Monday, 16 September 2013
Breckenridge Ballroom (Peak 14-17, 1st Floor) / Event Tent (Outside) (Beaver Run Resort and Conference Center)
For the production of radar Quantitative Precipitation Estimates (QPE), it is essential to ensure the quality of the radar observation and its conversion into the variable of interest (i.e., precipitation rates). We focus on the reconstruction of the meteorological signal for uncorrected scanning moment data in areas where the signal is lost or strongly contaminated due to ground or sea clutter, severe beam blockage or path attenuation due to heavy rain. Such reconstruction has been frequently done by horizontal interpolation or vertical extrapolation. The performance of these methods depends on the structure of the precipitation field: horizontal interpolation is preferred for widespread precipitation, whereas vertical extrapolation is more adequate convective situations. In this work, we have developed a reconstruction method that adapts to the different rainfall situations (scattered convection, organized convection, and widespread precipitation) by considering time and space variability of the rainfall ﬁeld. The n-dimensional semi-variogram is formulated to reconstruct the radar ﬁelds in a n-Dimensional Ordinary Kriging framework: i) 2D horizontal interpolation, ii) 3D interpolation in space using the observations from the closest non-contaminated PPI, and iii) 4D space-time interpolation. The last one takes into account the effect of the motion, similarly as done in extrapolating reﬂectivity fields to the future in many nowcasting algorithms. The reconstructed radar rainfall accumulations have been evaluated with collocated raingauge observations.
Shinju Park Current affiliation: Department of Astronomy and Atmospheric Sciences, Kyungpook National University, Daegu (South Korea)
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