{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T11:47:19Z","timestamp":1773056839595,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,19]],"date-time":"2021-08-19T00:00:00Z","timestamp":1629331200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFC1507401"],"award-info":[{"award-number":["2018YFC1507401"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41875030"],"award-info":[{"award-number":["41875030"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42075075"],"award-info":[{"award-number":["42075075"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2017"],"award-info":[{"award-number":["2017"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Satellite rainrate estimation is a great challenge, especially in mesoscale convective systems (MCSs), which is mainly due to the absence of a direct physical connection between observable cloud parameters and surface rainrate. The machine learning technique was employed in this study to estimate rainrate in the MCS domain via using cloud top temperature (CTT) derived from a geostationary satellite. Five kinds of machine learning models were investigated, i.e., polynomial regression, support vector machine, decision tree, random forest, and multilayer perceptron, and the precipitation of Climate Prediction Center morphing technique (CMORPH) was used as the reference. A total of 31 CTT related features were designed to be the potential inputs for training an algorithm, and they were all proved to have a positive contribution in modulating the algorithm. Random forest (RF) shows the best performance among the five kinds of models. By combining the classification and regression schemes of the RF model, an RF-based hybrid algorithm was proposed first to discriminate the rainy pixel and then estimate its rainrate. For the MCS samples considered in this study, such an algorithm generates the best estimation, and its accuracy is definitely higher than the operational precipitation product of FY-4A. These results demonstrate the promising feasibility of applying a machine learning technique to solve the satellite precipitation retrieval problem.<\/jats:p>","DOI":"10.3390\/rs13163273","type":"journal-article","created":{"date-parts":[[2021,8,19]],"date-time":"2021-08-19T04:13:54Z","timestamp":1629346434000},"page":"3273","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Rainrate Estimation from FY-4A Cloud Top Temperature for Mesoscale Convective Systems by Using Machine Learning Algorithm"],"prefix":"10.3390","volume":"13","author":[{"given":"Ping","family":"Lao","sequence":"first","affiliation":[{"name":"School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5454-2655","authenticated-orcid":false,"given":"Qi","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026, China"}]},{"given":"Yuhao","family":"Ding","sequence":"additional","affiliation":[{"name":"School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026, China"}]},{"given":"Yu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Environmental Science and Optoelectronic Technology, University of Science and Technology of China, Hefei 230026, China"},{"name":"Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, HFIPS, Chinese Academy of Sciences, Hefei 230031, China"}]},{"given":"Yuan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026, China"}]},{"given":"Meng","family":"Li","sequence":"additional","affiliation":[{"name":"School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2741","DOI":"10.5194\/amt-6-2741-2013","article-title":"Detection of potentially hazardous convective clouds with a dual-polarized C-band radar","volume":"6","author":"Adachi","year":"2013","journal-title":"Atmos. Meas. Tech."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Houze, R.A. (2004). Mesoscale convective systems. Rev. Geophys., 42.","DOI":"10.1029\/2004RG000150"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"396","DOI":"10.2151\/jmsj1965.60.1_396","article-title":"Cloud cluster and large-scale vertical motions in the trpics","volume":"60","author":"Houze","year":"1982","journal-title":"J. Meteorol. Soc. Jpn."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1175\/MWR-D-13-00111.1","article-title":"Initiation and Organizational Modes of an Extreme-Rain-Producing Mesoscale Convective System along a Mei-Yu Front in East China","volume":"142","author":"Luo","year":"2014","journal-title":"Mon. Weather Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1557","DOI":"10.1016\/j.scib.2019.09.005","article-title":"Science and prediction of monsoon heavy rainfall","volume":"64","author":"Luo","year":"2019","journal-title":"Sci. Bull."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3035","DOI":"10.1002\/2017JD027432","article-title":"Wavelet Scale Analysis of Mesoscale Convective Systems for Detecting Deep Convection from Infrared Imagery","volume":"123","author":"Klein","year":"2018","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1111\/j.1600-0870.2010.00503.x","article-title":"Ensemble prediction for nowcasting with a convection-permitting model-I: Description of the system and the impact of radar-derived surface precipitation rates","volume":"63","author":"Migliorini","year":"2011","journal-title":"Tellus Ser. A-Dyn. Meteorol. Oceanol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zeng, Q.L., Wang, Y.Q., Chen, L.F., Wang, Z.F., Zhu, H., and Li, B. (2018). Inter-Comparison and Evaluation of Remote Sensing Precipitation Products over China from 2005 to 2013. Remote Sens., 10.","DOI":"10.3390\/rs10020168"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4816","DOI":"10.1002\/jgrd.50354","article-title":"On the scale estimation using truncated swath measurements from low Earth orbiting satellites","volume":"118","author":"Liu","year":"2013","journal-title":"J. Geophys Res. Atmos."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"56","DOI":"10.18520\/cs\/v116\/i1\/56-78","article-title":"Rainfall estimation techniques over India and adjoining oceanic regions","volume":"116","author":"Mishra","year":"2019","journal-title":"Curr. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1175\/JHM-D-11-042.1","article-title":"Intercomparison of High-Resolution Precipitation Products over Northwest Europe","volume":"13","author":"Kidd","year":"2012","journal-title":"J. Hydrometeorol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"984","DOI":"10.1080\/09537325.2020.1732912","article-title":"A review of machine learning for big data analytics: Bibliometric approach","volume":"32","author":"Mohammed","year":"2020","journal-title":"Technol. Anal. Strateg. Manag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2349","DOI":"10.1175\/JTECH-D-19-0008.1","article-title":"Enhancing PMW Satellite Precipitation Estimation: Detecting Convective Class","volume":"36","author":"Orescanin","year":"2019","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1175\/WAF-D-18-0136.1","article-title":"Estimating Tropical Cyclone Intensity by Satellite Imagery Utilizing Convolutional Neural Networks","volume":"34","author":"Chen","year":"2019","journal-title":"Weather Forecast."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1175\/WAF-D-18-0201.1","article-title":"Prediction of Tropical Cyclone Genesis from Mesoscale Convective Systems Using Machine Learning","volume":"34","author":"Zhang","year":"2019","journal-title":"Weather Forecast."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1175\/WAF-D-19-0170.1","article-title":"Classifying Convective Storms Using Machine Learning","volume":"35","author":"Jergensen","year":"2020","journal-title":"Weather Forecast."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1175\/JHM-D-17-0077.1","article-title":"A Two-Stage Deep Neural Network Framework for Precipitation Estimation from Bispectral Satellite Information","volume":"19","author":"Hsu","year":"2018","journal-title":"J. Hydrometeorol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Szunyogh, I., Arcomano, T., Pathak, J., Wikner, A., Hunt, B., and Ott, E. (2020). A Machine Learning-Based Global Atmospheric Forecast Model. Geophys. Res. Lett., 47.","DOI":"10.1029\/2020GL087776"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"689","DOI":"10.2151\/jmsj.2019-040","article-title":"High Temporal Rainfall Estimations from Himawari-8 Multiband Observations Using the Random-Forest Machine-Learning Method","volume":"97","author":"Hirose","year":"2019","journal-title":"J. Meteorol. Soc. Jpn."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4890","DOI":"10.1175\/JCLI-D-14-00491.1","article-title":"Characteristics of Mesoscale Convective Systems over China and Its Vicinity Using Geostationary Satellite FY2","volume":"28","author":"Yang","year":"2015","journal-title":"J. Clim."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2693","DOI":"10.1175\/MWR-D-18-0455.1","article-title":"General Features of Extreme Rainfall Events Produced by MCSs over East China during 2016-17","volume":"147","author":"Zhang","year":"2019","journal-title":"Mon. Weather Rev."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1007\/s13351-017-6161-z","article-title":"Developing the science product algorithm testbed for Chinese next-generation geostationary meteorological satellites: Fengyun-4 series","volume":"31","author":"Min","year":"2017","journal-title":"J. Meteorol. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1637","DOI":"10.1175\/BAMS-D-16-0065.1","article-title":"Introducing the new generation of Chinese geostationary weather satellites, FENGYUN-4, Bull","volume":"98","author":"Yang","year":"2017","journal-title":"Amer. Meteor. Soc."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1175\/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2","article-title":"CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution","volume":"5","author":"Joyce","year":"2004","journal-title":"J. Hydrometeorol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1784","DOI":"10.1175\/JHM-D-12-017.1","article-title":"Evaluation of the High-Resolution CMORPH Satellite Rainfall Product Using Dense Rain Gauge Observations and Radar-Based Estimates","volume":"13","author":"Habib","year":"2012","journal-title":"J. Hydrometeorol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"8987","DOI":"10.1002\/2015JD023437","article-title":"Comprehensive evaluation of multisatellite precipitation estimates over India using gridded rainfall data","volume":"120","author":"Sunilkumar","year":"2015","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_27","first-page":"189","article-title":"A Research into the Character of CMORPH Remote Sensing Precipitation Error in China","volume":"29","author":"Xu","year":"2015","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1374","DOI":"10.1175\/1520-0477(1980)061<1374:MCC>2.0.CO;2","article-title":"Mesoscale convective complexes","volume":"61","author":"Maddox","year":"1980","journal-title":"B Am. Meteorol. Soc."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1002\/qj.194","article-title":"The propagation and diurnal cycles of deep convection in northern tropical Africa","volume":"134","author":"Laing","year":"2008","journal-title":"Q. J. Roy. Meteor. Soc."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1574","DOI":"10.1007\/s11434-008-0116-9","article-title":"Climatological distribution and diurnal variation of mesoscale convective systems over China and its vicinity during summer","volume":"53","author":"Zheng","year":"2008","journal-title":"Chin. Sci. Bull."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1921","DOI":"10.1002\/joc.2407","article-title":"Tracking mesoscale convective systems in the Sahel: Relation between cloud parameters and precipitation","volume":"32","author":"Goyens","year":"2012","journal-title":"Int. J. Climatol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2517","DOI":"10.1175\/MWR-D-15-0197.1","article-title":"Life Cycle Characteristics of MCSs in Middle East China Tracked by Geostationary Satellite and Precipitation Estimates","volume":"144","author":"Ai","year":"2016","journal-title":"Mon. Weather Rev."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2210","DOI":"10.1029\/2018JD029707","article-title":"Mesoscale Convective Systems in the Asian Monsoon Region from Advanced Himawari Imager: Algorithms and Preliminary Results","volume":"124","author":"Chen","year":"2019","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1683","DOI":"10.1175\/1520-0450(2001)040<1683:ASMTIS>2.0.CO;2","article-title":"A satellite method to identify structural properties of mesoscale convective systems based on the maximum spatial correlation tracking technique (MASCOTTE)","volume":"40","author":"Carvalho","year":"2001","journal-title":"J. Appl. Meteorol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"55","DOI":"10.7763\/IJCTE.2009.V1.9","article-title":"Atmospheric Temperature Prediction using Support Vector Machines","volume":"1","author":"Radhika","year":"2009","journal-title":"Int. J. Comput. Theory Eng."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1575","DOI":"10.1175\/JAMC-D-17-0293.1","article-title":"A Method for Identifying Midlatitude Mesoscale Convective Systems in Radar Mosaics. Part I: Segmentation and Classification","volume":"57","author":"Haberlie","year":"2018","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"13763","DOI":"10.3390\/s150613763","article-title":"Classification of Potential Water Bodies Using Landsat 8 OLI and a Combination of Two Boosted Random Forest Classifiers","volume":"15","author":"Ko","year":"2015","journal-title":"Sensors"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1029\/2018JD028795","article-title":"Downscaling Satellite Precipitation Estimates with Multiple Linear Regression, Artificial Neural Networks, and Spline Interpolation Techniques","volume":"124","author":"Sharifi","year":"2019","journal-title":"J. Geophys Res. Atmos."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1175\/1520-0450(1986)025<0184:GHAGST>2.0.CO;2","article-title":"Grid history\u2014A geostaionary satellite technique for estimating daily rainfall in the tropics","volume":"25","author":"Martin","year":"1986","journal-title":"J. Clim. Appl. Meteorol."},{"key":"ref_40","first-page":"79","article-title":"QPR-NN: A New Recommendation Algorithm Combining Quadric Polynomial Regression and Neural Network","volume":"53","author":"Liao","year":"2019","journal-title":"J. Xi\u2019an Jiaotong Univ."},{"key":"ref_41","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/16\/3273\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:46:57Z","timestamp":1760165217000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/16\/3273"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,19]]},"references-count":41,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["rs13163273"],"URL":"https:\/\/doi.org\/10.3390\/rs13163273","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,19]]}}}