Sensitivity studies of a four-dimensional local ensemble transform Kalman filter coupled with WRF-Chem version 3.9.1 for improving particulate matter simulation accuracy
<p>Accurately simulating severe haze events through numerical models remains a challenge because of uncertainty in anthropogenic emissions and physical parameterizations of particulate matter (PM<span class="inline-formula"><sub>2.5</sub></span> and PM<span...
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Copernicus Publications
2025-04-01
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| Series: | Geoscientific Model Development |
| Online Access: | https://gmd.copernicus.org/articles/18/2231/2025/gmd-18-2231-2025.pdf |
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| author | J. Lin J. Lin T. Dai L. Sheng W. Zhang S. Hai Y. Kong |
| author_facet | J. Lin J. Lin T. Dai L. Sheng W. Zhang S. Hai Y. Kong |
| author_sort | J. Lin |
| collection | DOAJ |
| description | <p>Accurately simulating severe haze events through numerical models remains a challenge because of uncertainty in anthropogenic emissions and physical parameterizations of particulate matter (PM<span class="inline-formula"><sub>2.5</sub></span> and PM<span class="inline-formula"><sub>10</sub></span>). In this study, a coupled Weather Research and Forecasting with Chemistry (WRF-Chem)–four-dimensional local ensemble transform Kalman filter (4D-LETKF) data assimilation system has been successfully developed to optimize particulate matter concentration by assimilating hourly ground-based observations in winter over the Beijing–Tianjin–Hebei (BTH) region and surrounding provinces. The effectiveness of the 4D-LETKF system and its sensitivity to the ensemble member size and length of the assimilation window are investigated. The promising results show that significant improvements have been made by analysis in the simulation of particulate matter during a severe haze event. The assimilation reduces root mean square errors in PM<span class="inline-formula"><sub>2.5</sub></span> from 69.93 to 31.19 <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−3</sup></span> and of PM<span class="inline-formula"><sub>10</sub></span> from 106.88 to 76.83 <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−3</sup></span>. Smaller root mean square errors and larger correlation coefficients in the analysis of PM<span class="inline-formula"><sub>2.5</sub></span> and PM<span class="inline-formula"><sub>10</sub></span> are observed across nearly all verification stations, indicating that the 4D-LETKF assimilation optimizes the simulation of PM<span class="inline-formula"><sub>2.5</sub></span> and PM<span class="inline-formula"><sub>10</sub></span> concentration efficiently. Sensitivity experiments reveal that the combination of 48 h of assimilation window length and 40 ensemble members shows the best performance for reproducing the severe haze event. In view of the performance of ensemble members, an increasing ensemble member size improves ensemble spread among each forecasting member, facilitates the spread of state vectors about PM<span class="inline-formula"><sub>2.5</sub></span> and PM<span class="inline-formula"><sub>10</sub></span> information in the first guess, favors the variances between each initial condition in the next assimilation cycle, and leads to better simulation performance in both severe and moderate haze events. This study advances our understanding of the selection of basic parameters in the 4D-LETKF assimilation system and the performance of ensemble simulations in a particulate-matter-polluted environment.</p> |
| format | Article |
| id | doaj-art-82a0fbf4db3c4ba6a2ede984d7fa3ac6 |
| institution | OA Journals |
| issn | 1991-959X 1991-9603 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Copernicus Publications |
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| series | Geoscientific Model Development |
| spelling | doaj-art-82a0fbf4db3c4ba6a2ede984d7fa3ac62025-08-20T02:16:48ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032025-04-01182231224810.5194/gmd-18-2231-2025Sensitivity studies of a four-dimensional local ensemble transform Kalman filter coupled with WRF-Chem version 3.9.1 for improving particulate matter simulation accuracyJ. Lin0J. Lin1T. Dai2L. Sheng3W. Zhang4S. Hai5Y. Kong6College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, 266100, ChinaState Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, ChinaState Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, ChinaCollege of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, 266100, ChinaCollege of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, 266100, ChinaCMA Earth System Modeling and Prediction Centre, China Meteorological Administration (CMA), Beijing 100081, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China<p>Accurately simulating severe haze events through numerical models remains a challenge because of uncertainty in anthropogenic emissions and physical parameterizations of particulate matter (PM<span class="inline-formula"><sub>2.5</sub></span> and PM<span class="inline-formula"><sub>10</sub></span>). In this study, a coupled Weather Research and Forecasting with Chemistry (WRF-Chem)–four-dimensional local ensemble transform Kalman filter (4D-LETKF) data assimilation system has been successfully developed to optimize particulate matter concentration by assimilating hourly ground-based observations in winter over the Beijing–Tianjin–Hebei (BTH) region and surrounding provinces. The effectiveness of the 4D-LETKF system and its sensitivity to the ensemble member size and length of the assimilation window are investigated. The promising results show that significant improvements have been made by analysis in the simulation of particulate matter during a severe haze event. The assimilation reduces root mean square errors in PM<span class="inline-formula"><sub>2.5</sub></span> from 69.93 to 31.19 <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−3</sup></span> and of PM<span class="inline-formula"><sub>10</sub></span> from 106.88 to 76.83 <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−3</sup></span>. Smaller root mean square errors and larger correlation coefficients in the analysis of PM<span class="inline-formula"><sub>2.5</sub></span> and PM<span class="inline-formula"><sub>10</sub></span> are observed across nearly all verification stations, indicating that the 4D-LETKF assimilation optimizes the simulation of PM<span class="inline-formula"><sub>2.5</sub></span> and PM<span class="inline-formula"><sub>10</sub></span> concentration efficiently. Sensitivity experiments reveal that the combination of 48 h of assimilation window length and 40 ensemble members shows the best performance for reproducing the severe haze event. In view of the performance of ensemble members, an increasing ensemble member size improves ensemble spread among each forecasting member, facilitates the spread of state vectors about PM<span class="inline-formula"><sub>2.5</sub></span> and PM<span class="inline-formula"><sub>10</sub></span> information in the first guess, favors the variances between each initial condition in the next assimilation cycle, and leads to better simulation performance in both severe and moderate haze events. This study advances our understanding of the selection of basic parameters in the 4D-LETKF assimilation system and the performance of ensemble simulations in a particulate-matter-polluted environment.</p>https://gmd.copernicus.org/articles/18/2231/2025/gmd-18-2231-2025.pdf |
| spellingShingle | J. Lin J. Lin T. Dai L. Sheng W. Zhang S. Hai Y. Kong Sensitivity studies of a four-dimensional local ensemble transform Kalman filter coupled with WRF-Chem version 3.9.1 for improving particulate matter simulation accuracy Geoscientific Model Development |
| title | Sensitivity studies of a four-dimensional local ensemble transform Kalman filter coupled with WRF-Chem version 3.9.1 for improving particulate matter simulation accuracy |
| title_full | Sensitivity studies of a four-dimensional local ensemble transform Kalman filter coupled with WRF-Chem version 3.9.1 for improving particulate matter simulation accuracy |
| title_fullStr | Sensitivity studies of a four-dimensional local ensemble transform Kalman filter coupled with WRF-Chem version 3.9.1 for improving particulate matter simulation accuracy |
| title_full_unstemmed | Sensitivity studies of a four-dimensional local ensemble transform Kalman filter coupled with WRF-Chem version 3.9.1 for improving particulate matter simulation accuracy |
| title_short | Sensitivity studies of a four-dimensional local ensemble transform Kalman filter coupled with WRF-Chem version 3.9.1 for improving particulate matter simulation accuracy |
| title_sort | sensitivity studies of a four dimensional local ensemble transform kalman filter coupled with wrf chem version 3 9 1 for improving particulate matter simulation accuracy |
| url | https://gmd.copernicus.org/articles/18/2231/2025/gmd-18-2231-2025.pdf |
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