Analysis Of Meteorological Data Research Articles

A Theoretical Analysis of Meteorological Data as a Road towards Optimizing Wind Energy Generation

The development of wind energy has been observed for many years. Both construction firms and the scientific world are analyzing new design solutions, atmospheric conditions and the technical performance achieved. The main goal of this research is to evaluate the requirements that have to be met to design wind power stations that would be an optimal fit for the climatic conditions in Poland. This study combines the results of empirical studies on wind velocity distributions with the physical fundamentals of wind power station design. This paper presents modelling of the relationships between wind velocity distributions observed in Poland and technical requirements for wind power stations design. The wind velocities distributions for various locations in Poland are determined and expressed in Weibull distribution parameters. Theoretical computations concerning the dependence of wind power stations as function of wind speed and air’s physical properties are presented. Conclusions important for the design of power stations fitted to the atmospheric conditions in Poland are given. LabVIEW 2021 was used for computer modeling.

Risk Evaluation Methodology for Weather-Related Degradation of Building façades

ABSTRACT Weather action is a relevant cause of façade degradation. Specific research evidence concerning its impact on the durability of building materials still needs to be comprehensively investigated and fully evaluated. This paper outlines a risk evaluation methodology for weather-related degradation of building façades. Two phases are considered: (1) laboratory work focussing on generic weather simulation in the wind tunnel in order to establish the hygric and thermo-aerodynamic vulnerabilities of constructions and to evaluate possible protective measures; (2) analysis of meteorological data and mapping of actual and projected hazards. Methods include the simulation and validation of wind-driven rain with gravimetric assessment of rainwater penetration into material specimens, the characterisation of the thermal behaviour of façades and that of the interaction between wind and building, using pressure measurements and flow visualization. The results enable decision-makers and professionals to evaluate the risk of weather-related degradation, individuating those areas of the construction more prone to damage, and thus to plan for adequate protection strategies. This would optimise the allocation of resources and increase the overall resilience of building structures. Future work is suggested with the intent of further validating the methodology both experimentally and with real-life applications.

GNSS signal ray-tracing algorithm for the simulation of satellite-to-satellite excess phase in the neutral atmosphere

Traditionally, GNSS space-based and ground-based estimates of tropospheric conditions are performed separately. It leads to limitations in the horizontal (e.g., a single space-based radio occultation profile covers a 300 km slice of the troposphere) and vertical resolution (e.g., ground-based estimates of troposphere conditions have spacing equal to stations’ distribution) of the tropospheric products. The first stage to achieve an integrated model is to create an effective 3D ray-tracing algorithm for the satellite-to-satellite (radio occultation) path reconstruction. We verify the consistency of the simulated data with the RO observations from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC-1) Data Analysis and Archive Center (CDAAC) in terms of excess phase and bending angle. The results show that our solution provides an effective RO excess phase, with a relative error varying from 35% at the height of 25–30 km (1.0–1.5 m) to 0.5% at heights 5–10 km (0.1–1 m) and 14 to 2% at heights below 5 km (2–14 m). The bending angle retrieval on simulated data attained for high-resolution ray-tracing, bias lower than 2% with respect to the observed bending angle. The optimal solution takes about 1 s for one transmitter–receiver pair with a tangent point below 5 km altitude. The high-resolution processing solution takes 3 times longer.

A century-long tendency of change in surface air temperature on the territory of Ukraine

A century-long tendency of change in surface air temperature on the territory of Ukraine

Spatiotemporal dynamics of forest fires in the context of climate change: a review.

Forest fires are sudden, destructive, hazardous, and challenging to manage and rescue, earning them a place on UNESCO's list of the world's eight major natural disasters. Currently, amid global warming, all countries worldwide have entered a period of high forest fire incidence. Due to global warming, the frequency of forest fires has accelerated, the likelihood of large fires has increased, and the spatial and temporal dynamics of forest fires have shown different trends. Therefore, the impact of climate change on the spatiotemporal dynamics of forest fires has become a hot issue in the field of forest fire research in recent years. Therefore, it is of great significance and necessity to conduct a review of the research in this area. This review delves into the interactions and impacts between climate change and the spatiotemporal dynamics of forest fires. To address this issue, scholars have mainly adopted the following research methods: first, statistical analysis methods, second, the establishment of spatiotemporal prediction models for meteorology and forest fires, and third, the coupling of climate models with forest fire risk forecasting models. The statistical analysis method relies on the analysis of historical meteorological and fire-related data to study the effects of climate change and meteorological factors on fire occurrence. Meanwhile, forest fire prediction models utilize technical tools such as remote sensing. These models synthesize historical meteorological and fire-related data, incorporating key meteorological factors such as temperature, rainfall, relative humidity, and wind. The models revealed the spatial and temporal distribution patterns of fires, identified key drivers, and explored the interactions between climate change and forest fire dynamics, culminating in the construction of predictive models. With the deepening of the study, the coupling of climate models and fire risk ranking systems became a trend in the prediction of forest fire risk trends. Moreover, as the climate warms, the increased frequency of extreme weather events like heatwaves, droughts, snow and ice storms, and El Niño-Southern Oscillation (ENSO) has accelerated forest fire occurrences and raised the risk of major fires. This review offers valuable technical insights by comprehensively analyzing the spatial and temporal characteristics of forest fires, elucidating key meteorological drivers, and exploring potential mechanisms. These insights serve as a scientific foundation for preventive measures and effective forest fire management. In the face of a changing climate, this synthesis contributes to the development of informed strategies to mitigate the escalating threat of forest fires.

How heating tracers drive self-lofting long-lived stratospheric anticyclones: simple dynamical models

Abstract. Long-lived “bubbles” of wildfire smoke or volcanic aerosol have recently been observed in the stratosphere, co-located with ozone, carbon monoxide, and water vapour anomalies. These bubbles often survive for several weeks, during which time they ascend through vertical distances of 15 km or more. Meteorological analysis data suggest that this aerosol is contained within strong, persistent anticyclonic vortices. Absorption of solar radiation by the aerosol is hypothesised to drive the ascent of the bubbles, but the dynamics of how this heating gives rise to a single-sign anticyclonic vorticity anomaly have thus far been unclear. We present a description of heating-driven stratospheric vortices, based on an axisymmetric balanced model. The simplest version of this model includes a specified localised heating moving upwards at fixed velocity and produces a steadily translating solution with a single-signed anticyclonic vortex co-located with the heating, with corresponding temperature anomalies forming a vertical dipole, matching observations. A more complex version includes the two-way interaction between a heating tracer, representing the aerosol, and the dynamics. An evolving tracer provides heating which drives a secondary circulation, and this in turn transports the tracer. Through this two-way interaction an initial distribution of tracer drives a circulation and forms a self-lofting tracer-filled anticyclonic vortex. Scaling arguments show that upward velocity is proportional to heating magnitude, but the magnitude of peak quasigeostrophic vorticity is O(f) (f is the Coriolis parameter) and independent of the heating magnitude. Estimates of vorticity from observations match our theoretical predictions. We discuss 3-D effects such as vortex stripping and dispersion of tracer outside the vortex by the large-scale flow, which cannot be captured explicitly by the axisymmetric model and are likely to be important in the real atmosphere. The large O(f) vorticity of the fully developed anticyclones explains their observed persistence and their effective confinement of tracers. To further investigate the early stages of formation of tracer-filled vortices, we consider an idealised configuration of a homogeneous tracer layer. A linearised calculation reveals that the upper part of the layer is destabilised due to the decrease in tracer concentrations with height there, which sets up a self-reinforcing effect where upward lofting of tracer results in stronger heating and hence stronger lofting. Small amplitude disturbances form isolated tracer plumes that ascend out of the initial layer, indicative of a self-organisation of the flow. The relevance of these idealised models to formation and persistence of tracer-filled vortices in the real atmosphere is discussed, and it is suggested that a key factor in their formation is the time taken to reach the fully developed stage, which is shorter for strong heating rates.

An Analysis of Meteorological Data in Sumatra and Nearby using Agglomerative Clustering

Sumatra is one of the biggest and the second most crowded islands in Indonesia. Sumatra is also a place of abundance of tropical flora and fauna. This paper aims to cluster the cities in Sumatra and nearby based on the meteorology data. It implements Agglomerative hierarchical clustering and uses a daily time series dataset from 17 cities from 1 January 2010 to 31 December 2023. The dataset contains variables minimum temperature, maximum temperature, average temperature, humidity, sunshine duration, and average wind speed. The preprocessing data was dedicated to managing the missing values and data aggregation to create single-form data. The single-form data contains cities and meteorological variables used as an input for the clustering algorithm, i.e. K-Means, Fuzzy C-Means, K-Medoid, intelligent K-KMeans, and Agglomerative clustering. The Agglomerative clustering outperforms other methods (i.e. K-Means, Fuzzy C-Means, K-Medoid, and intelligent K-KMeans) and produces Silhouette scores of 0.11. The clusters are then analyzed to find their unique pattern. The cut-off when the number cluster is two, Agglomerative hierarchical clustering gathers Aceh, Sabang, Pekanbaru, Padang, and Padang Lawas in Cluster 1. Other cities, i.e., Nagan Raya, Batam, Jambi, Bandar Lampung, Medan, Pangkalpinang, Palembang, Bengkulu, Belitung, Tapanuli, Deli Serdang, and Nias are in Cluster 2. The results can be briefly explained that the characteristic of Cluster 1 has a higher average temperature, lower humidity, and lower sunshine duration than cities in Cluster 2. However, Cluster 1 has a lower average minimum temperature than Cluster 2. The pairs of cities which have the most similarities are (Aceh, Sabang), (Pekanbaru, Padang Lawas), (Nagan Raya, Nias), (Jambi, Palembang), (Bengkulu, Tapanuli), and (Medan, Deli Serdang). The annual trend in several cities shows that there exists an increasing trend in minimum temperature, rising sunshine duration, and decreasing wind speed. These are signs of climate change that need a proper handling.

Multi-Time-Scale Optimal Scheduling Strategy for Marine Renewable Energy Based on Deep Reinforcement Learning Algorithm.

Surrounded by the Shandong Peninsula, the Bohai Sea and Yellow Sea possess vast marine energy resources. An analysis of actual meteorological data from these regions indicates significant seasonality and intra-day uncertainty in wind and photovoltaic power generation. The challenge of scheduling to leverage the complementary characteristics of various renewable energy sources for maintaining grid stability is substantial. In response, we have integrated wave energy with offshore photovoltaic and wind power generation and propose a day-ahead and intra-day multi-time-scale rolling optimization scheduling strategy for the complementary dispatch of these three energy sources. Using real meteorological data from this maritime area, we employed a CNN-LSTM neural network to predict the power generation and load demand of the area on both day-ahead 24 h and intra-day 1 h time scales, with the DDPG algorithm applied for refined electricity management through rolling optimization scheduling of the forecast data. Simulation results demonstrate that the proposed strategy effectively meets load demands through complementary scheduling of wave power, wind power, and photovoltaic power generation based on the climatic characteristics of the Bohai and Yellow Sea regions, reducing the negative impacts of the seasonality and intra-day uncertainty of these three energy sources on the grid. Additionally, compared to the day-ahead scheduling strategy alone, the day-ahead and intra-day rolling optimization scheduling strategy achieved a reduction in system costs by 16.1% and 22% for a typical winter day and a typical summer day, respectively.

Temperature Distribution Characteristics and Action Pattern of Concrete Box Girder under Low-Temperature and Cold Wave Conditions

In regions with severe cold and high latitudes, concrete structures are susceptible to cracking and displacement due to uneven temperature stress, which directly impacts their normal utilization. Therefore, to investigate the temperature distribution characteristics of concrete box girders under the combined influence of low temperatures and cold waves, a temperature test was conducted on a model of concrete box girders in Xinjiang Province, China. Based on the measured data, the distribution pattern of the most unfavorable negative temperature differential observed in high-latitude regions was determined. Long-term numerical simulation and extreme value analysis were performed using historical meteorological data, revealing that the vertical negative temperature gradient in the concrete box girder structures follows a composite exponential distribution. The temperature differential at the top complies with Chinese code requirements, while at the bottom, it aligns more closely with British standard BS5400. Statistical analysis of historical meteorological data predicts that the 50-year temperature differential will result in a drop amplitude of 26.42 °C, which is 1.44 times higher than measured values obtained from experiments. The proposed negative temperature gradient pattern for concrete box girders presented in this study can encompass general design codes and provide guidance for designing concrete bridges in severe cold areas.

Recurrence analysis of meteorological data from climate zones in India.

We present a study on the spatiotemporal pattern underlying the climate dynamics in various locations spread over India, including the Himalayan region, coastal region, and central and northeastern parts of India. We try to capture the variations in the complexity of their dynamics derived from temperature and relative humidity data from 1948 to 2022. By estimating the recurrence-based measures from the reconstructed phase space dynamics using a sliding window analysis on the data sets, we study the climate variability in different spatial locations. The study brings out the variations in the complexity of the underlying dynamics as well as their heterogeneity across the locations in India. We find almost all locations indicate shifts to more irregular and stochastic dynamics for temperature data around 1972-79 and shifts back to more regular dynamics beyond 2000. These patterns correlate with reported shifts in the climate and Indian Summer Monsoon related to strong and moderate El Niño-Southern Oscillation events and confirm their associated regional variability.

Precision Prediction for Dengue Fever in Singapore: A Machine Learning Approach Incorporating Meteorological Data.

This study aimed to improve dengue fever predictions in Singapore using a machine learning model that incorporates meteorological data, addressing the current methodological limitations by examining the intricate relationships between weather changes and dengue transmission. Using weekly dengue case and meteorological data from 2012 to 2022, the data was preprocessed and analyzed using various machine learning algorithms, including General Linear Model (GLM), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), Decision Tree (DT), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms. Performance metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R2) were employed. From 2012 to 2022, there was a total of 164,333 cases of dengue fever. Singapore witnessed a fluctuating number of dengue cases, peaking notably in 2020 and revealing a strong seasonality between March and July. An analysis of meteorological data points highlighted connections between certain climate variables and dengue fever outbreaks. The correlation analyses suggested significant associations between dengue cases and specific weather factors such as solar radiation, solar energy, and UV index. For disease predictions, the XGBoost model showed the best performance with an MAE = 89.12, RMSE = 156.07, and R2 = 0.83, identifying time as the primary factor, while 19 key predictors showed non-linear associations with dengue transmission. This underscores the significant role of environmental conditions, including cloud cover and rainfall, in dengue propagation. In the last decade, meteorological factors have significantly influenced dengue transmission in Singapore. This research, using the XGBoost model, highlights the key predictors like time and cloud cover in understanding dengue's complex dynamics. By employing advanced algorithms, our study offers insights into dengue predictive models and the importance of careful model selection. These results can inform public health strategies, aiming to improve dengue control in Singapore and comparable regions.

Seasonal and isotopic precipitation patterns in the semi-arid and high mountain areas

Precipitation plays a crucial role in the natural hydrological cycle. Understanding the spatial and temporal variations of precipitation isotopes is essential for identifying hydrological, meteorological, and ecological processes. In high mountain areas with arid and semi-arid conditions, especially in regions with endorheic basins, the portion of precipitation that infiltrates the groundwater as the primary source of water recharge. However, estimating this recharge is challenging and prone to high uncertainty.The main objective of this study was to implement a robust and detailed methodology to analyze the influence of meteorological variables and the origin of moisture sources on the stable isotopic composition (δ18O and δ2H) of precipitation. As an illustrative case, we focused on the Los Pozuelos Basin, an endorheic basin in the Altiplano-Puna region of the Andes. The analysis incorporated precipitation samples collected over a 3-year period (January 2020 to April 2023) along with comprehensive monitoring of local atmospheric variables, satellite imagery, and HYSPLIT backward trajectory models.The examination involved a multivariate analysis of meteorological and stable isotope data and atmospheric transport pattern. Precipitation characteristics exhibited seasonal variability, with summer precipitation being depleted in heavy isotopes due to its extended continental journey and the recycling it undergoes while crossing the Amazon basin with convective activity. Another moisture path from the Atlantic Ocean, via the Río de la Plata or Gran Chaco basin, represented an intermediate isotopic stage. La Niña events intensified westerly winds, drawing moist air masses from the Pacific Ocean and causing rainfall in the study area. In winter, precipitation comes from the Pacific Ocean and isotopically enriched due to the low amount of precipitation and lower convective activity.By employing a meticulous methodology and multivariate statistical analysis, the study contributes positively to the broader discourse on water resource management and conservation in arid and semi-arid environments.

Optimizing sowing dates increase solar radiation to mitigate maize lodging and yield variability: A five-year field study

Optimizing sowing dates (SDs) is a potential strategy for adjusting maize production to climate change and increasing yield. However, there is still a lack of research on the combined effects of lodging and yield in relation to climatic variables across various SDs. This study aims to investigate the changing patterns and distribution of important climatic variables during the maize growth season, their impact on yield and lodging, and the critical factors affecting lodging at crucial growth stages under different SD scenarios. In this study, we assessed the impact of climate change on yield and lodging by conducting field experiments over 5 years (2015, 2016, 2019–2021) encompassing 25 SDs in the Sichuan basin, China. Results demonstrated that the lodging rate had a significant effect on the coefficient of variation (CV, 3.31–10.50%) of maize yield. A 1% increase in lodging rate, led to a decrease of 58.05 kg ha-1 in yield. Changes in SDs notably affected solar radiation (Sr) from emergence to silking (E-R1). Additionally, the study found that Sr explained 34.7% of the lodging rate variation in E-R1. Analysis of historical meteorological data showed notable inter-annual variations in Sr trends, with a decline of -8.7763 MJ m-2 yr-1 from 1990 to 2021, especially noticeable from late May to early July. Variation Partitioning Analysis (VPA) revealed that climatic variables during the period from emergence to physiological maturity (E-R6) and E-R1 explained 43.9 and 53.2% of yield, respectively, across different SDs. These variables also contributed 56.0 and 45.4% to lodging. Using Random Forest (RF) determined that changes in SDs significantly impacted lodging rates mainly through modifications in basal internode morphology, which explained 69.79% of the variation. The study identified optimal sowing dates for achieving high and consistent yields, primarily occurring between late March and mid-April, attributed to increased Sr during E-R1. Overall, this research provides valuable insights into the effects of climate change on stalk lodging and offers guidance on adjusting sowing dates to mitigate maize lodging rates.

Integrated Hydrological Modeling for Watershed Analysis, Flood Prediction, and Mitigation Using Meteorological and Morphometric Data, SCS-CN, HEC-HMS/RAS, and QGIS

Flooding is a natural disaster with extensive impacts. Desert regions face altered flooding patterns owing to climate change, water scarcity, regulations, and rising water demands. This study assessed and predicted flash flood hazards by calculating discharge volume, peak flow, flood depth, and velocity using the Hydrologic Engineering Centre-River Analysis System and Hydrologic Modelling System (HEC-HMS and HEC-RAS) software. We employed meteorological and morphological data analyses, incorporating the soil conservation service (SCS) curve number method for precipitation losses and the SCS-Hydrograph for runoff transformation. The model was applied to two drainage basins (An-Nawayah and Al-Rashrash) in southeastern Cairo, Egypt, which recently encountered several destructive floods. The applied model revealed that 25-, 50-, and 100-year storms produced runoff volumes of 2461.8 × 103, 4299.6 × 103, and 5204.5 × 103 m3 for An-Nawayah and 6212 × 103, 8129.4 × 103, and 10,330.6 × 103 m3 for Al-Rashrash, respectively. Flood risk levels, categorised as high (35.6%), extreme (21.9%), and medium (21.12%) were assessed in low- and very-low-hazard areas. The study highlighted that the areas closer to the Nile River mouth faced greater flood impacts from torrential rain. Our findings demonstrate the effectiveness of these methods in assessing and predicting flood risk. As a mitigation measure, this study recommends the construction of five 10 m high dams to create storage lakes. This integrated approach can be applied to flood risk assessment and mitigation in comparable regions.

Prediction and Management of Building Energy Consumption Based on Building Environment Simulation Design Platform DeST and Meteorological Data Analysis Algorithm

Currently, the carbon emissions of building energy consumption account for a significant portion of all carbon emissions. How to reduce carbon emissions to achieve carbon neutrality is an important current research direction. Therefore this research builds a predictive algorithm model for analyzing energy consumption data of meteorological buildings using DeST platform for energy saving and emission reduction to achieve carbon neutrality. The new model uses Internet of Things and cloud platform technology to build a simulation building platform, and uses the support vector machine algorithm in the analysis algorithm to vectorize building energy consumption data, which can achieve normalization processing of building energy consumption and meteorological data. By processing building energy consumption data, prediction of building energy consumption at the next moment can be achieved. The experimental results show that the precision and accuracy of the new algorithm are higher than genetic algorithm 1 and 0.15 respectively, and 0.6 and 0.07 higher than clustering analysis algorithm respectively. Therefore, applying this algorithm model to building energy consumption prediction can significantly improve the accuracy and precision of the algorithm.

Solving the mysteries of Lahore smog: the fifth season in the country

South Asian smog is an annually recurring air pollution event that is characterized by high concentrations of air pollutants, low visibility, and severe socio-economic disruptions. It is most frequently observed across the north-western parts of the Indo-Gangetic Plains (IGP). Since 2016, it has become so frequent and pervasive that they are colloquially referred to as “the fifth season” in the region. During this season populations residing in this area including cities such as Lahore, Amritsar, Faisalabad, Multan, Delhi etc. are exposed to hazardous levels of air pollution. This study attempts to understand the reasons for the recent increase in the intensity and frequency of intense air pollution episodes by utilizing ground-based and satellite observations. Time-series analysis, seasonal and annual variations in PM2.5 and AOD were carried out. Satellite data of UVAAI, CO, and fire count were used to determine the fire incidences and aerosol characterization. The results indicate that during the last decade, there has been an increase in air pollution sources while crop residue burning, and motor vehicles have greatly contributed to the increased frequency and intensity of such events. The meteorological and topographical data analysis revealed that the IGP provides ample cloud-condensation nuclei and optimum conditions for the accumulations of pollutants, especially in the post-monsoon and winter periods. The Mann-Kendall test was performed to confirm the annual and seasonal trends of PM2.5. The major cities of South Asia such as Lahore, Delhi, Kathmandu, and Dhaka have recently shown a decreasing trend with respect to annual AOD. However, an increase has been observed for the period of post-monsoon especially for cities of Lahore and Delhi. To distinguish potential sources of air pollutants during extreme smog episodes in the region, Analytical Hierarchy Process (AHP) was conducted to estimate the most contributing factors to the annual smog episodes. According to the data, the transboundary pollution resulting from open-field agriculture fires has been assigned moderate importance over vehicular emissions. A HYSPLIT trajectory model coupled with ground observations and satellite data shows that the agricultural fires do have a profound impact on the air quality of the region. It highlights the importance of transboundary pollution and cooperation among cities, regions, and countries across the shared airshed of the Indo-Gangetic Plains.

Influence of climatic variables on maize grain yield and its components by adjusting the sowing date.

Yield and its components are greatly affected by climate change. Adjusting the sowing date is an effective way to alleviate adverse effects and adapt to climate change. Aiming to determine the optimal sowing date of summer maize and clarify the contribution of climatic variables to grain yield and its components, a consecutive 4-year field experiment was conducted from 2016 to 2019 with four sowing dates at 10-day intervals from 5 June to 5 July. Analysis of historical meteorological data showed that more solar radiation (SR) was distributed from early June to mid-August, and the maximum temperature (Tmax) > 32°C appeared from early July to late August, which advanced and lasted longer in 1991-2020 relative to 1981-1990. Additionally, the precipitation was mainly distributed from early June to late July. The climate change in the growing season of summer maize resulted in optimal sowing dates ranging from 5 June to 15 June, with higher yields and yield stability, mainly because of the higher kernel number per ear and 1,000-grain weight. The average contribution of kernel number per ear to grain yield was 58.7%, higher than that of 1,000-grain weight (41.3%). Variance partitioning analysis showed that SR in 15 days pre-silking to 15 days post-silking (SS) and silking to harvest (SH) stages significantly contributed to grain yield by 63.1% and 86.4%. The extreme growing degree days (EDD) > 32°C, SR, precipitation, and diurnal temperature range (DTR) contributed 20.6%, 22.9%, 14.5%, and 42.0% to kernel number per ear in the SS stage, respectively. Therefore, we concluded that the early sowing dates could gain high yield and yield stability due to the higher SR in the growing season. Meanwhile, due to the decreasing trend in SR and increasing Tmax trend in this region, in the future, new maize varieties with high-temperature resistance, high light efficiency, shade tolerance, and medium-season traits need to be bred to adapt to climate change and increased grain yield.

Detecting drought-prone regions through drought indices

Abstract Climate change has led to heightened variability in global rainfall patterns, resulting in greater unpredictability and inconsistency, and it has led to the origin of meteorological drought situation. This has amplified the frequency of droughts or drought-like conditions worldwide. India, being primarily agrarian, faces significant challenges due to drought, affecting various regions intermittently. Given the urgency of addressing recurring drought issues, it is crucial to determine specific ‘drought-prone’ areas through the analysis of historical and current meteorological data. It is still a challenge to quantitatively understand where and to what extent the impact of rainfall patterns could lead the drought. Whether any region likely comes under drought-prone area or not? Can we help policy makers to apply their knowledge effectively? It will help to undertake the long-term mitigation measures for drought assessment and management which encompasses early warning, monitoring, and relief toward the good health of the society. The present study is a further step in the same direction in which Akola district in Maharashtra, India has been assessed for drought-prone declaration using two drought measuring indices; seasonality index (SI) and aridity index (AI). For this, the measured meteorological data precipitation, and potential evapotranspiration, from 1952 to 2002 is made available from India Water Portal.

Solar PV technologies selection for the design of photovoltaic installations in Mexico based on the analysis of meteorological satellite data from the region

Mexico’s expansive territory spans diverse climatic conditions, which directly influences the selection of commercial photovoltaic technologies. This study utilizes solar irradiance, temperature, and cloud index data (derived from satellite sources) to generate a suitability map for commercial solar panel technologies through the Analytical Hierarchy Process-Geographical Information Systems methodology. The map illustrates that chalcopyrites and cadmium telluride emerge as the most suitable technologies in 47.12% of the national territory. Following closely behind is amorphous silicon, covering 30.45%, while monocrystalline and polycrystalline silicon account for 22.43%. The primary objective of this paper is to guide the proper selection of solar panel technology types that align optimally with Mexico’s climatic conditions. This strategic approach aims to strengthen the planning and viability of photovoltaic solar energy projects nationwide.

Winter and spring long-term dynamic of air temperature in Central Ukraine

This study aimed to analyze data from meteorological stations in central Ukraine that have the longest observation period and to search for patterns in the dynamics of temperature indicators over the past 140-200 years. Data and methods. To characterize the climate of central Ukraine, we analyzed the average monthly and average annual temperatures of Uman, Kropyvnytskyi, and Poltava, which have the longest continuous or almost continuous periods of observation. Based on these data, we have constructed graphs of changes in the average annual and average monthly temperatures for the winter and spring seasons. To analyze the dynamics of temperature indicators, we built linear and 11-year moving averages. The results. The analysis of meteorological data from weather stations in central Ukraine over the entire period of observation showed the following: average annual temperatures increased from 1.4 degrees in Kropyvnytskyi, 1.6 degrees in Uman to 2.5 degrees (since 1886 - 2.2 degrees) in Poltava. The highest average annual air temperatures at all weather stations were recorded in 2020 and 2021. The largest temperature increase occurred in the winter months. Over the entire observation period, the average monthly temperature in December increased by 2.0 degrees in Kropyvnytskyi, 2.3 degrees in Uman, and 3.6 degrees in Poltava (3.3 degrees since 1886). The average monthly temperature in January increased from 2.4 degrees in Uman and Kropyvnytskyi to 4.9 degrees (3.5 degrees since 1886) in Poltava. The average monthly temperature in February increased from 2.2 degrees in Kropyvnytskyi, 3.4 degrees in Uman to 4.1 degrees (since 1886 - 2.9 degrees) in Poltava. All three weather stations have common periods of rising and falling temperatures, with an increase in the average monthly temperature in the winter months from 1987-1989 to 2022. The air temperature in the spring months also increased significantly. Over the entire observation period, the average monthly temperature in March increased from 2.3 degrees in Kropyvnytskyi, 3.0 degrees in Uman to 3.6 degrees (since 1886 - 3.5 degrees) in Poltava. The average monthly temperature in April increased from 2.1 degrees in Kropyvnytskyi, 2.4 degrees in Uman to 4.2 degrees (since 1886 - 3.4 degrees) in Poltava. The average monthly temperature in May increased from 0.5 degrees in Uman and Kropyvnytskyi to 2.9 degrees (since 1886 - 1.3 degrees) in Poltava. All three weather stations have common periods of rising and falling temperatures, with a slight increase in average monthly spring temperatures from 1988-1990 to 2022. Analyzing the graphs of 11-year moving averages, one can notice the presence of periods of increase and decrease in average monthly temperatures lasting about 33 years or doubled periods lasting about 66 years. Scientific novelty. For the first time, the data of weather stations in central Ukraine for the entire period of observation (138 years – Uman, 148 years – Kropyvnytskyi, 198 years – Poltava) were analyzed and regularities in the dynamic of temperature indicators were determined. The practical significance lies in the possibility of using the researchers results to predict future climate change.