Improving sub-seasonal forecast skill of meteorological drought: a weather pattern approach
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Published:2020-01-14
Issue:1
Volume:20
Page:107-124
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ISSN:1684-9981
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Container-title:Natural Hazards and Earth System Sciences
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language:en
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Short-container-title:Nat. Hazards Earth Syst. Sci.
Author:
Richardson DougORCID, Fowler Hayley J.ORCID, Kilsby Christopher G., Neal Robert, Dankers RutgerORCID
Abstract
Abstract. 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.
Funder
Natural Environment Research Council European Research Council Wolfson Foundation
Publisher
Copernicus GmbH
Subject
General Earth and Planetary Sciences
Reference71 articles.
1. Ahrens, B. and Walser, A.: Information-Based Skill Scores for Probabilistic
Forecasts, Mon. Weather Rev., 136, 352–363, https://doi.org/10.1175/2007mwr1931.1, 2008. 2. Alexander, L. V. and Jones, P. D.: Updated Precipitation Series for the
U.K. and Discussion of Recent Extremes, Atmos. Sci. Lett., 1, 142–150, 2000. 3. Ansell, T. J., Jones, P. D., Allan, R. J., Lister, D., Parker, D. E., Brunet, M., Moberg, A., Jacobeit, J., Brohan, P., Rayner, N. A., Aguilar, E., Alexandersson, H., Barriendos, M., Brandsma, T., Cox, N. J., Della-Marta, P. M., Drebs, A., Founda, D., Gerstengarbe, F., Hickey, K., Jónsson, T., Luterbacher, J. Ø. N., Oesterle, H., Petrakis, M., Philipp, A., Rodwell, M. J., Saladie, O., Sigro, J., Slonosky, V., Srnec, L., Swail, V., García-Suárez, A. M., Tuomenvirta, H., Wang, X., Wanner, H., Werner, P., Wheeler, D., and Xoplaki, E.: Daily Mean Sea Level Pressure Reconstructions for the European–North Atlantic Region for the Period 1850–2003, J. Climate, 19, 2717–2742, https://doi.org/10.1175/jcli3775.1, 2006. 4. Arnal, L., Cloke, H. L., Stephens, E., Wetterhall, F., Prudhomme, C., Neumann, J., Krzeminski, B., and Pappenberger, F.: Skilful seasonal forecasts of streamflow over Europe?, Hydrol. Earth Syst. Sci., 22, 2057–2072, https://doi.org/10.5194/hess-22-2057-2018, 2018. 5. Baker, L. H., Shaffrey, L. C., and Scaife, A. A.: Improved seasonal prediction of UK regional precipitation using atmospheric circulation, Int. J. Climatol., 38, 437–453, https://doi.org/10.1002/joc.5382, 2018.
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