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Comparisons of Cloud Microphysical Characters between Mesoscale Model for Artificial Precipitation Enhancement and Satellite Observations
| Content Provider | Semantic Scholar |
|---|---|
| Author | Zhan-Shan, Ma Qi-Jun, Liu |
| Copyright Year | 2011 |
| Abstract | National Meteorological Center (abbreviated to NMC) has developed a new double-moment explicit microphysics scheme in 2006, and coupled it with mesoscale numerical model GRAPES, which established an operational numerical model forecast system for artificial rainfall enhancement in NMC. This system can provide information of cloud microphysical structure, macro properties and synoptic situations over China, which would be beneficial for the development of artificial rainfall enhancement. In order to have a better understanding of the system's performance and accuracy,the comparisons of cloud microphysical characters between the forecasting products and observations is significative and it will put forward an objective and reasonable proposal for improving and optimizing of the microphysical scheme at the same time. In previous studies, many comparisons of model forecast results with satellite data have already been undertaken abroad. For example, J.-P. Chaboureau et al. (2001) has adjusted the ice-to-snow autoconversion threshold of microphysical scheme according to the discrepancy of cloud top brightness temperature between Meso-NH model and METEOSAT observation. William. L. et al. (2006) has evaluated the results of operational NOAA Rapid Update Cycle (RUC) model in term of the analyzed and predicted condensation fields by directly comparing them with NASA Langley research Center (LaRC) satellite-derived cloud products and quantifying the differences. In this paper, we will mainly reveal the forecast performance of cloud characters and their 1 difference compared with MODIS and TRMM observations. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.wmo.int/pages/prog/arep/wwrp/new/documents/MOD.mazhanshan_China.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |