Regional Forecasts Research Articles

Modeling the Geomagnetic Response to the September 2017 Space Weather Event Over Fennoscandia Using the Space Weather Modeling Framework: Studying the Impacts of Spatial Resolution

AbstractWe must be able to predict and mitigate against geomagnetically induced current (GIC) effects to minimize socio‐economic impacts. This study employs the space weather modeling framework (SWMF) to model the geomagnetic response over Fennoscandia to the September 7–8, 2017 event. Of key importance to this study is the effects of spatial resolution in terms of regional forecasts and improved GIC modeling results. Therefore, we ran the model at comparatively low, medium, and high spatial resolutions. The virtual magnetometers from each model run are compared with observations from the IMAGE magnetometer network across various latitudes and over regional‐scales. The virtual magnetometer data from the SWMF are coupled with a local ground conductivity model which is used to calculate the geoelectric field and estimate GICs in a Finnish natural gas pipeline. This investigation has lead to several important results in which higher resolution yielded: (1) more realistic amplitudes and timings of GICs, (2) higher amplitude geomagnetic disturbances across latitudes, and (3) increased regional variations in terms of differences between stations. Despite this, substorms remain a significant challenge to surface magnetic field prediction from global magnetohydrodynamic modeling. For example, in the presence of multiple large substorms, the associated large‐amplitude depressions were not captured, which caused the largest model‐data deviations. The results from this work are of key importance to both modelers and space weather operators. Particularly when the goal is to obtain improved regional forecasts of geomagnetic disturbances and/or more realistic estimates of the geoelectric field.

Rainfall forecasting in arid regions using an ensemble of artificial neural networks

Water rainfall prediction is one of the most difficult tasks in hydrology because rainfall events are extremely random. This research presents a comparative analysis of different models that predict rainfall in an arid region. The forecasting models comprise the feed-forward, general regression, recurrent, cascade, and Elman neural networks. The performance of the aforementioned models is assessed using three evaluation metrics, namely the correlation coefficient, coefficient of efficiency, and Willmott’s index of agreement. Furthermore, the statistical significance of the neural network models is evaluated using the Wilcoxon-Mann-Whitney test. Finally, the correspondence of the neural network model results compared to the observations is examined using the Taylor diagram. The findings reveal that the general neural network exhibits the best performance compared to other models using the tropical rainfall measuring mission dataset at Suez city in Egypt. The Egyptian water municipality is intended to benefit from the proposed model in monthly rainfall forecasting in this arid region. The precise modeling of rainfall is vital for managing water resources such as food production, water allocation, and drought management.

A hybrid framework for forecasting monthly reservoir inflow based on machine learning techniques with dynamic climate forecasts, satellite-based data, and climate phenomenon information

In this study, we developed and evaluated a hybrid framework for reservoir inflow forecast. This framework is unprecedented, which integrates new quasi-globally available observation-, satellite-, or model-based datasets using machine learing models to forecast inflow at the local scale. Under this framework, we compared random forests, gradient boosting machine, extreme learning machine, M5-cubist, elastic net, as well as their multi-model ensemble using Bayesian model averaging (BMA), and tested contributions from different input datasets, including retrospective forecast (reforecast) from florecast oriented low ocean resolution (FLOR) dynamic climate model, satellite-based hydrologic products, and climate phenomenon information. The performance was evaluated using Kling–Gupta efficiency (KGE) and correlation coefficient (R) in two headwater reservoirs, Harris reservoir in the humid Alabama–Coosa–Tallapoosa river basin and the Navajo reservoir in the arid Upper Colorado River Basin (UCRB). The results showed that for the Harris reservoir, the BMA combining five models with antecedent inflow and satellite-based hydrologic information as model inputs provided the best performance (KGE = 0.66, R = 0.76). For the Navajo reservoir, the gradient boosting machine model with all variables combined as input showed the best performance (KGE = 0.76, R = 0.83). Satellite-based soil moisture and evaporation consistently showed significant contributions to the inflow forecast. Benefits from climate indices and FLOR reforecast varied by locations, with more benefits coming from climate indices than FLOR potential evaporation reforecast at the Navajo reservoir in UCRB. Given the global coverage of the model inputs, our approach can be potentially applicable to improve reservoir inflow forecasts in different regions of the world.

Global short-term forecasting of COVID-19 cases

The continuously growing number of COVID-19 cases pressures healthcare services worldwide. Accurate short-term forecasting is thus vital to support country-level policy making. The strategies adopted by countries to combat the pandemic vary, generating different uncertainty levels about the actual number of cases. Accounting for the hierarchical structure of the data and accommodating extra-variability is therefore fundamental. We introduce a new modelling framework to describe the pandemic’s course with great accuracy and provide short-term daily forecasts for every country in the world. We show that our model generates highly accurate forecasts up to seven days ahead and use estimated model components to cluster countries based on recent events. We introduce statistical novelty in terms of modelling the autoregressive parameter as a function of time, increasing predictive power and flexibility to adapt to each country. Our model can also be used to forecast the number of deaths, study the effects of covariates (such as lockdown policies), and generate forecasts for smaller regions within countries. Consequently, it has substantial implications for global planning and decision making. We present forecasts and make all results freely available to any country in the world through an online Shiny dashboard.

Hydrological Response of the Pampanga River Basin in the Philippines to Intense Tropical Cyclone Rainfall

AbstractThe Philippines is frequently affected by tropical cyclones (TCs), and understanding the flood response of the Pampanga River basin (PRB) from TC-induced rain is needed in effective disaster risk management. As large uncertainties remain in TC rain forecasting, we propose a simple checklist method for flood forecasting of the PRB that depends on the general TC track, season, and accumulated rainfall. To this end, flood events were selected based on the alert, alarm, and critical river height levels established by the Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA). Results show that all flood events in the PRB were induced by TCs. All intense TCs that directly traversed the PRB resulted in critical-level floods. These TCs also had the shortest flood onset of 7–27 h from alert to critical level. Flooding from distant landfalling TCs, on the other hand, are dependent on season. TCs traversing north (south) of the PRB induced flooding only during the southwest (northeast) monsoon season. These TCs can raise water levels from alert to critical in 11–48 h. Remote precipitation from non-landfalling TCs can also induce critical-level flooding but with a longer onset time of 59 h. These results indicate that a simple checklist method can serve as a useful tool for flood forecasting in regions with limited data and forecasting resources.

The Irreplaceable Utility of Sequential Data Assimilation for Numerical Weather Prediction System Development: Lessons Learned from an Experimental HWRF System

AbstractLimited-area numerical weather prediction models currently run operationally in the United States and follow a “partially cycled” schedule, where sequential data assimilation is periodically interrupted by replacing model states with solutions interpolated from a global model. While this strategy helps overcome several practical challenges associated with real-time regional forecasting, it is no substitute for a robust sequential data assimilation approach for research-to-operations purposes. Partial cycling can mask systematic errors in weather models, data assimilation systems, and data preprocessing techniques, since it introduces information from a different prediction system. It also adds extra heuristics to the model initialization steps outside the general Bayesian filtering framework from which data assimilation methods are derived. This study uses a research-oriented modeling system, which is self-contained in the operational Hurricane Weather Research and Forecasting (HWRF) Model package, to illustrate why next-generation modeling systems should prioritize sequential data assimilation at early stages of development. This framework permits the rigorous examination of all model system components—in a manner that has never been done for the HWRF Model. Examples presented in this manuscript show how sequential data assimilation capabilities can accelerate model advancements and increase academic involvement in operational forecasting systems at a time when the United States is developing a new hurricane forecasting system.

Criteria for petroleum potential and forecast of oil and gas prospective zones in the Achimov deposits of the Gydan Peninsula

The structure and seismogeological characteristics of the Achimov strata of the Gydan Peninsula are considered. Geological and geophysical criteria for the regional forecast of oil and gas potential, taking into account the clinoform structure of the Berriasian-Lower Aptian deposits, are proposed. Based on the interpretation of drilling data and 2D seismic exploration, predictive maps of the thickness of sand bodies were constructed, depocenters of accumulation of sandy material were mapped. Seismostratigraphic studies, which are based on a comprehensive analysis of the wave pattern of time seismic sections, dynamic and seismic facies analyzes, as well as paleotectonic and paleogeomorphological reconstructions, made it possible to identify the Salmanovskaya and Yuzhno-Gydanskaya oil and gas zones in the Achimov strata of the Gydan Peninsula.

The development and deployment of a model for hospital-level COVID-19 associated patient demand intervals from consistent estimators (DICE).

Hospitals commonly project demand for their services by combining their historical share of regional demand with forecasts of total regional demand. Hospital-specific forecasts of demand that provide prediction intervals, rather than point estimates, may facilitate better managerial decisions, especially when demand overage and underage are associated with high, asymmetric costs. Regional point forecasts of patient demand are commonly available, e.g., for the number of people requiring hospitalization due to an epidemic such as COVID-19. However, even in this common setting, no probabilistic, consistent, computationally tractable forecast is available for the fraction of patients in a region that a particular institution should expect. We introduce such a forecast, DICE (Demand Intervals from Consistent Estimators). We describe its development and deployment at an academic medical center in California during the ‘second wave’ of COVID-19 in the Unite States. We show that DICE is consistent under mild assumptions and suitable for use with perfect, biased and unbiased regional forecasts. We evaluate its performance on empirical data from a large academic medical center as well as on synthetic data.

МЕТОД РЕГИОНАЛЬНОГО ПРОГНОЗА НЕФТЕГАЗОНОСНОСТИ ТЕРРИТОРИЙ АЛГОРИТМАМИ МАШИННОГО ОБУЧЕНИЯ НА ПРИМЕРЕ ТЮМЕНСКОЙ СВИТЫ ЗАПАДНОЙ СИБИРИ

Актуальность исследования обусловлена сокращением фонда структурных ловушек и необходимостью расширения ресурсной базы углеводородов за счет повышения эффективности поиска и разведки месторождений в сложнопостроенных залежах нефти и газа. Цель: на примере прогноза нефтегазоносности территории исследования показать методику прогноза и набор применяемых технологических решений и алгоритмов. Объект: отложения средней юры (тюменская свита) Западной Сибири в пределах района (700×900 км), включающего в себя части Ямало-Ненецкого и Ханты-Мансийского административных округов и Томской области. Методы. С помощью алгоритмов машинного обучения и комплексирования технологического набора методов (геоинформатики, бассейнового моделирования, экспертных оценок) показаны следующие этапы реализации методики прогноза: 1) генерирование признакового пространства изучаемой территории на основе повышения пространственного разрешения структурных построений с применением алгоритмов генеративно-состязательной архитектуры нейронных сетей, где в качестве эталонных участков использованы результаты сейсморазведки 3D; 2) отбор признаков статистическим методом и методами машинного обучения; 3) создание подмножества моделей прогноза на основе градиентного бустинга над решающими деревьями; 4) объединение их в метамодель путем стекового обобщения логистической регрессией. Результаты. Формализован и апробирован подход к региональному прогнозу. Сделан прогноз вероятности нефтегазоносности тюменской свиты на изучаемой территории. На его основе и информации по открытым месторождениям оценена методом Монте-Карло ресурсная база УВ. Результаты представлены в виде суммарной таблицы геологических и извлекаемых ресурсов для вероятностей Р10, Р50, Р90 в сравнении с категориями запасов АВС1 и АВС1+С2 месторождений, числящихся на государственном балансе на территории исследования. В качестве примера приведены графические материалы результатов: работы алгоритма повышения пространственного разрешения; моделирования осадконакопления; моделирования миграции УВ; карта прогноза перспектив нефтегазоносности для северной части Надымского и Пуровского нефтегазовых районов.

Photoregime Affects Development, Reproduction, and Flight Performance of the Invasive Fall Armyworm (Lepidoptera: Noctuidae) in China.

The migratory fall armyworm, Spodoptera frugiperda (Smith), has become a worldwide agricultural pest. In this study, the effects of photoregime on the development, reproduction, and flight performance of fall armyworm were assessed based on two-sex life tables, ovarian and testis anatomy, and flight mill tests. The results indicated that photoregime had a significant effect on developmental duration of fall armyworm individuals, pupal survival and emergence, and adult fecundity. Among seven photoregimes tested, the 16:8 (L:D) h photoregime was the most suitable for fall armyworm fitness with the shortest pre-oviposition period and mean generation period (T), highest mating frequency and mating rate of female moths, largest intrinsic rate of natural increase (rm) and finite rate of increase (λ), and highest net reproduction rate (R0). Population growth for seven different photoregimes in decreasing order was 16:8 (L:D) h > 8:16 (L:D) h > 12:12 (L:D) h > 10:14 (L:D) h > 14:10 (L:D) h > 0:24 (L:D) h > 24:0 (L:D) h. The ovarian development level, mating frequency, and testis size did not significantly differ between long (16:8 (L:D) h) and short (10:14 (L:D) h) illumination. Photoregime had a significant effect on mass loss during flight of adults, but not on flight velocity, flight duration, and flight distance. These findings can be used to refine laboratory rearing protocols, accurately predict seasonal changes in population dynamics and should help improve regional forecasts and management of the fall armyworm.

Development and Evaluation of a Hydrometeorological Forecasting System Using the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) Model

In this study, an experimental hydrometeorological forecasting system was developed based on the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) model. The system downloads global real-time ocean, atmosphere, and wave forcing data, producing regional forecasts every day. A coastal area in South China, encompassing Hainan Island, Leizhou Peninsula, and surrounding sea areas, was chosen as the study domain. A series of 72-hour forecasting simulations were conducted in the area, lasting from July 27 to August 31, 2019. The forecasts throughout August were chosen for evaluation with station observations, along with two sets of reanalysis data, ERA5 and CLDAS. The evaluation results revealed that the COAWST model had high potential for routine forecasting operations. The 24 h forecasts, with a lead time of 24 hours, had high accuracy, while the 48 h and 72 h forecasts did not differ greatly in terms of performance. The distributions of bias between forecast and reanalysis data showed obvious differences between land and sea, with more forecasted precipitation and lower temperatures in land grids than in sea grids. In most cases, the forecasts were closer to ERA5 in terms of means and other statistical measures. The forecasts enlarged the land-sea differences of temperature when compared with ERA5 and strengthened summer monsoon with more moisture transported to land areas. Resulting from that, a forecasted bias of lower surface pressure, higher air humidity, stronger south wind, and so forth was also detected over the domain but at low values.

Stream Sediments of the Pestrinsk Silver-Bearing System (Northeastern Russia)

The composition, structure, and formation features of the exogenous anomalous geochemical fields (AGCFs) identified through stream sediments (SSs) are considered here within the Pestrinsk silver-bearing system and the Goltsovy silver-polymetallic deposit. The research was performed in the southern part of the Balygychan-Sugoy trough (northeastern Russia). The exogenous AGCFs of the main indicator elements of ores, formed in cryolithogenesis zone conditions, were studied. We used the results of multi-scale areal geochemical surveys of SSs. A survey of SSs at 1:200,000 scale was found to be effective at the stage of regional forecasting. Indeed, it is characterized by simplicity and the possibility of obtaining information operatively regarding the metallogeny of the area. It was found that at the local forecast stage, when prospecting for mineralization, the most effective was a survey of SSs at 1:50,000 scale. The AGCFs identified during this survey were distinguished by a richer component composition, higher contrast, and closer relationship with ores. During the lithochemical sampling of the watercourse heads, where alluvial sediments were found to be almost completely absent, a positive result was obtained by the bryolithochemical method, which is based on moss sampling together with a fine fraction of alluvium held by a moss cushion. The method enabled the sampling of watercourse heads and thus yielded information about the presence or absence of anomalous concentration fields of ore elements.

FACTOR ANALYSIS OF THE CENTRAL BLACK EARTH MACROREGION AREAS ECONOMIC DEVELOPMENT

Effective implementation of regional policy is impossible without assessing the current environment of the region’s functioning, which is formed under the influence of internal factors. Among the many factors that determine the socio-economic development of the region, we have identified: human potential, innovation potential, investment potential, digitalization of the economy, production potential, quality of life and infrastructure development. Each of the selected factors can be characterized by using a system of statistical indicators. In regional forecasts, internal factors act as control parameters, changing them it is possible to find an opportunity to change the course and direction of socio-economic processes in the region. This explains the necessity and relevance of the study. The purpose of this article is to form a set of indicators to assess the factor load on the socio-economic development of the region and determine its vector. The method of factor analysis on an indicative basis was used for achieving this goal. The importance of factorial analysis lies, first of all, in the fact that its results will make it possible to assess the share of influence of each factor on the “level of socio-economic development of the region” and to develop appropriate tools for managing growth factors. Taking into account the formed system of indicators, an analysis of the Central Black Earth macroregion areas economic development was carried out. The greatest factor load on the socio-economic development of the region is exerted by the innovation potential, production potential and human potential of the region. As a result of the study, a matrix of the regions distribution by the level of socio-economic development was built, reflecting the position of the region in dynamics. During the study period, the Voronezh region occupies a leading position, and the Tambov region is an outsider region. In addition, for each region, growth factors and restrictions on the development of the region were identified, which must be taken into account when building regional forecasts.

Post-processing methods for calibrating the wind speed forecasts in central regions of Chile

Post-processing methods for calibrating the wind speed forecasts in central regions of Chile

Northern Australia Climate Program: supporting adaptation in rangeland grazing systems through more targeted climate forecasts, improved drought information and an innovative extension program

The Northern Australia Climate Program (NACP) is a fully integrated research, development and extension (RDandE) program operating across extensive pastoral regions of northern Australia. The NACP aims to improve existing climate models and forecast tools, develop new products to meet user needs and build the capacity of rangeland producers to manage the challenges posed by droughts (or failed wet seasons) and climate variability. Climate information gaps identified through earlier surveys of graziers and communities in rural and remote Australia informed the design of the research component of the NACP, which aims to address the low and variable accuracy of seasonal climate forecasts in many regions, the need for proof of value of forecasts and relevance of existing forecast systems and technologies, and perceived lack of effective support from climate experts for the use of climate resources and technologies in agricultural decision making. The development and extension components of the program aim to improve climate literacy and the use of climate information. Building on the research program, they deliver a climate service that provides local extension and technical support, with a focus on building trust in climate information through locally sourced, industry connected NACP trained and supported extension advisers called Climate Mates. Two-way information flow between decision makers and researchers, facilitated by the Climate Mates, ensures that forecasts and decision- and discussion-support tools developed through the program are regionally relevant and targeted to the needs of end users. Monitoring and evaluation of the program indicates that this approach is contributing to positive outcomes in terms of awareness and knowledge of climate forecasting and products, and their adoption and use in decision making (i.e. practice change). In the longer term, the Climate Mates have potential for enduring impact beyond the program, leaving a knowledgeable and trusted climate resource across regional northern Australia.

Using Google Data to Estimate the Effects of Regional Mobility on Daily COVID-19 Cases: Evidence from Ontario Public Health Units

From September 2020 onwards, COVID-19 cases have rapidly increased across Canada. This study estimates the effects of Google population mobility indicators on daily COVID-19 cases to evaluate the impacts of public movements across different regions in Ontario. We focus on Ontario as Google mobility data are available for Public Health Units (PHUs) for that province. Results based on pooled data from May 1st – November 15th imply that higher mobility at retail stores is significantly correlated with an increase in daily COVID-19 cases. However, empirical estimates from individual PHU level time-series models reveal regional differences, as these findings are based primarily on the relationship between retail mobility and daily cases for the Public Health Units (PHUs) of Toronto and Peel. These results support the implementation of region-specific lockdowns. Further, different specifications generate daily COVID-19 forecasts for Peel and Toronto that are on average, approximately 6%-9% different from actual values. The models of this research should be of value to local health authorities who are in search of simple models that are not computationally intensive and are capable of generating reliable forecasts for specific regions.

Artificial Neural Networks in Inflation Forecasting at the Meso-Level

The aim of the article is to analyze inflation factors and their influence on the consumer price index in the regions. The article discusses the existing mathematical models for the forecasting of the regional inflation rate as an important national measure. Advantages, disadvantages and application fields of these models are presented. The appropriateness of recurrent neural network use for regional inflation forecasting is demonstrated. The article describes the process of neural network architecture creation, its training, inflation parameter forecasting.

A new propagation-based framework to enhance competency in regional drought monitoring

Drought is considered a regional phenomenon that usually covers large territorial extensions. It can occur anywhere in the world with severe impacts on water resources and socioeconomic activities. Therefore, it is compulsory to develop reliable tools and execute national plans based on the preeminent information and characterization of drought. There are numerous drought monitoring tools available in the literature to handle spatial and temporal behavior of the drought for regional forecasting and early warning mitigation policies. Standardized Drought Indices (SDI) are frequently used for drought characterization and comparing climatic characteristics of the regions. However, analyzing the spatiotemporal dynamics of the region requires more reliable methods and procedures for drought monitoring. In this perspective, the present study proposes a novel procedure for monitoring drought at a regional level: The Regional Propagation Spatially Weighted Accumulated Drought Index (RPSWADI). The first phase of the proposed procedure is intended to accumulate information from various meteorological stations placed in the homogenous region. In the second phase, accumulated information is used to propagate a new drought index. The proposed procedure is validated on six homogenous meteorological stations of the Northern areas of Pakistan. Furthermore, the commonly used standardized drought indices are used to observe the performance of the proposed procedure. The choice of the indices depends on the climatic conditions of the specific region and will be quantified accordingly. Results show that the RPSWADI can incorporate the spatiotemporal structure of various time series in various stations.

АКТУАЛЬНЫЕ ПРОБЛЕМЫ КОМПЛЕКСНЫХ ПРОСТРАНСТВЕННЫХ ИССЛЕДОВАНИЙ В РОССИИ С УЧЕТОМ МИРОВОГО ОПЫТА

The authors considered the background and conducted a brief retrospective analysis of complex spatial studies in the Russian Empire, in Soviet and modern Russia. The use of world theoretical developments in justifying the spatial location of economic objects in the Russian Federation is studied. Conclusions are drawn about the weak level of regional forecasting, programming and planning at different levels.

A Method for Identification of Anomalous Geological Zones

In the paper, a new approach to identifying zones with rare anomalous manifestations of geological processes is proposed. The approach is based on two one-class classification methods of machine learning: the method of minimum area of alarm and the method of preference. The algorithm of minimum area of alarm is nonparametric. It is trained on a sample of anomalous events and computes the field of anomalous zones. The knowledge obtained by this method is non-verbalized. The method of preference allows approximating the obtained solution by a rather simple logical rule that defines the anomalous region in terms of analyzed properties of the geological environment. Examples of this approach to finding areas of possible foci of strong earthquakes and to make a regional forecast of deposits are considered.