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.

Tendência monotônica e pontos de mudança na precipitação no sul do Brasil

Abstract: Scientific evidence on climate changes at global level has gained increasing interest in the scientific community in general. The impacts of climate change as well as anthropogenic actions may cause errors in hydro-agricultural projects existent in the watershed under study. This study aimed to identify the presence or absence of trend in total annual precipitation series of the watershed of the Mirim Lagoon, state of Rio Grande do Sul-RS / Brazil / Uruguay (Brazilian side) as well as to detect the period in which they occurred. For that, it was analyzed the precipitation data belonging to 14 weather stations. To detect the existence of monotonic trend and change points, it was used the nonparametric tests of Mann-Kendall and Mann-Whitney, the "t" test of Student for two samples of unpaired data (parametric), as well as the technique of progressive mean. The Weather Station 3152014 (Pelotas) presented changes in the trend in the series of annual precipitation in the period from 1953 to 2007. The methodologies that use subdivided series were more efficient in detecting change in trend when compared with the Mann-Kendall test, which uses the complete series (from 1921 to 2007).

Short-term wind speed forecasting of Oak Park Weather Station by using different ANN algorithms

Abstract: Renewable energy resources such as wind power generators are important alternatives in electric power systems considering their congenial environmental effects. Short-term wind speed forecasting have a huge impact on the load variation decisions and economic load dispatch in the wind-integrated power systems. Wind power is intermittent and is sometimes non-dispatchable because of its dependency on the atmospheric conditions, therefore accurate forecasting becomes necessary. In this paper different ANN algorithms i.e Levenberg-Marquardt back propagation, Bayesian Regularization & Scaled Conjugate Gradient algorithms has been applied in short-term wind speed forecasting that is one hour-ahead hourly forecast of the wind speed of Oak Park Weather Station, Ireland using MATLAB R14a. The data used in the forecasting are hourly historical data of the wind speed, temperature and wind direction. The simulation results have shown accurate one hour ahead forecasts with small error in wind speed forecasting.

Current Ohio Experience with an Automated Weather Station Network

Abstract: DURING the period of June 1980 to June 1981, the Ohio Agricultural Research and Development Center (OARDC) and Miami University jointly developed a network of eight automated weather stations in Ohio. The network was deemed a valuable resource for several of Miami University's environmentally-oriented programs and OARDC-Wooster's research programs in energy and agriculture. OARDC-Wooster has a 100-year history of weather data collection and it is currently the site of one of the National Oceanic and Atmospheric Administration's (NOAA) 20 or so special benchmark weather stations. The purpose of this paper is to summarize operational experience with the Ohio network for the 1 January 1982 to 31 December 1986 period. Descriptions of networks established in other states (e.g., Chang et al., 1983; Hubbard et al., 1983; Dugas and Whitis, 1984; Thompson et al., 1984) would be extremely useful for improving existing networks and designing new ones.

Enhanced Weather Modelling for Dynamic Line Rating

Abstract: Conventional approaches to dynamic line rating (DLR) forecasting provide single point estimates with no indication of the distribution of possible errors. Furthermore, most research related to DLR forecasting deals only with continuous or steady-state ratings while less attention has been given to short-term or transient-state ratings. This thesis describes (a) weather-based models to estimate probabilistic forecasts of steady-state DLRs for up to three 10-minutes time steps ahead for a particular span and a complete overhead line (OHL) and also (b) a fast-computational weather-based approach to probabilistic forecasting of transient-state DLRs for a particular span for time horizons of 10, 20 and 30 minutes. The percentiles of DLR forecasts can be used by a system operator within a chosen risk policy informed by the probability of a rating being exceeded. The thesis first develops time series forecasting models for different weather variables that impact on line rating (i.e. air temperature, wind speed, wind direction and solar radiation) at weather stations that are installed along the route of 132kV OHLs in North Wales. Predictive centres of weather variables are modelled as a sum of residuals predicted by a suitable auto-regressive process and temporal trends fitted by Fourier series. Conditional heteroscedasticity of the predictive distribution is modelled as a linear function of recent changes in residuals within one hour for air temperature and wind speed or concentration of wind direction observations within the most recent two hours. A technique of minimum continuous ranked probability score estimation is employed to determine predictive distributions of the measured weather variables. Then the thesis uses Monte Carlo simulation to generate a large number of random weather samples from the modelled predictive distributions which are paired to have rank correlations similar to those among their recent observations. The probabilistic steady-state DLR forecasts for a particular span in proximity to a weather station are estimated from the random weather samples combined with a maximum allowable conductor temperature using a thermal model of the conductors (i.e. a steady-state heat balance equation). For a complete OHL, possible weather predictions at each span are inferred from random weather samples at stations by using suitable spatial interpolation models; the steady-state DLR forecast of the OHL is then identified as the minimum DLR among all spans for each generated scenario. Using an enhanced analytical method which evolves from a non-steady-state heat balance equation to track the transient-state conductor temperature, the transient-state DLR forecast for a particular span is calculated as that which increases the conductor temperature from an initial value to the maximum allowable limit for a particular future time period (i.e. in this study, 10, 20 and 30 minutes) under each set of random weather samples. The calibration of probabilistic DLR forecasts estimated from independent or correlated random weather samples are then examined to determine which approaches are most suited to estimation of DLRs at the lower end of a predictive distribution consistent with a system operator’s risk policy. The potential use of DLR forecasting is then evaluated through estimating the degree to which wind generation curtailment for various assumed installed capacities at a wind farm that is connected to the 132kV network in North Wales can be alleviated through using the lower percentiles of steady-state DLR forecasts in place of the SLRs for each 132kV OHL.