Climatological Parameters Research Articles

Challenges on the implementation of wastewater-based epidemiology as a prediction tool: the paradigm of SARS-CoV-2.

Challenges on the implementation of wastewater-based epidemiology as a prediction tool: the paradigm of SARS-CoV-2.

Comparative Hydrological Analysis at Two Stations on the Boundary River Sotla/Sutla (Slovenia–Croatia)

This study examines hydrological processes at the Zelenjak (1958–2023) and Rakovec (1926–2022) stations on the Sotla/Sutla River, analyzed on an annual time scale. The analysis includes time series of annual minimum and maximum mean daily flows and mean annual flows. Additionally, data on annual precipitation and mean annual temperatures measured at the climatological station Bizeljsko in the period 1951 to 2024 were used to calculate annual runoff coefficients at the Zelenjak and Rakovec stations. The New Drought Index (NDI) was calculated using precipitation and air temperature data measured at the Bizeljsko climatological station. All analyses indicated a strong variability of the analyzed parameters over the available data period. A clear downward trend in mean annual flows is observed. In the recent period, from 2000 onward, there has been a sharp increase in mean annual air temperatures and a decline in all other analyzed hydrological and climatological parameters. Particularly concerning is the notable rise in the frequency and intensity of droughts in the 2000–2024 period. The causes of these trends could not be reliably determined through an analysis conducted on an annual time scale. It appears that natural factors, particularly the sharp rise in air temperatures, have played a significant role. However, it is important to emphasize that the natural characteristics of the Sotla/Sutla River basin have, to date, remained largely unaffected by human interventions. Furthermore, the insufficient accuracy in defining peak flows must be considered, as the rating curves used to define maximum flows may not have been reliable in certain periods.

A global systematic review and meta-analysis of virus prevalence in vegetables and fruits: meta-regression approaches of climatology and food security index effects.

This systematic review and meta-analysis was conducted to investigate the spread of viral contamination (hepatitis A, human norovirus, astrovirus and adenovirus) in vegetables and fruits. The most frequent types of vegetables involved were chicory (73%), silver beet (50%), watercress (34%), leek (30%) and purslane (21%). Vegetables exhibited a higher pooled prevalence of viral contamination (2%; 95% confidence interval = 1-3%) than fruits (no prevalence was observed). High levels of virus prevalence in vegetables were recorded in Argentina (35%), Egypt (23%) and Belgium (23%), whereas, for fruits, the most contaminated products were related to products from Egypt (20%). The most common viral contaminations in products were human astrovirus (29%) and human adenovirus (26%) for vegetables and porcine adenovirus (2%) for fruits. Fresh vegetables (2%) and frozen fruits (4%) had higher reports of viral prevalence than frozen vegetables and fresh fruits. In addition, frozen vegetables and fresh fruits were not reported for viral contamination. Our findings showed that the prevalence of virus prevalence has a direct relationship with climatological parameters, and regardless of Egypt (total 25.52% contamination), areas with humidity above the range of 65-70%, average temperature of 10-15 °C and annual rainfall of approximately 1000 mm, the prevalence showed higher viral contamination (P < 0.05). Moreover, the results show that countries with a low Global Food Security Index had high infection rates (P < 0.05). Governments should ensure that, through risk management protocols, the possibility of zoonotic infection transmission in fruits and vegetables is continuously monitored and controlled to reduce the risk of contamination. © 2025 Society of Chemical Industry.

Real-Time Estimation of Near-Surface Air Temperature over Greece Using Machine Learning Methods and LSA SAF Satellite Products

The air temperature near the Earth’s surface is one of the most important meteorological and climatological parameters. Yet, accurate and timely readings are not available in significant parts of the world. The development and first validation of a methodology for the estimation of the near-surface air temperature (NSAT) is presented here. Machine learning and satellite products are at the core of the developed model. Land Surface Analysis Satellite Application Facility (LSA SAF) products related to Earth’s surface radiation, temperature and humidity budgets, albedo and land cover, along with static topography parameters and weather station measurements, are used in the analysis. A series of experiments showed that the Random Forest regression with 20 selected satellite and topography predictors was the optimum selection for the estimation of the NSAT. The mean absolute error (MAE) of the NSAT estimation model was 0.96 °C, while the mean biased error (MBE) was −0.01 °C and the R2 was 0.976. Limited seasonality was present in the efficiency of the model, while an increase in errors was noted during the first morning and afternoon hours. The topography influence in the model efficiency was rather limited. Cloud-free conditions were associated to only marginally smaller errors, supporting the applicability of the model under both cloud-free and cloudy conditions.

Simultaneous Analytical View of Frequency Modulation (FM) Radio Signal Strength and Climatological Parameters in Radio Communication

Simultaneous Analytical View of Frequency Modulation (FM) Radio Signal Strength and Climatological Parameters in Radio Communication

Better with fewer features: climate dynamics estimation for Van Lake basin using feature selection.

Even though there exist many research efforts trying to develop forecasting models based on machine learning (ML) or statistical techniques, feature selection is not employed in a large majority of the studies. To fill this gap, this study builds prediction models involving feature selection through one-step ahead estimation of climatological parameters (i.e., temperature and evapotranspiration), considering the aforementioned shortcomings. In addition, the best models are used to make estimations for a long horizon of 30 years. The experimental results performed on three stations located at the Van Lake Closed basin of Turkey showed that the Bayesian Ridge regressor (BRR) often outperforms other regressors. The respective best models involving BRR also enabled us to obtain scores ranging from 0.961 to 0.988. On the other hand, feature selection helps us to reach or go beyond the respective baseline performance of any model by using a lower number of features. Finally, the overall evaluation is stated to have a limitation in that it needs non-sparse and complete time series data to produce satisfying results. It will also be a challenging task to employ our regression-based ML pipeline on any sparse time series dataset.

Thunderstorms with Extreme Lightning Activity in China: Climatology, Synoptic Patterns, and Convective Parameters

Intense convection is often accompanied by high-frequency lightning and is highly prone to producing heavy rainfall, strong winds, hail, and tornadoes, frequently resulting in significant damage and loss of life. It is necessary to understand the mechanisms and meteorological conditions of intense convection. This study utilizes the Thunderstorm Feature Dataset from 2010–2018 to analyze the characteristics of thunderstorms with extreme lightning activity (TELAs), defined as thunderstorms whose lightning frequency ranks in the top 1%. Four regions with relatively high thunderstorm activity were selected for analysis: Northeast China (NEC), North China (NC), South China (SC), and the Tibetan Plateau (TP). In NEC, TELAs primarily occur just west of upper-level westerly troughs (UWT), including cold vortices. In NC, TELAs are mainly associated with UWT and subtropical highs (STH). In SC, TELAs are related to frontal systems, easterly waves, tropical cyclones, and STH. In TP, TELAs are generated by TP vortices. Before the TELA process, vertically integrated moisture divergence (VIMD) and convective available potential energy (CAPE) show the most notable anomalies. Except for the TP, TELAs are typically located between centers of anomalies with positive and negative geopotential height (500 hPa) and near centers of anomalies with positive CAPE and negative VIMD, accompanied by notable increases in surface temperature and wind speed. These findings offer a valuable reference for the early warning and forecasting of intense convection.

Estimation of some Climatological Parameters by WEKA Software for Selective Regions in Iraq

An Estimation is a direct summation that can produce new data from past measurements. In this study, we confirm the possibility of using the (WEKA) program in estimating the monthly values of some climatological parameters and investigate the influence of the time series' length parameters on the accuracy of estimation for selected regions of Iraq. Satellite data were used, which represent the monthly values for each of the minimum and maximum temperature, wind velocity, and relative humidity for the 1981 – 2021 time period, the absolute error rate (MAE), and the square root of the error rate (RMSE) with the correlation ( R2) are also identified to test the confidence of the prediction. Using (WEKA) software, which depends only on the time series of the parameters in the estimation, 12 months were estimated for the mentioned parameters. The estimated values by WEKA were close to the satellite data, thus we can depend on the software as a good source of meteorological data. Also, the study indicates that the time series' length strongly affects the accuracy of the estimation, as the increase in the time series of weather parameters increases the estimate's accuracy, and this increase fluctuates among parameters and varies from one region to another.

Evaluating vegetation dynamics in the Yangtze river basin in relation to climatological parameters using remote sensing data from 2001 to 2022

Evaluating vegetation dynamics in the Yangtze river basin in relation to climatological parameters using remote sensing data from 2001 to 2022

Characterization of hydrogeochemical elements in determining the ground water quality for irrigation potential and its correlation with climatological parameters of chennai basin aquifer system, southern india.

An attempt has been made to comprehend the ground water quality and climate impacts of the Chennai River basin, which is aimed at its main socio-economic growth of the state of Tamil Nadu. The ground water samples collected from the study area were analyzed for its hydrogeochemical elements. The ground water quality and irrigation suitability were determined using several water quality assessment metrics. Ground water is extensively utilized for irrigation in the entire basin area for the past two decades, especially in the 38 over-exploited Firkas out of the 109 Firkas of the basin. It is inferred that the phreatic aquifer ground water quality is fresh in about 20%, as indicated by the EC value (< 750µs/cm) at 25°C. In about 63% of the ground water indicating the moderately fresh showing the EC varies between 751 and 2250µs/cm at 25°C, 11% of ground water exerted an EC ranging between 2251 and 3000µs/cm at 25°C indicating that the ground water is slightly mineralized, and in about 6% of groundwater, the EC is > 3000µs/cm at 25°C indicating that the ground water is highly mineralized. There were no water samples that exceeded the permissible limit of chloride either in phreatic aquifer or in fracture aquifer. The changes in rainfall frequency and atmospheric temperature affect the ground water movement and storage directly and indirectly. Similarly, the temperature data shows a positive relationship with the concentration of fluoride and nitrate ions in the water.

A Novel Approach for Identifying and Assessing MOR‐Based CMIP6 Model for Hydrological Analysis in an Ungauged Watershed

ABSTRACTThe identification of the onset and retreat dates of the monsoon season is a crucial and intricate phenomenon, given its annual spatiotemporal variability. The monsoon season contributes significantly to rainfall, replenishing water sources and hydrological systems but causes hydrological extremes, especially for the high‐altitude watersheds in Southeast Asia. Global Circulation Model (GCM)‐Coupled Model Intercomparison Project Phase 6 (CMIP6)‐based rainfall and temperature data are helpful for adequately representing present and future climate scenarios. However, the usability of uncorrected GCM‐CMIP6 datasets needs to be assessed regionally. This study focuses on identifying the best‐suited GCM‐CMIP6 based on the monsoon onset (MO) and retreat (MR) dates along with other climatological temporal parameters. A numerical definition for MO and MR has been formulated to find the best‐suited GCM‐CMIP6 (i.e., CMCC‐ESM2). In this context, runoff simulation is carried out using the meteorological inputs of the monsoon onset‐retreat (MOR)‐based best‐suited GCM to evaluate its usability. A multi‐model simulation approach has been carried out for runoff estimation based on observed datasets to find a better‐suited hydrological model. The proposed overall methodology is tested in a hydrological extreme‐prone ungauged watershed (i.e., Ranikhola). CMCC‐ESM2 and SSP2‐4.5 has been identified as best‐suited SSP based on statistical evolution (R2 [0.693], NSE [0.662] and RSR [0.581]) for future daily runoff prediction. Future hydrological analysis shows that the average monsoon peak runoff magnitude will increase from the calibrated period (2015–2020) by 18.01% in the coming years (2021–2049).

Integrated Analysis of Multi-Parameter Precursors to the Fukushima Offshore Earthquake (Mj = 7.3) on 13 February 2021 and Lithosphere–Atmosphere–Ionosphere Coupling Channels

The preparation phase of earthquakes (EQs) has been investigated by making full use of multi-parameter and multi-layer observations of EQ precursors, in order to better understand the lithosphere–atmosphere–ionosphere coupling (LAIC) process. For this purpose, we chose a specific target EQ, the huge EQ of Fukushima-ken-oki EQ on 13 February 2021 (magnitude Mj = 7.3). We initially reported on EQ precursors in different physical parameters not only of the lithosphere, but also of the atmosphere and ionosphere (Hayakawa et al. followed by Akhoondzadeh et al. and Draz et al., both based on satellite observations). Our first two papers dealt with seven electromagnetic precursors in the three layers (with emphasis on our own ground-based observations in the atmosphere and lower ionosphere), while the second paper dealt with Swarm satellite observations of magnetic field, electron density, and GPS TEC in the ionosphere, and the third paper dealt only with climatological parameters on and above the Earth’s surface (together with GPS TEC). We have extensively reviewed all of these results, and have coordinated the temporal evolutions of various physical parameters relevant to the LAIC system; we have sought to understand which hypothesis is more plausible in explaining the LAIC process. Then, we came to a conclusion that two possible LAIC channels seem to exist simultaneously for this EQ: a fast channel (nearly simultaneous responses on the ground and ionosphere), and a slow channel (or diffusion-type), with a time delay of a few to several days, in which the agent effects in the lithosphere and lowest atmosphere seem to propagate up to the ionosphere with a definite time delay. Finally, we have suggested some research directions for the future elucidation of LAIC channels, and also made some comments on an early EQ warning system.

Comparison of Monthly Rainfall Prediction using Long Short Term Memory and Multi Layer Perceptron Methods in South Tangerang City

Rainfall is one of the meteorological and climatological parameters whose information must be disseminated to the public and related stakeholders. Rainfall information has an important role in the sectors of people's lives. In agriculture, the amount of rainfall has an important role in determining the planting season, so that this can prevent potential crop failure. On Disaster, South Tangerang City during the 2016-2021 period experienced floods, landslides, and droughts. Therefore, the importance of rainfall prediction information can improve meteorological and climatological information services in various sectors. Nevertheless, it is still difficult for the community and stakeholders to get monthly rainfall predictions with high accuracy in the long term. In this research, monthly rainfall prediction is designed using MLP (Multi Layer Perceptron) and LSTM (Long Short Term Memory). The data used is the monthly rainfall data of Climate Hazards Group InfraRed Precipitations (CHIRPS) for 42 years (period 1981-2022) with coordinate boundaries according to the research location, namely South Tangerang City, which is located between 106.625 º - 106.825 º East and 6.4 ° - 6.2 ° LS as many as 16 grids with a resolution of 0.05 ° each grid. Monthly rainfall prediction using MLP produces an RMSE value of 90.19, and a MAPE of 40.55, while the LSTM method produces an RMSE value of 88.12 and a MAPE of 40.49. Monthly rainfall prediction results using the LSTM method are better than the MLP method; this can be seen from the RMSE value of the LSTM method is smaller than MLP.

Unveiling the nexus between atmospheric visibility, remotely sensed pollutants, and climatic variables across diverse topographies: A data-driven exploration empowered by artificial intelligence

Unveiling the nexus between atmospheric visibility, remotely sensed pollutants, and climatic variables across diverse topographies: A data-driven exploration empowered by artificial intelligence

Effects of meteorology and lunar cycle on the post-thawing quality of avian sperm.

Various climatological and lunar cycle parameters have a direct impact on animal reproduction, and in the case of the avian species, spermatozoa are extremely sensitive to heat stress. These parameters could influence sperm freezability, which will ultimately affect post-thawing semen quality, being sperm motility in roosters a relevant indicator of this quality as it is highly related to fertility. Therefore, the objective of the present study is to determine which are the climatological and lunar cycle parameters that have a greater effect on sperm freezability in roosters. Sperm was obtained from 16 Utrerana breed roosters and a total of 27 replicates were performed. A pool was made with those ejaculates that met the minimum quality criteria for each replicate, and four freezing-thawing samples per replicate were analyzed. The straws were thawed, and sperm motility was evaluated, classifying the results obtained into four seminal quality groups according to the guidelines of the Food and Agriculture Organization of the United Nations (Group 1: Good, Group 2: Satisfactory, Group 3: Acceptable but undesirable and Group 4: Unsatisfactory). The following traits were recorded for each day of semen collection: maximum temperature, minimum temperature, maximum barometric pressure, minimum barometric pressure, maximum gust, wind direction, mean wind speed, sunshine hours, rainfall, moon phase, and percentage of illuminated lunar surface over the total area. A discriminant canonical analysis was performed to determine which of these parameters offered the most information when classifying an ejaculate in each quality group, with minimum temperature, the new moon as moon phase, minimum barometric pressure, and rainfall being the most significant variables. According to the results obtained, semen quality decreases when temperature and precipitation are lower, pressure is higher, and when there is a new moon phase. Therefore, these environmental conditions should be avoided for sperm collection and processing.

Lake Kinneret and Hula Valley Ecosystems under Climate Change and Anthropogenic Involvement

The long-term record of ecological, limnological and climatological parameters that were documented in the Kinneret drainage basin was statistically evaluated. The dependent relations between environmental parameters and a change in climate conditions open a consequence dispute between three optional definitions: long-term instability, climate change impact and ecosystem resiliency. The Kinneret drainage basin during the Anthropocene era is marked by intensive anthropogenic involvement: Increase in population size, drainage of the wetlands and old lake Hula, agricultural development, enhancement of lake Kinneret utilization for water supply, hydrological management, fishery and recreation. Therefore, the impact of a combination of natural and anthropogenic environmental factors confounded each other, and the uniqueness of climate change is unclear.

Analysis of the Interaction of Oceanic Cloudiness with the Upper Oceanic Stratum

Objective: This paper addressed the relationship between two physical quantities of interest in climatology: the thermal energy present in the upper ocean stratum (UOS) and the oceanic cloudiness (OC), i.e., the one located above oceans. Methods: The interplay between these physical quantities was pointed out by analyzing the time series of seasonal and diurnal anomalies of ocean's total cloud cover, and the anomalies of the thermal energy present in the UOS. We examined these time series to identify signals indicative of interactions between the UOS and OC. We then aimed to explain these interactions at a climatological level. Finally, for validation, we demonstrated that our explanations are consistent with a global climatological model that we developed. Results: It was demonstrated that in both cases, the time series could be described as a Markov-1 alternating type, exhibiting similar structures. This led to the conclusion that the OC served as a natural thermostat in relation to the thermal energy contained in the UOS. By using a simple mathematical model previously introduced in a recent paper by the author (2023) to account for the thermal exchanges between the troposphere and the UOS, we confirmed that the OC acted as a natural thermostat for the thermal energy in the UOS. This "natural thermostat" effect, incorporated in the model, resulted in simulations projecting significantly less warming by 2095 compared to those from most existing global climate models (GCMs). Conclusion: Methodologically, this paper confirmed the interest of using Markov chains to identify climatological interactions. The developed GCM, utilized for validating hypotheses explaining these interactions, proved simple and efficient for simulating key climatological parameters in the evolution of the global climate. On a strictly scientific level, the work's main contribution lies in providing a definitive answer about cloud cover feedback in global warming, establishing it as significantly negative.

Radiative parameters specific to Braşov urban area

The paper objective is to present the input data needed for the renewable systems design, namely the recording and the analysis of the local meteorological and climatological parameters such as the solar radiation, the wind speed, the clearness index and the atmospheric turbidity factor. The diagrams presented are obtained on the basis of the meteorological data recorded during three years, 2006-2008. The used meteorological data, for this study, were recorded with a local weather station positioned on the building roof of the Transilvania University of Braşov. The paper ends with an analysis of some very important diagrams concerning the solar and wind power for Braşov area, and there are worded conclusions derived from the comparative analysis of these values.

A Relação da Altura Geopotencial em 850 hPa Com a Precipitação e Temperatura no Iraque

Abstract The study examines the connection between the geopotential height (Z850 hPa) and the temperature (°C) and precipitation (mm) in Iraq. The observation data for climatological parameters that are currently available from the Iraqi meteorological organization are used. The temporal analysis of precipitation had a homogeneous data with a single mean for the period but temperatures throughout the research period exhibit a fluctuating tendency, with most stations showing a rising trend after 1993. The northern mountain area of Iraq has the highest amounts of precipitation, while the southern portion of the country records the lowest amounts. The distribution of temperature is characterized in reverse by the precipitation pattern. A correlation map is used for investigating the link between Z850 hPa and the two Iraqi climatological factors. According to the research, cold air descending from the Polar Regions is the primary mechanism responsible for bringing cold air mass over the Iraqi region during winter precipitation. Iraq receives precipitation in the springtime mostly due to the advection of moist air from the Mediterranean Sea and North Africa. Additionally, during summer and autumn, the hot air advection from India and eastern Africa through the Arabian Peninsula is associated with Iraqi temperature patterns.

Modeling of Improved Sine Trigonometric Single Valued Neutrosophic Information based Air Pollution Prediction Approach

Industrialization and urbanization air is getting polluted due to human activities. CO, NO, C6H6, etc., are the major air pollutants. The focus of air pollutants in ambient air is controlled by the climatological parameters including wind direction, atmospheric speed of wind, temperature, and humidity. Air pollution prediction is a critical sector where machine learning (ML) technique plays a major role. Its main purpose is to tackle and understand the damaging effects of air pollutants on the environment and human health. By using a range of ML techniques such as neural networks, regression, and decision trees, we could analyze historical data on air quality alongside geographical and meteorological factors. This allows us to design model that could detect patterns and predict pollution levels. By taking proactive measures such as providing timely alerts to the public, adjusting controls on emissions, and, implementing strategies to reduce pollution, we can work towards creating healthier and cleaner environments. Embracing the potential of artificial intelligence (AI) in air pollution prediction empowers us to protect the well-being of our communities and make informed decisions. Therefore, this study develops an Improved Sine Trigonometric Single Valued Neutrosophic Information based Air Pollution Prediction (ISTSVNI-APP) approach. The major objective of the ISTSVNI-APP technique is to exploit AI concepts with neutrosophic sets (NS) models for the forecasting of air pollution. To do so, the ISTSVNI-APP technique makes use of min-max normalization as the initial preprocessing step. For predicting air pollution, the ISTSVNI-APP technique uses STSVNI approach. To improve the performance of the ISTSVNI-APP technique, modified crow search algorithm (MCSA) is used for the parameter tuning of the STSVNI system. The performance evaluation of the ISTSVNI-APP method is verified utilizing benchmark dataset. The experimental outcomes stated that the ISTSVNI-APP technique gains better performance in predicting air pollution