About: Weather station is a(n) research topic. Over the lifetime, 1789 publication(s) have been published within this topic receiving 42864 citation(s). The topic is also known as: meteorological station & meteorological observation post.
Papers published on a yearly basis
TL;DR: In this paper, the authors developed interpolated climate surfaces for global land areas (excluding Antarctica) at a spatial resolution of 30 arc s (often referred to as 1-km spatial resolution).
Abstract: We developed interpolated climate surfaces for global land areas (excluding Antarctica) at a spatial resolution of 30 arc s (often referred to as 1-km spatial resolution). The climate elements considered were monthly precipitation and mean, minimum, and maximum temperature. Input data were gathered from a variety of sources and, where possible, were restricted to records from the 1950–2000 period. We used the thin-plate smoothing spline algorithm implemented in the ANUSPLIN package for interpolation, using latitude, longitude, and elevation as independent variables. We quantified uncertainty arising from the input data and the interpolation by mapping weather station density, elevation bias in the weather stations, and elevation variation within grid cells and through data partitioning and cross validation. Elevation bias tended to be negative (stations lower than expected) at high latitudes but positive in the tropics. Uncertainty is highest in mountainous and in poorly sampled areas. Data partitioning showed high uncertainty of the surfaces on isolated islands, e.g. in the Pacific. Aggregating the elevation and climate data to 10 arc min resolution showed an enormous variation within grid cells, illustrating the value of high-resolution surfaces. A comparison with an existing data set at 10 arc min resolution showed overall agreement, but with significant variation in some regions. A comparison with two high-resolution data sets for the United States also identified areas with large local differences, particularly in mountainous areas. Compared to previous global climatologies, ours has the following advantages: the data are at a higher spatial resolution (400 times greater or more); more weather station records were used; improved elevation data were used; and more information about spatial patterns of uncertainty in the data is available. Owing to the overall low density of available climate stations, our surfaces do not capture of all variation that may occur at a resolution of 1 km, particularly of precipitation in mountainous areas. In future work, such variation might be captured through knowledgebased methods and inclusion of additional co-variates, particularly layers obtained through remote sensing. Copyright 2005 Royal Meteorological Society.
TL;DR: In this paper, the authors created a new dataset of spatially interpolated monthly climate data for global land areas at a very high spatial resolution (approximately 1 km2), including monthly temperature (minimum, maximum and average), precipitation, solar radiation, vapour pressure and wind speed, aggregated across a target temporal range of 1970-2000, using data from between 9000 and 60,000 weather stations.
Abstract: We created a new dataset of spatially interpolated monthly climate data for global land areas at a very high spatial resolution (approximately 1 km2). We included monthly temperature (minimum, maximum and average), precipitation, solar radiation, vapour pressure and wind speed, aggregated across a target temporal range of 1970–2000, using data from between 9000 and 60 000 weather stations. Weather station data were interpolated using thin-plate splines with covariates including elevation, distance to the coast and three satellite-derived covariates: maximum and minimum land surface temperature as well as cloud cover, obtained with the MODIS satellite platform. Interpolation was done for 23 regions of varying size depending on station density. Satellite data improved prediction accuracy for temperature variables 5–15% (0.07–0.17 °C), particularly for areas with a low station density, although prediction error remained high in such regions for all climate variables. Contributions of satellite covariates were mostly negligible for the other variables, although their importance varied by region. In contrast to the common approach to use a single model formulation for the entire world, we constructed the final product by selecting the best performing model for each region and variable. Global cross-validation correlations were ≥ 0.99 for temperature and humidity, 0.86 for precipitation and 0.76 for wind speed. The fact that most of our climate surface estimates were only marginally improved by use of satellite covariates highlights the importance having a dense, high-quality network of climate station data.
01 Sep 2000-Journal of Dairy Science
TL;DR: Production data obtained from AIPL USDA included 119,337 first-parity, test-day records of 15,012 Holsteins from 134 Georgia farms collected in 1990 to 1997 and the temperature-humidity index calculated with the available weather information can be used to account for the effect of heat stress on production.
Abstract: Production data obtained from AIPL USDA included 119,337 first-parity, test-day records of 15,012 Holsteins from 134 Georgia farms collected in 1990 to 1997. Weather information was obtained from the Georgia Automated Environmental Monitoring Network and included daily minimum, average, and maximum temperatures and humidity for 21 stations throughout the state. Each test-day record was augmented with weather information from the closest weather station. Analyses were based on models that included effects of herd-year-season, age, test day, milking frequency, and several types of heat and humidity. The best model used a temperature-humidity index. With this model, the average test-day yield for milk was about 26.3 kg for a temperature-humidity index <72 and decreased at about 0.2 kg per unit increase in the temperature-humidity index for a temperature-humidity index ≥72. For fat and protein, the test yield was 0.92 and 0.85 kg at a temperature-humidity index <72, respectively, and declined at a rate of 0.012 and 0.009 kg per degree of the temperature-humidity index, respectively. The temperature-humidity index calculated with the available weather information can be used to account for the effect of heat stress on production.
TL;DR: In this article, a methodology to generate scalefree climate data through the combination of interpolation techniques and elevation adjustments is presented, which is applied to monthly temperature and precipitation normals for 1961-90 produced by the Parameter-elevation Regressions on Independent Slopes Model (PRISM) for British Columbia, Yukon Territories, the Alaska Panhandle, and parts of Alberta and the United States.
Abstract: Applying climate data in resource management requires matching the spatial scale of the climate and resource databases. Interpolating climate data in mountainous regions is difficult. In this study, we present methodology to generate scalefree climate data through the combination of interpolation techniques and elevation adjustments. We apply it to monthly temperature and precipitation normals for 1961–90 produced by the Parameter-elevation Regressions on Independent Slopes Model (PRISM) for British Columbia, Yukon Territories, the Alaska Panhandle, and parts of Alberta and the United States. Equations were developed to calculate biologically relevant climate variables including various degree-days, number of frost-free days, frost-free period, and snowfall from monthly temperature and precipitation data. Estimates of climate variables were validated using an independent dataset from weather stations that were not included in the development of the model. Weather station records generally agreed well with estimated climate variables and showed significant improvements over original PRISM climate data. A stand-alone MS Windows application was developed to perform all calculations and to integrate future climate predictions from various global circulation models. We demonstrate the use of this application by showing how climate change may affect lodgepole pine seed planning zones for reforestation in British Columbia. Copyright 2006 Royal Meteorological Society.
••15 Dec 2011
TL;DR: This paper explores automatically creating site-specific prediction models for solar power generation from National Weather Service weather forecasts using machine learning techniques, and shows that SVM-based prediction models built using seven distinct weather forecast metrics are 27% more accurate for the authors' site than existing forecast-based models.
Abstract: A key goal of smart grid initiatives is significantly increasing the fraction of grid energy contributed by renewables. One challenge with integrating renewables into the grid is that their power generation is intermittent and uncontrollable. Thus, predicting future renewable generation is important, since the grid must dispatch generators to satisfy demand as generation varies. While manually developing sophisticated prediction models may be feasible for large-scale solar farms, developing them for distributed generation at millions of homes throughout the grid is a challenging problem. To address the problem, in this paper, we explore automatically creating site-specific prediction models for solar power generation from National Weather Service (NWS) weather forecasts using machine learning techniques. We compare multiple regression techniques for generating prediction models, including linear least squares and support vector machines using multiple kernel functions. We evaluate the accuracy of each model using historical NWS forecasts and solar intensity readings from a weather station deployment for nearly a year. Our results show that SVM-based prediction models built using seven distinct weather forecast metrics are 27% more accurate for our site than existing forecast-based models.
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