Regional Forecasts Research Articles

РЕГИОНАЛЬНОЕ ПРОГНОЗИРОВАНИЕ МЕСТОРОЖДЕНИЙ УРАНА НА ТЕРРИТОРИИ РЕСПУБЛИКИ НИГЕР

The paper deals with the methodology and results of forecasting sandstone-type uranium deposits on the territory of the Republic of Niger, based on the use of a set of regional geological and geophysical materials and a new technology for spatial analysis of data by the method of group accounting of arguments. The necessary information on the genesis and localization of sandstone-type uranium deposits in the study area is briefly outlined. Examples of identification of formal geological, tectonic and geophysical features that characterize the location of known large objects of uranium mineralization are given. On the example of reference ore and ore-less objects, an identification analysis of the features is carried out, focused on determining their aggregate information content. The results of the identification analysis made it possible to assess the weights of the features used and perform a comprehensive regional forecasting of sandstone-type uranium deposits in the territory of the Republic of Niger. Based on the results of the forecast, the three of the most promising sites were identified, where detailed work on the detection of industrial uranium mineralization is recommended.

Study on applicability of remote sensing precipitation products in hilly-plain-wetland complex area of northeast China

Abstract For the hilly-plain-wetland complex ecosystem in the cold region of Northeast China, in order to solve the problems, which include the scarcity of surface rainfall stations and the inability to provide accurate surface precipitation for hydrological process simulation, based on the observed precipitation of rainfall stations, three remote sensing precipitation products are taken as objects of evaluation. They include TRMM (Tropical Rainfall Measuring Mission) 3B42V7,3B42RT and CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data,CHIP). In this paper, the observation data of rainfall stations and IDWP precipitation data interpolated by IDW (Inverse Distance Weighted) are used as true value of precision comparison, and the detection accuracy of remote sensing precipitation products from 2001 to 2010 is evaluated on the time scale (day, month and quarter) and spatial scale in Naoli River Basin.The results of the study indicated that 3B42V7 and CHIP have a high detection accuracy for precipitation, and their CC (correlation coefficient) values are 0.47 and 0.49 respectively in daily time scale. The accuracy of their observation for monthly precipitation is better than that of daily precipitation, and the CC are 0.85 and 0.87 respectively. The multi-year average precipitation at different grid positions in the basin is overestimated by 3B42RT, and its evaluation results are poor at different time scales. For the precipitation intensity range of (0,20], the observed results of 3B42V7 and rainfall station are close to each other. For the precipitation intensity ranges of (0,1) and (50,+∞), 3B42RT and CHIP have overestimated or underestimated the precipitation in different degrees. Based on the above analysis results, 3B42RT can be considered as data that can detect whether precipitation occurs on different spatial positions in the basin. 3B42V7 and CHIP can be applied to flood forecasting and non-point source pollution control in cold regions.

Ways to improve the reliability of interpreting the results of geophysical surveys performed for the local forecast and prospecting for solid minerals: a geodynamic approach

Background. The geodynamic approach is effectively used in regional forecasting and prospecting works. However, its application for local forecasting and prospecting for solid minerals is limited and sometimes impossible. One of the key problems of local forecasting and prospecting for minerals (solid, liquid, gaseous) is the presence of non-standard (flickering) geophysical anomalies, which complicates the interpretation of the results of geophysical surveys performed at different times at different stages of geological exploration. The article is devoted to clarifying the possibility of using geodynamic research in local forecasting and prospecting for solid minerals on the basis of attracting the latest scientific knowledge from the field of studying the spatio-temporal patterns of the development of modern geodynamic processes and their influence on the Earth’s geophysical fields. Aim. To increase the reliability of interpreting the results of geophysical surveys performed for local forecasting and prospecting for solid minerals.Materials and methods. The research was carried out on the basis of a comprehensive analysis of literature data, fund materials and the results of many years of the author’s own research in the fields of modern geodynamics and prospecting and exploration geodynamics. The initial data were based on the monitoring data of various Earth’s geophysical fields (deformation, seismic, and surface magnetic).Results. A geodynamic reason for the appearance of flickering anomalies in the Earth’s geophysical fields (in particular, magnetic) has been established, and a mechanism for their formation under the influence of modern geodynamic processes has been proposed. The possibility of using the geodynamic approach in the prospecting for solid minerals has been expanded, and ways to increasing the efficiency of local searches have been proposed.Conclusions. The research demonstrates the possibility of using geodynamic studies in local prospecting for solid minerals, which helps to increase the reliability of the results of interpretation of geophysical survey data, and, as a result, to reduce the overall financial and time costs involved with searching for mineral deposits.

Impact of CYGNSS-Derived Winds on Tropical Cyclone Forecasts in a Global and Regional Model

AbstractAn observing system experiment was conducted to assess the impact of wind products derived from the Cyclone Global Navigation Satellite System (CYGNSS) on tropical cyclone track, maximum 10-m wind speed Vmax, and minimum sea level pressure forecasts. The experiment used a global data assimilation and forecast system, and the impact of both CYGNSS-derived scalar and vector wind retrievals was investigated. The CYGNSS-derived vector wind products were generated by optimally combining the scalar winds and a gridded a priori vector field. Additional tests investigated the impact of CYGNSS data on a regional model through the impact of lateral boundary and initial conditions from the global model during the developmental phase of Hurricane Michael (2018). In the global model, statistically significant track forecast improvements of 20–40 km were found in the first 60 h. The Vmax forecasts showed some significant degradations of ~2 kt at a few lead times, especially in the first 24 h. At most lead times, impacts were not statistically significant. Degradations in Vmax for Hurricane Michael in the global model were largely attributable to a failure of the CYGNSS-derived scalar wind test to produce rapid intensification in the forecast initialized at 0000 UTC 7 October. The storm in this test was notably less organized and symmetrical than in the control and CYGNSS-derived vector wind test. The regional model used initial and lateral boundary conditions from the global control and CYGNSS scalar wind tests. The regional forecasts showed large improvements in track, Vmax, and minimum sea level pressure.

Habitat suitability models based on opportunistic citizen science data: Evaluating forecasts from alternative methods versus an individual‐based model

AbstractAimTo evaluate the utility of opportunistic data from citizen science programmes for forecasting species distributions against forecasts with a model of individual‐based population dynamics.LocationSweden.MethodsWe evaluated whether alternative methods for building habitat suitability models (HSMs) based on opportunistic data from citizen science programmes produced forecasts that were consistent with forecasts from two benchmark models: (1) a HSM based on data from systematic monitoring and (2) an individual‐based model for spatially explicit population dynamics based on empirical demographic and movement data. We forecasted population numbers and habitat suitability for three realistic, future forest landscapes for a forest bird, the Siberian jay (Perisoreus infaustus). We ranked simulated forest landscapes with respect to their benefits to Siberian jays for each modelling method and compared the agreement of the rankings among methods.ResultsForecasts based on our two benchmark models were consistent with each other and with expectations based on the species’ ecology. Forecasts from logistic regression models based on opportunistic data were consistent with the benchmark models if species detections were combined with high‐quality inferred absences derived via retrospective interviews with experienced “super‐reporters.” In contrast, forecasts with three other widely used methods were inconsistent with the benchmark models, sometimes with misleading rankings of future scenarios.Main conclusionsOur critical evaluation of alternative HSMs against a spatially explicit IBM demonstrates that information on species absences critically improves forecasts of species distributions using opportunistic data from citizen science programmes. Moreover, high‐quality information on species absences can be retrospectively inferred from surveys of the consistency of reporting of individual species and the identification skills of participating reporters. We recommend that citizen science projects incorporate procedures to evaluate reporting behaviour. Inferred absences may be especially useful for improving forecasts for species and regions poorly covered by systematic monitoring schemes.

Methods for Small Area Population Forecasts: State-of-the-Art and Research Needs.

Small area population forecasts are widely used by government and business for a variety of planning, research and policy purposes, and often influence major investment decisions. Yet, the toolbox of small area population forecasting methods and techniques is modest relative to that for national and large subnational regional forecasting. In this paper, we assess the current state of small area population forecasting, and suggest areas for further research. The paper provides a review of the literature on small area population forecasting methods published over the period 2001–2020. The key themes covered by the review are extrapolative and comparative methods, simplified cohort-component methods, model averaging and combining, incorporating socioeconomic variables and spatial relationships, ‘downscaling’ and disaggregation approaches, linking population with housing, estimating and projecting small area component input data, microsimulation, machine learning, and forecast uncertainty. Several avenues for further research are then suggested, including more work on model averaging and combining, developing new forecasting methods for situations which current models cannot handle, quantifying uncertainty, exploring methodologies such as machine learning and spatial statistics, creating user-friendly tools for practitioners, and understanding more about how forecasts are used.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11113-021-09671-6.

An endemic–epidemic beta model for time series of infectious disease proportions

Time series of proportions of infected patients or positive specimens are frequently encountered in disease control and prevention. Since proportions are bounded and often asymmetrically distributed, conventional Gaussian time series models only apply to suitably transformed proportions. Here we borrow both from beta regression and from the well-established HHH model for infectious disease counts to propose an endemic–epidemic beta model for proportion time series. It accommodates the asymmetric shape and heteroskedasticity of proportion distributions and is consistent for complementary proportions. Coefficients can be interpreted in terms of odds ratios. A multivariate formulation with spatial power-law weights enables the joint estimation of model parameters from multiple regions. In our application to a flu activity index in the USA, we find that the endemic–epidemic beta model provides a better fit than a seasonal ARIMA model for the logit-transformed proportions. Furthermore, a multivariate approach can improve regional forecasts and reduce model complexity in comparison to univariate beta models stratified by region.

Impact of rainfall spatiotemporal variability and model structures on flood simulation in semi-arid regions

The spatiotemporal heterogeneity in precipitation and underlying surfaces and hybrid runoff generation mechanism make hydrological modelling and forecasting in semi-arid regions becoming a challenging work. Therefore, to provide a reference for the development of hydrological models in such regions, two nested hydrological experimental watersheds in semi-arid regions were selected for attribution analysis. Based on the concept of large-sample hydrology, large-scale numerical simulation experiments were performed by constructing different spatial and temporal scale rainfall schemes and combining three hydrological models with different runoff generation mechanisms. Finally, the influences of the time step, station density, and model structure on the flood simulations in semi-arid regions were evaluated. The spatial interpolation technique was used simultaneously to describe the high-dimensional complicated nonlinear relationships between the influencing factors and simulation results. The results show the following: (1) the flood simulation accuracy was more sensitive to the time step than the spatial station density of the rainfall schemes and was highly dependent on the time step of the original observation data, and (2) compared with the accuracy of the rainfall schemes, the model structure plays a dominant role in flood simulation accuracy. Thus, the hybrid model has significant potential for flood forecasting in semi-arid regions by combining different runoff generation mechanisms. (3) The spatial interpolation technique based on the k-nearest neighbour algorithm can construct a high-dimensional distribution between the influencing factors and model simulation accuracy, and describe the complicated relationships among the time step, station density, model structure, and flood simulations.

Evaluation of artificial intelligence models for flood and drought forecasting in arid and tropical regions

With the advancement of computer science, Artificial Intelligence (AI) is being incorporated into many fields to increase prediction performance. Disaster management is one of the main fields embracing the techniques of AI. It is essential to forecast the occurrence of disasters in advance to take the necessary mitigation steps and reduce damage to life and property. Therefore, many types of research are conducted to predict such events due to climate change in advance using hydrological, mathematical, and AI-based approaches. This paper presents a comparison of three major accepted AI-based approaches in flood and drought forecasting. In this study, fluvial floods are measured by the runoff change in rivers whereas meteorological droughts are measured using the Standard Precipitation Index (SPI). The performance of the Convolutional Neural Network (CNN), Long-Short Term Memory network (LSTM), and Wavelet decomposition functions combined with the Adaptive Neuro-Fuzzy Inference System (WANFIS) are compared in flood and drought forecasting, with five statistical performance criteria and accepted flood and drought indicators used for comparison, extending to two climatic regions: arid and tropical. The results suggest that the CNN performs best in flood forecasting with WANFIS for meteorological drought forecasting, regardless of the climate of the region under study. Besides, the results demonstrate the increased accuracy of the CNN in applications with multiple features in the input.

Anticipatory Culture in the Bering Sea: Weather, Climate and Temporal Dissonance

AbstractA major implication of climate change is the declining capacity for communities to anticipate future conditions and scenarios. In the Bering Sea region of Western Alaska, this situation is acute and holds manifold consequences, particularly for the region’s primarily Indigenous residents. Based upon interviews and fieldwork in two Bering Sea communities and among regional weather forecasters, this paper explores the intertwined temporalities of weather, climate, and social life. I demonstrate that anticipatory culture, which otherwise structures anticipatory practices regarding climate, local weather, and social life, is beset by temporal dissonance across three timescales. First, dramatic climatic and ecosystem shifts reshape how Indigenous Peoples envision themselves as culturally inhabiting a long-range history and future. Second, changes in weather patterns, ecological cycles, and sea ice dynamics upset evaluations of seasonality, leading to a pervasive sense of unpredictability. Third, on the everyday timescale, social and technological change complicates mariners’ evaluations of risk and economic (commercial and subsistence) decision-making. I conclude by connecting these three socio-environmental temporalities to the temporal frames that primarily characterize weather and climate services, with an emphasis on the US National Weather Service. The paper discusses how such services may further orient toward engaging socially embedded practices of anticipation in addition to formal prediction. Such an orientation can help to shape an anticipatory culture that more closely aligns meteorological and social patterns.

Climatological analysis of freezing level height over China and its implications using homogenized in‐situ data

ABSTRACTFreezing level height (FLH) is the altitude of a location where the air temperature is 0°C. The FLH denotes the approximate position for water phase transitions from liquid (or vapour) to solid but not vapour to liquid. Based on temperature and height data on the ground surface to heights with an atmospheric pressure of 400 hPa, the monthly FLH at 118 radiosonde stations located in China are derived for the 1958–2017 period. The monthly and annual climatology during 1981–2010 and the seasonal, intra‐annual and decadal variabilities of the FLH during 1958–2017, along with its long‐term trends, were quantitatively evaluated at the regional scale and for individual stations. FLH climatology is generally related to season, zone and topography. The annual FLH anomalies decline initially, followed by a rise, with synchronous changes in the temperature in the lower and middle troposphere; the anomalies in south‐eastern China are partly associated with El Niño–Southern Oscillation events. The increasing trends of FLH are closely associated with tropospheric warming and show evident regional and seasonal differences. Stronger increasing trends are observed in northern and western China in autumn or winter (particularly in recent decades). The changes in FLH have implications on long‐term trends in equilibrium line altitude (ELA) of glaciers in Tianshan Mountains, snow cover extent in melt season and hail frequency in China especially in high‐elevations. During recent decades, the evolution of the FLH and major composition in the cryosphere has accelerated as a response to tropospheric warming. The study on FLH change can provide valuable information for the evaluation of glaciers, snow cover extent and hail frequency in China from a new perspective; which is helpful for understanding regional hydrological processes and climate changes better. Further application of FLH in cryosphere monitoring can improve water management and flood forecasting in headwater regions in China.

Forecasting Particulate Pollution in an Urban Area: From Copernicus to Sub-Km Scale

Particulate air pollution has aggravated cardiovascular and lung diseases. Accurate and constant air quality forecasting on a local scale facilitates the control of air pollution and the design of effective strategies to limit air pollutant emissions. CAMS provides 4-day-ahead regional (EU) forecasts in a 10 km spatial resolution, adding value to the Copernicus EO and delivering open-access consistent air quality forecasts. In this work, we evaluate the CAMS PM forecasts at a local scale against in-situ measurements, spanning 2 years, obtained from a network of stations located in an urban coastal Mediterranean city in Greece. Moreover, we investigate the potential of modelling techniques to accurately forecast the spatiotemporal pattern of particulate pollution using only open data from CAMS and calibrated low-cost sensors. Specifically, we compare the performance of the Analog Ensemble (AnEn) technique and the Long Short-Term Memory (LSTM) network in forecasting PM2.5 and PM10 concentrations for the next four days, at 6 h increments, at a station level. The results show an underestimation of PM2.5 and PM10 concentrations by a factor of 2 in CAMS forecasts during winter, indicating a misrepresentation of anthropogenic particulate emissions such as wood-burning, while overestimation is evident for the other seasons. Both AnEn and LSTM models provide bias-calibrated forecasts and capture adequately the spatial and temporal variations of the ground-level observations reducing the RMSE of CAMS by roughly 50% for PM2.5 and 60% for PM10. AnEn marginally outperforms the LSTM using annual verification statistics. The most profound difference in the predictive skill of the models occurs in winter, when PM is elevated, where AnEn is significantly more efficient. Moreover, the predictive skill of AnEn degrades more slowly as the forecast interval increases. Both AnEn and LSTM techniques are proven to be reliable tools for air pollution forecasting, and they could be used in other regions with small modifications.

Long term electricity load forecasting based on regional load model using optimization techniques: A case study

ABSTRACT Long-term load forecasting is a significant and complex topic in electric distribution systems. Forecasters is need to proper forecasting methodologies and smart solutions to minimize complexity. In this study, regional long-term load forecasting is presented, for Sivas province of Turkey, taking into account the development plan of the municipality, and subscriber profiles. Firstly, the municipality development plan is divided into regions of similar load characteristics. The load demand values of each region are defined mathematically using the S curve. The optimal parameter values of the S curve are calculated using meta-heuristic methods such as Genetic Algorithm (GA), Grey Wolf Optimization (GWO) and Harris Hawk Optimization (HHO). The obtained results are compared with the results of the econometrics-based (top-to-bottom) approach and actual consumer projection. The consumption values between 2004 and 2014 are used for parameter estimation of S curves. The consumption values obtained as the result of analysis the period between 2015 and 2018 were selected as test data. The result is shown that S curve-based regional demand forecast demonstrated more convenient results using the HHO algorithm with statistical values of RE = 1.3362, MAE = 1.5145, RMSE = 1.80385 and STD = 2.122 can be applied to the forecast regional electricity consumption. The proposed method is simple and can be easily applied to forecast the total consumption of the power load for a province any load forecasting region. The presented approach can be used to define the future projections of electricity distribution systems and determine the correct investment strategies.

The ENEA-REG system (v1.0), a multi-component regional Earth system model: sensitivity to different atmospheric components over the Med-CORDEX (Coordinated Regional Climate Downscaling Experiment) region

Abstract. In this study, a new regional Earth system model is developed and applied to the Med-CORDEX (Coordinated Regional Climate Downscaling Experiment) region. The ENEA-REG system is made up of two interchangeable regional climate models as atmospheric components (RegCM, REGional Climate Model, and WRF, Weather Research and Forecasting), a river model (Hydrological Discharge, HD), and an ocean model (Massachusetts Institute of Technology General Circulation Model, MITgcm); processes taking place at the land surface are represented within the atmospheric models with the possibility to use several land surface schemes of different complexity. The coupling between these components is performed through the RegESM driver. Here, we present and describe our regional Earth system model and evaluate its components using a multidecadal hindcast simulation over the period 1980–2013 driven by ERA-Interim reanalysis. We show that the atmospheric components correctly reproduce both large-scale and local features of the Euro-Mediterranean climate, although we found some remarkable biases: in particular, WRF has a significant cold bias during winter over the northeastern bound of the domain and a warm bias in the whole continental Europe during summer, while RegCM overestimates the wind speed over the Mediterranean Sea. Similarly, the ocean component correctly reproduces the analyzed ocean properties with performances comparable to the state-of-art coupled regional models contributing to the Med-CORDEX initiative. Our regional Earth system model allows studying the Euro-Mediterranean climate system and can be applied to both hindcast and scenario simulations.

The current and projected drought-caused loss of the spring wheat yield in the south of European Russia

The study focuses on the linkage between droughts at the beginning of the crop growing season and the yield of spring wheat in southern regions of European Russia. Its extrapolation into the future helps to analyze possible climatic risks in the spring wheat growing. It was found that moderate droughts from May to June, detected by using Standardized Precipitation Index data, in most cases have led to low yields. It was revealed that changes in the air moisture conditions in late spring and early summer in the period 1991-2018 compared with the previous thirty-year period were generally more favorable for the cultivation of spring wheat in most southern regions of European Russia. These positive changes were due to a decrease in the frequency of droughts. At the same time, a significant increase in the frequency of such droughts was observed in Samarskaya and Orenburgskaya oblasts, where the largest sown areas of spring wheat are situated. According to model projections of the future climate, similar changes in the air moisture conditions in the study area will generally remain favorable by the middle of the 21st century. Unfavorable regional forecasts associated with a possible increase in the frequency of droughts at the start of the crop growing season are predicted in Kuban, the east of the Volga region, and the north of the Lower Volga region and the southeast of the Central Black Earth region, in the North-Western Caspian Sea region.

Regional Forecasting of Fine Particulate Matter Concentrations: A Novel Hybrid Model Based on Principal Component Regression and EOF

AbstractWhen many cities need quantitative forecasts of air quality to adjust industrial production plans and urbanization development, how to build an efficient forecast model remain a challenge. Methodology for quantitative prediction of air quality can no longer rely on single‐site observations, and thus approaches that require fewer input data and are more efficient and more reliable need to be explored. This paper proposes the principles and steps of a new model using the empirical orthogonal function (EOF) and principal component regression (PCR), which is a hybrid EOF‐PCR approach that decomposes the panel data of PM2.5 and predictors into spatial structures (EOFs) and time expansion coefficients (ECs) by EOF analysis, establishes the PCR of the ECs of PM2.5, and simulates the PM2.5 concentrations in each city by projecting the fitted ECs through EOFs. The very heart of the new model is the PCR modeling and projection. The results are presented for PM2.5 concentrations over Jiangsu Province in eastern China. The results show that this EOF‐PCR model, which is based on EC1s with a cumulative variance contribution rate above 90%, has an average prediction accuracy of 65%. The model performs best in spring and autumn, better in summer and worst in winter. Most predictors have a maximum lag of a week, and they are quite different among seasons. Considering the influence of the spatial distributions of predictors, rather than covariates at a single site, this model can reflect regional influences and effectively improve the simulation effect.

Influence of Near Real-Time Green Vegetation Fraction Data on Numerical Weather Prediction by WRF over North China

The green vegetation fraction (GVF) can greatly influence the partitioning of surface sensible and latent heat fluxes in numerical weather prediction (NWP) models. However, the multiyear averaged monthly GVF climatology—the most commonly used representation of the vegetation state in models—cannot capture the real-time vegetation state well. In this study, a near real-time (NRT) GVF dataset generated from an 8-day composite of the normalized difference vegetation index is compared with the 10-yr averaged monthly GVF provided by the WRF model. The annual variability of the GVF over North China is examined in detail. Many differences between the two GVF datasets are found over dryland, grassland, and cropland/grassland mosaic areas. Two experiments using different GVF datasets are performed to assess the impacts of GVF on forecasts of screen-level temperature and humidity. The results show that using NRT GVF can lead to a widespread reduction of 2-m temperature forecast errors from April to October. Evaluation against in-situ observations shows that the positive impact on 2-m temperature forecasts in the morning is more distinct than that in the afternoon. Our study demonstrates that NRT GVF can provide a more realistic representation of the vegetation state, which in turn helps to improve short-range forecasts in arid and semiarid regions of North China. Moreover, our study shows that the negative effect of using NRT GVF is closely related to the initial soil moisture.

The future of coal investment, trade, and stranded assets

The future of coal investment, trade, and stranded assets

Year of Polar Prediction – enhance observations to provide improved forecasts in polar regions

Year of Polar Prediction – enhance observations to provide improved forecasts in polar regions

Suresh Narayanan Comments on The Global Macroeconomic Impacts of COVID-19: Seven Scenarios

Suresh Narayanan Comments on The Global Macroeconomic Impacts of COVID-19: Seven Scenarios