Floods are the most frequent of natural disasters, affecting millions of people across the globe every year. The anticipation and forecasting of floods at the global scale is crucial to preparing for severe events and providing early awareness where local flood models and warning services may not exist. As numerical weather prediction models continue to improve, operational centers are increasingly using their meteorological output to drive hydrological models, creating hydrometeorological systems capable of forecasting river flow and flood events at much longer lead times than has previously been possible. Furthermore, developments in, for example, modelling capabilities, data, and resources in recent years have made it possible to produce global scale flood forecasting systems. In this paper, the current state of operational large-scale flood forecasting is discussed, including probabilistic forecasting of floods using ensemble prediction systems. Six state-of-the-art operational large-scale flood forecasting systems are reviewed, describing similarities and differences in their approaches to forecasting floods at the global and continental scale. Operational systems currently have the capability to produce coarse-scale discharge forecasts in the mediumrange and disseminate forecasts and, in some cases, early warning products in real time across the globe, in support of national forecasting capabilities. With improvements in seasonal weather forecasting, future advances may include more seamless hydrological forecasting at the global scale alongside a move towards multi-model forecasts and grand ensemble techniques, responding to the requirement of developing multi-hazard early warning systems for disaster risk reduction.

El Niño and La Niña events, the extremes of ENSO climate variability, influence river flow and flooding at the global scale. Estimates of the historical probability of extreme (high or low) precipitation are used to provide vital information on the likelihood of adverse impacts during extreme ENSO events. However, the nonlinearity between precipitation and flood magnitude motivates the need for estimation of historical probabilities using analysis of hydrological data sets. Here, this analysis is undertaken using the ERA-20CM-R river flow reconstruction for the twentieth century. Our results show that the likelihood of increased or decreased flood hazard during ENSO events is much more complex than is often perceived and reported; probabilities vary greatly across the globe, with large uncertainties inherent in the data and clear differences when comparing the hydrological analysis to precipitation.

Abstract. 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 hydrometeorological 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 hydrometeorological components and computational framework of GloFAS-Seasonal, alongside the forecast products available, before discussing initial evaluation results and next steps.

Ensemble flood forecasting has gained significant momentum over the past decade due to the growth of ensemble numerical weather and climate prediction, expansion in high performance computing, growing interest in shifting from deterministic to risk-based decision-making that accounts for forecast uncertainty, and the efforts of communities such as the international Hydrologic Ensemble Prediction Experiment (HEPEX), which focuses on advancing relevant ensemble forecasting capabilities and fostering its adoption. With this shift, comes the need to understand the current state of ensemble flood forecasting, in order to provide insights into current capabilities and areas for improvement, thus identifying future research opportunities to allow for better allocation of research resources. In this article, we provide an overview of current research activities in ensemble flood forecasting and discuss knowledge gaps and future research opportunities, based on a review of 70 papers focusing on various aspects of ensemble flood forecasting around the globe. Future research directions include opportunities to improve technical aspects of ensemble flood forecasting, such as data assimilation techniques and methods to account for more sources of uncertainty, and developing ensemble forecasts for more variables, for example, flood inundation, by applying techniques such as machine learning. Further to this, we conclude that there is a need to not only improve technical aspects of flood forecasting, but also to bridge the gap between scientific research and hydrometeorological model development, and real-world flood management using probabilistic ensemble forecasts, especially through effective communication.

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IMPORTANCE Despite guidelines recommending administration of intravenous (IV) magnesium sulfate for refractory pediatric asthma, the number of asthma-related hospitalizations has remained stable, and IV magnesium therapy is independently associated with hospitalization.OBJECTIVE To examine the association between IV magnesium therapy administered in the emergency department (ED) and subsequent hospitalization among pediatric patients with refractory acute asthma after adjustment for patient-level variables. DESIGN, SETTING, AND PARTICIPANTSThis post hoc secondary analysis of a double-blind randomized clinical trial of children with acute asthma treated from September 26, 2011, to November 19, 2019, at 7 Canadian tertiary care pediatric EDs was conducted between September and November 2020. In the randomized clinical trial, 816 otherwise healthy children aged 2 to 17 years with Pediatric Respiratory Assessment Measure (PRAM) scores of 5 points or higher after initial therapy with systemic corticosteroids and inhaled albuterol with ipratropium bromide were randomly assigned to 3 nebulized treatments of albuterol plus either magnesium sulfate or 5.5% saline placebo. EXPOSURES Intravenous magnesium sulfate therapy (40-75 mg/kg). MAIN OUTCOMES AND MEASURESThe association between IV magnesium therapy in the ED and subsequent hospitalization for asthma was assessed using multivariable logistic regression analysis.Analyses were adjusted for year epoch at enrollment, receipt of IV magnesium, PRAM score after initial therapy and at ED disposition, age, sex, duration of respiratory distress, previous intensive care unit admission for asthma, hospitalizations for asthma within the past year, atopy, and receipt of oral corticosteroids within 48 hours before arrival in the ED, nebulized magnesium, and additional albuterol after inhaled magnesium or placebo, with site as a random effect. RESULTS Among the 816 participants, the median age was 5 years (interquartile range, 3-7 years), 517 (63.4%) were boys, and 364 (44.6%) were hospitalized. A total of 215 children (26.3%) received IV magnesium, and 190 (88.4%) of these children were hospitalized compared with 174 of 601 children (29.0%) who did not receive IV magnesium. Multivariable factors associated with

This study has explored the prediction errors of tropical cyclones (TCs) in the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS) for the Northern Hemisphere summer period for five recent years. Results for the EPS are contrasted with those for the higher-resolution deterministic forecasts. Various metrics of location and intensity errors are considered and contrasted for verification based on IBTrACS and the numerical weather prediction (NWP) analysis (NWPa). Motivated by the aim of exploring extended TC life cycles, location and intensity measures are introduced based on lower-tropospheric vorticity, which is contrasted with traditional verification metrics. Results show that location errors are almost identical when verified against IBTrACS or the NWPa. However, intensity in the form of the mean sea level pressure (MSLP) minima and 10-m wind speed maxima is significantly underpredicted relative to IBTrACS. Using the NWPa for verification results in much better consistency between the different intensity error metrics and indicates that the lower-tropospheric vorticity provides a good indication of vortex strength, with error results showing similar relationships to those based on MSLP and 10-m wind speeds for the different forecast types. The interannual variation in forecast errors are discussed in relation to changes in the forecast and NWPa system and variations in forecast errors between different ocean basins are discussed in terms of the propagation characteristics of the TCs.

In June 2021, an unprecedented extreme heatwave impacted the Pacific Northwest of North America, resulting in more than 1000 excess deaths and affecting infrastructure and wildlife. Predicting such extreme events is key for preparedness and early action, and beyond temperature, it is important to consider biometeorological forecasts, accounting for the effects of the environment on the human body. The performance of ECMWF's (the European Centre for Medium‐Range Weather Forecasts) temperature and heat stress (Humidex and the Universal Thermal Climate Index) forecasts during this heatwave is explored, and highlights that several days in advance, an event surpassing the maximum of climatology was predicted with extremely high confidence (100% of ensemble members) – successfully predicting the unprecedented.