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Article Dans Une Revue IEEE Transactions on Geoscience and Remote Sensing Année : 2019

Ice cloud properties from himawari-8/ahi next-generation geostationary satellite: capability of the ahi to monitor the dc cloud generation process

Résumé

The Japan Meteorological Agency (JMA) successfully launched the Himawari-8 (H-8) new-generation geostationary meteorological satellite with the Advanced Himawari Imager (AHI) sensor on October 7, 2014. The H-8/AHI level-2 (L2) operational cloud property products were released by the Japan Aerospace Exploration Agency during September 2016. The Voronoi light scattering model, which is a fractal ice particle habit, was utilized to develop the retrieval algorithm called “Comprehensive Analysis Program for Cloud Optical Measurement” (CAPCOM-INV)-ice for the AHI ice cloud product. In this paper, we describe the CAPCOM-INV-ice algorithm for ice cloud products from AHI data. To investigate its retrieval performance, retrieval results were compared with 2000 samples of the ice cloud optical thickness and effective particle radius values. Furthermore, AHI ice cloud products are evaluated by comparing them with the MODIS collection-6 (C6) products. As an experiment, cloud property retrievals from AHI measurements, with an observation interval time of 2.5 min and ground-based rainfall observation radar data (the latter of which is supplied by the JMA, with a 1-km grid mesh), are used to investigate the generation processes of deep convective (DC) cloud in the vicinity of the Kyushu island, Japan. It revealed that AHI measurements have the capability of monitoring the growth processes, including variation of the cloud properties and the precipitation in the DC cloud.

Dates et versions

hal-04479277 , version 1 (27-02-2024)

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Citer

Husi Letu, Takashi Y. Nakajima, Jerome Riedi, Hiroshi Ishimoto, Anthony J. Baran, et al.. Ice cloud properties from himawari-8/ahi next-generation geostationary satellite: capability of the ahi to monitor the dc cloud generation process. IEEE Transactions on Geoscience and Remote Sensing, 2019, IEEE Transactions on Geoscience and Remote Sensing, 57, ⟨10.1109/TGRS.2018.2882803⟩. ⟨hal-04479277⟩
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