| CMA-GFS云预报的偏差分布特征 |
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| 引用本文： | 李喆,陈炯,马占山,陆慧娟,胡江凯,刘奇俊.CMA-GFS云预报的偏差分布特征[J].应用气象学报,2022,33(5):527-540. |
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| 作者姓名： | 李喆 陈炯 马占山 陆慧娟 胡江凯 刘奇俊 |
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| 作者单位： | 1.中国气象局地球系统数值预报中心，北京 100081 |
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| 摘 要： | 利用2021年3月—2022年2月ERA5再分析数据云量、云水凝物对中国气象局研发的全球数值预报系统CMA-GFS同期云量产品和由云量、云水凝物产品计算的云发生、云水凝物积分的偏差特征进行诊断评估, 初步探讨了CMA-GFS云预报偏差存在的可能原因。结果显示:CMA-GFS云量、云水凝物的分布较为合理, CMA-GFS能够描绘全球云量、云水凝物的分布特征, 并能够反映季节特征;CMA-GFS预报高云和中云的云量偏差大于低云的云量偏差, 而高云和中云的云量均方根误差较低云偏小, 说明模式高云和中云的预报稳定性优于低云;与ERA5再分析数据相比, CMA-GFS液相水凝物积分以负偏差为主, 冰相水凝物积分以正偏差为主;云量、云水凝物的偏差在不同地区成因不同, 在热带地区的偏差与对流参数化和微物理方案不协调有关, 在南北半球中高纬度地区的偏差与相对湿度偏差相关。
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| 关 键 词： | CMA-GFS 云发生 云量 水凝物积分 |
| 收稿时间： | 2022-03-25 |
Deviation Distribution Features of CMA-GFS Cloud Prediction |
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| Affiliation: | 1.CMA Earth System Modeling and Prediction Center, Beijing 1000812.State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 1000813.College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875 |
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| Abstract: | Clouds play a vital role in weather, climate system and the atmospheric water cycle. The diagnosis and evaluation of numerical model prediction results is important for numerical model research and development. Reasonable diagnosis and evaluation methods can not only provide references for model researchers to optimize model schemes, but also help users understand the performance of model prediction. The cloud characteristics of different regions and seasons should be considered for evaluation because the attributes in different regions are markedly different. The performance and deviation characteristics of the operational CMA-GFS of four seasons are evaluated, based on the reanalysis of ERA5 reanalysis data from March 2021 to February 2022. The frequency bias of cloud occurrence, cloud fraction, integrated cloud hydrometeors from various levels, and the bias and root mean square error of those variables are carefully diagnosed and evaluated via different methods. The deviation characteristics of cloud are emphatically analyzed according to different regions. The possible causes for the significant difference of cloud prediction deviation characteristics at different levels in different regions are preliminarily discussed. The results show that the overall distribution of cloud predicted by CMA-GFS is reasonable, which can describe the meridional peak and valley distribution characteristics of global cloud and reflect the seasonal trend. The cloud amount deviation of high cloud and medium cloud by CMA-GFS is greater than that of low cloud. The root mean square error of cloud amount of high cloud and medium cloud is smaller than that of low cloud. And the model stability for high cloud and medium cloud prediction is also better. The liquid-phased hydrometers integration is mainly negative deviation, and the ice-phased hydrometers integration is mainly positive deviation. The causes of the deviation of cloud predicted by CMA-GFS are different in different regions. In tropical region the deviation is related to the incongruity of convective parameterization and microphysical schemes, while in middle and high latitudes regions the deviations are related to the bias of relative humidity. It also shows that the diagnosis of model cloud features will cover up the actual problems only by a single method, and it needs to be evaluated comprehensively by combining a variety of methods. |
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