Mitigating Excessive Drying From the Use of Observations in Mesoscale Modeling.

摘要

Observations can be used to enhance mesoscale model forecasts both through creation of an initial condition analysis and through application of observations over a period of time within the model. Because observations are not available at the same density as the model grid points, assumptions must be made regarding spatial and temporal correlations in order to apply the observations to the model. Methods are developed that mitigate overly dry model conditions caused by these assumptions. The Weather Research and Forecasting model is applied to five cases centered over southern California to demonstrate the overdrying caused by incorporation of observations in the initial conditions, both at a discrete time and over a preforecast data assimilation period. Very dry conditions result from the assumption that model moisture error at the observation location is correlated with model moisture error at other locations in the model without regard to the relative magnitude of the moisture at those two locations. Modifications that account for this difference in the magnitude of moisture are made to both the initial condition analysis and the data assimilation methodology. These modifications greatly reduce the occurrence of excessive model dryness, while not degrading the overall model performance in predicting moisture.