The Assessment of Surface Precipitation Type Measurements

The type of precipitation reaching the surface (SPT) has a significant impact on society, yet both SPT observations and forecast products have poor skill. Improving any observation or forecast requires a framework of suitable assessment techniques. The assessment of SPT data is difficult because the commonly used categorisation standards lack specificity; slight differences exist between the standards, and some categories contain several SPTs, obfuscating the reference data. Furthermore, the rarity of some SPTs makes capturing a statistically significant and climatologically representative reference dataset challenging. Finally, assessing the skill of spatial SPT products from radar or forecasts, against a single point reference SPT dataset introduces representativity errors. Within the literature, the statistical metrics used to perform verification of any SPT data differ, obscuring the comparison of SPT diagnosis techniques and thereby suppressing the advancement of more accurate techniques into widespread operational use. This thesis tackles these assessment issues with a focus on observational techniques in the United Kingdom. Firstly, a new network of low-cost, real-time disdrometers were deployed to increase the number, quality and represented climatologies of SPT observations. These instruments were validated using case studies, followed by a 12-month evaluation with existing precipitation instruments. To facilitate the assessment, a new SPT classification standard, based on microphysical processes, is produced to reduce the ambiguity in SPT observations. In addition, an algorithm to amalgamate SPT observations over time is developed and applied successfully. Finally, a radar-based spatial SPT product from the UK Met Office is verified against all available ground-based datasets for five years. A novel tolerance technique is developed to address the representativity issues between spatial and point datasets. This technique is used to quantify the skill of the spatial SPT product and highlights several areas for improvement, which are being implemented by the UK Met Office.