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Moreover, meteorological data (rain, speed and direction wind) were used to investigate about the influence of natural events on high or low concentration situation.
Climatic and microclimatic data (rain, wind, solar radiation, relative humidity, air and surface temperature) were continuously collected outdoors nearby the rack, close to the samples exposed, they were analysed, and their temporal evolution for the whole duration of the exposure was studied.
It was created with three layers (input, hidden, and output) that consist of seven input neurons, eight neurons in the hidden layer and two output neurons in the output layer that represent the two classes corresponding to PR data (rain, no-rain).
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The received signal data for rain attenuation and rain rate were collected at 10 s intervals over a three year periods from 2013 to 2015.
But, the results demonstrate the potential of combining TRMM rainfall data with rain gauge data to drive distributed hydrological model for rainfall-runoff stimulation in data-sparse area.
In parallel, rainfall intensity data from rain gauge records was statistically processed over the same year periods as the rain attenuation data.
Utilizing simple climate data (humidity, rain fall, ambient temperature and wind speed) and load profile as model inputs, generator and PV output powers and fuel consumption can be accurately predicted.
The daily weather contained in weather data include: rain, fog, and thunder.
where A and b are parameters that can be estimated by fitting experimental data from rain gauges placed on the area investigated by the radar.
We obtained meteorological data (temperature, rain, wind speed, wind direction) from a meteorological station near the city center of Arnhem.
As Münster and Roxel are close to each other, the data for rain in Münster was taken for both locations.
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