Spatial-Temporal Mode Analysis and Prediction of Outgoing Longwave Radiation over China in 2002–2021 Based on Atmospheric Infrared Sounder Data
Outgoing longwave radiation (OLR) is a key factor to study the radiation balance of the earth–atmosphere system. It is of great significance to explore the temporal and spatial variation characteristics over the OLR value in China region and to predict its future variation trend. We investigate the...
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MDPI AG
2022-02-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/13/3/400 |
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| author | Chaoli Tang Dong Liu Yuanyuan Wei Xiaomin Tian Fengmei Zhao Xin Wu |
| author_facet | Chaoli Tang Dong Liu Yuanyuan Wei Xiaomin Tian Fengmei Zhao Xin Wu |
| author_sort | Chaoli Tang |
| collection | DOAJ |
| description | Outgoing longwave radiation (OLR) is a key factor to study the radiation balance of the earth–atmosphere system. It is of great significance to explore the temporal and spatial variation characteristics over the OLR value in China region and to predict its future variation trend. We investigate the characteristic distribution of OLR value over China and predict its results in time series using the seasonal autoregressive integrated moving average (SARIMA) and long short-term memory (LSTM) methods based on the OLR data by the Atmospheric Infrared Sounder (AIRS). The Mann–Kendall (MK) mutation test was used to analyze the annual average of OLR values in China and the mutation points in the four seasons. The empirical orthogonal function (EOF) is used to decompose the spatial characteristics and temporal variation of OLR values in China. The MK mutation test is used to obtain the mutation points in the three seasons of spring, summer and autumn. The cumulative variance contribution of the four modes obtained by EOF decomposition exceeds 70%, and the variance contribution of the first mode exceeds 50%. The prediction accuracy with SARIMA model is 99% and LSTM algorithm is 97%. The results of spatiotemporal analysis show that the OLR value near the equator is significantly higher than that of the north and south poles and decreases with the increase of latitude; the OLR value in spring, summer and autumn is higher than that in winter. The results of the MK test show that there are many mutation points in autumn, and the location of the mutation points cannot be determined. The mutation points in spring and summer meet the confidence interval; the first mode of EOF decomposition has a meridional structure, and the OLR value is dropped within 18 years as a whole. The spatial characteristics of modes 1 and 3 have obvious changes in the Qinghai-Tibet Plateau and Northeast China. The prediction results show that the prediction accuracy of SARIMA is higher than that of LSTM. Therefore, the results predicted by SARIMA may provide a reference for the study of the radiation balance of the earth–atmosphere system in China. |
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| language | English |
| last_indexed | 2024-03-09T20:07:24Z |
| publishDate | 2022-02-01 |
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| series | Atmosphere |
| spelling | doaj.art-10a46c5c79b7444a87f9e128a49b401d2023-11-24T00:26:15ZengMDPI AGAtmosphere2073-44332022-02-0113340010.3390/atmos13030400Spatial-Temporal Mode Analysis and Prediction of Outgoing Longwave Radiation over China in 2002–2021 Based on Atmospheric Infrared Sounder DataChaoli Tang0Dong Liu1Yuanyuan Wei2Xiaomin Tian3Fengmei Zhao4Xin Wu5Institute of Electrical & Information Engineering, Anhui University of Science and Technology, Huainan 232001, ChinaInstitute of Electrical & Information Engineering, Anhui University of Science and Technology, Huainan 232001, ChinaSchool of Internet, Anhui University, Hefei 230039, ChinaInstitute of Electrical & Information Engineering, Anhui University of Science and Technology, Huainan 232001, ChinaInstitute of Electrical & Information Engineering, Anhui University of Science and Technology, Huainan 232001, ChinaInstitute of Electrical & Information Engineering, Anhui University of Science and Technology, Huainan 232001, ChinaOutgoing longwave radiation (OLR) is a key factor to study the radiation balance of the earth–atmosphere system. It is of great significance to explore the temporal and spatial variation characteristics over the OLR value in China region and to predict its future variation trend. We investigate the characteristic distribution of OLR value over China and predict its results in time series using the seasonal autoregressive integrated moving average (SARIMA) and long short-term memory (LSTM) methods based on the OLR data by the Atmospheric Infrared Sounder (AIRS). The Mann–Kendall (MK) mutation test was used to analyze the annual average of OLR values in China and the mutation points in the four seasons. The empirical orthogonal function (EOF) is used to decompose the spatial characteristics and temporal variation of OLR values in China. The MK mutation test is used to obtain the mutation points in the three seasons of spring, summer and autumn. The cumulative variance contribution of the four modes obtained by EOF decomposition exceeds 70%, and the variance contribution of the first mode exceeds 50%. The prediction accuracy with SARIMA model is 99% and LSTM algorithm is 97%. The results of spatiotemporal analysis show that the OLR value near the equator is significantly higher than that of the north and south poles and decreases with the increase of latitude; the OLR value in spring, summer and autumn is higher than that in winter. The results of the MK test show that there are many mutation points in autumn, and the location of the mutation points cannot be determined. The mutation points in spring and summer meet the confidence interval; the first mode of EOF decomposition has a meridional structure, and the OLR value is dropped within 18 years as a whole. The spatial characteristics of modes 1 and 3 have obvious changes in the Qinghai-Tibet Plateau and Northeast China. The prediction results show that the prediction accuracy of SARIMA is higher than that of LSTM. Therefore, the results predicted by SARIMA may provide a reference for the study of the radiation balance of the earth–atmosphere system in China.https://www.mdpi.com/2073-4433/13/3/400outgoing longwave radiationMann–Kendall testEOF modes analysisSARIMA modellong short-term memory |
| spellingShingle | Chaoli Tang Dong Liu Yuanyuan Wei Xiaomin Tian Fengmei Zhao Xin Wu Spatial-Temporal Mode Analysis and Prediction of Outgoing Longwave Radiation over China in 2002–2021 Based on Atmospheric Infrared Sounder Data Atmosphere outgoing longwave radiation Mann–Kendall test EOF modes analysis SARIMA model long short-term memory |
| title | Spatial-Temporal Mode Analysis and Prediction of Outgoing Longwave Radiation over China in 2002–2021 Based on Atmospheric Infrared Sounder Data |
| title_full | Spatial-Temporal Mode Analysis and Prediction of Outgoing Longwave Radiation over China in 2002–2021 Based on Atmospheric Infrared Sounder Data |
| title_fullStr | Spatial-Temporal Mode Analysis and Prediction of Outgoing Longwave Radiation over China in 2002–2021 Based on Atmospheric Infrared Sounder Data |
| title_full_unstemmed | Spatial-Temporal Mode Analysis and Prediction of Outgoing Longwave Radiation over China in 2002–2021 Based on Atmospheric Infrared Sounder Data |
| title_short | Spatial-Temporal Mode Analysis and Prediction of Outgoing Longwave Radiation over China in 2002–2021 Based on Atmospheric Infrared Sounder Data |
| title_sort | spatial temporal mode analysis and prediction of outgoing longwave radiation over china in 2002 2021 based on atmospheric infrared sounder data |
| topic | outgoing longwave radiation Mann–Kendall test EOF modes analysis SARIMA model long short-term memory |
| url | https://www.mdpi.com/2073-4433/13/3/400 |
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