From months to minutes – exploring the value of high‐resolution rainfall observation and prediction during the UK winter storms of 2013/2014. (January 2015) - Lewis, Huw; Mittermaier, Marion; Mylne, Ken; Norman, Katie; Scaife, Adam; Neal, Robert; Pierce, Clive; Harrison, Dawn; Jewell, Sharon; Kendon, Michael; Saunders, Roger; Brunet, Gilbert; et al.
ABSTRACT As the societal impacts of hazardous weather and other environmental pressures grow, the need for integrated predictions that can represent the numerous feedbacks and linkages between sub‐systems is greater than ever. This was well illustrated during winter 2013/2014 when a prolonged series of deep Atlantic depressions over a 3 month period resulted in damaging wind storms and exceptional rainfall accumulations. The impact on livelihoods and property from the resulting coastal surge and river and surface flooding was substantial. This study reviews the observational and modelling toolkit available to operational meteorologists during this period, which focusses on precipitation forecasting months, weeks, days and hours ahead of time. The routine availability of high‐resolution (km scale) deterministic and ensemble rainfall predictions for short‐range weather forecasting as well as weather‐resolving seasonal prediction capability represent notable landmarks that have resulted from significant progress in research and development over the past decade. Latest results demonstrated that the suite of global and high‐resolution UK numerical weather prediction models provided excellent guidance during this period, supported by high‐resolution observations networks, such as weather radar, which proved resilient in difficult conditions. The specific challenges for demonstrating this performance for high‐resolution precipitation forecasts are discussed. Despite their good operational performance, there remains a need to further develop the capability and skill of these tools to fully meet user needs and to increase the value that they deliver. These challenges are discussed, notably to accelerate the progress towards understanding the value that might be delivered through more integrated environmental prediction.