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

TL;DR

This paper examines how spatial interpolation affects the accuracy of 3D wind retrievals from Doppler radar data, demonstrating that direct data assimilation at observation points enhances dynamic feature detection and reduces errors.

Contribution

It reveals that pre-gridding radial velocity data degrades retrieval accuracy and shows that direct assimilation at observation points improves wind field quality.

Findings

Direct data assimilation improves detection of dynamic features.

Pre-gridding radial velocities degrades accuracy.

Errors in vorticity and divergence are reduced with direct assimilation.

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 pre-gridding 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 a simulated supercell, while also reducing errors in vertical vorticity, horizontal divergence, and all three velocity components.