ADAPT seminar

Speaker: Masashi Minamide (Graduate student in Department of Meteorology, PSU)
Topic: "Application of Empirical Localization Functions to All-Sky Satellite Radiance Assimilation"

Room: 529 Walker Building (refreshments served)
Time: Friday August 25, 2017 3:30pm - 4:30pm
Abstract: Covariance localization has been used in ensemble-based data assimilation to control the sampling errors due to the limited ensemble size. One common method of covariance localization is the uniform application of circular localization functions for all the model state variables. However, this homogenous localizing method is potentially preventing us from analyzing optimal increments in all-sky satellite radiance assimilation since the covariance structures and effective radius of influence for radiances are highly flow- and situation-dependent.

In this study, an application of empirical localization functions (ELFs) for all-sky satellite radiance assimilation is investigated using convective-permitting models with an ensemble Kalman filter. ELFs are a method to find optimal localization functions for a given set of observations and model states using observation system simulation experiments. Here, a boot-strap version of empirical localization functions (BELFs) is proposed to obtain smooth localization functions from non-hydrostatic unbalanced model fields with nonlinear observation operators, which introduce huge representativeness errors. The BELFs method computes optimal localization functions by repeatedly comparing the estimated increments from subsets of (N-1) ensemble members and the differences between (N-1) members’ ensemble mean and the other unselected member. By applying this boot-strap method to variable sky conditions separately, computed BELFs clearly illustrates the cloud-dependency of optimal localizations. Higher cloud radiances are found to have vertically wider and horizontally narrower localization radius. Using BELFs significantly reduces the root mean square error of thermodynamic model state variables from manually tuned localization values. Since BELFs only require the set of ensemble priors for calculation, the applicability to real-data assimilation is also explored.