Abstract
We present a methodology for estimating the average profiles of daytime
and daily ambient temperature from a spatially-continuous database
for any location within Europe. The primary database with 1-km grid
resolution was developed by interpolation of monthly averages of
7 daily values of temperature: minimum and maximum and 5 measurements
at 3-h intervals from 6:00 to 18:00 hours Greenwich Mean Time. With
a little over 800 meteorological stations available, we obtained
a cross-validation root mean square error of 1.0-1.2 °C, while the
interpolation error is lower, at 0.5-0.7 °C. A polynomial fit was
applied to estimate the daytime temperature profile (assuming only
time from sunrise to sunset) from the interpolated 3-h measurements
for each month. The curve fit coefficients make it possible to calculate
a number of derived data, such as average daytime temperature, maximum
daytime temperature and time of its occurrence within the region.
An example demonstrates the coupling of the simulated daytime temperature
profile with a model for assessing the relative efficiency of electricity
generation by crystalline silicon photovoltaic modules. As an alternative
to the polynomial fitting, a double-cosine method was applied to
enable calculation of daily (24-h) temperature profiles for each
month using interpolated minimum and maximum temperatures. Compared
to the polynomial curve-fitting, this method does not offer lower
errors, but it provides data which are more suitable for estimation
of solar thermal heating or calculation of degree days for building
heating/cooling.
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