Data Validation and Mesoscale Assimilation of Himawari-8 Optimal Cloud Analysis Products

Himawari-8 optimal cloud analysis (OCA), which employs all 16 channels of the Advanced Himawari Imager, provides cloud properties such as cloud phase, top pressure, optical thickness, effective radius, and water path. By using OCA, the water vapor distribution can be inferred with high spatiotempora...

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Veröffentlicht in:Journal of atmospheric and oceanic technology 2021-02, Vol.38 (2), p.223-242
Hauptverfasser: Otsuka, Michiko, Seko, Hiromu, Hayashi, Masahiro, Koizumi, Ko
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container_title Journal of atmospheric and oceanic technology
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creator Otsuka, Michiko
Seko, Hiromu
Hayashi, Masahiro
Koizumi, Ko
description Himawari-8 optimal cloud analysis (OCA), which employs all 16 channels of the Advanced Himawari Imager, provides cloud properties such as cloud phase, top pressure, optical thickness, effective radius, and water path. By using OCA, the water vapor distribution can be inferred with high spatiotemporal resolution and with a wide coverage, including over the ocean, which can be useful for improving initial states for prediction of the torrential rainfalls that occur frequently in Japan. OCA products were first evaluated by comparing them with different kinds of datasets (surface, sonde, and ceilometer observations) and with model outputs, to determine their data characteristics. Overall, OCA data were consistent with observations of water clouds with moderate optical thicknesses at low to midlevels. Next, pseudorelative humidity data were derived from the OCA products, and utilized in assimilation experiments of a few heavy rainfall cases, conducted with the Japan Meteorological Agency’s nonhydrostatic model–based Variational Data Assimilation System. Assimilation of OCA pseudorelative humidities caused there to be significant differences in the initial conditions of water vapor fields compared to the control, especially where OCA clouds were detected, and their influence lasted relatively long in terms of forecast hours. Impacts of assimilation on other variables, such as wind speed, were also seen. When the OCA data successfully represented low-level inflows from over the ocean, they positively impacted precipitation forecasts at extended forecast times.
doi_str_mv 10.1175/JTECH-D-20-0015.1
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Assimilation of OCA pseudorelative humidities caused there to be significant differences in the initial conditions of water vapor fields compared to the control, especially where OCA clouds were detected, and their influence lasted relatively long in terms of forecast hours. Impacts of assimilation on other variables, such as wind speed, were also seen. 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source American Meteorological Society; Free E-Journal (出版社公開部分のみ); Alma/SFX Local Collection
subjects Cloud properties
Clouds
Data
Data assimilation
Data collection
Heavy rainfall
Humidity data
Initial conditions
Mathematical models
Meteorological satellites
Oceans
Optical properties
Optical thickness
Precipitation forecasting
Rain
Rainfall
Water vapor
Water vapor distribution
Water vapour
Weather forecasting
Wind speed
title Data Validation and Mesoscale Assimilation of Himawari-8 Optimal Cloud Analysis Products
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