Abstract
The present study evaluates the performance of three numerical weather forecasting models: Global Forecast System (GFS), Brazilian Regional Atmospheric Modelling System (BRAMS) and ETA Regional Model (ETA), by means of the Mean Error (ME) and the Root Mean Square Error (RMSE), during the most rainy four months period (January to April 2012) on Eastern Amazonia. The models displayed errors of superestimation and underestimation with respect to the observed precipitation, mainly over center-north of Pará and all of Amapá, where the precipitation is higher. Among the analyzed models, GFS shows the best performance, except during January and March, when the model to underestimated precipitation, possibly due to the anomalously high values recorded.
Highlights
Meteorological forecasting is a complex task, but such complexity has decreased, over the years, making the numerical forecast faster and more practical [1,2], with higher success rate for several variables
The present study evaluates the performance of three numerical weather forecasting models: Global Forecast System (GFS), Brazilian Regional Atmospheric Modelling System (BRAMS) and ETA Regional Model (ETA), by means of the Mean Error (ME) and the Root Mean Square Error (RMSE), during the most rainy four months period (January to April 2012) on Eastern Amazonia
The error analyses are displayed preceded by a discussion about the main meteorological systems responsible for the spatial and temporal distribution of the accumulated precipitation in each month (Figure 1), where dark green color represents the largest amounts of precipitation
Summary
Meteorological forecasting is a complex task, but such complexity has decreased, over the years, making the numerical forecast faster and more practical [1,2], with higher success rate for several variables. Precipitation is one variable that attracts more interest due to its relevance, for climate, and for several parts of society, such as mining, economics, industry, agriculture, and others [3,4,5,6,7]. Many social and economic sectors in Brazil presently use numerical weather forecasts for strategic planning of their activities [8,9,10]. The main meteorological centers in Brazil use operational models for numerical weather forecasting capable of accurate weather predictions, but in Amazonia, the largest tropical forest in the world, those models do not have a good parameterization of some essential physical processes to represent the atmospheric mechanisms that cause precipitation over that region [11].
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