The Local Unscented Transform Kalman Filter for the Weather Research and Forecasting Model
In this study, the local unscented transform Kalman filter (LUTKF) proposed in the previous study estimates the state of the Weather Research and Forecasting (WRF) model through local analysis. Real observations are assimilated to investigate the analysis performance of the WRF-LUTKF system. The WRF...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/14/7/1143 |
| _version_ | 1827734048232964096 |
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| author | Kwangjae Sung |
| author_facet | Kwangjae Sung |
| author_sort | Kwangjae Sung |
| collection | DOAJ |
| description | In this study, the local unscented transform Kalman filter (LUTKF) proposed in the previous study estimates the state of the Weather Research and Forecasting (WRF) model through local analysis. Real observations are assimilated to investigate the analysis performance of the WRF-LUTKF system. The WRF model as a regional numerical weather prediction (NWP) model is widely used to explain the atmospheric state for mesoscale meteorological fields, such as operational forecasting and atmospheric research applications. For the LUTKF based on the sigma-point Kalman filter (SPKF), the state of the nonlinear system is estimated by propagating ensemble members through the unscented transformation (UT) without making any linearization assumptions for nonlinear models. The main objective of this study is to examine the feasibility of mesoscale data assimilations for the LUTKF algorithm using the WRF model and real observations. Similar to the local ensemble transform Kalman filter (LETKF), by suppressing the impact of distant observations on model state variables through localization schemes, the LUTKF can eliminate spurious long-distance correlations in the background covariance, which are induced by the sampling error due to the finite ensemble size; therefore, the LUTKF used in the WRF-LUTKF system can efficiently execute the data assimilation with a small ensemble size. Data assimilation test results demonstrate that the LUTKF can provide reliable analysis performance in estimating the WRF model state with real observations. Experiments with various ensemble size show that the LETKF can provide better estimation results with a larger ensemble size, while the LUTKF can achieve accurate and reliable assimilation results even with a smaller ensemble size. |
| first_indexed | 2024-03-11T01:19:00Z |
| format | Article |
| id | doaj.art-db43fe09e63a41b89b235bc60e00fa80 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-11T01:19:00Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-db43fe09e63a41b89b235bc60e00fa802023-11-18T18:16:06ZengMDPI AGAtmosphere2073-44332023-07-01147114310.3390/atmos14071143The Local Unscented Transform Kalman Filter for the Weather Research and Forecasting ModelKwangjae Sung0Department of Software, Sangmyung University, Cheonan-si 31066, Republic of KoreaIn this study, the local unscented transform Kalman filter (LUTKF) proposed in the previous study estimates the state of the Weather Research and Forecasting (WRF) model through local analysis. Real observations are assimilated to investigate the analysis performance of the WRF-LUTKF system. The WRF model as a regional numerical weather prediction (NWP) model is widely used to explain the atmospheric state for mesoscale meteorological fields, such as operational forecasting and atmospheric research applications. For the LUTKF based on the sigma-point Kalman filter (SPKF), the state of the nonlinear system is estimated by propagating ensemble members through the unscented transformation (UT) without making any linearization assumptions for nonlinear models. The main objective of this study is to examine the feasibility of mesoscale data assimilations for the LUTKF algorithm using the WRF model and real observations. Similar to the local ensemble transform Kalman filter (LETKF), by suppressing the impact of distant observations on model state variables through localization schemes, the LUTKF can eliminate spurious long-distance correlations in the background covariance, which are induced by the sampling error due to the finite ensemble size; therefore, the LUTKF used in the WRF-LUTKF system can efficiently execute the data assimilation with a small ensemble size. Data assimilation test results demonstrate that the LUTKF can provide reliable analysis performance in estimating the WRF model state with real observations. Experiments with various ensemble size show that the LETKF can provide better estimation results with a larger ensemble size, while the LUTKF can achieve accurate and reliable assimilation results even with a smaller ensemble size.https://www.mdpi.com/2073-4433/14/7/1143regional numerical weather prediction modelensemble-based Kalman filterstate estimationdata assimilation |
| spellingShingle | Kwangjae Sung The Local Unscented Transform Kalman Filter for the Weather Research and Forecasting Model Atmosphere regional numerical weather prediction model ensemble-based Kalman filter state estimation data assimilation |
| title | The Local Unscented Transform Kalman Filter for the Weather Research and Forecasting Model |
| title_full | The Local Unscented Transform Kalman Filter for the Weather Research and Forecasting Model |
| title_fullStr | The Local Unscented Transform Kalman Filter for the Weather Research and Forecasting Model |
| title_full_unstemmed | The Local Unscented Transform Kalman Filter for the Weather Research and Forecasting Model |
| title_short | The Local Unscented Transform Kalman Filter for the Weather Research and Forecasting Model |
| title_sort | local unscented transform kalman filter for the weather research and forecasting model |
| topic | regional numerical weather prediction model ensemble-based Kalman filter state estimation data assimilation |
| url | https://www.mdpi.com/2073-4433/14/7/1143 |
| work_keys_str_mv | AT kwangjaesung thelocalunscentedtransformkalmanfilterfortheweatherresearchandforecastingmodel AT kwangjaesung localunscentedtransformkalmanfilterfortheweatherresearchandforecastingmodel |