Satellite observations sensitive to cloud and precipitation are key to improving global, regional and convective scale weather forecasts. All-sky assimilation of microwave radiances has been proven through operational assimilation. Further, all-sky infrared and visible radiances are in development, particularly to make use of rapidly updated geostationary observations. Cloud and precipitation radar, cloud lidar and lightning data should all start to contribute to NWP in the next decade. However, the assimilation of cloud and precipitation pushes data assimilation beyond its current design assumptions. Models often have state dependent biases in cloudy areas and do not represent details of the microphysical and sub-grid variability that are needed to drive the observation operators. These, based on scattering radiative transfer, require major simplification to make them fast enough for operational use. The random part of the observation error is dominated by the lack of predictability of cloud and precipitation processes. How can we use increasing satellite instrument resolutions when the predictable scales of cloud are much lower? Further, these predictability or representation errors are correlated in space and time and likely with the background errors, but to diagnose these errors is extremely difficult. The problem is not just correlations, because cloud and precipitation also give rise to nonlinear, non-Gaussian bounded processes and errors. To progress our ability to use cloud and precipitation data we may need to ‘learn’ better physical models and microphysical assumptions, improve fast scattering radiative transfer, and allow data assimilation to handle errors that are non-Gaussian, heterogeneous and heavily correlated.
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