Weather Parameters Research Articles

Incorporation of weather parameters in MMRC-K model for rainfall disaggregation

Incorporation of weather parameters in MMRC-K model for rainfall disaggregation

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.

Seasonal Incidence of Sucking Pests and Their Natural Enemies on Moth Bean [Vigna aconitifolia(Jacq.) Marechal

A field experiment was conducted at the College of Agriculture, NAU, Bharuch (Gujarat) to study the seasonal incidence of sucking pests and their natural enemies and their correlation with weather parameters on moth bean during kharif -2023. The studies on seasonal incidence indicated that jassid infestation began in the 1st week of August (31st SMW) and peaked during the 1st week of September (35th SMW). Whitefly infestation also began in the 1st week of August (31st SMW) achieved its peak during 2nd week of September (36th SMW). The population of thripscommenced from 1st week of August (31st SMW) reached its peak during2nd week of September (36th SMW). The population of ladybird beetle appeared from 2nd week of August (32th SMW) and reached its peak during 2nd week of September (36th SMW). The population of chrysoperla appeared from 2nd week of August(32nd SMW) and reached its peak population during 2nd week of September (36th SMW). Among the different environmental factors, morning relative humidity had a significant positive impact on the jassid and whitefly population. Bright sunshine hours showed significant positive influence on thrips and whitefly population. However no significant correlation was observed between ladybird beetle and Chrysoperla population with weather parameters.

Survey and molecular characterization of begomovirus, and assessment of yield losses caused by leaf curl disease of sunflower (Helianthus annuus)

A comprehensive survey was conducted during winter (rabi) and rainy (kharif) seasons of 2019–20 to 2021–22 in the Kurnool, Nandyal, and Prakasam districts of Andhra Pradesh, focusing on the incidence of leaf curl disease in hybrid sunflower (Helianthus annuus L.) crop. The results revealed a high incidence of the disease, ranging from 40–94.5% across most of the surveyed hybrid varieties. Leaf curl-infected samples collected from the surveyed fields were analyzed by PCR using specific primers for the DNA-A component of the virus to confirm the presence of the pathogen. The PCR-amplified fragments were cloned and sequenced. Sequence analysis revealed that the sunflower isolate (Snf-AP) shared 99.2% nucleotide identity with the tomato leaf curl Karnataka virus (ToLCKV), which infects sunflower crops in Karnataka. This indicates a strong geographical and genetic connection between the viral strains affecting sunflowers in Andhra Pradesh and Karnataka. This genetic similarity is significant, as it suggests that the same or closely related viral strains are responsible for sunflower leaf curl disease across broader regions. The disease was found to affect sunflowers at all growth stages, with the highest incidence (42.3%) observed at the star bud stage. Infection at the star bud stage leads to substantial seed yield losses, with reductions of up to 82.8%. This level of damage underscores the economic impact of early infections, as yield losses of this magnitude can severely affect the profitability of sunflower farming. Additionally, a positive correlation was found between the whitefly population, weather parameters, and disease development.

Impact of Key Insect Pests on Cowpea and their Relationship with Weather Parameters

Introduction: Cowpea (Vigna unguiculata L.) is a vital pulse crop with substantial nutritional value in India that faces production challenges due to various biotic and abiotic factors. Among the biotic factors that affects cowpea, pests such as Maruca vitrata and Riptortus pedestris significantly impact cowpea yields. This study aimed to evaluate the seasonal incidence of these pests and analyze their correlations with weather parameters in Thiruvananthapuram, Kerala. Methodology: Field observations were conducted over the cropping season to record pest populations across Standard Meteorological Weeks (SMWs). The population of the pod bugs and pod borers were taken from five plants. Statistical analyses were performed to identify correlations between pest incidence and key weather factors. Key Findings: The incidence of R. pedestris peaked in the 16th SMW, reaching 7.54 bugs per five plants. This pest displayed a significant positive correlation with maximum temperature (r = 0.637) and minimum temperature (r = 0.559) and a negative correlation with evening relative humidity (r =-0.480). Similarly, M. vitrata larval populations peaked during the same period at 2.65 larvae per five plants, showing a similar temperature-dependent correlation. Implications: The temperature-sensitive incidence patterns observed underscore the need for integrated pest management (IPM) strategies that address climatic factors to enhance cowpea sustainability. This data provides valuable insights for future pest forecasting and management efforts.

Climate Change as an Existential Threat to Tropical Fruit Crop Production—A Review

Climate change is an emerging threat to global food and nutritional security. The tropical fruits such as mango, bananas, passionfruit, custard apples, and papaya are highly sensitive to weather changes especially; changes of monsoon onset and elevated temperature are influencing crop growth and production. There is a need for more specific studies concerning individual crops and regional variations. Long-term effects and interactions of weather parameters and increased concentration of greenhouse gases, especially carbon dioxide, with phenological stages of the plant, pests, and diseases remain understudied, while adaptation strategies require further exploration for comprehensive understanding and effective mitigation. Few researchers have addressed the issues on the effect of climate change on tropical fruits. This paper focuses on the impact of abiotic (temperature, rainfall, humidity, wind speed, evaporation, carbon dioxide concentration) and biotic (pest and pathogens dynamics) factors affecting the fruit crop ecosystem. These factors influence flowering, pollination, fruit set, fruit yield and quality. This review paper will help develop adaptive strategies, policy interventions and technological innovations aimed at mitigating the adverse effects of climate change on tropical fruit production and safeguarding global food and nutritional security.

Earthquake Prediction and Alert System Using IoT Infrastructure and Cloud-Based Environmental Data Analysis

Earthquakes are one of the most life-threatening natural phenomena, and their prediction is of constant concern among scientists. The study proposes that abrupt weather parameter value fluctuations may influence the occurrence of shallow seismic events by focusing on developing an innovative concept that combines historical meteorological and seismic data collection to predict potential earthquakes. A machine learning (ML) model utilizing the ML.NET framework was designed and implemented. An analysis was undertaken to identify which modeling approach, value prediction, or data classification performs better in forecasting seismic events. The model was trained on a dataset of 8766 records corresponding to the period from 1 January 2001 to 5 October 2024. The achieved accuracy of the model was 95.65% for earthquake prediction based on weather conditions in the Vrancea region, Romania. The authors proposed a unique alerting algorithm and conducted a case study that evaluates multiple predictive models, varying parameters, and methods to identify the most effective model for seismic event prediction in specific meteorological conditions. The findings demonstrate the potential of combining Internet of Things (IoT)-based environmental monitoring with AI to improve earthquake prediction accuracy and preparedness. An IoT-based application was developed using C# with ASP.NET framework to enhance earthquake prediction and public warning capabilities, leveraging Azure cloud infrastructure. The authors also created a hardware prototype for real-time earthquake alerting, integrating the M5Stack platform with ESP32 and MPU-6050 sensors for validation. The testing phase and results describe the proposed methodology and various scenarios.

An Approach to Multivariable Regression Analysis of a Rooftop PV System in Households’ Sector in Tirana. A Novel Approach using Energy Modeling Tool

Solar power prediction plays an essential role in functioning, mapping, and obtaining energy and climate goals in 2030 and beyond and contributing to real-time balancing of the power system. On the other side, electricity consumption is influenced by Heating Degree Days (HDD), Cooling Degree Days (CDD), average monthly temperature, energy management, human behaviour, architecture, orientation, and many other factors. The simulations are performed based on three consecutive energy consumption data for a typical dwelling in Tirana city. Once the base case scenario is designed, the model is validated based on the monthly electricity bills. In the base case scenario are included some energy efficiency measures (EEM) afterward the optimization of the supply side using PV and Solar Water Heating is part of our simulation, too. The focus of our work is to design a correlation between the relationship between consumption and generation of electricity given as a dependent variable (Y), which is regressed with weather parameters given as independent variables (Xi) under Durbin Watson statistics. The output of the study can help designers to compile a reliable power system, better utilization of energy resources, and forecasting accuracy analysis from both sides of the energy system (demand and supply side). The tested household and EEM applied in the proposed scenario may lead to an electricity reduction level of 8655 kWh per year and 76.6 % of solar fraction is used to meet the hot water demand.

Atmospheric icing meteorological parameter study using field experiments and simulation

AbstractAtmospheric icing on ground structures is a concern from design, operation, and safety perspectives. Supercooled water droplets size and liquid water content (LWC) are important weather parameters to better understand the ice accretion physics on ground structures. Most existing studies are based on measurements at high altitude. The study is based on the field results of a specific event (from 9:30 to 22:27 h on October 29, 2022) in Arctic region of northern Norway. The data from this event are presented and used for analytical validation and simulation. Field measurements of different meteorological weather parameters including the droplet size and LWC are carried out leading to recording of resultant atmospheric ice load and intensity. A comprehensive study is also carried out to validate droplet collision efficiency and ice load using the existing analytical model ISO‐12494 and computational fluid dynamics (CFD)–based numerical simulations. Furthermore, the differences in icing simulation using parameters such as median volume diameter (MVD), Langmuir B –J as alternatives to the actual droplet size distribution (DSD) spectrum are also analyzed. The results show that under natural meteorological conditions, the characteristics of water DSD change in real time. Using MVD alone to calculate the water droplet collision efficiency on circular cylinders can lead to significant errors. Accurately selecting the Langmuir distribution as a substitute for the actual DSD can reduce simulation errors to within 5%. Compared to the analytical model, the numerical simulations result better reflects the collision characteristics of water droplets of different sizes on the cylindrical object.

Impact of weather parameters on groundnut yield in Dharwad: A statistical analysis

Impact of weather parameters on groundnut yield in Dharwad: A statistical analysis

Predictability of Severe Convective Storm Environments in Global Ensemble Forecast System, Version 12, Reforecasts

Abstract Prediction of severe convective storms at time scales of 2–4 weeks is of interest to forecasters and stakeholders due to their impacts on life and property. Prediction of severe convective storms on this time scale is challenging, since the large-scale weather patterns that drive this activity begin to lose dynamic predictability beyond week 1. Previous work related to severe convective storms on the subseasonal time scale has mostly focused on observed relationships with teleconnections. The skill of numerical weather prediction forecasts of convective-related variables has been comparatively less explored. In this study over the United States, a forecast evaluation of variables relevant in the prediction of severe convective storms is conducted using Global Ensemble Forecast System, version 12, reforecasts at lead times of up to 4 weeks. We find that kinematic and thermodynamic fields are predicted with skill out to week 3 in some cases, while composite parameters struggle to achieve meaningful skill into week 2. Additionally, using a novel method of weekly summations of daily maximum composite parameters, we suggest that the aggregation of certain variables may assist in providing additional predictability beyond week 1. These results should serve as a reference for forecast skill for the relevant fields and help inform the development of convective forecasting tools at time scales beyond current operational products. Significance Statement Prediction of severe weather beyond 1 week in advance is of interest to stakeholders given their impacts on life and property. In this study, we evaluate 20 years of forecast data generated by a numerical model ensemble to determine whether variables relevant in severe weather forecasts can be predicted in weeks 2–4. The variables with the best skill measures generally represent large-scale weather patterns that are more predictable on longer time scales, although some manipulation of other severe weather parameters yielded additional results. We suggest that the results found in this study can inform future work that assesses the predictability of severe weather in weeks 2–4 using more complex methods such as machine learning.

Solar Plasma Noise in TianQin Laser Propagation: An Extreme Case and Statistical Analysis

TianQin (TQ) proposes to detect gravitational-wave signals by using laser interferometry. However, the laser propagation effect introduces a potential noise factor for TQ. In this work, we used magnetohydrodynamic (MHD) simulations to obtain the space magnetic field and plasma distributions during an extremely strong solar eruption, and based on the MHD simulation result, we investigated laser propagation noise for TQ. For the extremely strong solar eruption event, we find that the laser propagation noise closely approaches 100% of TQ’s displacement noise requirement for the Michelson combination, while the laser propagation noise is still about 30% of TQ’s displacement noise requirement for time-delay interferometry (TDI)-X combination. In addition, we investigate the laser propagation noise for 12 cases with different solar wind conditions. Our finding reveals a linear correlation between the laser propagation noise and several space weather parameters, e.g., solar wind dynamic pressure, Sym-H, and Dst, where the correlation coefficients for solar wind dynamic pressure are strongest. Combining the cumulative distribution of solar wind dynamic pressure from 1999 to 2021 with the linear correlation between solar wind dynamic pressure and laser propagation noise, we have determined that the occurrence rate of the laser propagation noise to be greater than 30% of TQ’s displacement noise requirement for the Michelson combination over the entire solar activity week is about 15%. In addition, we find that TDI can suppress the laser propagation noise, and reduce the occurrence rate of the laser propagation noise exceeding 30% of TQ’s requirement to less than 1%.

GIS-based solar radiation modelling for photovoltaic potential in cities: A sensitivity analysis for the evaluation of output variability range

Currently, there is increasing attention to matching the buildings’ energy demand with energy produced by renewable sources. This approach serves to both reduce the environmental footprint, the target of several European policies and initiatives and ensure access to energy for everyone worldwide, aligning with one of the aims of the United Nations Agenda 2030. Urban environments are suitable for the installation of photovoltaic panels, which can be easily integrated within roofs to minimise land use and installation costs. Multiple studies focused on solar radiation as the most critical element affecting the estimation of the photovoltaic potential, in particular by resorting to Geographic Information Systems (GIS). Among different tools, which are useful for integrating layers with different spatial resolutions, the r.sun function embedded in GRASS GIS has been selected as the most suitable to capture complex variations resulting from weather and geographical parameters. However, a gap in the literature has emerged as no study is available to define the precision degree of the tool when inputting parameters derived from various data sources based on different model assumptions. The scope of this research is to collect open-access weather inputs and quantify the solar radiation output along with its variability range. Based on 28 test plans, resulting from the combination of input parameters from alternative data sources, this research quantified the output variability range to be ±20 %, thus requiring additional processing to increase the precision. The paper concludes with a critical assessment of the strengths and weaknesses of the current research, emphasizing the importance of validating the results with measured values in forthcoming research, to move from precision to accuracy evaluation.

Understanding the effects of weather parameters on the population dynamics of an endangered geophyte supports monitoring efficiency.

Due to their complex life cycles geophytes are often neglected in conservation programs, despite they are important elements of early spring communities. Their life cycle is strongly affected by weather parameters, i.e. temperature, precipitation, and light, but the effects of these parameters are often contradictory and show high intra-annual variability even within species. Deeper knowledge about the abiotic factors affecting the population dynamics of geophytes is needed to support the designation of effective conservation plans. We aimed to explore the link between weather parameters and population dynamics of Colchicum bulbocodium, an endangered and strictly protected geophyte. We monitored three life cycle stages (flowering, growing, fruiting) of 1069 individuals in permanent plots for six consecutive years. Our results showed that life cycle of C. bulbocodium was strongly related to the actual weather parameters; the lagged effect of the previous year was weaker. Increasing temperature and lack of cold periods had negative effect on all life stages. We highlighted that population estimation based on the number of flowering individuals in a single year can underestimate population size by 40-83%. Monitoring in years following wet and cold springs and cold winters could increase the accuracy of population estimations of the flowering individuals.

Effects of Weather Conditions on Pod Yield in Groundnut Varieties

Experiments were conducted at the Department of Agricultural Meteorology, Anand Agricultural University, Anand during the Summer of 2015 and 2016 with four varieties of groundnut (GG-2, GG-20, GJG-31 and TG-26) sown on three different dates to study the effect of weather parameters and agrometeorological indices on pod yield of groundnut. The results of the present investigation indicated that pod yield of groundnut was negatively correlated with maximum, minimum and mean temperature during most of the phenophases except P9 (pod maturity to physiological maturity), where the mean temperature has varied between 32.70C to 35.10C during pod maturity to physiological maturity. In our experiment, we have found that more than 33.90C is required for adequate seed development, resulting in higher test weight and higher pod yield. Agrometeorological indices have a significant and positive correlation with pod yield only during P7 (pod initiation to pod development) phase.

Survey of Rice Sheath Blight Disease in Kuttanad and Effect of Biocontrol Agents and Fungicides against Rhizoctonia solani

Aims: Rice is an important crop cultivated worldwide, whose production is influenced by numerous factors like pest, disease, weather parameters. In India, diseases accounts for 35 per cent in yield reduction, with sheath blight being significant. This disease is notably prevalent in Kuttanad region of Kerala. This study involves a survey conducted in Kuttanad to assess the extent of the disease and in vitro management using biocontrol agents and fungicides. Study Design: Study was outlined in Completely Randomised Design (CRD) to evaluate the in vitro efficacy of bioagents. Place and Duration of Study: Department of Plant Pathology, College of Agriculture, Vellayani and M.S. Swaminathan Rice Research Station, Moncompu, between 2022 and 2023 Methodology: Survey was conducted in forty locations of Kuttanad in which the disease incidence was recorded and samples were collected. The isolates obtained were tested for pathogenicity by artificial inoculation on rice variety, Uma. Cultural characteristics of isolates were studied. The virulent culture obtained was further advanced for the in vitro studies with biocontrol agents and fungicides. The inhibition of fungal growth was also recorded. Results: Forty isolates of sheath blight pathogen (Rhizoctonia solani) were obtained. The virulent isolate among these was identified on the basis of number of days taken for symptom development by artificial inoculation and this was further advanced for the in vitro studies. Bacillus amyloliquefaciens was found to be more effective with an inhibition percentage of 68.64. Among the tested fungicides, Kresoxim methyl 40% + Hexaconazole 8% WG showed cent per cent growth inhibition of pathogen at 500 and 1000 ppm concentrations which was on par with Azoxystrobin 18.2% + Difenoconazole 11.4% SC at 1000 ppm. Conclusion: From this study, it can be concluded that, that these biocontrol agents and fungicides are very effective in the management of the pathogen and hence can be used for further field-level studies in management of the disease.

On the issue of forecasting crop yields for the purposes of adaptive landscape agriculture

Long-term monitoring (1998–2023) of the yield of clover-timothy grass stands of the first year of use was carried out in order to find patterns of influence of landscape environmental conditions on it in various agroclimatic conditions. The research was conducted within the moraine hill located at the Gubino VNIIMZ agro-testing site in the Tver region. Soil-forming rocks are two-membered deposits consisting of an upper layer composed of relatively light rocks, underlain by moraine bouldery loam. The grass stands were exploited without fertilizers in a single-cut mode on a field divided into 120 plots. Using regression analysis, the influence of landscape and soil environment factors was determined: relief, physical and agrochemical properties of soils on grass yield, as well as the dependence of the degree of this impact on climatic conditions. It was found that the yield of perennial grasses is most strongly influenced by various fractions of the granulometric composition of soils – from stones to dust (up to 16 % of its variability) and the altitude of the location (up to 38 %), since the thermal and water-air characteristics of soils and vegetation largely depend on them. Terrain characteristics such as steepness and curvature of the surface have a minor impact on grass yield (up to 12 %). The degree of influence of agro-landscape environmental factors on the growth of grasses is largely regulated by fluctuations in weather conditions. “Climate scenarios” of a specific factor – sets of weather parameters under which its effect on the production process of a crop is manifested – in the years of sowing and mowing, as a rule, do not differ fundamentally. Knowing the nature of the influence of climatic factors allows us to more accurately predict crop yields within an agricultural landscape and, thus, optimize the location of crops on the territory of a particular farm.

Revealing the Feedback Loop: Comparing Satellite and Sensor-derived Meteorological Parameters in Groundnut Cultivation

Background: Studying soil moisture’s impact on groundnut, particularly under rainfed conditions, is crucial for improving quality and yield. This research explores soil moisture dynamics using satellite and in-situ sensor data to enhance water management and crop predictions, focusing on the feedback mechanisms between soil moisture and weather variables. Methods: The study was conducted at the Oil Seed Research Station (TNAU) in Tindivanam, Tamil Nadu, using the groundnut variety TMV 14. Soil moisture and weather parameters were monitored at critical crop growth stages, using an in-situ soil moisture sensor (Cr 300) and compared with satellite data from SMAP, ERA5 and Sentinel 1A with environmental factors. Statistical analysis was carried out using SAS JMP Pro and network analysis via Cytoscape 3.8.2 software. Result: Pre-monsoon soil moisture SMAP, SMAP-RZSM and sensor (VWC at 10cm) were found to increase in post-monsoon. Strong positive correlations were attained between SMAP-SM and SMAP-RZSM (r=0.94), indicating a reinforcing loop. Whereas, the negative feedback was observed between SMAP-SM and deeper soil temperatures (-0.91). It underscores the dynamic interplay between soil moisture and atmospheric conditions, essential for developing efficient water management strategies and enhancing crop resilience to climate variability.

Effects of environmental factors on host-parasite interaction patterns in backyard-tethered goats of Kerala, India

The environment is the most important stratum in the epidemiological triad of a parasitic disease and any variations in the environmental factors may decide the dynamic occurrence and existence of different lifecycle stages of these parasites. The present study investigated the correlations between key biometeorological and demographical parameters with the incidence of different gastrointestinal parasites and hemoparasites among goats. Four hundred and thirty-two goats were screened for parasitic infection in a yearlong survey conducted from July 2022 to June 2023 in the Teaching Veterinary Clinical Complex, Mannuthy, Kerala, India. The weather parameters (Tmax, Tmin, RH, THI, and bright sunshine hours), air quality parameters [AQI, PM2.5, and PM10], and demographic parameters (gender and age) were recorded along with the test positivity of different categories of gastrointestinal parasites and hemoparasites in goats by routine fecal sample examination and blood smear examination, respectively. The infection level was ranked based on the severity of the infection. The mean and daily variations in biometeorological parameters were calculated and the data were statistically analyzed to figure out the pertinent correlations in host-parasite-environment interaction patterns. High levels of parasitic infections with significant month-wise variations and climate-correlated peak infection patterns were noticed. The incidence of parasites was negatively correlated to all parameters except humidity, indicating more severe parasitism during monsoon months. The significant variations in the host-parasite interaction dynamics point towards the need for detailed explorations concerning the lifecycle of each specific parasite with a focus on the possible environment-favourable and resistant lifecycle stages. Future studies may be designed from a biometeorological perspective to develop a crucial understanding of host-parasite-environment interactions in goats ensuring sustainable goat farming.

Epidemiological Study of Downy Mildew Disease of Opium Poppy

The downy mildew caused by Peronosporaspp is a major disastrous disease of many plant species. The symptoms of downy mildew disease incidence on opium poppy (Papaver somniferum L.) were collected in three years from 2019-20 to 2021-22 for prediction of weather parameters on the progression of downy mildew disease. The downy mildew disease initiation was recorded in the 52nd SMW (10.0%DMI) when the maximum temperature was 19.430C. At 3rd SMW, the maximum temperature dropped 17.330C and DMI reached up to 20.00 percent depicting the progression and spread of downy mildew disease in opium poppy with the decrease of maximum temperature. The maximum disease incidence was recorded 92.53 percent in 10th SMW when maximum and minimum temperature were 29.030C and 13.530C. Regression analysis between dependent variable downy mildew disease incidence Vs. independent variables (viz., rainfall, maximum and minimum temperature and relative humidity) showed that all the weather parameters contributed more than 85 percent variation (R2 = 0.869, 0.957, 0.859) in the downy mildew incidence of opium poppy. One unit change of maximum temperature, minimum temperature (1.00c), maximum and minimum relative humidity (1.0%) might cause to change 0.128, 0.70, 0.117 0.130 units in downy mildew incidence, respectively.