| 摘要: |
| 误差订正是提高模式模拟和预报性能的有效方法。基于CWRF(regional Climate-Weather Research and Forecasting model)25套不同物理参数化方案的日降水量模拟资料,对比仅进行降水日数订正(OCD)、仅进行降水量订正(OCM)和先订正降水日数再订正降水量(COR)三种订正方法,先订正再等权重集成和先等权重集成再订正两种订正思路,重点对1997—2015年华中和华南地区夏季日降水进行订正效果的对比。结果表明:(1) 降水日的订正是必要的,综合而言COR方法对CWRF模式日降水的订正效果更佳,尤其是小量级降水,但降水强度的表现不如OCM;(2) 先集成后订正的效果更好;(3) CWRF模式不同参数化方案对日降水的模拟能力有显著差别,经过订正后模拟能力均有所提升,但对于不同的模拟方案,其订正效果也不同。表明,误差订正确实能有效提高模式模拟及预报性能,但其效果存在不确定性。提高模式的预报性能,关键还是提高模式对真实大气动力学的表述能力。 |
| 关键词: CWRF模式 误差订正 分位数映射方法 |
| DOI:10.16032/j.issn.1004-4965.2019.076 |
| 分类号: |
| 基金项目: |
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| COMPARISON OF BIAS CORRECTION TECHNIQUES BASED ON CWRF MODEL FOR DAILY PRECIPITATION IN SUMMER |
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LI Xin-yun1, YU Jin-hua1, LIANG Xin-zhong2
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1. Key Laboratory of Meteorological Disaster, Ministry of Education(KLME)/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD), Nanjing University of Information Science &Technology, Nanjing 210044, China;2. Earth System Science Interdisciplinary Center,University of Maryland,MD,USA 20742
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| Abstract: |
| Error correction is an effective method to improve the performance of simulation and prediction in a model. Based on summer daily precipitation simulated by CWRF (regional Climate-Weather Research and Forecasting model), focusing on central and southern China, the effectiveness of three correction methods including only corrected precipitation day (OCD), only corrected magnitude of precipitation (OCM) and corrected both (COR) as well as two schemes that correction before or after equal-weighted integration were compared in the paper. The result are as follows. (1) The correction of precipitation day is necessary. Compared with OCM, COR was better on the effect of CWRF daily precipitation correction, especially of smaller magnitude. (2) Equal-weighted integration before correction performed better. (3) Different parametric schemes of CWRF model have significant differences in the simulation ability of daily precipitation, and the simulation ability has been improved after the revision. The revision effect is different for different simulated schemes. The key to improve the prediction performance of CWRF is to improve the ability of the model to express real atmospheric dynamics. |
| Key words: CWRF bias correction quantile mapping |