INFLUENCE OF DIFFERENT-SCALE ERRORS INTERACTIONS ON ANALYSIS AND FORECAST OF REGIONAL NWP MODEL |
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| Authors: | ZHANG Xu-bin and TAN Zhe-min |
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| Institution: | Key Laboratory of Mesoscale Severe Weather/MOE, and School of Atmospheric Sciences, Nanjing University, Nanjing 210093 China; Guangdong Provincial Key Laboratory of Regional Numerical Weather Prediction, CMA, Guangzhou 510080 China; Institute of Tropical |
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| Abstract: | In the previous study, the influences of introducing larger- and smaller-scale errors on the background error covariances estimated at the given scales were investigated, respectively. This study used the covariances obtained in the previous study in the data assimilation and model forecast system based on three-dimensional variational method and the Weather Research and Forecasting model. In this study, analyses and forecasts from this system with different covariances for a period of one month were compared, and the causes for differing results were presented. The variations of analysis increments with different-scale errors are consistent with those of variances and correlations of background errors that were reported in the previous paper. In particular, the introduction of smaller-scale errors leads to greater amplitudes in analysis increments for medium-scale wind at the heights of both high- and low-level jets. Temperature and humidity analysis increments are greater at the corresponding scales at the middle- and upper-levels. These analysis increments could improve the intensity of the jet-convection system that includes jets at different levels and the coupling between them that is associated with latent heat release. These changes in analyses will contribute to more accurate wind and temperature forecasts in the corresponding areas. When smaller-scale errors are included, humidity analysis increments are significantly enhanced at large scales and lower levels, to moisten southern analyses. Thus, dry bias can be corrected, which will improve humidity forecasts. Moreover, the inclusion of larger- (smaller-) scale errors will be beneficial for the accuracy of forecasts of heavy (light) precipitation at large (small) scales because of the amplification (diminution) of the intensity and area in precipitation forecasts. |
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| Keywords: | background error covariances errors at different scales data assimilation |
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