@Article{RozanteRaSiFeAlSi:2019:TeVa,
author = "Rozante, Jos{\'e} Roberto and Ramirez, Enver and Silva Dias,
Pedro Leite da and Fernandes, Alex de Almeida and Alvim,
D{\'e}bora Souza and Silva, Vinicius 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)}",
title = "Development of an index for frost prediction: technique and
validation",
journal = "Meteorological Applications",
year = "2019",
volume = "6",
pages = "1807",
keywords = "Temperature, Frost Index, Frost Prediction.",
abstract = "An index for frost prediction is proposed and calibrated against
observations. It takes into account: a) the main meteorological
variables that favor or oppose to frost; b) weights attributed to
these variables; c) means and standard deviations of these
variables, only for cases in which frost occurs, as defined by
observation of temperatures that are equal to or less than 6°C.
The meteorological variables used for the frost index IG (from the
Portuguese, {\'{\i}}ndice de geada) are numerically predicted by
a regional weather forecast model. An outcome of the calibration
processes results 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 one. After index calibration and threshold determination, the
method was applied for 2017 winter season a case study for May
2018 was also considered. In order to verify whether the new index
is able to satisfactorily contribute to the weather forecasting,
the results using the IG were compared to 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. It was thus 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 = "lattes: 3781543923591839 1 RozanteGuSiFeAlMa:2019:TeVa",
language = "en",
targetfile = "met.1807.pdf",
urlaccessdate = "28 mar. 2024"
}