The Effects of Spatial Interpolation on a Novel, Dual-Doppler 3D Wind Retrieval Technique

Abstract

Abstract Three-dimensional wind retrievals from ground-based Doppler radars have played an important role in meteorological research and nowcasting over the past four decades. However, in recent years, the proliferation of open-source software and increased demands from applications such as convective parameterizations in numerical weather prediction models has led to a renewed interest in these analyses. In this study, we analyze how a major, yet often-overlooked, error source effects the quality of retrieved 3D wind fields. Namely, we investigate the effects of spatial interpolation, and show how the common practice of pregridding radial velocity data can degrade the accuracy of the results. Alternatively, we show that assimilating radar data directly at their observation locations improves the retrieval of important dynamic features such as the rear flank downdraft and mesocyclone within supercells, while also reducing errors in vertical vorticity, horizontal divergence, and all three velocity components. Significance Statement We can attempt to estimate the wind speed and direction within a weather system when two weather radars measure it simultaneously. However, radars do not scan the whole atmosphere at once—instead, they measure along many cross sections, each at different heights. We show that a method commonly used to stitch the observations together degrades the accuracy of the winds. Additionally, we describe a way to feed the data directly into the analysis without stitching it together first, and show that this improves the wind retrievals considerably. We hope these improvements will help researchers better understand how various weather systems work, and help forecasters warn for dangerous weather such as tornadoes.