Impact of 3DVAR Data Assimilation on the Prediction of Heavy Rainfall over Southern China
This study examines the impact of three-dimensional variational data assimilation (3DVAR) on the prediction of two heavy rainfall events over Southern China by using a real-time storm-scale forecasting system. Initialized from the European Centre for Medium-Range Weather Forecasts (ECMWF) high-resol...
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2013/129642 |
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| author | Tuanjie Hou Fanyou Kong Xunlai Chen Hengchi Lei |
| author_facet | Tuanjie Hou Fanyou Kong Xunlai Chen Hengchi Lei |
| author_sort | Tuanjie Hou |
| collection | DOAJ |
| description | This study examines the impact of three-dimensional variational data assimilation (3DVAR) on the prediction of two heavy rainfall events over Southern China by using a real-time storm-scale forecasting system. Initialized from the European Centre for Medium-Range Weather Forecasts (ECMWF) high-resolution data, the forecasting system is characterized by combining the Advanced Research Weather Research and Forecasting (WRF-ARW) model and the Advanced Regional Prediction System (ARPS) 3DVAR package. Observations from Doppler radars, surface Automatic Weather Station (AWS) network, and radiosondes are used in the experiments to evaluate the impact of data assimilation on short-term quantitative precipitation forecast (QPF) skill. Results suggest that extrasurface AWS data assimilation has slight but general positive impact on rainfall location forecasts. Surface AWS data also improve model results of near-surface variables. Radiosonde data assimilation improves the QPF skill by improving rainfall position accuracy and reducing rainfall overprediction. Compared with radar data, the overall impact of additional surface and radiosonde data is smaller and is reflected primarily in reducing rainfall overestimation. The assimilation of all radar, surface, and radiosonde data has a more positive impact on the forecast skill than the assimilation of either type of data only for the two rainfall events. |
| format | Article |
| id | doaj-art-d737096030ab498aaa169ff63f66fdb8 |
| institution | OA Journals |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-d737096030ab498aaa169ff63f66fdb82025-08-20T02:19:31ZengWileyAdvances in Meteorology1687-93091687-93172013-01-01201310.1155/2013/129642129642Impact of 3DVAR Data Assimilation on the Prediction of Heavy Rainfall over Southern ChinaTuanjie Hou0Fanyou Kong1Xunlai Chen2Hengchi Lei3Laboratory of Cloud-Precipitation Physics and Severe Storms, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, ChinaCenter for Analysis and Prediction of Storms, University of Oklahoma, Norman, OK 73072, USACenter for Analysis and Prediction of Storms, University of Oklahoma, Norman, OK 73072, USALaboratory of Cloud-Precipitation Physics and Severe Storms, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, ChinaThis study examines the impact of three-dimensional variational data assimilation (3DVAR) on the prediction of two heavy rainfall events over Southern China by using a real-time storm-scale forecasting system. Initialized from the European Centre for Medium-Range Weather Forecasts (ECMWF) high-resolution data, the forecasting system is characterized by combining the Advanced Research Weather Research and Forecasting (WRF-ARW) model and the Advanced Regional Prediction System (ARPS) 3DVAR package. Observations from Doppler radars, surface Automatic Weather Station (AWS) network, and radiosondes are used in the experiments to evaluate the impact of data assimilation on short-term quantitative precipitation forecast (QPF) skill. Results suggest that extrasurface AWS data assimilation has slight but general positive impact on rainfall location forecasts. Surface AWS data also improve model results of near-surface variables. Radiosonde data assimilation improves the QPF skill by improving rainfall position accuracy and reducing rainfall overprediction. Compared with radar data, the overall impact of additional surface and radiosonde data is smaller and is reflected primarily in reducing rainfall overestimation. The assimilation of all radar, surface, and radiosonde data has a more positive impact on the forecast skill than the assimilation of either type of data only for the two rainfall events.http://dx.doi.org/10.1155/2013/129642 |
| spellingShingle | Tuanjie Hou Fanyou Kong Xunlai Chen Hengchi Lei Impact of 3DVAR Data Assimilation on the Prediction of Heavy Rainfall over Southern China Advances in Meteorology |
| title | Impact of 3DVAR Data Assimilation on the Prediction of Heavy Rainfall over Southern China |
| title_full | Impact of 3DVAR Data Assimilation on the Prediction of Heavy Rainfall over Southern China |
| title_fullStr | Impact of 3DVAR Data Assimilation on the Prediction of Heavy Rainfall over Southern China |
| title_full_unstemmed | Impact of 3DVAR Data Assimilation on the Prediction of Heavy Rainfall over Southern China |
| title_short | Impact of 3DVAR Data Assimilation on the Prediction of Heavy Rainfall over Southern China |
| title_sort | impact of 3dvar data assimilation on the prediction of heavy rainfall over southern china |
| url | http://dx.doi.org/10.1155/2013/129642 |
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