\hypertarget{allscheme_page_allscheme_overview}{}\doxysubsection{Physical Parameterizations and Suites}\label{allscheme_page_allscheme_overview}
The UFS-\/\+SRW App. assembles the parameterizations in suites.

{\bfseries{Radiation}} 
\begin{DoxyItemize}
\item \mbox{\hyperlink{GFS_RRTMG}{GFS RRTMG Shortwave/\+Longwave Radiation Scheme}}
\end{DoxyItemize}

{\bfseries{PBL}} {\bfseries{and}} {\bfseries{Turbulence}} 
\begin{DoxyItemize}
\item \mbox{\hyperlink{GFS_SATMEDMFVDIFQ}{GFS Scale-\/aware TKE-\/based Moist Eddy-\/\+Diffusion Mass-\/\+Flux (EDMF) PBL and Free Atmospheric Turbulence Scheme}}
\item \mbox{\hyperlink{MYNNEDMF}{MYNN-\/\+EDMF Boundary Layer and Shallow Cloud Scheme}} with \mbox{\hyperlink{SGSCLOUD_page}{Subgrid-\/scale Cloud Interstitial}}
\end{DoxyItemize}

{\bfseries{Land}} {\bfseries{Surface}} {\bfseries{Model}} 
\begin{DoxyItemize}
\item \mbox{\hyperlink{GFS_NOAH}{GFS Noah Land Surface Model}}
\item \mbox{\hyperlink{RUCLSM}{RUC Land Surface Model}}
\item \mbox{\hyperlink{NoahMP}{GFS Noah\+MP Land Surface Model}}
\item \mbox{\hyperlink{CLM_LAKE_model}{CLM Lake Model}}
\end{DoxyItemize}

{\bfseries{Cumulus}} {\bfseries{Parameterizations}} 
\begin{DoxyItemize}
\item \mbox{\hyperlink{GFS_SAMFdeep}{GFS Scale-\/\+Aware Simplified Arakawa-\/\+Schubert (sa-\/\+SAS) Deep Convection Scheme}}
\item \mbox{\hyperlink{GFS_SAMFshal}{GFS SAS-\/based Mass-\/\+Flux Scheme for Shallow convection (sa-\/\+MF)}}
\item \mbox{\hyperlink{CU_GF}{Grell-\/\+Freitas Scale and Aerosol Aware Convection Scheme}}
\end{DoxyItemize}

{\bfseries{Microphysics}} 
\begin{DoxyItemize}
\item \mbox{\hyperlink{GFDL_cloud}{GFDL Cloud Microphysics Scheme}}
\item \mbox{\hyperlink{THOMPSON}{Thompson Aerosol-\/\+Aware Cloud Microphysics Scheme}}
\item \mbox{\hyperlink{NSSLMICRO_page}{NSSL 2-\/moment Cloud Microphysics Scheme}}
\end{DoxyItemize}

{\bfseries{Ozone}} {\bfseries{Photochemical}} {\bfseries{Production}} {\bfseries{and}} {\bfseries{Loss}} 
\begin{DoxyItemize}
\item \mbox{\hyperlink{GFS_OZPHYS}{GFS Ozone Photochemistry (2015) Scheme}}
\end{DoxyItemize}

{\bfseries{Water}} {\bfseries{Vapor}} {\bfseries{Photochemical}} {\bfseries{Production}} {\bfseries{and}} {\bfseries{Loss}} 
\begin{DoxyItemize}
\item \mbox{\hyperlink{GFS_H2OPHYS}{GFS Stratospheric H2O Scheme}}
\end{DoxyItemize}

{\bfseries{Gravity}} {\bfseries{Wave}} {\bfseries{Drag}} 
\begin{DoxyItemize}
\item \mbox{\hyperlink{GFS_UGWP_v0}{Unified Gravity Wave Physics Scheme -\/ Version 0}}
\item \mbox{\hyperlink{GFS_drag_suite}{GSL Drag Suite Scheme}}
\end{DoxyItemize}

{\bfseries{Surface}} {\bfseries{Layer}} 
\begin{DoxyItemize}
\item \mbox{\hyperlink{GFS_SFCLYR}{GFS Surface Layer Scheme}}
\item \mbox{\hyperlink{SFC_MYNNSFL}{MYNN Surface Layer Scheme}}
\end{DoxyItemize}

{\bfseries{Simplified}} {\bfseries{Ocean}} {\bfseries{and}} {\bfseries{Sea}} {\bfseries{Ice}} {\bfseries{Representation}} 
\begin{DoxyItemize}
\item \mbox{\hyperlink{GFS_NSST}{GFS Near-\/\+Surface Sea Temperature Scheme}}
\item \mbox{\hyperlink{GFS_OCEAN}{GFS Simple Ocean Scheme}}
\item \mbox{\hyperlink{GFS_SFCSICE}{GFS Sea Ice Scheme}}
\end{DoxyItemize}

{\bfseries{Others}} 
\begin{DoxyItemize}
\item \mbox{\hyperlink{GFS_SPP}{Stochastically-\/\+Perturbed Parameterizations (SPP)}}
\end{DoxyItemize}

The input information for the parameterizations includes the values of the gridbox mean prognostic variables (wind components, temperature, specific humidity, cloud fraction, water contents for cloud liquid, cloud ice, rain, snow, graupel, and ozone concentration), the provisional dynamical tendencies for the same variables and various surface fields, both fixed and variable.

The time integration of the physics suites is based on the following\+:
\begin{DoxyItemize}
\item The tendencies from the different physical processes are computed by the parameterizations or derived in separate interstitial routines.
\item The first part of the suite, comprised of the parameterizations for radiation, surface layer, surface (land, ocean, and sea ice), and boundary layer is computed using a hybrid of parallel and sequential splitting described in Donahue and Caldwell (2018) \cite{donahue_and_caldwell_2018}, a method in which the various parameterizations use the same model state as input but are impacted by the preceding parameterizations. The tendencies from the various parameterizations are then added together and used to update the model state.
\item The surface parameterizations (land, ocean and sea ice) are invoked twice in a subcycling loop, with the first time to create a guess, and the second time to produce the tendencies.
\item The second part of the physics suite, comprised of the parameterizations of gravity wave physics, ozone, stratospheric water vapor, deep and shallow convection (if using), and microphysics, is computed using sequential splitting in the order listed above, in which the model state is updated between calls to the parameterization.
\item If the in-\/core saturation adjustment is used ({\ttfamily do\+\_\+sat\+\_\+adj=}.true.), it is invoked at shorter timesteps along with the dynamical solver.
\end{DoxyItemize}

The UFS Short Range Weather Application (SRW App) v2.\+2.\+0 supports four physics suites.

Table 1. Physics suites and primary schemes supported in SRW v2.\+2.\+0 ~\newline


\tabulinesep=1mm
\begin{longtabu}spread 0pt [c]{*{6}{|X[-1]}|}
\hline
\PBS\centering \cellcolor{\tableheadbgcolor}\textbf{ Physics suites   }&\PBS\centering \cellcolor{\tableheadbgcolor}\textbf{ GFS\+\_\+v16   }&\PBS\centering \cellcolor{\tableheadbgcolor}\textbf{ HRRR   }&\PBS\centering \cellcolor{\tableheadbgcolor}\textbf{ RRFS\+\_\+v1beta   }&\PBS\centering \cellcolor{\tableheadbgcolor}\textbf{ Wo\+FS\+\_\+v0   }&\PBS\centering \cellcolor{\tableheadbgcolor}\textbf{ RAP    }\\\cline{1-6}
\endfirsthead
\hline
\endfoot
\hline
\PBS\centering \cellcolor{\tableheadbgcolor}\textbf{ Physics suites   }&\PBS\centering \cellcolor{\tableheadbgcolor}\textbf{ GFS\+\_\+v16   }&\PBS\centering \cellcolor{\tableheadbgcolor}\textbf{ HRRR   }&\PBS\centering \cellcolor{\tableheadbgcolor}\textbf{ RRFS\+\_\+v1beta   }&\PBS\centering \cellcolor{\tableheadbgcolor}\textbf{ Wo\+FS\+\_\+v0   }&\PBS\centering \cellcolor{\tableheadbgcolor}\textbf{ RAP    }\\\cline{1-6}
\endhead
{\bfseries{Deep}} {\bfseries{Cu}}   &\mbox{\hyperlink{GFS_SAMFdeep}{GFS Scale-\/\+Aware Simplified Arakawa-\/\+Schubert (sa-\/\+SAS) Deep Convection Scheme}}   &{\itshape off}   &{\itshape off}   &{\itshape off}   &\mbox{\hyperlink{CU_GF}{Grell-\/\+Freitas Scale and Aerosol Aware Convection Scheme}}    \\\cline{1-6}
{\bfseries{Shallow}} {\bfseries{Cu}}   &\mbox{\hyperlink{GFS_SAMFshal}{GFS SAS-\/based Mass-\/\+Flux Scheme for Shallow convection (sa-\/\+MF)}}   &\mbox{\hyperlink{MYNNEDMF}{MYNN-\/\+EDMF Boundary Layer and Shallow Cloud Scheme}}   &\mbox{\hyperlink{MYNNEDMF}{MYNN-\/\+EDMF Boundary Layer and Shallow Cloud Scheme}}   &\mbox{\hyperlink{MYNNEDMF}{MYNN-\/\+EDMF Boundary Layer and Shallow Cloud Scheme}}   &\mbox{\hyperlink{CU_GF}{Grell-\/\+Freitas Scale and Aerosol Aware Convection Scheme}}    \\\cline{1-6}
{\bfseries{Microphysics}}   &\mbox{\hyperlink{GFDL_cloud}{GFDL Cloud Microphysics Scheme}}   &\mbox{\hyperlink{THOMPSON}{Thompson Aerosol-\/\+Aware Cloud Microphysics Scheme}}   &\mbox{\hyperlink{THOMPSON}{Thompson Aerosol-\/\+Aware Cloud Microphysics Scheme}}   &\mbox{\hyperlink{NSSLMICRO_page}{NSSL 2-\/moment Cloud Microphysics Scheme}}   &\mbox{\hyperlink{THOMPSON}{Thompson Aerosol-\/\+Aware Cloud Microphysics Scheme}}    \\\cline{1-6}
{\bfseries{PBL/\+TURB}}   &\mbox{\hyperlink{GFS_SATMEDMFVDIFQ}{GFS Scale-\/aware TKE-\/based Moist Eddy-\/\+Diffusion Mass-\/\+Flux (EDMF) PBL and Free Atmospheric Turbulence Scheme}}   &\mbox{\hyperlink{MYNNEDMF}{MYNN-\/\+EDMF Boundary Layer and Shallow Cloud Scheme}}   &\mbox{\hyperlink{MYNNEDMF}{MYNN-\/\+EDMF Boundary Layer and Shallow Cloud Scheme}}   &\mbox{\hyperlink{MYNNEDMF}{MYNN-\/\+EDMF Boundary Layer and Shallow Cloud Scheme}}   &\mbox{\hyperlink{MYNNEDMF}{MYNN-\/\+EDMF Boundary Layer and Shallow Cloud Scheme}}    \\\cline{1-6}
{\bfseries{Radiation}}   &\mbox{\hyperlink{GFS_RRTMG}{GFS RRTMG Shortwave/\+Longwave Radiation Scheme}}   &\mbox{\hyperlink{GFS_RRTMG}{GFS RRTMG Shortwave/\+Longwave Radiation Scheme}}   &\mbox{\hyperlink{GFS_RRTMG}{GFS RRTMG Shortwave/\+Longwave Radiation Scheme}}   &\mbox{\hyperlink{GFS_RRTMG}{GFS RRTMG Shortwave/\+Longwave Radiation Scheme}}   &\mbox{\hyperlink{GFS_RRTMG}{GFS RRTMG Shortwave/\+Longwave Radiation Scheme}}    \\\cline{1-6}
{\bfseries{Surface}} {\bfseries{Layer}}   &\mbox{\hyperlink{GFS_SFCLYR}{GFS Surface Layer Scheme}}   &\mbox{\hyperlink{SFC_MYNNSFL}{MYNN Surface Layer Scheme}}   &\mbox{\hyperlink{SFC_MYNNSFL}{MYNN Surface Layer Scheme}}   &\mbox{\hyperlink{SFC_MYNNSFL}{MYNN Surface Layer Scheme}}   &\mbox{\hyperlink{SFC_MYNNSFL}{MYNN Surface Layer Scheme}}    \\\cline{1-6}
{\bfseries{LSM}}   &\mbox{\hyperlink{GFS_NOAH}{GFS Noah Land Surface Model}}   &\mbox{\hyperlink{RUCLSM}{RUC Land Surface Model}} and \mbox{\hyperlink{CLM_LAKE_model}{CLM Lake Model}}   &\mbox{\hyperlink{NoahMP}{GFS Noah\+MP Land Surface Model}}   &\mbox{\hyperlink{GFS_NOAH}{GFS Noah Land Surface Model}}   &\mbox{\hyperlink{RUCLSM}{RUC Land Surface Model}}    \\\cline{1-6}
{\bfseries{Gravity}} {\bfseries{Wave}} {\bfseries{Drag}}   &\mbox{\hyperlink{GFS_UGWP_v0}{Unified Gravity Wave Physics Scheme -\/ Version 0}}   &\mbox{\hyperlink{GFS_drag_suite}{GSL Drag Suite Scheme}}   &\mbox{\hyperlink{GFS_UGWP_v0}{Unified Gravity Wave Physics Scheme -\/ Version 0}}   &\mbox{\hyperlink{GFS_UGWP_v0}{Unified Gravity Wave Physics Scheme -\/ Version 0}}   &\mbox{\hyperlink{GFS_drag_suite}{GSL Drag Suite Scheme}}    \\\cline{1-6}
{\bfseries{Ice}} and {\bfseries{Snow}}   &\mbox{\hyperlink{GFS_SFCSICE}{GFS Sea Ice Scheme}}   &\mbox{\hyperlink{RUCLSM}{RUC Land Surface Model}}   &\mbox{\hyperlink{GFS_SFCSICE}{GFS Sea Ice Scheme}}   &\mbox{\hyperlink{GFS_SFCSICE}{GFS Sea Ice Scheme}}   &\mbox{\hyperlink{RUCLSM}{RUC Land Surface Model}}   \\\cline{1-6}
\end{longtabu}
\hypertarget{GFS_RRTMG}{}\doxysubsection{GFS RRTMG Shortwave/\+Longwave Radiation Scheme}\label{GFS_RRTMG}
\hypertarget{_g_f_s__r_r_t_m_g_des_rrtmg}{}\doxysubsubsection{Description}\label{_g_f_s__r_r_t_m_g_des_rrtmg}
Radiative processes are among the most complex and computationally intensive parts of all model physics. As an essential component of modeling the atmosphere, radiation directly and indirectly connects all physics processes with model dynamics, and it regulates the overall earth-\/atmosphere energy exchanges and transformations.

The schematic radiation module structure is shown in Figure 1. 

GFS radiation package is intended to provide a fast and accurate method of determining the total radiative flux at any given location. These calculations provide both the total radiative flux at the ground surface, which is needed to establish the surface energy budget, and the vertical radiative net flux divergence, which is used to calculate the radiative heating and cooling rates of a given atmospheric layer. The magnitude of the terms in the surface energy budget can set the stage for moist deep convection and are crucial to the formation of low-\/level clouds. In addition, the vertical radiative flux divergence can produce substantial cooling, particularly at the tops of clouds, which can have strong dynamical effects on cloud evolution.

Since 2007, the longwave (LW) and the shortwave (SW) radiation parameterizations in NCEP\textquotesingle{}s operational GFS are both modified and optimized versions of the Rapid Radiative Transfer Model for GCMs (RRTMG\+\_\+\+LW and RRTMG\+\_\+\+SW, respectively) developed at AER {\itshape inc}. (Atmospheric and Environmental Research) (Iacono et al.(2008) \cite{iacono_et_al_2008}; Mlawer et al.(1997) \cite{mlawer_et_al_1997}; Iacono et al.(2000) \cite{iacono_et_al_2000}; Clough et al. 2005 \cite{clough_et_al_2005}). The LW algorithm contains 140 unevenly distributed g-\/points (quadrature points) in 16 broad spectral bands, while the SW algorithm includes 112 g-\/points in 14 bands. In addition to the major atmospheric absorbing gases of ozone, water vapor, and carbon dioxide, the algorithm also includes various minor absorbing species such as methane, nitrous oxide, oxygen, and in the longwave up to four types of halocarbons (CFCs). To represent statistically the unresolved subgrid cloud variability when dealing multi layered clouds, a Monte-\/\+Carlo Independent Column Approximation ({\bfseries{Mc\+ICA}}) method is used in the RRTMG radiative transfer. Several cloud overlap methods, including maximum-\/random, exponential, and exponential-\/random are available in both LW and SW radiation calculations.

Radiative fields from model outputs ( $W m^{-2}$) include\+:
\begin{DoxyItemize}
\item At surface total sky
\begin{DoxyItemize}
\item DLWRFsfc\+: Downward LW
\item DSWRFsfc\+: Downward SW
\item ULWRFsfc\+: Upward LW
\item USWRFsfc\+: Upward SW
\item NBDSFsfc\+: Near IR beam downward
\item NDDSFsfc\+: Near IR diffuse downward
\item VBDSFsfc\+: UV+\+Visible beam downward
\item VDDSFsfc\+: UV+\+Visible diffuse downward
\item DUVBsfc\+: UV-\/B downward flux
\end{DoxyItemize}
\item At surface clear sky
\begin{DoxyItemize}
\item CSDLFsfc\+: Downward LW
\item CSDSFsfc\+: Downward SW
\item CSULFsfc\+: Upward LW
\item CSULFsfc\+: Upward LW
\item CSUSFsfc\+: Upward sw
\item CDUVBsfc\+: UV-\/B downward flux
\end{DoxyItemize}
\item At TOA total sky
\begin{DoxyItemize}
\item DSWRFtoa\+: Downward SW
\item ULWRFtoa\+: Upward LW
\item USWRFtoa\+: Upward SW
\end{DoxyItemize}
\item At TOA clear sky\+:
\begin{DoxyItemize}
\item CSULFtoa\+: Upward LW
\item CSUSFtoa\+: Upward SW
\end{DoxyItemize}
\end{DoxyItemize}\hypertarget{_g_f_s__r_r_t_m_g_rrtmg_enh}{}\doxysubsubsection{CCPP Physics Updates}\label{_g_f_s__r_r_t_m_g_rrtmg_enh}
\begin{DoxyVersion}{Version}
CCPP v6.\+0.\+0
\end{DoxyVersion}
Requests have been made by many physics developers and users to rewrite the cloud routines (routines progcld) for radiation computation in the program radiation\+\_\+clouds.\+f. Those cloud subroutines are very similar, and have many lines of common code. We modified the radiation\+\_\+clouds.\+f module, and includes all the calculations of the cloud properties to a new subroutine radiation\+\_\+clouds\+\_\+prop. We also moved the common code from subroutines progcld to this new subroutine. Subroutine radiation\+\_\+clouds\+\_\+prop can be called by RRTMG and RRTMGP. A single call to the subroutine radiation\+\_\+clouds\+\_\+prop can connect to the calculations of the cloud radiation properties for all the microphysics schemes.

Summary of the major changes\+:
\begin{DoxyItemize}
\item radiation\+\_\+clouds.\+f

A new subroutine “radiation\+\_\+clouds\+\_\+prop” was added to radiation\+\_\+clouds.\+f. This new subroutine calculates all cloud radiation properties for all the microphysics schemes. Subroutines \char`\"{}progcld$\ast$\char`\"{} were renamed based on the input variables {\ttfamily imp\+\_\+physics}, and the inactive subroutines were removed from file radiation\+\_\+clouds.\+f
\begin{DoxyItemize}
\item \textquotesingle{}progcld1\textquotesingle{} --- \texorpdfstring{$>$}{>} progcld\+\_\+zhao\+\_\+carr
\item \textquotesingle{}progcld3\textquotesingle{} --- \texorpdfstring{$>$}{>} progcld\+\_\+zhao\+\_\+carr\+\_\+pdf
\item \textquotesingle{}progcld4\textquotesingle{} --- \texorpdfstring{$>$}{>} progcld\+\_\+gfdl\+\_\+lin
\item \textquotesingle{}progcld5\textquotesingle{} --- \texorpdfstring{$>$}{>} progcld\+\_\+fer\+\_\+hires
\item \textquotesingle{}progcld6\textquotesingle{} --- \texorpdfstring{$>$}{>} progcld\+\_\+thompson\+\_\+wsm6
\item \textquotesingle{}progclduni\textquotesingle{} --- \texorpdfstring{$>$}{>} progclduni
\item \textquotesingle{}progcld\+\_\+thompson\textquotesingle{}--- \texorpdfstring{$>$}{>} progcld\+\_\+thompson
\end{DoxyItemize}
\item GFS\+\_\+rrtmg\+\_\+pre.\+F90

Removed the “progcld” subroutine calls, and replaced them with a single subroutine call to “radiation\+\_\+clouds\+\_\+prop”.
\item radiation\+\_\+cloud\+\_\+overlap.\+F90

Replaced subroutine “get\+\_\+alpha\+\_\+exp” with “get\+\_\+alpha\+\_\+exper”. The new subroutine revises alpha for exponential random cloud overlap option. This new subroutine is used in programs GFS\+\_\+rrtmgp\+\_\+cloud\+\_\+overlap\+\_\+pre.\+F90 and GFS\+\_\+rrtmgp\+\_\+gfdlmp\+\_\+pre.\+F90.
\item Subroutine getml() has been modified. The subroutine computes low, mid, high, total and boundary clouds, and is used in GFS\+\_\+cloud\+\_\+diagnostics.\+F90.
\end{DoxyItemize}\hypertarget{_g_f_s__r_r_t_m_g_intraphysics_rrtmg}{}\doxysubsubsection{Intraphysics Communication}\label{_g_f_s__r_r_t_m_g_intraphysics_rrtmg}

\begin{DoxyItemize}
\item {\bfseries{GFS\+\_\+suite\+\_\+interstitial\+\_\+rad\+\_\+reset\+:}} \mbox{\hyperlink{namespacegfs__suite__interstitial__rad__reset_a608c5cba3c0ae722066fa616a718992c_arg_table_GFS_suite_interstitial_rad_reset_run}{Argument Table}}
\item {\bfseries{\mbox{\hyperlink{namespacegfs__rrtmg__pre}{gfs\+\_\+rrtmg\+\_\+pre}}\+:}} \mbox{\hyperlink{group___g_f_s__rrtmg__pre__mod_ga6d9841bd404b47f27921b221f427e716_arg_table_GFS_rrtmg_pre_run}{Argument Table}}
\item {\bfseries{GFS\+\_\+radiation\+\_\+surface\+:}} \mbox{\hyperlink{namespacegfs__radiation__surface_adf74c669fa67ec92d00a4ecb3156c7ca_arg_table_GFS_radiation_surface_run}{Argument Table}}
\item {\bfseries{\mbox{\hyperlink{namespacerad__sw__pre}{rad\+\_\+sw\+\_\+pre}}\+:}} \mbox{\hyperlink{group__rad__sw__pre_arg_table_rad_sw_pre_run}{Argument Table}}
\item {\bfseries{\mbox{\hyperlink{namespacerrtmg__sw}{rrtmg\+\_\+sw}}\+:}} \mbox{\hyperlink{group__module__radsw__main_arg_table_rrtmg_sw_run}{Argument Table}}
\item {\bfseries{\mbox{\hyperlink{namespacerrtmg__sw__post}{rrtmg\+\_\+sw\+\_\+post}}\+:}} \mbox{\hyperlink{group__rrtmg__sw__post_arg_table_rrtmg_sw_post_run}{Argument Table}}
\item {\bfseries{\mbox{\hyperlink{namespacerrtmg__lw__pre}{rrtmg\+\_\+lw\+\_\+pre}}\+:}} \mbox{\hyperlink{group__rrtmg__lw__pre_arg_table_rrtmg_lw_pre_run}{Argument Table}}
\item {\bfseries{\mbox{\hyperlink{namespacerrtmg__lw}{rrtmg\+\_\+lw}}\+:}} \mbox{\hyperlink{group__module__radlw__main_arg_table_rrtmg_lw_run}{Argument Table}}
\item {\bfseries{\mbox{\hyperlink{namespacerrtmg__lw__post}{rrtmg\+\_\+lw\+\_\+post}}\+:}} \mbox{\hyperlink{group__rrtmg__lw__post_ga3ce62517f26e262a788b653c7fd49f94_arg_table_rrtmg_lw_post_run}{Argument Table}}
\item {\bfseries{GFS\+\_\+rrtmg\+\_\+post\+:}} \mbox{\hyperlink{group___g_f_s__rrtmg__post__mod_arg_table_GFS_rrtmg_post_run}{Argument Table}}
\end{DoxyItemize}\hypertarget{_g_f_s__r_r_t_m_g_gen_al_rrtmg}{}\doxysubsubsection{General Algorithm}\label{_g_f_s__r_r_t_m_g_gen_al_rrtmg}

\begin{DoxyItemize}
\item \mbox{\hyperlink{group__module__radlw__main_gen_lwrad}{RRTMG Longwave Radiation Scheme General Algorithm}}
\item \mbox{\hyperlink{group__module__radsw__main_gen_swrad}{RRTMG Shortwave Radiation Scheme General Algorithm}} 
\end{DoxyItemize}\hypertarget{GFS_SATMEDMFVDIFQ}{}\doxysubsection{GFS Scale-\/aware TKE-\/based Moist Eddy-\/\+Diffusion Mass-\/\+Flux (EDMF) PBL and Free Atmospheric Turbulence Scheme}\label{GFS_SATMEDMFVDIFQ}
\hypertarget{_g_f_s__s_a_t_m_e_d_m_f_v_d_i_f_q_des_satmedmfvdifq}{}\doxysubsubsection{Description}\label{_g_f_s__s_a_t_m_e_d_m_f_v_d_i_f_q_des_satmedmfvdifq}
The current operational GFS scale-\/aware TKE-\/\+EDMF PBL scheme is an extended version of GFS Hybrid Eddy-\/\+Diffusivity Mass-\/\+Flux PBL and Free Atmospheric Turbulence scheme (Han et al. 2016 \cite{Han_2016}; Han et al. 2017 \cite{han_et_al_2017}) with below enhancement\+:


\begin{DoxyEnumerate}
\item Eddy diffusivity (K) is now a function of TKE which is prognostically predicted
\item EDMF approach is applied for all the unstable PBL
\item EDMF approach is also applied to the stratocumulus-\/top-\/driven turbulence mixing
\item It includes a moist-\/adiabatic process when updraft thermal becomes saturated
\item Scale-\/aware capability
\item It includes interaction between TKE and cumulus convection
\end{DoxyEnumerate}

The CCPP-\/compliant subroutine \mbox{\hyperlink{group__module__satmedmfvdifq_gab8c41e2463540b19aee80386e7a1fbdc_gab8c41e2463540b19aee80386e7a1fbdc}{satmedmfvdifq\+\_\+run()}} computes subgrid vertical turbulence mixing using scale-\/aware TKE-\/based moist eddy-\/diffusion mass-\/flux paramterization (Han et al. 2019 \cite{Han_2019})
\begin{DoxyItemize}
\item For the convective boundary layer, the scheme adopts EDMF parameterization (Siebesma et al. (2007)\cite{Siebesma_2007}) to take into account nonlocal transport by large eddies(mfpbltq.\+f)
\item A new mass-\/flux paramterization for stratocumulus-\/top-\/induced turbulence mixing has been introduced (mfscuq.\+f; previously, it was an eddy diffusion form)
\item For local turbulence mixing, a TKE closure model is used.
\item Wind shear effect in characteristic mixing length calculation is included, which reduces the mixing length in a strong shear environment such as a hurricane.
\item To better predict surface inversion as well as capping inversion near the PBL top, background diffusivity in the inversion layers is reduced as a function of surface roughness and green vegetation fraction.
\item To reduce the PBL overgrowth, the PBL updraft overshoot is not only limited by bulk Richardson number-\/based PBL depth, but the virtual potential temperature at the top of the surface layer rather than that at the model first layer is also used as the near-\/surface virtual potential temperature in the bulk-\/\+Richardson number computation. This helps to largely suppress the unrealistic widespread popcorn-\/like precipitation.
\item Updraft entrainment rates for moisture, hydrometeors, and tracers are increased by about 30\%.
\item A positive definite total variation diminishing (TVD) mass-\/flux transport scheme for moisture, hydrometeors, and tracers and a method for removing negative tracer mixing ratio values have been implemented.
\end{DoxyItemize}

\begin{DoxySeeAlso}{See also}
NCEP Office Note 505 \cite{https://doi.org/10.25923/cybh-w893} and 506 \cite{https://doi.org/10.25923/5051-3r70}
\end{DoxySeeAlso}
\hypertarget{_g_f_s__s_a_t_m_e_d_m_f_v_d_i_f_q_v6_pbl_enh}{}\doxysubsubsection{CCPP Physics Updates}\label{_g_f_s__s_a_t_m_e_d_m_f_v_d_i_f_q_v6_pbl_enh}
\begin{DoxyVersion}{Version}
UFS-\/\+SRW v2.\+2.\+0
\begin{DoxyItemize}
\item To reduce the negative hurricane intensity biases, a parameterization for environmental wind shear effect is included in the GFS TKE-\/\+EDMF PBL and cumulus schemes. In addition, the entrainment rates are enhanced proportional to the sub-\/cloud or PBL mean TKE (turbulent kinetic energy) when TKE is larger than a threshold value.
\item To enhance the underestimated CAPE forecasts in the GFS, the entrainment rate in the TKE-\/\+EDMF PBL scheme is increased as a function of vegetation fraction and surface roughness length.
\end{DoxyItemize}
\end{DoxyVersion}
\hypertarget{_g_f_s__s_a_t_m_e_d_m_f_v_d_i_f_q_intra_satmedmfvdifq}{}\doxysubsubsection{Intraphysics Communication}\label{_g_f_s__s_a_t_m_e_d_m_f_v_d_i_f_q_intra_satmedmfvdifq}

\begin{DoxyItemize}
\item \mbox{\hyperlink{group__module__satmedmfvdifq_gab8c41e2463540b19aee80386e7a1fbdc_arg_table_satmedmfvdifq_run}{Argument Table}}
\end{DoxyItemize}\hypertarget{_g_f_s__s_a_t_m_e_d_m_f_v_d_i_f_q_gen_pbl_satmedmfvdifq}{}\doxysubsubsection{General Algorithm}\label{_g_f_s__s_a_t_m_e_d_m_f_v_d_i_f_q_gen_pbl_satmedmfvdifq}

\begin{DoxyItemize}
\item \mbox{\hyperlink{group__module__satmedmfvdifq_gab8c41e2463540b19aee80386e7a1fbdc_gen_satmedmfvdifq}{GFS satmedmfvdifq General Algorithm}} 
\end{DoxyItemize}\hypertarget{MYNNEDMF}{}\doxysubsection{MYNN-\/\+EDMF Boundary Layer and Shallow Cloud Scheme}\label{MYNNEDMF}
\hypertarget{_m_y_n_n_e_d_m_f_mynnedmf_descrip}{}\doxysubsubsection{Description}\label{_m_y_n_n_e_d_m_f_mynnedmf_descrip}
The Mellor-\/\+Yamada-\/\+Nakanishi-\/\+Niino (Nakanishi and Niino 2009 \cite{NAKANISHI_2009}) eddy diffusivity-\/mass flux (EDMF) scheme was implemented into CCPP to introduce an alternative turbulent kinetic energy (TKE)-\/based planetary boundary layer (PBL) scheme which could serve as a candidate PBL parameterization for future operational implementations of the Unified Forecast System. The MYNN-\/\+EDMF is currently employed in NOAA\textquotesingle{}s operational Rapid Refresh (RAP; Benjamin et al.\+2016 \cite{Benjamin_2016}) and High-\/\+Resolution Rapid Refresh (HRRR) forecast systems.

The original MYNN scheme was demonstrated to be an improvement over predecessor Mellor-\/\+Yamada-\/type PBL schemes (e.\+g., Mellor and Yamada 1974,1982 \cite{Mellor_1974} \cite{Mellor_1982}) when compared against large-\/eddy simulation (LES) of a convective PBL (Nakanishi and Niino 2004, 2009 \cite{Nakanishi_2004} \cite{NAKANISHI_2009}), the prediction of advection fog (Nakanishi and Niino 2006 \cite{Nakanishi_2006}), and for the representation of coastal barrier jets (Olson and Brown 2009 \cite{olson_and_brown_2009}). The MYNN scheme can be configured to function at level 2.\+5, 2.\+6 (current default) or 3.\+0 closure and includes a partial-\/condensation scheme (also known as a cloud PDF or a statistical-\/cloud scheme) to represent the effects of subgrid-\/scale(SGS) clouds on the buoyancy flux (Nakanishi and Niino 2004, 2006, and 2009 \cite{Nakanishi_2004} \cite{Nakanishi_2006} \cite{NAKANISHI_2009}). The closure constants for the original MYNN scheme were tuned to a database of LES as opposed to observational data.

The MYNN-\/\+EDMF scheme has been extensively developed to improve upon the forecast skill of the original MYNN, largely driven by requirements to improve forecast skill in support of the NOAA\textquotesingle{}s National Weather Service (NWS), the Federal Aviation Administration (FAA) and users within the renewable-\/energy industry.\+Specifically, fundamental changes were made to the formulation of the mixing lengths and representation of subgrid-\/scale(SGS) clouds, but new components have also been added to improve the representation of non-\/local mixing, the turbulence interaction with clouds, and the coupling to other model components (i.\+e., radiation). A description of the changes to the MYNN scheme are available in Olson et al.(2019) \cite{https://doi.org/10.25923/n9wm-be49}.\hypertarget{_m_y_n_n_e_d_m_f_intra_mynnpbl}{}\doxysubsubsection{Intraphysics Communication}\label{_m_y_n_n_e_d_m_f_intra_mynnpbl}

\begin{DoxyItemize}
\item \mbox{\hyperlink{SGSCLOUD_page}{Subgrid-\/scale Cloud Interstitial}}
\end{DoxyItemize}

The SGS clouds produced by the MYNN-\/\+EDMF scheme are coupled to the longwave and shortwave radiation schemes if the namelist parameter {\ttfamily icloud\+\_\+bl} is set to 1. In this case, the SGS cloud fraction, {\ttfamily CLDFRA\+\_\+\+BL}, and the SGS cloud-\/mixing ratio, {\ttfamily QC\+\_\+\+BL}, are added to the microphysics arrays within the radiation driver (mynnrad\+\_\+pre\+\_\+run()). The following two steps are performed\+:

(1) Use either Xu and Randall (1996) \cite{xu_and_randall_1996} or Chaboureau and Bechtold (2005) \cite{Chaboureau_2005} cloud fraction for the convection scheme subgrid clouds, chosen by the switch {\ttfamily conv\+\_\+cf\+\_\+opt} = 0\+: CB2005, 1\+: XR1996. Note that the MYNN-\/\+EDMF PBL scheme subgrid clouds input into this scheme are already partitioned into qc and qi and already have assigned cloud fractions, but now qi is further partitioned into qi and qsnow.

(2) if the resolved-\/scale cloud liquid ( $q_c$) is less than $10^{-6}kg kg^{-1}$ , and there exists a nonzero SGS cloud fraction, then the SGS components are added to their respective resolved-\/scale components by a temperature weighting, according to linear approximation of Hobbs et al.(1974) \cite{HOBBS_1974} . Next the frozen species are crudely split into 50\% ice and 50\% snow below approximately 700 h\+Pa. The percentage of snow decreases with altitude and tapers off at approximately 300 h\+Pa\+: \[ W_{snow}=min(0.5,max((P-30000,0.0)/140000.0) \]

\[ W_{ice}=1.0-W_{snow} \]

Next the SGS cloud water, liquid, and snow are sorted as\+:

$q_c$=QC\+\_\+\+BL$\ast$\+CLDFRA\+\_\+\+BL

$q_i$=QI\+\_\+\+BL$\ast$ $W_{ice}$$\ast$\+CLDFRA\+\_\+\+BL

$q_s$=QI\+\_\+\+BL$\ast$ $W_{snow}$$\ast$\+CLDFRA\+\_\+\+BL

This allows using a single 3-\/D array for both SGS cloud water,ice and snow. The updated $q_{c}$, $q_{i}$, $q_{s}$and {\ttfamily CLDFRA} are then used as input into the radiation schemes. After exiting the radiation schemes, the original values of $q_c, q_i, q_s$ and {\ttfamily CLDFRA} are restored, so the SGS clouds do not impact the resolved-\/scale moisture budget.

The MYNN-\/\+EDMF CCPP-\/compliant interface\+: \mbox{\hyperlink{group__gp__mynnedmf_arg_table_mynnedmf_wrapper_run}{Argument Table}}\hypertarget{_m_y_n_n_e_d_m_f_gen_mynnedmf_conv}{}\doxysubsubsection{General Algorithm}\label{_m_y_n_n_e_d_m_f_gen_mynnedmf_conv}


\mbox{\hyperlink{namespacemodule__bl__mynn_a59d997b466c1b7edc5ff85fcf61b42ce_gen_mynn_bl_driver}{GSD mynn\+\_\+bl\+\_\+driver General Algorithm}} \hypertarget{SGSCLOUD_page}{}\doxysubsection{Subgrid-\/scale Cloud Interstitial}\label{SGSCLOUD_page}
\hypertarget{_s_g_s_c_l_o_u_d_page_rrfs_sgscloud_descrip}{}\doxysubsubsection{Description}\label{_s_g_s_c_l_o_u_d_page_rrfs_sgscloud_descrip}
This interstitial module adds the subgrid-\/scale cloud information to the resolved-\/scale (microphysics) clouds. This procedure is required when using microphysics schemes that only produce clouds in fully saturated grid cells, like the Thompson microphysics scheme, and when using boundary layer and convection schemes that produce subgrid-\/scale cloud information (mixing ratio and cloud fraction). This allows the subgrid-\/scale cloud information to be assembled into the rest of the cloud information prior to calling the radiation schemes.\hypertarget{_s_g_s_c_l_o_u_d_page_intra_rrfssgs}{}\doxysubsubsection{Intraphysics Communication}\label{_s_g_s_c_l_o_u_d_page_intra_rrfssgs}

\begin{DoxyItemize}
\item sgscloud\+\_\+radpre\+\_\+run\+: \mbox{\hyperlink{group__sgsradpre__group_arg_table_sgscloud_radpre_run}{Argument Table}}
\item sgscloud\+\_\+radpost\+\_\+run\+:\mbox{\hyperlink{group__sgscloud__radpost__mod_arg_table_sgscloud_radpost_run}{Argument Table}}
\end{DoxyItemize}\hypertarget{_s_g_s_c_l_o_u_d_page_gen_rrfssgs}{}\doxysubsubsection{SGSCLOUD Pre-\/radiation Interstitial General Algorithm}\label{_s_g_s_c_l_o_u_d_page_gen_rrfssgs}
The order of procedures is outlined below\+: ~\newline
 -\/ Back up the original qc, qi, and qs in \char`\"{}save arrays\char`\"{} qc\+\_\+save, qi\+\_\+save and qs\+\_\+save. ~\newline
 -\/ Add MYNN boundary layer clouds cldfra\+\_\+bl as qc, qi and qs ~\newline
 -\/ Add Grell-\/\+Freitas deep convection clouds. Partition the condensate from the convection scheme into liquid, ice and snow. ~\newline
 -\/ Calculate effective radius cloud water, ice and snow ~\newline
 -\/ Calculate Chaboureau-\/\+Bechtold (CB) convective component based on both Chaboureau and Bechtold (2002 \cite{Chaboureau_2002} and 2005 \cite{Chaboureau_2005}) ~\newline
 -\/ Leverage CB stratus clouds from MYNN in saturated condtions into cloud fraction. \hypertarget{GFS_NOAH}{}\doxysubsection{GFS Noah Land Surface Model}\label{GFS_NOAH}
\hypertarget{_g_f_s__n_o_a_h_des_noah}{}\doxysubsubsection{Description}\label{_g_f_s__n_o_a_h_des_noah}
Land-\/atmosphere interactions are a main driver of Earth\textquotesingle{}s surface water and energy budgets. The importance of the land surface is rather intuitive, and has been demonstrated not only in terms of predictability on daily to seasonal timescale (Betts et al. (2017) \cite{betts_et_al_2017}), but also in terms of influencing extremes such as drought and heatwaves (Paimazumder and Done (2016) \cite{paimazumder_and_done_2016}), PBL evolution and cloud formation (Milovac et al. (2016) \cite{milovac_et_al_2016}) and afternoon convection (Guillod et al. (2015) \cite{guillod_et_al_2015}), and tropical cyclone re-\/intensification (Andersen and Shepherd (2014) \cite{andersen_and_shepherd_2014}). Other linkages, such as the role of soil moisture (SM) or vegetation heterogeneity in mesoscale circulation (Hsu et al. (2017) \cite{hsu_et_al_2017}) and planetary waves (Koster et al. (2014) \cite{koster_et_al_2014}), and those driven by land use and land cover change or management (Hirsch et al. (2015) \cite{hirsch_et_al_2015}; Findell et al. (2017) \cite{findell_et_al_2017}) are topics of active research.

Figure 1 is a schematic of local land-\/atmosphere interactions in a quiescent synoptic regime, including the soil moisture-\/precipitation (SM-\/P) feedback pathways (Ek and Mahrt (1994) \cite{ek_and_mahrt_1994}; Ek and Holtslag (2004) \cite{ek_and_holtslag_2004} ). Solid arrows indicate a positive feedback pathway, and large dashed arrows represent a negative feedback, while red indicates radiative, black indicates surface layer and PBL, and brown indicates land surface processes. Thin red and grey dashed lines with arrows also represent positive feedbacks. The single horizontal gay-\/dotted line (no arrows) indicates the top of the PBL, and the seven small vertical dashed lines (no arrows) represent precipitation  Recently, the land surface updates in 2017 GFS operational physics includes\+:
\begin{DoxyItemize}
\item IGBP 20-\/type 1-\/km land classification
\item STASGO 19-\/type 1-\/km soil classification
\item MODIS-\/based snow free albedo
\item MODIS-\/based maximum snow albedo
\item Diurnal albedo treatment
\item Unify snow cover, albedo between radiation and land surface model
\item Increase ground heat flux under deep snow
\item Upgrade surface layer parameterization scheme \mbox{\hyperlink{GFS_SFCLYR}{GFS Surface Layer Scheme}} to modify the roughness-\/length formulation and introduce a stability parameter constraint in the Monin-\/\+Obukhov similarity theory to prevent the land-\/atmosphere system from fully decoupling leading to excessive cooling of 2m temperature during sunset 
\end{DoxyItemize}\hypertarget{_g_f_s__n_o_a_h_intra_noah}{}\doxysubsubsection{Intraphysics Communication}\label{_g_f_s__n_o_a_h_intra_noah}

\begin{DoxyItemize}
\item \mbox{\hyperlink{group___noah___l_s_m_arg_table_lsm_noah_run}{Argument Table}}
\end{DoxyItemize}\hypertarget{_g_f_s__n_o_a_h_gen_al_noah}{}\doxysubsubsection{General Algorithm}\label{_g_f_s__n_o_a_h_gen_al_noah}

\begin{DoxyItemize}
\item \mbox{\hyperlink{group___noah___l_s_m_ga5d624f72a0da3ffcdf7a01fb9862e0e8_ga5d624f72a0da3ffcdf7a01fb9862e0e8}{lsm\+\_\+noah\+::lsm\+\_\+noah\+\_\+run}} 
\end{DoxyItemize}\hypertarget{RUCLSM}{}\doxysubsection{RUC Land Surface Model}\label{RUCLSM}
\hypertarget{_r_u_c_l_s_m_ruclsm_descrip}{}\doxysubsubsection{Description}\label{_r_u_c_l_s_m_ruclsm_descrip}
The land surface model (LSM) was originally developed as part of the NOAA Rapid Update Cycle (RUC) model development effort; with ongoing modifications, it is now used as an option for the WRF community model. The RUC model and its WRF-\/based NOAA successor, the Rapid Refresh (RAP) and High-\/\+Resolution Rapid Refresh (HRRR), are hourly updated and have an emphasis on short-\/range, near-\/surface forecasts including aviation-\/impact variables and pre-\/convective environment. Therefore, coupling to this LSM (hereafter the RUC LSM) has been critical to provide more accurate lower boundary conditions.

The RUC LSM became operational at the NOAA/\+National Centers for Environmental Prediction (NCEP) first, as part of the RUC from 1998–2012, and then as part of the RAP from 2012 through the present and as part of HRRR from 2014 through the present. The simple treatments of basic land surface processes in the RUC LSM (Smirnova et al. 2016 \cite{Smirnova_2016} ) have proven to be physically robust and capable of realistically representing the evolution of soil moisture, soil temperature, and snow in cycled models. Extension of the RAP domain to encompass all of North America and adjacent high-\/latitude ocean areas necessitated further development of the RUC LSM for application in the tundra permafrost regions and over Arctic sea ice (Smirnova et al. 2000 \cite{Smirnova_2000}). Other modifications include refinements in the snow model (snow \char`\"{}mosaic\char`\"{} approach, improvements in computation of snow cover fraction and snow thermal conductivity) and a more accurate specification of albedo, roughness length, and other surface properties. Some of these modifications in the RUC LSM are described and evaluated in Smirnova et al. 2016 \cite{Smirnova_2016} .

The parameterizations in the RUC LSM describe complicated atmosphere–land surface interactions (Fig.\+1) in an intentionally simplified fashion to avoid excessive sensitivity to multiple uncertain surface parameters. Nevertheless, the RUC LSM, when coupled with the hourly-\/assimilating atmospheric model, demonstrated over years of ongoing cycling (Benjamin et al. 2004a,b \cite{Benjamin_2004a} \cite{Benjamin_2004b} ; \+Berbery et al. 1999 \cite{Berbery_1999}) that it can produce a realistic evolution of hydrologic and time-\/varying soil fields (i.\+e., soil moisture and temperature) that cannot be directly observed over large areas, as well as the evolution of snow cover on the ground surface. This result is possible only if the soil–vegetation–snow component of the coupled model, constrained only by atmospheric boundary conditions and the specification of surface characteristics, has sufficient skill to avoid long-\/term drift.

International projects for intercomparison of land surface and snow parameterization schemes were essential in providing the testing environment and afforded an excellent opportunity to evaluate the RUC LSM with different land use and soil types and within a variety of climates. The RUC LSM was included in phase 2(d) of the Project for the Intercomparison of Land Surface Prediction Schemes \mbox{[}PILPS-\/2(d)\mbox{]}, in which tested models performed 18-\/yr simulations of the land surface state for the Valdai site in Russia (Schlosser et al. 1997 \cite{Schlosser_1997} ; \+Slater et al. 2001 \cite{Slater_2001} ;  Luo et al. 2003 \cite{Luo_2003} ). The RUC LSM was also tested during the Snow Models Intercomparison Project (Snow\+MIP, Snow\+MIP2, ESM-\/\+Snow\+MIP), with emphasis on snow parameterizations for both grassland and forest locations in different parts of the world (Etchevers et al. 2002, 2004 \cite{Etchevers_2002} \cite{Etchevers_2004}; \+Essery et al. 2009 \cite{Essery_2009} ; \+Rutter et al. 2009 \cite{Rutter_2009} , Krinner et al. 2018 \cite{Krinner_2018} ). The analysis of RUC LSM performance over 10 reference sites in ESM-\/\+Snow\+MIP rated it 4th place among the 26 participating models. The results were published in Menard et al.(2021) \cite{Menard_2021} and Essery et al. (2020) \cite{essery_et_al_2020}. RUC LSM received high rankings in ESM-\/\+Snow\+MIP experiement in terms of multi-\/year snow cover and surface temperature simulations for several sites located in different parts of the world (Fig.\+2, Menard et al.\+2021 \cite{Menard_2021}).

RUC LSM is used in several weather prediction models around the world (Austria, New Zealand, Switzerland, RAP/\+HRRR in US). Recent RUC LSM implementation in the 1km-\/resolution model over Europe revealed some issues in the snow-\/covered high terrain (Swiss Alps), and this led to some small modifications and adjustments to the snow model. These adjustments are available in the current CCPP public release.

Coupling of the RUC LSM to physically-\/based stochastic snow model (He et al.(2021) \cite{he_et_al_2021}) is also available in the current public release.

The sensitivity of surface fluxes and turbine-\/height winds to the RUC LSM parameters has been explored by Geng Xia, NREL to determine the uncertainty range for the selected parameters in the RUC LSM.\hypertarget{_r_u_c_l_s_m_autotoc_md2}{}\doxyparagraph{RUC LSM characteristics that differ from Noah LSM\+:}\label{_r_u_c_l_s_m_autotoc_md2}
 
\begin{DoxyItemize}
\item {\bfseries{Implicit}} {\bfseries{solution}} of energy and moisture budgets in the layer spanning the ground surface
\item {\bfseries{9}} {\bfseries{soil}} {\bfseries{levels}} with high vertical resolution near surface RUC LSM has more levels in soil than \mbox{\hyperlink{GFS_NOAH}{GFS Noah Land Surface Model}} model with higher resolution near the interface with the atmosphere
\item {\bfseries{Prognostic}} {\bfseries{soil}} moisture variable ( $\theta-\theta_r$) The prognostic variables for soil moisture is volumetric soil moisture content minus residual value of soil moisture which is tied to soil particles and does not participate in moisture transport.
\item {\bfseries{Frozen}} {\bfseries{soil}} {\bfseries{physics}} algorithm RUC LSM has a different approach to take into account freezing and thawing processes in soil.
\item Treatment of {\bfseries{mixed}} {\bfseries{phase}} {\bfseries{precipitation}} accounts for mixed phase precipitation provided by \mbox{\hyperlink{THOMPSON}{Thompson Aerosol-\/\+Aware Cloud Microphysics Scheme}} used in RAP and HRRR.
\item Simple treatment of {\bfseries{sea}} {\bfseries{ice}} which solves heat diffusion in sea ice and allows evolving snow cover on top of sea ice
\item Sub-\/grid-\/scale {\bfseries{heterogeneity}} of surface parameters in RUC LSM (Fig.\+3). With the certain level of confidence in the skill of the model, the next requirement is to provide land static fields and surface parameters with the best possible accuracy. RAP and HRRR use the same soil/vegetation calssifications as \mbox{\hyperlink{GFS_NOAH}{GFS Noah Land Surface Model}}. But in addition to specifying surface parameters for the dominant soil and land-\/use category in the grid box, RUC LSM has an option to take into account the sub-\/grid scale heterogeneity in the computation of such parameters as roughness length, emissivity, soil porosity, soil heat capacity and others. The difference in roughness between the mosaic and dominant category presented on Figure 3, is positive from contribution of the forests, which helped to reduce high biases of surface wind speeds in these regions. In cropland regions, roughness length also has a seasonal variability depending on the growing phase of the plants. This again helped to improve the wind forecasts during the warm season. Turning on sub-\/grid-\/scale heterogeneity option requries\+: {\ttfamily mosaic\+\_\+lu} = 1 and {\ttfamily mosaic\+\_\+soil} = 1 in the namelist, and fractions of soil and vegetation types in a grid cell. 
\item New\+: simple irrigation in the cropland area with {\ttfamily mosaic\+\_\+lu} = 1
\item New\+: water/snow intercepted by canopy as function of vegetation fraction and leaf area index (LAI)
\end{DoxyItemize}\hypertarget{_r_u_c_l_s_m_autotoc_md3}{}\doxyparagraph{RUC snow model characteristics\+:}\label{_r_u_c_l_s_m_autotoc_md3}
Snow forms additional two layers on top of soil in RUC LSM
\begin{DoxyItemize}
\item {\bfseries{2-\/layer}} {\bfseries{snow}} model\+: when SWE \texorpdfstring{$<$}{<} 1.\+6 cm -\/ snow layer is combined with top soil layer;
\item Fractional snow cover (SWE \texorpdfstring{$<$}{<} 3 cm);
\item Weighted average of snow-\/covered and snow-\/free areas to compute snow parameters (roughness, albedo);
\item \char`\"{}\+Mosaic\char`\"{} approach for patchy snow (Fig.\+4)\+:
\begin{DoxyItemize}
\item Separate treatment of energy and moisture budgets for snow-\/covered and snow-\/free portions of the grid cell
\item Aggregate solutions at the end of time step
\item Outcome\+: reduced cold bias for areas with thin snow 
\item New\+: additional options to compute snow cover fraction ({\ttfamily isncovr\+\_\+opt} =2 and 3, Niu and Yang (2007) \cite{Niu_2007}). These options allowed to reduce overprediction of number of grid cells fully covered with snow which further reduced cold-\/biases over snow. Figure 5 demonstrates that option 3 of snow cover fraction computation ({\ttfamily isncovr\+\_\+opt} = 3) in the UFS-\/based regional model matches better the satellite data for the test case on 6 February 2022.
\end{DoxyItemize}
\item New\+: added an option to use of a new formulation of snow thermal conductivity ({\ttfamily isncond\+\_\+opt} = 2, Sturm et al. (1997) \cite{sturm_1997}); 
\item Iterative snow melting algorithm;
\item Density of snow on the ground -\/ a function of compaction parameter and snow depth and temperature;
\item Snow albedo -\/ a function of temperature and snow fraction;
\item Snow interception by canopy -\/ a function of vegetation fraction and leaf area index (LAI);
\item Density of falling snow/graupel/ice precipitation (Fig.\+6)
\begin{DoxyItemize}
\item The density of falling snow/graupel/ice is computed inside RUC LSM using empirical temperature-\/dependent equations;
\item Averaged density of frozen precipitation is defined from weighted contribution of each hydrometeor species\+: \[ \rho_{fr}=\rho_{sn}*\alpha_{sn}+\rho_{gr}*\alpha_{gr}+\rho_{ice}*\alpha_{ice} \] Where subscripts sn, gr, ice -\/ snow, graupel and ice precipitation, respectively.
\end{DoxyItemize}
\item The depth of new snow is defined from its liquid equivalent and $\rho_{fr}$ 
\end{DoxyItemize}

Snow accumulation with variable density is provided as an additional product in the model guidance. Figure 6 shows one example of this product from the 23-\/h HRRR forecast for snowstorm on 29 Dec 2015. This product is in the middle panel. The panel on the left uses traditional 10\+:1 ratio, and the right panel is observed snow accumulation. We can see that the new product in the middle here has a better, further north location of maximum snow accumulation, and high amounts of snow in the product with 10\+:1 ratio are trimmed in central and southern Iowa where both observed and model precipitation had a high content of sleet. There is even larger improvement in the Chicago area, where observed and model precipitation were almost totally sleet.\hypertarget{_r_u_c_l_s_m_v6_updates_ruc}{}\doxysubsubsection{Physics Updates}\label{_r_u_c_l_s_m_v6_updates_ruc}
\begin{DoxyVersion}{Version}
UFS-\/\+SRW v2.\+2.\+0
\begin{DoxyItemize}
\item Initialization of land and ice emissivity with consideration of partial snow cover
\item Initialization of land and ice albedo with consideration of partial snow cover
\item Initialization of water vapor mixing ratio over land and ice
\item Initialization of fractions of soil and vegetation types in a grid cell
\item Changes in the computation of a flag for sea ice\+: it is set to true only if {\ttfamily flag\+\_\+cice} = .false. (uncoupled sea ice model)
\item Introduced separate variables for sea ice, for example\+: {\ttfamily showfallac} is replaced with {\ttfamily snowfallac\+\_\+ice} 
\item Added accommodation of fractional surface grid (land and ice fractions are possible within the grid cell)
\item Solar angle dependence of albedo for snow-\/free land
\item SPP option for stochastic perturbations for emissivity, albedo and vegetation fraction
\item Based on RRFS testing, the coefficient in the soil resistance formulation (Sakaguchi and Zeng (2009) \cite{sakaguchi_and_zeng_2009}) was increased from 0.\+7 to 1.\+0 to increase soil resistance to evaporation
\end{DoxyItemize}
\end{DoxyVersion}
\hypertarget{_r_u_c_l_s_m_intra_ruclsm}{}\doxysubsubsection{Intraphysics Communication}\label{_r_u_c_l_s_m_intra_ruclsm}

\begin{DoxyItemize}
\item \mbox{\hyperlink{group__lsm__ruc__group_arg_table_lsm_ruc_run}{Argument Table}}
\end{DoxyItemize}\hypertarget{_r_u_c_l_s_m_gen_ruclsm}{}\doxysubsubsection{General Algorithm}\label{_r_u_c_l_s_m_gen_ruclsm}

\begin{DoxyItemize}
\item \mbox{\hyperlink{group__lsm__ruc__group_gab76161e7a2cd6fafa9f7be5e596bca10_gab76161e7a2cd6fafa9f7be5e596bca10}{module\+\_\+sf\+\_\+ruclsm\+::lsmruc}} 
\end{DoxyItemize}\hypertarget{NoahMP}{}\doxysubsection{GFS Noah\+MP Land Surface Model}\label{NoahMP}
\hypertarget{_noah_m_p_des_noahmp}{}\doxysubsubsection{Description}\label{_noah_m_p_des_noahmp}
This implementation of the Noah\+MP Land Surface Model (LSM) is adapted from the version implemented in WRF v3.\+7 with additions by NOAA EMC staff to work with the UFS Atmosphere model. Authoritative documentation of the Noah\+MP scheme can be accessed at the following links\+:


\begin{DoxyItemize}
\item Technical documentation freely available at He et al. (2023) \cite{He_2023}.
\item A primary reference for the Noah\+MP LSM is Niu et al. (2011) \cite{niu_et_al_2011}.
\end{DoxyItemize}\hypertarget{_noah_m_p_noahmp_update}{}\doxysubsubsection{CCPP Physics Updates}\label{_noah_m_p_noahmp_update}
\begin{DoxyVersion}{Version}
UFS-\/\+SRW v2.\+2.\+0
\begin{DoxyItemize}
\item As part of a larger-\/scale effort to unify how microphysics outputs (in particular snow) are used in the land models and outputs, an additional option for using the unified frozen precipitation fraction in Noah\+MP was added
\item Diagnostic 2-\/meter temperature and humidity are based on vegetation and bare-\/ground tiles
\item Bug fixes for GFS-\/based thermal roughness length scheme
\end{DoxyItemize}
\end{DoxyVersion}
\hypertarget{_noah_m_p_intra_noahmp}{}\doxysubsubsection{Intraphysics Communication}\label{_noah_m_p_intra_noahmp}

\begin{DoxyItemize}
\item \mbox{\hyperlink{group___noah_m_p___l_s_m_ga930e0de939b2785f5bf9e6f7da7ce13e_arg_table_noahmpdrv_run}{Argument Table}}
\end{DoxyItemize}\hypertarget{_noah_m_p_gen_al_noahmp}{}\doxysubsubsection{General Algorithm}\label{_noah_m_p_gen_al_noahmp}

\begin{DoxyItemize}
\item \mbox{\hyperlink{group___noah_m_p___l_s_m_ga930e0de939b2785f5bf9e6f7da7ce13e_general_noahmpdrv}{Noah\+MP Driver General Algorithm}} 
\end{DoxyItemize}\hypertarget{CLM_LAKE_model}{}\doxysubsection{CLM Lake Model}\label{CLM_LAKE_model}
\hypertarget{_c_l_m__l_a_k_e_model_des_clmlake}{}\doxysubsubsection{Description}\label{_c_l_m__l_a_k_e_model_des_clmlake}
The Community Land Model (CLM) lake model is a multi-\/level one-\/dimensional lake model that has been implemented within the operational 3-\/km HRRR and 13-\/km RAP for small lakes (Benjamin et al. (2022) \cite{gmd-15-6659-2022}). This implementation is from the Community Land Model version 4.\+5 (Oleson et al. (2013) \cite{Oleson2013}). Subin et al. (2012) \cite{Subin_2012} describe the 1-\/d CLM lake model as applied within the Community Earth System Model (CESM) as a component of the overall CESM CLM (Lawrence et al. (2019) \cite{Lawrence_2019}). Gu et al. (2015) \cite{Gu2015} describe the introduction of the CLM lake model into the WRF model and inital experiments using its 1-\/d solution for both lakes Superior (average depth of 147 m) and Erie (average depth of 19 m).

The atmospheric inputs into the model are temperature, water vapor, horizontal wind components from the lowest atmospheric level and shortwave and longwave radiative fluxes. The CLM lake model then provides latent heat and sensible heat fluxes back to the atmosphere. It also computes 2-\/m temperature/moisture, skin temperature, lake temperature, ice fraction, ice thickness, snow water equivalent and snow depth. The CLM lake model divides the vertical lake profile into 10 layers driven by wind-\/driven eddies. The thickness of the top layer is fixed to 10-\/cm and the rest of the lake depth is divided evenly into the other 9 layers. Energy transfer (heat and kinetic energy) occurs between lake layers via eddy and molecular diffusion as a function of the vertical temperature gradient. The CLM lake model also uses a 10-\/layer soil model beneath the lake, a multi-\/layer ice formation model and up to 5-\/layer snow-\/on-\/ice model. Multiple layers in lake model have the potential to better represent vertical mixing processes in the lake.

Testing of the CLM lake model within RAP/\+HRRR applications showed computational efficiency of the model with no change of even 0.\+1\% in run time. The lake/snow variables have to be continuously transfered within the CLM lake model from one forecast to another, constrained by the atmospheric data assimilation. The lake-\/cycling initialization in RAP/\+HRRR has been effective overall, owing to accurate hourly estimates of near-\/surface temperature, moisture and winds, and shortwave and longwave estimates provided to the 1-\/d CLM lake model every time step (Benjamin et al. (2022) \cite{gmd-15-6659-2022}). Cycling techniques showed improvements over initializing lake temperatures from the SST analysis, which is problematic for small water bodies. The improvements are particularly eminent during transition periods between cold and warm seasons, and in the regions with anomalous weather conditions. The CLM lake model has the potential to improve surface prediction in the vicinity of small lakes.

The CLM lake model requires bathymetry for the lake points in the model domain. Grid points are assigned as lake points when the fraction of lake coverage in the grid cell exceeds 50\% and when this point is disconnected from oceans. The lake water mask is therefore binary, set to either 1 or 0. This binary approach for models with higher horizontal resolution --- for example, 3-\/km resolution in in the UFS SRW App --- is capable of capturing the effect of lakes on regional heat and moisture fluxes.

Lake depths for the RRFS lake configuration (Fig.\+1) are assigned from a global dataset provided by Kourzeneva et al.(2012) \cite{Kourzeneva_2012}, this dataset is referred to as GLOBv3 bathymetry in the UFS\+\_\+\+UTL.



To cold-\/start the CLM lake model in regional configurations of the UFS\+:


\begin{DoxyItemize}
\item Use the CLM option in the input.\+nml ~\newline
 -\/ lkm = 1 ~\newline
 -\/ iopt\+\_\+lake = 2
\item Lake temperature is initialized from interpolation between SST at the surface and $-4^oC$ at 50-\/m depth ~\newline
 -\/ A special case is for the Great Salt Lake, the temperature is limited with +/-\/ 3 K from the bi-\/weekly climatology
\item Temperature for soil under the lake is initialized from bottom lake temperature at the top to the substrate soil temperature at the bottom of soil layer
\item Lake ice at the top level is initialized from the GFS ice concentration
\end{DoxyItemize}

The differences of surface variables from the experimental RRFS 6-\/h forecast with/without CLM lake model are shown in Figure 2 for 2-\/m temperature and in Figure 3 for 2-\/m dewpoint.  \hypertarget{_c_l_m__l_a_k_e_model_intra_clmlake}{}\doxysubsubsection{Intraphysics Communication}\label{_c_l_m__l_a_k_e_model_intra_clmlake}

\begin{DoxyItemize}
\item \mbox{\hyperlink{namespaceclm__lake_a4e1b1be945f1e11a3c1a488c3c8a1e29_arg_table_clm_lake_run}{Argument Table}} 
\end{DoxyItemize}\hypertarget{GFS_SAMFdeep}{}\doxysubsection{GFS Scale-\/\+Aware Simplified Arakawa-\/\+Schubert (sa-\/\+SAS) Deep Convection Scheme}\label{GFS_SAMFdeep}
\hypertarget{_g_f_s__s_a_m_fdeep_des_deep}{}\doxysubsubsection{Description}\label{_g_f_s__s_a_m_fdeep_des_deep}
The scale-\/aware mass-\/flux (SAMF) deep convection scheme is an updated version of the previous Simplified Arakawa-\/\+Schubert (SAS) scheme with scale and aerosol awareness and parameterizes the effect of deep convection on the environment (represented by the model state variables) in the following way. First, a simple cloud model is used to determine the change in model state variables due to one entraining/detraining cloud type, per unit cloud-\/base mass flux. Next, the total change in state variables is retrieved by determining the actual cloud base mass flux using the quasi-\/equilibrium assumption (for grid sizes larger than a threshold value \mbox{[}currently set to 8 km\mbox{]}) or a mean updraft velocity (for grid sizes smaller than the threshold value). With a scale-\/aware parameterization, the cloud mass flux decreases with increasing grid resolution. A simple aerosol-\/aware parameterization is employed, where rain conversion in the convective updraft is modified by aerosol number concentration. The name SAS is replaced with SAMF as for the smaller grid sizes, the parameterization does not use Arakawa-\/\+Schubert\textquotesingle{}s quasi-\/equilibrium assumption any longer where the cloud work function (interpreted as entrainment-\/moderated convective available potential energy \mbox{[}CAPE\mbox{]}) by the large scale dynamics is in balance with the consumption of the cloud work function by the convection.

The SAS scheme uses the working concepts put forth in Arakawa and Schubert (1974) \cite{arakawa_and_schubert_1974} but includes modifications and simplifications from Grell (1993) \cite{grell_1993} such as saturated downdrafts and only one cloud type (the deepest possible), rather than a spectrum based on cloud top heights or assumed entrainment rates. The scheme was implemented for the GFS in 1995 by Pan and Wu (1995) \cite{pan_and_wu_1995}, with further modifications discussed in Han and Pan (2011) \cite{han_and_pan_2011} , including the calculation of cloud top, a greater CFL-\/criterion-\/based maximum cloud base mass flux, updated cloud model entrainment and detrainment, improved convective transport of horizontal momentum, a more general triggering function, and the inclusion of convective overshooting.

The SAMF scheme updates the SAS scheme with scale-\/ and aerosol-\/aware parameterizations from Han et al. (2017) \cite{han_et_al_2017} based on the studies by Arakawa and Wu (2013) \cite{arakawa_and_wu_2013} and Grell and Freitas (2014) \cite{grell_and_freitas_2014} for scale awareness and Lim (2011) by \cite{lim_2011} for aerosol awareness. The ratio of advective time to convective turnover time is also taken into account for the scale-\/aware parameterization. Along with the scale-\/ and aerosol-\/aware parameterizations, more changes are made to the SAMF scheme. The cloud base mass-\/flux computation is modified to use convective turnover time as the convective adjustment time scale. The rain conversion rate is modified to decrease with decreasing air temperature above the freezing level. Convective inhibition in the sub-\/cloud layer is used as an additional trigger condition. Convective cloudiness is enhanced by considering suspended cloud condensate in the updraft. The lateral entrainment is also enhanced to more strongly suppress convection in a drier environment.

In further update for FY19 GFS implementation, interaction with turbulent kinetic energy (TKE), which is a prognostic variable used in a scale-\/aware TKE-\/based moist EDMF vertical turbulent mixing scheme, is included. Entrainment rates in updrafts and downdrafts are proportional to sub-\/cloud mean TKE. TKE is transported by cumulus convection. TKE contribution from cumulus convection is deduced from cumulus mass flux. On the other hand, tracers such as ozone and aerosol are also transported by cumulus convection.

Occasional model crashes have been occurred when stochastic physics is on, due to too much convective cooling and heating tendencies near the cumulus top which are amplified by stochastic physics. To reduce too much convective cooling at the cloud top, the convection schemes have been modified for the rain conversion rate, entrainment and detrainment rates, overshooting layers, and maximum allowable cloudbase mass flux (as of June 2018).\hypertarget{_g_f_s__s_a_m_fdeep_ca_page}{}\doxyparagraph{Cellular Automata Stochastic Convective Organization Scheme}\label{_g_f_s__s_a_m_fdeep_ca_page}
{\bfseries{Scientific}} {\bfseries{Background}} 

Cumulus clouds in the atmosphere can organize into a variety of sizes, ranging from small fair‐weather cumulus clouds, rain showers and thunderstorms, to larger scale weather systems. In weather and climate models, such organization is traditionally not well-\/represented as the motions associated with cumulus clouds are generally too small to be resolved by the numerical model. In this scheme we use a stochastic cellular automaton (CA), a mathematical model often used to describe self‐organizing behavior in physical systems to represent the effects of convective organization. The scheme addresses the effect of convective organization in a bulk-\/plume cumulus convection parameterizations (sa\+SAS), where this type of organization has to be represented in terms of how the resolved flow would “feel” convection if more coherent structures were present on the subgrid. ~\newline


In addition, for longer range forecasts (seasonal, decadal, climate), the relevance of stochastic cumulus convection in numerical models can also be discussed in terms of noise induced forcing. As an example, on the time scale of organized convectively coupled waves, the small scale individual convective plumes grow and decay so rapidly that they are not predictable on time-\/scales longer than a few hours, whereas the organized larger scale convectively coupled wave envelope can have a deterministic limit of predictability of about two weeks. Thus, for longer range forecasts, individual convective plumes can be viewed as stochastic noise -\/ they can have an impact on the convectively coupled waves (due to noise forcing), but they are not predictable on their own. By providing the CA with a stochastic initialization, the effect of stochastic cumulus convection is also represented by the scheme.

The scientific motivation for the scheme, the CA rulesets explored, and the impact on convectively coupled equatorial waves can be found in the following references; Bengtsson et al. 2011 \cite{Bengtsson_2011}, Bengtsson et al. 2013 \cite{bengtsson_et_al_2013}, Bengtsson and Kornich (2016) \cite{bengtsson_and_kornich_2016}, Bengtsson et al 2019 \cite{Bengtsson_2019}, and Bengtsson et al. 2021 \cite{bengtsson_et_al_2021}.

{\bfseries{Technical}} {\bfseries{remarks}} 

The CA source code is located in the stochastic physics submodule in the ufs-\/weather-\/model\+: \href{https://github.com/noaa-psd/stochastic_physics}{\texttt{ https\+://github.\+com/noaa-\/psd/stochastic\+\_\+physics}} . In the UFS Weather Model, the main call to the CA routines are made from FV3/stochastic\+\_\+physics\+\_\+driver.\+F90.

There are currently two options to evolve the CA (can be done simultaneously); ({\ttfamily ca\+\_\+global}) a large scale global pattern which evolves the ruleset according to game of life with cell history, or ({\ttfamily ca\+\_\+sgs}) a sub-\/grid scale pattern which is conditioned on a forcing from the atmospheric model. The two options are controlled by namelist and are evolved in cellular\+\_\+automata\+\_\+globa.\+F90 and cellular\+\_\+automata\+\_\+sgs.\+F90 respectively. Both approaches use the main CA module update\+\_\+ca.\+F90 to evolve the CA in time. Since the CA needs to know about its neighborhood it uses the halo information to gather the state in adjacent MPI domains and/or adjacent cube sphere interfaces.

{\bfseries{The}} {\ttfamily ca\+\_\+sgs} {\bfseries{option}} -\/ {\bfseries{Coupled}} {\bfseries{to}} {\bfseries{sa\+SAS}} {\bfseries{cumulus}} {\bfseries{convection}} {\bfseries{scheme}} 

The evolution of the CA is an extension to the automaton family known as “\+Generations,” which in turn is based on the “\+Game of Life”(Chopard \& Droz, 1998 \cite{Chopard_1998}) but adds cell history to the rule set. It is a deterministic CA ruleset, initialized with Gaussian white noise. Thus, when used in an ensemble system, each ensemble member can provide a different seed to the random number generator governing the initial state to then generate a different evolution for each member. By cell history we refer to newborn cells being given a “lifetime,”τ, that is incrementally reduced by 1 each time step where the rules are not met, in contrast to going directly from 1 to 0. The CA is conditioned on a forcing from the host model through the lifetime variable τ such that\+:

\[ \tau =N\left( \frac{\int_{l=1}^{l=top}E\frac{dp }{g} }{\max\left( \int_{l=1}^{l=top} E\frac{d p}{g}\right)} \right) \]

here, N is an integer that when multiplied by the model time-\/step represents a physical time scale, such that τ is longer in regions where the forcing is larger, E is the vertically integrated convective rain evaporation from the sa\+SAS cumulus convection scheme stored in Couplingcondition. The denominator is the maximum value of the forcing in the global domain. While the grid-\/scale forcing in practice could be any two-\/dimensional field, we choose here to set it as the vertically integrated subgrid rain evaporation amount, serving as an indicator of geographical regions where enhanced subgrid organization may arise through convective cold-\/pools.

The CA is evolved on a finer grid than the numerical prediction host model (size controlled by namelist), and can be either coarse grained back to the host model grid as a fraction, or (in case of {\ttfamily nca\+\_\+plumes} = .true.) give back the maximum number of connected “plumes” (represented by connected CA cells), and their associated size within each numerical prediction host model grid-\/box. nca\+\_\+plumes is default true and the maximum cluster size is passed to the sa\+SAS cumulus convection scheme in the Couplingca\+\_\+deep container.

Depending on the activated namelist options, the CA can feed back to the sa\+SAS convection scheme via the entrainment ({\ttfamily ca\+\_\+entr}), closure ({\ttfamily ca\+\_\+closure}) or convective initiation ({\ttfamily ca\+\_\+trigger}) in the following way\+:


\begin{DoxyItemize}
\item Entrainment ({\ttfamily ca\+\_\+entr})\+: In entraining plume model bulk mass-\/flux schemes, the upward mass-\/flux is typically parameterized as a function of environmental air being entrained into the rising plume (as well as parcel properties at cloud base). The fractional entrainment is described as a function of the plume radius. Larger thermals (plumes) have smaller fractional entrainment, which is a consequence of the fact that larger areas have relatively smaller perimeters. In this scheme, the assumption is that subgrid organization will lead to a few larger plumes rather than several smaller plumes, such that the grid-\/box average fractional entrainment is reduced. Thus, after the CA is updated, we count the number of plumes, and their associated size within each NWP grid-\/box ({\ttfamily nca\+\_\+plumes} = true). If the largest cluster of cells found on the subgrid is larger than a set radius, then the fractional entrainment rate is reduced at that grid-\/point by 30\% (selected based on experimentation)
\item Triggering ({\ttfamily ca\+\_\+trigger})\+: In NWP models physical processes are parameterized in columns, and the horizontal interaction between physical processes takes place only through advection and diffusion. As the CA can organize clusters across adjacent NWP model grid-\/boxes, the method offers a novel approach to enhance the probability of triggering of convection in nearby areas, representing subgrid fluctuations in temperature and humidity, and triggering in premoistened regions if convection is triggered in a cluster. The stochastic nature of the CA may enhance organization in different directions within the grid-\/box, and across grid-\/boxes, depending on the initial seed. If the model is run as an ensemble, the convection scheme\textquotesingle{}s stochastic triggering function can help to improve uncertainty estimates associated with subgrid fluctuations of temperature and humidity and randomness in organization. In this work, model grid boxes in which the CA\textquotesingle{}s largest connecting plume exceeds a given threshold will be considered as candidates for convective activation, in addition to sa\+SAS’s current triggering criteria.
\item Closure ({\ttfamily ca\+\_\+closure})\+: We assume that convection that organizes into plumes with larger radii tends to cover a larger area fraction of the grid-\/box and thereby acts to enhance the cloud base mass flux. In this coupling strategy, we again count the number of plumes (represented by connected cellular automaton cells), and their associated size within each NWP grid-\/box. If the largest cluster of cells found on the subgrid is larger than a set radius, then the cloud base mass-\/flux is enhanced in that grid-\/box by 25\% (selected based on experimentation). This option is being revisited by reformulating the entire closure using a prognostic evolution of the updraft area fraction, and is in its current formulation not recommended.
\end{DoxyItemize}\hypertarget{_g_f_s__s_a_m_fdeep_gen_enh}{}\doxyparagraph{Physics Updates in GFS Cumulus Convection}\label{_g_f_s__s_a_m_fdeep_gen_enh}

\begin{DoxyItemize}
\item To enhance the surface-\/based convective available potential energy (CAPE), more strict convection trigger conditions are applied.
\item Enhanced downdraft detrainments start from 60 mb above the ground surface rather than from the cloud base.
\item Reduced rain evaporation with the removal of wind shear dependency, which helps to reduce cold bias in tropospheric temperature profile especially over Tropics.
\item Separation cloud depth of deep and shallow convection is increased to 200 h\+Pa from 150 h\+Pa.
\item Updraft entrainment rates for moisture, hydrometeors, and tracers are increased by about 30\%.
\item A positive definite TVD (Total Variation Diminishing) mass-\/flux transport scheme for moisture, hydrometeors and tracers and a method for removing negative tracer mixing ratio values have been implemented.
\end{DoxyItemize}

\begin{DoxySeeAlso}{See also}
NCEP Office Note 505 \cite{https://doi.org/10.25923/cybh-w893} and 506 \cite{https://doi.org/10.25923/5051-3r70} 
\end{DoxySeeAlso}
\hypertarget{_g_f_s__s_a_m_fdeep_intra_deep}{}\doxysubsubsection{Intraphysics Communication}\label{_g_f_s__s_a_m_fdeep_intra_deep}
\mbox{\hyperlink{group___s_a_m_fdeep_arg_table_samfdeepcnv_run}{Argument Table}}\hypertarget{_g_f_s__s_a_m_fdeep_gen_al_deep}{}\doxysubsubsection{General Algorithm}\label{_g_f_s__s_a_m_fdeep_gen_al_deep}
\mbox{\hyperlink{group___s_a_m_fdeep_general_samfdeep}{GFS samfdeepcnv General Algorithm}} \hypertarget{GFS_SAMFshal}{}\doxysubsection{GFS SAS-\/based Mass-\/\+Flux Scheme for Shallow convection (sa-\/\+MF)}\label{GFS_SAMFshal}
\hypertarget{_g_f_s__s_a_m_fshal_des_shal}{}\doxysubsubsection{Description}\label{_g_f_s__s_a_m_fshal_des_shal}
\mbox{\hyperlink{group___s_a_m_f__shal}{GFS sa\+SAS Shallow Convection Module}} is an updated version of the previous mass-\/flux shallow convection scheme with scale and aerosol awareness and parameterizes the effect of shallow convection on the environment. \mbox{\hyperlink{group___s_a_m_f__shal}{GFS sa\+SAS Shallow Convection Module}} is similar to \mbox{\hyperlink{group___s_a_m_fdeep}{GFS sa\+SAS Deep Convection Module}} but with a few key differences. First, no quasi-\/equilibrium assumption is used for any grid size and the shallow cloud base mass flux is parameterized using a mean updraft velocity. Further, there are no convective downdrafts, the entrainment rate is greater than for deep convection, and the shallow convection is limited to not extend over the level where $p=0.7p_{sfc}$. The paramerization of scale and aerosol awareness follows that of the SAMF deep convection scheme, although it can be interpreted as only having the \char`\"{}static\char`\"{} and \char`\"{}feedback\char`\"{} control portions, since the \char`\"{}dynamic\char`\"{} control is not necessary to find the cloud base mass flux.

The previous version of the shallow convection scheme (shalcnv.\+f) is described in Han and Pan (2011) \cite{han_and_pan_2011} and differences between the shallow and deep convection schemes are presented in Han and Pan (2011) \cite{han_and_pan_2011} and Han et al. (2017) \cite{han_et_al_2017} . Details of scale-\/ and aerosol-\/aware parameterizations are described in Han et al. (2017) \cite{han_et_al_2017} .

In further update for FY19 GFS implementation, interaction with turbulent kinetic energy (TKE), which is a prognostic variable used in a scale-\/aware TKE-\/based moist EDMF vertical turbulent mixing scheme, is included. Entrainment rates in updrafts are proportional to sub-\/cloud mean TKE. TKE is transported by cumulus convection. TKE contribution from cumulus convection is deduced from cumulus mass flux. On the other hand, tracers such as ozone and aerosol are also transported by cumulus convection.

To reduce too much convective cooling at the cloud top, the convection schemes have been modified for the rain conversion rate, entrainment and detrainment rates, overshooting layers, and maximum allowable cloudbase mass flux (as of June 2018).\hypertarget{_g_f_s__s_a_m_fshal_intr_shal}{}\doxysubsubsection{Intraphysics Communication}\label{_g_f_s__s_a_m_fshal_intr_shal}
\mbox{\hyperlink{group___s_a_m_f__shal_arg_table_samfshalcnv_run}{Argument Table}}\hypertarget{_g_f_s__s_a_m_fshal_gen_shal}{}\doxysubsubsection{General Algorithm}\label{_g_f_s__s_a_m_fshal_gen_shal}
\mbox{\hyperlink{group___s_a_m_f__shal_gen_samfshalcnv}{GFS samfshalcnv General Algorithm}} \hypertarget{CU_GF}{}\doxysubsection{Grell-\/\+Freitas Scale and Aerosol Aware Convection Scheme}\label{CU_GF}
\hypertarget{_c_u__g_f_gfcu_descrip}{}\doxysubsubsection{Description}\label{_c_u__g_f_gfcu_descrip}
The Grell-\/\+Freitas (GF) scheme, as described in Grell and Freitas (2014) \cite{grell_and_freitas_2014}, Freitas et al. (2018) \cite{freitas_et_al_2018}, Freitas et al. (2021) \cite{freitas_et_al_2021}, and Lin et al. (2022) \cite{Lin_2022} follows the mass flux approach published by Grell (1993) \cite{grell_1993}. Further developments by Grell and $D\acute{e}v\acute{e}nyi$ (2002) \cite{Grell_2002} included implementing stochastics through allowing parameter perturbations. The GF scheme includes mixed phase physics impact, momentum transport, a diurnal cycle closure (Bechtold et al. (2014) \cite{bechtold_et_al_2014} ), and a trimodal spectral size to simulate the interaction and transition from shallow, congestus and deep convection regimes. The vertical mass flux distribution of shallow, congestus and deep convection regimes is characterized by Probability Density Functions (PDFs). The three PDFs are meant to represent the average statistical mass flux characteristic of deep, congestus, and shallow (respectively) plumes in the grid area. Each PDF therefore represents a spectrum of plumes within the grid box. Forcing is different for each characteristic type. Entrainment and detrainment are derived from the PDFs. The GF scheme takes into account aerosol dependence (considered experimental and not supported in this release), which is implemented through rain generation (following Berry (1968) \cite{berry_1968} and evaporation formulations depending on the cloud concentration nuclei at cloud base (Jiang et al. (2010) \cite{Jiang_2010}), and Lee and Feingold (2010) \cite{lee_and_feingold_2010}). Wet scavenging is considered to add a memory impact. GF is able to transport tracers. Recently, GPU capabilities and cap suppressing ({\ttfamily do\+\_\+cap\+\_\+suppress}) based on radar data assimilation have been added, and they are used only for the RAP suite.

The impacts of GF scheme in operational RAP/\+HRRR include\+: (a)uses mass-\/flux schemes, which are more physically realistic than (sounding) adjustment schemes; (b)takes parameterization uncertainty into account by allowing parameters from multiple convective schemes which can be perturbed internally or with temporal and spatial correlation patterns; (c)for higher resolutions (less than 10 km), in addition to scale awareness as in Arakawa et al. (2011) \cite{Arakawa_2011} GF can transition as grid spacing decreases into a shallow convection scheme; (d)Coupled to the grid-\/scale precipitation and radiation schemes through passing of diagnosed cloud liquid and ice from simulated precipitating convective cloud and shallow convective clouds.\hypertarget{_c_u__g_f_version_cugf_enh}{}\doxysubsubsection{CCPP Physics Updates}\label{_c_u__g_f_version_cugf_enh}
\begin{DoxyVersion}{Version}
UFS-\/\+SRW v2.\+2.\+0
\end{DoxyVersion}
{\bfseries{The Implementation of GF in RRFS prototypes}}
\begin{DoxyItemize}
\item Updates for aerosol-\/awareness (experimental)
\item Scale-\/awareness is turned off when explicit microphysics is not active anywhere in the column
\item GF is completely turned off at grid points when MYNN produces shallow convection at that point
\item Radar reflectivity considers mass flux PDF as well as whether scale-\/awareness is turned on at the grid point in equation.
\end{DoxyItemize}

{\bfseries{The implementation of GF in HAFS is ongoing.}}\hypertarget{_c_u__g_f_intra_rough_gf}{}\doxysubsubsection{Intraphysics Communication}\label{_c_u__g_f_intra_rough_gf}
The GF scheme passes cloud hydrometeors to the grid-\/scale microphysics scheme (\mbox{\hyperlink{THOMPSON}{Thompson Aerosol-\/\+Aware Cloud Microphysics Scheme}} ) through detrainment from each convective cloud layer containing convective cloud. The detrained condensate interacts with short-\/ and longwave radiation by contributing to the \char`\"{}opaqueness\char`\"{} to radiation of each grid layer. Additionally, detrained condensate is added to any existing condensate, to be treated by the complex grid-\/scale microphysics scheme. This allows for a crude emulation of stratiform precipitation regions in the RAP.

Additionally, the shallow convection and PBL schemes pass cloud information to the radiation scheme, which improved cloud/radiation interaction and retention of the inversion typically found above mixed layers.


\begin{DoxyItemize}
\item \mbox{\hyperlink{group__cu__gf__group_ga5946f8b09cdcaf4fe6cee01377f4de7a_arg_table_cu_gf_driver_run}{Argument Table}}
\end{DoxyItemize}\hypertarget{_c_u__g_f_gen_gfgsd}{}\doxysubsubsection{General Algorithm}\label{_c_u__g_f_gen_gfgsd}

\begin{DoxyItemize}
\item \mbox{\hyperlink{group__cu__gf__group_ga5946f8b09cdcaf4fe6cee01377f4de7a_gen_gf_driver}{Grell-\/\+Freitas Cumulus Scheme Driver General Algorithm}} 
\end{DoxyItemize}\hypertarget{GFDL_cloud}{}\doxysubsection{GFDL Cloud Microphysics Scheme}\label{GFDL_cloud}
\hypertarget{_g_f_d_l_cloud_des_GFDL_cloud}{}\doxysubsubsection{Description}\label{_g_f_d_l_cloud_des_GFDL_cloud}
GFDL cloud microphysics (MP) scheme is a six-\/category MP scheme to replace Zhao-\/\+Carr MP scheme, and moves the GFS from a total cloud water variable to five predicted hydrometeors (cloud water, cloud ice, rain, snow and graupel). This scheme utilizes the \char`\"{}bulk water\char`\"{} microphysical parameterization technique in Lin et al. (1983) \cite{lin_et_al_1983} and has been significantly improved over years at GFDL (Lord et al. (1984) \cite{lord_et_al_1984}, Krueger et al. (1995) \cite{krueger_et_al_1995}, Chen and Lin (2011) \cite{chen_and_lin_2011}, Chen and Lin (2013) \cite{chen_and_lin_2013}). Physics processes of GFDL cloud MP are described in Figure 1 (also see \mbox{\hyperlink{group__mod__gfdl__cloud__mp_gacc8783950135d8875dbfe23ef5013c76_gacc8783950135d8875dbfe23ef5013c76}{warm\+\_\+rain()}} and \mbox{\hyperlink{group__mod__gfdl__cloud__mp_ga6d4a8494f1530ef44e39c61be5d819fe_ga6d4a8494f1530ef44e39c61be5d819fe}{icloud()}}) and are feature with time-\/split between warm-\/rain (faster) and ice-\/phase (slower) processes (see \textquotesingle{}conversion time scale\textquotesingle{} in gfdl\+\_\+cloud\+\_\+microphys.\+F90 for default values).



Some unique attributes of GFDL cloud microphysics include\+:\hypertarget{_g_f_d_l_cloud_autotoc_md0}{}\doxysubsubsection{Precipitation and Cloud Effects on Dynamics}\label{_g_f_d_l_cloud_autotoc_md0}
 The leftmost column of Figure 1 shows the external API calls used during a typical process-\/split model integration procedure. First, the solver is called, which advances the solver a full \char`\"{}physics\char`\"{} time step. This updated state is then passed to the physical parameterization package, which then computes the physics tendencies over the same time interval. Finally, the tendencies are then used to update the model state using a forward-\/in-\/time evaluation consistent with the dynamics. ~\newline
 There are two levels of time-\/stepping inside FV3. The first is the \char`\"{}remapping\char`\"{} loop, the green column in Figure 1. This loop has three steps\+:
\begin{DoxyEnumerate}
\item Perform the Lagrangian dynamics, the loop shown in the blue column of Figure 1.
\item Perform the subcycled tracer advection along Lagrangian surfaces, using accumulated mass fluxes from the Lagrangian dynamics. Subcycling is done independently within each layer to maintain local (within each layer) stability.
\item Remap the deformal Lagrangian surfaces on to the reference, or \char`\"{}\+Eulerian\char`\"{}, coordinate levels.
\end{DoxyEnumerate}

This loop is typically performed once per call to the solver, although it is possible to improve the model\textquotesingle{}s stability by executing the loop (and thereby the vertical remapping) multiple times per solver call.

At grid spacing of less than $\sim$10 km, model dynamics should be able to \char`\"{}see\char`\"{} and \char`\"{}feel\char`\"{} the cloud and precipitation condensate; heat content, heat exchange with the environment, and momentum of condensate should be accounted for. The GFDL microphysics scheme is formulated to accomplish this through strict moist energy conservation during phase changes, and keeping heat and momentum budgets for all condensate. This results in thermodynamic consistency between the FV3 microphysics scheme and FV3 dyanmics.

In current fv3gfs, GFDL in-\/core fast saturation adjustment (phase-\/changes only) is called after the \char`\"{}\+Lagrangian-\/to-\/\+Eulerain\char`\"{} remapping. When \mbox{\hyperlink{group__fast__sat__adj}{GFDL In-\/\+Core Fast Saturation Adjustment Module}} is activated (do\+\_\+sat\+\_\+adj=.true. in {\bfseries{fv\+\_\+core\+\_\+nml}} block), it adjusts cloud water evaporation (cloud water $\rightarrow$water vapor), cloud water freezing (cloud water $\rightarrow$cloud ice), and cloud ice deposition (water vapor $\rightarrow$cloud ice). The process of condensation is an interesting and well known example. Say dynamics lifts a column of air above saturation, then an adjustment is made to temperature and moisture in order to reach saturation. The tendency of the dynamics has been included in this procedure in order to have the correct balance.\hypertarget{_g_f_d_l_cloud_autotoc_md1}{}\doxysubsubsection{Scale-\/awareness}\label{_g_f_d_l_cloud_autotoc_md1}
Scale-\/awareness provided by assumed subgrid variability that is directly proportional to grid spacing. Horizontal sub-\/grid variability is a function of cell area\+:
\begin{DoxyItemize}
\item Over land\+: \[ h_{var}=\min \left\{0.2,\max\left[0.01, D_{land}(\frac{A_{r}}{10^{10}})^{0.25}\right]\right\} \]
\item Over Ocean\+: \[ h_{var}=\min \left\{0.2,\max\left[0.01, D_{ocean}(\frac{A_{r}}{10^{10}})^{0.25}\right]\right\} \]
\end{DoxyItemize}

Where $A_{r}$ is cell area, $D_{land}$ and $D_{ocean}$ are base values for sub-\/grid variability over land and ocean (larger sub-\/grid variability appears in larger area). Horizontal sub-\/grid variability is used in cloud fraction, relative humidity calculation, evaporation and condensation processes. Scale-\/awareness is achieved by this horizontal subgrid variability and a $2^{nd}$ order FV-\/type vertical reconstruction (Lin et al. (1994) \cite{lin_et_al_1994}).\hypertarget{_g_f_d_l_cloud_intro_GFDL_cloud}{}\doxysubsubsection{Intraphysics Communication}\label{_g_f_d_l_cloud_intro_GFDL_cloud}

\begin{DoxyItemize}
\item GFDL Cloud MP\+: \mbox{\hyperlink{group__gfdlmp_arg_table_gfdl_cloud_microphys_run}{Argument Table}}
\item GFDL In-\/core Fast Saturation Adjustment\+: \mbox{\hyperlink{group__fast__sat__adj_arg_table_fv_sat_adj_run}{Argument Table}}
\end{DoxyItemize}\hypertarget{_g_f_d_l_cloud_Gen_GFDL_cloud}{}\doxysubsubsection{General Algorithm}\label{_g_f_d_l_cloud_Gen_GFDL_cloud}

\begin{DoxyItemize}
\item GFDL Cloud MP\+: \mbox{\hyperlink{group__mod__gfdl__cloud__mp_ga9a157f35f4472577a555978027d97c65_ga9a157f35f4472577a555978027d97c65}{mpdrv()}}
\item GFDL In-\/core Fast Saturation Ajustment\+: \mbox{\hyperlink{group__fast__sat__adj_ga703504f9798ee0914aa0dee05465caf7_ga703504f9798ee0914aa0dee05465caf7}{fv\+\_\+sat\+\_\+adj\+\_\+work()}} 
\end{DoxyItemize}\hypertarget{THOMPSON}{}\doxysubsection{Thompson Aerosol-\/\+Aware Cloud Microphysics Scheme}\label{THOMPSON}
\hypertarget{_t_h_o_m_p_s_o_n_thompson_descrp}{}\doxysubsubsection{Description}\label{_t_h_o_m_p_s_o_n_thompson_descrp}
The scheme computes sources, sinks, and conversions for the mixing ratios of cloud water, rainwater, cloud ice, snow, and graupel. Number concentration for cloud ice (particles per cubic meter) is also forecast, based on statistical relationships of number concentration, density, and ice mass from recent observational studies. Since April 2014 (WRFv3.\+6), this is also the \char`\"{}aerosol-\/aware\char`\"{} scheme as described in Thompson and Eidhammer (2014) \cite{Thompson_2014} .

The original RAP/\+HRRR microphysics implementation represents the most aggressive attempt to include explicit prediction of cloud and precipitation microphysical processes in the NCEP operational forecast model suite. The RAP and HRRR are important guidance to NWS aviation forecasts, and any microphysics improvements are aimed at least in part, to improve that guidance. The scheme is particularly beneficial for aircraft icing forecasts. Recently, this scheme is now being tested with a GFS\+\_\+v17 prototype\+: the GFS\+\_\+v17\+\_\+p8 suite at Environmental Modeling Center (EMC) as a candidate for the next operational implementation.

The microphysical processes accounted for are shown in the graphic below\+: 

Descriptions of these preocesses follow\+:
\begin{DoxyItemize}
\item {\bfseries{Deposition\+:}} Diffusional growth of ice particles under conditions of vapor supersaturation with respect to ice. Growth is \char`\"{}aerosol-\/aware\char`\"{}, meaning the number of ice-\/friendly nulei and ice crystals is estimated by the microphysics scheme. Deposition of ice is estimated using the properties of these ice-\/friendly aerosols. Latent heat associated with the phase change from vapor to ice is released to the atmosphere.
\item {\bfseries{Sublimation\+:}} Diffusional shrinkage of ice particles under conditions of vapor subsaturation with respect to ice. The effect of aerosols on the sublimation process is estimated. Latent heat associated with the phase change from ice to vapor is removed from the atmosphere.
\item {\bfseries{Riming\+:}} Rapid freezing of supercooled cloud-\/size drops as a result of collisions with frozen precipitation particles, or foreign objects with sub $0^{o}C$ surface temperatures, such as airplane wings. Latent heat of freezing is released to the atmosphere during the process.
\item {\bfseries{Evaporation\+:}} Process by which liquid is transformed into the gaseous state. The evaporation process is \char`\"{}aerosol-\/aware\char`\"{} through estimation of aerosol impacts. Latent heat is removed from the atmosphere and stored in water vapor during this process.
\item {\bfseries{Condensation\+:}} Process by which vapor is transformed into a liquid state. The impact of aerosols on condensation is estimated. latent heat is released into the atmosphere during this process.
\item {\bfseries{Ice}} {\bfseries{multiplication\+:}} Formation of small ice particles as a result of freezing of supercooled cloud or raindrops. Ice multiplication occurs only under very restrictive conditions, but can be a large source of ice particles when these conditions are met.
\item {\bfseries{Aggregation/autoconversion}} {\bfseries{of}} {\bfseries{snow\+:}} Process through which frozen precipitation particles grow by collision and the assimilation of cloud particles or other precipitation particles. The autoconversion rate increases as ambient temperatures rise toward freezing.
\item {\bfseries{Accretion/autoconversion}} {\bfseries{of}} {\bfseries{cloud}} {\bfseries{liquid\+:}} Process through which some cloud drops grow to become raindrops by collision and the assimilation of cloud drops.
\item {\bfseries{Supercooled}} {\bfseries{water}} {\bfseries{formation\+:}} Liquid cloud particles that remain in their liquid state despite being cooled below the freezing point of water.
\end{DoxyItemize}

The scheme also assumes precipitation particle-\/size distributions for the three predicted precipitation types\+: rain, snow, and graupel. These size distributions are requied to emulate growth of precipitation by accretion of cloud-\/size particles, coalescence with other precipitation particles, and fall speeds of precipitation hydrometeors. The rainwater fall speed depends on the rainwater mixing ratio, with small values associated with fall speeds (and allowing the model to parameterize the behavior) of drizzle. The forecast precipitation type is based on what prognostic precipitation type actually reaches the surface (Benjamin et al.(2016) \cite{Benjamin_2016b} )\hypertarget{_t_h_o_m_p_s_o_n_autotoc_md4}{}\doxysubsubsection{Advantages of the Thompson Scheme and  Updating Cloud Fields}\label{_t_h_o_m_p_s_o_n_autotoc_md4}
Some of the more general advantages of the upgraded MP scheme implementation in the RAP on the model\textquotesingle{}s cloud and precipitation forecasts are listed below.


\begin{DoxyItemize}
\item Includes five hydrometeor types and their interactions, plus a sixth forecast variable for cloud ice number concentration. Drizzle is parameterized through using low fall velocities when there are low rain mixing ratios.
\item Includes impact of types of aerosol on condensation of water/deposition of ice
\item Accounts for horizontal and vertical advection of hydrometeors
\item Allows for more accurate prediction of precipitation in the form of snow, because slow fall velocities give time for snow to advect between grid columns
\item Includes cloud ice sedimentation, which positively influences RH and cloud forecasts
\item Because the scheme is a relatively complete mixed-\/phase scheme, it can account for the formation of supercooled water, a prerequisite for the model to provide useful guidance for aircraft icing forecasts
\item Can account for cloud phase changes and provides a sound physical basis for diagnosing precipitation type reaching the ground
\end{DoxyItemize}

Recently, three mechanisms have been implemented to improve the stability of the scheme for weather forecast applications\+: {\itshape inner} {\itshape loop}, {\itshape subcycle}, and {\itshape semi-\/\+Lagrangian} {\itshape sedimentation} {\itshape of} {\itshape rain} {\itshape and} {\itshape graupel}. The inner loop and the subcycle are similar in that the physics time step is subdivided and the scheme is activated more often than others in the physics suite. However, they differ in implementation. With the inner loop method, namelist variable {\ttfamily dt\+\_\+inner} is used to set the short step. Conversely, the subcycle method is controlled by CCPP Framework through the \char`\"{}subcycle loop\char`\"{} in the suite definition file. The two methods should be used exclusively. The Semi-\/\+Lagrangian sedimentation of rain and graupel (based on Juang and Hong 2010 \cite{Henry_Juang_2010} ) increases numerical stability by applying the subtime step only to sedimentation computation. Two namelist variables control the usage of the semi-\/\+Lagrangian sedimentation, {\ttfamily sedi\+\_\+semi} and {\ttfamily decfl}. {\ttfamily sedi\+\_\+semi} is set to ‘true’ to activate the method. {\ttfamily Decfl} is a parameter that needs to avoid deformation of the arriving grids, currently, \char`\"{}10\char`\"{}.\hypertarget{_t_h_o_m_p_s_o_n_v6_enh_thompson}{}\doxysubsubsection{CCPP Physics Updates}\label{_t_h_o_m_p_s_o_n_v6_enh_thompson}
\begin{DoxyVersion}{Version}
UFS-\/\+SRW v2.\+2.\+0
\end{DoxyVersion}

\begin{DoxyItemize}
\item The ice generation supersaturation requirement for nonaerosol option is reduced from 0.\+25 to 0.\+15. The purpose is to generate more ice in the upper level and reduce the OLR bias.
\item For the non-\/aerosol option of the scheme, the cloud number concentration is divided into two parts (over land and others). The number concentration over the ocean is reduced to a smaller number (50/L) from its default (100/L). The purpose is to reduce the bias in surface downward shortwave radiative flux off the coastal region including the Southeast Pacific.
\end{DoxyItemize}\hypertarget{_t_h_o_m_p_s_o_n_intra_thompson}{}\doxysubsubsection{Intraphysics Communication}\label{_t_h_o_m_p_s_o_n_intra_thompson}

\begin{DoxyItemize}
\item \mbox{\hyperlink{group__aathompson_ga8602fe3ada97d51564bf424533d96392_arg_table_mp_thompson_run}{Argument Table}}
\end{DoxyItemize}\hypertarget{_t_h_o_m_p_s_o_n_gal_thompson}{}\doxysubsubsection{General Algorithm}\label{_t_h_o_m_p_s_o_n_gal_thompson}

\begin{DoxyItemize}
\item \mbox{\hyperlink{group__aathompson_ga913721b93920f568db16540ff0dac1e9_gen_thompson_init}{thompson\+\_\+init General Algorithm}}
\item \mbox{\hyperlink{group__aathompson_ga8602fe3ada97d51564bf424533d96392_gen_thompson_hrrr}{Thompson MP General Algorithm}}
\item \mbox{\hyperlink{group__aathompson_ga34cc9c069935417137a1db6f87326142_gen_mpgtdriver}{Thompson mp\+\_\+gt\+\_\+driver General Algorithm}}
\item \mbox{\hyperlink{group__aathompson_ga30c4e9adcaeeca50d836b2ead15bc05a_gen_mp_thompson}{mp\+\_\+thompson General Algorithm}} 
\end{DoxyItemize}\hypertarget{NSSLMICRO_page}{}\doxysubsection{NSSL 2-\/moment Cloud Microphysics Scheme}\label{NSSLMICRO_page}
\hypertarget{_n_s_s_l_m_i_c_r_o_page_nssl2m_descrp}{}\doxysubsubsection{Description}\label{_n_s_s_l_m_i_c_r_o_page_nssl2m_descrp}
The NSSL two-\/moment bulk microphysical parameterization scheme that describes form and phase changes among a range of liquid and ice hydrometeors, as described in Mansell et al. (2010) \cite{Mansell_etal_2010} and Mansell and Ziegler (2013) \cite{Mansell_2013}. The microphysical parameterization predicts the mass mixing ratio and number concentration of cloud droplets, raindrops, cloud ice crystals (columns), snow particles (including large crystals and aggregates), graupel, and (optionally) hail. ~\newline


The graupel and hail particle densities are also calculated by predicting the total particle volume. The graupel category therefore emulates a range of characteristics from high-\/density frozen drops (includes small hail) to low-\/density graupel (from rimed ice crystals/snow) in its size and density spectrum. The hail category is designed to simulate larger hail sizes. Hail is only produced from higher-\/density large graupel.

Hydrometeor size distributions are assumed to follow a gamma functional form. Microphysical processes include cloud droplet and cloud ice nucleation, condensation, deposition, evaporation, sublimation, collection–coalescence, variable-\/density riming, shedding, ice multiplication, cloud ice aggregation, freezing and melting, and conversions between hydrometeor categories.

Cloud concentration nuclei (CCN) concentration is predicted as in Mansell et al. (2010) \cite{Mansell_etal_2010} with a bulk activation spectrum approximating small aerosols. The model tracks the number of unactivated CCN, and the local CCN concentration is depleted as droplets are activated, either at cloud base or in cloud. The CCN are subjected to advection and subgrid turbulent mixing but have no other interactions with hydrometeors; for example, scavenging by raindrops is omitted. CCN are restored by droplet evaporation and by a gradual regeneration when no hydrometeors are present. Aerosol sensitivity is enhanced by explicitly treating droplet condensation instead of using a saturation adjustment. Supersaturation (within reason) is allowed to persist in updraft with low droplet concentration.

Excessive size sorting (common in 2-\/moment schemes) is effectively controlled by an adaptive breakup method that prevents reflectivity growth by sedimentation (Mansell 2010 \cite{Mansell_2010}).

The NSSL scheme is designed with deep (severe) convection in mind at grid spacings of up to 4 km, but can also be run at larger grid spacing as needed for nesting etc. It is also able to capture non-\/severe and winter weather.\hypertarget{_n_s_s_l_m_i_c_r_o_page_intra_nssl2m}{}\doxysubsubsection{Intraphysics Communication}\label{_n_s_s_l_m_i_c_r_o_page_intra_nssl2m}

\begin{DoxyItemize}
\item \mbox{\hyperlink{group__nsslmp_gac1994df6febb0ba7ab30688eff8eb72f_arg_table_mp_nssl_run}{Argument Table}}
\end{DoxyItemize}\hypertarget{_n_s_s_l_m_i_c_r_o_page_gen_nssl2m}{}\doxysubsubsection{General Algorithm}\label{_n_s_s_l_m_i_c_r_o_page_gen_nssl2m}

\begin{DoxyItemize}
\item \mbox{\hyperlink{group__nsslmp_gac1994df6febb0ba7ab30688eff8eb72f_gen_nssl}{NSSL MP General Algorithm\+: interface to driver}} 
\end{DoxyItemize}\hypertarget{GFS_OZPHYS}{}\doxysubsection{GFS Ozone Photochemistry (2015) Scheme}\label{GFS_OZPHYS}
\hypertarget{_g_f_s__o_z_p_h_y_s_des_ozone}{}\doxysubsubsection{Description}\label{_g_f_s__o_z_p_h_y_s_des_ozone}
In recent years, the leading NWP centers have extended the vertical range of their NWP and DA systems from the surface up through the stratosphere ($\sim$10-\/50 km altitude) and lower mesosphere ($\sim$50-\/65 km). Some have also added stratospheric ozone ( $O_3$) as a prognostic trace constituent to their models. Examples include the Integrated Forecast System (IFS) of the European Centre for Medium Range Weather Forecasts (ECMWF) (Untch et al.(1999) \cite{untch_et_al_1999}; Dethof and holm (2004) \cite{dethof_and_holm_2004}), and the NCEP GFS (NCEP 2003). The addition of stratospheric ozone as a prognostic variable is expected to improve overall forecast and analyses skill of other fields, such as temperature and wind. The primary reasons for this are\+:
\begin{DoxyItemize}
\item more accurate assimilation of satellite radiances from channels that are sensitive to ozone (Derber and Wu (1998) \cite{derber_and_wu_1998}; John and Buehler (2004) \cite{john_and_buehler_2004});
\item utilization of correlations between ozone and wind to improve wind analyses in the upper troposphere and lower stratosphere (UTLS) (e.\+g. Riishojgaard (1996) \cite{riishojgaard_1996});
\item more accurate radiative heating rates, and hence temperatures arising from using analysed ozone rather than climatological ozone in the forecast model radiation sheme (often referred to as \char`\"{}ozone/radiation interaction\char`\"{}) (Jackson and Saunders (2002) \cite{jackson_and_saunders_2002});
\item more accurate analyses and forecasts of surface ultraviolet (UV) radiation.
\end{DoxyItemize}

With the extension of NWP/\+DA systems into the upper stratosphere, assimilation and advection alone cannot yield accurate ozone forecasts -\/ photochemical effects must also be included. Due to the complexity of ozone photochemistry, it is not currently feasible to implement full ozone photochemistry within a high-\/resolution operational global NWP system. Instead, the operational GFS currently parameterizes photochemical production and destruction based on monthly mean coefficients provided by Naval Research Laboratory CHEM2D chemistry model through an ozone photochemistry parameterization (OPP), known as CHEM2\+D-\/\+OPP (Mccormack et al.(2006) \cite{mccormack_et_al_2006}). CHEM2D is a global model extending from the surface to $\sim$120 km that solves 280 chemical reactions for 100 different species within a transformed Eulerian mean framework with fully interactive raditative heating and dynamics. CHEM2\+D-\/\+OPP consists of four coefficients describing the residual ozone photochemical tendency as the difference between the production and loss rates $(P-L)$ and its sensitivity to local changes in ozone mixing ratio $r$, temperature $T$, and overhead ozone column amount $\Sigma$ (see Figs. 1, 2, 5, and 6 in Mccormack et al.(2006) \cite{mccormack_et_al_2006}). The local time rate of change of ozone mixing ratio due to photochemistry is then\+: \[ \frac{\partial r}{\partial t}=(P-L)\left[r,T,\Sigma\right] \]

Linearization of the ozone photochemical tendency above has become a standard method for NWP and climate models. The latest generation of linearized ozone photochemistry scheme approximate the unknown function $(P-L)\left[r,T,\Sigma\right]$ by defining it about some reference state $r_0$, $T_0$, $\Sigma_0$, then expanding it about this reference state using a first-\/order Taylor series expansion. \[ \frac{\partial r(\lambda,\phi,p,t)}{\partial t}=(P-L)_0+\frac{\partial (P-L)}{\partial r}\bracevert_0(r-r_0)+\frac{\partial (P-L)}{\partial T}\bracevert_0(T-T_0)+\frac{\partial (P-L)}{\partial \Sigma}\bracevert_0(\Sigma-\Sigma_{0}) \] where $\lambda$ represents longtitude, $\phi$ represents latitude, and $p$ represents pressure as the model vertical coordinate. The subscript \char`\"{}0\char`\"{} attached to $(P-L)$ and its partial derivatives refers to their values at the reference state $r_0$, $T_0$, $\Sigma_0$, and are all zonally averaged quantities. The reference-\/state production and loss, $(P-L)_0$, and the various partial derivative terms about the state (the last three terms on the right) are computed from odd oxygen ( $O_X\equiv O_3+O$) reaction rates in the CHEM2D photochemical transport model. These coefficients are then stored in tabular form as functions of latitude, pressure, and month, with suitable linear interpolation to the desired location. The ozone photochemistry scheme has been upgraded recently through latest coefficients and adding two more terms that depend on the temperature and column ozone climatology ({\ttfamily global\+\_\+o3prdlos.\+f77} is replaced by {\ttfamily ozprdlos\+\_\+2015\+\_\+new\+\_\+sbuv\+O3\+\_\+tclm15\+\_\+nuchem.\+f77} in ozphys\+\_\+2015\+\_\+run).\hypertarget{_g_f_s__o_z_p_h_y_s_intra_ozone}{}\doxysubsubsection{Intraphysics Communication}\label{_g_f_s__o_z_p_h_y_s_intra_ozone}

\begin{DoxyItemize}
\item \mbox{\hyperlink{group___g_f_s__ozphys__2015_gaa58a04159ef5c9afe851f74df5080666_arg_table_ozphys_2015_run}{Argument Table}}
\end{DoxyItemize}\hypertarget{_g_f_s__o_z_p_h_y_s_gen_ozone}{}\doxysubsubsection{General Algorithm}\label{_g_f_s__o_z_p_h_y_s_gen_ozone}

\begin{DoxyItemize}
\item \mbox{\hyperlink{group___g_f_s__ozphys__2015_gaa58a04159ef5c9afe851f74df5080666_genal_ozphys_2015}{GFS ozphys\+\_\+2015\+\_\+run General Algorithm}} 
\end{DoxyItemize}\hypertarget{GFS_H2OPHYS}{}\doxysubsection{GFS Stratospheric H2O Scheme}\label{GFS_H2OPHYS}
\hypertarget{_g_f_s__h2_o_p_h_y_s_des_h2o}{}\doxysubsubsection{Description}\label{_g_f_s__h2_o_p_h_y_s_des_h2o}
To improve the treatment of stratospheric water vapor in the global model, NCEP implemented a parameterization of photochemical production and loss (P-\/L) of water vapor through methane oxidation and photolysis of H2O in the upper mesosphere due to solar Lyman alpha absorption is implemented in GFS.

The Navy Research Laboratory (NRL) linearized parameterization of stratospheric and mesospheric water vapor photochemistry (Mc\+Cormack at al. (2008) \cite{mccormack_et_al_2008}) applies a linearized photochemical tendency to specific humidity q in the form \[ \frac{dq}{dt}=(P-L)_{0}+\frac{\partial (P-L)}{\partial q}\mid_{0}(q-q_{0}) \] The second term on the right-\/hand side quantifies the linearized sensitivity to local changes in $q$, and yields photochemical relaxation to an equilibrium specific humidity $q_{0}$ \[ -[\frac{\partial (P-L)}{\partial q}]_{0}=\tau_*^{-1} \] The $(P-L)_{0}$ and $\tau_*$ values were computed from perturbation experiments with the NRL two-\/dimensional photochemical model (CHEM2D). The equilibrium profile $q_{0}$ is specified using a climatology based on a combination of reanalysis and satellite observations.\hypertarget{_g_f_s__h2_o_p_h_y_s_intra_h2o}{}\doxysubsubsection{Intraphysics Communication}\label{_g_f_s__h2_o_p_h_y_s_intra_h2o}

\begin{DoxyItemize}
\item \mbox{\hyperlink{group___g_f_s__h2ophys_arg_table_h2ophys_run}{Argument Table}}
\end{DoxyItemize}\hypertarget{_g_f_s__h2_o_p_h_y_s_gen_h2o}{}\doxysubsubsection{General Algorithm}\label{_g_f_s__h2_o_p_h_y_s_gen_h2o}

\begin{DoxyItemize}
\item \mbox{\hyperlink{group___g_f_s__h2ophys_genal_h2ophys}{GFS H2O Physics Scheme General Algorithm}} 
\end{DoxyItemize}\hypertarget{GFS_UGWP_v0}{}\doxysubsection{Unified Gravity Wave Physics Scheme -\/ Version 0}\label{GFS_UGWP_v0}
\hypertarget{_g_f_s__u_g_w_p_v0_des_UGWP}{}\doxysubsubsection{Description}\label{_g_f_s__u_g_w_p_v0_des_UGWP}
Gravity waves (GWs) are generated by a variety of sources in the atmosphere including orographic GWs (OGWs; quasi-\/stationary waves) and non-\/orographic GWs (NGWs; non-\/stationary oscillations). When the Version 0 of the Unified Gravity Wave Physics (UGWP v0) is invoked, the subgrid OGWs and NGWs are parameterized. For the subgrid-\/scale parameterization of OGWs, the UGWP invokes a separate scheme, the \mbox{\hyperlink{GFS_GWDPS}{GFS Orographic Gravity Wave Drag Scheme}}, which is used in the operational Global Forecast System (GFS) version 15.

The NGW physics scheme parameterizes the effects of non-\/stationary waves unresolved by dynamical cores. These non-\/stationary oscillations with periods bounded by Coriolis and Brunt-\/\+Väisälä frequencies and typical horizontal scales from tens to several hundreds of kilometers, are forced by the imbalance of convective and frontal/jet dynamics in the troposphere and lower stratosphere (Fritts 1984 \cite{fritts_1984}; Alexander et al. 2010 \cite{alexander_et_al_2010}; Plougonven and Zhang 2014 \cite{plougonven_and_zhang_2014}). The NGWs propagate upwards and the amplitudes exponentially grow with altitude until instability and breaking of waves occur. Convective and dynamical instability induced by GWs with large amplitudes can trigger production of small-\/scale turbulence and self-\/destruction of waves. The latter process in the theory of atmospheric GWs is frequently referred as the wave saturation (Lindzen 1981 \cite{lindzen_1981}; Weinstock 1984 \cite{weinstock_1984}; Fritts 1984 \cite{fritts_1984}). Herein, “saturation” or \char`\"{}breaking\char`\"{} refers to any processes that act to reduce wave amplitudes due to instabilities and/or interactions arising from large-\/amplitude perturbations limiting the exponential growth of GWs with height. Background dissipation processes such as molecular diffusion and radiative cooling, in contrast, act independently of GW amplitudes. In the middle atmosphere, impacts of NGW saturation (or breaking) and dissipation on the large-\/scale circulation, mixing, and transport have been acknowledged in the physics of global weather and climate models after pioneering studies by Lindzen 1981 \cite{lindzen_1981} and Holton 1983 \cite{holton_1983}. Comprehensive reviews on the physics of NGWs and OGWs in climate and weather models have been discussted in Alexander et al. 2010 \cite{alexander_et_al_2010}, Geller et al. 2013 \cite{geller_et_al_2013}, and Garcia et al. 2017 \cite{garcia_et_al_2017}. They are formulated using different aspects of the nonlinear and linear propagation, instability, breaking and dissipation of waves along with different specifications of GW sources (Garcia et al. 2007 \cite{garcia_et_al_2007}; Richter et al 2010 \cite{richter_et_al_2010}; Eckermann et al. 2009 \cite{eckermann_et_al_2009}; Eckermann 2011 \cite{eckermann_2011}; Lott et al. 2012 \cite{lott_et_al_2012}).

Several studies have demonstrated the importance of NGW physics to improve model predictions in the stratosphere and upper atmosphere (Alexander et al. 2010 \cite{alexander_et_al_2010}; Geller et al. 2013). In order to describe the effects of unresolved GWs in global forecast models, the representation of subgrid OGWs and NGWs has been implemented in the self-\/consistent manner using the UGWP framework.

The concept of UGWP was first proposed and implemented in the Unified Forecast System (UFS)with model top at different levels by scientists from the University of Colorado Cooperative Institute for Research in the Environmental Sciences (CIRES) at NOAA\textquotesingle{}s Space Weather Prediction Center (SWPC) and from NOAA\textquotesingle{}s Environmental Modeling Center (EMC) (Alpert et al. 2019 \cite{alpert_et_al_2019}; Yudin et al. 2016 \cite{yudin_et_al_2016}; Yudin et al. 2018 \cite{yudin_et_al_2018}). The UGWP considers identical GW propagation solvers for OGWs and NGWs with different approaches for specification of subgrid wave sources. The current set of the input and control paramters for UGWP version 0 (UGWP v0) enables options for GW effects, including momentum deposition (also called GW drag), heat deposition, and mixing by eddy viscosity, conductivity and diffusion; however, note that the eddy mixing effects induced by instability of GWs are not activated in this version.

Namelist paramters control the number of directional azimuths in which waves can propagate, number of waves in a single direction, and the level above the surface at which NGWs can be launched. Among the input parameters, the GW efficiency factors reflect intermittency of wave excitation. They should vary with horizontal resolution, reflecting the capability of the dynamical core to resolve mesoscale wave activity with the enhancement of model resolution.

Prescribed distributions for vertical momentum flux (VMF) of NGWs have been employed in global numerical weather prediction and reanalysis models to ease tuning of GW schemes to the climatology of the middle atmosphere dynamics in the absence of the global wind data above about 35 km (Eckermann et al. 2009 \cite{eckermann_et_al_2009}; Molod et al. 2015 \cite{molod_et_al_2015}). These distributions of VMF qualitatively describe the general features of the latitudinal and seasonal variations of the global GW activity in the lower stratosphere, observed from the ground and space (Ern et al. 2018 \cite{ern_et_al_2018}). Subgrid GW sources can also be parameterized to respond to year-\/to-\/year variations of solar input and anthropogenic emissions (Richter et al 2010 \cite{richter_et_al_2010}; 2014 \cite{richter_et_al_2014}).

Note that in UGWP v0, the momentum and heat deposition due to GW breaking and dissipation have been tested in the multi-\/year simulations and medium-\/range forecasts using a configuration of the UFS weather model using 127 levels with model top at approximately 80 km.

Along with the GW heat and momentum depositions, GW eddy mixing is an important element of the Whole Atmosphere Model (WAM) physics, as shown in WAM simulations with the spectral dynamics (Yudin et al. 2018 \cite{yudin_et_al_2018}). The impact of eddy mixing effects in the middle and upper atmosphere, which is not included in this version, need to be tested, evaluated, and orchestrated with the representation of the subgrid turbulent diffusion and the numerical dissipation.

The representation of subgrid GWs is particularly important for WAMs that extend into the thermosphere (top lid at $\sim$600 km). In the mesosphere and thermosphere, the background attenuation of subgrid waves due to molecular and turbulent diffusion, radiative damping and ion drag will be the additional mechanism of NGW and OGW dissipation along with convective and dynamical instability of waves described by the linear (Lindzen 1981 \cite{lindzen_1981}) and nonlinear (Weinstock 1984 \cite{weinstock_1984}; Hines 1997 \cite{hines_1997}) saturation theories.\hypertarget{_g_f_s__u_g_w_p_v0_ugwp_updates}{}\doxysubsubsection{CCPP Physics Updates}\label{_g_f_s__u_g_w_p_v0_ugwp_updates}
\begin{DoxyVersion}{Version}
UFS-\/\+SRW v2.\+2.\+0
\end{DoxyVersion}
We have added optional diagnostic outputs for the various tendencies supplied by the UGWP. They can be switched on by setting the two following input namelist variables equal to “.\+true.\+”\+: {\ttfamily ldiag3d} and {\ttfamily ldiag\+\_\+ugwp}.

The optional diagnostic outputs are\+:
\begin{DoxyItemize}
\item {\bfseries{dws3dt\+\_\+ogw\+:}} time-\/averaged wind speed tendency due to mesoscale gravity wave drag
\item {\bfseries{dws3dt\+\_\+obl\+:}} time-\/averaged wind speed tendency due to blocking drag
\item {\bfseries{dws3dt\+\_\+oss\+:}} time-\/averaged wind speed tendency due to small-\/scale gravity wave drag
\item {\bfseries{dws3dt\+\_\+ofd\+:}} time-\/averaged wind speed tendency due to turbulent orographic form drag
\item {\bfseries{ldu3dt\+\_\+ogw\+:}} time-\/averaged x wind tendency due to mesoscale orographic gravity wave drag
\item {\bfseries{ldu3dt\+\_\+obl\+:}} time-\/averaged x wind tendency due to blocking drag
\item {\bfseries{ldu3dt\+\_\+oss\+:}} time-\/averaged x wind tendency due to small scale gravity wave drag
\item {\bfseries{ldu3dt\+\_\+ofd\+:}} time-\/averaged x wind tendency due to form drag
\item {\bfseries{ldu3dt\+\_\+ngw\+:}} time-\/averaged u momentum tendency due to non-\/stationary gravity wave drag
\item {\bfseries{ldv3dt\+\_\+ngw\+:}} time-\/averaged v momentum tendency due to non-\/stationary gravity wave drag
\item {\bfseries{ldt3dt\+\_\+ngw\+:}} time-\/averaged temperature tendency due to non-\/stationary gravity wave drag
\item {\bfseries{dudt\+\_\+ogw\+:}} instantaneous x wind tendency from mesoscale orographic gravity wave drag
\item {\bfseries{dvdt\+\_\+ogw\+:}} instantaneous y wind tendency from mesoscale orographic gravity wave drag
\item {\bfseries{dudt\+\_\+obl\+:}} instantaneous x wind tendency from blocking drag
\item {\bfseries{dvdt\+\_\+obl\+:}} instantaneous y wind tendency from blocking drag
\item {\bfseries{dudt\+\_\+oss\+:}} instantaneous x wind tendency from small scale GWD
\item {\bfseries{dvdt\+\_\+oss\+:}} instantaneous y wind tendency from small scale GWD
\item {\bfseries{dudt\+\_\+ofd\+:}} instantaneous x wind tendency from form drag
\item {\bfseries{dvdt\+\_\+ofd\+:}} instantaneous y wind tendency from form drag
\item {\bfseries{du\+\_\+ogwcol\+:}} instantaneous integrated x momentum flux from mesoscale orographic gravity wave drag
\item {\bfseries{dv\+\_\+ogwcol\+:}} instantaneous integrated y momentum flux from mesoscale orographic gravity wave drag
\item {\bfseries{du\+\_\+oblcol\+:}} instantaneous integrated x momentum flux from blocking drag
\item {\bfseries{dv\+\_\+oblcol\+:}} instantaneous integrated y momentum flux from blocking drag
\item {\bfseries{du\+\_\+osscol\+:}} instantaneous integrated x momentum flux from small scale gwd
\item {\bfseries{dv\+\_\+osscol\+:}} instantaneous integrated y momentum flux from small scale gwd
\item {\bfseries{du\+\_\+ofdcol\+:}} instantaneous integrated x momentum flux from form drag
\item {\bfseries{dv\+\_\+ofdcol\+:}} instantaneous integrated y momentum flux from form drag
\item {\bfseries{du3\+\_\+ogwcol\+:}} time-\/averaged surface x momentum flux from mesoscale orographic gravity wave drag
\item {\bfseries{dv3\+\_\+ogwcol\+:}} time-\/averaged surface y momentum flux from mesoscale orographic gravity wave drag
\item {\bfseries{du3\+\_\+oblcol\+:}} time-\/averaged surface x momentum flux from blocking drag
\item {\bfseries{dv3\+\_\+oblcol\+:}} time-\/averaged surface y momentum flux from blocking drag
\item {\bfseries{du3\+\_\+osscol\+:}} time-\/averaged surface x momentum flux from small scale gravity wave drag
\item {\bfseries{dv3\+\_\+osscol\+:}} time-\/averaged surface y momentum flux from small scale gravity wave drag
\item {\bfseries{du3\+\_\+ofdcol\+:}} time-\/averaged surface x momentum flux from form drag
\item {\bfseries{dv3\+\_\+ofdcol\+:}} time-\/averaged surface y momentum flux from form drag
\end{DoxyItemize}

Note that the relevant diag\+\_\+table entries for these variables are included in\+: ufs-\/weather-\/model/tests/parm/diag\+\_\+table/diag\+\_\+table\+\_\+rap\hypertarget{_g_f_s__u_g_w_p_v0_intra_UGWPv0}{}\doxysubsubsection{Intraphysics Communication}\label{_g_f_s__u_g_w_p_v0_intra_UGWPv0}

\begin{DoxyItemize}
\item \mbox{\hyperlink{group__cires__ugwp__run__mod_ga79e2ff37434fd5d1e820a02807c20f5e_arg_table_cires_ugwp_run}{Argument Table}}
\end{DoxyItemize}\hypertarget{_g_f_s__u_g_w_p_v0_gen_al_ugwpv0}{}\doxysubsubsection{General Algorithm}\label{_g_f_s__u_g_w_p_v0_gen_al_ugwpv0}

\begin{DoxyItemize}
\item \mbox{\hyperlink{group__cires__ugwp__run__mod_ga79e2ff37434fd5d1e820a02807c20f5e_gen_cires_ugwp}{CIRES UGWP V0 Scheme General Algorithm}} 
\end{DoxyItemize}\hypertarget{GFS_GWDPS}{}\doxysubsubsection{GFS Orographic Gravity Wave Drag Scheme}\label{GFS_GWDPS}
\hypertarget{_g_f_s__g_w_d_p_s_des_gwdps}{}\doxyparagraph{Description}\label{_g_f_s__g_w_d_p_s_des_gwdps}
The GFS orographic gravity wave drag parameterization calculates the effect of gravity waves produced by flow over irregularities at the Earth\textquotesingle{}s surface such as mountains and valleys and highly dynamic atmospheric processes such as jet streams and fronts on the horizontal wind. At present, global models must be run with horizontal resolutions that cannot typically resolve atmospheric phenomena shorter than $\sim$10-\/100 km or greater for weather prediction and $\sim$100-\/1000 km or greater for climate predicition. Many atmospheric processes have shorter horizontal scales than these \char`\"{}subgrid-\/scale\char`\"{} processes interact with and affect the larger-\/scale atmosphere in important ways.

Atmospheric gravity waves are one such unresolved processes. These waves are generated by lower atmospheric sources. e.\+g., flow over irregularities at the Earth\textquotesingle{}s surface such as mountains and valleys, uneven distribution of diabatic heat sources asscociated with convective systems, and highly dynamic atmospheric processes such as jet streams and fronts. The dissipation of these waves produces synoptic-\/scale body forces on the atmospheric flow, known as \char`\"{}gravity wave drag\char`\"{}(GWD), which affects both short-\/term evolution of weather systems and long-\/term climate. However, the spatial scales of these waves (in the range of $\sim$5-\/500 km horizontally) are too short to be fully captured in models, and so GWD must be parameterized. In addition, the role of GWD in driving the global middle atmosphere circulation and thus global mean wind/temperature structures is well established. Thus, GWD parametrizations are now critical components of virtually all large-\/scale atmospheric models. GFS physics includes parameterizations of gravity waves from two important sources\+: mountains and convection. This parameterization address the former.

Atmospheric flow is significantly influenced by orography creating lift and frictional forces. The representation of orography and its influence in numerical weather prediction models are necessarily divided into the resolvable scales of motion and treated by primitive equations, the remaining sub-\/grid scales to be treated by parameterization. In terms of large scale NWP models, mountain blocking of wind flow around sub-\/grid scale orograph is a process that retards motion at various model vertical levels near or in the boundary layer. Flow around the mountain encounters larger frictional forces by being in contact with the mountain surfaces for longer time as well as the interaction of the atmospheric environment with vortex shedding which occurs in numerous observations. Lott and Miller (1997) \cite{lott_and_miller_1997}, incorporated the dividing streamline and mountain blocking in conjunction with sub-\/grid scale vertically propagating gravity wave parameterization in the context of NWP. The dividing streamline is seen as a source of gravity waves to the atmosphere above and nonlinear subgrid low-\/level mountain drag effect below.

Gravity-\/wave drag is simulated as described by Alpert et al. (1988) \cite{alpert_et_al_1988}. The parameterization includes determination of the momentum flux due to gravity waves at the surface, as well as upper levels. The surface stress is a nonlinear function of the surface wind speed and the local Froude number, following Pierrehumbert (1986) \cite{pierrehumbert_1986}. Vertical variations in the momentum flux occur when the local Richardson number is less than 0.\+25 (the stress vanishes), or when wave breaking occurs (local Froude number becomes critical); in the latter case, the momentum flux is reduced according to Lindzen (1981) \cite{lindzen_1981} wave saturation hypothesis. Modifications are made to avoid instability when the critical layer is near the surface, since the time scale for gravity-\/wave drag is shorter than the model time step. The treatment of the GWD in the lower troposphere is enhanced according to Kim and Arakawa (1995) \cite{kim_and_arakawa_1995} . Orographic Std Dev (HPRIME), Convexity(\+OC), Asymmetry (OA4) and Lx (CLX4) are input topographic statistics needed (see Appendix in Kim and Arakawa (1995) \cite{kim_and_arakawa_1995}) .

Mountain blocking influences are incorporated following ~\newline
 Lott and Miller (1997) \cite{lott_and_miller_1997} parameterization with minor changes, including their dividing streamline concept. The model subgrid scale orography is represented by four parameters, after Baines and Palmer (1990) \cite{baines_and_palmer_1990}, the standard deviation (HPRIME), the anisotropy (GAMMA), the slope (SIGMA) and the geographical orientation of the orography (THETA). These are calculated off-\/line as a function of model resolution in the fortran code ml01rg2.\+f, with script mlb2.\+sh (see Appendix\+: Specification of subgrid-\/scale orography in Lott and Miller (1997) \cite{lott_and_miller_1997}).

The orographic GWD parameterizations automatically scales with model resolution. For example, the T574\+L64 version of GFS uses four times stronger mountain blocking and one half the strength of gravity wave drag than the T383\+L64 version.\hypertarget{_g_f_s__g_w_d_p_s_intra_gwdps}{}\doxyparagraph{Intraphysics Communication}\label{_g_f_s__g_w_d_p_s_intra_gwdps}
\mbox{\hyperlink{group__gfs__gwdps_arg_table_gwdps_run}{Argument Table}}\hypertarget{_g_f_s__g_w_d_p_s_gen_al_gwdps}{}\doxyparagraph{General Algorithm}\label{_g_f_s__g_w_d_p_s_gen_al_gwdps}
\mbox{\hyperlink{group__gfs__gwdps_gen_gwdps}{GFS Orographic GWD Scheme General Algorithm}} \hypertarget{GFS_drag_suite}{}\doxysubsection{GSL Drag Suite Scheme}\label{GFS_drag_suite}
\hypertarget{_g_f_s_drag_suite_des_drag}{}\doxysubsubsection{Description}\label{_g_f_s_drag_suite_des_drag}
The GSL drag suite, developed by NOAA\textquotesingle{}s Global Systems Laboratory, is a set of subgrid-\/scale orographic drag parameterizations that calculate momentum tendencies due to the effects of unresolved topography. The drag forces they represent are those due to\+: 1) large-\/scale gravity (mountain) waves that propagate vertically and break in the free atmosphere of the troposphere, stratosphere and above; 2) low-\/level flow blocking; 3) small-\/scale gravity wave drag (GWD) due to mountain waves generated in stable planetary boundary layer (PBL) conditions, typically at nighttime, which break at or near the PBL top; and 4) turbulent orographic form drag (TOFD), which is generated by turbulent pressure perturbations that are correlated with the terrain slope. The distinction between the large-\/scale and small-\/scale gravity waves are that the former are generated by topography with horizontal scales on the order of 5 km and greater, which can support vertical propagation through the typical static stabilities found in the free atmosphere, while the latter are generated by topography with smaller horizontal scales down to about 1 km, which can support vertical propagation only in very stable conditions, typically found in nocturnal PBLs.

The large-\/scale GWD scheme is based on Kim and Doyle (2005) \cite{kim_and_doyle_2005} and Choi and Hong (2015)\cite{choi_and_hong_2015} and the code originated from the NCAR Weather Research and Forecasting (WRF) model and NOAA RAP/\+HRRR. The low-\/level blocking scheme is adapted from Kim and Doyle (2005)\cite{kim_and_doyle_2005}, with the code also originating from the WRF and RAP/\+HRRR. The small-\/scale orographic GWD scheme is based on Steeneveld et al.(2008)\cite{steeneveld_et_al_2008} and Tsiringakis et al. (2017) \cite{tsiringakis_et_al_2017}, and the TOFD scheme is adapted from Beljaars et al. (2004)\cite{beljaars_et_al_2004}.

All four orographic GWD schemes require static input data files that contain statistical information about the subgrid terrain within each model grid cell, such as the standard deviation of the subgrid topography, which comes from the high resolution USGS 30-\/second GMETED2010 dataset. These data files augment the usual \char`\"{}oro\+\_\+data.\+tile$\ast$.\+nc\char`\"{} files, which contain orographic height data and GWD static data for the GFSv16 GWD parameterizations. The static data files for the large-\/scale GWD and blocking schemes are named \char`\"{}oro\+\_\+data\+\_\+ls.\+tile$\ast$.\+nc\char`\"{}. The source topography for the datasets are calculated from a 2.\+5-\/minute lat-\/lon grid to filter out small-\/scale topographic variations. The static data files for the small-\/scale GWD and TOFD schemes are named \char`\"{}oro\+\_\+data\+\_\+ss.\+tile$\ast$.\+nc\char`\"{}. The data is from the 30-\/second topographic dataset, but band-\/passed filtered from $\sim$20km down to $\sim$2km as per Beljaars et al.(2004) \cite{beljaars_et_al_2004}.

The large-\/scale GWD and blocking schemes are explicitly tapered off from horizontal grid resolutions starting at $\sim$13km down to 3km resolution, at and below which the scheme is not active.\hypertarget{_g_f_s_drag_suite_instra_drag}{}\doxysubsubsection{Intraphysics Communication}\label{_g_f_s_drag_suite_instra_drag}

\begin{DoxyItemize}
\item \mbox{\hyperlink{group__gfs__drag__suite__mod_gaac1dcb9c3c6a9180becf43d946b4b33b_arg_table_drag_suite_run}{Argument Table}}
\end{DoxyItemize}\hypertarget{_g_f_s_drag_suite_gen_drag}{}\doxysubsubsection{General Algorithm}\label{_g_f_s_drag_suite_gen_drag}

\begin{DoxyItemize}
\item \mbox{\hyperlink{group__gfs__drag__suite__mod_gaac1dcb9c3c6a9180becf43d946b4b33b_gen_drag_suite}{GFS Orographic GWD Scheme General Algorithm}} 
\end{DoxyItemize}\hypertarget{GFS_SFCLYR}{}\doxysubsection{GFS Surface Layer Scheme}\label{GFS_SFCLYR}
\hypertarget{_g_f_s__s_f_c_l_y_r_des_rough}{}\doxysubsubsection{Description}\label{_g_f_s__s_f_c_l_y_r_des_rough}
The lowest model layer is assumed to be the surface layer and apply the Monin-\/\+Obukhov similarity profile relationship to obtain the surface stress and sensible and latent heat fluxes. The formulation was based on Miyakoda and Sirutis (1986) \cite{miyakoda_and_sirutis_1986} and has been modified by P. Long \cite{long_1984} \cite{long_1986} in the very stable and very unstable situations.

\mbox{\hyperlink{group___noah___l_s_m}{GFS Noah LSM Model}} are largely responsible for the quality of model forecasts produced for near-\/surface weather parameters, such as 2-\/meter air temperature ( $T_{2m}$) and surface skin temperature ( $LST$). $LST$ is derived from the surface energy budget, and is particularly important to remote sensing and data assimilation. How precise these two parameters can be simulated by the model strongly depends on how accurate the surface heat fluxes are parameterized, particularly the surface sensible heat flux ( $SH$). The surface thermal roughness length is a key parameter to determine $SH$. Previous GFS version do not distinguish between the roughness length for heat and momentum. The aerodynamic roughness $Z_{0m}$ is used for wind, while the thermal roughness $Z_{0t}$ is used for heat and water vapor. In the surface layer, the GFS applies the MO similarity profile scheme with modified stability functions (Miyakoda and Sirutis 1986 \cite{miyakoda_and_sirutis_1986}; Long 1984, 1986 \cite{long_1984} \cite{long_1986}). Momentum and thermal roughness lengths are necessary to estimate the surface fluxes from the atmospheric surface layer similarity theory. In the current operational GFS, the momentum roughtness length $Z_{0m}$ is specified according to the fixed vegetation types but has no seasonal variation.

In May 2011, the new vegetation-\/dependent formulations of thermal roughness formulation ( Zheng et al. (2012) \cite{zheng_et_al_2012}) was implemented to deal with the cold $LST$ bias over the arid western continental United States (CONUS) during daytime. The thermal roughness length $Z_{0H}$ is derived by a seasonlly varying formulation dependent on the seasonal cycle of green vegetation fraction. In this $Z_{0H}$ formulation, a key parameter known as $C_{zil}$ is specified according to a dependence on canopy height.

The NCEP GFS global prediction model has experienced a longstanding problem of severe cold bias in the $T_{2m}$ forecasts over land in the late afternoon and nighttime during moist seasons. This cold bias is closely associated with the nocturnal stable boundary layer and is accompanied by a corresponding warm air temperature bias in the first model level above the ground. In 2017, Zheng et al. (2017) \cite{zheng_et_al_2017} identified the bias and introduced a stability parameter constraint $(z/L)_{lim}$ to prevent the land-\/atmosphere system from fully decoupling\+: \[ (z/L)_{lim}=\frac{ln(\frac{z}{z_{0M}})}{2\alpha(1-\frac{z_{0M}}{z})} \]

Here $z$ is the height, $L$ is the Obukhov length, $z_{0M}$ is the momentum roughness length, and $\alpha = 5$.

The pertinent features of the GFS stable surface layer parameterization scheme are described in the appendix of Zheng et al. (2017) \cite{zheng_et_al_2017}.\hypertarget{_g_f_s__s_f_c_l_y_r_version_gfsslay_enh}{}\doxysubsubsection{Physics Updates}\label{_g_f_s__s_f_c_l_y_r_version_gfsslay_enh}
\begin{DoxyVersion}{Version}
CCPP v6.\+0.\+0
\end{DoxyVersion}

\begin{DoxyItemize}
\item A new canopy heat storage algorithm was implemented. The reduction of the sensible heat flux into the PBL, as a function of surface roughness and vegetation fraction, helps to reduce nighttime cold and daytime warm 2-\/meter temperature biases over forested regions.
\item A sea spray effect algorithm was included to enhance sensible and latent heat fluxes, especially for strong wind conditions.
\item To better represent sub-\/grid scale turbulence variability in the surface layer, a new algorithm for maximum surface layer stability parameter was developed as an inverse function of the background turbulent eddy diffusivity.
\item The thermal roughness length calculation over land has been modified.
\end{DoxyItemize}

\begin{DoxySeeAlso}{See also}
Han et al.(2021) \cite{https://doi.org/10.25923/cybh-w893} and Han et al.(2022) \cite{https://doi.org/10.25923/5051-3r70}
\end{DoxySeeAlso}
\hypertarget{_g_f_s__s_f_c_l_y_r_intra_rough}{}\doxysubsubsection{Intraphysics Communication}\label{_g_f_s__s_f_c_l_y_r_intra_rough}
\mbox{\hyperlink{group___g_f_s__diff__main_arg_table_sfc_diff_run}{Argument Table}}\hypertarget{_g_f_s__s_f_c_l_y_r_gen_rough}{}\doxysubsubsection{General Algorithm}\label{_g_f_s__s_f_c_l_y_r_gen_rough}
\mbox{\hyperlink{group___g_f_s__diff__main_general_diff}{GFS Surface Layer Scheme General Algorithm}} \hypertarget{SFC_MYNNSFL}{}\doxysubsection{MYNN Surface Layer Scheme}\label{SFC_MYNNSFL}
\hypertarget{_s_f_c__m_y_n_n_s_f_l_mynnsfclayer_descrip}{}\doxysubsubsection{Description}\label{_s_f_c__m_y_n_n_s_f_l_mynnsfclayer_descrip}
The surface layer scheme controls the degree of coupling between the model surface and the atmosphere. Traditionally, surface layer schemes have been developed to be paired with certain PBL schemes, but this pairing is too narrow-\/scoped, since the surface layer physics should be equally as integrated with the land-\/surface model (LSM), modern gravity wave drag suites, and wave models. The expansion of model complexity, such as the inclusion of subgrid-\/scale landuse variations, vertically distributed sources of drag \mbox{[}i.\+e., wind farm drag (Fitch et al. 2012 \cite{fitch_et_al_2012} ), small-\/scale gravity wave drag (Steeneveld et al. 2008 \cite{steeneveld_et_al_2008}) and topographic form drag (Beljaars et al. 2004) \cite{beljaars_et_al_2004} \mbox{]}, requires that surface layer scheme be developed within a broader context so assumptions made across all model components are physically consistent.

The MYNN surface layer scheme was originally developed for the Mellor-\/\+Yamada-\/\+Nakanishi-\/\+Niino (MYNN)-\/Eddy Diffusivity-\/\+Mass Flux (EDMF) scheme (Nakanishi and Niino 2009 \cite{NAKANISHI_2009}, Olson et al. 2019 \cite{https://doi.org/10.25923/n9wm-be49}) and has been used in NOAA\textquotesingle{}s operational Rapid Refresh (RAP; Benjamin et al. 2016 \cite{Benjamin_2016} ) and High-\/\+Resolution Rapid Refresh (HRRR) forecast systems since 2014. During this time, the scheme has undergone significant development in tandem with other components of the forecast systems. More recently, several new features have been added in order to accommodate different capabilities in the Common Community Physics Package (CCPP) (Heinzeller et al. 2019). This updated version of the surface layer scheme is a candidate to be used in the Rapid Refresh Forecast System (RRFS), which is a component of NOAA\textquotesingle{}s Unified Forecast System, and a successor to the RAP/\+HRRR forecast systems.

The surface layer schemes in CCPP only compute transfer coefficients and pass them to other modules that compute the surface fluxes and 2-\/m and 10-\/m diagnostics. Therefore, switches were added to bypass the calculation of surface fluxes and diagnostics for typical use, but kept as an option to used in idealized model configurations.

The surface layer physics represented by the scheme utilizes traditional Monin-\/\+Obukhov stability theory (MOST) (Monin and Obukhov 1954 \cite{monin_and_obukhov_1954} ), which is not novel, but still represents a respectable performance benchmark. This scheme is built in a modular sense, which allows for flexible testing of a variety of different subcomponents within the MOST-\/type bulk-\/flux algorithm. The specific subcomponents that exploit this modular design include the specification of the surface roughness lengths, the scalar roughness lengths, and the flux-\/profile relationships (a.\+k.\+a. stability functions). The configuration options for testing different forms of these subcomponents will be discussed below.\hypertarget{_s_f_c__m_y_n_n_s_f_l_intra_mynnsfc}{}\doxysubsubsection{Intraphysics Communication}\label{_s_f_c__m_y_n_n_s_f_l_intra_mynnsfc}
The surface-\/layer scheme is call directly after the radiation and prior to the surface modules (land-\/surface, sea-\/ice, and sea-\/surface temperature models). The surface layer scheme and the surface modules collectively calculate the necessary input for the boundary-\/layer schemes (beyond the basic state variables)\+: u$\ast$, z/L, surface heat and moisture fluxes.

\mbox{\hyperlink{group__mynn__sfc_ga97763dd6119f171b98a43e45ef0677f5_arg_table_mynnsfc_wrapper_run}{Argument Table}}\hypertarget{_s_f_c__m_y_n_n_s_f_l_gen_mynnsfclay}{}\doxysubsubsection{MYNN Surface Layer Scheme General Algorithm}\label{_s_f_c__m_y_n_n_s_f_l_gen_mynnsfclay}


Within the MYNN surface layer scheme, there is a dependency check for the first timestep. If true, several arrays are initialized at every i point. This is done because (1) some variables are calculated in schemes called after the surface layer call and (2) some variables are used within iterative processes and may not be specified until later in the surface layer scheme. The main order of operations and hightlight relevant subroutines in \mbox{\hyperlink{group__mynn__sfc_ga2d3c8b61b5bd2d5ef2aa4aa1c8393354_ga2d3c8b61b5bd2d5ef2aa4aa1c8393354}{sfclay1d\+\_\+mynn()}}. \hypertarget{GFS_NSST}{}\doxysubsection{GFS Near-\/\+Surface Sea Temperature Scheme}\label{GFS_NSST}
\hypertarget{_g_f_s__n_s_s_t_des_nst}{}\doxysubsubsection{Description}\label{_g_f_s__n_s_s_t_des_nst}
Sea Surface Temperature (SST) is required in a Numerical Weather Prediction (NWP) system as the lower thermal boundary condition of air-\/sea heat fluxes calculation at forecast step and a radiative transfer model (CRTM or RTTOV) at analysis step. SST analysis has been previously produced independently and then provided to NWP system as an input. The GFS NSST scheme was initially developed at NCEP by Xu Li in 2007, and subsequently received contributions by S. Moorthi, Y.-\/T. Hu and J. Derber, leading to operational implementation in the GFS in 2017 (Li (2015)\cite{Li_2015} and Li and Derber (2009) \cite{li_and_derber_2009}).

At the forecast step, Near-\/\+Surface Sea Temperature (NSST) is an oceanic vertical temperature profile just below the sea surface. Here, only the vertical thermal structure due to {\bfseries{diurnal}} {\bfseries{thermocline}} {\bfseries{layer}} {\bfseries{warming}} and {\bfseries{thermal}} {\bfseries{skin}} {\bfseries{layer}} {\bfseries{cooling}} is resolved, both with an origin on Fairall et al. (1996) \cite{fairall_et_al_1996} .



Linear T-\/\+Profile\+: \[ T(z)=T_{f}(z_{w})+(1-z/z_{w})T_w^{\textnormal{\textquotesingle}}(0)-(1-z/\delta_{c})T_c^{\textnormal{\textquotesingle}}(0) \] where\+:
\begin{DoxyItemize}
\item $T_{f}$\+: foundation temperature(the analysis variable)
\item $T_w^{\textnormal{\textquotesingle}}(z,t)$\+: diurnal warming profile, $z\in z_{w}\sim O(5m)$
\item $T_c^{\textnormal{\textquotesingle}}(z,t)$\+: sub-\/layer cooling profile, $z\in \delta_{c}\sim O(1mm)$
\item $z_{w}$\+: diurnal warming layer thickness
\item $\delta_{c}$\+: sub-\/layer cooling layer thickness
\end{DoxyItemize}

The GFS NSST scheme is used to forecast the NSST for two main purposes\+: supply SSTs to the atmospheric model for the calculation of air-\/sea heat and moisture fluxes and providing a sub-\/layer temperature profile forecast for use as a first guess in the Gridpoint Statistical Interpolator (GSI) data assimilation code. Atmospheric inputs include short-\/ and long-\/wave radiation, surface pressure, surface layer winds, temperature and specific humidity, and rainfall. The NSST scheme and has shown positive impact on the analysis and prediction of the ocean and atmosphere in weather prediction time scale.\hypertarget{_g_f_s__n_s_s_t_intra_nst}{}\doxysubsubsection{Intraphysics Communication}\label{_g_f_s__n_s_s_t_intra_nst}

\begin{DoxyItemize}
\item \mbox{\hyperlink{group__gfs__nst__main__mod_arg_table_sfc_nst_run}{Argument Table}}
\end{DoxyItemize}\hypertarget{_g_f_s__n_s_s_t_gen_nst}{}\doxysubsubsection{General Algorithm}\label{_g_f_s__n_s_s_t_gen_nst}

\begin{DoxyItemize}
\item \mbox{\hyperlink{group__gfs__nst__main__mod_gaf1d4588f94fa0249277318bf34bd687b_gaf1d4588f94fa0249277318bf34bd687b}{sfc\+\_\+nst\+::sfc\+\_\+nst\+\_\+run}} 
\end{DoxyItemize}\hypertarget{GFS_OCEAN}{}\doxysubsection{GFS Simple Ocean Scheme}\label{GFS_OCEAN}
\hypertarget{_g_f_s__o_c_e_a_n_des_sfcocean}{}\doxysubsubsection{Description}\label{_g_f_s__o_c_e_a_n_des_sfcocean}
The Sea Surface Temperature (SST) is a required field in Numerical Weather Prediciton (NWP) systems because it functions as the lower boundary condition for the calculation of air-\/sea heat fluxes. The GFS Simple Ocean Scheme does not change the SST. Therefore, the SST stays constant throughout the forecast unless it is updated by other processes. In some models, such as the UFS atmosphere, the SST can change if forcing towards the climatology is turned on.\hypertarget{_g_f_s__o_c_e_a_n_intra_sfcocean}{}\doxysubsubsection{Intraphysics Communication}\label{_g_f_s__o_c_e_a_n_intra_sfcocean}

\begin{DoxyItemize}
\item \mbox{\hyperlink{group__gfs__ocean__main_arg_table_sfc_ocean_run}{Argument Table}}
\end{DoxyItemize}\hypertarget{_g_f_s__o_c_e_a_n_gen_gfs_sos}{}\doxysubsubsection{General Algorithm}\label{_g_f_s__o_c_e_a_n_gen_gfs_sos}

\begin{DoxyItemize}
\item \mbox{\hyperlink{group__gfs__ocean__main_ga73fa6c3ece5a87e564dbebc1bbab6f29_ga73fa6c3ece5a87e564dbebc1bbab6f29}{sfc\+\_\+ocean\+::sfc\+\_\+ocean\+\_\+run}} 
\end{DoxyItemize}\hypertarget{GFS_SFCSICE}{}\doxysubsection{GFS Sea Ice Scheme}\label{GFS_SFCSICE}
\hypertarget{_g_f_s__s_f_c_s_i_c_e_des_sice}{}\doxysubsubsection{Description}\label{_g_f_s__s_f_c_s_i_c_e_des_sice}
Sea ice is a thin skin of frozen water covering the polar oceans. The sea ice strongly interacts with both the atmosphere above and the ocean underneath in the high latitudes. In a coupled weather/climate system, changes in sea ice extent, thickness and concentration regionally or globally would influence oceanic and atmospheric conditions, which in turn affect the sea ice distribution. The physical and dynamical processes affecting the weather and climate are considered as follows\+:
\begin{DoxyItemize}
\item The high albedo of the sea ice reflects more solar radiation back to the space. The feedbacks are considered as positive. The broader the sea ice cover, the higher the surface albedo, which result in less amount of solar radiation absorbed at the Earth\textquotesingle{}s surface. A cooler surface would favor more sea ice to form. The process would be reversed in less sea ice situation.
\item The sea ice restricts the heat/water exchange between the air and ocean. The presence of extensive areas of sea ice would suppress the heat loss in winter and the heat gain in summer by the ocean. Even a thin ice cover influences the turbulent heat transfer significantly between ocean and atmosphere. The surface fluxes of sensible and latent heat can be greater by up to two orders of magnitude at the open water surface of a lead or polynya than that through (snow covered) pack ice.
\item The sea ice modifies air/sea momentum transfer, ocean fresh water balance and ocean circulation. The freezing and melting of the ocean surface and the associated fluxes of salt and heat produce major changes in the density structure of the polar water. Formation of sea ice injects salt into the ocean makes the water heavier and more convectively unstable, conversely when melting occurs, stable and fresh layers can prevent deep covective activity.
\end{DoxyItemize}

A sea ice model, in general, may contain subcomponents treating 1) dynamics (ice motion), 2) ice transport, 3) multiple ice thickness categories (including leads), 4) surface albedo, and 5) vertical thermodynamics. GFS sea ice scheme is concerned with a scheme for the last of these processes. A three-\/layer thermodynamic sea ice model (Winton (2000) \cite{winton_2000}) has been coupled to GFS. It predicts sea ice/snow thickness, the surface temperature and ice temperature structure. In each model grid box, the heat and moisture fluxes and albedo are treated separately for the ice and the open water.\hypertarget{_g_f_s__s_f_c_s_i_c_e_intra_sice}{}\doxysubsubsection{Intraphysics Communication}\label{_g_f_s__s_f_c_s_i_c_e_intra_sice}

\begin{DoxyItemize}
\item \mbox{\hyperlink{group__gfs__sice__main_arg_table_sfc_sice_run}{Argument Table}}
\end{DoxyItemize}\hypertarget{_g_f_s__s_f_c_s_i_c_e_gen_sice}{}\doxysubsubsection{General Algorithm}\label{_g_f_s__s_f_c_s_i_c_e_gen_sice}

\begin{DoxyItemize}
\item \mbox{\hyperlink{group__gfs__sice__main_general_sice_run}{General Algorithm}}
\item \mbox{\hyperlink{group__gfs__sice__main_ga7ef26147a1c8d851bfaf0a7574ba9fbb_gen_ice3lay}{Three-\/layer Thermodynamics Sea Ice Model General Algorithm}} 
\end{DoxyItemize}\hypertarget{GFS_SPP}{}\doxysubsection{Stochastically-\/\+Perturbed Parameterizations (SPP)}\label{GFS_SPP}
\hypertarget{_g_f_s__s_p_p_des_spp}{}\doxysubsubsection{Description}\label{_g_f_s__s_p_p_des_spp}
For both regional and global ensembles, sufficient initial condition and physics uncertainty representation is critically important to ensemble performance. Otherwise, members may tend toward similar solutions, resulting in forecasts not capturing the full envelope of possible outcomes. To improve the effectiveness of the ensemble, stochastic physics can be employed. Stochastically-\/\+Perturbed Parameterizations (SPP) provides one method through which unknown or uncertain physical processes can be represented in ensemble forecasting. As it is currently employed in CCPP (Beck et al.(2022) \cite{beck_et_al_2022}), SPP can be applied to the following physics parameterizations\+: \mbox{\hyperlink{MYNNEDMF}{MYNN-\/\+EDMF Boundary Layer and Shallow Cloud Scheme}}, \mbox{\hyperlink{SFC_MYNNSFL}{MYNN Surface Layer Scheme}}, \mbox{\hyperlink{GFS_RRTMG}{GFS RRTMG Shortwave/\+Longwave Radiation Scheme}}, GFS\+\_\+\+UNIFIED\+\_\+\+UGWP,\mbox{\hyperlink{GFS_drag_suite}{GSL Drag Suite Scheme}} and \mbox{\hyperlink{THOMPSON}{Thompson Aerosol-\/\+Aware Cloud Microphysics Scheme}} (note that both \mbox{\hyperlink{RUCLSM}{RUC Land Surface Model}} and \mbox{\hyperlink{GFS_NOAH}{GFS Noah Land Surface Model}} can also be run with stochastic parameter perturbations). A list of the parameters perturbed within each scheme can be found below\+:


\begin{DoxyItemize}
\item \mbox{\hyperlink{MYNNEDMF}{MYNN-\/\+EDMF Boundary Layer and Shallow Cloud Scheme}}
\begin{DoxyItemize}
\item Eddy diffusivity
\item Viscosity
\item Background water vapor
\item Lateral entrainment rate
\end{DoxyItemize}
\item \mbox{\hyperlink{SFC_MYNNSFL}{MYNN Surface Layer Scheme}}
\begin{DoxyItemize}
\item Thermal roughness length
\item Moisture roughness length
\item Aerodynamic roughness length
\end{DoxyItemize}
\item GFS\+\_\+\+UNIFIED\+\_\+\+UGWP and \mbox{\hyperlink{GFS_drag_suite}{GSL Drag Suite Scheme}}
\begin{DoxyItemize}
\item Subgrid-\/scale terrain variations
\item Froude number
\item Wind speed tendencies
\end{DoxyItemize}
\item \mbox{\hyperlink{GFS_RRTMG}{GFS RRTMG Shortwave/\+Longwave Radiation Scheme}}
\begin{DoxyItemize}
\item Effective cloud water, snow, and ice radii
\end{DoxyItemize}
\item \mbox{\hyperlink{THOMPSON}{Thompson Aerosol-\/\+Aware Cloud Microphysics Scheme}}
\begin{DoxyItemize}
\item Graupel intercept parameter
\item Cloud droplet shape parameter
\item Vertical velocity used in CCN activation
\item Ice number concentration
\end{DoxyItemize}
\end{DoxyItemize}

Magnitudes, time decorrelation lengths, and other namelist settings can be modified to control the perturbations for each scheme; however, for now, all parameters are perturbed identically within each scheme. For detailed information on how to activate and modify SPP settings for these schemes, the reader is referred to \href{https://ufs-srweather-app.readthedocs.io/en/latest/index.html}{\texttt{ the SRW App Users Guide}}.

Each parameter listed above was chosen through consultation with physics experts, often the author of the parameterization, in order to target fields that could benefit from the application of SPP. However, there are many other parameters which could be effectively perturbed to improve uncertainty representation and ensemble spread generation. In most of the perturbed parameterizations, the perturbation magnitudes are multiplied by coefficients to generate reasonable parameter values; however, users should be careful when making changes to the magnitudes provided as a guideline in the SRW App documentation, since it is still possible to generate unphysical parameter values. Default magnitudes and decorrelation lengths in the SRW App documentation were the result of extensive testing with 3-\/km CONUS simulations. If the user would like to apply SPP at other resolutions, it is recommended that retrospective simulations be conducted over the area of interest to ensure reasonable application of the perturbations.\hypertarget{_g_f_s__s_p_p_autotoc_md5}{}\doxyparagraph{GFS Surface Parameter Perturbation}\label{_g_f_s__s_p_p_autotoc_md5}
Land surface perturbation (Gehne et al. (2019) \cite{Gehne_2019}) has been recently introduced into UFS. This treatment is based on the hypothesis that one of the major causes of the insufficient spread in current global NWP model,especially near the surface, is a lack of treatment of uncertainty in the soil state and in the associated model parameters. It allows for land surface parameters such as surface albedo, vegetation fraction, soil hydraulic conductivity, leaf area index (LAI), surface roughness lengths for heat and momentom to vary in space. These parameters and variables have been shown to impact forecasts of 2m temperature, 10m wind and precipitation. Based on the parameter or variable,different strategies to perturb are necessary. 