Improving Observations of Precipitation Type at the Surface: A 5-year Verification of a Radar-derived Product from the United Kingdom Met Office.

Ben S Pickering,Maryna Lukach,Ryan R Neely,Darren Lyth,Steven Best,David Dufton

doi:10.1175/jhm-d-20-0172.1

Abstract

AbstractThis study aims to verify the skill of a radar-based surface precipitation type (SPT) product with observations on the ground. Social and economic impacts can occur from SPT because it is not well forecast or observed. Observations from the United Kingdom Meteorological Office’s weather radar network are combined with post-processed numerical weather prediction (NWP) freezing level heights in a Boolean logic algorithm to create a 1 km resolution cartesian-gridded map of SPT. Here 5 years of discrete non-probabilistic outputs of rain, mixed phase, and snow are compared against surface observations made by trained observers, automatic weather stations, and laser disdrometers. The novel skill verification method developed as part of this study employs several tolerances of space and time from the SPT product, indicating the precision of the product for a desired accuracy. In general the results indicate that the tolerance verification method works well and produces reasonable statistical score ranges grounded in physical constraints. Using this method, we find that the mixed precipitation class is the least well diagnosed which is due to a negative bias in the input temperature height field, resulting in rain events frequently being classified as mixed. Snowis capturedwell by the product which is entirely reliant upon a post-processed NWP temperature field, although a single period of anomalously cold temperatures positively skewed snow scores with low-skill events. Furthermore, we conclude that more verification consistency is needed amongst studies to help identify successful approaches and thus improve SPT forecasts.

Highlights

The type of hydrometeors reaching the surface, known as the surface precipitation type (SPT), can severely impact human activities
The higher-dimension generalized Heidke skill score (HSS) is examined to give an overall value to the SPT product, before examining each precipitation class
The Met Office surface precipitation type (SPT) product was examined with three datasets of ground-based observations over 5 years (2014–18)

Summary

Introduction

The type of hydrometeors reaching the surface, known as the surface precipitation type (SPT), can severely impact human activities. In regions where solid precipitation types are common and expected occurrences, long-term adaptations are cost effective, but where solid precipitation types are infrequent and uncommon (midlatitudinal, certain mountainous regions) these adaptations are not cost effective and (as in the case of the United Kingdom) events can significantly disrupt daily life (Kay 2016; Curtis et al 2017). In the winter of 2009/10, the cost to the U.K. National Health Service from falls on snow and surface ice was £42 million (Beynon et al 2011). Mitigative actions such as clearing roads, covering exposed crops, and redirecting aircraft are cost associated and require sufficient lead time and confidence (Cornford and Thornes 1996; Rasmussen et al 2001; Handa et al 2006; Clark et al 2009).

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