The Research On The Impacts Of Model Effective Topography In GRAPES

For a given numerical model, the kinetic energy spectra analysis shows that the model is limited to effectively simulate the atmospheric motion in certain scales. Similarly, a numerical model is limited to effectively represent the topography. If an inappropriate topography is used in a numerical model, it would cause the noises to harm the model predictions. Generally, some filter could be used to smooth the high resolution topography, in order to generate the model effective topography. For GRAPES, which scale of topography is effective? What is the relationship between the model effective topography and the model resolution? What is the good method to generate the effective topography for GRAPES_Meso model? Those are the basic questions to be answered for the scientific design and application of GRAPES model.By intercomparing a series of numerical simulation tests with ideal data sets and real data sets, this paper is mainly focused on the issues of the highest effective scale of topography for GRAPES and the methods to generate the model effective topography. The five points smoothing or high-order low-pass implicit tangent filters have been tested. The following main remarks were concluded:The model prediction is very sensitive to the scale of model topography. The numerical prediction models, which have different resolutions, need to choose suitable model topography. It is important to reduce the noises, which often cause false terrain rainfall, for improving the prediction performances of the models.The ideal data sets show that GRAPES can reproduce the airflow across the hill effectively using ten times grid scale of the model topography. This result is consistent with the best resolved runs. The GRAPES can still simulate the airflow motion by using six times grid scale of the model topography. Simulations with six times grid scale of the model topography capture about 60-80% of the drag in the best resolved runs. When GRAPES uses smaller scale of the model topography than six times grid scale, the result is very poor. Therefore, the six times grid scale of the model topography is the highest effective scale for GRAPES. The scales, which are smaller than the six times scale, need to be filtered. These influences are considered to be reduced by sub-grid topography parameterizations.By intercomparing the results of the different smoothing methods, the high-order low-pass implicit tangent filter was a better smoothing to deal with model topographies. We can adjust filter parameters to filter off the scale of model topographies for GRAPES according the different needs. The ideal tests show that the six times grid scale of the model topography (6Δx) is the highest effective scales. And we configure the filter parameters (p=5,ε=10) to smooth the five times grid scale and smaller than that scales off. We have made the model effective topography for GRAPES in this way to deal with the original topography.In the numerical simulation experiments, we get the smoother results by using the smoothed topography. Comparing the results with the observation, the prediction by using high-order low-pass filtered topography is more consistent with the observation. The impacts of different model topographies on the results of precipitation forecast are very significant. The precipitation forecast by model topography has stronger rainfall and extended rainfall areas. And the precipitation forecast is the best by using high-order low-pass filtered topography, which is most consistent with the observed precipitation. The whole rainfall reduces and the majority of sporadic rainfall completely disappears. TS and BS score also show the same result .At the same time, other major elements of weather forecasting has been the result of varying degrees of improvement.