@Article{RozanteRaSiFeAlSi:2020:TeVa,
author = "Rozante, Jos{\'e} Roberto and Ramirez Gutierrez, Enver and Silva
Dias, Pedro Leite da and Fernandes, Alex de Almeida and Alvim,
D{\'e}bora Souza and Silva, Vin{\'{\i}}cius Matoso",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Universidade de
S{\~a}o Paulo (USP)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)} and {Universidade Federal do ABC (UFABC)} and
{Instituto Nacional de Pesquisas Espaciais (INPE)}",
title = "Development of an index for frost prediction: technique and
validation",
journal = "Meteorological Applications",
year = "2020",
volume = "27",
number = "1",
pages = "e1807",
month = "Jan.",
keywords = "frost index, frost prediction, temperature.",
abstract = "An index for frost prediction is proposed and calibrated against
observations. It takes into account: (1) the main meteorological
variables that favour or oppose to frost; (2) weights attributed
to these variables; and (3) means and standard deviations of these
variables, only for cases in which frost occurs, as defined by
observation of temperatures that are <= 6 degrees C. The
meteorological variables used for the frost index IG (from the
Portuguese, indice de Geada) are numerically predicted by a
regional weather forecast model. An outcome of the calibration
processes results is that temperature has the largest
contribution, followed by pressure and winds, while the other
variables were adjusted to obey the restriction that the sum of
weights are equal to 1. After index calibration and threshold
determination, the method was applied for the 2017 winter season,
and a case study for May 2018 was also considered. In order to
verify whether the new index can satisfactorily contribute to the
weather forecasting, the results using the IG were compared with
the temperature outputs of the numerical regional model. It was
found that for three selected areas, and for all the forecasted
hours, the IG produces better results than the model's direct
temperature forecasts. Thus, it was concluded that the use of the
IG in an operational environment potentially provides considerable
improvement in the predictive skill of frost events.",
doi = "10.1002/met.1807",
url = "http://dx.doi.org/10.1002/met.1807",
issn = "1350-4827",
label = "self-archiving-INPE-MCTIC-GOV-BR",
language = "en",
targetfile = "rozante_development.pdf",
urlaccessdate = "02 maio 2024"
}