TY - JOUR
T1 - High-resolution retrieval of cloud microphysical properties and surface solar radiation using Himawari-8/AHI next-generation geostationary satellite
AU - Letu, Husi
AU - Yang, Kun
AU - Nakajima, Takashi Y.
AU - Ishimoto, Hiroshi
AU - Nagao, Takashi M.
AU - Riedi, Jérôme
AU - Baran, Anthony J.
AU - Ma, Run
AU - Wang, Tianxing
AU - Shang, Huazhe
AU - Khatri, Pradeep
AU - Chen, Liangfu
AU - Shi, Chunxiang
AU - Shi, Jiancheng
N1 - Funding Information:
This work was supplied by National Key R&D Program of China , Grant Award Number: 2018YFA0605401 ; Second Tibetan Plateau Scientific Expedition and Research Program (STEP), Grant No. 2019QZKK0206 ; National Natural Science Foundation of China , No: 41771395 and JST CREST Grant Number JPMJCR15K4 , Japan. The Himawari-8 cloud property products used in this paper was supplied by the P-Tree System, Japan Aerospace Exploration Agency (JAXA).
Funding Information:
This work was supplied by National Key R&D Program of China, Grant Award Number: 2018YFA0605401; Second Tibetan Plateau Scientific Expedition and Research Program (STEP), Grant No. 2019QZKK0206; National Natural Science Foundation of China, No: 41771395 and JST CREST Grant Number JPMJCR15K4, Japan. The Himawari-8 cloud property products used in this paper was supplied by the P-Tree System, Japan Aerospace Exploration Agency (JAXA).
Publisher Copyright:
© 2019 The Authors
PY - 2020/3/15
Y1 - 2020/3/15
N2 - Optical properties of clouds and heavy aerosol retrieved from satellite measurements are the most important elements for calculating surface solar radiation (SSR). The Himawari-8/Advanced Himawari Imager (AHI) satellite measurements receive high spatial, temporal and spectral signals, which provides an opportunity to estimate cloud, aerosol and SSR accurately. In this study, we developed the AHI official cloud property product (version 1.0) for JAXA P-Tree system. A look-up table (LUT) method was used to calculate high-temporal (10 min) and high-spatial (5 km) SSR from AHI cloud properties. First, the LUT of the SSR estimation was optimized through a radiative transfer model to account for solar zenith angle, cloud optical thickness (COT), effective particle radius (CER), aerosol optical thickness and surface albedo. Following this, COT and CER were retrieved from the AHI data, with ice cloud parameters being retrieved from an extended Voronoi ice crystal scattering database and water cloud parameters being retrieved from the Mie–Lorenz scattering model. The retrieved COT and CER for water clouds were compared well with MODIS collection 6 cloud property products, with correlation coefficients of 0.77 and 0.82, respectively. The COT of ice cloud also shows good consistency, with a correlation coefficient of 0.85. Finally, the SSR was calculated based on the SSR LUT and the retrieved cloud optical parameters. The estimated SSR was validated at 122 radiation stations from several observing networks covering the disk region of Himawari-8. The root-mean-square error (RMSE) at CMA (China Meteorological Administration) stations was 101.86 Wm− 2 for hourly SSR and 31.42 Wm− 2 for daily SSR; RMSE at non-CMA stations was 119.07 Wm− 2 for instantaneous SSR, 81.10 Wm− 2 for hourly SSR and 26.58 Wm− 2 for daily SSR. Compared with the SSR estimated from conventional geostationary satellites, the accuracy of the SSR obtained in this study was significantly improved.
AB - Optical properties of clouds and heavy aerosol retrieved from satellite measurements are the most important elements for calculating surface solar radiation (SSR). The Himawari-8/Advanced Himawari Imager (AHI) satellite measurements receive high spatial, temporal and spectral signals, which provides an opportunity to estimate cloud, aerosol and SSR accurately. In this study, we developed the AHI official cloud property product (version 1.0) for JAXA P-Tree system. A look-up table (LUT) method was used to calculate high-temporal (10 min) and high-spatial (5 km) SSR from AHI cloud properties. First, the LUT of the SSR estimation was optimized through a radiative transfer model to account for solar zenith angle, cloud optical thickness (COT), effective particle radius (CER), aerosol optical thickness and surface albedo. Following this, COT and CER were retrieved from the AHI data, with ice cloud parameters being retrieved from an extended Voronoi ice crystal scattering database and water cloud parameters being retrieved from the Mie–Lorenz scattering model. The retrieved COT and CER for water clouds were compared well with MODIS collection 6 cloud property products, with correlation coefficients of 0.77 and 0.82, respectively. The COT of ice cloud also shows good consistency, with a correlation coefficient of 0.85. Finally, the SSR was calculated based on the SSR LUT and the retrieved cloud optical parameters. The estimated SSR was validated at 122 radiation stations from several observing networks covering the disk region of Himawari-8. The root-mean-square error (RMSE) at CMA (China Meteorological Administration) stations was 101.86 Wm− 2 for hourly SSR and 31.42 Wm− 2 for daily SSR; RMSE at non-CMA stations was 119.07 Wm− 2 for instantaneous SSR, 81.10 Wm− 2 for hourly SSR and 26.58 Wm− 2 for daily SSR. Compared with the SSR estimated from conventional geostationary satellites, the accuracy of the SSR obtained in this study was significantly improved.
KW - Cloud property retrieval
KW - Himawari-8 satellite
KW - Ice scattering model
KW - Surface solar radiation
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U2 - 10.1016/j.rse.2019.111583
DO - 10.1016/j.rse.2019.111583
M3 - Article
AN - SCOPUS:85076867070
SN - 0034-4257
VL - 239
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
M1 - 111583
ER -