8 December 2006 Prediction of the diurnal cycle of clouds using a multimodel superensemble and ISCCP data sets
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Abstract
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.
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Arindam Chakraborty, Arindam Chakraborty, T. N. Krishnamurti, T. N. Krishnamurti, C. Gnanaseelan, C. Gnanaseelan, } "Prediction of the diurnal cycle of clouds using a multimodel superensemble and ISCCP data sets", Proc. SPIE 6404, Remote Sensing and Modeling of the Atmosphere, Oceans, and Interactions, 64040A (8 December 2006); doi: 10.1117/12.697088; https://doi.org/10.1117/12.697088
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