Monitoring Deep Convection and Convective Overshooting From 60° S to 60° N Using MHS: A Cloudsat/CALIPSO-Based Assessment - Université Pierre et Marie Curie Accéder directement au contenu
Article Dans Une Revue IEEE Geoscience and Remote Sensing Letters Année : 2017

Monitoring Deep Convection and Convective Overshooting From 60° S to 60° N Using MHS: A Cloudsat/CALIPSO-Based Assessment

Résumé

Spaceborne passive microwave sounders allow to detect convective storms thanks to their sensitivity to ice cloud particles. However, a worldwide assessment of the conditions under which these instruments can detect storms as well as a characterization of the microphysics of the detected storms is still missing. In this letter, we used ten-year measurements from Cloudsat radar and CALIPSO lidar to assess and characterize two convection diagnostics, namely, deep convection (DC) and convective overshooting (COV), derived from microwave humidity sounder measurements. When snow and sea ice-covered regions, such as Siberia and elevated regions (>1800 m) are discarded, DC and COV are associated with convective clouds, as identified by Cloudsat, more than 90% of time. COV reaches the Tropopause in 51% of cases. Results also show that ice water content (IWC) profiles peak higher for COV (9 km) than DC (7 km), with a heavier average ice loading for DC (0.9 g/m3) than for COV (0.8 g/m3). Maximal altitude reached by ice clouds is higher in the tropics than in the midlatitudes (16.1 km against 12.7 km in average for COV events), while average IWC is slightly higher in the tropics (0.21 g/m3 against 0.18 g/m3 for DC events). This evaluation and characterization open the doors to the development of a unique 60° S/60° N, 1999-present database of DC and COV using spaceborne passive microwave sounders.
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insu-01505756 , version 1 (11-04-2017)

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Citer

Jean-François Rysman, Chantal Claud, Julien Delanoë. Monitoring Deep Convection and Convective Overshooting From 60° S to 60° N Using MHS: A Cloudsat/CALIPSO-Based Assessment. IEEE Geoscience and Remote Sensing Letters, 2017, 14 (2), pp.159 - 163. ⟨10.1109/LGRS.2016.2631725⟩. ⟨insu-01505756⟩
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