@Article{ValverdeRamirezFerrVelh:2006:LiNoSt,
author = "Valverde Ramirez, Maria Cleofe and Ferreira, Nelson Jesus and
Velho, Haroldo Fraga de Campos",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)}",
title = "Linear and nonlinear statistical dowscaling for rainfall
forecasting over southeasthern Brazil",
journal = "Weather and Forecasting",
year = "2006",
volume = "21",
number = "6",
pages = "969--989",
month = "Dec.",
keywords = "METEOROLOGY, Linear downscaling, Nonlinear downscaling, Rainfall,
METEOROLOGIA, Escala linear, Escala n{\~a}o-linear, Chuvas.",
abstract = "In this work linear and nonlinear downscaling are developed to
establish empirical relationships between the synoptic-scale
circulation and observed rainfall over southeastern Brazil. The
methodology uses outputs from the regional Eta Model; prognostic
equations for local forecasting were developed using an artificial
neural network (ANN) and multiple linear regression (MLR). The
final objective is the application of such prognostic equations to
Eta Model output to generate rainfall forecasts. In the first
experiment the predictors were obtained from the Eta Model and the
predictand was rainfall data from meteorological stations in
southeastern Brazil. In the second experiment the observed
rainfall on the day prior to the forecast was included as a
predictor. The threat score (TS) and bias, used to quantify the
performance of the forecasts, showed that the ANN was superior to
MLR in most seasons. When compared with Eta Model forecasts, it
was observed that the ANN has a tendency to forecast moderate and
high rainfall with greater accuracy during the austral summer.
Also, when the observed rainfall of the previous day is included
as a predictor, the TS showed the best performance in continuous
rain and well-organized meteorological systems. On the other hand,
in the austral winter period, characterized by slight rain, the
ANN showed better forecasting ability than did the Eta Model. The
obtained results also suggest that in the austral winter rainfall
is more predictable because convection is less frequent, and when
this occurs the forcing is dynamic instead of thermodynamic.",
copyholder = "SID/SCD",
doi = "10.1175/WAF981.1",
url = "http://dx.doi.org/10.1175/WAF981.1",
issn = "0882-8156",
language = "en",
targetfile = "Valverde Ramirez_Linear.pdf",
urlaccessdate = "02 maio 2024"
}