Topic
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.
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01 Jan 2015
TL;DR: SmartIrrigation as discussed by the authors provides real-time irrigation schedules for selected crops (i.e., avocado, citrus, cotton, peanut, strawberry, vegetables) based on evapotranspiration (ET) or a water balance methodology using realtime weather data from the Florida Automated Weather Network and the Georgia Environmental Monitoring Network.
Abstract: . SmartIrrigation apps were developed to provide real-time irrigations schedules for selected crops (i.e., avocado, citrus, cotton, peanut, strawberry, vegetables). Irrigation schedules in the smartphone apps are based on evapotranspiration (ET) or a water balance methodology using real-time weather data from the Florida Automated Weather Network and the Georgia Environmental Monitoring Network. The FAO Penman Monteith method is used for calculating reference ET and crop coefficients (Kc) are applied based on time after planting, calendar month, or a crop‘s phenological stage. The functionality of each app was customized for each user group considering the most common irrigation systems used. Custom features include water conservation options, splitting irrigation events, spreadsheet output emails, and the use of notifications. App inputs varied by crop (primarily due to the irrigation system used); however, all apps required root depth, irrigation rate, and soil type except the strawberry app. App outputs also varied and included estimated reference ET, days between irrigation events, irrigation depth and duration, accumulated rain for previous 7 days, and growing degree days. National Weather Service forecast data are also provided in the apps. The apps are available in Android and iOS stores. A limitation to the app irrigation schedules is the spatial variation in rainfall given the finite set of weather station; future efforts will focus on more accurate inclusion of rainfall into the irrigation schedules generated by the SmartIrrigation apps.
10 citations
TL;DR: Using the hourly weather data produced for Tainan City, Taiwan, the long-term cumulative UHI intensity (UHII) and urban bioclimatic indexes and investigated how urban form and structure are related to UHII, thermal stress, use of natural ventilation, and cooling degree day can inform urban policy making.
10 citations
TL;DR: In this article, a novel use of a numerical weather prediction mesoscale model, the Global Forecast System (GFS) sflux model, as a source of input data for transient thermal simulations was analyzed.
10 citations
09 Apr 2015
TL;DR: In this article, the authors proposed a novel method for wind energy estimation in the state of Georgia based on Artificial Neural Network (ANN) using real data obtained from several weather station sites around the state.
Abstract: Wind energy resources are ideally suited for distributed generation systems to provide electricity for residential use. This paper proposes a novel method for wind energy estimation in the state of Georgia. This method is based on Artificial Neural Network (ANN) using real data obtained from several weather station sites around the state. The proposed ANN model was trained and then tested using a local station located in Savannah. The ANN inputs are elevation, latitude, longitude, day, temperatures (min/max), and the output is the daily wind speed. The model was efficiently implemented in Simulink environment using closed-form algebraic equations which eliminated the need for repeated training. The ANN model was formulated with suitable numbers of layers/neurons which was trained and tested with excellent regression constant. Furthermore, the ANN model has the ability to interpolate between learning curves to generate wind speed estimates for different locations. It is anticipated that this model will be able to successfully select sites for wind turbine installations for residential applications in the state of Georgia.
10 citations
17 Mar 2016
TL;DR: In this paper, the authors analyzed the trends in weather dependency in energy demand and monthly load forecasting and developed linear and non-linear regression models to check the dependency of energy demand on different climatic factors like temperature and relative humidity.
Abstract: Electrical energy demand of domestic consumers depends on many factors like population, regional development, weather, electricity price, industrialization etc. For those regions having different climatic conditions in a year, it is found that the energy demand depends on prevailing weather. So a study on weather dependency of energy demand would help in accurate load forecasting and maintenance schedules. This paper aims at analyzing the trends in weather dependency in energy demand and monthly load forecasting. Linear and non linear regression models are developed. Multiple linear regression to check the dependency of energy demand on different climatic factors like temperature and relative humidity is developed. Non linear forecast model of Energy consumption as per the variation in the temperature is also developed. Energy consumption details of Attavara area, obtained from Attavar sub-station, is related with weather data obtained from Mangalore Bajpe weather station for the analysis of seasonal demand of energy demand.
10 citations