The humanitarian crises caused by the recent droughts (2008–2009 and 2010–2011) in East Africa have illustrated that the ability to make accurate drought forecasts with sufficient lead time is essential. The use of dynamical model precipitation forecasts in combination with drought indices, such as the Standardized Precipitation Index (SPI), can potentially lead to a better description of drought duration, magnitude and spatial extent. This study evaluates the use of the European Centre for Medium-Range Weather Forecasts (ECMWF) products in forecasting droughts in East Africa. ECMWF seasonal precipitation shows significant skill for March–May and October–December rain seasons when evaluated against measurements from the available in situ stations from East Africa. The forecast for October–December rain season has higher skill than for the March–May season. ECMWF forecasts add value to the consensus forecasts produced during the Greater Horn of Africa Climate Outlook Forum (GHACOF), which is the present operational product for precipitation forecast over East Africa. Complementing the original ECMWF precipitation forecasts with SPI provides additional information on the spatial extent and intensity of the drought event.

Forecasting droughts in East Africa

. 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.

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

Seasonal climate forecasts are a key component of sectoral climate services. Skill and reliability in predicting agro-climate indicators, co-designed with and for European wheat farmers, are here assessed. The main findings show how seasonal climate forecast provides useful information for decision-making processes in the European winter wheat-producing sector. Flowering time can be reliably predicted already at the beginning of the growing season in central and eastern Europe, thus supporting effective variety selection and timely planning of agro-management practices. The predictability of climate events relevant for winter wheat production is strongly dependent on the forecast initialization time as well as the nature of the event being predicted. Overall, regionally skillful and reliable predictions of drought events during the sensitive periods of wheat flowering and grain filling can be made already at the end of winter. On the contrary, predicting excessive wetness seems to be very challenging as no or very limited skill is estimated during the entire wheat growing season. Other approaches, e.g., linked to the use of large-scale atmospheric patterns, should be identified to enhance the predictability of those harmful events.

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Seasonal climate forecast can inform the European agricultural sector well in advance of harvesting

Non-optimal temperatures, both warm and cold, are associated with enhanced mortality in the United Kingdom (UK). In this study we demonstrate a pathway to sub-seasonal and medium range forecasting of temperature-related mortality risk by quantifying the impact of large-scale weather regimes and synoptic scale weather patterns on temperature-associated excess deaths in 12 regions across the UK. We find a clear dominance of the NAO− regime in leading to high wintertime excess mortality across all regions. In summer, we note that cold spells lead to comparable cumulative excess mortality as moderate hot days, with cold days accounting for 11 (London) to 100% (Northern Ireland) of the summer days with the highest 5% cumulative excess mortality. However, exposure to high temperatures is typically associated with an immediate but short lived spike in mortality, while the impact of cold weather tends to be more delayed and spread out over a longer period. Weather patterns with a Scandinavian high component are most likely to be associated with summer hot extremes, while a strong zonal jet stream weather pattern which rarely occurs in summer is most likely to be associated with summer cold spells.

Weather regimes and patterns associated with temperature-related excess mortality in the UK: a pathway to sub-seasonal risk forecasting

Hydrological drought forecasts outperform meteorological ones, which is anticipated coming from catchment memory. Yet, the importance of catchment memory in explaining hydrological drought forecast performance has not been studied. Here, we use the Baseflow Index (BFI) and the groundwater Recession Coefficient (gRC), which through the streamflow, give information on the catchment memory. Performance of streamflow drought forecasts was evaluated using the Brier Score (BS) for rivers across Europe. We found that BS is negatively correlated with BFI, meaning that rivers with high BFI (large memory) yield better drought prediction (low BS). A significant positive correlation between gRC and BS demonstrates that catchments slowly releasing groundwater to streams (low gRC), i.e. large memory, generates higher drought forecast performance. The higher performance of hydrological drought forecasts in catchments with relatively large memory (high BFI and low gRC) implies that Drought Early Warning Systems have more potential to be implemented there and will appear to be more useful. 

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Catchment memory explains hydrological drought forecast performance

Medium- to long-range precipitation forecasts are a crucial component in mitigating the impacts of fluvial flood events. Although precipitation is difficult to predict at these lead times, the forecast skill of atmospheric circulation tends to be greater. The study explores using weather patterns (WPs) as a preliminary step in producing forecasts of upper-tail precipitation threshold exceedance probabilities for the UK. The WPs are predefined, discrete states representing daily mean sea-level pressure (MSLP) over a European–North Atlantic domain. The WPs most likely to be associated with flooding are highlighted by calculating upper-tail exceedance probabilities derived from the conditional distributions of regional precipitation given each WP. WPs associated with higher probabilities of extreme precipitation are shown to have occurred during two well-known flood events: the 2014 Somerset Levels floods in southwest England; and Storm Desmond over the northern UK in December 2015. To illustrate the potential of this WP-based prediction framework, a forecast guidance tool called Fluvial Decider is introduced. It is intended for use by hydro-meteorologists in the England and Wales Flood Forecasting Centre (FFC). Forecasts of the MSLP from ensemble prediction systems (EPSs) are assigned to the closest-matching WP, providing daily probabilistic forecasts of WPs out to the chosen lead time. Combining these probabilities with observed precipitation threshold exceedance probabilities provides a parsimonious tool for highlighting potential periods with increased risk of flooding. Model forecasts using the European Centre for Medium-range Weather Forecasts (ECMWF) EPS highlighted both flood events as being at a higher than average risk of heavy extreme precipitation at lead times of over five days.

Doug Richardson

Papers

Linking weather patterns to regional extreme precipitation for highlighting potential flood events in medium- to long-range forecasts

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