National Forecasts Research Articles

Energy storage scenarios and design of a new Italian innovation infrastructure for energy transition

Abstract The revised European Renewable Energy Directive sets the EU’s binding renewable energy target for 2030 to a minimum of 42.5% from renewable sources in the overall energy mix. To achieve the fit-for-55 objectives, Italy intends to increase solar and wind power plants installations by 2030, with expected power capacities of 79.3 GW and 28.1 GW respectively. Renewable energy penetration is unlocked by an adequate storage capacity availability which should increase by up to 95 GWh (2030) in the Country. The present paper aims at providing an outlook on the national energy scenarios and forecasts on energy storage systems penetration, sharing the design of an innovation facility to support the industry in advancing the technological maturity of energy storage systems with specific reference to hydrogen and e-fuel production as well as electrochemical storage.

Multi-model ensembles for regional and national wheat yield forecasts in Argentina

Abstract While multi-model ensembles (MMEs) of seasonal climate models (SCMs) have been used for crop yield forecasting, there has not been a systematic attempt to select the most skillful SCMs to optimize the performance of a MME and improve in-season yield forecasts. Here, we propose a statistical model to forecast regional and national wheat yield variability from 1993–2016 over the main wheat production area in Argentina. Monthly mean temperature and precipitation from the four months (August–November) before harvest were used as features. The model was validated for end-of-season estimation in December using reanalysis data (ERA) from the European Centre for Medium-Range Weather Forecasts (ECMWF) as well as for in-season forecasts from June to November using a MME of three SCMs from 10 SCMs analyzed. A benchmark model for end-of-season yield estimation using ERA data achieved a R 2 of 0.33, a root-mean-square error (RMSE) of 9.8% and a receiver operating characteristic (ROC) score of 0.8 on national level. On regional level, the model demonstrated the best estimation accuracy in the northern sub-humid Pampas with a R 2 of 0.5, a RMSE of 12.6% and a ROC score of 0.9. Across all months of initialization, SCMs from the National Centers for Environmental Prediction, the National Center for Atmospheric Research and the Geophysical Fluid Dynamics Laboratory had the highest mean absolute error of forecasted features compared to ERA data. The most skillful in-season wheat yield forecasts were possible with a 3-member-MME, combining data from the SCMs of the ECMWF, the National Aeronautics and Space Administration and the French national meteorological service. This MME forecasted wheat yield on national level at the beginning of November, one month before harvest, with a R 2 of 0.32, a RMSE of 9.9% and a ROC score of 0.7. This approach can be applied to other crops and regions.

Predicting subnational incidence of COVID-19 cases and deaths in EU countries

BackgroundRecurring COVID-19 waves highlight the need for tools able to quantify transmission risk, and identify geographical areas at risk of outbreaks. Local outbreak risk depends on complex immunity patterns resulting from previous infections, vaccination, waning and immune escape, alongside other factors (population density, social contact patterns). Immunity patterns are spatially and demographically heterogeneous, and are challenging to capture in country-level forecast models.MethodsWe used a spatiotemporal regression model to forecast subnational case and death counts and applied it to three EU countries as test cases: France, Czechia, and Italy. Cases in local regions arise from importations or local transmission. Our model produces age-stratified forecasts given age-stratified data, and links reported case counts to routinely collected covariates (e.g. test number, vaccine coverage). We assessed the predictive performance of our model up to four weeks ahead using proper scoring rules and compared it to the European COVID-19 Forecast Hub ensemble model. Using simulations, we evaluated the impact of variations in transmission on the forecasts. We developed an open-source RShiny App to visualise the forecasts and scenarios.ResultsAt a national level, the median relative difference between our median weekly case forecasts and the data up to four weeks ahead was 25% (IQR: 12–50%) over the prediction period. The accuracy decreased as the forecast horizon increased (on average 24% increase in the median ranked probability score per added week), while the accuracy of death forecasts was more stable. Beyond two weeks, the model generated a narrow range of likely transmission dynamics. The median national case forecasts showed similar accuracy to forecasts from the European COVID-19 Forecast Hub ensemble model, but the prediction interval was narrower in our model. Generating forecasts under alternative transmission scenarios was therefore key to capturing the range of possible short-term transmission dynamics.DiscussionOur model captures changes in local COVID-19 outbreak dynamics, and enables quantification of short-term transmission risk at a subnational level. The outputs of the model improve our ability to identify areas where outbreaks are most likely, and are available to a wide range of public health professionals through the Shiny App we developed.

Near-term forecasting of Covid-19 cases and hospitalisations in Aotearoa New Zealand.

Near-term forecasting of infectious disease incidence and consequent demand for acute healthcare services can support capacity planning and public health responses. Despite well-developed scenario modelling to support the Covid-19 response, Aotearoa New Zealand lacks advanced infectious disease forecasting capacity. We develop a model using Aotearoa New Zealand's unique Covid-19 data streams to predict reported Covid-19 cases, hospital admissions and hospital occupancy. The method combines a semi-mechanistic model for disease transmission to predict cases with Gaussian process regression models to predict the fraction of reported cases that will require hospital treatment. We evaluate forecast performance against out-of-sample data over the period from 2 October 2022 to 23 July 2023. Our results show that forecast performance is reasonably good over a 1-3 week time horizon, although generally deteriorates as the time horizon is lengthened. The model has been operationalised to provide weekly national and regional forecasts in real-time. This study is an important step towards development of more sophisticated situational awareness and infectious disease forecasting tools in Aotearoa New Zealand.

INSTITUTIONAL SECURITY IN RELATIONS OF OWNERSHIP OF NATURAL RESOURCES: STATE ENVIRONMENTAL AND ECONOMIC POLICY AND DECENTRALIZATION

The article defines the natural resource sphere of the national economy as the largest territorial and sectoral complex, which includes land, water, forest, mineral, natural and recreational and other types of resources. The lack of implementation of the allocation of a sufficient amount of funds aimed at the rationalization of nature use and the modernization of environmental infrastructure facilities, as well as the uneven distribution of capital investments for the protection and rational use of natural resources in the region for the period 2016-2022, was established. It is substantiated that in the conditions of decentralization of power, the general basis of institutional support for property relations over natural resources should be corporatization, development of rent relations, improvement of fiscal mechanisms in resource use, market-economic evaluation and capitalization of all-natural resources without exception at the local, regional and national levels. The theoretical foundations of the problems of institutional support in property relations in the conditions of decentralization of power have been deepened by determining the need to create an institutional system of property. This system provides for the development of lease relationships, approaches to investment projects, improvement of natural resource taxation mechanisms by improving the system of local taxes on the use of resources, and assessment of the value of natural resources at the micro, meso, and macro levels. The definition of ecological and economic problems of property relations is formulated, which, unlike the existing ones, are focused on the problems of attracting capital investments and current costs of the state for the protection of the natural environment in conditions of decentralization, as well as the fundamental basis for the formation of an institutional system of property in nature management, taking into account foreign experience. The results of the research can be used in the development of regional and national programs and forecasts on issues of nature management and environmental protection.

Seasonal climate models for national wheat yield forecasts in Brazil

National wheat yield depends on climate conditions and usually remains unknown until harvest. In-season knowledge can be provided by wheat yield forecast systems, supporting the decision-making of farmers, food traders, or policymakers. In this study, we improved a previously developed statistical wheat yield model to forecast trend-corrected wheat yield in Brazil with monthly temperature and precipitation data from seasonal climate models (SCM) from the last three months before harvest. We chose SCM from the European Center for Medium-Range Weather Forecasts (ECMWF), the National Centers for Environmental Prediction (NCEP), and the UK-based Met-Office (UKMO). A multi-model ensembles (MME) approach from the three individual models as well as a climatology (CLIMATE) approach were also tested. Wheat yield forecasts were issued at the beginning of each month from planting in April to harvest in November. Each month, features from future months are forecasted by SCM, and past features are supplemented with observations from weather stations. Our approach shows a 12% RMSE in forecasting yield early in the season, from April to June. Forecasts start to improve from July onwards, with shorter lead times and including observed features from September onwards. At the beginning of October, about two months before harvest is completed, wheat yield can be forecasted with 7.6%, 7.9%, 7.9%, 9.1%, and 9.3% RMSE using climate data from UKMO, ECMWF, MME, NCEP, and CLIMATE respectively. Seasonal climate models can be useful tools to forecast national wheat yield, even shortly before harvest to prepare for possible food shortages. Our approach could be applied to other staple crops and regions.

Joint COVID-19 and influenza-like illness forecasts in the United States using internet search information

BackgroundAs the prolonged COVID-19 pandemic continues, severe seasonal Influenza (flu) may happen alongside COVID-19. This could cause a “twindemic”, in which there are additional burdens on health care resources and public safety compared to those occurring in the presence of a single infection. Amidst the raising trend of co-infections of the two diseases, forecasting both Influenza-like Illness (ILI) outbreaks and COVID-19 waves in a reliable and timely manner becomes more urgent than ever. Accurate and real-time joint prediction of the twindemic aids public health organizations and policymakers in adequate preparation and decision making. However, in the current pandemic, existing ILI and COVID-19 forecasting models face shortcomings under complex inter-disease dynamics, particularly due to the similarities in symptoms and healthcare-seeking patterns of the two diseases.MethodsInspired by the interconnection between ILI and COVID-19 activities, we combine related internet search and bi-disease time series information for the U.S. national level and state level forecasts. Our proposed ARGOX-Joint-Ensemble adopts a new ensemble framework that integrates ILI and COVID-19 disease forecasting models to pool the information between the two diseases and provide joint multi-resolution and multi-target predictions. Through a winner-takes-all ensemble fashion, our framework is able to adaptively select the most predictive COVID-19 or ILI signals.ResultsIn the retrospective evaluation, our model steadily outperforms alternative benchmark methods, and remains competitive with other publicly available models in both point estimates and probabilistic predictions (including intervals).ConclusionsThe success of our approach illustrates that pooling information between the ILI and COVID-19 leads to improved forecasting models than individual models for either of the disease.

Scalable Crop Yield Prediction with Sentinel-2 Time Series and Temporal Convolutional Network

One of the precepts of food security is the proper functioning of the global food markets. This calls for open and timely intelligence on crop production on an agroclimatically meaningful territorial scale. We propose an operationally suitable method for large-scale in-season crop yield estimations from a satellite image time series (SITS) for statistical production. As an object-based method, it is spatially scalable from parcel to regional scale, making it useful for prediction tasks in which the reference data are available only at a coarser level, such as counties. We show that deep learning-based temporal convolutional network (TCN) outperforms the classical machine learning method random forests and produces more accurate results overall than published national crop forecasts. Our novel contribution is to show that mean-aggregated regional predictions with histogram-based features calculated from farm-level observations perform better than other tested approaches. In addition, TCN is robust to the presence of cloudy pixels, suggesting TCN can learn cloud masking from the data. The temporal compositing of information do not improve prediction performance. This indicates that with end-to-end learning less preprocessing in SITS tasks seems viable.

Toward Experimental Heat–Health Early Warning in Africa

Abstract Heat is one of the most serious hazards in the world as it affects human health and is extremely dangerous to vulnerable populations such as the elderly, people with preexisting cardiovascular or respiratory conditions, and even healthy people with prolonged sunlight exposure during heat waves. As the globe has warmed over the past several decades, extreme heat has become more frequent and intense than ever before, and Africa, especially the Sahel in West Africa, is one of the regions of the world where heat is a major public health concern exacerbated by livelihood activities during the heat season. Yet, there is a major gap in monitoring and forecasting heat waves in Africa. This paper describes NOAA’s role in enabling heat–health early warning in Africa, working with meteorological services and health professionals. Emphasis is on real-time heat wave forecasting at week 2, including the postprocessing of the NCEP model outputs, and providing the information to the meteorological services in Africa to serve as guidance in national heat wave forecasts. In addition, the paper describes the end-to-end process of heat hazard outlooks and translating the forecasts into early action and early planning to reduce heat risk to human health. Furthermore, the paper addresses the very important aspect of capacity development tailored at enhancing forecasters’ skills to prepare and issue heat wave forecasts and training of a cadre of health professionals to work with meteorologists to coproduce heat–health bulletins and to issue heat–health early warnings.

COVID-19 forecasts using Internet search information in the United States

As the COVID-19 ravaging through the globe, accurate forecasts of the disease spread are crucial for situational awareness, resource allocation, and public health decision-making. Alternative to the traditional disease surveillance data collected by the United States (US) Centers for Disease Control and Prevention (CDC), big data from Internet such as online search volumes also contain valuable information for tracking infectious disease dynamics such as influenza epidemic. In this study, we develop a statistical model using Internet search volume of relevant queries to track and predict COVID-19 pandemic in the United States. Inspired by the strong association between COVID-19 death trend and symptom-related search queries such as “loss of taste”, we combine search volume information with COVID-19 time series information for US national level forecasts, while leveraging the cross-state cross-resolution spatial temporal framework, pooling information from search volume and COVID-19 reports across regions for state level predictions. Lastly, we aggregate the state-level frameworks in an ensemble fashion to produce the final state-level 4-week forecasts. Our method outperforms the baseline time-series model, while performing reasonably against other publicly available benchmark models for both national and state level forecast.

Measuring and Forecasting the Rise of China: Reality over Image

ABSTRACT The rise of Chinese capabilities relative to those of the United States has received widespread attention. Some argue that a transition in relative capabilities has already occurred, others that it is unlikely within this century. This article presents a new multidimensional measure of relative national capabilities and forecasts using the International Futures model across 29 alternative scenarios. This article finds that Chinese capabilities surpass the United States in 26 scenarios before 2060, with the most frequent period of power transition being the early 2040s. This analysis offers an opportunity for leaders to reconcile national images with reality, potentially reducing the risk of conflict associated with great power transition.

Data fusion using climatology and seasonal climate forecasts improves estimates of Australian national wheat yields

National and regional yield forecasts can provide important insights into agribusiness beyond the farm gate. The incorporation of dynamical climate models into these forecasting systems strengthen their predictive performance in many cases but may contribute inherent biases to the final yield estimates. Downscaling the native climate model output so that is suitable for crop simulation modelling can also present challenges in representing realistic conditions for plant growth from a climate model. This study evaluated the performance of an operational national wheat yield forecast system for the Australian wheatbelt using climatology and seasonal climate model-based input data, and introduces an alternative approach using a data fusion method. The crop forecasting system uses the APSIM wheat model to estimate water-limited potential yield. The climatology-based forecast tended to over predict national yield (high yield bias; 1.5 to 7% across forecast months), while the model-based method (using ACCESS-S1 dynamical model) tended to under predict yield (low yield bias; -5.9 to -0.5% across forecast months) and had a lower spread than climatology (10 to 50% lower across forecast months). The model-based forecast had skill in terms of accuracy and reliability during the second half of the season. The newly developed data fusion method used a weighting method calibrated for separate forecast locations (stations) to remove bias in the mean forecast yield and reduce ensemble spread. This resulted in improvements in the Australia-wide yield forecasts across all forecast start dates. This study provides a demonstration of how a data-driven approach can be applied to a crop forecast to improve accuracy and resolution of crop yield forecasts without the need for more computationally intensive downscaling approaches.

Near-term, national solar capacity factor forecasts aided by trend attributes and artificial intelligence

Near-term, national solar capacity factor forecasts aided by trend attributes and artificial intelligence

Machine learning for regional crop yield forecasting in Europe

Crop yield forecasting at national level relies on predictors aggregated from smaller spatial units to larger ones according to harvested crop areas. Such crop areas come from land cover maps or reported statistics, both of which can have errors and uncertainties. Sub-national or regional crop yield forecasting minimizes the propagation of these errors to some extent. In addition, regional forecasts provide added value and insights to stakeholders on regional differences within a country, which would otherwise compensate each other at national level. We propose a crop yield forecasting approach for multiple spatial levels based on regional crop yield forecasts from machine learning. Machine learning, with its data-driven approach, can leverage larger data sizes and capture nonlinear relationships between predictors and yield at regional level. We designed a generic machine learning workflow to demonstrate the benefits of regional crop yield forecasting in Europe. To evaluate the quality and usefulness of regional forecasts, we predicted crop yields for 35 case studies, including nine countries that are major producers of six crops (soft wheat, spring barley, sunflower, grain maize, sugar beets and potatoes). Machine learning models at regional level had lower normalized root mean squared errors (NRMSE) and uncertainty than a linear trend model, with Wilcoxon p-values of 3e-7 and 2e-7 for 60 days before harvest and end of season respectively. Similarly, regional machine learning forecasts aggregated to national level had lower NRMSEs than forecasts from an operational system in 18 out of 35 cases 60 days before harvest, with a Wilcoxon p-value of 0.95 indicating similar performance. Our models have room for improvement, especially during extreme years. Nevertheless, regional crop yield forecasts from machine learning and aggregated national forecasts provide a consistent forecasting method across spatial levels and insights from regional differences to support important policy decisions. • Crop yield forecasts for multiple spatial levels using machine learning. • Evaluated machine learning on 35 case studies covering nine countries and six crops. • Regional forecasts had lower errors and uncertainty than trend forecasts. • Forecasts captured spatial patterns quite well for average but not extreme harvests. • Aggregated national forecasts were comparable to the European MARS System forecasts.

Evaluation of the national innovative potential of Ukraine

The essence of index methods of assessing the innovative national potential is investigated in this paper. The objective of the paper is to characterize the index methods of assessing the national innovation potential. The following scientific tasks are solved: determination of the main methods of assessment based on the use of international assessment indices, characteristics of the city of Ukraine in the world in terms of innovation potential; development of directions of use of the received estimations of innovative potential for its further improvement. The main research method is the index method. As a result of using the global innovation index, it is determined that during the period 2014–2021, Ukraine has improved its innovation performance. It is established that Ukraine has the best results in the field of knowledge and technology, and the worst results are characterized by indicators of innovation infrastructure development. Assessment of Ukraine's innovation potential according to the Bloomberg Innovation Index and the Innovation Union Scoreboard (IUS) shows that the country lags far behind European countries. The EU Innovation Scoreboard places Ukraine in the emerging Innovator group. Ukraine's main strengths include its advantageous geographical location, market capacity, deep and comprehensive free trade area between Ukraine and the European Union, and human potential with relatively high level of development. The value of the constituent indices should determine the specific priorities and objectives of Ukraine's innovation development, make adjustments to innovation strategies and programs. Public administration bodies should carry out constant assessment, control over the components of innovative development, development of financing measures and state support for priority areas of innovative development, taking into account international indices. The state innovation policy should take into account international assessments, the dynamics of changes in national innovation components, forecasts and trends in global innovation development.

The uncertainties involved in measuring national solar photovoltaic electricity generation

The measurement of near real-time national solar PV electricity output is an increasingly important component for managing electricity systems. PV systems embedded in the distribution network are problematic for system operators since their output is not directly visible through existing transmission system metering. Typically, national solar PV output is measured indirectly by upscaling the output from a sample of reference PV systems using an estimate of the national capacity. National PV output is then used, along with similar measurements for other embedded technologies such as wind, to train and validate electricity forecasts which ensure efficient electricity market operation. For the first time and using Great Britain as a case study, we investigate the accuracy of this general approach by characterising different sources of uncertainty in national PV output measurements. We find that the capacity error, at ±5%, dominates the yield calculation error, at < ±1% and leads to an overall error in GB solar PV output estimates of ±5.1%. We conclude that solar PV measurements, and consequently national electricity demand forecasts, are currently limited by the state of national PV capacity registers. To make PV output measurements more accurate nations must develop a coherent and comprehensive approach to PV system registration, and output measurement methods must move away from using static solar PV capacity registers towards estimating an operational grid-connected solar PV capacity. This could involve moving away from capacity registers altogether and instead estimating capacity from other sources, for example, from network power flows, satellite imagery, or more likely, a combination of complementary datasets.

Projections for obesity, smoking and hypertension based on multiple imputation.

Information on the future development of prevalences of risk factors and health indicators is needed to prepare for the forthcoming burden of disease in the population and to allocate resources properly for prevention. We aim to present how multiple imputation can be used flexibly to project future prevalences. The proposed approach uses data on repeated cross-sectional surveys from different years. We create future samples with age and sex distributions corresponding to the official national population forecasts. Then, the risk factors are simulated using multiple imputation by chained equations. Finally, the imputations are pooled to obtain the prevalences of interest. Covariates, such as sociodemographic variables as well as their possible interactions and non-linear terms, can be included in the modelling. The future development of these covariates is also projected simultaneously. We apply the procedure to data from five Finnish health examination surveys conducted between 1997 and 2017, and project the prevalences of obesity, smoking and hypertension to 2020 and 2025. The prevalence of obesity is projected to increase to 24% for both men and women in 2025. The prevalences of hypertension and smoking are expected to continue decreasing, and the differences between men and women are projected to remain so that men will have higher prevalences. Simulation of future observations by multiple imputation can be used as a flexible yet relatively easy-to-use projection method.

Pattern‐based conditioning enhances sub‐seasonal prediction skill of European national energy variables

AbstractSub‐seasonal forecasts are becoming more widely used in the energy sector to inform high‐impact, weather‐dependent decisions. Using pattern‐based methods (such as weather regimes) is also becoming commonplace, although until now an assessment of how pattern‐based methods perform compared with gridded model output has not been completed. We compare four methods to predict weekly‐mean anomalies of electricity demand and demand‐net‐wind across 28 European countries. At short lead times (days 0–10) grid‐point forecasts have higher skill than pattern‐based methods across multiple metrics. However, at extended lead times (day 12+) pattern‐based methods can show greater skill than grid‐point forecasts. All methods have relatively low skill at weekly‐mean national impact forecasts beyond day 12, particularly for probabilistic skill metrics. We therefore develop a method of pattern‐based conditioning, which is able to provide windows of opportunity for prediction at extended lead times: when at least 50% of the ensemble members of a forecast agree on a specific pattern, skill increases significantly. The conditioning is valuable for users interested in particular thresholds for decision‐making, as it combines the dynamical robustness in the large‐scale flow conditions from the pattern‐based methods with local information present in the grid‐point forecasts.

Input-Output Table Regionalization and Multiregional Input-Output Model Development Algorithm

The main problems of constructing multiregional input-output (IO) models used for the development of spatial and sectoral long-term national economy forecasts are caused by the absence of proper statistics. Therefore, it is necessary to make up the input-output tables (IOTs) based on a limited set of direct and indirect indicators that measure spatial patterns of production, consumption, capital formation, etc., sufficiently accurate for filling up multiregional IO models. The paper discusses Russian IOT spatial mapping problems and proposes an approach to partial automation of the procedures necessary for regional IOT construction (by federal districts). The regionalization was carried out using a static IO model with the bounding sum control. As a result, a set of consistent regional tables for 2015 was developed, with the eight IOT sum equalling Russia’s IOT. Based on the estimated regional IOTs, a static multiregional input-output model (OMIOM) across federal districts for 2015 was made up. The model allows us to proceed to the follow-up phase, i. e., developing a semi-dynamic model for long-term national economic projection computations

FACTORS AFFECTING URBAN AGRICULTURE DEVELOPMENT IN THAI NGUYEN PROVINCE, VIET NAM

The process of urbanization is taking place fast and vigorously in large urban and peri-urban areas in Vietnam. According to national forecasts, the rate of urbanization nationwide will reach 39.3% by 2020 and 50-55% by 2035. Thai Nguyen is a province in the midland and mountainous region. The province is located at the northern gateway and bordered with Hanoi capital. In recent years, the agricultural land area of Thai Nguyen province has narrowed due to the urban-industrial development. Urban agriculture development is an inevitable direction, creating safe and high quality food products, protecting the ecological environment, and increasing people's income. Thai Nguyen is one of the provinces promoting sustainable urban agricultural development. Secondary and primary data sources are analyzed and synthesized by descriptive statistical methods. The article will analyze five groups of factors affecting urban agricultural development in Thai Nguyen province in the period 2015-2018, including Socio-economic; Natural conditions and infrastructure; Policy factors; Planning factors; Links and integration.