Publication Date:
2016-03-01
Description:
Infrared satellite observations are strongly affected by clouds, which complicates their effective use in data assimilation. While observation minus model first guess (FG departure) statistics for cloud-free data are close to a normal (Gaussian) distribution, the occurrence of clouds leads to strongly increased uncertainty, systematic differences between observations and model forecasts, and subsequently to a clear deviation of the FG departures from Gaussianity that is usually assumed in data assimilation. This study aims to classify the cloud impact on MSG SEVIRI infrared brightness temperature observations and model equivalents to mitigate the issues of non-Gaussian FG departure statistics for data assimilation. A threshold brightness temperature is introduced that allows to quantify the cloud impact and to derive an error estimate for FG departures as function of the cloud impact. The use of the dynamic error estimate leads to substantially more Gaussian FG departure statistics. Based on the dynamic error estimate, an observation error model is derived for the assimilation of infrared brightness temperature observations in an all-sky approach. The proposed method allows to treat cloud-free and cloud-affected observations in a uniform way without the need for cloud-screening.
Print ISSN:
0035-9009
Electronic ISSN:
1477-870X
Topics:
Geography
,
Physics
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