Improving Weather Forecasting Accuracy by Using r-Adaptive Methods Coupled to Data Assimilation Algorithms

Chris Budd (), Mike Cullen () and Chiara Piccolo ()
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Chris Budd: University of Bath
Mike Cullen: Met Office
Chiara Piccolo: Met Office

A chapter in UK Success Stories in Industrial Mathematics, 2016, pp 11-18 from Springer

Abstract: Abstract Weather impacts all of our lives and we all take a close interest in it, with every news report finishing with a weather forecast watched by millions. Accurate weather forecasting is essential for the transport, agricultural and energy industries and the emergency and defence services. The Met Office plays a vital role by making 5-day forecasts, using advanced computer algorithms which combine numerical weather predictions (NWP) with carefully measured data (a process known as data assimilation). However, a major limitation on the accuracy of these forecasts is the sub-optimal use of this data. Adaptive methods, developed in a partnership between Bath and the Met Office have been employed to make better use of the data, thus improving the Met Office operational data assimilation system. This has lead to a significant improvement in forecast accuracy as measured by the UK Index [9] with great societal and economic impact. Forecasts, of surface temperatures, in particular, are pivotal for the OpenRoad forecasting system used by local authorities to plan road clearing and gritting when snow or ice are predicted.

Keywords: Data Assimilation; Numerical Weather Prediction; Adaptive Mesh; Inversion Layer; Monitor Function (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/978-3-319-25454-8_2

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