Long-range Forecasts Research Articles

Mathematical modelling for pandemic preparedness in Canada: Learning from COVID-19.

The COVID-19 pandemic underlined the need for pandemic planning but also brought into focus the use of mathematical modelling to support public health decisions. The types of models needed (compartment, agent-based, importation) are described. Best practices regarding biological realism (including the need for multidisciplinary expert advisors to modellers), model complexity, consideration of uncertainty and communications to decision-makers and the public are outlined. A narrative review was developed from the experiences of COVID-19 by members of the Public Health Agency of Canada External Modelling Network for Infectious Diseases (PHAC EMN-ID), a national community of practice on mathematical modelling of infectious diseases for public health. Modelling can best support pandemic preparedness in two ways: 1) by modelling to support decisions on resource needs for likely future pandemics by estimating numbers of infections, hospitalized cases and cases needing intensive care, associated with epidemics of "hypothetical-yet-plausible" pandemic pathogens in Canada; and 2) by having ready-to-go modelling methods that can be readily adapted to the features of an emerging pandemic pathogen and used for long-range forecasting of the epidemic in Canada, as well as to explore scenarios to support public health decisions on the use of interventions. There is a need for modelling expertise within public health organizations in Canada, linked to modellers in academia in a community of practice, within which relationships built outside of times of crisis can be applied to enhance modelling during public health emergencies. Key challenges to modelling for pandemic preparedness include the availability of linked public health, hospital and genomic data in Canada.

Accuracy of COVID-19 Prediction Modeling Techniques

The unprecedented impact of the COVID-19 pandemic has revealed that forecasting capability is critically needed in making strategic decisions and formulating reasonable countermeasures. This study aimed to assess the predictive accuracy in forecasting the numbers of COVID-19 cases using Thailand’s national COVID-19 surveillance database from January 2020– June 2021 based on three analytical models: a susceptible-exposed-infectious-recovery compartmental model, an auto-regressive integrated moving average model, and a long short-term memory (LSTM) network model. All forecasting methods had model parameters adjusted weekly according to the most recent situation and predictive accuracy measures, including the mean absolute percent error (MAPE). We found that the MAPE values ranged from 19.65%–22.54%, 28.95%–32.35%, 47.78%–53.55%, and 75.03–84.91% for forecasting one, two, four, and eight weeks ahead, respectively. Among the three models, the LSTM model had slightly higher accuracy than the other two models within the same forecasting range. These prediction models can be used for short-range forecasts in other similar settings while long-range forecasting requires monitoring and updating periodically.

Remote Midlatitude Control of Rainfall Onset at the Southern African Tropical Edge

Abstract Rainfall over the southern African tropical boundary is highly variable, particularly around the onset of the summer rains. However, understanding of the causes of variability in onset timing is poor. A lack of observational data in the region is compounded by the complexity of climate dynamics and variability at the interface of the tropics and the midlatitudes. The key to onset over the region is the Congo air boundary (CAB), a distinguishing circulation feature present in September and declining in frequency until November. The CAB is crucial to early season rainfall; precipitation is broadly inhibited when the CAB is present, while its breakdown allows large-scale rainfall and onset over central southern Africa. This work identifies a remote midlatitude influence on the timing of CAB breakdown using the high-resolution reanalysis dataset ERA5. Propagating and (upstream) breaking low-wavenumber Rossby waves in the Southern Hemisphere westerly jet are shown to be related to >70% of breakdown events. Breakdown is forced by 1) the replacement of subsidence over eastern southern Africa with synoptically forced ascent and 2) wind field modification that leads to positive 850-hPa continental convergence anomalies and enhanced moisture advection from the Congo basin and Indian Ocean. Upper-level anticyclonic vorticity tendency induced by enhanced convection slows Rossby wave propagation aloft, contributing to wave breaking and the persistence of conditions favorable to convection. The identification of this midlatitude influence on CAB breakdown reveals new potential sources of predictability for onset and demonstrates the significant equatorward extent of midlatitude influences to the tropical edge. Significance Statement This study aims to improve our understanding of the causes of variability in the start date of the summer rainy season in central southern Africa. The research reveals that certain upper-level waves in the Southern Hemisphere westerly jet are associated with patterns of weather that force a large-scale transition from dry pre-onset conditions to widespread rainfall in a time scale of 2–5 days, and that these waves account for a large proportion of such transitions. This finding is of interest to subseasonal weather forecasters and may help to improve the long-range forecasting of onset date.

An efficient spatial-temporal transformer with temporal aggregation and spatial memory for traffic forecasting

Traffic forecasting technology has widespread applications in various domains, such as urban traffic planning and intelligent transportation systems. Traffic forecasting encounters challenges in effectively capturing the intricate spatial–temporal correlations in traffic data. While the latest methods have achieved satisfactory performance, they still suffer from two limitations: (i) Most methods overlook the memory of valuable traffic patterns at each traffic node, thus making it struggle to reveal dynamic spatial–temporal correlations using their inherent periodicity and trend characteristics from a broader perspective. (ii) As the research progresses, recently proposed models become increasingly complex and massive. To address these issues, we propose a Spatial-Temporal Aggregation Memory Transformer (STAMT) for traffic forecasting. Specifically, we propose a memory bank to enhance vanilla spatial attention and cache the traffic patterns of historical input. By querying these traffic patterns, which contain rich spatial–temporal semantic information, the model can optimize prediction performance by extracting trends and regularities across various periods. To reduce the computational costs, we introduce a temporal module to capture temporal correlations while reducing temporal dimension information. In addition, we leverage the random feature map and matrix multiplication associativity property, which reduce the quadratic complexity of spatial attention to linearity with regard to the number of nodes. Ultimately, our theoretical analysis concludes that a single-layer spatial attention network is sufficient to capture spatial–temporal correlations deeply without stacking. Extensive experiments on nine real-world datasets demonstrate that STAMT outperforms state-of-the-art baselines in regular, long-range, and large-scale traffic forecasting tasks while significantly reducing the computational costs. Codes are available at https://github.com/LiuAoyu1998/STAMT.

Learning from Polar Representation: An Extreme-Adaptive Model for Long-Term Time Series Forecasting

In the hydrology field, time series forecasting is crucial for efficient water resource management, improving flood and drought control and increasing the safety and quality of life for the general population. However, predicting long-term streamflow is a complex task due to the presence of extreme events. It requires the capture of long-range dependencies and the modeling of rare but important extreme values. Existing approaches often struggle to tackle these dual challenges simultaneously. In this paper, we specifically delve into these issues and propose Distance-weighted Auto-regularized Neural network (DAN), a novel extreme-adaptive model for long-range forecasting of stremflow enhanced by polar representation learning. DAN utilizes a distance-weighted multi-loss mechanism and stackable blocks to dynamically refine indicator sequences from exogenous data, while also being able to handle uni-variate time-series by employing Gaussian Mixture probability modeling to improve robustness to severe events. We also introduce Kruskal-Wallis sampling and gate control vectors to handle imbalanced extreme data. On four real-life hydrologic streamflow datasets, we demonstrate that DAN significantly outperforms both state-of-the-art hydrologic time series prediction methods and general methods designed for long-term time series prediction.

Temperature forecasts for the continental United States: a deep learning approach using multidimensional features

Accurate weather forecasts are critical for saving lives, emergency services, and future developments. Climate models such as numerical weather prediction models have made significant advancements in weather forecasts, but these models are computationally expensive and can be subject to inaccurate representations of complex natural interconnections. Alternatively, data-driven machine learning methods have provided new dimensions in assisting weather forecasts. In this study, we used convolutional neural networks (CNN) to assess how geopotential height at different levels of the troposphere may affect the predictability of extreme surface temperature (t2m) via two cases. Specifically, we analyzed temperature forecasts over the continental United States at lead times from 1 day to 30 days by incorporating z100, z200, z500, z700, and z925 hPa levels as inputs to the CNN. In the first case, we applied the framework to predict summer temperatures of 2012, which contributed to one of the extreme heatwave events in the U.S. history. The results show that z500 leads to t2m forecasts with relatively less root mean squared errors (RMSE) than other geopotential heights at most of the lead time under consideration, while the inclusion of more atmospheric pressure levels improves t2m forecasts to a limited extent. At the same lead time, we also predicted the z500 patterns with different levels of geopotential height and temperature as the inputs. We found that the combination of z500, t2m, and t850 (temperature at 850 hPa) is associated with less RMSE for the z500 forecasts compared to other inputs. In contrast to the 2012 summer, our second case examined the wintertime temperature of 2014 when the upper Midwest and Great Lakes regions experienced the coldest winter on record. We found that z200 contributes to better t2m predictions for up to 7-days lead times whereas z925 gives better results for z500 forecasts during this cold event. Collectively, the results suggest that for long-range temperature forecasts based on the CNN, including various levels of geopotential heights could be beneficial.

A new approach of coupled long-range forecasts for streamflow and groundwater level

Methods to produce seasonal to annual forecasts of surface and groundwater availability have been developed and implemented nationally and globally. Such forecasts allow water managers to create effective irrigation schedules and better plan and manage water supply. However, one limitation of existing approaches that the forecasts of surface streamflow and groundwater are independent and therefore neglect inter-variable correlations that allow for reliable forecasting of total water availability. In this study, we develop an approach to jointly forecast streamflow and groundwater level and demonstrate its performance for the Lockyer Valley, Australia, where both surface water and groundwater are critical for irrigation. Informed by analysis of the processes influencing dynamics of groundwater levels in the alluvial aquifer (as a substitute to available groundwater storage) and streamflow dynamics, an existing hydrological model is adapted to better represent the ephemeral nature of streamflow in the catchment and also to simulate changes in groundwater levels. The modified hydrological model is then integrated into a statistical-dynamical forecasting framework to jointly forecast streamflow and groundwater level. Verification results indicate that forecasts skilful to lead times of up to 3 months for streamflow and 12 months for groundwater levels, even though rainfall forecasts are not skilful beyond the first month. Forecasts for both streamflow and groundwater are also statistically reliable with low bias. The approach developed can be extended to forecast the spatial distribution of groundwater levels across a large region, as well as be used to infill missing groundwater observations.

Probabilistic rainy season onset prediction over the greater horn of africa based on long-range multi-model ensemble forecasts

This works proposes a probabilistic framework for rainy season onset forecasts over Greater Horn of Africa derived from bias-corrected, long range, multi-model ensemble precipitation forecasts. A careful analysis of the contribution of the different forecast systems to the overall multi-model skill shows that the improvement over the best performing individual model can largely be explained by the increased ensemble size. An alternative way of increasing ensemble size by blending a single model ensemble with climatology is explored and demonstrated to yield better probabilistic forecasts than the multi-model ensemble. Both reliability and skill of the probabilistic forecasts are better for OND onset than for MAM and JJAS onset where forecasts are found to be late biased and have only minimal skill relative to climatology. The insights gained in this study will help enhance operational subseasonal-to-seasonal forecasting in the GHA region.

Joint-outcome prediction markets for climate risks.

Predicting future climate requires the integration of knowledge and expertise from a wide range of disciplines. Predictions must account for climate-change mitigation policies which may depend on climate predictions. This interdependency, or "circularity", means that climate predictions must be conditioned on emissions of greenhouse gases (GHGs). Long-range forecasts also suffer from information asymmetry because users cannot use track records to judge the skill of providers. The problems of aggregation, circularity, and information asymmetry can be addressed using prediction markets with joint-outcome spaces, allowing simultaneous forecasts of GHG concentrations and temperature. The viability of prediction markets with highly granular, joint-outcome spaces was tested with markets for monthly UK rainfall and temperature. The experiments demonstrate these markets can aggregate the judgments of experts with relevant expertise, and suggest similarly structured markets, with longer horizons, could provide a mechanism to produce credible forecasts of climate-related risks for policy making, planning, and risk disclosure.

Long-range forecasting of daily rainfall using machine learning techniques

Long-range forecasting of daily rainfall using machine learning techniques

Displacement Error Characteristics of 500-hPa Cutoff Lows in Operational GFS Forecasts

Abstract Cutoff lows are often associated with high-impact weather; therefore, it is critical that operational numerical weather prediction systems accurately represent the evolution of these features. However, medium-range forecasts of upper-level features using the Global Forecast System (GFS) are often subjectively characterized by excessive synoptic progressiveness, i.e., a tendency to advance troughs and cutoff lows too quickly downstream. To better understand synoptic progressiveness errors, this research quantifies seven years of 500-hPa cutoff low position errors over the globe, with the goal of objectively identifying regions where synoptic progressiveness errors are common and how frequently these errors occur. Specifically, 500-hPa features are identified and tracked in 0–240-h 0.25° GFS forecasts during April 2015–March 2022 using an objective cutoff low and trough identification scheme and compared to corresponding 500-hPa GFS analyses. In the Northern Hemisphere, cutoff lows are generally underrepresented in forecasts compared to verifying analyses, particularly over continental midlatitude regions. Features identified in short- to long-range forecasts are generally associated with eastward zonal position errors over the conterminous United States and northern Asia, particularly during the spring and autumn. Similarly, cutoff lows over the Southern Hemisphere midlatitudes are characterized by an eastward displacement bias during all seasons. Significance Statement Cutoff lows are often associated with high-impact weather, including excessive rainfall, winter storms, and severe weather. GFS forecasts of cutoff lows over the United States are often subjectively noted to advance cutoff lows too quickly downstream, and thus limit forecast skill in potentially impactful scenarios. Therefore, this study quantifies the position error characteristics of cutoff lows in recent GFS forecasts. Consistent with typically anecdotal impressions of cutoff low position errors, this analysis demonstrates that cutoff lows over North America and central Asia are generally associated with an eastward position bias in medium- to long-range GFS forecasts. These results suggest that additional research to identify both environmental conditions and potential model deficiencies that may exacerbate this eastward bias would be beneficial.

Долгосрочное прогнозирование сроков вскрытия реки Юкон синоптико-статистическим методом

A scheme for obtaining a long-range forecast of the dates of ice break-up is proposed for the Yukon River (North America). The scheme is based on a well-proven national practice of ice forecasting, namely, on the meteorological statistical method. The method utilizes a linear dependence of the predicted value on the characteristics of temperature and pressure fields in the North Atlantic and the North Pacific. The most informative predictors are selected. Statistical stability of the forecast formula parameters is verified. The average forecast lead time is 40 days. The verification of the proposed methodology performed for three stretches of the Yukon River on the basis of independent data for the period from 2009 to 2015 showed that it allows obtaining quite satisfactory results with a fairly low root-mean-square error and a fairly high accuracy of forecasts. Keywords: river ice break-up, long-range forecast, synoptic statistical method, temperature and pressure fields, predictors, stability, method verification

ОЦЕНКА ИЗМЕНЕНИЙ ТЕМПЕРАТУРНОГО РЕЖИМА В СЕВЕРНОЙ ЕВРАЗИИ НА ПРЕДСТОЯЩЕЕ ПЯТИЛЕТИЕ ПО ПРОГНОЗАМ МОДЕЛИ ЗЕМНОЙ СИСТЕМЫ ИВМ РАН И ИХ ВОЗМОЖНЫХ ПОСЛЕДСТВИЙ ДЛЯ СЕЛЬСКОГО ХОЗЯЙСТВА

Estimates of future changes in the characteristics of the air temperature regime in Northern Eurasia over a five-year time interval are presented. The estimates are based on the forecasts of the Earth climate model developed at Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences. The skill of the forecasts for the territory of Northern Eurasia is discussed using standard skill scores for long-range forecasts. Regions where surface air temperature anomalies are likely to exceed the threshold targets of the Paris Agreement are identified. The consequences of a possible change in the growing season and dates of the stable air temperature above 5°C for agriculture are analyzed. The presented estimates of possible changes in the air temperature characteristics in Northern Eurasia over a five-year interval provide a more objective pattern of expected climate change and can be useful to determine optimal solutions for the development of agricultural sector in the changing climate and to minimize possible negative consequences.

Seasonal soil moisture and crop yield prediction with fifth-generation seasonal forecasting system (SEAS5) long-range meteorological forecasts in a land surface modelling approach

Abstract. Long-range weather forecasts provide predictions of atmospheric, ocean and land surface conditions that can potentially be used in land surface and hydrological models to predict the water and energy status of the land surface or in crop growth models to predict yield for water resources or agricultural planning. However, the coarse spatial and temporal resolutions of available forecast products have hindered their widespread use in such modelling applications, which usually require high-resolution input data. In this study, we applied sub-seasonal (up to 4 months) and seasonal (7 months) weather forecasts from the latest European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system (SEAS5) in a land surface modelling approach using the Community Land Model version 5.0 (CLM5). Simulations were conducted for 2017–2020 forced with sub-seasonal and seasonal weather forecasts over two different domains with contrasting climate and cropping conditions: the German state of North Rhine-Westphalia (DE-NRW) and the Australian state of Victoria (AUS-VIC). We found that, after pre-processing of the forecast products (i.e. temporal downscaling of precipitation and incoming short-wave radiation), the simulations forced with seasonal and sub-seasonal forecasts were able to provide a model output that was very close to the reference simulation results forced by reanalysis data (the mean annual crop yield showed maximum differences of 0.28 and 0.36 t ha−1 for AUS-VIC and DE-NRW respectively). Differences between seasonal and sub-seasonal experiments were insignificant. The forecast experiments were able to satisfactorily capture recorded inter-annual variations of crop yield. In addition, they also reproduced the generally higher inter-annual differences in crop yield across the AUS-VIC domain (approximately 50 % inter-annual differences in recorded yields and up to 17 % inter-annual differences in simulated yields) compared to the DE-NRW domain (approximately 15 % inter-annual differences in recorded yields and up to 5 % in simulated yields). The high- and low-yield seasons (2020 and 2018) among the 4 simulated years were clearly reproduced in the forecast simulation results. Furthermore, sub-seasonal and seasonal simulations reflected the early harvest in the drought year of 2018 in the DE-NRW domain. However, simulated inter-annual yield variability was lower in all simulations compared to the official statistics. While general soil moisture trends, such as the European drought in 2018, were captured by the seasonal experiments, we found systematic overestimations and underestimations in both the forecast and reference simulations compared to the Soil Moisture Active Passive Level-3 soil moisture product (SMAP L3) and the Soil Moisture Climate Change Initiative Combined dataset from the European Space Agency (ESA CCI). These observed biases of soil moisture and the low inter-annual differences in simulated crop yield indicate the need to improve the representation of these variables in CLM5 to increase the model sensitivity to drought stress and other crop stressors.

Review article: Scaling, dynamical regimes, and stratification. How long does weather last? How big is a cloud?

Abstract. Until the 1980s, scaling notions were restricted to self-similar homogeneous special cases. I review developments over the last decades, especially in multifractals and generalized scale invariance (GSI). The former is necessary for characterizing and modelling strongly intermittent scaling processes, while the GSI formalism extends scaling to strongly anisotropic (especially stratified) systems. Both of these generalizations are necessary for atmospheric applications. The theory and some of the now burgeoning empirical evidence in its favour are reviewed. Scaling can now be understood as a very general symmetry principle. It is needed to clarify and quantify the notion of dynamical regimes. In addition to the weather and climate, there is an intermediate “macroweather regime”, and at timescales beyond the climate regime (up to Milankovitch scales), there is a macroclimate and megaclimate regime. By objectively distinguishing weather from macroweather, it answers the question “how long does weather last?”. Dealing with anisotropic scaling systems – notably atmospheric stratification – requires new (non-Euclidean) definitions of the notion of scale itself. These are needed to answer the question “how big is a cloud?”. In anisotropic scaling systems, morphologies of structures change systematically with scale even though there is no characteristic size. GSI shows that it is unwarranted to infer dynamical processes or mechanisms from morphology. Two “sticking points” preventing more widespread acceptance of the scaling paradigm are also discussed. The first is an often implicit phenomenological “scalebounded” thinking that postulates a priori the existence of new mechanisms, processes every factor of 2 or so in scale. The second obstacle is the reluctance to abandon isotropic theories of turbulence and accept that the atmosphere's scaling is anisotropic. Indeed, there currently appears to be no empirical evidence that the turbulence in any atmospheric field is isotropic. Most atmospheric scientists rely on general circulation models, and these are scaling – they inherited the symmetry from the (scaling) primitive equations upon which they are built. Therefore, the real consequence of ignoring wide-range scaling is that it blinds us to alternative scaling approaches to macroweather and climate – especially to new models for long-range forecasts and to new scaling approaches to climate projections. Such stochastic alternatives are increasingly needed, notably to reduce uncertainties in climate projections to the year 2100.

Dynamics of codfish stocks in the North Atlantic region

Purpose: to investigate the features of the long-term dynamics of codfish stocks in the North Atlantic region as a basis for predicting their state and sustainable fishing (exploitation). Materials: data on spawning stock biomass, abundance of recruitment at age 0 to 3 years, survival indices at early ontogenesis stage for 11 Atlantic cod stocks, 6 Atlantic haddock stocks, and 4 saithe stocks for the period 1946–2020 Methods: a comparative analysis of the above time series based on their statistical processing. Novelty: For the first time, a 50–60-year cycle which characterizes the long-term variations of the Atlantic Multidecadal Oscillation index, were revealed in the long-term fluctuations of the Northeast Arctic cod recruitment. This assumes a relationship in the long-term variations of two parameters under consideration. Results: The significant interannual variability of all characteristics was identified for each of the 21 commercial fish stocks under consideration. Statistically significant positive correlations were found between changes in recruitment abundance of Atlantic cod in the Northwest Atlantic and Atlantic cod, Atlantic haddock, and saithe in the areas of Greenland, Iceland, North and Baltic Seas. Changes in the recruitment abundance of Labrador cod and Northeast Arctic cod in the Barents Sea are opposite. Practical significance: the results of this work can be used to improve the methods of the medium-range and long-range forecasts of codfish catches in the North Atlantic region.

Large-scale seasonal forecasts of river discharge by coupling local and global datasets with a stacked neural network: Case for the Loire River system

Accurate prediction of river discharge is critical for a wide range of sectors, from human activities to environmental hazard management, especially in the face of increasing demand for water resources and climate change. To address this need, a multivariate model that incorporates both local and global data sources, including river and piezometer gauges, sea level, and climate parameters. By employing phase shift analysis, the model optimizes correlations between the target discharge and 12 parameters related to hydrologic and climatic systems, all sampled daily. In addition, a stacked LSTM - a more complex neural network architecture - is used to improve information extraction ability.Exploring river dynamics in the Loire-Bretagne basin and its surroundings, the investigation delves into predictions in daily time steps for one, three, and six months ahead. The resulting forecast features high accuracy and efficiency in predicting river discharge fluctuations, showcasing superior performance in forecasting drought periods over flood peaks. A detailed examination on data used highlights the significance of both local and global datasets in predicting river discharge, where the former dictates short-term predictions, while the latter drives long-range forecasts. Seasonally extended forecasting confirms a strong connection between the forecast leading time and the shift in data correlation, with lower correlation at a lag of 3 months due to seasonal changes affecting forecast quality, compensated by a higher correlation at a longer lag of 6 months. Such mutual effect in this multi-time-step forecasting improves the predictive quality of a six-month horizon, thus encourages progress in long-term prediction to a seasonal scale. The research establishes a practical foundation for effectively utilizing big data to leverage long-term forecasting of environmental dynamics.

Numerical modeling as a support tool for groundwater permits in the state of Bahia, Brazil

The western region of Bahia State is an important agricultural frontier in Brazil, generating a large increase in requests for groundwater usage in the last decades, mainly through large capacity water wells tapping the Urucuia Aquifer System (UAS). This study aims to develop a groundwater numerical model using the Visual MODFLOW software and the additional packages MODPATH and ZONEBUDGET to evaluate the effects of intensive groundwater pumping in eight possible scenarios of two areas (Central and Eastern) in the Alto Grande - São Desidério rivers sub-basin. Drawdown ranging from 4 to 20 m was observed in the different areas and scenarios. The analysis of the local flow path lines showed that the wells have an area of influence that captures part of the groundwater flow. The water balance indicated that the total volume pumped in the sub-basin did not reach 10% of the recharge in any scenario, considering the entire model domain. The high concentration of irrigation pumping wells in restricted areas resulted in a significant impact on the aquifer, reaching up to 84.6% of the local recharge, a value that is far above what is considered sustainable for the region. Results show that the eastern area is more susceptible to drawdown and changes in flow path lines by pumping. The applied method has proven to be useful to predict the effects of water abstraction on the aquifer before the implementation of projects, improving water resources management. Long-range forecasts and taking into account possible climate changes are also some of the advantages of the method, though it depends on reliable field hydrological and climate data.

GEFSv12 High- and Low-Skill Day-10 Tornado Forecasts

Abstract On average, modern numerical weather prediction forecasts for daily tornado frequency exhibit no skill beyond day 10. However, in this extended-range lead window, there are particular model cycles that have exceptionally high forecast skill for tornadoes because of their ability to correctly simulate the future synoptic pattern. Here, model initial conditions that produced a more skillful forecast for tornadoes over the United States were exploited while also highlighting potential causes for low-skill cycles within the Global Ensemble Forecasting System, version 12 (GEFSv12). There were 88 high-skill and 91 low-skill forecasts in which the verifying day-10 synoptic pattern for tornado conditions revealed a western U.S. thermal trough and an eastern U.S. thermal ridge, a favorable configuration for tornadic storm occurrence. Initial conditions for high skill forecasts tended to exhibit warmer sea surface temperatures throughout the tropical Pacific Ocean and Gulf of Mexico, an active Madden–Julian oscillation, and significant modulation of Earth-relative atmospheric angular momentum. Low-skill forecasts were often initialized during La Niña and negative Pacific decadal oscillation conditions. Significant atmospheric blocking over eastern Russia—in which the GEFSv12 overforecast the duration and characteristics of the downstream flow—was a common physical process associated with low-skill forecasts. This work helps to increase our understanding of the common causes of high- or low-skill extended-range tornado forecasts and could serve as a helpful tool for operational forecasters. Significance Statement This research provides a framework for the anticipation of a more (or less) skillful 10-day tornado forecast in an operational numerical weather prediction system. High-skill forecasts were associated with substantial tropical convection and warm sea surface temperature throughout the Pacific Ocean and Gulf of Mexico, whereas the underlying cause of low-skill forecasts were typically associated with a blocking anticyclone over eastern Russia. These findings are important because they permit increased or decreased confidence in a long-range forecast of tornado occurrence based on a dynamical prediction system.

A co-generation success story: Improving drinking water management through hydro-climate services

Hydro-climate services have been emerging to address the needs in the drinking water supply sector. Here, we demonstrate how co-generation can lead to the uptake of a service for supporting decision-making and ensuring a sustainable use of water resources. Insights are based on the storyline of the SMHI Aqua service co-generation. A framework consisting on four pillars (co-design, co-development, co-delivery and co-evaluation) was implemented based on the active involvement and close collaboration of service purveyors, data providers and users. The service consists of three main components: data assimilation, forecast production and a decision support system. Two hydrological models were locally customized to simulate freshwater availability in surface and groundwater reservoirs, generating further short- and long-range forecasts updated twice a day. Real-time hydro-meteorological measurements are used to update the model and initialize the forecasts, while different water extraction strategies allow simulating freshwater availability scenarios for the upcoming months. The hydro-meteorological measured and modelled data are accessible in a web-based platform and presented in a user-intuitive and informative way. We conclude that knowledge co-evolution is crucial for ensuring a hydro-climate service uptake. For water managers to take informed decisions, the hydro-meteorological information (including propagated uncertainty) generated by the service needs to be efficiently communicated. We finally note that the benefits resulting from the adoption of the hydro-climate service go beyond water-related users reaching to the general public by guaranteeing access to freshwater and by improving awareness and preparedness to extreme conditions.