3.3 A Priori filtering of Radio Occultation Bending Angles in the Moist Atmosphere for Data Assimilation Application

Tuesday, 12 January 2016: 2:00 PM
Room 335/336 ( New Orleans Ernest N. Morial Convention Center)
Hui Liu, NCAR, Boulder, CO; and B. Kuo, S. Sokolovskiy, Z. Zeng, and R. Anthes

Radio occultation (RO) measurements have become an important data source for global numerical weather prediction (NWP). Most of the operational centers assimilate RO data in the form of bending angles, because they represents an earlier step in the processing chain and their error characterization is better defined. However, in the moist atmosphere, vertical gradients of water vapor due to convective and boundary layer processes can induce substantial irregularities in the bending angle profiles, with complex and sharp vertical structures unresolvable by current assimilation models. Consequently, large random representativeness errors can occur in the assimilation of bending angle profiles, and these may induce systematic errors due to strong nonlinearities in the assimilation, especially when such data are assimilated in ensemble-based assimilation systems. A priori filtering of bending angle data to reduce representativeness errors prior to the assimilation can be beneficial because it is a linear process and can reduce potential systematic errors in the assimilation process.

In this study, we first estimate the representativeness errors for typical NWP models including the Weather Research and Forecast (WRF) model and the ECMWF model and investigate how to reduce the errors by a priori filtering of the raw RO bending angle observations. Then we explore the systematic errors of the analyses induced by the representativeness errors and the benefits of the a priori filtering by performing assimilation experiments of the raw and filtered bending angles with the WRF model using a sequential ensemble filtering technique. It is found that random representativeness errors can reach up to 8% in the moist lower troposphere, but a priori filtering of the raw bending angles can reduce the errors substantially to less than 2%. The large representativeness errors associated with raw bending angle data can introduce noticeable systematic errors in the analysis of water vapor in the lower troposphere and temperature in the upper troposphere. Assimilation of the a priori filtered bending angle observations substantially reduces such systematic errors and improves the analyses. We also find that the induced systematic errors are sensitive to the physical parameterizations of the model used in the assimilation. The results should help improve assimilation of current and, particularly the coming COSMIC-2 RO bending angle observations.

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