Rainrate Estimation from FY-4A Cloud Top Temperature for Mesoscale Convective Systems by Using Machine Learning Algorithm
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 ra...
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doaj-3de19013785144d09912044f5b4cede52021-08-26T14:17:52ZengMDPI AGRemote Sensing2072-42922021-08-01133273327310.3390/rs13163273Rainrate Estimation from FY-4A Cloud Top Temperature for Mesoscale Convective Systems by Using Machine Learning AlgorithmPing Lao0Qi Liu1Yuhao Ding2Yu Wang3Yuan Li4Meng Li5School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026, ChinaSchool of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026, ChinaSchool of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026, ChinaSchool of Environmental Science and Optoelectronic Technology, University of Science and Technology of China, Hefei 230026, ChinaSchool of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026, ChinaSchool of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026, ChinaSatellite 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.https://www.mdpi.com/2072-4292/13/16/3273rainrate estimationmesoscale convective systemmachine learning algorithmcloud top temperatureFY-4A meteorological satellite |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ping Lao Qi Liu Yuhao Ding Yu Wang Yuan Li Meng Li |
spellingShingle |
Ping Lao Qi Liu Yuhao Ding Yu Wang Yuan Li Meng Li Rainrate Estimation from FY-4A Cloud Top Temperature for Mesoscale Convective Systems by Using Machine Learning Algorithm Remote Sensing rainrate estimation mesoscale convective system machine learning algorithm cloud top temperature FY-4A meteorological satellite |
author_facet |
Ping Lao Qi Liu Yuhao Ding Yu Wang Yuan Li Meng Li |
author_sort |
Ping Lao |
title |
Rainrate Estimation from FY-4A Cloud Top Temperature for Mesoscale Convective Systems by Using Machine Learning Algorithm |
title_short |
Rainrate Estimation from FY-4A Cloud Top Temperature for Mesoscale Convective Systems by Using Machine Learning Algorithm |
title_full |
Rainrate Estimation from FY-4A Cloud Top Temperature for Mesoscale Convective Systems by Using Machine Learning Algorithm |
title_fullStr |
Rainrate Estimation from FY-4A Cloud Top Temperature for Mesoscale Convective Systems by Using Machine Learning Algorithm |
title_full_unstemmed |
Rainrate Estimation from FY-4A Cloud Top Temperature for Mesoscale Convective Systems by Using Machine Learning Algorithm |
title_sort |
rainrate estimation from fy-4a cloud top temperature for mesoscale convective systems by using machine learning algorithm |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2021-08-01 |
description |
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. |
topic |
rainrate estimation mesoscale convective system machine learning algorithm cloud top temperature FY-4A meteorological satellite |
url |
https://www.mdpi.com/2072-4292/13/16/3273 |
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