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Accurate river streamflow forecasts are a vital tool in the fields of water security, flood preparation and agriculture, as well as in industry more generally. Traditional physics-based models used to produce streamflow forecasts have become increasingly sophisticated, with forecasts improving accordingly. However, the development of such models is often bound by two soft limits: empiricism – many physical relationships are represented empirical formulae; and data sparsity – long time series of observational data are often required for the calibration of these models. Artificial neural networks have previously been shown to be highly effective at simulating non-linear systems where knowledge of the underlying physical relationships is incomplete. However, they also suffer from issues related to data sparsity. Recently, hybrid forecasting systems, which combine the traditional physics-based approach with statistical forecasting techniques, have been investigated for use in hydrological applications. In this study, we test the efficacy of a type of neural network, the long short-term memory (LSTM), at predicting streamflow at 10 river gauge stations across various climatic regions of the western United States. The LSTM is trained on the catchment-mean meteorological and hydrological variables from the ERA5 and Global Flood Awareness System (GloFAS)–ERA5 reanalyses as well as historical streamflow observations. The performance of these hybrid forecasts is evaluated and compared with the performance of both raw and bias-corrected output from the Copernicus Emergency Management Service (CEMS) physics-based GloFAS. Two periods are considered, a testing phase (June 2019 to June 2020), during which the models were fed with ERA5 data to investigate how well they simulated streamflow at the 10 stations, and an operational phase (September 2020 to October 2021), during which the models were fed forecast variables from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS), to investigate how well they could predict streamflow at lead times of up to 10 d. Implications and potential improvements to this work are discussed. In summary, this is the first time an LSTM has been used in a hybrid system to create a medium-range streamflow forecast, and in beating established physics-based models, shows promise for the future of neural networks in hydrological forecasting.

The European Centre for Medium-Range Weather Forecasts (ECMWF) mission is to deliver high-quality global medium-range numerical weather predictions and monitoring of the Earth system to its member states. The modelling and forecasting of natural hazards are an important part of this mission. Challenges in this area include the integration of innovative observations into the Earth system; realistic representations of water, energy and carbon cycles; coupling and initialisation of all Earth system components; adequate representation of uncertainties; supporting the development of user-specific products to enable optimal decision-making under uncertainties; and advances in software engineering. The new ECMWF strategy identified three pillars to sustain its future development (ECMWF, 2021a): science and technology (world-leading weather and Earth system science, cutting-edge technology and computational science), impact (high-quality products fit for purpose, efficient and easy access to products), and people (inspiring and hiring the best experts). Progress in all these areas will need enhanced collaboration with member states and partners across Europe and beyond.

Weather forecasts rely heavily on general circulation models of the atmosphere and other components of the Earth system. National meteorological and hydrological services and intergovernmental organizations, such as the European Centre for Medium-Range Weather Forecasts (ECMWF), provide routine operational forecasts on a range of spatio-temporal scales by running these models at high resolution on state-of-the-art high-performance computing systems. Such operational forecasts are very demanding in terms of computing resources. To facilitate the use of a weather forecast model for research and training purposes outside the operational environment, ECMWF provides a portable version of its numerical weather forecast model, OpenIFS, for use by universities and other research institutes on their own computing systems. In this paper, we describe a new project (OpenIFS@home) that combines OpenIFS with a citizen science approach to involve the general public in helping conduct scientific experiments. Volunteers from across the world can run OpenIFS@home on their computers at home, and the results of these simulations can be combined into large forecast ensembles. The infrastructure of such distributed computing experiments is based on our experience and expertise with the climateprediction.net (https://www.climateprediction.net/, last access: 1 June 2021) and weather@home systems. In order to validate this first use of OpenIFS in a volunteer computing framework, we present results from ensembles of forecast simulations of Tropical Cyclone Karl from September 2016 studied during the NAWDEX field campaign. This cyclone underwent extratropical transition and intensified in mid-latitudes to give rise to an intense jet streak near Scotland and heavy rainfall over Norway. For the validation we use a 2000-member ensemble of OpenIFS run on the OpenIFS@home volunteer framework and a smaller ensemble of the size of operational forecasts using ECMWF's forecast model in 2016 run on the ECMWF supercomputer with the same horizontal resolution as OpenIFS@home. We present ensemble statistics that illustrate the reliability and accuracy of the OpenIFS@home forecasts and discuss the use of large ensembles in the context of forecasting extreme events.

Estimating how much water is flowing through rivers at the global scale is challenging due to a lack of observations in space and time. A way forward is to optimally combine the global network of earth system observations with advanced numerical weather prediction (NWP) models to generate consistent spatio-temporal maps of land, ocean, and atmospheric variables of interest, which is known as a reanalysis. While the current generation of NWP models output runoff at each grid cell, they currently do not produce river discharge at catchment scales directly and thus have limited utility in hydrological applications such as flood and drought monitoring and forecasting. This is overcome in the Global Flood Awareness System (GloFAS; http://www.globalfloods.eu/, last access: 28 June 2020) by coupling surface and sub-surface runoff from the Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (HTESSEL) land surface model used within ECMWF's latest global atmospheric reanalysis (ERA5) with the LISFLOOD hydrological and channel routing model. The aim of this paper is to describe and evaluate the GloFAS-ERA5 global river discharge reanalysis dataset launched on 5 November 2019 (version 2.1 release). The river discharge reanalysis is a global gridded dataset with a horizontal resolution of 0.1∘ at a daily time step. An innovative feature is that it is produced in an operational environment so is available to users from 1 January 1979 until near real time (2 to 5 d behind real time). The reanalysis was evaluated against a global network of 1801 daily river discharge observation stations. Results found that the GloFAS-ERA5 reanalysis was skilful against a mean flow benchmark in 86 % of catchments according to the modified Kling–Gupta efficiency skill score, although the strength of skill varied considerably with location. The global median Pearson correlation coefficient was 0.61 with an interquartile range of 0.44 to 0.74. The long-term and operational nature of the GloFAS-ERA5 reanalysis dataset provides a valuable dataset to the user community for applications ranging from monitoring global flood and drought conditions to the identification of hydroclimatic variability and change and as raw input for post-processing and machine learning methods that can add further value. The dataset is openly available from the Copernicus Climate Change Service Climate Data Store: https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-glofas-historical?tab=overview (last access: 28 June 2020) with the following DOI: https://doi.org/10.24381/cds.a4fdd6b9 (C3S, 2019).

New precipitation (P) datasets are released regularly, following innovations in weather forecasting models, satellite retrieval methods, and multi-source merging techniques. Using the conterminous US as a case study, we evaluated the performance of 26 gridded (sub-)daily P datasets to obtain insight into the merit of these innovations. The evaluation was performed at a daily timescale for the period 2008–2017 using the Kling–Gupta efficiency (KGE), a performance metric combining correlation, bias, and variability. As a reference, we used the high-resolution (4 km) Stage-IV gauge-radar P dataset. Among the three KGE components, the P datasets performed worst overall in terms of correlation (related to event identification). In terms of improving KGE scores for these datasets, improved P totals (affecting the bias score) and improved distribution of P intensity (affecting the variability score) are of secondary importance. Among the 11 gauge-corrected P datasets, the best overall performance was obtained by MSWEP V2.2, underscoring the importance of applying daily gauge corrections and accounting for gauge reporting times. Several uncorrected P datasets outperformed gauge-corrected ones. Among the 15 uncorrected P datasets, the best performance was obtained by the ERA5-HRES fourth-generation reanalysis, reflecting the significant advances in earth system modeling during the last decade. The (re)analyses generally performed better in winter than in summer, while the opposite was the case for the satellite-based datasets. IMERGHH V05 performed substantially better than TMPA-3B42RT V7, attributable to the many improvements implemented in the IMERG satellite P retrieval algorithm. IMERGHH V05 outperformed ERA5-HRES in regions dominated by convective storms, while the opposite was observed in regions of complex terrain. The ERA5-EDA ensemble average exhibited higher correlations than the ERA5-HRES deterministic run, highlighting the value of ensemble modeling. The WRF regional convection-permitting climate model showed considerably more accurate P totals over the mountainous west and performed best among the uncorrected datasets in terms of variability, suggesting there is merit in using high-resolution models to obtain climatological P statistics. Our findings provide some guidance to choose the most suitable P dataset for a particular application.

An early warning system for drought events can provide valuable information for decision makers dealing with water resources management and international aid. However, predicting such extreme events is still a big challenge. In this study, we compare two approaches for drought predictions based on forecasted precipitation derived from the Ensemble extended forecast model (ENS) of the ECMWF, and on forecasted monthly occurrence anomalies of weather regimes (MOAWRs), also derived from the ECMWF model. Results show that the MOAWRs approach outperforms the one based on forecasted precipitation in winter in the north-eastern parts of the European continent, where more than 65 % of droughts are detected 1 month in advance. The approach based on forecasted precipitation achieves better performance in predicting drought events in central and eastern Europe in both spring and summer, when the local atmospheric forcing could be the key driver of the precipitation. Sensitivity tests also reveal the challenges in predicting small-scale droughts and drought onsets at longer lead times. Finally, the results show that the ENS model of the ECMWF successfully represents most of the observed linkages between large-scale atmospheric patterns, depicted by the weather regimes and drought events over Europe.

Global overviews of upcoming flood and drought events are key for many applications, including disaster risk reduction initiatives. Seasonal forecasts are designed to provide early indications of such events weeks or even months in advance, but seasonal forecasts for hydrological variables at large or global scales are few and far between. Here, we present the first operational global-scale seasonal hydro-meteorological forecasting system: GloFAS-Seasonal. Developed as an extension of the Global Flood Awareness System (GloFAS), GloFAS-Seasonal couples seasonal meteorological forecasts from ECMWF with a hydrological model to provide openly available probabilistic forecasts of river flow out to 4 months ahead for the global river network. This system has potential benefits not only for disaster risk reduction through early awareness of floods and droughts, but also for water-related sectors such as agriculture and water resources management, in particular for regions where no other forecasting system exists. We describe the key hydro-meteorological components and computational framework of GloFAS-Seasonal, alongside the forecast products available, before discussing initial evaluation results and next steps.

The atmospheric composition analysis and forecast for the European Copernicus Atmosphere Monitoring Services (CAMS) relies on biomass-burning fire emission estimates from the Global Fire Assimilation System (GFAS). The GFAS is a global system and converts fire radiative power (FRP) observations from MODIS satellites into smoke constituents. Missing observations are filled in using persistence, whereby observed FRP values from the previous day are progressed in time until a new observation is recorded. One of the consequences of this assumption is an increase of fire duration, which in turn translates into an increase of emissions estimated from fires compared to what is available from observations. In this study persistence is replaced by modelled predictions using the Canadian Fire Weather Index (FWI), which describes how atmospheric conditions affect the vegetation moisture content and ultimately fire duration. The skill in predicting emissions from biomass burning is improved with the new technique, which indicates that using an FWI-based model to infer emissions from FRP is better than persistence when observations are not available.

This paper considers whether there is any added value in using seasonal climate forecasts instead of historical meteorological observations for forecasting streamflow on seasonal timescales over Europe. A Europe-wide analysis of the skill of the newly operational EFAS (European Flood Awareness System) seasonal streamflow forecasts (produced by forcing the Lisflood model with the ECMWF System 4 seasonal climate forecasts), benchmarked against the ensemble streamflow prediction (ESP) forecasting approach (produced by forcing the Lisflood model with historical meteorological observations), is undertaken. The results suggest that, on average, the System 4 seasonal climate forecasts improve the streamflow predictability over historical meteorological observations for the first month of lead time only (in terms of hindcast accuracy, sharpness and overall performance). However, the predictability varies in space and time and is greater in winter and autumn. Parts of Europe additionally exhibit a longer predictability, up to 7 months of lead time, for certain months within a season. In terms of hindcast reliability, the EFAS seasonal streamflow hindcasts are on average less skilful than the ESP for all lead times. The results also highlight the potential usefulness of the EFAS seasonal streamflow forecasts for decision-making (measured in terms of the hindcast discrimination for the lower and upper terciles of the simulated streamflow). Although the ESP is the most potentially useful forecasting approach in Europe, the EFAS seasonal streamflow forecasts appear more potentially useful than the ESP in some regions and for certain seasons, especially in winter for almost 40 % of Europe. Patterns in the EFAS seasonal streamflow hindcast skill are however not mirrored in the System 4 seasonal climate hindcasts, hinting at the need for a better understanding of the link between hydrological and meteorological variables on seasonal timescales, with the aim of improving climate-model-based seasonal streamflow forecasting.

We undertook a comprehensive evaluation of 22 gridded (quasi-)global (sub-)daily precipitation (P) datasets for the period 2000–2016. Thirteen non-gauge-corrected P datasets were evaluated using daily P gauge observations from 76 086 gauges worldwide. Another nine gauge-corrected datasets were evaluated using hydrological modeling, by calibrating the HBV conceptual model against streamflow records for each of 9053 small to medium-sized ( 2) catchments worldwide, and comparing the resulting performance. Marked differences in spatio-temporal patterns and accuracy were found among the datasets. Among the uncorrected P datasets, the satellite- and reanalysis-based MSWEP-ng V1.2 and V2.0 datasets generally showed the best temporal correlations with the gauge observations, followed by the reanalyses (ERA-Interim, JRA-55, and NCEP-CFSR) and the satellite- and reanalysis-based CHIRP V2.0 dataset, the estimates based primarily on passive microwave remote sensing of rainfall (CMORPH V1.0, GSMaP V5/6, and TMPA 3B42RT V7) or near-surface soil moisture (SM2RAIN-ASCAT), and finally, estimates based primarily on thermal infrared imagery (GridSat V1.0, PERSIANN, and PERSIANN-CCS). Two of the three reanalyses (ERA-Interim and JRA-55) unexpectedly obtained lower trend errors than the satellite datasets. Among the corrected P datasets, the ones directly incorporating daily gauge data (CPC Unified, and MSWEP V1.2 and V2.0) generally provided the best calibration scores, although the good performance of the fully gauge-based CPC Unified is unlikely to translate to sparsely or ungauged regions. Next best results were obtained with P estimates directly incorporating temporally coarser gauge data (CHIRPS V2.0, GPCP-1DD V1.2, TMPA 3B42 V7, and WFDEI-CRU), which in turn outperformed the one indirectly incorporating gauge data through another multi-source dataset (PERSIANN-CDR V1R1). Our results highlight large differences in estimation accuracy, and hence the importance of P dataset selection in both research and operational applications. The good performance of MSWEP emphasizes that careful data merging can exploit the complementary strengths of gauge-, satellite-, and reanalysis-based P estimates.

In light of strong encouragement for disaster managers to use climate services for flood preparation, we question whether seasonal rainfall forecasts should indeed be used as indicators of the likelihood of flooding. Here, we investigate the primary indicators of flooding at the seasonal timescale across sub-Saharan Africa. Given the sparsity of hydrological observations, we input bias-corrected reanalysis rainfall into the Global Flood Awareness System to identify seasonal indicators of floodiness. Results demonstrate that in some regions of western, central, and eastern Africa with typically wet climates, even a perfect tercile forecast of seasonal total rainfall would provide little to no indication of the seasonal likelihood of flooding. The number of extreme events within a season shows the highest correlations with floodiness consistently across regions. Otherwise, results vary across climate regimes: floodiness in arid regions in southern and eastern Africa shows the strongest correlations with seasonal average soil moisture and seasonal total rainfall. Floodiness in wetter climates of western and central Africa and Madagascar shows the strongest relationship with measures of the intensity of seasonal rainfall. Measures of rainfall patterns, such as the length of dry spells, are least related to seasonal floodiness across the continent. Ultimately, identifying the drivers of seasonal flooding can be used to improve forecast information for flood preparedness and to avoid misleading decision-makers.

El Niño and La Niña (ENSO) events influence global river flow and are often used as an early indicator of potential flooding. Here, the authors show that the probability of ENSO-driven flood hazard is more complex than is often perceived, and highlight the importance of considering hydrological response.

Many fields, such as drought-risk assessment or reservoir management, can benefit from long-range streamflow forecasts. Climatology has long been used in long-range streamflow forecasting. Conditioning methods have been proposed to select or weight relevant historical time series from climatology. They are often based on general circulation model (GCM) outputs that are specific to the forecast date due to the initialisation of GCMs on current conditions. This study investigates the impact of conditioning methods on the performance of seasonal streamflow forecasts. Four conditioning statistics based on seasonal forecasts of cumulative precipitation and the standardised precipitation index were used to select relevant traces within historical streamflows and precipitation respectively. This resulted in eight conditioned streamflow forecast scenarios. These scenarios were compared to the climatology of historical streamflows, the ensemble streamflow prediction approach and the streamflow forecasts obtained from ECMWF System 4 precipitation forecasts. The impact of conditioning was assessed in terms of forecast sharpness (spread), reliability, overall performance and low-flow event detection. Results showed that conditioning past observations on seasonal precipitation indices generally improves forecast sharpness, but may reduce reliability, with respect to climatology. Conversely, conditioned ensembles were more reliable but less sharp than streamflow forecasts derived from System 4 precipitation. Forecast attributes from conditioned and unconditioned ensembles are illustrated for a case of drought-risk forecasting: the 2003 drought in France. In the case of low-flow forecasting, conditioning results in ensembles that can better assess weekly deficit volumes and durations over a wider range of lead times.

Too often, credible scientific early warning information of increased disaster risk does not result in humanitarian action. With financial resources tilted heavily towards response after a disaster, disaster managers have limited incentive and ability to process complex scientific data, including uncertainties. These incentives are beginning to change, with the advent of several new forecast-based financing systems that provide funding based on a forecast of an extreme event. Given the changing landscape, here we demonstrate a method to select and use appropriate forecasts for specific humanitarian disaster prevention actions, even in a data-scarce location. This action-based forecasting methodology takes into account the parameters of each action, such as action lifetime, when verifying a forecast. Forecasts are linked with action based on an understanding of (1) the magnitude of previous flooding events and (2) the willingness to act "in vain" for specific actions. This is applied in the context of the Uganda Red Cross Society forecast-based financing pilot project, with forecasts from the Global Flood Awareness System (GloFAS). Using this method, we define the "danger level" of flooding, and we select the probabilistic forecast triggers that are appropriate for specific actions. Results from this methodology can be applied globally across hazards and fed into a financing system that ensures that automatic, pre-funded early action will be triggered by forecasts.

Meteorological centres make sustained efforts to provide seasonal forecasts that are increasingly skilful, which has the potential to benefit streamflow forecasting. Seasonal streamflow forecasts can help to take anticipatory measures for a range of applications, such as water supply or hydropower reservoir operation and drought risk management. This study assesses the skill of seasonal precipitation and streamflow forecasts in France to provide insights into the way bias correcting precipitation forecasts can improve the skill of streamflow forecasts at extended lead times. We apply eight variants of bias correction approaches to the precipitation forecasts prior to generating the streamflow forecasts. The approaches are based on the linear scaling and the distribution mapping methods. A daily hydrological model is applied at the catchment scale to transform precipitation into streamflow. We then evaluate the skill of raw (without bias correction) and bias-corrected precipitation and streamflow ensemble forecasts in 16 catchments in France. The skill of the ensemble forecasts is assessed in reliability, sharpness, accuracy and overall performance. A reference prediction system, based on historical observed precipitation and catchment initial conditions at the time of forecast (i.e. ESP method) is used as benchmark in the computation of the skill. The results show that, in most catchments, raw seasonal precipitation and streamflow forecasts are often more skilful than the conventional ESP method in terms of sharpness. However, they are not significantly better in terms of reliability. Forecast skill is generally improved when applying bias correction. Two bias correction methods show the best performance for the studied catchments, each method being more successful in improving specific attributes of the forecasts: the simple linear scaling of monthly values contributes mainly to increasing forecast sharpness and accuracy, while the empirical distribution mapping of daily values is successful in improving forecast reliability.

Probabilistic hydro-meteorological forecasts have over the last decades been used more frequently to communicate forecast uncertainty. This uncertainty is twofold, as it constitutes both an added value and a challenge for the forecaster and the user of the forecasts. Many authors have demonstrated the added (economic) value of probabilistic over deterministic forecasts across the water sector (e.g. flood protection, hydroelectric power management and navigation). However, the richness of the information is also a source of challenges for operational uses, due partially to the difficulty in transforming the probability of occurrence of an event into a binary decision. This paper presents the results of a risk-based decision-making game on the topic of flood protection mitigation, called "How much are you prepared to pay for a forecast?". The game was played at several workshops in 2015, which were attended by operational forecasters and academics working in the field of hydro-meteorology. The aim of this game was to better understand the role of probabilistic forecasts in decision-making processes and their perceived value by decision-makers. Based on the participants' willingness-to-pay for a forecast, the results of the game show that the value (or the usefulness) of a forecast depends on several factors, including the way users perceive the quality of their forecasts and link it to the perception of their own performances as decision-makers.

Soil moisture memory is a key component of seasonal predictability. However, uncertainty in current memory estimates is not clear and it is not obvious to what extent these are dependent on model uncertainties. To address this question, we perform a global sensitivity analysis of memory to key hydraulic parameters, using an uncoupled version of the H-TESSEL land surface model. Results show significant dependency of estimates of memory and its uncertainty on these parameters, suggesting that operational seasonal forecasting models using deterministic hydraulic parameter values are likely to display a narrower range of memory than exists in reality. Explicitly incorporating hydraulic parameter uncertainty into models may then give improvements in forecast skill and reliability, as has been shown elsewhere in the literature. Our results also show significant differences with previous estimates of memory uncertainty, warning against placing too much confidence in a single quantification of uncertainty.

For many years, meteorological models have been run with perturbated initial conditions or parameters to produce ensemble forecasts that are used as a proxy of the uncertainty of the forecasts. However, the ensembles are usually both biased (the mean is systematically too high or too low, compared with the observed weather), and has dispersion errors (the ensemble variance indicates a too low or too high confidence in the forecast, compared with the observed weather). The ensembles are therefore commonly post-processed to correct for these shortcomings. Here we look at one of these techniques, referred to as Ensemble Model Output Statistics (EMOS) (Gneiting et al., 2005). Originally, the post-processing parameters were identified as a fixed set of parameters for a region. The application of our work is the European Flood Awareness System (http://www.efas.eu), where a distributed model is run with meteorological ensembles as input. We are therefore dealing with a considerably larger data set than previous analyses. We also want to regionalize the parameters themselves for other locations than the calibration gauges. The post-processing parameters are therefore estimated for each calibration station, but with a spatial penalty for deviations from neighbouring stations, depending on the expected semivariance between the calibration catchment and these stations. The estimated post-processed parameters can then be used for regionalization of the postprocessing parameters also for uncalibrated locations using top-kriging in the rtop-package (Skøien et al., 2006, 2014). We will show results from cross-validation of the methodology and although our interest is mainly in identifying exceedance probabilities for certain return levels, we will also show how the rtop package can be used for creating a set of post-processed ensembles through simulations.

The African Flood Forecasting System (AFFS) is a probabilistic flood forecast system for medium- to large-scale African river basins, with lead times of up to 15 days. The key components are the hydrological model LISFLOOD, the African GIS database, the meteorological ensemble predictions by the ECMWF (European Centre for Medium-Ranged Weather Forecasts) and critical hydrological thresholds. In this paper, the predictive capability is investigated in a hindcast mode, by reproducing hydrological predictions for the year 2003 when important floods were observed. Results were verified by ground measurements of 36 sub-catchments as well as by reports of various flood archives. Results showed that AFFS detected around 70 % of the reported flood events correctly. In particular, the system showed good performance in predicting riverine flood events of long duration (> 1 week) and large affected areas (> 10 000 km2) well in advance, whereas AFFS showed limitations for small-scale and short duration flood events. The case study for the flood event in March 2003 in the Sabi Basin (Zimbabwe) illustrated the good performance of AFFS in forecasting timing and severity of the floods, gave an example of the clear and concise output products, and showed that the system is capable of producing flood warnings even in ungauged river basins. Hence, from a technical perspective, AFFS shows a large potential as an operational pan-African flood forecasting system, although issues related to the practical implication will still need to be investigated.

Timely forecasts of the onset or possible evolution of droughts are an important contribution to mitigate their manifold negative effects. In this paper we therefore analyse and compare the performance of the first month of the probabilistic extended range forecast and of the seasonal forecast from the European Centre for Medium-range Weather Forecasts (ECMWF) in predicting droughts over the European continent. The Standardized Precipitation Index (SPI-1) is used to quantify the onset or likely evolution of ongoing droughts for the next month. It can be shown that on average the extended range forecast has greater skill than the seasonal forecast, whilst both outperform climatology. No significant spatial or temporal patterns can be observed, but the scores are improved when focussing on large-scale droughts. In a second step we then analyse several different methods to convert the probabilistic forecasts of SPI into a Boolean drought warning. It can be demonstrated that methodologies which convert low percentiles of the forecasted SPI cumulative distribution function into warnings are superior in comparison with alternatives such as the mean or the median of the ensemble. The paper demonstrates that up to 40 % of droughts are correctly forecasted one month in advance. Nevertheless, during false alarms or misses, we did not find significant differences in the distribution of the ensemble members that would allow for a quantitative assessment of the uncertainty.

The Canadian Forest Fire Weather Index (FWI) System is the mostly widely used fire danger rating system in the world. We have developed a global database of daily FWI System calculations, beginning in 1980, called the Global Fire WEather Database (GFWED) gridded to a spatial resolution of 0.5° latitude by 2/3° longitude. Input weather data were obtained from the NASA Modern Era Retrospective-Analysis for Research and Applications (MERRA), and two different estimates of daily precipitation from rain gauges over land. FWI System Drought Code calculations from the gridded data sets were compared to calculations from individual weather station data for a representative set of 48 stations in North, Central and South America, Europe, Russia, Southeast Asia and Australia. Agreement between gridded calculations and the station-based calculations tended to be most different at low latitudes for strictly MERRA-based calculations. Strong biases could be seen in either direction: MERRA DC over the Mato Grosso in Brazil reached unrealistically high values exceeding DC = 1500 during the dry season but was too low over Southeast Asia during the dry season. These biases are consistent with those previously identified in MERRA's precipitation, and they reinforce the need to consider alternative sources of precipitation data. GFWED can be used for analyzing historical relationships between fire weather and fire activity at continental and global scales, in identifying large-scale atmosphere–ocean controls on fire weather, and calibration of FWI-based fire prediction models.

Large-scale fires occur frequently across Indonesia, particularly in the southern region of Kalimantan and eastern Sumatra. They have considerable impacts on carbon emissions, haze production, biodiversity, health, and economic activities. In this study, we demonstrate that severe fire and haze events in Indonesia can generally be predicted months in advance using predictions of seasonal rainfall from the ECMWF System 4 coupled ocean–atmosphere model. Based on analyses of long, up-to-date series observations on burnt area, rainfall, and tree cover, we demonstrate that fire activity is negatively correlated with rainfall and is positively associated with deforestation in Indonesia. There is a contrast between the southern region of Kalimantan (high fire activity, high tree cover loss, and strong non-linear correlation between observed rainfall and fire) and the central region of Kalimantan (low fire activity, low tree cover loss, and weak, non-linear correlation between observed rainfall and fire). The ECMWF seasonal forecast provides skilled forecasts of burnt and fire-affected area with several months lead time explaining at least 70% of the variance between rainfall and burnt and fire-affected area. Results are strongly influenced by El Niño years which show a consistent positive bias. Overall, our findings point to a high potential for using a more physical-based method for predicting fires with several months lead time in the tropics rather than one based on indexes only. We argue that seasonal precipitation forecasts should be central to Indonesia's evolving fire management policy.

Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks 1 . Accurate and timely warnings are critical for mitigating flood risks 2 , but hydrological simulation models typically must be calibrated to long data records in each watershed. Here we show that artificial intelligence-based forecasting achieves reliability in predicting extreme riverine events in ungauged watersheds at up to a five-day lead time that is similar to or better than the reliability of nowcasts (zero-day lead time) from a current state-of-the-art global modelling system (the Copernicus Emergency Management Service Global Flood Awareness System). In addition, we achieve accuracies over five-year return period events that are similar to or better than current accuracies over one-year return period events. This means that artificial intelligence can provide flood warnings earlier and over larger and more impactful events in ungauged basins. The model developed here was incorporated into an operational early warning system that produces publicly available (free and open) forecasts in real time in over 80 countries. This work highlights a need for increasing the availability of hydrological data to continue to improve global access to reliable flood warnings., (© 2024. The Author(s).)

The Wet Bulb Globe Temperature (WBGT) is an international standard heat index used by the health, industrial, sports, and climate sectors to assess thermal comfort during heat extremes. Observations of its components, the globe and the wet bulb temperature (WBT), are however sparse. Therefore WBGT is difficult to derive, making it common to rely on approximations, such as the ones developed by Liljegren et al. (2008, https://doi.org/10.1080/15459620802310770, W B G T L i l j e g r e n ) and by the American College of Sports Medicine ( W B G T A C S M 87 ). In this study, a global data set is created by implementing an updated WBGT method using ECMWF ERA5 gridded meteorological variables and is evaluated against existing WBGT methods. The new method, W B G T B r i m i c o m b e , uses globe temperature calculated using mean radiant temperature and is found to be accurate in comparison to W B G T L i l j e g r e n across three heatwave case studies. In addition, it is found that W B G T A C S M 87 is not an adequate approximation of WBGT. Our new method is a candidate for a global forecasting early warning system., Competing Interests: The authors declare no conflicts of interest relevant to this study., (© 2023 The Authors. GeoHealth published by Wiley Periodicals LLC on behalf of American Geophysical Union.)

Forty years (1980-2019) of reanalysis data were used to investigate climatology and trends of heat stress in the Caribbean region. Represented via the Universal Thermal Climate Index (UTCI), a multivariate thermophysiological-relevant parameter, the highest heat stress is found to be most frequent and geographically widespread during the rainy season (August, September, and October). UTCI trends indicate an increase of more than 0.2°C·decade -1 , with southern Florida and the Lesser Antilles witnessing the greatest upward rates (0.45°C·decade -1 ). Correlations with climate variables known to induce heat stress reveal that the increase in heat stress is driven by increases in air temperature and radiation, and decreases in wind speed. Conditions of heat danger, as depicted by the heat index (HI), have intensified since 1980 (+1.2°C) and are found to occur simultaneously to conditions of heat stress suggesting a synergy between heat illnesses and physiological responses to heat. This work also includes the analysis of the record-breaking 2020 heat season during which the UTCI and HI achieved above average values, indicating that local populations most likely experienced heat stress and danger higher than the ones they are used to. These findings confirm the gradual intensification of heat stress in the Caribbean and aim to provide a guidance for heat-related policies in the region., Competing Interests: The authors declare no potential conflict of interest., (© 2022 The Authors. International Journal of Climatology published by John Wiley & Sons Ltd on behalf of Royal Meteorological Society.)

Air temperature has been the most commonly used exposure metric in assessing relationships between thermal stress and mortality. Lack of the high-quality meteorological station data necessary to adequately characterize the thermal environment has been one of the main limitations for the use of more complex thermal indices. Global climate reanalyses may provide an ideal platform to overcome this limitation and define complex heat and cold stress conditions anywhere in the world. In this study, we explored the potential of the Universal Thermal Climate Index (UTCI) based on ERA5 - the latest global climate reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF) - as a health-related tool. Employing a novel ERA5-based thermal comfort dataset ERA5-HEAT, we investigated the relationships between the UTCI and daily mortality data in 21 cities across 9 European countries. We used distributed lag nonlinear models to assess exposure-response relationships between mortality and thermal conditions in individual cities. We then employed meta-regression models to pool the results for each city into four groups according to climate zone. To evaluate the performance of ERA5-based UTCI, we compared its effects on mortality with those for the station-based UTCI data. In order to assess the additional effect of the UTCI, the performance of ERA5-and station-based air temperature (T) was evaluated. Whilst generally similar heat- and cold-effects were observed for the ERA5-and station-based data in most locations, the important role of wind in the UTCI appeared in the results. The largest difference between any two datasets was found in the Southern European group of cities, where the relative risk of mortality at the 1st percentile of daily mean temperature distribution (1.29 and 1.30 according to the ERA5 vs station data, respectively) considerably exceeded the one for the daily mean UTCI (1.19 vs 1.22). These differences were mainly due to the effect of wind in the cold tail of the UTCI distribution. The comparison of exposure-response relationships between ERA5-and station-based data shows that ERA5-based UTCI may be a useful tool for definition of life-threatening thermal conditions in locations where high-quality station data are not available., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

We introduce the Precipitation Probability DISTribution (PPDIST) dataset, a collection of global high-resolution (0.1°) observation-based climatologies (1979-2018) of the occurrence and peak intensity of precipitation (P) at daily and 3-hourly time-scales. The climatologies were produced using neural networks trained with daily P observations from 93,138 gauges and hourly P observations (resampled to 3-hourly) from 11,881 gauges worldwide. Mean validation coefficient of determination (R 2 ) values ranged from 0.76 to 0.80 for the daily P occurrence indices, and from 0.44 to 0.84 for the daily peak P intensity indices. The neural networks performed significantly better than current state-of-the-art reanalysis (ERA5) and satellite (IMERG) products for all P indices. Using a 0.1 mm 3 h -1 threshold, P was estimated to occur 12.2%, 7.4%, and 14.3% of the time, on average, over the global, land, and ocean domains, respectively. The highest P intensities were found over parts of Central America, India, and Southeast Asia, along the western equatorial coast of Africa, and in the intertropical convergence zone. The PPDIST dataset is available via www.gloh2o.org/ppdist .

In human biometeorology, the estimation of mean radiant temperature (MRT) is generally considered challenging. This work presents a general framework to compute the MRT at the global scale for a human subject placed in an outdoor environment and irradiated by solar and thermal radiation both directly and diffusely. The proposed framework requires as input radiation fluxes computed by numerical weather prediction (NWP) models and generates as output gridded globe-wide maps of MRT. It also considers changes in the Sun's position affecting radiation components when these are stored by NWP models as an accumulated-over-time quantity. The applicability of the framework was demonstrated using NWP reanalysis radiation data from the European Centre for Medium-Range Weather Forecasts. Mapped distributions of MRT were correspondingly computed at the global scale. Comparison against measurements from radiation monitoring stations showed a good agreement with NWP-based MRT (coefficient of determination greater than 0.88; average bias equal to 0.42 °C) suggesting its potential as a proxy for observations in application studies.

Heat stress and forest fires are often considered highly correlated hazards as extreme temperatures play a key role in both occurrences. This commonality can influence how civil protection and local responders deploy resources on the ground and could lead to an underestimation of potential impacts, as people could be less resilient when exposed to multiple hazards. In this work, we provide a simple methodology to identify areas prone to concurrent hazards, exemplified with, but not limited to, heat stress and fire danger. We use the combined heat and forest fire event that affected Europe in June 2017 to demonstrate that the methodology can be used for analysing past events as well as making predictions, by using reanalysis and medium-range weather forecasts, respectively. We present new spatial layers that map the combined danger and make suggestions on how these could be used in the context of a Multi-Hazard Early Warning System. These products could be particularly valuable in disaster risk reduction and emergency response management, particularly for civil protection, humanitarian agencies and other first responders whose role is to identify priorities during pre-interventions and emergencies., (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)

In this work, the potential of the Universal Thermal Climate Index (UTCI) as a heat-related health risk indicator in Europe is demonstrated. The UTCI is a bioclimate index that uses a multi-node human heat balance model to represent the heat stress induced by meteorological conditions to the human body. Using 38 years of meteorological reanalysis data, UTCI maps were computed to assess the thermal bioclimate of Europe for the summer season. Patterns of heat stress conditions and non-thermal stress regions are identified across Europe. An increase in heat stress up to 1 °C is observed during recent decades. Correlation with mortality data from 17 European countries revealed that the relationship between the UTCI and death counts depends on the bioclimate of the country, and death counts increase in conditions of moderate and strong stress, i.e., when UTCI is above 26 and 32 °C. The UTCI's ability to represent mortality patterns is demonstrated for the 2003 European heatwave. These findings confirm the importance of UTCI as a bioclimatic index that is able to both capture the thermal bioclimatic variability of Europe, and relate such variability with the effects it has on human health.

Warning systems with the ability to predict floods several days in advance have the potential to benefit tens of millions of people. Accordingly, large-scale streamflow prediction systems such as the Advanced Hydrologic Prediction Service or the Global Flood Awareness System are limited to coarse resolutions. This article presents a method for routing global runoff ensemble forecasts and global historical runoff generated by the European Centre for Medium-Range Weather Forecasts model using the Routing Application for Parallel computatIon of Discharge to produce high spatial resolution 15-day stream forecasts, approximate recurrence intervals, and warning points at locations where streamflow is predicted to exceed the recurrence interval thresholds. The processing method involves distributing the computations using computer clusters to facilitate processing of large watersheds with high-density stream networks. In addition, the Streamflow Prediction Tool web application was developed for visualizing analyzed results at both the regional level and at the reach level of high-density stream networks. The application formed part of the base hydrologic forecasting service available to the National Flood Interoperability Experiment and can potentially transform the nation's forecast ability by incorporating ensemble predictions at the nearly 2.7 million reaches of the National Hydrography Plus Version 2 Dataset into the national forecasting system.

Although over a hundred thermal indices can be used for assessing thermal health hazards, many ignore the human heat budget, physiology and clothing. The Universal Thermal Climate Index (UTCI) addresses these shortcomings by using an advanced thermo-physiological model. This paper assesses the potential of using the UTCI for forecasting thermal health hazards. Traditionally, such hazard forecasting has had two further limitations: it has been narrowly focused on a particular region or nation and has relied on the use of single 'deterministic' forecasts. Here, the UTCI is computed on a global scale, which is essential for international health-hazard warnings and disaster preparedness, and it is provided as a probabilistic forecast. It is shown that probabilistic UTCI forecasts are superior in skill to deterministic forecasts and that despite global variations, the UTCI forecast is skilful for lead times up to 10 days. The paper also demonstrates the utility of probabilistic UTCI forecasts on the example of the 2010 heat wave in Russia.