Clouds play a major role in the radiation budget of the earth-atmosphere system. They contribute to a high amplitude of variation on the time scale of one day. This has significant impacts on the climate of the earth. Current cloud parameterization schemes have significant deficiency to predict the diurnal cycle
of cloud cover a few days in advance. The present study addresses this issue utilizing a two fold approach.
We used four versions of the Florida State University (FSU) global spectral model (GSM) including four different cloud parameterization schemes in order to construct ensemble/superensemble forecasts of cloud covers. The results show that it is possible to substantially reduce the 1-5 days forecast errors of phase
and amplitude of the diurnal cycle of clouds with this methodology. Further, a unified cloud parameterization scheme is developed for climate models, which,
when implemented in the FSU GSM, carries a higher skill compared to those of the individual cloud schemes.
This study shows that while the multimodel superensemble is still the best product in forecasting the diurnal cycle of clouds, a unified cloud
parameterization scheme, used in a single model, also provides higher skills
compared to the individual cloud models. Moreover, since this unified scheme is
an integral part of the model, the overall forecast skill improves both in terms of
radiative fluxes and precipitation and thus has a greater impact on both weather
and climate time scales.