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Discriminating raining from non‐raining cloud areas at mid‐latitudes using meteosat second generation SEVIRI night‐time data
Author(s) -
Thies B.,
Nauss T.,
Bendix J.
Publication year - 2008
Publication title -
meteorological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.672
H-Index - 59
eISSN - 1469-8080
pISSN - 1350-4827
DOI - 10.1002/met.56
Subject(s) - environmental science , meteorology , precipitation , brightness temperature , nowcasting , radar , cloud top , remote sensing , satellite , cloud computing , brightness , computer science , geology , geography , telecommunications , physics , engineering , aerospace engineering , optics , operating system
A new method for the delineation of precipitation during night‐time using multispectral satellite data is proposed. The approach is not only applicable to the detection of mainly convective precipitation by means of the commonly used relation between infrared cloud‐top temperature and rainfall probability but enables also the detection of stratiform precipitation (e.g. in connection with mid‐latitude frontal systems). The presented scheme is based on the conceptual model that precipitating clouds are characterized by a combination of particles large enough to fall, an adequate vertical extension [both represented by the cloud water path (CWP)], and the existence of ice particles in the upper part of the cloud. As no operational retrieval exists for Meteosat Second Generation (MSG) to compute the CWP during night‐time, suitable combinations of brightness temperature differences (Δ T ) between the thermal bands of Meteosat Second Generation‐Spinning Enhanced Visible and InfraRed Imager (MSG SEVIRI, Δ T 3.9–10.8 , Δ T 3.9–7.3 , Δ T 8.7–10.8 , Δ T 10.8–12.1 ) are used to infer implicit information about the CWP and to compute a rainfall confidence level. Δ T 8.7–10.8 and Δ T 10.8–12.1 are particularly considered to supply information about the cloud phase. Rain area delineation is realized by using a minimum threshold of the rainfall confidence. To obtain a statistical transfer function between the rainfall confidence and the channel differences, the value combination of the channel differences is compared with ground‐based radar data. The retrieval is validated against independent radar data not used for deriving the transfer function and shows an encouraging performance as well as clear improvements compared to existing optical retrieval techniques using only IR thresholds for cloud‐top temperature. Copyright © 2008 Royal Meteorological Society

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