Lili Lei;
Jeff Whitaker;
Jeff Anderson
Nanjing University
The 8th EnKF Data Assimilation Workshop
Adaptive localization for satellite radiance observations in an ensemble Kalman filter
Talk:
lili_201805.pdf
Previous studies have shown that localization is an essential component to effectively assimilate satellite radiances in ensemble Kalman filters with affordable ensemble sizes. But the vertical location and separation from a grid point model variable for a radiance observation are not well defined, which results in complexities when localizing the impact of radiance observations. Lei et al. (2017) used a global group filter (GGF) to provide a theoretical estimate of vertical localization functions for the AMSU-A radiance observations. The fitted GGF experiment that uses the Gaspri-Cohn (GC) function to fit the GGF produces slightly better forecasts than the unfitted GGF experiments. Motivated by these results, a similar fitted GGF is applied here, which uses correlations between observations and state variables in assimilation cycles to estimate localization functions that reduce sampling error in ensemble correlations. This GGF adaptively provides theoretical estimates of vertical localization functions for each channel of every satellite being assimilated. Based on the GGFs, three localization parameters, the localization width, maximum, and vertical location of the radiance observations are obtained. These adaptively estimated localization parameters are used in experiments with the NCEP GFS and the NOAA operational EnKF. Verifications relative to the conventional observations show that using the adaptive localization width and vertical location of radiance observations is more beneficial than including the maximum localization value. Experiments using the adaptive localization width and vertical location of radiance observations produce smaller errors than the control experiment and similar errors to the optimal GC experiment.
Previous studies have shown that localization is an essential component to effectively assimilate satellite radiances in ensemble Kalman filters with affordable ensemble sizes. But the vertical location and separation from a grid point model variable for a radiance observation are not well defined, which results in complexities when localizing the impact of radiance observations. Lei et al. (2017) used a global group filter (GGF) to provide a theoretical estimate of vertical localization functions for the AMSU-A radiance observations. The fitted GGF experiment that uses the Gaspri-Cohn (GC) function to fit the GGF produces slightly better forecasts than the unfitted GGF experiments. Motivated by these results, a similar fitted GGF is applied here, which uses correlations between observations and state variables in assimilation cycles to estimate localization functions that reduce sampling error in ensemble correlations. This GGF adaptively provides theoretical estimates of vertical localization functions for each channel of every satellite being assimilated. Based on the GGFs, three localization parameters, the localization width, maximum, and vertical location of the radiance observations are obtained. These adaptively estimated localization parameters are used in experiments with the NCEP GFS and the NOAA operational EnKF. Verifications relative to the conventional observations show that using the adaptive localization width and vertical location of radiance observations is more beneficial than including the maximum localization value. Experiments using the adaptive localization width and vertical location of radiance observations produce smaller errors than the control experiment and similar errors to the optimal GC experiment.
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