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Irrigation is the most significant consumer of freshwater worldwide. Deciding on the right amount of irrigation is crucial for sustainable water management and food production. The Penman-Monteith (P-M) reference crop evapotranspiration (ETO) is the gold standard in irrigation management and scheduling; however, its calculation requires measurements from multiple sensors over an extended reference grass surface. The cost of land, sensors, maintenance, and water to keep the grass surface green impedes having a dense network of ETO stations. To solve this challenge, this research aims to develop an input-limited ETO estimation approach based on historical weather data and machine learning (ML) algorithms to relax the need for a reference grass surface. This approach, called “SolarET,” takes solar radiation (RS) data as its sole input. RS is the only meteorological driving factor of ETO that does not rely on the measuring surface. To test the generalizability of SolarET, we test its performance over unseen arbitrary locations across California. California is chosen as the case study since it is one of the world's most hydrologically altered and agriculturally productive regions. In total, 19,088,736 hourly data samples from 131 automated weather stations have been used in this study. The ML models have been trained over 114 stations and tested over 17 unseen stations, each representing a California climatic zone. Our findings point to the superiority of decision tree-based algorithms versus neural networks. SolarET works best in irrigation-oriented regions of California (e.g., Central Valley) and is less accurate in coastal and desert zones. Our results demonstrate the higher accuracy of SolarET using hourly (RMSE = 0.93 mm/day) and daily (RMSE = 0.97 mm/day) RS data in comparison to well-known empirical alternatives like Priestley-Taylor (PT) (RMSE = 1.35 mm/day) and Hargreaves-Samani (HS) (RMSE = 2.69 mm/day).

Energy generation and economic development are closely linked, with energy playing a pivotal role in wealth creation. However, the finite nature of fossil fuel resources and associated environmental challenges has emphasized the need for sustainable energy alternatives based on renewable sources. Among these alternatives, photovoltaic (PV) systems hold significant potential for fostering a sustainable energy system. Despite ongoing material research, achieving a major breakthrough in enhancing the conversion efficiency of commercial PV modules remains a challenge. To optimize PV system yield, installation must consider geographic location and design factors. PV modules often yield different results compared to manufacturer specifications due to factors such as high temperatures, dust, and arid to semiarid climates. Therefore, it is crucial to conduct outdoor testing and characterization of PV modules tailored to specific locations, such as Jordan, to maximize system performance. This research aims to examine the impact of ambient temperature, dust accumulation, and solar irradiance intensity on PV system performance in Jordan, providing valuable insights through monitoring power output variations. The findings will contribute to improving PV system efficiency in Jordan's unique climate and aid manufacturers in developing innovative PV applications. The collected data includes solar radiation, temperature, voltage, current, and output power. The results confirm that the Azraq site is a better location when compared to Mafraq based on the possibility of generated more output power using a PV system. [ABSTRACT FROM AUTHOR]

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Modeling the atmospheric boundary layer (ABL) in the area of a wind farm using computational fluid dynamics (CFD) methods allows us to study the characteristics of air movement, the shading effect, the influence of relief, etc., and can be actively used in studies of local territories where powerful wind farms are planned to be located. The operating modes of a wind farm largely depend on meteorological phenomena, the intensity and duration of which cause suboptimal operating modes of wind farms, which require the use of modern tools for forecasting and classifying precipitation. The methods and approaches used to predict meteorological phenomena are well known. However, for designed and operated wind farms, the influence of meteorological phenomena on the operating modes, such as freezing rain and hail, remains an urgent problem. This study presents a multi-layered neural network for the classification of precipitation zones, designed to identify adverse meteorological phenomena for wind farms according to weather stations. The neural network receives ten inputs and has direct signal propagation between six hidden layers. During the training of the neural network, an overall accuracy of 81.78%, macro-average memorization of 81.07%, and macro-average memorization of 75.05% were achieved. The neural network is part of an analytical module for making decisions on the application of control actions (control of the boundary layer of the atmosphere by injection of silver iodide, ionization, etc.) and the formation of the initial conditions for CFD modeling. Using the example of the Ulyanovsk wind farm, a study on the movement of air masses in the area of the wind farm was conducted using the initial conditions of the neural network. Digital models of wind turbines and terrain were created in the Simcenter STAR-CCM+ software package, version 2022.1; an approach based on a LES model using an actuating drive disk model (ADM) was implemented for modeling, allowing calculation with an error not exceeding 5%. According to the results of the modeling of the current layout of the wind turbines of the Ulyanovsk wind farm, a significant overlap of the turbulent wake of the wind turbines and an increase in the speed deficit in the area of the wind farm were noted, which significantly reduced its efficiency. A shortage of speed in the near and far tracks was determined for special cases of group placement of wind turbines. [ABSTRACT FROM AUTHOR]

In developing nations, outdated technologies and sulfur-rich heavy fossil fuel usage are major contributors to air pollution, affecting urban air quality and public health. In addition, the limited resources hinder the adoption of advanced monitoring systems crucial for informed public health policies. This study addresses this challenge by introducing an affordable internet of things (IoT) monitoring system capable of tracking atmospheric pollutants and meteorological parameters. The IoT platform combines a Bresser 5-in-1 weather station with a previously developed air quality monitoring device equipped with Alphasense gas sensors. Utilizing MQTT, Node-RED, InfluxDB, and Grafana, a Raspberry Pi collects, processes, and visualizes the data it receives from the measuring device by LoRa. To validate system performance, a 15-day field campaign was conducted in Santa Clara, Cuba, using a Libelium Smart Environment Pro as a reference. The system, with a development cost several times lower than Libelium and measuring a greater number of variables, provided reliable data to address air quality issues and support health-related decision making, overcoming resource and budget constraints. The results showed that the IoT architecture has the capacity to process measurements in tropical conditions. The meteorological data provide deeper insights into events of poorer air quality. [ABSTRACT FROM AUTHOR]

The aim of this study is to evaluate changes in variance components for dairy cows under heat stress conditions using a random regression model. The daily milk yield and pedigree records used in the research were obtained from a dairy farm in Sanliurfa, Türkiye. The records were from Holstein dairy cows registered between 2017 and 2019 at the farm. A total of 690 lactations from 690 healthy dairy cows were used in the study and the total number of cow-days was 207,003. In order to evaluate heat stress on animals meteorological data were used and collected from a public weather station located 15.04 km away from the farm. In the study, variance components were separately estimated for the comfort period (CP) and the heat stress period (HSP) using a random regression test-day model and six-knot linear spline function was used. In the study, it was observed that heat stress resulted in an increase in additive genetic, permanent environmental, and consequently, phenotypic variance. During the lactation period, the average heritability was determined to be 0.13±0.007 for CP, while it was found to be 0.18±0.010 for HSP. According to the findings obtained from the study, it was concluded that the time periods for selection should coincide with the peak milk yield under heat stress conditions, while for the period without heat stress, it should be around the 120th day of lactation. These results indicate that climatic factors such as temperature and humidity should be included in the models used for genetic parameter and breeding value estimation. Thus, it may be possible to identify dairy cattle that are genetically more tolerant to hot conditions. In this way, more successful outcomes can be achieved in selection studies.

In this research study, a cost-effective and reliable weather station using a microcontroller system containing instruments and sensors for measuring and recording ambient variables was designed, fabricated, and tested. The dataset recorded and stored in the meteorological system can be applied to conduct various research in the field of energy and environment, especially in solar systems. Employing a microcontroller system reduces costs and provides special features such as accessing data on the web-based spreadsheets and adding control devices. In this system, meteorological information including solar radiation, air temperature, wind velocity, and air relative humidity is measured and saved in user-defined time intervals such as 30 seconds. The total cost for measuring equipment, sensors, and microcontroller along with a data logger is about 110 USD. To demonstrate the importance of using local meteorological data, in the vicinity of the case studies, the dataset provided by the local weather station was compared with the meteorological data of two nearby national stations for one month. The results revealed that the values reported by the national stations were different from the actual values measured by the local weather station. The deviations for solar radiation, wind velocity, air temperature and humidity values were at least 5, 9, 7%, and more than 100%, respectively.

Weather station devices are used to monitor weather parameter conditions, such as wind direction, speed, rainfall, solar radiation level, temperature, and humidity. This article discusses the design of a customized weather station embedded with gas concentration readings, whereby the gas concentration measurement includes oxygen (O2), carbon dioxide (CO2), and carbon monoxide (CO). The measurements and data processing of input sensors were transmitted to an Arduino Uno microcontroller, and the input data were then remitted to Wemos D1 Mini to be uploaded to a cloud server. Furthermore, the gas sensors' characterization methods were also considered to reveal the obtained results of accuracy, precision, linearity, and hysteresis. An android-based mobile application was also designed for monitoring purposes. The system in our experiment utilized an internet connection with a field station, base station, and database server. [ABSTRACT FROM AUTHOR]

Paciorek and Wehner raise important questions around our use of the Mann‐Kendall nonparametric trend test on smoothed data for analyzing long‐term hydrometeorological trends in Zhang et al. (2021, https://doi.org/10.1029/2020gl092293). We thank them for initiating this important conversation and their gracious cooperation in exploring the issues addressed in their comment. In this reply we confirm the inflation of significant p‐values by our choice to smooth, illustrate the relatively minor impacts on the main conclusions of our paper, and add our voices to those of Paciorek and Wehner in highlighting the lack of methodology for hypothesis testing across multiple stations that have spatial structure (i.e., testing for regionally consistent trends). Plain Language Summary: Our colleagues Drs. Paciorek and Wehner have raised concerns about our paper (Zhang et al., 2021, https://doi.org/10.1029/2020gl092293), which showed widespread increases in the duration of drought events over the last five decades in the western United States. They point out that our decision to smooth the data using a moving average inflated the number of weather stations at which the trends toward longer droughts were deemed significant by a statistical test. We agree with them on this point, and we have recomputed all our results using unsmoothed data to determine the impacts. We find that for most stations and regions, trend magnitudes remained largely unchanged, with many stations nearby one another often suggesting similar trends. Finally, we agree with Paciorek and Wehner that there is a lack of statistical methods to test such coherent regional patterns, and we caution that over‐reliance on p‐values limits the power of regional data to identify important climate trends. Key Points: We agree that smoothing to 5‐year moving windows introduced serial correlation into time series of annual statistics of daily rainfall data, inflating the number of weather stations individually showing significant trends (p < 0.05) with the Mann‐Kendall testRecomputation with unsmoothed values produced substantially the same dry intervals trend magnitude and direction at most stations individually and had only minimal impacts on dry interval trends computed for National Ecological Observatory Network domains using the Regional Kendall testNo perfect statistical approach leverages the capacity of coherent regional patterns among spatially correlated weather stations, and an over‐reliance on p‐values as a binary (significant vs. insignificant) determinant of trends limits the power of regional data [ABSTRACT FROM AUTHOR]

At the General Santander Educational Institution, located in the municipality of Soacha, Cundinamarca, a portable weather station with temperature sensor was designed and implemented for the classroom. The system, programed with Arduino, allowed users to obtain real-time weather information. The data collected revealed that the temperature in the classrooms ranged from 24°C to 29°C. This piece of research, in addition to summarizing the current environmental conditions and existing problems in the classrooms, evidenced how the integration of sensors into the system allows the monitoring and computation of the temperature level. By creating a learning environment with an appropriate temperature, academic success, and personal development of students can be fostered. [ABSTRACT FROM AUTHOR]

The aim of this study is to evaluate changes in variance components for dairy cows under heat stress conditions using a random regression model. The daily milk yield and pedigree records used in the research were obtained from a dairy farm in Sanliurfa, Türkiye. The records were from Holstein dairy cows registered between 2017 and 2019 at the farm. A total of 690 lactations from 690 healthy dairy cows were used in the study and the total number of cow-days was 207,003. In order to evaluate heat stress on animals meteorological data were used and collected from a public weather station located 15.04 km away from the farm. In the study, variance components were separately estimated for the comfort period (CP) and the heat stress period (HSP) using a random regression test-day model and six-knot linear spline function was used. In the study, it was observed that heat stress resulted in an increase in additive genetic, permanent environmental, and consequently, phenotypic variance. During the lactation period, the average heritability was determined to be 0.13±0.007 for CP, while it was found to be 0.18±0.010 for HSP. According to the findings obtained from the study, it was concluded that the time periods for selection should coincide with the peak milk yield under heat stress conditions, while for the period without heat stress, it should be around the 120th day of lactation. These results indicate that climatic factors such as temperature and humidity should be included in the models used for genetic parameter and breeding value estimation. Thus, it may be possible to identify dairy cattle that are genetically more tolerant to hot conditions. In this way, more successful outcomes can be achieved in selection studies. [ABSTRACT FROM AUTHOR]

Various studies on local crop monitoring are presented in the literature, however there is insufficient research on low-cost IoT weather stations that can obtain weather information over a long period of time. To overcome the difficulties described above, this paper proposes the development of an IoT weather station for low-cost, sustainable and multi-functional agricultural crop monitoring. The objective of the work is to develop a local weather observation device for agricultural crops, to set up a server for storing and providing meteorological information and to perform an analysis of the acquired weather data. The weather station architecture developed and proposed in this project consists of four main blocks: sensor and controller module, power module, local data storage module and data communication module (gateway). [ABSTRACT FROM AUTHOR]

Irrigation is the most significant consumer of freshwater worldwide. Deciding on the right amount of irrigation is crucial for sustainable water management and food production. The Penman-Monteith (P-M) reference crop evapotranspiration (ETO) is the gold standard in irrigation management and scheduling; however, its calculation requires measurements from multiple sensors over an extended reference grass surface. The cost of land, sensors, maintenance, and water to keep the grass surface green impedes having a dense network of ETO stations. To solve this challenge, this research aims to develop an input-limited ETO estimation approach based on historical weather data and machine learning (ML) algorithms to relax the need for a reference grass surface. This approach, called ''SolarET,'' takes solar radiation (RS) data as its sole input. RS is the only meteorological driving factor of ETO that does not rely on the measuring surface. To test the generalizability of SolarET, we test its performance over unseen arbitrary locations across California. California is chosen as the case study since it is one of the world's most hydrologically altered and agriculturally productive regions. In total, 19,088,736 hourly data samples from 131 automated weather stations have been used in this study. The ML models have been trained over 114 stations and tested over 17 unseen stations, each representing a California climatic zone. Our findings point to the superiority of decision tree-based algorithms versus neural networks. SolarET works best in irrigation-oriented regions of California (e.g., Central Valley) and is less accurate in coastal and desert zones. Our results demonstrate the higher accuracy of SolarET using hourly (RMSE = 0.93 mm/day) and daily (RMSE = 0.97 mm/day) RS data in comparison to well-known empirical alternatives like Priestley-Taylor (PT) (RMSE = 1.35 mm/day) and Hargreaves-Samani (HS) (RMSE = 2.69 mm/day).

The evolution of the Internet of Things (IoT) has ushered in innovative approaches facilitated by breakthrough technologies. This paper presents a comprehensive review of recent advancements in smart weather stations, focusing on IoT-enabled solutions. The integration of Internet of Things (IoT) technologies has revolutionized traditional weather monitoring systems, enabling seamless data collection, analysis, and dissemination. Commercially available automated weather stations offer cost-effective solutions for comprehensive meteorological data collection. However, challenges such as limited local deployment and reliance on expensive options persist, hindering comprehensive monitoring efforts. The critical need for improved data collection methods is underscored to enhance the accuracy of weather forecasts and address evolving climatic conditions. Climate change impacts, including shifts in weather patterns and rising temperatures, highlight the importance of effective weather monitoring for agriculture, infrastructure, and national security. Additionally, the dependence on non-renewable energy sources for electricity generation emphasizes the environmental and economic implications of energy production. In response to these challenges, numerous IoT based smart weather station systems have been proposed by earlier researchers. The introduction of IoT-enabled smart weather stations represents a significant advancement in weather monitoring technology. These stations leverage IoT technologies to collect, analyse, and visualize meteorological data in real time. In order to identify the challenges and prospects in this area of technology, the purpose of this study is to present a thorough analysis of the suggested designs as well as to compare, evaluate, and assess the outcomes, contributing to the development of robust and efficient weather monitoring systems.

People who work in dangerous environments include farmers, sailors, travelers, and mining workers. Due to the fact that they must evaluate the changes taking place in their immediate surroundings, they must gather information and data from the real world. It becomes crucial to regularly monitor meteorological parameters such air quality, rainfall, water level, pH value, wind direction and speed, temperature, atmospheric pressure, humidity, soil moisture, light intensity, and turbidity in order to avoid risks or calamities. Enhancing environmental standards is largely influenced by IoT. It greatly advances sustainable living with its innovative and cutting-edge techniques for monitoring air quality and treating water. With the aid of various sensors, microcontroller (Arduino Uno), GSM, Wi-Fi, and HTTP protocols, the suggested system is a real-time smart monitoring system based on the Internet of Things. Also, the proposed system has HTTP-based webpage enabled by Wi-Fi to transfer the data to remote locations. This technology makes it feasible to track changes in the weather from any location at any distance. The proposed system is a sophisticated, efficient, accurate, cost-effective, and dependable weather station that will be valuable to anyone who wants to monitor environmental changes on a regular basis.

The large-scale deployment of low-cost monitoring systems has the potential to revolutionize the field of urban hydrology monitoring, bringing improved urban management, and a better living environment. Even though low-cost sensors emerged a few decades ago, versatile and cheap electronics like Arduino could give stormwater researchers a new opportunity to build their own monitoring systems to support their work. To find out sensors which are ready for low-cost stormwater monitoring systems, for the first time, we review the performance assessments of low-cost sensors for monitoring air humidity, wind speed, solar radiation, rainfall, water level, water flow, soil moisture, water pH, conductivity, turbidity, nitrogen, and phosphorus in a unified metrological framework considering numerous parameters. In general, as these low-cost sensors are not initially designed for scientific monitoring, there is extra work to make them suitable for in situ monitoring, to calibrate them, to validate their performance, and to connect them with open-source hardware for data transmission. We, therefore, call for international cooperation to develop uniform low-cost sensor production, interface, performance, calibration and system design, installation, and data validation guides which will greatly regulate and facilitate the sharing of experience and knowledge. HIGHLIGHTS Low-cost sensors have the potential to revolutionize water monitoring.; We provide an up-to-date scientific review of commercially available low-cost sensors.; We introduce a unified metrological framework for various low-cost sensors.; Suggestions and recommendations for sensor modules choice and uses are given.; The development of low-cost monitoring systems requires elaborating common guidelines.;

Abstract Paciorek and Wehner raise important questions around our use of the Mann‐Kendall nonparametric trend test on smoothed data for analyzing long‐term hydrometeorological trends in Zhang et al. (2021, https://doi.org/10.1029/2020gl092293). We thank them for initiating this important conversation and their gracious cooperation in exploring the issues addressed in their comment. In this reply we confirm the inflation of significant p‐values by our choice to smooth, illustrate the relatively minor impacts on the main conclusions of our paper, and add our voices to those of Paciorek and Wehner in highlighting the lack of methodology for hypothesis testing across multiple stations that have spatial structure (i.e., testing for regionally consistent trends).

In the present work, the production results obtained in the area of Langovet were presented, over a period of four years, in the period 2019-2022, a locality located in the immediate vicinity of the border with Serbia. The data used in this work were provided by the Banat Crisana Regional Meteorological Center, data that reported the meteorological aspects of risk reported at two meteorological stations in Caras Severin county, the meteorological station from Oravita and the one from Moldova Noua, for the analyzed period. At the beginning of the work, I presented the geographical location of Campia (Langovet), a village belonging to Socol commune, and then I carried out a climatological description of certain risk factors prevalent in this area of Caras Severin county. The Danube gorge is under the influence of the air masses that circulate towards the northeast, it is distinct and contributes to the increase of the originality of the landscape. We thus emphasize the presence of more abundant precipitation if we consider the temperate continental climate of our country's territory. In addition, precipitation manifests itself with a spasmodic character, recording the highest values during autumn and spring with variables registered by the exposure of the slopes. Regarding the average annual temperature of the area, we mention that it is higher. The average annual isotherm of 110 delimits the Danube corridor. Thanks to these evaluations, we signal the presence of biocenoses specific to the area. For several decades, our family has been working in the field of agriculture in the town of Langovet, being among the first to purchase machinery in our town. In total, we cultivate 95 ha, of which 40 ha are our property, the remaining 55 ha are leased, the surfaces being cultivated with corn, sunflower and wheat. Regarding the share held by the three crops, in the last year, in 2022, we have 20 ha of sunflower, the remaining 75 ha being almost equally divided between the other two crops, corn and wheat. The paper presents the production results obtained during the four analyzed years, 2019-2022. [ABSTRACT FROM AUTHOR]

In this research study, a cost-effective and reliable weather station using a microcontroller system containing instruments and sensors for measuring and recording ambient variables was designed, fabricated, and tested. The dataset recorded and stored in the meteorological system can be applied to conduct various research in the field of energy and environment, especially in solar systems. Employing a microcontroller system reduces costs and provides special features such as accessing data on the web-based spreadsheets and adding control devices. In this system, meteorological information including solar radiation, air temperature, wind velocity, and air relative humidity is measured and saved in user-defined time intervals such as 30 seconds. The total cost for measuring equipment, sensors, and microcontroller along with a data logger is about 110 USD. To demonstrate the importance of using local meteorological data, in the vicinity of the case studies, the dataset provided by the local weather station was compared with the meteorological data of two nearby national stations for one month. The results revealed that the values reported by the national stations were different from the actual values measured by the local weather station. The deviations for solar radiation, wind velocity, air temperature and humidity values were at least 5, 9, 7%, and more than 100%, respectively. [ABSTRACT FROM AUTHOR]

Climate variability has increased the need for atmospheric monitoring systems at the local level. Meteorological data are useful for climate prediction, agriculture and crop management, water resources management, and air quality estimation. Current solutions to obtain meteorological data are typically regional, expensive, and depend on telecommunications providers, limiting the value of the information and its accessibility in rural areas or specific places. Corresponding to the development of more affordable systems, the following article presents a monitoring solution for meteorological variables based on the TTGO T-Beam development board and the Bresser 5-in-1 weather station, using LoRa communication. The proposed hardware design includes the Bresser 5-in-1 station, two T-Beam development modules, and a personal computer. The software architecture uses the TTGO T-Beam modules to transmit and store the weather station measurements, the IoT platform "Node-RED" is used for data acquisition, while the "Grafana" platform is used for the visualization. Link tests with LoRa technology (868MHz) demonstrated the ability to operate at distances greater than one kilometer. The research results confirmed the possibility and reliability of remotely integrating measurements from the Bresser 5-in-1 weather station to an open-source IoT platform. This use of open software and hardware solutions allows their generalization by other researchers. [ABSTRACT FROM AUTHOR]

This paper presents a collection of small-scale atmospheric datasets obtained from a PCE-FWS 20 N weather station in Pangandaraan, a region situated in the southern part of Java Island. The datasets cover a period from March 2022 to April 2023, with hourly measurements of air temperature, humidity, wind speed, wind direction, and daily rainfall. The instrument was cleaned and calibrated every three months according to the manufacturer's guidelines. Every week the data was downloaded from the memory card, resulting in a total of 48,468 data points available in a publicly accessible repository. The collected data were organized into .csv format and visualized to facilitate analysis. Our study aims to explore the microclimate of Pangandaraan over an extended period and highlights its potential applications in various fields, such as applied oceanography, meteorology, fishing grounds, and agriculture.

Autonomous power systems serving remote areas with weather stations with small settlements are characterized by a fairly high cost of generating electricity and the purchase and delivery of fuel. In addition, diesel power plants require regular maintenance, have a relatively short service life during continuous operation and produce a large amount of emissions into the environment. This article discusses various methods of placing solar panels in the space for the autonomous power supply of weather station equipment. The principles of these methods are described and their advantages and disadvantages are outlined. The optimal algorithms of functioning for photomodules are described and their comparison regarding the main, significant parameters is carried out. The choice of the most effective algorithm for use at a weather station is made. The effective positioning of solar panels is also calculated, and positioning conditions are determined depending on the territorial location and various environmental conditions. Simulation of the power supply system of a weather station consisting of solar panels, batteries and inverters is performed. As a result, a practical example of the application of the method of selecting the optimal composition of equipment for a hybrid power system of a weather station territorially located in Siberia with different configurations of equipment is considered. In numerical terms, it was possible to reduce the cost of power equipment operation by more than 60% with a fairly low payback period of 5.5 years and an increased reliability of the power system, which is very important for autonomous power systems of northern weather stations. [ABSTRACT FROM AUTHOR]

This paper addresses problems associated with modelling of a meteorological station needed by small agricultural producers for monitoring and improving agricultural production process. The proposed meteorological station model represents an autonomous solution for remote monitoring of meteorological and spatio-temporal attributes. The efficiency of this solution is reflected in the constant monitoring of meteorological parameters using custom selected group of sensors, and significantly lower cost of realization compared to the available commercial solutions. The proposed meteorological station is applicable in fruit, crop and vegetable production. Due to its features and design, it is suitable for installation and outdoor use, enabling coverage of a large number of smaller surfaces through the creation of a wireless network of weather stations. This paper provides not only technical details about the meteorological station, but also practical use cases for improving agricultural production. It is a symbiosis of modem IT solutions and agriculture, presented both from scientific and practical aspect. Therefore, it can be useful not only to IT experts and data scientist, but also to agronomists. Furthermore, ideas presented in this paper are rather novel, and can be used as a starting point for domain specific data exploration. [ABSTRACT FROM AUTHOR]

For small ectotherm species, dealing with microclimatic variation during sensitive stages of their development is a critical component of their ecological interactions within and among different landscape settings. However, it is often unknown whether the use of habitats in different landscape settings may affect microclimatic conditions at the level of microhabitats (e.g., at host plants for herbivorous insects). As opposed to standard ecological monitoring experiments, we aim to identify the microclimatic variables that accurately represent the environment in which a small arthropod spends most of its life. As a case study, we selected grassy sites that corresponded to the microhabitat of grass‐feeding insects in general, and larvae of the butterfly Pararge aegeria in particular, as this insect recently expanded its habitat use from forest (edges) to agricultural and urban environments. We tested the extent to which local microclimates and microclimatic buffering capacity differed between tufts of grass in forest and in two anthropogenic (i.e., agricultural and urban) landscape settings by measuring microclimatic variables with sensors at the level of the grasses. We compared temperature, relative humidity, and vapor pressure deficit (VPD) during an exceptionally warm and dry summer period with parallel data from weather stations and tested for differences between microclimatic profiles among the three landscape settings. Microclimatic conditions correspond only partly to variables based on weather station data. Differences are particularly significant for relative humidity and VPD, but not for day and night temperatures. Effects of canopy openness on the variability of microclimatic variables are landscape‐specific. Host plants in agricultural settings experience strong microclimatic fluctuations and low short‐term variability in VPD. In urban settings, urban heat island effects increase night temperature but also show microclimatic buffering effects similar to the ones in forest settings. Our findings stress the functional implications of landscape‐specific microclimatic profiles at the appropriate organism‐centered scale. We discuss the significance of such microclimatic profiles for ecological studies dealing with ectotherms under climate and land use change. [ABSTRACT FROM AUTHOR]

The time data series of weather stations are a source of information for floods. The study of the previous wintertime series allows knowing the behavior of the variables and the result that will be applied to analysis and simulation models that feed variables such as flow and level of a study area. One of the most common problems is the acquisition and transmission of data from weather stations due to atypical values and lost data; this generates difficulties in the simulation process. Consequently, it is necessary to propose a numerical strategy to solve this problem. The data source for this study is a real database where these problems are presented with different variables of weather. This study is based on comparing three methods of time series analysis to evaluate a multivariable process offline. For the development of the study, we applied a method based on the discrete Fourier transform (DFT), and we contrasted it with methods such as the average and linear regression without uncertainty parameters to complete missing data. The proposed methodology entails statistical values, outlier detection, and the application of the DFT. The application of DFT allows the time series completion, based on its ability to manage various gap sizes and replace missing values. In sum, DFT led to low error percentages for all the time series (1% average). This percentage reflects what would have likely been the shape or pattern of the time series behavior in the absence of misleading outliers and missing data. [ABSTRACT FROM AUTHOR]

The 10th percentiles (10P) of the daily minimum (Tmin) and maximum (Tmax) during 1971–2000 were determined to estimate a threshold for cold days. This 10P (a standard of extreme climatic condition suggested by the World Meteorological Organization) threshold was applied with the daily Tmin and Tmax in the winter months (December, January, and February) of 2000 to 2021 to calculate the number of cold days, and consecutively, cold spells, and their trends. A cold day was declared when the daily Tmax and/or Tmin was lower than that of the 10P threshold, and the average temperature was ≤17 °C in a weather station. In this research, the cold days and spells were categorized into five classes, namely extreme (≤13 °C), severe (>13–14 °C), very (>14–15 °C), moderate (>15–16 °C), and Mild (>16–17 °C). Moreover, a cold spell was considered when such cold days persisted for ≥2 consecutive days in at least two nearby stations. The results revealed a higher number of average cold days during winter in the western and northwestern districts of Bangladesh, and it reduced gradually in the south, southeast, and northeast. Dinajpur and Rajshahi districts showed the highest number of extreme and severe categories of cold days, i.e., 4.81 and 3.24 days/year, respectively. Rajshahi division had the highest number of cold spells on average (3.24/year), and Rangpur division had the highest number of extreme-category (the category that carries the lowest temperature range, ≤13 °C) cold spells (1.29/year). January was the coldest month, with the maximum number of cold days and spells. The highest average number of cold days (25.54%) was observed during the second ten days of January (i.e., 11–20 January). Significant increasing trends were found in the cold days of 11–20 December (5 stations), 21–31 December (3 stations), and the month of December (13 stations). In contrast, significant decreasing trends were noticed for the 1–10 January period in three weather stations. Our proposed 10P method could be used to determine the cold days and spells in Bangladesh that might be useful for the policy makers in formulating appropriate strategies in minimizing the impact of cold regimes during the winter season. [ABSTRACT FROM AUTHOR]

Infrared thermography is a predictive maintenance tool used in substations to identify a disturbance in electrical equipment that could lead to poor operation and potential failure in the future. According to Joule's law, the temperature of electrical equipment is proportional to the current flowing through it. Other external factors, such as solar incidence, air humidity, wind speed, and air temperature, can interfere with its operating temperatures. Based on this premise, this article aims to analyze the influence of atmospheric and load conditions on the operational cycle of thermography-monitored equipment in order to describe the operating temperature of the object using only external data and to show the impacts of external influences on the final temperature reached by the object. Five multivariate time series regression models were developed to describe the maximum equipment temperature. The final model achieved the best fit between the measured and model temperature based on the Akaike information criterion (AIC) metric, where all external variables were used to compose the model. The proposed model shows the impacts of each external factor on equipment temperature and could be used to create a predictive maintenance strategy for power substations to avoid failure. [ABSTRACT FROM AUTHOR]

Most statistical models of microclimate focus on the difference or 'offset' between standardized air temperatures (macroclimate) and those of a specific habitat such as forest understorey, grassland or under a log. However, these offsets can fluctuate from positive to negative over a single day such that common practice consists in aggregating data into daily mean, minimum and maximum before modelling monthly offsets for each summary statistic. Here, we propose a more parsimonious and flexible approach relying on just two parameters: the slope and equilibrium. The slope captures the linear relationship between microclimate and macroclimate, while the equilibrium is the point at which microclimate equals macroclimate. Although applicable to other habitats, we demonstrate the relevance of our method by focusing on forest understoreys.We installed temperature sensors at 1‐m height inside forest stands and in nearby open grasslands equipped with standardized weather stations, across 13 sites in France spanning a wide climatic gradient. From a year of hourly temperatures and for each sensor, we established relationships between microclimate and macroclimate temperatures using two linear mixed‐effects models, during the leaf‐on (May–November) and leaf‐off period (December–April). We extracted the monthly equilibrium and slope for each sensor, and used another set of linear mixed‐effects models to investigate their main determinants.The slope was chiefly determined by stand structure variables interacting with the leaf‐on/leaf‐off period: stand type (conifer vs broadleaf); shade‐casting ability; stand age; dominant height; stem density; and cover of the upper and lower shrub layer. In contrast, forest structure had no explanatory power on the equilibrium. We found the equilibrium to be positively related to mean macroclimate temperature, interacting with the open/forest habitat.The method introduced here overcomes several shortcomings of modelling microclimate offsets. By demonstrating that the slope and equilibrium vary in predictable ways, we have established a general linkage between microclimate and macroclimate temperatures that can be applied to any location or time if we know the mean macroclimate temperature (equilibrium) and buffering or amplifying capacity of the habitat (slope). We also warn about methodological biases due to the reference used for macroclimate. [ABSTRACT FROM AUTHOR]

Many weather monitoring networks such as the Oklahoma Mesonet provide estimates of reference evapotranspiration (ETref) to facilitate weather-informed irrigation decisions. However, weather stations that collect the required input data to estimate ETref using the widely applied ASCE standardized ETref equation are not typically installed over a reference surface, defined as a large expanse of dense, well-watered, stress-free grass or alfalfa having a specified height, surface resistance, and albedo. The deviation of actual surface conditions in the surrounding environment of the weather stations from the reference condition creates station aridity effects that can lead to overestimation of ETref. Daily hydroclimate datasets for a period of 20 years (2000-2019) were used to evaluate the prevalence and spatiotemporal characteristics of station aridity across the Oklahoma Mesonet. Station aridity was characterized based on mean dew point deviation (MDD = Tmin - Tdew), maximum relative humidity (RHmax), and normalized difference vegetation index (NDVI). Results demonstrate that station aridity is prevalent and highly variable in both space and time across the Oklahoma Mesonet, as it increases from southeast to northwest in the Oklahoma Panhandle. Larger average seasonal MDD (up to 13°C), lower RHmax (e.g., 57%), and lower NDVI (e.g., 0.22) were observed during extreme to exceptional drought of 2011 in western Oklahoma, where a majority of the state’s irrigated agriculture (88%) is located. Spatiotemporal patterns of station aridity demonstrate the profound effect of wet and dry periods that influence the utility of ETref estimates to improve agricultural water conservation during high irrigation requirement times in water-scarce irrigated areas. [ABSTRACT FROM AUTHOR]

Abstract For small ectotherm species, dealing with microclimatic variation during sensitive stages of their development is a critical component of their ecological interactions within and among different landscape settings. However, it is often unknown whether the use of habitats in different landscape settings may affect microclimatic conditions at the level of microhabitats (e.g., at host plants for herbivorous insects). As opposed to standard ecological monitoring experiments, we aim to identify the microclimatic variables that accurately represent the environment in which a small arthropod spends most of its life. As a case study, we selected grassy sites that corresponded to the microhabitat of grass‐feeding insects in general, and larvae of the butterfly Pararge aegeria in particular, as this insect recently expanded its habitat use from forest (edges) to agricultural and urban environments. We tested the extent to which local microclimates and microclimatic buffering capacity differed between tufts of grass in forest and in two anthropogenic (i.e., agricultural and urban) landscape settings by measuring microclimatic variables with sensors at the level of the grasses. We compared temperature, relative humidity, and vapor pressure deficit (VPD) during an exceptionally warm and dry summer period with parallel data from weather stations and tested for differences between microclimatic profiles among the three landscape settings. Microclimatic conditions correspond only partly to variables based on weather station data. Differences are particularly significant for relative humidity and VPD, but not for day and night temperatures. Effects of canopy openness on the variability of microclimatic variables are landscape‐specific. Host plants in agricultural settings experience strong microclimatic fluctuations and low short‐term variability in VPD. In urban settings, urban heat island effects increase night temperature but also show microclimatic buffering effects similar to the ones in forest settings. Our findings stress the functional implications of landscape‐specific microclimatic profiles at the appropriate organism‐centered scale. We discuss the significance of such microclimatic profiles for ecological studies dealing with ectotherms under climate and land use change.

The development of power generators that can function in harsh snowy environments and in contact with snow can be beneficial but challenging to accomplish. Herein, we introduce the first snow-based triboelectric nanogenerator (snow-TENG) that can be used as an energy harvester and a multifunctional sensor based on the principle of snow-triboelectrification. In this work, we used a 3D printing technique for the precise design and deposition of the electrode and triboelectric layer, leading to flexible, stretchable and metal-free triboelectric generators. Based on the single electrode mode, the device can generate an instantaneous output power density as high as 0.2 mW/m2, an open circuit voltage up to 8 V, and a current density of 40 μA/m2. In addition, the snow-TENG can function as a miniaturized weather station to monitor the weather in real time to provide accurate information about the snowfall rate, snow accumulation depth, wind direction, and speed in snowy and/or icy environments. In addition, the snow-TENG can be used as a wearable power source and biomechanical sensor to detect human body motions, which may prove useful for snow-related sports. Unlike conventional sensor platforms, our design works without the need for batteries or image processing systems. We envision these devices could potentially be integrated into solar panels to ensure continuous power supply during snowy weather conditions.

Previous studies have documented that surface wind speed (u) has been increasing over the ocean but decreasing over land for the past several decades. The decreasing u at the surface over land has been referred to as terrestrial stilling. A plausible hypothesis for terrestrial stilling is an increase in surface roughness associated with changes in land surface (e.g. enhanced vegetation growth, landscape fragmentation or urbanization). One of the most widespread land surface changes is enhanced vegetation leaf area index (LAI) known as greening, particularly over the middle to high latitudes of the Northern Hemisphere where strong stilling is observed from weather station data. In this study, we examine the hypothesis that enhanced vegetation LAI is a key driver of global terrestrial stilling. We first characterized the trend in u over the ocean using long-term satellite altimeter measurements, and the trend in u over land using continuous wind records from 4305 in situ meteorological stations. We then performed initial condition ensemble Atmospheric Model Intercomparison Project-type simulations using two state-of-the-art Earth system models (IPSL-CM and CESM) to isolate the response of u to the historical increase in LAI (representing the greening) for the period 1982-2011. Both models, forced with observed sea surface temperature and sea ice and with LAI from satellite observation, captured the observed strengthening of Pacific trade winds and Southern Ocean westerly winds. However, these simulations did not reproduce the weakening of surface winds over land as significantly as it appears in the observations (-0.006 m s -1 versus -0.198 m s -1 during 1982-2011), indicating that enhanced LAI (greening) is not a dominant driver for terrestrial stilling.

ABSTRACT A common scenario in the developing countries is the low income and less education of producers. Thus, the tools used for irrigation management must be cheap and easy to handle. In this work, an autonomous and low-cost network of micro-weather stations has been developed for irrigation management. Simulations were performed to evaluate the ability of intelligent systems to compute evapotranspiration with noisy and insufficient data. The network of micro-weather stations was then applied to autonomous irrigation management of a crop of bell peppers. Statistical analysis was performed on data from the developed system and a standard weather station. The results show no statistical difference between the values of evapotranspiration calculated with data from these two sources. The developed system performed with a coefficient of determination of 0.968, mean absolute error of 0.055 mm day−1, and root mean square error of 0.063 mm day−1. The study shows that low-cost intelligent systems can be used as viable tools for efficient irrigation management.

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This article presents the development of a model center using agricultural intelligent center technology. The goal of this research is: 1. To develop a wireless sensor network. 2. To be a source of learning on the use of sensor technology in agriculture. For local and nearby farmers Using the Sufficiency Economy Learning Center, according to King's Science. The Rajamangala University of Technology Suvarnabhumi is a research area. With the problems faced in farming today. It found that the world's climate change whether it's drought. Rains leave ranges and toxic airborne particulate matter caused by farming to match current problem conditions. The researchers then designed a two-part system: 1. Node Moisture Sensor that measures soil moisture and commands the opening - It also controls on-off with a manual switch. Wind speed and wind direction sensors, light intensity sensors, temperature, and humidity sensors, and Particulate Matters Sensor 1.0, 2.5, 10 with environmental reports within the growing area via Wi-F signals to (Sever) Raspberry Pi record real-time data. Every 30 seconds According to research, node moisture sensors can measure soil moisture and record results, and the station measures the environment within the growing area via a Wi-F signal to (Sever) Raspberry Pi. Rainfall values measured by local rainfall sensors measuring up to 35.3 mm are within the threshold of heavy rain. The maximum wind speed measured is 8.5 km/h, the maximum temperature of 35.8 degrees Celsius, and the maximum humidity of 99.9 percent, the light intensity is up to 58,002 Lux, and the Final Particles, with pm 1.0 up to 40.1 microns, PM 2.5 up to 51.3 microns and PM 10 up to 63.5 microns. Apply agriculture to 50 interested farmers after receiving knowledge transfer of smart sensor technology. The expansion has resulted in 3 farmers and will continue to expand in the future. Promote the use of agricultural technology. Intensifying communities and supporting global climate change. [ABSTRACT FROM AUTHOR]

Introduction: Climate change along with human development created additional pressure on ecosystems, so in many cases it has caused a decrease in biodiversity. Global climate change continues as a global crisis. Climate change does not affect all habitats and species equally. Rather, large species are more affected by climate change and habitat change and are more vulnerable than other species. Also, studies show that the countries of the Middle East have been severely affected by the effects of climate change. Large herbivores, especially red deer (Cervus elaphus), are not only affected by habitat changes resulting from climate change, but also the increase in temperature has reduced the survival rate of calves and impaired reproduction. The Caspian red deer in Iran has lost a lot of its population under the influence of habitat destruction and illegal hunting, and today the remaining population tolerate additional pressure from climate change. Central Alborz Protected Area is considered as a main breeding habitat of Caspian red deer, which is affected by human development and climate change today. Material and methods: In this study, in order to investigate the impact of climate change on the red deer population, temperature and precipitation data of two meteorological stations, Nowshahr in the lower elevations, and Baladeh in the southern elevations, were used. Also, presence and non-presence points of the species were collected random transects direct observation, as well as pictures of camera traps. Results and discussion: Using 19 climate parameters to evaluate habitat suitability in the future, it revealed that continuation of the climate change process in four optimistic to pessimistic scenarios using five MaxEnt, RF, ANN, GLM, MARS algorithms, the suitability of low altitude areas in the sectors of north and east will be reduced, and will be increased towards to highlands in southwest. The results showed that the parameters of average annual temperature, average daily temperature, average temperature of the dry season, average temperature of the hottest season, annual rainfall, rainfall in the wettest season, and rainfall in the hottest season have an effective role in determination of the habitat suitability of the species. The results of the climate data showed that the altitude plays an effective role in reducing the effects of climate change, so that the climate change in the downstream areas has a significant intensity compared to the high areas. According to the change of habitat suitability resulting from the continuation of the climate change, Kojoor forest zone shows the relative stability of desirability for the studied species in the present and future, but the Kheyrud forest road with a total length of more than 50 km is located in the depth of this area, where the traffic of vehicles of herders, ecotourists and especially illegal hunters have facilitated access to this habitat, which is the most main threat to survival of red deer in this habitat. Conclusion: With the continuation of climate change and its effects on the habitat in the future, it is expected that female deers will be observed in unsafe parts of the habitat. Therefore, for conservation of the Caspian red deer population in the central Alborz Protected Area, robust management measures are needed based on created habitat changes in the future. [ABSTRACT FROM AUTHOR]