Conditional Forecasts Research Articles

Advances and Challenges in Weather Nowcasting : A Comprehensive Review of Modern Techniques and Models

Weather nowcasting, the short-term forecasting of weather conditions, is a critical area of study due to its impact on safety and planning. Recent advances in machine learning (ML) and deep learning (DL) have significantly improved nowcasting accuracy, particularly in precipitation prediction. This review examines modern techniques and models used for weather nowcasting, focusing on their advancements and challenges. Motivation for this study arises from the need for more accurate and timely weather predictions to mitigate adverse effects on daily life and infrastructure. Despite advancements, current methods face limitations such as high computational costs, data quality issues, and the challenge of generalizing models across different geographic regions. The aim of this review is to provide a comprehensive overview of recent developments in nowcasting techniques, highlight their strengths and weaknesses, and discuss future directions for research. The objective is to synthesize the latest findings, assess the performance of various models, and propose potential improvements. This review underscores the importance of continued innovation and integration of new technologies, including transfer learning and hybrid models, to enhance the effectiveness of weather nowcasting systems.

Краткосрочный прогноз смерчеопасных ситуаций для прибрежной акватории Черного моря с использованием усовершенствованного индекса WRI

The paper considers the issues of improving the quality of short-term forecasts of waterspout-risk conditions for the Black Sea coastal water. The results related to the development of new versions for calculating the regional waterspout risk index (WRI) for both the warm (WRI21) and cold (WRIW) seasons are presented. The WRI is used to identify zones of high-risk waterspout formation and subsequent localization of waterspout-risk areas of the coast. Calculations of the WRI fields are based on the output of the COSMO-Ru2 mesoscale model with a grid spacing of 2.2 km. Calculations are carried out in real time within the technology for assessing the risk of waterspouts. The skill of forecasting new WRI versions for different periods is analyzed. It is shown that the probability of waterspout detection in the warm season can reach 82% on average. At the same time, there is also a high false alarm rate (about 70%), mainly due to the excessively predicted waterspout risk in certain areas of the coast (Sochi and Tuapse). In the cold season, as expected, a significantly smaller number of waterspout-risk days is predicted as compared to the warm period. Examples of forecasts for different seasons are presented. Recommendations on the use of forecasts for preparing storm warnings are given.

Holistic opportunistic maintenance scheduling and routing for offshore wind farms

Despite the high growth of the offshore wind market, the economic benefits of wind energy sources are still being undermined by its high operation and maintenance expenses. On the one hand, high maintenance expenses are a direct result of offshore-specific challenges such as complex ocean meteorology, varying vessel accessibility, and shifting transportation requirements. Besides, component degradation and weather conditions largely limit the ability of turbine operators to estimate a workable maintenance time window. This study aims to develop a holistic opportunistic maintenance strategy to address the practical challenges of offshore wind farms. The proposed strategy starts by deriving the preventive maintenance interval of each turbine component based on its degradation trend and predictable cost rates. Then, each turbine is maintained in groups based on the maintenance opportunities arising from preventive maintenance, unexpected failures, and cable damage. Following that, given weather condition forecasts of key parameters (wind speed and wave height), the proposed strategy optimizes the daily allocations and vessel routes to maintain turbines in a timely and cost-effective manner. Finally, experimental results based on real-world data from an actual offshore wind farm demonstrate that the proposed strategy outperforms several universal maintenance strategies in key metrics. Compared to the simple, widely employed, and easy-to-implement strategies, it can help reduce the total cost by 85.9 %, 65.9 %, and 41.6 %, respectively. This work helps turbine operators implement comprehensive and cost-effective maintenance schemes, which can lead to tariff duction and wind farm promotion benefits in the long term.

Transforming Crop Management Through Advanced AI and Machine Learning: Insights into Innovative Strategies for Sustainable Agriculture

The integration of artificial intelligence (AI) and machine learning (ML) into crop management is transforming modern agriculture by enhancing efficiency, sustainability, and resilience. This review explores the multifaceted applications of AI and ML in key areas such as precision farming, pest and disease management, and harvest optimization. The use of AI-driven predictive analytics allows for more accurate forecasting of crop yields, pest outbreaks, and weather conditions, enabling farmers to make data-driven decisions that optimize resource use and reduce environmental impacts. A significant advancement is the integration of AI and ML with the Internet of Things (IoT) and autonomous farming equipment. These technologies enable real-time monitoring and precise interventions, enhancing productivity and minimizing labor costs. In crop breeding and genomics, AI accelerates the development of resilient crop varieties, which is crucial for adapting to climate change and increasing food demands. Despite the many benefits, challenges such as data quality, infrastructure limitations, and high implementation costs remain. The adoption of AI technologies is uneven, with small-scale farmers in developing regions facing barriers due to limited access to data and resources. Ethical concerns related to data privacy and the digital divide must also be addressed to ensure equitable access to these technologies. The future of AI and ML in agriculture lies in the development of more advanced predictive models, enhanced integration with the IoT, and the widespread use of autonomous farming systems.

Enhancing Weather Forecast Accuracy Through the Integration of WRF and BP Neural Networks: A Novel Approach

AbstractIn the past century, scholars from both domestic and international communities have delved into the study of numerical weather prediction models to promptly understand meteorological factors and mitigate the impacts of extreme weather events on humanity. Effective and precise prediction models enable the forecasting of meteorological conditions in the upcoming days, empowering individuals to implement proactive measures to minimize the adverse effects of extreme weather (Liang et al., 2021). The WRF (Weather Research and Forecasting) modeling system is commonly used for forecasting meteorological elements. However, uncertainties terribly hamper the correctness of the forecasting results. To this end, the present study was conducted to build a secondary model on the basis of the WRF forecast model. The WRF‐BPNN model was proposed for verification after constructing the network, the temperature vertical profile and the mixing ratio vertical profile were predicted, and the results on the validation set were tested. The results showed that the WRF‐BPNN model could effectively predict the temperature profile and mixing ratio profile, presenting better performance than the traditional WRF model.

Статистический анализ изменения температуры воздуха по данным многолетних наблюдений Метеорологической обсерватории имени В. А. Михельсона РГАУ – МСХА

Introduction. The purpose of the study is to assess the dynamics of changes in air temperature indicators and its impact on agronomic systems, based on long-term observations of the V. A. Mikhelson Meteorological Observatory of RSAU – MTAA. Materials and Methods. Using the Mathcad program, graphs of changes in average annual temperatures were constructed using a third-order approximation polynomial, graphs of the theoretical normal density distribution and normal temperature distribution function, as well as graphs of intra-annual temperature distribution for 30 years. Research results and Discussion. The values of the relative error of average annual temperatures were calculated, the normal distribution of average annual temperatures was checked using the Pearson criterion, after which the probability of deviation of the empirical hours falling into intervals from the theoretical hours was found. Conclusion. As a result of the research, a significant increase in average annual air temperature was established against the background of a general reduction in the duration of the cold period. Increase in temperature over the period 1961–1990 amounted to 0.9 °C, which is significantly higher than the average values for the globe. The results of the study reflect the general trend of warming in the context of global climate change and can be used to improve the forecasting of weather conditions, as well as to optimize technological processes in crop production.

Agricultural Drought Monitoring: A Comparative Review of Conventional and Satellite-Based Indices

Drought is a natural hazard that causes significant economic and human losses by creating a persistent lack of precipitation that impacts agriculture and hydrology. It has various characteristics, such as delayed effects and variability across dimensions like severity, spatial extent, and duration, making it difficult to characterize. The agricultural sector is especially susceptible to drought, which is a primary cause of crop failures and poses a significant threat to global food security. To address these risks, it is crucial to develop effective methods for identifying, classifying, and monitoring agricultural drought, thereby aiding in planning and mitigation efforts. Researchers have developed various tools, including agricultural drought indices, to quantify severity levels and determine the onset and evolution of droughts. These tools help in early-stage forecasting and ongoing monitoring of drought conditions. The field has been significantly advanced by remote sensing technology, which now offers high-resolution spatial and temporal data, improving our capacity to monitor and assess agricultural drought. Despite these technological advancements, the unpredictable nature of environmental conditions continues to pose challenges in drought assessment. It remains essential to provide an overview of agricultural drought indices, incorporating both conventional methods and modern remote sensing-based indices used in drought monitoring and assessment.

Forecasting of meteorological drought using ensemble and machine learning models

This study highlights drought forecasting for understanding the semi-arid area in India, where drought phenomena play vital role in the irrigation, drinking water supplies, and sustaining the ecological with economic balance for every nation. Therefore, drought forecasting is important for the future drought planning based on the machine learning (ML) models. Hence, The Standardized Precipitation Index (SPI) at 3- and 6-month periods have been selected and used for future drought forecasting scenarios in area. The combinations of ten inputs SPI-1- and SPI-10 were used for predicting modeling for SPI-3 and SPI-6 timescales, that modeling developed based on the historical SPI datasets from 1989 to 2019 years. The SPI-3 and SPI-6 maximum and minimum values are shown SPI-3 (2.03 and -5.522) and SPI-6 (1.94 and -6.93). The SPI is a popular method for estimating the drought analysis and has been used everywhere at global level. The developed models have been compared with each other, with the best combination of input variables selected using subset regression models and sensitivity studies. After that, the active input parameters were used for forecasting of SPI-3 and SPI-6 values to understanding of drought in semi-arid area. The finest input variables combination have been used in the Ml models and established the novel five models such as robust linear regression, bagged trees, boosted trees, support vector regression (SVM-Linear), and Matern Gaussian Process Regression (Matern GPR) models. Such kind of models first time has been applied for the forecasting of future drought conditions. Whole models were fine and improved modeling by using hyperparameters tuning, bagging, and boosting models. Entire ML models’ accuracy was compared using different statistical metrics. Compared with five ML models accuracy, we have found that the Matern GPR model better accuracy than other ML models. The best model accuracy is R2 = 0.95 and 0.93, RMSE, MSE, MAE, MARE, and NSE values, respectively, for predicting SPI-3 and SPI-6 values in the area. Therefore, the Matern GPR model was identified as the finest ML algorithm for predicting SPI-3 and SPI-6 associated with other algorithms. This research demonstrates the Matern GPR model's efficacy in predicting multiscale SPI-3 and SPI-6 under climate variations. It can be helpful in soil and water resource conservation planning and management and understanding droughts in the entire basin areas of the country India.

A Novel Energy-Efficient Routing Protocol for Industrial WSN Using Hybrid Coot-Ls Algorithm with LSTM-Based Dom Prediction

In industrial Wireless Sensor Networks (WSNs), energy efficiency and reliable data transmission are critical challenges that need to be addressed to ensure sustainable and robust network operations. This paper proposes a novel energy-efficient routing protocol that integrates a Hybrid COOT-LS (Coot- Levy Search) algorithm with Long Short-Term Memory (LSTM)-based Dominant Object Motion (DOM) prediction. The routing protocol leverages the strengths of Hybrid Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) to enhance routing efficiency and reduce energy consumption. The Hybrid PSO and ACO algorithms are employed to optimize routing paths by balancing exploration and exploitation, considering multiple factors such as energy levels, node distance, and reliability. The COOT-LS algorithm further refines these paths by incorporating a Levy flight mechanism to enhance the search process. Additionally, the LSTM-based DOM prediction provides accurate forecasts of network conditions, enabling dynamic adjustments to routing strategies in real time. Simulation results demonstrate that the proposed protocol significantly improves network lifetime, reduces energy consumption, and enhances data transmission reliability compared to traditional routing protocols. This approach provides a robust and scalable solution for industrial WSN applications, ensuring efficient and reliable network performance in dynamic and complex industrial environments.

Impact of ocean in-situ observations on ECMWF sub-seasonal forecasts

We assess for the first time the impact of in-situ ocean observations on European Centre for Medium-Range Weather Forecasts (ECMWF) sub-seasonal forecasts of both ocean and atmospheric conditions. A series of coupled reforecasts have been conducted for the period 1993-2015, in which different sets of ocean observations were withdrawn in the production of the ocean initial conditions. Removal of all ocean in-situ observations in the initial conditions leads to significant degradation in the forecasts of ocean surface and subsurface mean state at lead times from week 1 to week 4. The negative impact is predominantly caused by the removal of the Argo observing system in recent decades. Changes in the mean state of atmospheric variables are comparatively small but significant in the forecasts of lower and upper atmospheric circulation over large regions. Our results highlight the value of continuous, real-time in-situ observations of the surface and subsurface ocean for coupled forecasts in the sub-seasonal range.

Определение индивидуального риска травматизма горнорабочих в результате динамического проявления

The article analyses the effect of gas-dynamic manifestations on occupational injuries. It has been established that the most hazardous gas-dynamic manifestation for underground deep mines is a rock burst. As the rock burst is identified by the authors as the most hazardous effect by its manifestations, the authors offer their methodology of identification of occupational injury risk caused by a rock burst for a wider discussion. The main characteristics and causes of rock bursts and their signs and forecasting methods have been described. The statistics of accidents caused by rock bursts have been provided. The article contains statistical information on mild, severe, and lethal injuries caused by rock bursts in coal mines of the Kuznetsk coal basin. Multifunctional safety systems able to obtain information on the condition of rock mass have been analyzed. Special attention has been paid to the system of current control and forecast of rock mass conditions based on the «Mikon-GEO» system. In the majority of cases, based on the problems to solve, such a system is a part of multifunctional safety systems. The authors suggest that the forecast methodology is to be based on the data obtained by such systems operating based on the seismoacoustics principles. The mathematical model using the source data obtained from the «Mikon-GEO» system has been considered. The calculation algorithm enabling the assessment of the rock burst probability has been provided. The algorithm and the results of measurements of rock mass basic parameters are used to determine the individual risk value that is advisable to compare with the average value of occupational risk at production. The results obtained by the authors have been tested in one of the Kuznetsk Basin coal mines. The preliminary results of the forecast methodology use have shown the relevance of the studies and identified certain issues requiring specification and further research.

Mathematical modelling of dissolved oxygen levels in a multi-cage salmon farm

Dissolved oxygen concentrations were monitored at 12 points distributed over 3 depths in one out of six cages in an Atlantic salmon farm and compared to the output of a mathematical model using the advection-diffusion equation and a model of the salmon's oxygen consumption to estimate spatial oxygen distributions. The effects of oxygen consumption in neighbouring fish cages on the oxygen levels observed in the monitored cage were found to be significant, and several model variants modified to better represent the real system were tested. Position data from 8 fish in the cage equipped with acoustic telemetry tags was used to refine the model configuration.Results show that simulating the full farm with all cages improves model fit significantly over simulating a single cage only. Even better fit was achieved in a single cage model with ad hoc modifications, indicating that there are additional non-modelled effects that should be investigated further. This study establishes the oxygen model as a suitable tool for monitoring and forecasting of oxygen conditions in salmon cages, and gives indications about he accuracy and limitations of the model.

A Dual- Linearly polarized Ku/K Band Reflectarray for Broadcast and Fixed Satellite Services

A novel single layer, dual polarized dual band reflectarray is constructed by implementing independent control over each band. The downlink element in the Ku-band employs a vertical slot embedded with semi-circular rings, featuring 841 elements, and is complemented by two delay lines attached to the outer ring to ensure orthogonal polarization. The uplink element containing 841 elements is formed by 90° rotation of downlink element to avoid mutual coupling effect. The application of the degree of freedom (DOF ) technique enables the attainment of phase ranges of 720° and 840° within the unit cell for both frequency bands. Thus, the total 1682 elements etched over the Taconic TLx-09 substrate with 0.8mm thick substrate of aperture dimensions of 16.5 × 16.5 mm is designed, simulated, and fabricated for Ku and K band applications. The centre-fed reflectarray upon construction exhibits a measured gain of 26.5dB and 25.7 dB and 1dB gain bandwidth of 26.4% for Ku band and 11.7% for K band respectively. Moreover , the proposed reflectarray demonstrates a maximum aperture efficiency of 30.4 at 12.8 GHz and 19% at 23 GHz. Thus, the proposed dual-polarization reflectarray covers the downlink frequencies for the broadcasting satellite communication services like DTH connectivity and uplink frequencies for earth exploration satellite services like forecast of dangerous weather conditions like drought, heavy rain.

Prediction of voltage stability index in buses without measurement in distribution systems

AbstractIn power systems, voltage collapse during overload can be a significant threat. Accurate forecasting of critical operational conditions within power grids is crucial for preventing such situations. Precise predictions of voltage collapse enable operators to monitor the system closely and implement necessary corrective measures promptly, avoiding potential issues. However, monitoring networks can be costly due to the numerous loads and transformers in the distribution system. A comprehensive approach known as the voltage stability index (VSI) forecast without measurement buses (VFWMB) has been introduced to address this challenge. This approach involves innovative methods, including the seeking observation zone with weight least square (SOZWLS) technique for determining the number and location of measurements in the network based on its topology. Additionally, short‐term load forecasting is performed using the long short‐term memory (LSTM) forecasting method, followed by voltage estimation for buses without measurements. Finally, the proposed method calculates the modern voltage stability index for distribution systems (MVSIDS) for upcoming hours. All indicators and techniques in the VFWMB method have been validated. The algorithm has been thoroughly tested on various networks, including small and large, balanced and unbalanced, and both real and test networks, showing high efficiency in the electricity industry.

Application of a single-equation SARIMA model for short-term conditional forecast (projection) of CPI price dynamics in Poland

The aim of the article is to construct an optimal SARIMA model for short-term conditional forecasting (projection) of the dynamics of prices expressed by the Consumer Price Index (CPI), as understood within the extended Box-Jenkins procedure. The construction of such a forecast aims to influence, through the expectations channel, the institutional trust of society in monetary authorities and assess the effectiveness of achieving the monetary policy goal within the framework of the democratic responsibility of the decision-making body of the National Bank of Poland – the Monetary Policy Council. The selection of the optimal SARIMA model was carried out using an iterative method within the Box-Jenkins procedure, with the goal of reducing the systematic bias of estimators – coherence with empirical data. The analysis was conducted on compiled secondary data of the monthly Consumer Price Index for goods and services from the Central Statistical Office for the years 2010-2023 (on a monthly basis). Results show that the short-term forecast demonstrated accuracy within a specified confidence interval. The application of the SARIMA model serves as a useful methodological tool for constructing elaborate DSGE models (for example, the NECMOD model) using procedures such as SEM (System for Averaging Models) from Norges Bank.

Perceptions About Monetary Policy

Abstract We estimate perceptions about the Federal Reserve’s monetary policy rule from panel data on professional forecasts of interest rates and macroeconomic conditions. The perceived dependence of the federal funds rate on economic conditions varies substantially over time, in particular over the monetary policy cycle. Forecasters update their perceptions about the Fed’s policy rule in response to monetary policy actions, measured by high-frequency interest rate surprises, suggesting that they have imperfect information about the rule. Monetary policy perceptions matter for monetary transmission, as they affect the sensitivity of interest rates to macroeconomic news, term premia in long-term bonds, and the response of the stock market to monetary policy surprises. A simple learning model with forecaster heterogeneity and incomplete information about the policy rule motivates and explains our empirical findings.

Calculating a conductor’s temperature-related sag as the limiting value for a Dynamic Line Rating

The ampacity — a term that refers to the maximum current in amperes — of existing overhead lines can be increased using Dynamic Line Rating (DLR) systems. Based on ambient online conditions and electrical power flow measurements, together with a forecast of future ambient conditions, the ampacity of overhead lines can be dynamically adjusted. In essence, the ampacity of an overhead line is determined by the operating temperature of the conductor that still guarantees the required safe distances to the objects under the overhead line. This paper presents a calculation algorithm for the sag of a conductor that addresses an unequal temperature of the conductor along the line as a response to an expected problem in the future, when increasing numbers of DLR sensors will be installed on or even near overhead lines. A novel approach to the sag-temperature calculation useful for a DLR is developed based on the linearization of cable equations. As a result, the computational costs are greatly reduced, which is an important factor when the DLR system includes a large number of overhead lines and processes them simultaneously. The presented methods are open and can be adapted for an arbitrary DLR system.

Subseasonal Potential Predictability of Horizontal Water Vapor Transport and Precipitation Extremes in the North Pacific

Abstract Accurate forecasts of weather conditions have the potential to mitigate the social and economic damages they cause. To make informed decisions based on forecasts, it is important to determine the extent to which they could be skillful. This study focuses on subseasonal forecasts out to a lead time of four weeks. We examine the differences between the potential predictability, which is computed under the assumption of a “perfect model,” of integrated vapor transport (IVT) and precipitation under extreme conditions in subseasonal forecasts across the northeast Pacific. Our results demonstrate significant forecast skill of extreme IVT and precipitation events (exceeding the 90th percentile) into week 4 for specific areas, particularly when anomalously wet conditions are observed in the true model state. This forecast skill during weeks 3 and 4 is closely associated with a zonal extension of the North Pacific jet. These findings of the source of skillful subseasonal forecasts over the U.S. West Coast could have implications for water management in these regions susceptible to drought and flooding extremes. Additionally, they may offer valuable insights for governments and industries on the U.S. West Coast seeking to make informed decisions based on extended weather prediction. Significance Statement The purpose of this study is to understand the differences between the ability to predict high amounts of the transport of water vapor and precipitation over the North Pacific 3 and 4 weeks into the future. The results indicate that differences do exist in a region that is relevant to precipitation on the U.S. West Coast. To physically explain why differences in predictability exist, the relationship between weekly extremes of the extension of the jet stream, IVT, and precipitation over the North Pacific is explored. These findings may impact decisions relevant to water management on the U.S. West Coast susceptible to drought and flooding extremes.

Precision Regulation and Forecasting of Greenhouse Tomato Growth Conditions Using an Improved GA-BP Model

Tomatoes are annual herbaceous plants of the family Solanaceae. They have very stringent requirements for their growing environment and climate conditions. To precisely control the greenhouse environment for tomato growth, this project designed and implemented a monitoring system utilizing programmable logic controllers and a data acquisition system for monitoring. Sensors are installed at key locations in the greenhouse, such as near the ground, in the plant canopy, and under the roof, to monitor environmental parameters such as temperature, humidity, light intensity, and carbon dioxide concentration in real time. A three-layer feedforward GA-BP neural network model was established using soil temperature, soil humidity, air humidity, and illumination as predictive parameters. This technology predicts optimal environmental parameters and performs real-time monitoring and intelligent control, overcoming the traditional BP neural network’s drawbacks of slow convergence, susceptibility to disturbance, and poor generalization ability, with the current model’s average prediction error being less than 5%. It provides a theoretical basis and decision support for precision control and related environmental forecasting in agricultural projects. Based on the growth characteristics, physiological and morphological features of tomatoes, and the comprehensive impact of planting conditions, the technology enhances tomato yield and quality while reducing production costs and labor inputs. It mainly includes the establishment of tomato growth models, measurement and control of environmental parameters, and the design and optimization of intelligent decision control systems. Research outcomes in this field are significant for improving agricultural production efficiency, ensuring food security, and promoting sustainable agricultural development.

Developing a predictive maintenance model for the rehabilitation of roads’ assets

Maintaining road infrastructure is a significant challenge in minimizing public fund waste from reactive maintenance. Preventive maintenance is considered a new era in this field. To achieve cost savings and prolong the life cycle of road networks, it’s crucial to determine their lifespan at the time of construction. This article proposes an emerging predictive methodology to select road network rehabilitation policy. This methodology facilitates long-term planning based on technical and budgetary perspectives to identify the most cost-effective rehabilitation policy for the road network’s assets over the planning horizon. To accomplish this, the Pavement Rehabilitation Predictive Model (PRPM) is developed for long-term forecasting of future asset conditions as the network deteriorates over time. It takes into account effective planning for different rehabilitation policies predicts asset deterioration, and aids in making informed decisions by identifying the best rehabilitation policy for pavement sections. An empirical example is used to illustrate the proposed approach in detail, based on the evaluation of three scenarios of the rehabilitation policies. The results indicate that the prediction model is highly efficient, resulting in an average budget savings of up to 20% compared to other relevant maintenance methods.