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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 16 Jul 2019

Submitted as: research article | 16 Jul 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Natural Hazards and Earth System Sciences (NHESS).

Improving sub-seasonal forecast skill of meteorological drought: a weather pattern approach

Doug Richardson1,2, Hayley J. Fowler2, Chris G. Kilsby2, Robert Neal3, and Rutger Dankers3,4 Doug Richardson et al.
  • 1CSIRO Oceans & Atmosphere, Hobart, Australia, 7001
  • 2School of Engineering, Newcastle University, Newcastle-upon-Tyne, NE1 7RU, United Kingdom
  • 3Weather Science, Met Office, Exeter, EX1 3PB, United Kingdom
  • 4Environmental Research, Wageningen University & Research, Wageningen, 6708 PB, Netherlands

Abstract. Dynamical model skill in forecasting extratropical precipitation is limited beyond the medium-range (around 15 days), 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 time scales. MSLP forecasts from the ECMWF 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 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 for 31- and 46-day lead-times, dynamical, and to a lesser extent Markov, model forecasts using WPs can achieve higher skill scores that the non-WP method, particularly for precipitation. 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.

Doug Richardson et al.
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Doug Richardson et al.
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Publications Copernicus
Short summary
Models are not particularly skilful at forecasting rainfall more than 15 days in advance. However, they are often better at predicting atmospheric variables such as mean sea level pressure (MSLP). Comparing a range of models, we show that UK winter and autumn rainfall and drought prediction skill can be improved by utilising forecasts of MSLP-based weather patterns (WPs) and subsequently estimating rainfall using the historical WP-precipitation relationships.
Models are not particularly skilful at forecasting rainfall more than 15 days in advance....