Remote Sensing Technology and Application, Volume. 39, Issue 4, 1000(2024)

Research on a Real-time Precipitation Recognition Method based on Geostationary Satellite Observation Data

Mengyuan CUI, Dabin JI, Li JIA, Chaolei ZHENG, and Weiguo JIANG
Author Affiliations
  • Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing100101,China
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    References(29)

    [1] LIU Yuanbo, FU Qiaoni, SONG Ping et al. Satellite retrieval of precipitation: An overview. Advances in Earth Science, 26, 1162-1172(2011).

    [2] HE Xingwei, FENG Xiaohu, HAN Qi et al. Advances of the geostationary meteorological satellite in the world: A review. Advances in Meteorological Science and Technology, 10, 22-29,41(2020).

    [3] ARKIN P A, MEISNER B N. The relationship between large-scale convective rainfall and cold cloud over the western hemisphere during 1982-84. Monthly Weather Review, 115, 51-74(1987).

    [4] ADLER R F, NEGRI A J. A satellite infrared technique to estimate tropical convective and stratiform rainfall. Journal of Applied Meteorology and Climatology, 27, 30-51(1988).

    [5] BA M B, GRUBER A. GOES Multispectral Rainfall Algorithm (GMSRA). Journal of Applied Meteorology, 40, 1500-1514(2001).

    [6] KULIGOWSKI R J. A. Self-calibrating real-time GOES rainfall algorithm for short-term rainfall estimates. Journal of Hydro-meteorology, 3, 112-130(2002).

    [7] YOU Ran. Satellite quantitative precipitation estimation method. 卫星定量降水估计方法, C.

    [8] JOYCE R J, JANOWIAK J E, ARKIN P A et al. CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. Journal of Hydrometeorology, 5, 487-503(2004).

    [9] USHIO T, SASASHIGE K, KUBOTA T et al. A Kalman Filter approach to the Global Satellite Mapping of Precipitation (GSMaP) from combined passive microwave and infrared radiometric data. Journal of the Meteorological Society of Japan.Ser.II, 137-151(2009).

    [11] HSU K L, GAO X, SOROOSHIAN S et al. Precipitation estimation from remotely sensed information using artificial neural networks. Journal of Applied Meteorology, 36, 1176-1190(1997).

    [12] HONG Y, HSU K L, SOROOSHIAN S et al. Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system. Journal of Applied Meteorology, 43, 1834-1853(2004).

    [13] SHI Chunxiang, LU Naimang, ZHANG Wenjian. Precipitation estimation from remotely sensed information using neural networks. Climate and Environment Research, 467-472(2001).

    [14] LU Zhiying, REN Yimo, SUN Xiaolei et al. Recognition of short-time heavyrainfall based on deep learning. Journal of Tianjin University (Science and Engineering Technology), 51, 111-119(2018).

    [15] SEHAD M, LAZRI M, AMEUR S. Novel SVM-based technique to improve rainfall estimation over the Mediterranean region (north of Algeria) using the multispectral MSG SEVIRI imagery. Advances in Space Research, 59, 1381-1394(2017).

    [16] ZHANG Jiahua, YAO Yibin, CAO Na. Prediction of whether precipitation based on decision tree. Journal of Geomatics, 42, 107-109(2017).

    [17] KÜHNLEIN M, APPELHANS T, THIES B et al. Improving the accuracy of rainfall rates from optical satellite sensors with machine learning — A random forests-based approach applied to MSG SEVIRI. Remote Sensing of Environment, 129-143(02).

    [18] HUANG Y, BAO Y, PETROPOULOS G P et al. Precipitation estimation using FY-4B/AGRI satellite data based on random forest. Remote Sensing, 16, 1267(2024).

    [19] GUAN Li, ZHONG Yulu. Retrieval of surface rainfall using random forest algorithm based on FY-4A AGRI observations. Progress in Geophysics, 38, 1931-1938(2023).

    [20] MA Z, ZHU S, YANG J. FY4QPE-MSA: An all-day near-real-time quantitative precipitation estimation framework based on multi-spectral analysis from AGRI onboard Chinese FY-4 series satellites. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-15(2022).

    [21] ZHU S, MA Z. PECA-FY4A: Precipitation Estimation using Chromato-graphic Analysis methodology for full-disc multispectral observations from FengYun-4A/AGRI. Remote Sensing of Environment, 282, 113234(2022).

    [22] BESSHO K, DATE K, HAYASHI M et al. An introduction to Hima-wari-8/9— Japan’s new-generation geostationary meteorological satellites. Journal of the Meteorological Society of Japan. Ser.II, 94, 151-183(2016).

    [23] BREIMAN L. Random forests. Machine Learning, 45, 5-32(2001).

    [24] MYLES A J, FEUDALE R N, LIU Y et al. An introduction to decision tree modeling. Journal of Chemometrics, 18, 275-285(2004).

    [25] BELGIU M, DRĂGUŢ L. Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photo-grammetry and Remote Sensing, 114, 24-31(2016).

    [26] LIU Xiuyu, JIANG Tao, LI Yanyi. Application of moderate resolution remote sensing image classification based on machine learning. Geographic Information World, 28, 66-73(2021).

    [27] ZHANG Qirui, ZHANG Ling, DONG Shoubin et al. Effects of category distribution in a training set on text categorization. Journal of Tsinghua University (Science and Technology), 45, 1802-1805(2005).

    [29] LI Qilun, ZHANG Wanchang, YI Lu et al. Accuracy evaluation and comparison of GPM and TRMM precipitation product over Mainland China. Advances in Water Science, 29, 303-313(2018).

    [30] YU Jingjing, SHEN Yan, PAN Yang et al. Comparative assessment between the daily merged precipitation dataset over China and the world’s popular counterparts. Acta Meteorology Sinica, 73, 394-410(2015).

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    Mengyuan CUI, Dabin JI, Li JIA, Chaolei ZHENG, Weiguo JIANG. Research on a Real-time Precipitation Recognition Method based on Geostationary Satellite Observation Data[J]. Remote Sensing Technology and Application, 2024, 39(4): 1000

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    Paper Information

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    Received: Feb. 14, 2023

    Accepted: --

    Published Online: Jan. 6, 2025

    The Author Email:

    DOI:10.11873/j.issn.1004-0323.2024.4.1000

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