This module defines four algorithms that are called to calculate dominant precipitation type, and the tallies are sumed in calwxt_dominant().
Functions/Subroutines | |
subroutine | calpreciptype (kdt, nrcm, im, ix, lm, lp1, randomno, xlat, xlon, gt0, gq0, prsl, prsi, prec, phii, tskin, domr, domzr, domip, doms) |
subroutine | calwxt (lm, lp1, t, q, pmid, pint, d608, rog, epsq, zint, iwx, twet) |
This subroutine computes precipitation type using a decision tree approach that uses variables such as integrated wet bulb temperatue below freezing and lowest layer temperature (Baldwin et al. 1994 [11]) | |
subroutine | calwxt_ramer (lm, lp1, t, q, pmid, rh, td, pint, ptyp) |
This subroutine is written and provided by Jim Ramer at NOAA/ESRL (Ramer (1993) [167]). | |
real(kind=kind_phys) function | xmytw (t, td, p) |
subroutine | calwxt_bourg (lm, lp1, rn, g, t, q, pmid, pint, zint, ptype) |
this routine computes precipitation type using a decision tree approach that uses the so-called "energy method" of Bourgouin(2000) [25]. | |
subroutine | calwxt_revised (lm, lp1, t, q, pmid, pint, d608, rog, epsq, zint, twet, iwx) |
This subroutine computes precipitation type using a decision tree approach that uses variables such as integrated wet bulb temperature below freezing and lowest layer temperature (Baldwin et al.1994 [11]). Since the original version of the algorithm has a high bias for freezing rain and sleet, the revised version is to balance that bias with a version more likely to predict snow. | |
subroutine | calwxt_dominant (nalg, rain, freezr, sleet, snow, domr, domzr, domip, doms) |
This subroutine takes the precipitation type solutions from different algorithms and sums them up to give a dominant type. | |