The distribution and diurnal variation of short-duration heavy rainfall (SDHR) and the influence of a complex underlying surface were studied by using fine-scale hourly precipitation data in the ...Beijing–Tianjin–Hebei (BTH) region during the summers of 2014–2020. Areas prone to SDHR are located mainly in the southern foothills of the Yanshan Mountains, the foothills area, and the trumpet-shaped topographic entrance area north of Beijing, areas inland of the west coast of Bohai Bay, and the northern Beijing urban area. Owing to the influence of topography and the geographical location, the distribution and diurnal variation SDHR is significantly different in the western and northern mountainous areas, the foothills, and the plains. Compared to the underlying urban surface, the topography and the land–sea interface have considerable effects on the distribution of SDHR. A key finding is that the foothills of northern of Beijing, eastern slope and piedmont area of the Taihang Mountains, and the land–sea interface of Bohai Bay play important roles in the formation and propagation of SDHR.
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.
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.
Demand forecasting is and has been for years a topic of great interest in the electricity sector, being the temperature one of its major drivers. Indeed, one of the challenges when modelling the load ...is to choose the right weather station, or set of stations, for a given load time series. However, only a few research papers have been devoted to this topic. This paper reviews the most relevant methods that were applied during the Global Energy Forecasting Competition of 2014 (GEFCom2014) and presents a new approach to weather station selection, based on Genetic Algorithms (GA), which allows finding the best set of stations for any demand forecasting model, and outperforms the results of existing methods. Furthermore its performance has also been tested using GEFCom2012 data, providing significant error improvements. Finally, the possibility of combining the weather stations selected by the proposed GA using the BFGS algorithm is briefly tested, providing promising results.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Monitoring efficiently urban microclimates is necessary for rendering cities resilient against climate change impacts. Aiming to retrieve data of high spatial resolution, mobile monitoring techniques ...have been introduced and applied within various urban contexts around the globe. Mobile monitoring campaigns may indeed contribute toward a high-granularity evaluation of the intra-urban characteristics of each city. In addition, it can help exploit resiliency planning at different aggregation levels when implemented in conjunction with traditional monitoring systems, such as fixed stations and satellites. Under this scenario, this study presents for the first time a systematic and critical review of the recent and most comprehensive mobile monitoring systems, and the corresponding field campaigns and protocols followed. The reviewed monitoring systems are developed on either motorized (e.g. cars, vans) or non-motorized (e.g. bicycles, carts, humans) agents for measuring the main parameters regulating urban canopy layers with respect to the (i) hygrothermal environment, (ii) air quality, (iii) light pollution, (iv) noise pollution. Overall, 94 articles are analyzed for concluding to the current state of the art, the corresponding scientific gaps, and proposed future research paths. Most of the reported monitoring systems are developed to monitor only one environmental domain, while the lack of standardized monitoring protocols is found to be the biggest challenge that needs to be addressed for minimizing biases and rendering the outcomes of different studies comparable. Relevant technological advancements and human-centric frameworks are further discussed aiming to exploit mobile monitoring for establishing efficient decision-making and risk assessment within urban environments.
•Systematic literature review of urban mobile monitoring is performed for the first time.•Thermal environment, air quality, noise and light pollution monitoring protocols are reviewed.•The review reports on monitoring campaigns performed at a global scale.•The challenges and future paths for an efficient urban mobile monitoring are devised.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Many applications in urban areas require high‐resolution rainfall measurements. Typical operational weather radars can provide rainfall intensities at 1‐km2 grid cells every 5 min. Opportunistic ...sensing with commercial microwave links yields path‐averaged rainfall intensities (typically 0.1–10 km) within urban areas. Additionally, large amounts of urban in situ rainfall measurements from amateur weather observers are obtainable in real‐time. The accuracy of these three techniques is evaluated for an urban study area of 20 × 20 km, taking into account their respective network layouts and sampling characteristics. We use two simulated rainfall events described in terms of drop size distributions on a 100‐m grid and with a temporal resolution of 30 s. Accurate radar rainfall estimation with the Z‐R relationship relies heavily on an appropriate choice of parameters, and a dual‐polarization strategy is more suitable for higher intensities. Under ideal measurement conditions, the weather station network is the most promising, with a Pearson correlation coefficient above 0.86 and a relative bias below 4% for 100‐m rainfall estimates at 5‐min resolution. Microwave link rainfall observations contain the largest error, shown by a consistently larger coefficient of variation. The accuracy of all techniques improves when considering rainfall at larger scales, especially by increasing time intervals, with the strongest improvements found for microwave links for which errors are largely caused by their temporal sampling. Sparser networks are examined, showing that the decline in measurement accuracy only becomes significant when the link and station network density are reduced to less than half their levels in Amsterdam.
Key Points
Assuming perfect measurement accuracy, the personal weather station network captures small‐scale rainfall dynamics best in Amsterdam
Measurement accuracy increases at larger temporal and spatial scales, most significantly for commercial microwave links
With current network layouts, similar accuracy is achieved by half the number of commercial microwave links and personal weather stations
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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).
•SolarET is a generalizable data-driven approach that uses only solar radiation data to estimate daily ETO.•Using only RS, SolarET relaxes the need for a reference surface, saving the cost of land, irrigation, and maintenance.•SolarET has higher accuracy (RMSE = 0.93 mm/day) compared to Priestley-Taylor and Hargreaves-Samani equations.
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In recent years, the monitoring systems play significant roles in our life. So, in this paper, we propose an automatic weather monitoring system that allows having dynamic and real-time climate data ...of a given area. The proposed system is based on the internet of things technology and embedded system. The system also includes electronic devices, sensors, and wireless technology. The main objective of this system is sensing the climate parameters, such as temperature, humidity, and existence of some gases, based on the sensors. The captured values can then be sent to remote applications or databases. Afterwards, the stored data can be visualized in graphics and tables form.
Implementation of a weather station to monitor agricultural crops Udrescu, Ionuț Claudiu; Viorel, Alina Cristina; Crăciunaș, Gabriela ...
International Journal of Advanced Statistics and IT&C for Economics and Life Sciences,
12/2023, Volume:
13, Issue:
1
Journal Article
Peer reviewed
Open access
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).
Automatic weather station (AWS) observations are used to select tropical cyclones (TCs) affecting the Taiwan Strait during 2007–2016. Applying these data, the influence of TCs on the Taiwan Strait is ...measured and TCs with weak or no impact are removed. Objective statistical properties of the remaining TCs are analysed by clustering them into three groups using the fuzzy C‐means method. Clusters are differentiated as (a) TCs affecting the Taiwan Strait but recurving at the edge of the TC alert area, (b) TCs moving straight across The Philippines and the South China Sea, and (c) TCs striking Taiwan and eastern China and recurving after landfall. Differences among the clusters include in the spatial range and duration of gales monitored by AWS, and the track, intensity, and genesis location of TCs. Also analysed are the influence of large‐scale atmospheric environments on TC motion, sea‐surface temperature responses to TCs, and the influence of El Niño‐Southern Oscillation on TC classifications.
Automatic weather station (AWS) observations are used to select the tropical cyclones (TCs) affecting the Taiwan Strait during 2007–2016 (shown in the image), by which the influence of TCs on the region are measured quantitatively. Objective statistical properties for three TC clusters are given in terms of the gales monitored by AWS, the track, intensity, and genesis location of TCs. The roles of large‐scale environments on TC motion, as well as the influence of El Niño–Southern Oscillation on TC classifications, are analysed.
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