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.

Three dimensional numerical simulation of residential building on shrink–swell soils in response to climatic conditions

Abstract: Summary Shrink–swell soils can cause distresses in buildings, and every year, the economic loss associated with this problem is huge. This paper presents a comprehensive system for simulating the soil–foundation–building system and its response to daily weather conditions. Weather data include rainfall, solar radiation, air temperature, relative humidity, and wind speed, all of which are readily available from a local weather station or the Internet. These data are used to determine simulation flux boundary conditions. Different methods are proposed to simulate different boundary conditions: bare soil, trees, and vegetation. A coupled hydro-mechanical stress analysis is used to simulate the volume change of shrink–swell soils due to both mechanical stress and water content variations. Coupled hydro-mechanical stress-jointed elements are used to simulate the interaction between the soil and the slab, and general shell elements are used to simulate structural behavior. All the models are combined into one finite element program to predict the entire system's behavior. This paper first described the theory for the simulations. A site in Arlington, Texas, is then selected to demonstrate the application of the proposed system. Simulation results are shown, and a comparison between measured and predicted movements for four footings in Arlington, Texas, over a 2-year period is presented. Finally, a three-dimensional simulation is made for a virtual residential building on shrink–swell soils to identify the influence of various factors. Copyright © 2015 John Wiley & Sons, Ltd.

Road weather hazard system

Abstract: A method and system for assessing road conditions is provided. The method includes determining a road hazard condition for a road segment that may include a precipitation type, a pavement condition, and a visibility level. The precipitation type may be determined using radar data, satellite cloud classification data, weather station air temperature data, wiper status, mobile air data, speed ratio, or headlight status. The pavement condition may be determined using pavement temperature, precipitation type, automatic brake system status, traction status or a stability control observation, and a yaw rate. The visibility level may be determined using wind speed, relative humidity, percentage of fog lights on, percentage of high beams on, speed ratio, station visibility, station-reported visibility type, wildfire existence, wind direction, a dust existence indicator.

Meteorological parameters effects on solar energy power generation

Abstract: As Turkey lies near the sunny belt between 36 and 42°N latitudes, most of the locations in Turkey receive abundant solar energy. The yearly average solar radiation is 3.6 kWh/m2 day, and the total yearly radiation period is approximately 2610 h. Meteorological data such as solar radiation, ambient temperature, relative humidity, wind speed, air pressure and sunshine duration are accepted as dependable and widely variable renewable energy resources. These data play a very important role in photovoltaic systems. In this study, permanent resistive load directly fed from photovoltaic panel which produce DC electrical energy. Experiments were done during 23 month period from 2008 to 2010. Permanent resistive load currents and voltages measured with power analyzer continuously during the day. At the same time meteorological parameters like outdoor temperature, air pressure, humidity, wind speed and solar radiation etc. measured and recorded with digital weather station. These measurement results compared with the graphics at the same time bases. Photovoltaic panel output power calculated with current and voltage measurements. A mathematical equation found with curve fitting method from power graphics to examine dependencies for meteorological parameters. Thus correlation between photovoltaic performance and meteorological conditions is examined for Istanbul-Goztepe.

Seasonal Characterization of Solar Radiation Estimates Obtained from a MSG-SEVIRI-Derived Dataset and a RAMS-Based Operational Forecasting System over the Western Mediterranean Coast

Abstract: Solar radiation is a key factor in the Earth’s energy balance and it is used as a crucial input parameter in many disciplines such as ecology, agriculture, solar energy and hydrology. Thus, accurate information of the global downward surface shortwave flux integration into the grid is of significant importance. From the different strategies used for grid integration of the surface solar radiation estimates, satellite-derived and numerical weather prediction forecasts are two interesting alternatives. In the current work, we present a comprehensive evaluation of the global downward solar radiation forecasts provided by the Regional Atmospheric Modeling System (RAMS) and the Downwelling Surface Shortwave Flux (DSSF) product, derived from the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI). Both solar radiation estimates are compared to thirteen ground-based weather station measurements for the winter 2010–2011 and the summer 2011 seasons. For these periods, the most recent versions of RAMS (4.4 and 6.0) were running in parallel within the real-time weather forecasting system implemented over the Valencia Region. The solar radiation performance and accuracy are evaluated for these datasets segmented into two atmospheric conditions (clear and cloudy skies) and two terrain classes (flat and hilly). DSSF shows a very good agreement over the study area. Statistical daily evaluations show that corresponding errors vary between seasons, with absolute bias ranging from −30 to 40 W·m−2, absolute root mean square errors (RMSE) from 25 to 60 W·m−2, relative bias ranging from −11% to 7% and relative RMSE from 7% to 22%, depending on the sky condition and the terrain location as well, thus reproducing the observations more faithfully than RAMS, which produces higher errors in comparison to the measurements. In this regard, statistical daily evaluations show absolute bias values varying from −50 to 160 W·m−2, absolute RMSE from 60 to 240 W·m−2, relative bias ranging from −30% to 40% and relative RMSE from 10% to 80%, also depending on the daily initialization and the forecast horizon. This bias variability demonstrates that there is a different trend in the deviation of the model results in relation to the observations, both for the DSSF product and RAMS forecasts, and considering the summer and the winter seasons independently. In this regard, although there is an overestimation of the observed solar radiation within the summer months, this magnitude is underestimated during the winter. Finally, comparing this solar radiation estimates for different atmospheric conditions and different terrain classes, the best results are found under clear skies over flat terrain. This result is achieved using both methodologies.