Weather Variables Research Articles

Principle Component Analysis for Forecasting of Pre-harvest Rapeseed and Mustard Yield Based on Meteorological Parameters

The pre-harvest forecast model was developed using time series data on Rapeseed and Mustard yields as well as weekly data on six weather variables from the 40th SMW of one year to the 8th SMW of the next year, which covers the years 1990–1991 to 2014–2015. Multiple regression and principal component analysis are two statistical techniques that have been reported for creating pre-harvest forecast models. The most accurate model found by applying step-by-step regression analysis to weekly weather data was based on adj R2, RMSE, and%SE. One and a half months prior to harvest, these models can be utilised to obtain a trustworthy forecast of the yield of Rapeseed and Mustard.

Environmental Influences on the Drying Rate of Spray Applied Disinfestants from Horticultural Production Surfaces

Drying rates of disinfestants commonly applied to horticultural plant production surfaces were evaluated under cool to hot weather and under laboratory conditions to characterize the range of drying times and how this relates to contact times specified on product labels. Drying rates of six disinfestants [isopropyl alcohol (IPA), two quaternary ammonium compounds (QACs), two peroxygen compounds (PXs), sodium hypochlorite (bleach)] and water were evaluated when applied to six substrate materials (concrete, galvanized metal, polypropylene ground fabric, polyethylene plastic sheet, pressure-treated pine, and twin-wall clear polycarbonate) based on the weather variables of solar radiation, temperature, and relative humidity. Differences were evident at the point of application. Disinfestants with low (IPA, both QACs and one PX) and high (bleach, one PX and water) surface tension provided approximately 100% and 60% coverage, respectively, when applied to horizontal, non-porous solid materials. Disinfestants applied to horizontal porous materials (concrete, fabric and wood) persisted on the surface for a mean of only 9 to 113 sec because solutions were actively drawn into the substrates internal structure. Disinfestants applied to vertical twin-wall greenhouse material flowed off, while retaining only a maximum beaded wetness coverage of 14%. A Bayesian analysis procedure was used to model drying effects of disinfestants correlated with substrate and weather variables based on posterior marginal and prediction trends. Generally, the fastest drying rate occurred in the first 2.5 min, and approximately 50% of coverage was retained by 5 min. The evaporative process was variable with distinct interactions occurring among the experimental variables.

Association between exposure to ambient air pollution, meteorological factors and atopic dermatitis consultations in Singapore—a stratified nationwide time-series analysis

Atopic dermatitis (AD) is a chronic inflammatory skin disease affecting approximately 20% of children globally. While studies have been conducted elsewhere, air pollution and weather variability is not well studied in the tropics. This time-series study examines the association between air pollution and meteorological factors with the incidence of outpatient visits for AD obtained from the National Skin Centre (NSC) in Singapore. The total number of 1,440,844 consultation visits from the NSC from 2009 to 2019 was analysed. Using the distributed lag non-linear model and assuming a negative binomial distribution, the short-term temporal association between outpatient visits for AD and air quality and meteorological variability on a weekly time-scale were examined, while adjusting for long-term trends, seasonality and autocorrelation. The analysis was also stratified by gender and age to assess potential effect modification. The risk of AD consultation visits was 14% lower (RR10th percentile: 0.86, 95% CI 0.78–0.96) at the 10th percentile (11.9 µg/m3) of PM2.5 and 10% higher (RR90th percentile: 1.10, 95% CI 1.01–1.19) at the 90th percentile (24.4 µg/m3) compared to the median value (16.1 µg/m3). Similar results were observed for PM10 with lower risk at the 10th percentile and higher risk at the 90th percentile (RR10th percentile: 0.86, 95% CI 0.78–0.95, RR90th percentile: 1.10, 95% CI 1.01–1.19). For rainfall for values above the median, the risk of consultation visits was higher up to 7.4 mm in the PM2.5 model (RR74th percentile: 1.07, 95% CI 1.00–1.14) and up to 9 mm in the PM10 model (RR80th percentile: 1.12, 95% CI 1.00–1.25). This study found a close association between outpatient visits for AD with ambient particulate matter concentrations and rainfall. Seasonal variations in particulate matter and rainfall may be used to alert healthcare providers on the anticipated rise in AD cases and to time preventive measures to reduce the associated health burden.

Analysis of effect of weather variables on diseases of winter season fodder crops

Analysis of effect of weather variables on diseases of winter season fodder crops

Experimental study of an on-grid hybrid solar air conditioner with evaporative pre-cooling of condenser inlet air

This paper presents an experimental study of a split type solar air conditioning system with evaporative pre-cooling at the condenser. The main objective of this research is to evaluate the thermal and electrical performance of the system under hot summer conditions in a Mediterranean climate. Previous studies have separately shown the benefits of both condenser inlet air pre-cooling and photovoltaic solar cooling. The novelty of this work will be the study of the coupling of these two techniques in a real installation, where the viability of the system depends on the component-level design and the operating conditions, since weather variables such as ambient temperature, relative humidity and solar radiation significantly affect the thermal behaviour of the refrigeration cycle, the evaporative pre-cooling capacity and the performance of the photovoltaic panels. Experimental tests were carried out over a summer season to evaluate the behaviour of the system and to obtain performance correlations validated with experimental data. These correlations will allow the modelling of the system and will be used to carry out an economic and environmental feasibility study of the proposed hybrid solar air conditioning system. The results show that a 100mm-thick evaporative pad achieved an average evaporative cooling efficiency for all tests performed of ɛevap=67.4%, reducing the air inlet temperature to the condenser by around 6°C. This resulted in an 18.3% improvement in the EER of the air conditioner. In addition, the use of evaporative pre-cooling resulted in a significant 23% reduction in peak power consumption. The study also evaluated the impact of the air pressure drop caused by the evaporative pad when not in use, with results favouring its installation. The system achieved a solar contribution of more than 30% under different solar radiation conditions when using pre-cooling, compared to 25% with a standard condenser. In addition, the combination of photovoltaic contribution and evaporative pre-cooling reduced energy consumption by 43%.

Impact of Climate Change on Wheat Yields in Australia

Purpose: The aim of the study was to analyze the impact of climate change on wheat yields in Australia. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Research on climate change's impact on wheat yields in Australia reveals significant challenges: Rising temperatures reduce yields by accelerating crop maturation (Thompson, 2019). Variability in precipitation and extreme weather further destabilize yields (Foster & Ryan, 2020). While increased CO2 can enhance growth, it also worsens outcomes under heat and drought (Lee & Kumar, 2021). Regional differences necessitate tailored adaptation strategies (Patel, 2022). Advanced technologies like AI offer potential for adapting cultivation practices to changing conditions (Zhang, 2024). Unique Contribution to Theory, Practice and Policy: Environmental determinism, resource dependence theory & ecological modernization theory may be used to anchor future studies on analyze the impact of climate change on wheat yields in Australia. Training programs and technical support should also be increased to ensure farmers can effectively utilize these advanced technologies. Government policies should include increased investment in agricultural research, particularly in the development of new wheat varieties that are resilient to extreme weather conditions.

Weather variability risks slow climate adaptation: An empirical analysis of forestry

The timing of climate adaptation decisions can have substantial consequences for the assessment of climate damages. Since weather variability can create risks for natural resource management that differ across adaptation choices, such variability has the potential to alter the speed of climate adaptation. This paper estimates the effect of weather variability on the timing of adaptation decisions of forest landowners in the Eastern United States. A discrete-choice econometric model of forest management is estimated and used in a bio-economic simulation that shows how variability in cold temperatures can significantly slow the rate of adapting from cold-tolerant natural hardwood forests to cold-sensitive, but highly valuable pine plantations. The range of weather variability in climate projections and across the landscape generates large differences in adaptation timing. Ignoring projected future decreases in weather variability results in a large downward bias in estimating future paths of climate adaptation. Since pine plantations produce fewer non-market ecosystem services than natural hardwood forests, an important source of future conservation uncertainty is the economic response of private forest landowners to changing weather variability.

Unmanned high speed aerial vehicles in medical logistics: A comparative analysis of blood sample transportation with weather variability

Unmanned high speed aerial vehicles in medical logistics: A comparative analysis of blood sample transportation with weather variability

Anthropogenic and climatic factors interact to influence reproductive timing and effort.

Reproduction, although absolutely essential to a species' persistence, is in itself challenging. As anthropogenic change increasingly affects every landscape on Earth, it is critical to understand how specific pressures impact the reproductive efforts of individuals, which directly contribute to the success or failure of populations. However, organisms rarely encounter a single burden at a time, and the interactions of environmental challenges can have compounding effects. Understanding environmental and physiological pressures is difficult because they are often context-dependent and not generalizable, but long-term monitoring across variable landscapes and weather patterns can improve our understanding of these complex interactions. We tested the effects of urbanization, climate, and individual condition on the reproductive investment of wild side-blotched lizards (Uta stansburiana) by measuring physiological/reproductive metrics from six populations in urban and rural areas over six consecutive years of variable precipitation. We observed that reproductive stage affected body condition, corticosterone concentration, and oxidative stress. We also observed that reproductive patterns differed between urban and rural populations depending on rainfall, with rural animals increasing reproductive investment during rainier years compared to urban conspecifics, and that reproductive decisions appeared to occur early in the reproductive process. These results demonstrate the plastic nature of a generalist species optimizing lifetime fitness under varying conditions.

Spatio-temporal prediction of total energy consumption in multiple regions using explainable deep neural network

Energy consumption forecasting is essential for energy system integration and management. However, existing studies mainly focus on temporal features of energy consumption, which neglects the spatial correlation of variables with time information. Capturing the spatio-temporal relationships helps to improve forecasting accuracy and further promote energy dispatch. To tackle this problem, an explainable Convolutional Neural Network-Long Short Term Memory forecasting model is employed to effectively predict the total energy consumption by capturing the spatial and temporal features of multivariate time series. In the model, the autoencoder is used to achieve the nonlinear dimensionality reduction and transfer the data to a low-dimensional space. Furthermore, a Convolutional Neural Network is used to extract more effective features from the decoded data, and long short-term memory is employed to identify the temporal dependencies between extracted features and total energy consumption. Shapley additive explanation is introduced to interpret the outputs of the black-box model. The superior performance of the proposed method with high accuracy and good adaptability is verified by the comparisons with conventional forecasting models. This method provides an insight into the regional energy consumption analyzing contributions of weather variables to energy consumption, which helps administers in understanding regional energy performance for enhancing energy efficiency.

Inverse modeling of frequency domain-based one-dimensional soil water flow in layered soils

Soils are heterogeneous at multiple scales. Estimates of spatially varying saturated hydraulic conductivity (Ks) of soils dictate the resolution of modeling water flux based on the Richardson-Richards equation (RRE), but direct measurements of Ks everywhere in a field are practically impossible. As a result, this study develops an inverse approach to estimate spatially varying Ks utilizing soil moisture observations driven by periodic weather variability at different frequencies. Using a numerical solution to vertical, one-dimensional RRE based on Gardner-Kozeny (GK) model in a heterogeneous soil with periodic flux, we investigate the spatial cross-correlation between Ks and observations (the amplitude or phase shift) of soil moisture fluctuation at different frequencies without high computational cost in the conventional time-domain model. These correlation patterns vary with the spatial location and frequency, suggesting that soil moisture fluctuations at different frequencies at one monitoring location carry non-redundant information about the heterogeneous distribution of Ks. Guided by cross-correlation maps, several inverse experiments reveal the effectiveness of multi-frequency data of soil moisture fluctuation for mapping the heterogeneous distribution of Ks. Monte Carlo simulations suggest that multi-frequency data can provide non-redundant and useful information about the estimation of heterogeneous Ks. Synthetic data based on a commonly-applied nonlinear van Genuchten-Mualem (VG) model is then tested in our inversion algorithm. The results indicate that the spatial pattern of the inverted GK Ks is very similar to that of the VG Ks, although these two parameters are inherently not identical since they are used in two different models. In addition, the inverted GK Ks can successfully predict the soil moisture fluctuation of frequencies that were not used in the inversion. Despite having no field experiments to validate this model, we believe this is the first attempt to conduct frequency-domain inverse modeling in vadose zone hydrology using soil moisture fluctuation.

날씨 변수를 고려한 전력 수요량 예측구간 시계열 분석 연구

Annual electricity demand is steadily increasing and the importance of forecasting the electricity demand is emerging since the dependence on domestic electricity is also increasing. The purpose of this study is analysis of the electricity demand considering seasonality and weather variables, for which we study time series modelling and forecasting analysis. ARIMA, SARIMA, ARIMAX, SARIMAX models are handled with optimally selected orders. Out-of-sample forecasting analysis is carried out to evaluate predicted values and their errors such as RMSE, MAE, MAPE and MSLE. The errors by four models are compared. As the optimal model, SARIMAX(2,1,1) (2,1,2,12) model with CDD, HDD and insolation has been chosen. For strict statistical analysis of prediction intervals, normal distribution, laplace distribution and bootstrap method are adopted to construct 80%, 95% prediction intervals. Coverage probability (CP), average length (AL) and mean interval score (MIS) of the prediction intervals by three approximations are evaluated. This study contributes with novelty in that it provides three approaches of prediction intervals along with their comparison. The results of this study are expected to be of practical help in the efficient operation of electricity supply system.

Part 2: Lessons Learned from Predicting Wildfire Occurrence for CONUS Using Deep Learning and Fire Weather Variables

Abstract This paper illustrates the lessons learned as we applied the Unet3+ deep learning model to the task of building an operational model for predicting wildfire occurrence for the contiguous United States (CONUS) in the 1-to-10-day range. Through the lens of model performance, we explore the reasons for performance improvements made possible by the model. Lessons include the importance of labeling, the impact of information loss in input variables, and the role of operational considerations in the modeling process. This work offers lessons learned for other interdisciplinary researchers working at the intersection of deep learning and fire occurrence prediction with an eye towards operationalization.

Part 1: Improving Wildfire Occurrence Prediction for CONUS Using Deep Learning and Fire Weather Variables

Abstract The purpose of this research is to build an operational model for predicting wildfire occurrence for the contiguous United States (CONUS) in the 1-to-10-day range using the UNet3+ machine-learning model. This paper illustrates the range of model performance resulting from choices made in the modeling process, such as how labels are defined for the model, and how input variables are codified for the model. By combining the capabilities of the UNet3+ model with a neighborhood loss function, Fractions Skill Score (FSS), we can quantify model success by predictions made both in and around the location of the original fire occurrence label. The model is trained on weather, weather-derived fuel, and topography observational inputs and labels representing fire occurrence. Observational weather, weather-derived fuel, and topography data are sourced from the gridMET data set, a daily, CONUS-wide, high-spatial resolution data set of surface meteorological variables. Fire occurrence labels are sourced from the U.S. Department of Agriculture’s Fire Program Analysis Fire-Occurrence Database (FPA-FOD), which contains spatial wildfire occurrence data for CONUS, combining data sourced from the reporting systems of federal, state, and local organizations. By exploring the many aspects of the modeling process with the added context of model performance, this work builds understanding around the use of deep learning to predict fire occurrence in CONUS.

Synchrony in adult survival is remarkably strong among common temperate songbirds across France.

Synchronous variation in demographic parameters across species increases the risk of simultaneous local extinction, which lowers the probability of subsequent recolonization. Synchrony therefore tends to destabilize meta-populations and meta-communities. Quantifying interspecific synchrony in demographic parameters, like abundance, survival, or reproduction, is thus a way to indirectly assess the stability of meta-populations and meta-communities. Moreover, it is particularly informative to identify environmental drivers of interspecific synchrony because those drivers are important across species. Using a Bayesian hierarchical multisite multispecies mark-recapture model, we investigated temporal interspecific synchrony in annual adult apparent survival for 16 common songbird species across France for the period 2001-2016. Annual adult survival was largely synchronous among species (73%, 95% credible interval [47%-94%] of the variation among years was common to all species), despite species differing in ecological niche and life history. This result was robust to different model formulations, uneven species sample sizes, and removing the long-term trend in survival. Synchrony was also shared across migratory strategies, which suggests that environmental forcing during the 4-month temperate breeding season has a large-scale, interspecific impact on songbird survival. However, the strong interspecific synchrony was not easily explained by a set of candidate weather variables we defined a priori. Spring weather variables explained only 1.4% [0.01%-5.5%] of synchrony, while the contribution of large-scale winter weather indices may have been stronger but uncertain, accounting for 12% [0.3%-37%] of synchrony. Future research could jointly model interspecific variation and covariation in breeding success, age-dependent survival, and age-dependent dispersal to understand when interspecific synchrony in abundance emerges and destabilizes meta-communities.

High-resolution early warning system for human Puumala hantavirus infection risk in Germany

The fluctuation of human infections by the Puumala orthohantavirus (PUUV) in Germany has been linked to weather and phenology parameters that drive the population growth of its host species. We quantified the annual PUUV-outbreaks at the district level by binarizing the reported infections in the period 2006–2021. With these labels we trained a model based on a support vector machine classifier for predicting local outbreaks and incidence well in advance. The feature selection for the optimal model was performed by a heuristic method and identified five monthly weather variables from the previous two years plus the beech flowering intensity of the previous year. The predictive power of the optimal model was assessed by a leave-one-out cross-validation in 16 years that led to an 82.8% accuracy for the outbreak and a 0.457 coefficient of determination for the incidence. Prediction risk maps for the entire endemic area in Germany will be annually available on a freely-accessible permanent online platform of the German Environment Agency. The model correctly identified 2022 as a year with low outbreak risk, whereas its prediction for large-scale high outbreak risk in 2023 was not confirmed.

Trend and change-point analyses of meteorological variables using Mann–Kendall family tests and innovative trend assessment techniques in New Bhupania command (India)

Abstract Climate change (CC) significantly influences agricultural water productivity, necessitating increased irrigation. Therefore, the present study was undertaken to assess the trend and change-point analyses of weather variables such as temperature (T), rainfall (R), and reference evapotranspiration (ET0) using 31-year long-term data for semi-arid climate. The analysis was carried out employing Mann–Kendall (MK), Modified Mann–Kendall (MMK), Innovative Trend Analysis (ITA), and Innovative Polygon Trend Analysis (IPTA) methods. Homogeneity tests, including Pettitt's test, Standard Normal Homogeneity Test (SNHT) , Buishand range test, and Von Neumann Ratio Test (VNRT), were employed to detect change points (CPs) in the time series data. The results indicated that, for maximum temperature, MK and MMK revealed a positive trend for September and July, respectively, while minimum temperatures indicated increasing trends in August and September. Rainfall exhibited an increasing trend during the Zaid season (April–May). ET0 exhibited a negative trend in January. ITA and IPTA displayed a mixture of positive and negative trends across months and seasons. The change-point analysis revealed that for Tmax, the CP occurred in 1998 for time-series data for the month of April. Likewise, for Tmin, the change points for April and August time series were found in 1997.

EFFECTS OF GENOTYPE, WEATHER, FHB FUNGICIDE AND PRE-HARVEST GLYPHOSATE ON GRAIN QUALITY OF HARD RED SPRING WHEAT IN WESTERN CANADA

Diverse growing season weather in Western Canada has large effects on wheat quality. Management practices, such as pesticide application, may also affect wheat quality, but are largely unknown. This study measured pesticide application effects on grain quality for six hard red spring wheat cultivars over three growing seasons at four prairie locations. Each Siteyear included (i) an untreated control, (ii) fungicide applied at anthesis for Fusarium head blight (FHB) control, (iii) pre-harvest glyphosate applied at physiological maturity, and (iv) a combination of both pesticides. Generally warmer and drier conditions in 2015 and 2017 compared to 2016, resulted in wheat with higher grades, test weight, thousand-kernel weight, and grain protein content but lower Fusarium damaged kernel (FDK) content. Siteyear, reflecting weather variation by location, was the major factor affecting grain quality, contributing from 39% to 77% of total variance. Rainfall variation was greater than that for air temperature and appeared to be the main weather factor affecting quality. Genotype had a significant impact on grain quality but contributed 1% to 20% of total variance. The pesticide treatments had a significant effect on several quality parameters, but they contributed only 0.2% to 2% to total variance, implying that they have no detrimental effect on wheat grain quality when applied as recommended. Fungicide significantly reduced FDK level in four of ten Siteyears, all with high FDK levels, but not when applied at the low FDK Siteyears. Fungicide for FHB control should be used only when weather is conducive to high FHB disease pressure.

ASSESSMENT OF SOLAR PV OUTPUT PERFORMANCE WITH VARYING TILT ANGLES AND WEATHER DATA FROM ERA5: CASE OF MUNTINLUPA CITY, PHILIPPINES

Abstract. Solar photovoltaic (PV) technology has been gaining popularity in the Philippines as an alternative source of sustainable energy. In such technology, module tilt angle and weather conditions are among the system parameters that have substantial impacts on PV system performance. Previous studies have considered either tilt angles or weather conditions, but not the combined impact of these two parameters on solar PV power output. The objective of this study is to examine the effects of weather variables and tilt angle on the output of solar photovoltaic (PV) systems in Muntinlupa City, the Philippines. Three 120W monocrystalline solar PV panels were used and set up to three different tilt angles (i.e., 5°, 10°, and 15°). The fifth generation of the ECMWF's global climate and weather reanalysis (ERA5) dataset was used to gather hourly weather information such as surface solar radiation, wind speed, wind direction, relative humidity, ambient temperature, and total precipitation. Three principal components (PC), which together account for 95% of the variability, were identified using principal components analysis (PCA), which was used to address multicollinearity among the weather parameters. To assess the effects of tilt angle, time, and PCs on solar PV production, analysis of covariance (ANCOVA) was carried out. Results show that all weather variables, except for wind speed and total precipitation, have a significant impact on solar PV production with configuration producing the best results. Moreover, a significant difference in mean solar PV production was observed among the three tilt angles. From 6:00 AM to 2:00 PM, solar PV output gradually increases and declines thereafter. Outputs this study can help in optimizing the design and configuration of solar PV systems in the Philippines by considering weather variables and module tilt angle. Lastly, this study provides useful information for system designers, installers, and policymakers in improving energy generation and utilization, encouraging the use of renewable energy sources, and advancing sustainable energy objectives.

Genetic Variation and Heritability for Hydrogen Cyanide in Fresh Cassava Roots: Implications for Low-Cyanide Cassava Breeding.

Breeding for low-hydrogen-cyanide (HCN) varieties is a major objective of programs targeting boiled cassava food products. To enhance the breeding of low-HCN varieties, knowledge of genetic variation and trait heritability is essential. In this study, 64 cassava clones were established across four locations and evaluated for HCN using three HCN assessment methods: one with a 1 to 9 scale, on with a 0 ppm to 800 ppm scale, and a quantitative assay based on spectrophotometer readings (HCN_Spec). Data were also collected on the weather variables precipitation, relative humidity, and temperature. Highly significant differences were observed among clones (p < 0.001) and locations (p < 0.001). There was also significant clone-environment interactions, varying from p < 0.05 to p < 0.001. Locations Arua and Serere showed higher HCN scores among clones and were associated with significantly higher (p < 0.001) mean daily temperatures (K) and lower relative humidity values (%) across 12 h and 18 h intervals. Within locations, HCN broad sense heritability estimates ranged from 0.22 to 0.64, while combined location heritability estimates ranged from 0.14 to 0.32. Relationships between the methods were positive and strong (r = 0.75-0.92). The 1 to 9 scale is more accurate and more reproducible than either the 0 to 800 ppm scale or spectrophotometric methods. It is expected that the information herein will accelerate efforts towards breeding for low-HCN cassava varieties.