基于机器学习的华北气温多模式集合预报的订正方法

模式预报的订正是决定局地天气预报结果的一个重要步骤,基于机器学习的后处理模型近年来开始崭露头角。本文发展了基于岭回归（Ridge）、随机森林（Random Forest,RF）和深度学习（Deep Learning,DL）的3种后处理模型,基于中国气象局（CMA）的BABJ模式、欧洲中期天气预报中心（ECMWF）的ECMF模式、日本气象厅（JMA）的RJTD模式和NCEP的KWBC模式这4个数值天气预报模式2014年2月至2016年9月（训练期）近地面2 m气温预报和实况资料确定各模型参数,进而对2016年10月至2017年9月（预报期）华北地区（38°N~43°N,113°E~119°E）的逐日地面2 m气温预报进行了多模式集合预报分析。采用均方根误差对预报效果进行评估,这3种后处理模型的预报效果和4个数值天气预报模式以及通常的多模式集合平均（Ensemble Mean,EMN）的预报效果的对比表明:1）随着预报时长增加,4个数值预报模式及各种后处理模型的均方根误差均呈上升趋势;但区域平均而言,Ridge、RF和DL的预报效果在任何预报时长上都明显优于EMN和单个天气预报模式;特别是前几天的短期预报DL的预报效果更好,中后期预报Ridge的预报效果略好。2）华北地区的东南部均方根误差较小,其余格点上均方根误差较高,从空间分布而言,DL的订正预报效果最好,3种机器学习模型的误差在1.24~1.26℃之间,而EMN的误差达1.69℃。3）夏季各种方法的预报效果都较好,冬季预报效果都较差;但是Ridge、RF和DL的预报效果明显优于EMN,这3种模型预报的平均均方根误差在2.15~2.18℃之间,而EMN的平均均方根误差达2.45℃。     

基于1873年以来器测气温的二十四节气气候变化

2000多年来二十四节气已广泛用于指导人们生产和生活。然而传统二十四节气的应用意义在当前全球变暖背景下正在发生变化。气候学二十四节气的提出赋予了二十四节气动态变化的内涵,有利于更好地发挥其现实指导作用。本文利用北京观象台1940~2017年和上海徐家汇站1873~2017年均一化的逐日气温观测序列,分析了近百余年二十四节气气候变化特征。结果显示,北京1941~2016年和上海1874~2016年的年平均气温和二十四节气气温都呈现变暖趋势,导致早春到初夏阶段的气候学节气呈现提前的趋势,而夏末到初冬阶段的节气呈现推迟的趋势;这些趋势大部分是统计显著的。北京和上海的极端冷事件（以大寒标准定义）均呈现显著的减少趋势,上海的极端热事件（以大暑标准定义）呈现显著的增多趋势。除了长期趋势之外,上海极端热事件频数和夏季平均气温演变中都存在明显的60~80年周期的多年代际变率,和大西洋多年代际振荡相关。相比以往基于1960年以来的观测所做的二十四节气气候趋势分析,本文揭示了更完善的长期气候变化特征,有助于从精细化的季节循环演变角度丰富关于近代中国气候变暖的认识,为适应气候变化提供科学基础。     

“05.4.8”四川冰雹个例定量降水的预报技巧研究

从集合预报的角度研究了"05.4.8"四川冰雹个例的定量降水的预报技巧随水平尺度以及地形的变化。初步结论为当水平尺度小于100km时,预报技巧随着水平尺度的增加而迅速增加,当水平尺度达到200km及以上时,预报技巧增加地非常缓慢,基本维持不变;在不同的地理位置,预报技巧的差别很大。总体而言,预报技巧在山地要较平原地区来得高。     

山西省夏季三类典型强降水的集合预报试验

利用我国自主研发的多尺度通用的新一代同化与数值预报系统--GRAPES模式,通过对4种积云参数化方案的比较,确定集合预报成员,对山西省夏季3类典型强降水过程进行了集合预报试验.试验结果表明:(1)对所模拟的个例,Betts-Miler-Janjic对流调整方案和Kain-Fritsch对流参数化方案优于对流调整方案和郭氏参数化方案;(2)积云参数化方案对降水的影响比边界层参数化方案对降水的影响大,不同个例对2种不同积云参数化方案和3种不同边界层参数化方案的6种组合的反应敏感程度也各不相同;(3)不同集合成员对降水的预报结果各不相同,其预报偏差也各不相同;(4)对于不同的天气系统,不同的方案其预报效果是不同的;(5)通过集合平均,降水预报偏差明显减小,降水预报偏大的情况得到改善,且其24～36 h预报效果最好.     

两种短期降水集合预报方案的对比试验

将MM5V3作为试验模式,利用初值扰动和物理参数化方案,分别对东南沿海地区2004年7—9月的短期降水进行了模拟,并运用均方根误差法对模拟试验效果进行了评估检验,以比较两种方案的集合预报效果。结果表明,对于汛期短期降水预报而言,两种方案的预报效果都有一定的改进。相对于初值误差而言,积云对流过程在汛期降水过程中起着更为重要的作用,因而物理参数化方案扰动的集合预报效果,总体优于基于初值扰动的集合预报效果。研究还发现,模式物理参数化方案扰动的集合预报效果在9月出现了明显的下滑,这主要由于7—8月对流活动旺盛,而9月对流活动随热力因素减弱而减弱的原因所致。     

Progress on the Key Technology Development in Application of Ensemble Prediction Products Associated with TIGGE

This project is supported by the 2007 R & D Special Fund for Public Welfare by Ministry of Science and Technology and Ministry of Finance.Research tasks in this project are proposed based on the implementation plan of theTHORPEX(The Observing System Research and Predictability Experiment) Interactive Grand Global Ensemble(TIGGE),a sub-project of the THORPEX international program.The principal tasks of this project are to develop the key ap-     

临夏州大—暴雨物理量诊断集合预报模型

针对临夏州大—暴雨产生的物理条件,总结了相关的物理量及其指标,根据地面、高空及T213数值预报格点资料,采用三角形插值法计算各站点的相关物理量,利用集合的方法建立了临夏州大—暴雨物理量诊断集合预报模型。通过个例检验,该模型对临夏州大—暴雨具有一定的预报能力。     

Heavy Rainfall Ensemble Prediction： Initial Condition Perturbation vs Multi-Physics Perturbation

Mesoscale ensemble is an encouraging technology for improving the accuracy of heavy rainfall predictions. Occurrences of heavy rainfall are closely related to convective instability and topography. In mid-latitudes, perturbed initial fields for medium-range weather forecasts are often configured to focus on the baroclinic instability rather than the convective instability. Thus, alternative approaches to generate initial perturba- tions need to be developed to accommodate the uncertainty of the convective instability. In this paper, an initial condition perturbation approach to mesoscale heavy rainfall ensemble prediction, named as Different Physics Mode Method （DPMM）, is presented in detail. Based on the PSU/NCAR mesoscale model MM5, an ensemble prediction experiment on a typical heavy rainfall event in South China is carried out by using the DPMM, and the structure of the initial condition perturbation is analyzed. Further, the DPMM ensem- ble prediction is compared with a multi-physics ensemble prediction, and the results show that the initial perturbation fields from the DPMM have a reasonable mesoscale circulation structure and could reflect the prediction uncertainty in the sensitive regions of convective instability. An evaluation of the DPMM ini- tial condition perturbation indicates that the DPMM method produces better ensemble members than the multi-physics perturbation method, and can significantly improve the precipitation forecast than the control non-ensemble run.     

An analysis of the difference between the multiple linear regression approach and the multimodel ensemble mean

An investigation of the difference in seasonal precipitation forecast skills between the multiple linear regression (MLR) ensemble and the simple multimodel ensemble mean (EM) was based on the forecast quality of individual models. The possible causes of difference in previous studies were analyzed. In order to make the simulation capability of studied regions relatively uniform, three regions with different temporal correlation coefficients were chosen for this study. Results show the causes resulting in the incapability of the MLR approach vary among different regions. In the Nino3.4 region, strong co-linearity within individual models is generally the main reason. However, in the high latitude region, no significant co-linearity can be found in individual models, but the abilities of single models are so poor that it makes the MLR approach inappropriate for superensemble forecasts in this region. In addition, it is important to note that the use of various score measurements could result in some discrepancies when we compare the results derived from different multimodel ensemble approaches.     

Dust storm ensemble forecast experiments in East Asia

The ensemble Kalman filter (EnKF), as a unified approach to both data assimilation and ensemble forecasting problems, is used to investigate the performance of dust storm ensemble forecasting targeting a dust episode in the East Asia during 23–30 May 2007. The errors in the input wind field, dust emission intensity, and dry deposition velocity are among important model uncertainties and are considered in the model error perturbations. These model errors are not assumed to have zero-means. The model error me...