Weather station

About: Weather station is a research topic. Over the lifetime, 1789 publications have been published within this topic receiving 42864 citations. The topic is also known as: meteorological station & meteorological observation post.

Evapotranspiration-based Irrigation System for Mustard Green Crop Cultivation using Public Weather Forecast

Abstract: Irrigation system is gaining more attention in the field of research due to its large impact in water conservation. This study will design a system based on evapotranspiration (ET) of mustard green crop. Parameters such as temperature, humidity, solar radiation, and wind speed were attained from weather information - Microclimatic Weather Station and Public Weather Forecast. These parameters were used to compute ET and evaluate Penman-Monteith method. The mustard green crops have been observed under three different methods of irrigation: Manual Irrigation; Automatic Irrigation based on ET with parameters from Microclimatic Weather Station; and Automatic Irrigation based on ET with parameters from Public Weather Forecast. These methods of irrigation system were evaluated by the growth parameters: height of the crop and length of the outer leaf. ET-based irrigation system using Public Weather Forecast conserved 71.70% of water than the manual irrigation system while the ET-based irrigation system using Microclimatic Weather Station conserved 71.81% of water than the manual irrigation system.

Modelling maximum discharge of the catastrophic flood at the Iya River (Irkutsk region, Russia) in 2019

Abstract: The aim of the study is to estimate the maximum discharge of the catastrophic flood in June 2019 at the Iya River (Irkutsk Region, Russia). The main cause of this flood was extreme precipitation (170 mm for 3 days). The distributed deterministic hydrological model Hydrograph was applied. The schematization of the Iya river basin, parametrization and verification of the Hydrograph model were performed. The median value of the Nash-Sutcliff criteria was 0.69 for the period 1970-1996 for three catchments of the Iya River basin. Based on the data of weather stations and global weather forecast model ICON, maximum daily discharge values of the flood were estimated as 6570 and 4780 m3 s-1 respectively with the possible value range assessed by the dependence of Q(H) 6250-7500 m3 s-1 . The flood hydrograph estimated from weather station data coincides in magnitude of flood peak, but its formation is delayed by 1 day. ICON data underestimates maximum value but provides proper timing of the flood peak. The ensemble of input meteorological data from various sources could potentially be used to satisfactorily predict the magnitude and duration of the catastrophic flood and minimize the consequences of the flood.

Long-term structural response prediction models for concrete structures using weather data, fiber-optic sensing, and convolutional neural network

Abstract: This study proposes a long-term strain prediction model for concrete structures using weather data. In the proposed model, the relationship between weather and the strain data for a concrete structure is defined by a convolutional neural network (CNN), which is a machine learning technique, based on the strong correlation between the two types of data. The weather data collected from a weather station located near the monitored structure are used in the input layer of the CNN; the strain data measured by fiber-optic sensors (FOSs) at the structure are used in the output layer of the CNN. The trained CNN can predict the strain using only weather data in the case of sensor malfunctions or data loss. Various types of weather data, including the air temperature, relative humidity, and wind speed, are used to determine the environmental factors that are valid for predicting the long-term deformation of concrete structures. Six prediction models are proposed, in which the three types of weather data are used individually or jointly in the input layer of the CNN. The proposed models are applied to predict the strain of a footbridge located at Princeton University. To build the prediction models, the strain data measured at the bridge over a long-term period and the weather data obtained from a nearby local weather station are used. The performance of the prediction models is verified through long-term strain prediction. Furthermore, the prediction performance of the analyzed models is compared, and the weather data types that are significant for predicting the long-term deformation of concrete structures are elucidated.

Data Journeys as an approach for exploring the socio-cultural shaping of (big) data: the case of climate science in the United Kingdom

Abstract: The paper reports on a pilot study aimed at developing, and assessing the utility of, a data journeys approach for critically exploring the socio-cultural shaping of interconnected data infrastructures. At various points along the journey of a (metaphorical) datum from production through processing, re-use and intersection with other data journeys selected organisations and projects are brought into focus and empirical data about the socio-cultural values and practices shaping the life of data within that particular space are collected using a variety of qualitative data collection methods. These empirical data are then critically and thematically analysed in relation to the broader social context. This paper outlines the rationale for the data journeys approach prior to presenting initial findings from The Secret Life of a Weather Datum research project which applies the approach to explore the socio-cultural values and practices interacting with weather and climate data as they move through a variety of data infrastructures. The initial findings presented in this paper focus specifically on weather data production and climate science at Weston Park Museum Weather Station in Sheffield, UK; the Met Office (the UK’s national meteorological organisation); the Climatic Research Unit at the University of East Anglia, UK (a world leading climate research institute); and the Old Weather Project (a citizen science project involved in historical weather data recovery and rescue). Emerging themes of vulnerability and resilience within the weather and climate data infrastructure are presented. These initial findings highlight the importance of situating our understanding of data infrastructures firmly within the social. Through drawing on the data journeys concept to guide and inform the selection of sites for data collection, we begin to demonstrate the utility of the approach for beginning to build a picture of the “contingent and contested” (Dalton and Thatcher, 2014) relations between people, interconnected in time and space through data infrastructures, that are core to the development and shaping of climate data and knowledge. We also begin to draw out the interrelations between local and global spaces and infrastructures; and to ground amorphous ‘big’ data infrastructures in local sites and cultures of production.

Virtual instrument developed for adcon weather station

Abstract: This paper presents a virtual instrument for communication with a weather station that transmits information on measured environmental parameters using a radio telemetry units. The user interface is built in LabVIEW visual programming environment. This makes a PC serial or TCP/IP communication between a terminal and the central unit of the weather station, the information received is decoded and played back using the user interface program.