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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TL;DR: A short-term solar power forecasting system that employs Neural Network models to forecast irradiance and PV power and determines the correction coefficients based on the characteristics and temperature of PV modules.
Abstract: Background/Objectives: For efficient PhotoVoltaic (PV) power generation, computing and information technologies are increasingly used in irradiance forecasting and correction. Methods/Statistical Analysis: Today the majority of PV modules are used for grid-connected power generation, so solar generation forecasting that predicts available PV output ahead is essential for integrating PV resources into electricity grids. This paper proposes a short-term solar power forecasting system that employs Neural Network (NN) models to forecast irradiance and PV power. Results: The proposed system uses the weather observations of a ground weather station, the medium-term weather forecasts of a physical model, and the short-term weather forecasts of the Weather Research and Forecasting (WRF) model as input. To increase prediction accuracy, the proposed system performs forecast corrections and determines the correction coefficients based on the characteristics and temperature of PV modules. The proposed system also analyzes the inclination angle of PV modules to predict PV power outputs. Conclusion/Application: In the future, the proposed forecasting system for solar power generation resources will be further refined and run in real environments.
7 citations
TL;DR: Gray tree frogs were temperature sensitive and calling was significantly related to increased water and air temperatures as well as day time high temperatures over the previous 2 weeks, and it is recommended that an additional monitoring run could be added to reduce detection variability of this species.
Abstract: Anuran populations are sensitive to changing environmental conditions and act as useful indicators. Presently, much information collected concerning frog populations comes from volunteers following the North American Amphibian Monitoring Protocol. Does weather variability allowed within protocol affect the abundance of calling frogs? For 10 years, Credit Valley Conservation (Ontario, Canada) has been collecting anuran data concerning nine frog species employing three frog monitoring runs. Records include frog abundance by protocol code and five weather variables. Antecedent precipitation and temperature were determined from the nearest weather station. Locations with large source populations of two Hylidae species were selected (spring peeper calling in April and gray tree frog in May). Spearman correlations suggested there were no significant relationships between calling abundance of Hylidae species and ambient wind speed or humidity. However, gray tree frogs were temperature sensitive and calling was significantly related to increased water and air temperatures as well as day time high temperatures over the previous 2 weeks. Both species of calling Hylidae were affected by the volume and timing of precipitation (though, in different ways). Gray tree frogs seem to prefer drier conditions (when temperatures are significantly warmer) while spring peepers prefer to call during, or closely following, precipitation. Monitors targeting gray tree frog should track local weather conditions and focus on evenings when it is (a) warmer than the minimum temperatures and (b) drier than suggested by the protocol. It is recommended that an additional monitoring run could be added to reduce detection variability of this species.
7 citations
TL;DR: The mesoscale variation of the empirical probability of snow on a winter precipitation day in the New York urban-suburban metropolitan area is determined from first-order and cooperative weather station records, and clearly reveals the effect of the urban heat island on snowfall as discussed by the authors.
Abstract: The mesoscale variation of the empirical probability of snow on a winter precipitation day in the New York urban-suburban metropolitan area is determined from first-order and cooperative weather station records, and clearly reveals the effect of the urban “heat island” on snowfall. Conditional empirical probabilities are also determined as functions of the precipitation type and daily maximum temperature at Central Park. Mesoclimatology is shown to be of potential value for the translation of large-scale weather predictions into low forecasts.
7 citations
TL;DR: Simulation of crop yields, drainage and nitrogen leaching for an agroecosystem in the North China Plain using the Daisy model demonstrates the importance of explicit consideration of weather and soil variability for agro-environmental simulation studies at regional scale.
Abstract: Single or multiple weather station data were combined with soil textural data ranging from low to high detail, i.e., point data from a field station, the FAO Digital Soil Map of the World and a comprehensive data from national soil survey, as input to the Daisy model to simulate and upscale crop yields, drainage and nitrogen leaching for an agroecosystem in the North China Plain. Increasing the detail of the weather data increased the spatial variation of all simulated variables and decreased their regional median. Regional crop yields were simulated well with high-detail input data, though at a weak response to data detail. Simulated regional drainage and nitrogen leaching, and their spatial variability, however, responded well and increased two-to threefold, but their regional medians were similar for medium- and high-detail soil data. This work demonstrates the importance of explicit consideration of weather and soil variability for agro-environmental simulation studies at regional scale.
7 citations
TL;DR: In this paper, the authors describe a major community initiative underway in the Southland Region of New Zealand involving geographic information systems mapping of 850,000 ha at a 1:50,000 scale.
Abstract: Risk assessment at the regional level requires quality resource information on critical factors for sustainable land-use decisions. This paper describes a major community initiative underway in the Southland Region of New Zealand involving geographic information systems mapping of 850,000 ha at a 1:50,000 scale. At a southerly latitude of 46° in the southern part of the South Island of New Zealand, the critical factors affecting plant growth are soil quality and air temperature. Air temperature is being measured with a network of 900 automatic data loggers positioned to represent key features of the landscape and recording maximum and minimum temperatures every hour for a full year. Data are then differentially adjusted using the nearest long-term weather station to the site to generate a 30-year normal temperature record and consequent growing-degree-day value for each site. Isocontour maps are then generated to identify microclimates of interest within the landscape. Soils of the area are being mapped at the same time and to the same scale to allow integrated development of the critical information for sustainable land-use decisions. Basic soil attributes will be interpreted to produce maps for crop suitability, vulnerability to structural degradation, and leaching risk. High-class soils will also be identified. An Internet site and a bureau service will be established. This will provide technical support and interpretation of the data for clients to encourage wide use of the information produced by the project. The project is being supported and funded by the local community in the belief that the provision of high-quality resource information will lead to significant employment opportunities in the region.
7 citations