Adaptive Localization for Satellite Radiance Observations in an Ensemble Kalman Filter
Abstract Localization is essential to effectively assimilate satellite radiances in ensemble Kalman filters. However, the vertical location and separation from a model grid point variable for a radiance observation are not well defined, which results in complexities when localizing the impact of rad...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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American Geophysical Union (AGU)
2020-08-01
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| Series: | Journal of Advances in Modeling Earth Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2019MS001693 |
| _version_ | 1819002368044302336 |
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| author | Lili Lei Jeffrey S. Whitaker Jeffrey L. Anderson Zhemin Tan |
| author_facet | Lili Lei Jeffrey S. Whitaker Jeffrey L. Anderson Zhemin Tan |
| author_sort | Lili Lei |
| collection | DOAJ |
| description | Abstract Localization is essential to effectively assimilate satellite radiances in ensemble Kalman filters. However, the vertical location and separation from a model grid point variable for a radiance observation are not well defined, which results in complexities when localizing the impact of radiance observations. An adaptive method is proposed to estimate an effective vertical localization independently for each assimilated channel of every satellite platform. It uses sample correlations between ensemble priors of observations and state variables from a cycling data assimilation to estimate the localization function that minimizes the sampling error. The estimated localization functions are approximated by three localization parameters: the localization width, maximum value, and vertical location of the radiance observations. Adaptively estimated localization parameters are used in assimilation experiments with the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model and the National Oceanic and Atmospheric Administration (NOAA) operational ensemble Kalman filter (EnKF). Results show that using the adaptive localization width and vertical location for radiance observations is more beneficial than also including the maximum localization value. The experiment using the adaptively estimated localization width and vertical location performs better than the default Gaspari and Cohn (GC) experiment, and produces similar errors to the optimal GC experiment. The adaptive localization parameters can be computed during the assimilation procedure, so the computational cost needed to tune the optimal GC localization width is saved. |
| first_indexed | 2024-12-20T23:03:59Z |
| format | Article |
| id | doaj.art-f9972f6990d14417a8571945b741d187 |
| institution | Directory Open Access Journal |
| issn | 1942-2466 |
| language | English |
| last_indexed | 2024-12-20T23:03:59Z |
| publishDate | 2020-08-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | Journal of Advances in Modeling Earth Systems |
| spelling | doaj.art-f9972f6990d14417a8571945b741d1872022-12-21T19:23:55ZengAmerican Geophysical Union (AGU)Journal of Advances in Modeling Earth Systems1942-24662020-08-01128n/an/a10.1029/2019MS001693Adaptive Localization for Satellite Radiance Observations in an Ensemble Kalman FilterLili Lei0Jeffrey S. Whitaker1Jeffrey L. Anderson2Zhemin Tan3Key Laboratory of Mesoscale Severe Weather, Ministry of Education Nanjing University Nanjing ChinaNOAA/Earth System Research Laboratory/Physical Sciences Division Boulder CO USANational Center for Atmospheric Research Boulder CO USAKey Laboratory of Mesoscale Severe Weather, Ministry of Education Nanjing University Nanjing ChinaAbstract Localization is essential to effectively assimilate satellite radiances in ensemble Kalman filters. However, the vertical location and separation from a model grid point variable for a radiance observation are not well defined, which results in complexities when localizing the impact of radiance observations. An adaptive method is proposed to estimate an effective vertical localization independently for each assimilated channel of every satellite platform. It uses sample correlations between ensemble priors of observations and state variables from a cycling data assimilation to estimate the localization function that minimizes the sampling error. The estimated localization functions are approximated by three localization parameters: the localization width, maximum value, and vertical location of the radiance observations. Adaptively estimated localization parameters are used in assimilation experiments with the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model and the National Oceanic and Atmospheric Administration (NOAA) operational ensemble Kalman filter (EnKF). Results show that using the adaptive localization width and vertical location for radiance observations is more beneficial than also including the maximum localization value. The experiment using the adaptively estimated localization width and vertical location performs better than the default Gaspari and Cohn (GC) experiment, and produces similar errors to the optimal GC experiment. The adaptive localization parameters can be computed during the assimilation procedure, so the computational cost needed to tune the optimal GC localization width is saved.https://doi.org/10.1029/2019MS001693radiance observationensemble Kalman filteradaptive localization |
| spellingShingle | Lili Lei Jeffrey S. Whitaker Jeffrey L. Anderson Zhemin Tan Adaptive Localization for Satellite Radiance Observations in an Ensemble Kalman Filter Journal of Advances in Modeling Earth Systems radiance observation ensemble Kalman filter adaptive localization |
| title | Adaptive Localization for Satellite Radiance Observations in an Ensemble Kalman Filter |
| title_full | Adaptive Localization for Satellite Radiance Observations in an Ensemble Kalman Filter |
| title_fullStr | Adaptive Localization for Satellite Radiance Observations in an Ensemble Kalman Filter |
| title_full_unstemmed | Adaptive Localization for Satellite Radiance Observations in an Ensemble Kalman Filter |
| title_short | Adaptive Localization for Satellite Radiance Observations in an Ensemble Kalman Filter |
| title_sort | adaptive localization for satellite radiance observations in an ensemble kalman filter |
| topic | radiance observation ensemble Kalman filter adaptive localization |
| url | https://doi.org/10.1029/2019MS001693 |
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