All-sky satellite data assimilation at operational weather forecasting centres
Geer, Alan J. ; Lonitz, Katrin ; Weston, Peter ; Kazumori, Masahiro ; Okamoto, Kozo ; Zhu, Yanqiu ; Liu, Emily Huichun ; Collard, Andrew ; Bell, William ; Migliorini, Stefano ; Chambon, Philippe ; Fourrié, Nadia ; Kim, Min-Jeong ; Köpken-Watts, Christina ; Schraff, Christoph
This article reviews developments towards assimilating cloud and precipitation-affected satellite radiances at operational forecasting centres. Satellite data assimilation is moving beyond the 'clear-sky' approach that discards any observations affected by cloud. Some centres already assimilate cloud and precipitation-affected radiances operationally and the most popular approach is known as 'all-sky', which assimilates all observations directly as radiances, whether they are clear, cloudy or precipitating, using models (both for radiative transfer and forecasting) that are capable of simulating cloud and precipitation with sufficient accuracy. Other frameworks are being tried including the assimilation of humidity retrieved from cloudy observations using Bayesian techniques. Although the all-sky technique is now proven for assimilation of microwave radiances, it has yet to be demonstrated operationally for infrared radiances, though several centres are getting close. Assimilating frequently-available all-sky infrared observations from geostationary satellites could give particular benefit for short-range forecasting. More generally, assimilating cloud and precipitation-affected satellite observations improves forecasts into the medium-range globally, and it can also improve the analysis and shorter-range forecast of otherwise poorly-observed weather phenomena as diverse as tropical cyclones and wintertime low cloud.
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