Correcting Forecast Time Biases in CMA-MESO Using Himawari-9 and Time-Shift Method
The accurate forecasting of time, intensity, and spatial distribution is fundamental to weather prediction. However, the limitations of numerical weather prediction (NWP) models, as well as uncertainties in inital conditions, often lead to temporal biases in forecasts. This study addresses these bia...
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MDPI AG
2025-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/4/617 |
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| author | Xingtao Song Wei Han Haofei Sun Hao Wang Xiaofeng Xu |
| author_facet | Xingtao Song Wei Han Haofei Sun Hao Wang Xiaofeng Xu |
| author_sort | Xingtao Song |
| collection | DOAJ |
| description | The accurate forecasting of time, intensity, and spatial distribution is fundamental to weather prediction. However, the limitations of numerical weather prediction (NWP) models, as well as uncertainties in inital conditions, often lead to temporal biases in forecasts. This study addresses these biases by employing visible reflectance data from the Himawari-9/AHI satellite and RTTOV (TOVS radiation transfer) simulations derived from CMA-MESO model outputs. The time-shift method was applied to analyze two precipitation events—20 October 2023 and 30 April 2024—in order to assess its impact on precipitation forecasts. The results indicate the following: (1) the time-shift method improved cloud simulations, necessitating a 30 min advance for Case 1 and a 3.5 h delay for Case 2; (2) time-shifting reduced the standard deviation of observation-minus-background (OMB) bias in certain regions and enhanced spatial uniformity; (3) the threat score (TS) demonstrated an improvement in forecast accuracy, particularly in cases exhibiting significant movement patterns. The comparative analysis demonstrates that the time-shift method effectively corrects temporal biases in NWP models, providing forecasters with a valuable tool to optimize predictions through the integration of high-temporal- and spatial-resolution visible light data, thereby leading to more accurate and reliable weather forecasts. |
| format | Article |
| id | doaj-art-b51932809ff545e2b36943358483cf5a |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-b51932809ff545e2b36943358483cf5a2025-08-20T02:03:30ZengMDPI AGRemote Sensing2072-42922025-02-0117461710.3390/rs17040617Correcting Forecast Time Biases in CMA-MESO Using Himawari-9 and Time-Shift MethodXingtao Song0Wei Han1Haofei Sun2Hao Wang3Xiaofeng Xu4School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaCMA Earth System Modeling and Prediction Centre (CEMC), China Meteorological Administration, Beijing 100081, ChinaState Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 101408, ChinaCMA Earth System Modeling and Prediction Centre (CEMC), China Meteorological Administration, Beijing 100081, ChinaSchool of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaThe accurate forecasting of time, intensity, and spatial distribution is fundamental to weather prediction. However, the limitations of numerical weather prediction (NWP) models, as well as uncertainties in inital conditions, often lead to temporal biases in forecasts. This study addresses these biases by employing visible reflectance data from the Himawari-9/AHI satellite and RTTOV (TOVS radiation transfer) simulations derived from CMA-MESO model outputs. The time-shift method was applied to analyze two precipitation events—20 October 2023 and 30 April 2024—in order to assess its impact on precipitation forecasts. The results indicate the following: (1) the time-shift method improved cloud simulations, necessitating a 30 min advance for Case 1 and a 3.5 h delay for Case 2; (2) time-shifting reduced the standard deviation of observation-minus-background (OMB) bias in certain regions and enhanced spatial uniformity; (3) the threat score (TS) demonstrated an improvement in forecast accuracy, particularly in cases exhibiting significant movement patterns. The comparative analysis demonstrates that the time-shift method effectively corrects temporal biases in NWP models, providing forecasters with a valuable tool to optimize predictions through the integration of high-temporal- and spatial-resolution visible light data, thereby leading to more accurate and reliable weather forecasts.https://www.mdpi.com/2072-4292/17/4/617Himawari-9CMA-MESOtime shift |
| spellingShingle | Xingtao Song Wei Han Haofei Sun Hao Wang Xiaofeng Xu Correcting Forecast Time Biases in CMA-MESO Using Himawari-9 and Time-Shift Method Remote Sensing Himawari-9 CMA-MESO time shift |
| title | Correcting Forecast Time Biases in CMA-MESO Using Himawari-9 and Time-Shift Method |
| title_full | Correcting Forecast Time Biases in CMA-MESO Using Himawari-9 and Time-Shift Method |
| title_fullStr | Correcting Forecast Time Biases in CMA-MESO Using Himawari-9 and Time-Shift Method |
| title_full_unstemmed | Correcting Forecast Time Biases in CMA-MESO Using Himawari-9 and Time-Shift Method |
| title_short | Correcting Forecast Time Biases in CMA-MESO Using Himawari-9 and Time-Shift Method |
| title_sort | correcting forecast time biases in cma meso using himawari 9 and time shift method |
| topic | Himawari-9 CMA-MESO time shift |
| url | https://www.mdpi.com/2072-4292/17/4/617 |
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