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31 October 2005 Upper level divergence estimated from wind field observation using Meteosat-8 data
Régis Borde, Marianne König, Johannes Schmetz
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Proceedings Volume 5979, Remote Sensing of Clouds and the Atmosphere X; 59790M (2005) https://doi.org/10.1117/12.626423
Event: SPIE Remote Sensing, 2005, Bruges, Belgium
Abstract
Upper-level divergence is often associated to low-level convergence through the principle of mass continuity, inducing an ascending motion of the air mass. Vertical motion, either upward or downward, is recognized as an important parameter in the atmosphere because it affects the formation or dissipation of clouds. An important application of vertical motion observations is to use this quantity as diagnostic for the occurrence of rain. For instance, extensive regions of precipitation associated with extratropical cyclones are regions of large-scale upward motion. Similarly, the nearly cloud-free regions in large anticyclones are regions in which air is subsiding. Previous studies indicated that the upper tropospheric humidity (UTH) field is also governed by large scale dynamics, and is in a general good agreement with the patterns of high level wind divergence. That suggests that a divergence parameter could be very useful in the analysis for numerical weather prediction. Holmlund (2000a) has described the possibility to infer divergence fields from Atmospheric Motion Vectors (AMVs) that have been derived from tracking cloud and humidity features in the 6.2 μm WV channel of Meteosat. Such algorithm has been developed at EUMETSAT, and tested on Meteosat 8 data. This paper describes the calculation's process of the upper level divergence, and presents some results over large scale convective systems observed by Meteosat 8 over tropical areas.
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Régis Borde, Marianne König, and Johannes Schmetz "Upper level divergence estimated from wind field observation using Meteosat-8 data", Proc. SPIE 5979, Remote Sensing of Clouds and the Atmosphere X, 59790M (31 October 2005); https://doi.org/10.1117/12.626423
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KEYWORDS
Clouds
Algorithm development
Humidity
Atmospheric sensing
Detection and tracking algorithms
Diagnostics
Remote sensing
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