16B.3 The Radar quality control and quantitative precipitation estimation inter-comparison project

Thursday, 29 September 2011: 12:00 AM
Urban Room (William Penn Hotel)
Paul Joe, EC, Toronto, ON, Canada

The use of radar has advanced from local (nowcasting, severe weather, flash flooding) to regional and global (numerical weather prediction, data assimilation and climate) applications. Radar data exchange is occurring at the regional/trans-national scale. However, considering the different weather, climate and sensing environments and legacy issues, radars have considerable artifacts (ground clutter, anomalous propagation, etc) and are also configured to scan differently (from spatial and temporal sampling, to areal coverage, to data parameters collected). With the globalization of the use of weather radar, the World Meteorological Organization has initiated an inter-comparison project on radar quality control and quantitative precipitation estimation. It is under the auspices of the Commission of Instruments and Methods for Observations. The goals of the project are to Inter-compare radar quality control algorithms for QPE applications, to articulate best practices and make recommendations for WMO members and to quantify the quality of QPE radar products globally. Many of the artifacts and issues are confounded by radar quality, scan strategy, application, local siting and weather/propagation environments. The adjustment procedures have been segmented into static bias correction (ground clutter and anomalous propagation, etc), target classification (e.g., insects from precipitation), vertical profile correction for long range QPE, bias adjustment (with disdrometers or gauges), network merging and space-time sampling adjustment. A series of inter-comparison workshops are planned where various data adjustment techniques are applied to various scenarios/cases from around the globe. A critical question is the development of a simple metric to describe the improvement. Also, nowcasting and data assimilation for numerical weather prediction are also emerging operational issues. Results from a pilot study to study the inter-comparison process will be presented
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