Linear and Nonlinear Statistical Downscaling for Rainfall Forecasting over Southeastern Brazil

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.

Satellite-driven downscaling of global reanalysis precipitation products for hydrological applications

“…The critical role of high-resolution gridded rainfall data sets for hydrological simulations has led to the development of several rainfall disaggregation algorithms (e.g., Brussolo et al, 2008;Ferraris et al, 2003;Fowler et al, 2007;Frei et al, 2006;Maraun et al, 2010;Ning et al, 2011;Park, 2013;Rahman et al, 2009;Ramírez et al, 2006;Tao and Barros, 2010). The main assumption for some recently developed downscaling methods for satellite-based products is the relationship between spatial variability of rainfall and environmental factors such as topography and land surface conditions.…”

Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America

“…One can see that in both downscaling methods used the highest correlations occur in winter in all regions under study, indicating that the models are better able to represent the variability of precipitation during this season. Ramírez et al (2006) performed statistical downscaling for the precipitation forecast for the southeast of Brazil, using ANNs and MLR with the ETA model. The results suggested that the precipitation forecasts using ANNs performed better in winter than in summer, since the synoptic forcing is more pronounced and the deep convective activity is less common.…”

“…Dynamic techniques focus on numerical models with more detailed resolution, while statistical (or empirical) techniques use transfer functions between scales. Currently, numerical weather prediction (NWP) models can forecast various meteorological variables with acceptable accuracy (Ramírez et al, 2006). Specifically, rainfall is of great interest, both for its climatic and meteorological relevance and for its direct effect on agricultural output, hydropower generation, and other important economic factors.…”

“…Ramírez et al (2005), using observed daily rainfall in the São Paulo region, found that ANNs outperformed MLR, which showed a high bias for days without rain. Ramírez et al (2006) analyzed daily rainfall in southeastern Brazil and concluded that the ANN method tended to predict moderate rainfall with greater accuracy during austral summer compared to ETA model forecasts. Mendes and Marengo (2010) reported that the daily rainfall in the Amazon Basin is better represented by ANNs than autocorrelation models.…”

A hybrid generalised linear and Levenberg–Marquardt artificial neural network approach for downscaling future rainfall in North Western England

“…However, it performed slightly better for simulating monthly total precipitation with correlation coefficient of 0.65. Later, Ramirez and Ferreira, (2006) have compared the ANN and MLR approaches to downscale rainfall in Eta region of Brazil. They concluded that ANN has tendency to forecast moderate and high rainfall with greater accuracy during the austral summer and was superior to multiple linear regression (MLR).…”