All-sky satellite data assimilation at operational weather forecasting centres

作者：Geer, Alan J.^*; Lonitz, Katrin; Weston, Peter; Kazumori, Masahiro; Okamoto, Kozo; Zhu, Yanqiu; Liu, Emily Huichun; Collard, Andrew; Bell, William; Migliorini, Stefano; Chambon, Philippe; Fourrie, Nadia; Kim, Min-Jeong; Koepken-Watts, Christina; Schraff, Christoph

来源：Quarterly Journal of the Royal Meteorological Society, 2018, 144(713): 1191-1217.

DOI：10.1002/qj.3202

• all-sky
• cloud and precipitation
• data assimilation
• infrared
• microwave
• NWP
• satellite

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 (for both 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 in the medium range globally and can also improve the analysis and shorter-range forecasting of otherwise poorly observed weather phenomena as diverse as tropical cyclones and wintertime low cloud.

• 出版日期2018-4