CONICET | Buscador de Institutos y Recursos Humanos

The Weather and Research Forecast Model is tested over South America in different configurations to identify the one that gives best estimates of observed surface variables. Systematic, non-systematic and total errors are computed for 48 hour forecasts initialized with GDAS. There is no unique model design that fits best all variables over the whole domain, and non-systematic errors for all configurations differ little from one another, such differences are in all cases smaller than the observed day to day variability. An ensemble mean consisting of runs with different parameterizations gives the best skill for the whole domain. Surface variables are highly sensitive to the choice of land surface models. Surface temperature is well represented by the NOAH land model, but dew-point temperature is best estimated by the simplest land surface model considered here, which specifies soil moisture based on climatology. This underlines the need for better understanding of humid processes at the sub-grid scale. Surface wind errors decrease the intensity of the low level jet, reducing expected heat and moisture advection over south-east South America (SESA), with negative precipitation errors over SESA and positive biases over the SACZ (South Atlantic Convergence Zone). This pattern of errors suggests feed-backs between wind errors, precipitation and surface processes as follows: an increase of precipitation over the SACZ produces compensating descent in SESA, with more stable stratification, less rain, less soil moisture and decreased rain. This is a clear example of how local errors are related to regional circulation, and suggests that improvement of model performance requires not only better parameterizations at the sub-grid scales, but also improved regional models.

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