Characterizing Objective Analysis Errors for Dual Polarization Weather Radar

Title: Characterizing Objective Analysis Errors for Dual Polarization Weather Radar Authors: Jared Marquis, Joseph Hardin, V. Chandrasekar The natural coordinate system for radar sampled data lies on a spherical grid. These grids however do not lend themselves easily to either analysis, or model ingest. The process of converting the data that lies on a spherical grid, to a uniform Cartesian grid is called objective radar analysis. This process is a form of interpolation, and as such, induces errors in the underlying data. This work examines the errors that are generated by the gridding process, extending the pioneering work of Trapp and Doswell[1]. We use two different approaches to characterize the errors generated by several different objective analysis algorithms. These algorithms span from the simple linear and nearest neighbor, to the more complex Barnes and adaptive Cressman algorithms.. Analytically generated fields give us a method to generate exact ground truth and compare the errors in both spatial and frequency domains. This allows us to examine the performance of these algorithms over storms of differing spatial frequency scales. We then use real data drawn from field campaigns to characterize the error on different storm types. We characterize the error using several statistical measures, as well as structural similarity metrics[2]. We finish up with recommendations on algorithm use for different meteorological phenomenon. References: [1] Trapp, R. J., and C. A. Doswell III, 2000: Radar Data Objective Analysis. J. Atmos. Oceanic Technol., 17, 105-120. [2] Wang, Zhou, Alan Conrad Bovik, Hamid Rahim Sheikh, and Eero P. Simoncelli, 2004: Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Transactions on Image Processing, Vol. 13 No. 4, 600-612.