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Media type:
E-Article
Title:
Precipitation process and rainfall intensity differentiation using Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager data
Published in:Journal of Geophysical Research: Atmospheres
Language:
English
DOI:
10.1029/2008jd010464
ISSN:
0148-0227
Origination:
Footnote:
Description:
<jats:p>A new day and night technique for precipitation process separation and rainfall intensity differentiation using the Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager is proposed. It relies on the conceptual design that convective clouds with higher rainfall intensities are characterized by a larger vertical extension and a higher cloud top. For advective‐stratiform precipitation areas, it is assumed that areas with a higher cloud water path (CWP) and more ice particles in the upper parts are characterized by higher rainfall intensities. First, the rain area is separated into areas of convective and advective‐stratiform precipitation processes. Next, both areas are divided into subareas of differing rainfall intensities. The classification of the convective area relies on information about the cloud top height gained from water vapor‐IR differences and the IR cloud top temperature. The subdivision of the advective‐stratiform area is based on information about the CWP and the particle phase in the upper parts. Suitable combinations of temperature differences (Δ<jats:italic>T</jats:italic><jats:sub>3.9–10.8</jats:sub>, Δ<jats:italic>T</jats:italic><jats:sub>3.9–7.3</jats:sub>, Δ<jats:italic>T</jats:italic><jats:sub>8.7–10.8</jats:sub>, Δ<jats:italic>T</jats:italic><jats:sub>10.8–12.1</jats:sub>) are incorporated to infer information about the CWP during nighttime, while a visible and a near‐IR channel are considered during the daytime. Δ<jats:italic>T</jats:italic><jats:sub>8.7–10.8</jats:sub> and Δ<jats:italic>T</jats:italic><jats:sub>10.8–12.1</jats:sub> are particularly included to supply information about the cloud phase. Intensity differentiation is realized by using pixel‐based confidences for each subarea calculated as a function of the respective value combinations of the previously mentioned variables. For the calculation of the confidences, the value combinations are compared with ground‐based radar data. The proposed technique is validated against ground‐based radar data and shows an encouraging performance (Heidke skill score 0.07–0.2 for 15‐min intervals).</jats:p>