Quantitative analysis of different methods for merging radar reflectivity data
Jennifer S. Green, National Weather Center Research Experience for Undergrads, Sterling Heights, MI; and V. Lakshmanan and T. M. Smith
Merging radar data is a useful aid in visualizing data from multiple radars covering large domains. It involves getting the base data from several radars and combining the data to find the best estimate of a radar parameter at every grid point in the domain. In this study, we study the effects of two possible pre-processing methods – quality control of radar data, and advection of radar echoes to match the grid time -- on the quality of the merged result. We study the effects of using quality control, or quality control and advection to four WSR-88D's and their reflectivity data before they are merged. In order to get a quantitative analysis, precipitation rates from Oklahoma Mesonet stations were compared to precipitation rates of merged radar data broken into three categories 1.) raw reflectivity data, 2.) quality controlled, and 3.) quality controlled and advected. A scoring program was developed to get a quantitative analysis of each of the different methods with each of the four cases analyzed. We found, rather counter-intuitively, that neither quality control nor advection led to better precipitation estimates. Closer examination revealed some problems with our methodology. Adding advection to reflectivity data gives amounts of high error, but this may be caused by the temporal resolution. We found that the use of quality controlled data by the precipitation estimation method needs to be improved so that cases with high amounts of virga can provide more accurate precipitation rates.
Poster Session 1, Student Conference Poster Session
Sunday, 9 January 2005, 5:30 PM-5:30 PM
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