Improvement of Cloud Radiative Forcing and Its Impact on Weather Forecasts

Abstract

The global numerical weather prediction model GRAPES at the National Meteorological Center of the China Meteorological Administration is subject to substantial systematic discrepancies from satellite-retrieved cloud cover, cloud water contents, and radiative fluxes. In particular, GRAPES produces insufficient total cloud cover and liquid water amounts and, consequently, greatly underestimates cloud radiative forcings and causes substantial radiation budget errors. Along with updates of several physics components, new parameterization schemes are incorporated in this study to more realistically represent cloud-radiation interactions. These schemes include predictions for cloud cover, liquid water, and effective radius as well as radiative effects of partial clouds and in-cloud inhomogeneity. As a result, radiation fluxes and cloud radiative forcings at both the surface and top of the atmosphere agree much better with the best available satellite data. The global mean model biases in most radiation fluxes using the new physics are approximately three times smaller than using the original physics. These improvements enhance the model weather forecast skills for key surface variables, including precipitation and 2 m temperature, and for height and temperature in the lower troposphere. Although non- trivial biases still exist, this study nonetheless represents the first essential step toward correcting the radiation imbalance before tackling other formulation deficiencies so that significantly enhanced GRAPES weather forecast skills can eventually be achieved.