Premium
All‐sky satellite data assimilation at operational weather forecasting centres
Author(s) -
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 MinJeong,
KöpkenWatts Christina,
Schraff Christoph
Publication year - 2018
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.3202
Subject(s) - environmental science , data assimilation , meteorology , satellite , geostationary orbit , sky , cloud computing , precipitation , remote sensing , weather forecasting , numerical weather prediction , geostationary operational environmental satellite , weather satellite , computer science , geography , aerospace engineering , engineering , operating system
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.