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High temporal and spatial resolution of radar measurements enables to continuously observe dynamically evolving meteorological phenomena. Three-dimensional (3D) weather radar reflectivity data assimilated into the numerical weather prediction model has the potential to improve initial description of the atmospheric model state. The paper is concentrated on the development of radar reflectivity assimilation technique into COAMPS mesoscale model using an Ensemble Kalman Filter (EnKF) type assimilation schemes available in Data Assimilation Research Testbed (DART) programming environment. Before weather radar data enter into the assimilation system, the measurement errors are eliminated through quality control procedures. At first artifacts associated with non-meteorological errors are removed using the algorithms based on analysis of reflectivity field pattern. Then procedures for correction of the reflectivity data are employed, especially due to radar beam blockage and attenuation in rain. Each of the correction algorithms is connected with generation of the data quality characteristic expressed quantitatively by so called quality index (QI). In order to avoid transformation of data uncertainty into assimilation scheme only the radar gates successfully verified by means of the quality algorithms were employed in the assimilation. The proposed methodology has been applied to simulate selected intense precipitation events in Poland in May and August 2010.

Experiments with three-dimensional radar reflectivity data assimilation into the COAMPS model