@ARTICLE {ParrottKokLacroix1996,
AUTHOR = {Parrott, L. and Kok, R. and Lacroix, R.},
TITLE = {Daily average temperatures: Modeling and generation with a Fourier
transform approach},
JOURNAL = {Transactions of the ASAE},
YEAR = {1996},
VOLUME = {39},
PAGES = {1911-1922},
NUMBER = {5},
NOTE = {Vq591 Times Cited:2 Cited References Count:12},
ABSTRACT = {A mathematical model has been developed To generate daily average
temperatures (DATs) on a yearly basis for any climate. The model
contains a complete description of the patterns and variations in
the DATs for a given location using a set of 29 descriptor values,
the magnitudes of which are determined using a Fourier frequency
analysis of physical weather data. The model was tuned to imitate
the climates at three Canadian sites - Montreal, Winnipeg, and Vancouver
Daily average temperatures were then synthesized for all three locations
and these were compared with the physical weather data from which
the descriptor values had been derived. A number of evaluation methods
were used to assess the effectiveness of the model in reproducing
realistic DAT values. First, the model's ability to imitate patterns
in the physical data was judged by comparing the distributions and
statistics of the DATs at three granularities daily, weekly, and
monthly. Second, variability in the data sets was compared by considering
the pattern in the differences between consecutive daily average
temperatures. Thirdly, a number of characterization measures, such
as the heat units for a given crop (e.g., degree-days for corn),
were calculated. Lastly, a set of DAT values from Halifax was used
to tune the model, and its ability to predict temperature patterns
for that climate was assessed. Overall, the model effectively captured
the essence of real temperature patterns. Synthetic DATs were similar
to the physical data for all three sites studied, at all three granularities.
They also showed similar day-to-day variation. Values of the characterization
measures were also comparable. The model is conceptually simple,
and allows for the rapid generation of unlimited temperature data
representative of a given climate. Furthermore, the model descriptors
have physical meaning so that it is easy to compose a purely hypothetical
climate and synthesize data. The Fourier approach proved to be a
practical method of analyzing and reproducing temperature data,
and should be widely applicable to the modeling of other natural
phenomena as well.},
KEYWORDS = {weather model temperature model fourier transform neural-network solar},
OWNER = {brugerolles},
TIMESTAMP = {2007.12.05},
}