Abstract
Abstract. Starting in February 2017, a network of 14 Thies laser precipitation monitors (LPMs) were installed at various locations around the United Kingdom to create the Disdrometer Verification Network (DiVeN). The instruments were installed for verification of radar hydrometeor classification algorithms but are valuable for much wider use in the scientific and operational meteorological community. Every Thies LPM is able to designate each observed hydrometeor into one of 20 diameter bins from ≥0.125 to >8 mm and one of 22 speed bins from >0.0 to >20.0 m s−1. Using empirically derived relationships, the instrument classifies precipitation into one of 11 possible hydrometeor classes in the form of a present weather code, with an associated indicator of uncertainty. To provide immediate feedback to data users, the observations are plotted in near-real time (NRT) and made publicly available on a website within 7 min. Here we describe the Disdrometer Verification Network and present specific cases from the first year of observations. Cases shown here suggest that the Thies LPM performs well at identifying transitions between rain and snow, but struggles with detection of graupel and pristine ice crystals (which occur infrequently in the United Kingdom) inherently, due to internal processing. The present weather code quality index is shown to have some skill without the supplementary sensors recommended by the manufacturer. Overall the Thies LPM is a useful tool for detecting hydrometeor type at the surface and DiVeN provides a novel dataset not previously observed for the United Kingdom.
Highlights
Precipitation in all its various forms is one of the most important meteorological variables
The instruments used in Disdrometer Verification Network (DiVeN) are the ThiesTM laser precipitation monitor (LPM), model number 5.4110.00.200, which is described in detail in Adolf Thies GmbH & Co
The Disdrometer Verification Network is the largest network of laser precipitation measurements in the UK
Summary
Precipitation in all its various forms is one of the most important meteorological variables. In the UK, severe precipitation events cause millions of pounds worth of damage every year (Thornes, 1992; Penning-Rowsell and Wilson, 2006; Muchan et al, 2015). In winter, limited resources such as flood defences, ploughs, and grit will be allocated differently based on forecasts of hydrometeor type (Elmore et al, 2015; Gascón et al, 2018, and references therein). Accurate observations and forecasts of precipitation amount and type are essential
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