Effects of cloud microphysics on the universal performance of neural
network radiation scheme
Abstract
The stability of radiation emulator on cloud microphysics changes is
essential for utilization in operational weather-forecasting models with
frequent updates. This study examined the effects of 15 microphysics
schemes on a radiation emulator for real and ideal cases. In the real
case, although the forecast errors (compared to a control run) were
higher with different microphysics schemes compared to those with the
trained scheme, the forecast error for the 2-m temperature rather
improved by 0.9-5.4% compared to observations. The radiation emulator
for the real case was applied to a two-dimensional ideal simulation to
test the universal applicability of the emulator; the resulting forecast
errors in heating rates and fluxes for 14 microphysics schemes increased
by 8.6-41.3% compared to the trained scheme. The errors were reduced by
26.5-50.4% by utilizing compound parameterization. Therefore, the
stability and accuracy of the radiation emulator were confirmed for
various microphysics schemes.