Meteorological Indices Research Articles

Research on the Index Calculation Method for the Impact of Drought on Water Quality in the Nakdong River, Korea

The impact of drought is intensifying due to climate change, leading to significant environmental consequences, particularly concerning river water quality. While drought is typically classified as meteorological or hydrological, studies assessing its environmental impacts remain limited. Drought-induced hydrological alterations in rivers often degrade water quality, necessitating the development of an environmental drought index. This study introduces a novel methodology for calculating an index to evaluate the effects of drought on river water quality, specifically applied to tributaries of the Nakdong River in South Korea. The index was constructed by reviewing existing water quality and drought indices, selecting relevant parameters, and weighting each factor following the National Sanitation Foundation Water Quality Index (NSFWQI) methodology. Factors integrated into the index encompass both meteorological and hydrological indicators, with priority given to variables measurable in real time. Real-time parameters—such as flow rate, cumulative precipitation, days without rainfall, and sensor-based metrics (pH, electrical conductivity [EC], dissolved oxygen [DO], and total organic carbon [TOC])—were incorporated. Additionally, for rivers with upstream dams, dam discharge data were included to reflect its influence on flow conditions. The applicability of the calculated index was assessed by comparing index values to observed water quality data. A class interval structure was implemented to enhance the index’s usability across diverse riverine conditions. Furthermore, the utility of the index was validated by comparing it to the basin’s target water quality, thereby assessing its sensitivity to drought-induced water quality deterioration. The environmental drought index proposed in this study enables the proactive and real-time monitoring of water quality under drought conditions. When applied to 10 tributaries of the Nakdong River, the index demonstrated a clear correlation between drought conditions and water quality deterioration. This index provides a practical tool for river management, facilitating early response strategies to mitigate water quality impacts associated with environmental drought.

A novel structural equation modelling-based framework for identifying hydrometeorological multi-factor interaction

Study regionThe Hanjiang River Basin, China. Study focusIn this study, we proposed a new integrated framework based on structural equation models to identify hydrometeorological multi-factor interaction relationship. The spatiotemporal distribution and interaction relationship of hydrological, meteorological, and vegetation indicators in the Hanjiang River Basin were analyzed based on the national long-series monitoring data. New hydrological insights for the regionThe findings reveal that: (1) The rainfall is the maximum in the lower reaches (968.87 mm), and the runoff depth fluctuated the most in the lower reaches (1181.70 mm to 2934.88 mm). (2) Dew point temperature is an important influencing factor of rainfall in the upper and middle reaches, and relative humidity is an important influencing factor of rainfall in the lower reaches. High vegetation cover is an important influencing factor for runoff depth. (3) Rainfall and runoff depth are significantly negatively affected by meteorological characteristics, with effect values ranging from −0.24 to −0.68. However, runoff depth is positively affected by rainfall, relative humidity, vegetation characteristics, and canopy interception, with effect values ranging from 0.02 to 0.58. The size and mode of influence have significant regional differences.

Construction of Freezing Injury Grade Index for Nanfeng Tangerine Plants Based on Physiological and Biochemical Parameters.

Low-temperature freezing stress constitutes the most significant meteorological disaster during the overwintering period in the Nanfeng Tangerine (NT) production area, severely impacting the normal growth and development of the plants. Currently, the accuracy of meteorological disaster warnings and forecasts for NT orchards remains suboptimal, primarily due to the absence of quantitative meteorological indicators for low-temperature freezing stress. Therefore, this study employed NT plants as experimental subjects and conducted controlled treatment experiments under varying intensities of low-temperature freezing stress (0 °C, -2 °C, -5 °C, -7 °C, and -9 °C) and durations (1 h, 4 h, and 7 h). Subsequently, physiological and biochemical parameters were measured, including photosynthetic parameters, chlorophyll fluorescence parameters, reactive oxygen species, osmoregulatory substances, and antioxidant enzyme activities in NT plants. The results demonstrated that low-temperature freezing stress adversely affected the photosynthetic system of NT plants, disrupted the dynamic equilibrium of the antioxidant system, and compromised cellular stability. The severity of freezing damage increased with decreasing temperature and prolonged exposure. Chlorophyll (a/b) ratio (Chl (a/b)), maximum quantum yield of photosystem II (Fv/Fm), soluble sugar, and malondialdehyde (MDA) were identified as key indicators for assessing physiological and biochemical changes in NT plants. Utilizing these four parameters, a comprehensive score (CS) model of freezing damage was developed to quantitatively evaluate the growth status of NT plants across varying low-temperature freezing damage gradients and durations. Subsequently, the freezing damage grade index for NT plants during the overwintering period was established. Specifically, Level 1 for CS ≤ -0.50, Level 2 for -0.5 < CS ≤ 0, Level 3 for 0 < CS ≤ 0.5, and Level 4 for 0.5 < CS. The research results provide valuable data for agricultural meteorological departments to carry out disaster monitoring, early warning, and prevention and control.

Multi-Index Approach to Assess and Monitor Meteorological and Agricultural Drought in the Mediterranean Region: Case of the Upper Oum Er Rabia Watershed, Morocco

Drought is a severe disaster, increasingly exacerbated by climate change, and poses significant challenges worldwide, particularly in arid and semi-arid regions like Morocco. This study aims to assess and monitor drought using a multi-index approach to provide a comprehensive understanding of its spatio-temporal dynamics at both meteorological and agricultural levels. The research focuses on the Upper Oum Er Rabia watershed, which spans 35,000 km2 and contributes approximately a quarter of Morocco’s renewable water resources. We propose a methodology that combines ERA5 temperature data from remote sensing with ground-based precipitation data to analyze drought characteristics. Three meteorological indices were utilized: the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), and the Reconnaissance Drought Index (RDI). Additionally, three remote-sensing indices were employed to capture agricultural drought: the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Crop Water Stress Index (CWSI), with a total of 528 NDVI and EVI images and 1016 CWSI images generated through Google Earth Engine (GEE), using machine-learning techniques. Trend analyses were conducted to monitor drought patterns spatio-temporally. Our results reveal that the three-month interval is critical for effective drought monitoring and evaluation. Among the indices, SPEI emerged as the most effective for capturing drought in combination with remote-sensing data, while CWSI exhibited the highest correlation with SPEI over the three-month period, outperforming NDVI and EVI. The trend analysis indicates a significant precipitation deficit, alongside increasing trends in temperature and evapotranspiration over both the short and long term. Furthermore, all drought indices (SPI, SPEI, and RDI) demonstrate an intensification of drought conditions. Adaptation strategies are essential for managing water resources in the Upper Oum Er Rabia watershed under these evolving climate conditions. Continuous monitoring of climate variables and drought indices will be crucial for tracking changes and informing future water management strategies.

A data-driven framework for the definition of heat in CV research for better comprehension of climate changes on human health

Abstract Background Recent literature has identified numerous combinations of meteorological indicators, methods and comparison thresholds to define heat in order to analyse its impact on adverse cardiovascular (CV) events. In fact, despite increasing attention to the issue due to ongoing climate change, there is no standardised definition of heat nor guidelines for its establishment, and in studies often little attention is given to the selected definition, despite its impact on the results. Purpose To fill this gap, our aim was to propose a data-driven framework for selecting the temperature threshold for defining heat in the context of studies on adverse CV outcomes, combining the most common approaches identified in the literature. Methods The following five-step framework is proposed: 1) Selection of temperature lags based on the contour plot obtained by the distributed lag non-linear model (DLNM); 2) Estimation of the relative risk (RR) of having a CV emergency in heat versus non-heat days by Poisson regression considering the 90th-99th percentile (%ile) of the temperature distribution for each lag; 3) Selection of the final lag based on the curvatures of the 10 included %iles; 4) Calculation of the % change in RR from %ile i to %ile i+1; 5) Selection of the heat threshold, defined as the %ile from which all the next %iles are associated with a higher % change, or, if not existing, the %ile preceding the one with the highest % change. We tested this framework, for each year, on retrospective CV emergencies in May-September 2017-2022 in Milan, Italy, using the apparent temperature data, accounting for air temperature, relative humidity and wind speed. Results The contour plot of the DLNM, controlled for air pollution, seasonality and long-term trend, showed that lags 0 and 1 were characterised by an increased RR for a CV emergency. The RR estimated for each %ile for each considered lag (i.e., heat on day i, heat on day i or on day i-1, and heat on day i and on day i-1), produced a similar curvature, so first lag option was arbitrarily selected. For Milan, the 95th %ile was the point from which all the subsequent %iles were associated to progressively higher % changes, resulting in a RR of having a CV emergency compared to non-heat days equal to 1.113 (95% CI: 1.087-1.139). The mean daily apparent temperature values associated to such %ile, varying from 27.7 °C to 29.9 °C depending on the year, should then be considered as the heat threshold in the examined context. Conclusion We proposed a flexible data-driven framework for the definition of heat that could easily be extended to similar scenarios. Although the framework still requires some arbitrary choices to be made, and the need of validation on other real-world data, it could provide a valuable guideline for relevant research, and could contribute to the standardization of heat definition for CV health research aiming at a better comprehension of climate changes on human health.

Google Earth Engine application for mapping and monitoring drought patterns and trends: A case study in Arkansas, USA

Drought is a prolonged dry period that can have severe impacts on the environment, human health, economies, agriculture, and energy resources. It can lead to water shortages, ruin crops, dry out forests, and reduce the availability of food and water for wildlife and livestock. The primary objectives of this study are to: 1) quantify the variability and distributions of drought patterns in Arkansas, United States (US), 2) use remotely sensed indices to investigate the correlation between drought and vegetation cover in the area, and 3) develop a cloud-based framework (user-friendly app) to facilitate the assessment of drought impact in Arkansas over the past decades. A correlation analysis was also performed between the Vegetation Health Index (VHI) and meteorological indices to better understand the impact of meteorological drought on vegetation stress. In addition, Mann-Kendall trend analysis was used to assess trends in meteorological drought indices. The results indicate that drought is most prevalent during March and August months. The results of this study revealed that approximately 31% of the study area fell under the four drought classes (i.e., 1% Extreme drought, 4% Severe drought, 9% moderate drought, and 19% mild drought), with spring and the growing season experiencing moderate drought, particularly in agricultural areas, most notably within the Mississippi Alluvial Valley Plain at both state and county levels. In August, approximately 31% of the study area fell under the Four drought classes (i.e., 1% Extreme drought, 4% Severe drought, 9% moderate drought, and 19% mild drought), with spring and the growing season experiencing moderate drought, particularly in agricultural areas, most notably within the Mississippi Alluvial Valley Plain at both state and county levels. This study provides an essential foundation for policymakers, environmental scientists, and agricultural stakeholders aiming to mitigate drought impacts and safeguard against future climate uncertainties.

Eco-physiological approach to assessing the functional state of workers in geological prospecting expeditions during meridian movement between mid-latitudes and the Arctic region

BACKGROUND: The current challenge lies in defining the unique characteristics of compensatory and adaptive mechanisms formation, as well as the development of healthcare technologies for various forms of work in Arctic region conditions. AIM: To study associations between climate patterns and physiological responses to daily fluctuations in meteorological factors. Specifically, we aimed to investigate how these factors impact the cardiovascular system and psychophysiological well-being of workers during geological prospecting expeditions along the meridian travel between Tyumen (57° 09’ N) and Yamal (71° 11ʹ N). MATERIAL AND METHODS: A total of 115 men aged 20–40 years in mid-latitude conditions (baseline data) and during occupational relocation to the Arctic region comprised the sample. The examination included recording key indicators of the cardiovascular system (heart rate, blood pressure, mean arterial pressure, pulse pressure), ECG in standard unipolar and augmented leads, and psychophysiological assessments (evaluation of trait and state anxiety, stress diagnostics, regulation of self-control, and emotional lability). Additionally, to calculate the natural and climatic contrast of regions in Western Siberia, meteorological data and geomagnetic activity indicators were analyzed based on eight daily measurements over 12 months across 11 years using a unified system of meteorological station indicators in Tyumen and Kharasavey. RESULTS: The cardiovascular system experiences desynchronosis during meridian movement from middle latitudes to the Arctic region. This condition is influenced by the interregional climatic contrast and the differences in daily rhythm characteristics of meteorological data between the initial and final destinations. The interaction within the system of meteorological rhythms plays an important role in these variations. CONCLUSION: The established associations within the ‘meteorhythms–biorhythms’ system underscore the necessity for developing regional norms for chronophysiological parameters of a healthy individual under specific environmental and climatic conditions. It also highlights the importance of dynamic monitoring of deviations in these parameters during occupational relocations between mid-latitudes and the Arctic region across different seasons. This approach is essential for the effective monitoring of maladaptive disorders and for predicting the potential development of pathologies.

ОБҐРУНТУВАННЯ РОЗШИРЕНОГО СКЛАДУ СПОСТЕРЕЖЕНЬ НА ВОДНО-БАЛАНСОВИХ СТАНЦІЯХ ТА ДОСЛІДНИЦЬКИХ ГІДРОГЕОФІЗИЧНИХ ПОЛІГОНАХ

The current level of understanding of the subordination and multifactorial dependence of the determining processes in the atmosphere, lithosphere and hydrosphere of the Earth requires a corresponding reorganization of the basic system of environmental monitoring, improvement and expansion of research on water these stations, which can become the supporting 'nodes' of the balance of this system. The appearance of fundamentally new theoretical developments, modern devices and equipment, a large number of software tools, etc., prompts a significant reorganization and strengthening of the environmental monitoring system. The article substantiates an additional set of studies, which should be included in the regulation of observations at already existing water balance stations, with their mandatory modernization. Spheres are subject to control - the atmosphere, surface and underground (subsurface) hydrospheres, which change over time at different rates and pedosphere. A certain inertia of hydrogeological processes implies the possibility of using meteorological indicators, which can be used to predict changes in the moisture regime in the aeration zone and shallow groundwater in the near future; based on the reliably predictable changes of the latter - to forecast changes in interlayer groundwater resources, etc. It is proposed to include in the monitoring regulations the following indicators of the state of the environment, which will allow to identify and analyze the causes of changes in the water situation, balance and resources, to determine the mechanisms of moisture transfer and accumulation, as well as to build models and perform predictive assessments. The results of comprehensive research at the «Lutiz» landfill demonstrate significant variability of hydrogeophysical indicators and changes in the intensity of natural signals depending on the landscape timing, the latest tectonics, the geological basis and the composition of the overlying sediments. Original devices are presented, which are used to determine indicators of electric and thermal fields, the ratio of positive and negative air ions, etc.

Construction of a comprehensive meteorological risk index for wheat Fusarium head blight prediction based on more than a half-century of monitoring data.

Fusarium head blight (FHB) represents a critical threat to wheat production globally, not only reducing yields but also contaminating crops with harmful mycotoxins. This study aimed to elucidate new spatiotemporal patterns of FHB incidence and to develop a comprehensive meteorological risk index to enhance scientific prevention and control of the disease. Through the analysis of annual and decadal variations from 1965 to 2023, the study assessed FHB trends across four agricultural regions (I, II, III, and IV) in Jiangsu Province, located in the middle and lower reaches of the Yangtze River-a hotspot for FHB in China. Key findings include: Since 1965, Regions I and III consistently exhibited higher FHB incidence rates compared to Regions II and IV. Post-2000, there was a notable increase in years with high incidence rates, with Region III overtaking Region I as the region with the highest incidence. Since 2010, occurrences of FHB reaching the most severe grade (Grade 5) have surpassed those in previous decades across all regions. The study also revealed a stronger correlation between meteorological factors (cumulative precipitation, number of days with rainfall ≥ 0.1 mm, total rainy days with ≥ 2 and ≥3 consecutive days of rain, total rainy days with both average daily air temperature ≥ 15 °C and daily rainfall ≥ 0.1 mm, days with average daily relative humidity ≥ 85%, cumulative sunshine hours, and cumulative cloudy days) and the FHB incidence rates during the heading-flowering-grain filling period in Regions I, II, and III, compared to the heading-flowering period alone. This indicates that optimal temperature and high humidity during the grain filling stage significantly contribute to the final FHB incidence rates. Despite the less apparent correlation between temperature changes and disease rates, the significant warming trend observed since 2000 has likely fostered conditions conducive to the proliferation of FHB. The comprehensive meteorological risk index, constructed to incorporate key meteorological factors during the heading-flowering-filling period, showed a strong correlation with actual disease incidences. The index demonstrated fitting accuracy rates of 84.7% for Region I, 72.9% for Region II, 83.1% for Region III, and 90.9% for Region IV, underscoring its effectiveness in predicting FHB occurrences. This tool offers both convenience and practicality, providing valuable insights for strategically managing FHB risks based on local weather conditions.

Effect of Sowing Dates and Wheat Cultivars on Agro Meteorological Indices of Wheat under Conditions of North-western Himalayas, India

Climate change in recent times have presented a scenario of elevated susceptibility of wheat to rising temperatures. Agro-meteorological indices can present a scenario of variable response of wheat to diverse conditions. A field investigation was conducted in rabi season of 2016-17 at Research farm, Department of Agronomy, CSK Himachal Pradesh Krishi Vishvavidyalaya, Palampur to evaluate the influence of sowing dates and wheat cultivars on agro-meteorological indices such as growing degree days, photothermal units, Helio thermal units and phenothermal index of wheat crop. The field investigation involved five sowing dates (15th October, 30th October, 15th November, 30th November and 15th December) and wheat cultivars (HPW-349 and HPW-155) which were studied in a factorial randomized block design with three replications. The results of the investigation revealed that timely sown crop i.e., on 15th October recorded the highest values for agro-meteorological indices for both the wheat cultivars such as growing degree days, Helio thermal units, photothermal units and phenothermal index across various wheat development stages such as complete emergence, crown root initiation, tillering, ear emergence and physiological maturity. Therefore, timely sowing of wheat by 15th October was concluded to be the best for optimized agro-meteorological indices such as growing degree days, photothermal units, Helio thermal units and phenothermal index under conditions of North-Western Himalayas.

Insurance loss model vs. meteorological loss index – how comparable are their loss estimates for European windstorms?

Abstract. Windstorms affecting Europe are among the natural hazards with the largest socio-economic impacts. Therefore, many sectors like society, the economy, or the insurance industry are highly interested in reliable information on associated impacts and losses. In this study, we compare – for the first time – estimated windstorm losses using a simplified meteorological loss index (LI) with losses obtained from a complex insurance loss (catastrophe) model, namely the European Windstorm Model of Aon Impact Forecasting. To test the sensitivity of LI to different meteorological input data, we furthermore contrast LI based on the reanalysis dataset ERA5 and its predecessor ERA-Interim. We focus on similarities and differences between the datasets in terms of loss values and storm rank for specific historical storm events in the common reanalysis period across 11 European countries. Our results reveal higher LI values for ERA5 than for ERA-Interim for all of Europe (by roughly a factor of 10), coming mostly from the higher spatial resolution in ERA5. The storm ranking is comparable for western and central European countries for both reanalyses, confirmed by high correlation values between 0.6 and 0.89. Compared to the Aon Impact Forecasting model, LI ERA5 shows comparable storm ranks, with correlation values ranging between 0.45 and 0.8. In terms of normalized loss, LI exhibits overall lower values and smaller regional differences. Compared to the market perspective represented by the insurance loss model, LI seems to have particular difficulty in distinguishing between high-impact events at the tail of the wind gust distribution and moderate-impact events. Thus, the loss distribution in LI is likely not steep enough, and the tail is probably underestimated. Nevertheless, it is an effective index that is suitable for estimating the impacts of storm events and ranking storm events, precisely because of its simplicity.

Satellite Based Composite Weather Insurance Product for assessing mid-term adversary

The present study formulated a satellite-based rainfall insurance product (SRIP) at sub-seasonal scale during 2015–2020 by integrating meteorological, geophysical and vegetation index products e.g. rainfall (CHIRPS:Climate Hazards Group InfraRed Precipitation with Station), soil moisture (SMAP), normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) from moderate resolution imaging spectro-radiometer (MODIS). The SRIP was meticulously designed to integrate both cause (rainfall) and impact (NDVI, NDWI, soil moisture) parameters to enhance the assessment of min-season crop risks, with a particular emphasis on deficit rainfall. The performance evaluation of SRIP was conducted in two distinct agro-climatic zones viz. Deoria district of Uttar Pradesh (Upper Gangetic Plain Region) and Krishna district of Andhra Pradesh (Southern Plateau and Hills Region) spanned from June to October, which aligns with the major crop-growing season corresponding to the Southwest monsoon period in India. Three sets of SRIPs were investigated i.e. SRIP with cause parameter (SRIP1), SRIP with cause-impact parameters with no lag response (SRIP2) and SRIP with cause-impact parameters with one-month lag response (SRIP3). The SRIP was structured to provide a numerical index on a scale ranging from 0 to 1, representing poor to good crop-growing conditions that could effectively identify the rainfall deficit threshold (SRIP≤0.43) corresponding to negative percent deviation in rainfall (PDR≤−20%). This threshold level was recognized for compensation of triggers at the multiple crop growth phases during the crop-growing season. The district average of SRIP and PDR showed fair correlation co-efficient (0.77 ≤ r ≤ 0.88). Good correlation between sub-seasonal (monthly, bi-monthly, tri-monthly) SRIP and normalized MODIS Gross Primary Productivity (0.65 ≤ r ≤ 0.98), and SRIP deviation and yield deviation (0.45 ≤ r ≤ 0.97) reflect sensitivity of SRIP to crop carbon productivity and crop yield. The findings of the present study recommend SRIP not only a multi-phase composite index for assessing mid-season crop risks in terms of rainfall deficit for clusters of insurance units, but it has the potential to be used by insurance agencies to settle the claims by farmers in a transparent and faster manner for crop loss due to large-scale rainfall deficits. However, SRIP needs to be customized in future to make it more adaptable to settle insurance claims due to weather adversaries at individual farm level.

Increased exposure of rice to compound drought and hot extreme events during its growing seasons in China

Against the backdrop of global warming, compound drought and hot extreme events (CDHEEs) have intensified markedly, attracting attention from both the scientific community and society. However, a comprehensive understanding of the characteristics of the CDHEEs and their impacts on rice production during growing seasons remains limited. Thus, in this study, we use daily maximum and minimum air temperatures to derive a meteorological drought composite index for CDHEE identification. Additionally, we employ the Crop Calendar and the CROPGRIDS data to define the rice-growing seasons and regions. Detailed analyses reveal that from 1961 to 2023, the CDHEE frequency in both single-cropping rice regions (Rice1) and double-cropping rice regions (Rice2) increased at rates of 0.79 days (decade)−1 and 1.26 days (decade)−1, respectively, with a notable acceleration in the post-1990 s. The duration of the CDHEEs has lengthened, and prolonged events have become more frequent. The intensity of the CDHEEs has also enhanced, primarily driven by hot extreme events in Rice1 and the synergistic effects of droughts and hot extreme events in Rice2. Furthermore, the time span of CDHEE occurrence has expanded, starting earlier (rates of − 3.21 days (decade)−1 in Rice1 and − 4.37 days (decade)−1 in Rice2) and ending later (rates of 4.81 days (decade)−1 in Rice1 and 8.20 days (decade)−1 Rice2) during rice-growing seasons, thereby extending the exposure duration of rice. Since the 1990 s, rice exposure to the CDHEEs has also intensified, especially for the persistent CDHEEs. These findings indicate the upward trends in the frequency, intensity and duration of the CDHEEs, as well as the exposure of rice to the CDHEEs, during the rice-growing seasons in China. This research provides crucial insights into CDHEE impacts and underscores the necessity of implementing targeted adaptation and mitigation strategies to safeguard rice production amidst escalating climate challenges.

Research on dangerous flight weather prediction based on machine learning

Abstract With the continuous expansion of the scale of air transport, the demand for aviation meteorological support also continues to grow. The impact of hazardous weather on flight safety is critical. How to effectively use meteorological data to improve the early warning capability of flight dangerous weather and ensure the safe flight of aircraft is the primary task of aviation meteorological services. In this work, support vector machine (SVM) models are used to predict hazardous flight weather, especially for meteorological conditions with high uncertainty such as storms and turbulence. SVM is a supervised learning method that distinguishes between different classes of data by finding optimal decision boundaries in a high-dimensional space. To meet the needs of this study, we chose the radial basis function (RBF) as the kernel function, which helps to deal with nonlinear problems and enables the model to better capture complex meteorological data structures. During the model training phase, we used historical meteorological observations from multiple weather stations, including temperature, humidity, wind speed, wind direction, and other meteorological indicators closely related to flight safety. From this data, the SVM model learns how to distinguish between normal and dangerous flight weather conditions.

The study of the city of Rudny as a “heat island”

The annual increase in surface air temperatures contributes to climate change. This phenomenon is widespread over the entire surface of the Earth and is typical for any point of the planet. The purpose of this study was to identify patterns of changes in meteorological indicators in the city of Rudny, Kostanay region, Republic of Kazakhstan over the summer period 2018–2022. The scientific novelty of the work lies in the fact that for the first time the spatial distribution of the heat index in the territory of the city of Rudny was determined. To conduct the study, data from local automated weather stations were used, meteorological data was analyzed, compared for each year of the study, and the heat index was calculated. The reliability of the results is ensured by mathematical processing of daily empirical temperature and humidity data (calculation of the average for the day, the average for the month), coefficient of variation, coefficient of dispersion. The conducted research provides a clear description of climate changes in the city of Rudny, which can be used by municipal authorities to form measures to reduce the thermal impact on public health, heat loss in the housing and communal services system, and to improve air quality when planning urban infrastructure in the territory of the studied city.

Thermal regime of peatlands in Western Siberia near the southern border of the permafrost

Relevance. The permafrost degradation under the effect of global warming. It determines the necessity for a predictive assessment of permafrost stability to minimize disturbances to engineering installations in the permafrost zone. Aim. To assess the stability of frozen peatlands in the southern part of the zone of insular distribution of permafrost. Objects. Soils of flat-mound and high-mound palsa mires, which preserve permafrost on the southern border of the insular permafrost zone. Methods. Measurements of the temperature of peat and mineral soil in geocryological boreholes from 0 to 10 m deep using the SAM-N automatic monitoring surveillance network; determination of surface temperature from thermal channels of MODIS satellite images from 2000 to 2022; analysis of meteorological indices and determination of air temperature trends for predictive assessment of permafrost stability; calculation of indicator values of permafrost state, such as freezing degree-days and thawing degree-days, frost index, freezing and thawing N-factors. Results. Numerous features of unstable permafrost have been observed. In particular, the mean annual temperature of the surface layer was positive in all studied boreholes (+0.8...+1.3°C), and the temperature at the depth of zero amplitudes (10 m) is close to the melting point (with the predominance of temperatures in the range of –0.2...–0.3°C). Besides, a layer of soil that does not freeze throughout the year (non-merging permafrost) was identified. The thawing degree-days and frost number values correspond to areas with unfrozen soils. Positive trends in air temperature, Earth's surface temperature, and snow depth were observed. For 2000–2022, surface air temperature increased by 0.76°C/10 years on average. The land surface temperature increased in summer by an average of 0.42°C/10 years. If the positive trend in surface air temperatures continues, frozen peatlands in the southern part of the permafrost zone will completely melt in 50–70 years.

Analysis of long-term dynamics of air temperature fluctuations in the Nura River basin

In recent decades, a different frequency of dry years has been observed in the Nura River basin in Central Kazakhstan. This article examines the trend of changes in temperature fluctuations over a long period using materials from a separate climate element from the Besoba and Karaganda meteorological stations located in the Nura River basin and the Arkalyk meteorological station located outside the basin, to the west of it, but characterized by similar natural conditions. In the course of research over an 81-year time interval from 1939 to 2019, the absolute and relative frequency of average monthly air temperatures in July in the Nura River basin were established. The meteorological stations under consideration are part of the global international network of meteorological data of Kazhydromet and have constant, accurate series of values of meteorological indicators. Based on the use of the method of statistical analysis, an assessment of the spatio-temporal fluctuations of the temperature regime over a long-term period in the Nura River basin was identified. The study expressed the formation of an arid climate associated with an increase in the average monthly air temperature in the basin of the study area.

Air pollutant prediction based on a attention mechanism model of the Yangtze River Delta region in frequent heatwaves

Heatwaves pose significant threats to urban environments, affecting both ecological systems and public health, primarily through the exacerbation of air pollution. Accurate prediction of air pollutant concentrations during heatwave periods is crucial for authorities to develop timely prevention and control strategies. Thus, we developed the 1D-CNN-BiLSTM-attention model, specifically designed to account for the unique data characteristics associated with heatwave conditions. Our model leverages an attention mechanism to enhance its ability to learn and predict air pollutant behavior during heatwaves. Across six scenario-based experiments, the model demonstrated high predictive accuracy, achieving a MAPE of 2.93 %. The model integrates meteorological indicators such as temperature, humidity, wind speed, cloud cover, and precipitation, extending its predictive capability across a spatial range of 150 km. In experiments testing the model's applicability to three typical city types in the Yangtze River Delta region, the results confirmed its effectiveness in predicting air pollutants. These findings highlight the model's usefulness for studying air pollution during urban heatwave periods on a regional scale, demonstrating its robustness and reliability under varying weather conditions.

Random forest analysis of the relative importance of meteorological indicators for heatstroke cases in Japan based on the degree of severity and place of occurrence

Heatstroke is a serious health concern in Japan. To reduce heatstroke risk, the government of Japan implemented the “Heatstroke Alert” nationwide in 2021, employing the wet bulb globe temperature (WBGT) as a criterion. Although the WBGT is a useful meteorological indicator for assessing the risk of heatstroke, other important meteorological indicators must also be investigated. Therefore, using a random forest approach, this study analyzed the relative importance of several meteorological indicators, including those representing heat acclimatization, for each of the 47 Japanese prefectures. Using the generalized linear model, important meteorological indicators were employed as explanatory variables in the heatstroke prediction model to determine the predictive meteorological indicator. Heatstroke cases were evaluated separately by the degree of severity and the place of occurrence. The results showed that the relative temperature (RelTemp), which represents heat acclimatization and was calculated considering past temperature history, was the most predictive (i.e., provided the best goodness of fit) concerning the degree of severity, place of occurrence, and prefectures. RelTemp can be a complementary indicator of WBGT in countries and regions such as Japan, where seasonal differences in heat acclimatization must be considered. In addition, the findings of this study contribute to the development of a more accurate assessment of heatstroke risk.

Exploring the influence of environmental indicators and forecasting influenza incidence using ARIMAX models.

Influenza is a respiratory infection that poses a significant health burden worldwide. Environmental indicators, such as air pollutants and meteorological factors, play a role in the onset and propagation of influenza. Accurate predictions of influenza incidence and understanding the factors influencing it are crucial for public health interventions. Our study aims to investigate the impact of various environmental indicators on influenza incidence and apply the ARIMAX model to integrate these exogenous variables to enhance the accuracy of influenza incidence predictions. Descriptive statistics and time series analysis were employed to illustrate changes in influenza incidence, air pollutants, and meteorological indicators. Cross correlation function (CCF) was used to evaluate the correlation between environmental indicators and the influenza incidence. We used ARIMA and ARIMAX models to perform predictive analysis of influenza incidence. From January 2014 to September 2023, a total of 21,573 cases of influenza were reported in Fuzhou, with a noticeable year-by-year increase in incidence. The peak of influenza typically occurred around January each year. The results of CCF analysis showed that all 10 environmental indicators had a significant impact on the incidence of influenza. The ARIMAX(0, 0, 1) (1, 0, 0)12 with PM10(lag5) model exhibited the best prediction performance, as indicated by the lowest AIC, AICc, and BIC values, which were 529.740, 530.360, and 542.910, respectively. The model achieved a fitting RMSE of 2.999 and a predicting RMSE of 12.033. This study provides insights into the impact of environmental indicators on influenza incidence in Fuzhou. The ARIMAX(0, 0, 1) (1, 0, 0)12 with PM10(lag5) model could provide a scientific basis for formulating influenza control policies and public health interventions. Timely prediction of influenza incidence is essential for effective epidemic control strategies and minimizing disease transmission risks.