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Lookup NU author(s): Doug Richardson, Professor Hayley Fowler, Professor Chris Kilsby
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Dynamical model skill in forecasting extratropical precipitation is limited beyond the medium-range (around 15 d), but such models are often more skilful at predicting atmospheric variables. We explore the potential benefits of using weather pattern (WP) predictions as an intermediary step in forecasting UK precipitation and meteorological drought on sub-seasonal timescales. Mean sea-level pressure forecasts from the European Centre for Medium-Range Weather Forecasts ensemble prediction system (ECMWF-EPS) are post-processed into probabilistic WP predictions. Then we derive precipitation estimates and dichotomous drought event probabilities by sampling from the conditional distributions of precipitation given the WPs. We compare this model to the direct precipitation and drought forecasts from the ECMWF-EPS and to a baseline Markov chain WP method. A perfect-prognosis model is also tested to illustrate the potential of WPs in forecasting. Using a range of skill diagnostics, we find that the Markov model is the least skilful, while the dynamical WP model and direct precipitation forecasts have similar accuracy independent of lead time and season. However, drought forecasts are more reliable for the dynamical WP model. Forecast skill scores are generally modest (rarely above 0.4), although those for the perfect-prognosis model highlight the potential predictability of precipitation and drought using WPs, with certain situations yielding skill scores of almost 0.8 and drought event hit and false alarm rates of 70 % and 30 %, respectively.
Author(s): Richardson D, Fowler HJ, Kilsby CG, Neal R, Dankers R
Publication type: Article
Publication status: Published
Journal: Natural Hazards and Earth System Sciences
Year: 2020
Volume: 20
Issue: 1
Pages: 107-124
Online publication date: 14/01/2020
Acceptance date: 05/12/2019
Date deposited: 12/08/2020
ISSN (print): 1561-8633
ISSN (electronic): 1684-9981
Publisher: Copernicus GmbH
URL: https://doi.org/10.5194/nhess-20-107-2020
DOI: 10.5194/nhess-20-107-2020
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