Preprints
https://doi.org/10.5194/egusphere-2026-4631
https://doi.org/10.5194/egusphere-2026-4631
07 Aug 2026
 | 07 Aug 2026
Status: this preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).

Derivation of the multi-Doppler 3D wind field during the 2023 WesCon-WOEST campaign in southern England

Robert J. Thompson, Thorwald H. M. Stein, Chun Hay Brian Lo, Robert Jackson, Christopher D. Westbrook, Scott Collis, Lindsay Rhodes, Timothy Darlington, Emily G. Norton, Paul A. Barrett, and Ryan R. Neely III

Abstract. Convection drives high-impact weather events but remains challenging to represent accurately in modern convection-resolving weather prediction models. Robust evaluation and development of these models therefore require detailed observations of convective dynamics, with enough data for statistical application. In this study we present the first multi-Doppler radar 3D wind analysis in the UK. Five radars used in the WesCON-WOEST field campaign are used with an updated version of the PyDDA algorithm. This includes a newly implemented "ORIGAMI" unfolding algorithm based on model winds, a combined VAD initialisation and a cloud-top boundary condition. The resulting dataset provides a complete 3D wind grid with 1 km horizontal and 500 m vertical resolution every 10 minutes over the 3 month field campaign. Validation against two radar wind profilers show the horizontal wind components have RMSE better than 2.4 ms1, with a small bias in the westerly component, u. Statistical comparison to aircraft flight data shows similar distribution, but underestimates the strongest up- and downdrafts, as a result of not resolving the scale of the smallest updrafts. This data provides important dynamical information on convective processes and offers a valuable resource for evaluating and improving convection-resolving models.

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Robert J. Thompson, Thorwald H. M. Stein, Chun Hay Brian Lo, Robert Jackson, Christopher D. Westbrook, Scott Collis, Lindsay Rhodes, Timothy Darlington, Emily G. Norton, Paul A. Barrett, and Ryan R. Neely III

Status: open (until 12 Sep 2026)

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Robert J. Thompson, Thorwald H. M. Stein, Chun Hay Brian Lo, Robert Jackson, Christopher D. Westbrook, Scott Collis, Lindsay Rhodes, Timothy Darlington, Emily G. Norton, Paul A. Barrett, and Ryan R. Neely III
Robert J. Thompson, Thorwald H. M. Stein, Chun Hay Brian Lo, Robert Jackson, Christopher D. Westbrook, Scott Collis, Lindsay Rhodes, Timothy Darlington, Emily G. Norton, Paul A. Barrett, and Ryan R. Neely III
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Latest update: 07 Aug 2026
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Short summary
Thunderstorms have complex flow patterns that are difficult to observe directly. Observational data is needed for robust evaluation to confirm that forecast models behave correctly. Five Doppler weather radars in Southern England, an updated 3D variational algorithm used, to generate a 3D grid of the 3 components of the wind. The dataset is verified with wind profiler, aircraft & separate radar, finding accuracy better than 2.4 ms−1. This data is valuable for evaluation of models.
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