Cloud reflectivity profile classification using MSG/SEVIRI infrared multichannel and TRMM data

Autor(es)

Lima, Wagner F. A. ^[1] ; Machado, Luiz A. T. ^[1]

Afiliação do(s) autor(es)

^[1] Inst Nacl Pesquisas Espaciais, CPTEC, BR-12630000 Cachoeira Paulista, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: International Journal of Remote Sensing; v. 34, n. 12, p. 4384-4405, 2013.
• Citações Web of Science: 0

Resumo

This work analyses the capability of utilizing cloud-top multispectral radiation to extract information about the vertical reflectivity profile of clouds. Reflectivity profiles and cloud type classification were collected using the Tropical Rainfall Measuring Mission (TRMM) 2A25 algorithm and brightness temperature multispectral channels (3.9, 6.2, 8.7, 10.8, and 12 mu m) from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the Meteosat Second Generation (MSG) satellite. The analysis was performed on four cloud types: convective, warm, and stratiform with and without bright band, using a four-channel combination (10.8-3.9, 6.2-10.8, 8.7-10.8, and 10.8-12.0 mu m). The study was applied over Tropical Africa at the MSG subsatellite point, in August 2006. Sixteen individual profile types were detected: three warm, four convective, three stratiform without bright band, and six stratiform with bright band. These cloud profile types were examined using cloud-top multichannel brightness temperature differences. The channel combination results demonstrated that the information obtained from cloud-top radiation enables us to detect specific individual characteristics within the cloud reflectivity profile. The channel combinations employed in this study were effective in identifying warm and cold cloud types. In the 10.8-3.9 and 8.7-10.8 mu m channels, brightness temperature differences were indicated in the detection of warm clouds, while the 6.2-10.8 mu m channel was noted to be very efficient in classifying cold clouds. Cold clouds types were much more difficult to classify because they possess a similar multichannel signature, which caused ambiguity in the classification. In order to reduce this uncertainty, it was necessary to use texture information (space variability) to acquire a clearer distinction between different cloud types. The survey analysis showed good performance in classifying cloud types, with an accuracy of about 77.4% and 73.5% for night and day, respectively. (AU)

• Processo FAPESP: 09/15235-8 - Processos de nuvens associados aos principais sistemas precipitantes no Brasil: uma contribuição à modelagem da escala de nuvens e ao GPM (Medida Global de Precipitação)
• Beneficiário: Luiz Augusto Toledo Machado
• Linha de fomento: Auxílio à Pesquisa - Temático