Challenges exist for assessing the impacts of climate and climate change on the hydrological cycle on local and regional scales, and in turn on water resources, food, energy, and natural hazards. Potential evapotranspiration (PET) represents atmospheric demand for water, which is required at high spatial and temporal resolutions to compute actual evapotranspiration and thus close the water balance near the land surface for many such applications, but there are currently no available high-resolution datasets of PET. Here we develop an hourly PET dataset (hPET) for the global land surface at 0.1° spatial resolution, based on output from the recently developed ERA5-Land reanalysis dataset, over the period 1981 to present. We show how hPET compares to other available global PET datasets, over common spatiotemporal resolutions and time frames, with respect to spatial patterns of climatology and seasonal variations for selected humid and arid locations across the globe. We provide the data for users to employ for multiple applications to explore diurnal and seasonal variations in evaporative demand for water.

Reliable information on the likelihood of drought is of crucial importance in agricultural planning and humanitarian decision-making. Acting based upon probabilistic forecasts of drought, rather than responding to prevailing drought conditions, has the potential to save lives, livelihoods and resources, but is accompanied by the risk of acting in vain. The suitability of a novel forecasting

Approximately 886 million people in Africa rely on agriculture as their main means of survival. They are therefore susceptible to changes in seasonal rains from year to year that can result in agricultural drought. Agricultural drought is determined by low soil moisture content. Soil moisture responds to rainfall, but also depends on many other factors, including the soil characteristics and, crucially, on the past soil moisture. Here we demonstrate that predictive skill can be gained from knowledge of the current state of the land surface – how wet or dry the soil is – as the growing season evolves. This skill arises from the land surface memory – the soil moisture content at a particular time depends to a large extent on the historical soil moisture. By forcing a land surface model with observed data up to a ‘present day’ and then forward in time with climatological data (to represent the range of possible future conditions) we show that it is possible to be confident of an ensuing agricultural drought several weeks before the end of the growing season. This system is illustrated using results from an operational trial for Tamale in northern Ghana.

Abstract. Early warning of weather-related hazards enables farmers, policy makers and aid agencies to mitigate their exposure to risk. We present a new operational framework, Tropical Applications of Meteorology using SATellite data and ground based measurements-AgricuLtural EaRly warning sysTem (TAMSAT-ALERT), which aims to provide early warning for meteorological risk to agriculture. TAMSAT-ALERT combines information on land-surface properties, seasonal forecasts and historical weather to quantitatively assess the likelihood of adverse weather-related outcomes, such as low yield. This article describes the modular TAMSAT-ALERT framework and demonstrates its application to risk assessment for low maize yield in northern Ghana (Tamale). The modular design of TAMSAT-ALERT enables it to accommodate any impact or land-surface model driven with meteorological data. The implementation described here uses the well-established General Large Area Model (GLAM) for annual crops to provide probabilistic assessments of the meteorological hazard for maize yield in northern Ghana (Tamale) throughout the growing season. The results show that climatic risk to yield is poorly constrained in the beginning of the season, but as the season progresses, the uncertainty is rapidly reduced. Based on the assessment for the period 2002–2011, we show that TAMSAT-ALERT can estimate the meteorological risk on maize yield 6 to 8 weeks in advance of harvest. The TAMSAT-ALERT methodology implicitly weights forecast and observational inputs according to their relevance to the metric being assessed. A secondary application of TAMSAT-ALERT is thus an evaluation of the usefulness of meteorological forecast products for impact assessment. Here, we show that in northern Ghana (Tamale), the tercile seasonal forecasts of seasonal cumulative rainfall and mean temperature, which are routinely issued to farmers, are of limited value because regional and seasonal temperature and rainfall are poorly correlated with yield. This finding speaks to the pressing need for meteorological forecast products that are tailored for individual user applications.

The availability of seasonal weather forecast information in Africa has potential to provide advanced early warning of rainfall variability, informing preparedness actions to minimise adverse impacts. Obtaining accurate forecast information for the spatial scales at which decisions are made is vital. Here we examine the impact of spatial scales on the utility of seasonal rainfall forecasts in Africa. Using observations alongside seasonal forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF), we combine measures of local representativity and skill to assess optimal spatial scales for anticipating local rainfall conditions. The results reveal regions where spatial aggregation of gridded forecast data improves the quality of information provided at the local scale, and regions where forecasts have useful skill without aggregation. More generally this study presents a novel approach for evaluating the utility of forecast information which is applicable both globally and at all timescales.