Performance of the Seasonal Forecasting of the Asian Summer Monsoon by BCC_CSM1.1(m)

This paper provides a comprehensive assessment of Asian summer monsoon prediction skill as a function of lead time and its relationship to sea surface temperature prediction using the seasonal hindcasts of the Beijing Climate Center Climate System Model, BCC_CSM1. l（m）. For the South and Southeast Asian summer monsoon, reasonable skill is found in the model＇s forecasting of certain aspects of monsoon climatology and spatiotemporal variability. Nevertheless, deficiencies such as significant forecast errors over the tropical western North Pacific and the eastern equatorial Indian Ocean are also found. In particular, overestimation of the connections of some dynamical monsoon indices with large-scale circulation and precipitation patterns exists in most ensemble mean forecasts, even for short lead-time forecasts. Variations of SST, measured by the first mode over the tropical Pacific and Indian oceans, as well as the spatiotemporal features over the Nifio3.4 region, are overall well predicted. However, this does not necessarily translate into successful forecasts of the Asian summer monsoon by the model. Diagnostics of the relationships between monsoon and SST show that difficulties in predicting the South Asian monsoon can be mainly attributed to the limited regional response of monsoon in observations but the extensive and exaggerated response in predictions due partially to the application of ensemble average forecasting methods. In contrast, in spite of a similar deficiency, the Southeast Asian monsoon can still be forecasted reasonably, probably because of its closer relationship with large-scale circulation patterns and E1 Nifio-Southern Oscillation.

Impacts of Land Process on the Onset and Evolution of Asian Summer Monsoon in the NCEP Climate Forecast System

Impacts of land models and initial land conditions （ICs） on the Asian summer monsoon, especially its onset, were investigated using the NCEP Climate Forecast System （CFS）. Two land models, the Oregon State University （OSU） land model and the NCEP, OSU, Air Force, and Hydrologic Research Laboratory （Noah） land model, were used to get parallel experiments NCEP/Department of Energy （DOE） Global Reanalysis 2 System （GLDAS）. The experiments also used land ICs from the （GR2） and the Global Land Data Assimilation Previous studies have demonstrated that, a systematic weak bias appears in the modeled monsoon, and this bias may be related to a cold bias over the Asian land mass. Results of the current study show that replacement of the OSU land model by the Noah land model improved the model＇s cold bias and produced improved monsoon precipitation and circulation patterns. The CFS predicted monsoon with greater proficiency in E1 Nifio years, compared to La Nifia years model in monsoon predictions for individual years. and the Noah model performed better than the OSU These improvements occurred not only in relation to monsoon onset in late spring but also to monsoon intensity in summer. Our analysis of the monsoon features over the India peninsula, the Indo-China peninsula, and the South Chinese Sea indicates different degrees of improvement. Furthermore, a change in the land models led to more remarkable improvement in monsoon prediction than did a change from the GR2 land ICs to the GLDAS land ICs.

Monthly Forecast of Indian Southwest Monsoon Rainfall Based on NCEP’s Coupled Forecast System

The monthly forecast of Indian monsoon rainfall during June to September is investigated by using the hindcast data sets of the National Centre for Environmental Prediction (NCEP)’s operational coupled model (known as the Climate Forecast System) for 25 years from 1981 to 2005 with 15 ensemble members each. The ensemble mean monthly rainfall over land region of India from CFS with one month lead forecast is underestimated during June to September. With respect to the inter-annual variability of monthly rainfall it is seen that the only significant correlation coefficients (CCs) are found to be for June forecast with May initial condition and September rainfall with August initial conditions. The CFS has got lowest skill for the month of August followed by that of July. Considering the lower skill of monthly forecast based on the ensemble mean, all 15 ensemble members are used separately for the preparation of probability forecast and different probability scores like Brier Score (BS), Brier Skill Score (BSS), Accuracy, Probability of Detection (POD), False Alarm Ratio (FAR), Threat Score (TS) and Heidke Skill Score (HSS) for all the three categories of forecasts (above normal, below normal and normal) have been calculated. In terms of the BS and BSS the skill of the monthly probability forecast in all the three categories are better than the climatology forecasts with positive BSS values except in case of normal forecast of June and July. The “TS”, “HSS” and other scores also provide useful probability forecast in case of CFS except the normal category of July forecast. Thus, it is seen that the monthly probability forecast based on NCEP CFS coupled model during the southwest monsoon season is very encouraging and is found to be very useful.

Forecasting Monsoon Precipitation Using Artificial Neural Networks

This paper explores the application of Artificial Intelligent (AI) techniques for climate forecast. It presents a study on modelling the monsoon precipitation forecast by means of Artificial Neural Networks (ANNs). Using the historical data of the total amount of summer rainfall over the Delta Area of Yangtze River in China, three ANNs models have been developed to forecast the monsoon precipitation in the corresponding area one year, five-year, and ten-year forward respectively. Performances of the models have been validated using a 'new' data set that has not been exposed to the models during the processes of model development and test. The experiment results are promising, indicating that the proposed ANNs models have good quality in terms of the accuracy, stability and generalisation ability.

Indian summer monsoon rainfall (ISMR) forecasting using time series data: A fuzzy-entropy-neuro based expert system

This study presents a model to forecast the Indian summer monsoon rainfall（ISMR）（June-September）based on monthly and seasonal time scales. The ISMR time series data sets are classified into two parts for modeling purposes, viz.,（1） training data set（1871-1960）, and（2） testing data set（1961-2014）.Statistical analyzes reflect the dynamic nature of the ISMR, which couldn＇t be predicted efficiently by statistical and mathematical based models. Therefore, this study suggests the usage of three techniques,viz., fuzzy set, entropy and artificial neural network（ANN）. Based on these techniques, a novel ISMR time series forecasting model is designed to deal with the dynamic nature of the ISMR. This model is verified and validated with training and testing data sets. Various statistical analyzes and comparison studies demonstrate the effectiveness of the proposed model.

Tropical Stratospheric Circulation and Monsoon Rainfall

Interannual variability of both SW monsoon (June-September) and NE monsoon (October-December) rainfall over subdivisions of Coastal Andhra Pradesh, Rayalaseema and Tamil Nadu have been examined in relation to monthly zonal wind anomaly for 10 hPa, 30 hPa and 50 hPa at Balboa (9°N, 80°W) for the 29 year period (1958-1986). Correlations of zonal wind anomalies to SW monsoon rainfall (r = 0.57, significant at 1% level) is highest with the longer lead time (August of the previous year) at 10 hPa level suggesting some predictive value for Coastal Andhra Pradesh. The probabilities estimated from the contingency table reveal non-occurrence of flood during easterly wind anomalies and near non-occurrence of drought during westerly anomalies for August of the previous year at 10 hPa which provides information for forecasting of performance of SW monsoon over Coastal Andhra Pradesh. However, NE monsoon has a weak relationship with zonal wind anomalies of 10 hPa, 30 hPa and 50 hPa for Coastal Andhra Pradesh, Rayalaseema and Tamil Nadu.Tracks of the SW monsoon storms and depressions in association with the stratospheric wind were also examined to couple with the fluctuations in SW monsoon rainfall. It is noted that easterly / westerly wind at 10 hPa, in some manner, suppresses / enhances monsoon storms and depressions activity affecting their tracks.

Statistical Downscaling for Multi-Model Ensemble Prediction of Summer Monsoon Rainfall in the Asia-Pacific Region Using Geopotential Height Field

The 21-yr ensemble predictions of model precipitation and circulation in the East Asian and western North Pacific （Asia-Pacific） summer monsoon region （0°-50°N, 100° 150°E） were evaluated in nine different AGCM, used in the Asia-Pacific Economic Cooperation Climate Center （APCC） multi-model ensemble seasonal prediction system. The analysis indicates that the precipitation anomaly patterns of model ensemble predictions are substantially different from the observed counterparts in this region, but the summer monsoon circulations are reasonably predicted. For example, all models can well produce the interannual variability of the western North Pacific monsoon index （WNPMI） defined by 850 hPa winds, but they failed to predict the relationship between WNPMI and precipitation anomalies. The interannual variability of the 500 hPa geopotential height （GPH） can be well predicted by the models in contrast to precipitation anomalies. On the basis of such model performances and the relationship between the interannual variations of 500 hPa GPH and precipitation anomalies, we developed a statistical scheme used to downscale the summer monsoon precipitation anomaly on the basis of EOF and singular value decomposition （SVD）. In this scheme, the three leading EOF modes of 500 hPa GPH anomaly fields predicted by the models are firstly corrected by the linear regression between the principal components in each model and observation, respectively. Then, the corrected model GPH is chosen as the predictor to downscale the precipitation anomaly field, which is assembled by the forecasted expansion coefficients of model 500 hPa GPH and the three leading SVD modes of observed precipitation anomaly corresponding to the prediction of model 500 hPa GPH during a 19-year training period. The cross-validated forecasts suggest that this downscaling scheme may have a potential to improve the forecast skill of the precipitation anomaly in the South China Sea, western North Pacific and the East Asia Pacific regions, where the anomaly correlation coefficient （ACC） has been improved by 0.14, corresponding to the reduced RMSE of 10.4% in the conventional multi-model ensemble （MME） forecast.

广东“18.8”季风槽极端降水事件对流尺度集合预报分析

受季风槽影响,2018年8月30—31日华南地区出现一次极端暴雨过程,单日站点累计降水量达1056.7 mm,刷新了广东有历史纪录以来新的极值。对于此次极端降水事件,常用的业务模式包括欧洲中期天气预报中心全球模式(ECMWF)、日本气象厅谱模式(JMA)和中国气象局广东快速更新同化数值预报系统(CMA-GD),都低估了降水强度。利用深圳市气象局业务对流尺度集合预报系统分析了此次特大暴雨过程,结果表明:对流尺度集合预报系统对本次特大暴雨过程具有比较好的预报能力,概率匹配平均最大雨量达348.7 mm·(24 h)^(-1),集合平均的强降水中心和观测基本一致,观测极值附近区域发生大暴雨(≥150 mm)概率最大值达到80%。选取了较“好”和较“差”集合成员预报进行对比分析,发现较“好”成员预报的强降水中心位置和观测基本一致,而较“差”成员预报的降水中心位置则偏向福建地区。较“好”成员预报出莲花山南侧地面中尺度辐合线较长时间的维持和缓慢移动,导致强降水雨团在莲花山脉附近不断地触发和维持,同时地形的阻挡作用使得对流系统在地形附近区域持续维持,造成了罕见的特大暴雨;而较“差”成员辐合区位于莲花山以北,对流形成后向东、向北移动,最终导致强降水预报位置偏向福建地区。

德国气候预测系统中东亚冬季风的季节预测及可预报性

东亚冬季风(EAWM)作为北半球冬季最强的中纬度环流系统之一,主导着东亚的冬季气候。因此,开展东亚冬季风季节预测和可预报性研究具有十分重要的意义。本研究使用德国气候预测系统(German Climate Forecast System,简称GCFS2)输出的回报数据(1993~2016年)对EAWM的预测性能进行全面评估。GCFS2很好地预测了EAWM气候态的主要特征,包括西伯利亚高压、阿留申低压、东亚大槽、东亚高空急流及东亚上空的地表气温和降水,并可以熟练地预测东亚大槽及东亚地表气温的年际变化。GCFS2对一个海平面气压定义的EAWM指数(EAWMI)显示出了预测技巧,同时可以很好地预测与EAWM相关的位于海洋上的大气环流、地表气温及降水异常。GCFS2中EAWM的预测技巧主要得益于对观测中的EAWM–ENSO关系及ENSO遥相关的成功再现,模式中增强的EAWM–ENSO[强于观测,观测中整个24年(1993~2016)EAWM与ENSO的相关系数为-0.46]关系,有助于提前2个月或更长时间预测EAWM。GCFS2中12月初始化的EAWMI在去除ENSO信号后仍有0.42的预测技巧,说明有另一预测源,为冬季巴伦支—喀拉海区域海冰覆盖度(BK_SIC)。观测中BK_SIC减少,增强西伯利亚高压,EAWM从而增强;模式中BK_SIC的变化可以增加西伯利亚高压东北部的可预测性,使得12月初始化的EAWM预测技巧增加。

POAMA海气耦合模式对2003和2004年南海夏季风预报能力的评估

运用澳大利亚大气海洋耦合预报模式(Predictive Ocean Atmosphere Model for Australia,POAMA)的输出结果,采用泰勒图与分类统计分析方法,评估了该模式对2003和2004年南海夏季风的爆发和演变进行实时预报的能力。通过对泰勒图的分析发现,随着预报初始时间越来越接近实际的季风爆发时间,模式预报南海夏季风爆发和演变的能力越来越强。当提前1—30d预报南海夏季风时,模式能够很好地预报风场、射出长波辐射OLR(Outgoing Longwave Radiation)和降水场的空间分布,其中对风场的预报最好。通过对季风爆发指数和分类统计的分析,定量分析了模式预报南海夏季风爆发的能力,结果表明该模式对南海夏季风爆发时间有一定的预报能力,其最大预报时限可以提前10—15d左右,这与目前中期预报的上限(2周)是一致的。

广东沿海强东北季风的概率预报方法

通过对1988～1997年十年中冷空气造成的广东沿海强东北季风过程的统计研究,挑选出多个与强东北季风相关程度较高的因子作最优因子组合,分月份、分时次共设计了24条概率预报方程,从而建立起冬春季强东北季风概率预报方法,因子结构合理,处理技术科学,所有计算过程完全客观定量。预报检验和业务试验表明,该方法的预报结果效果良好,有较高的应用参考价值。

青藏高原季风的气候振荡和预测试验

利用奇异谱分析等方法 ,分析了 1 96 1～ 1 995年青藏高原季风的气候振荡。结果表明 :自 6 0年代以来 ,季风的总趋势是加强的 ,即夏季青藏高原暖低压加强 ;冬季青藏高原冷高压减弱 ,这可能是对全球变暖的响应。季风的主要周期是 3～ 4年 ,其次是 6年 ,高原季风在 1 985年存在着由偏弱向偏强的突变。利用 SSA- MEM方法可以预报春季高原季风指数未来 2年的趋势和季高原季风指数未来 9个季的趋势。

东亚梅雨季节内振荡的气候特征

影响中国、日本、朝鲜半岛的东亚梅雨是夏季风向北推进过程中的特有雨季。利用NCEP/NCAR逐日再分析资料、CMAP降水资料,将夏季风影响及夏季风降水的季节转换相结合,定义东亚梅雨的入、出梅指标;进而采用集合经验模态分解信号提取方法对东亚梅雨区降水季节内振荡及其大尺度环流条件的气候特征进行了详细分析;并对东亚梅雨季节内振荡对降水事件的指示作用进行讨论,为东亚梅雨区降水的延伸预报提供依据和参考。研究结果表明:(1)采用标准化候降水量的空间覆盖率,同时兼顾夏季风影响等条件确定的东亚梅雨入、出梅划分指标可较好地反映东亚梅雨的气候特征及东亚梅雨期的大尺度环流形势。(2)东亚梅雨全年降水量存在三峰型分布特征,峰值分别位于第27、36及47候。该三峰型特征主要受10—20及30—60d的低频振荡影响。比较而言,30—60d振荡对梅雨区降水三峰型的贡献较10—20d振荡大。(3)东亚梅雨区峰值降水与热带环流及北方高位涡冷空气输送的低频演变密切关联。在梅雨区北侧,中高纬度里海附近冷空气(高位涡)低频波列的东传及鄂霍次克海高位涡的西南向输送共同影响东亚梅雨区。在梅雨区南侧,通过热带低频异常强对流的激发作用,热带西太平洋至中国东北—鄂霍次克海地区形成沿经向分布的低层气旋-反气旋-气旋-反气旋波列,进而导致梅雨区低层形成低频偏北风和偏南风的辐合;而印度西海岸和阿拉伯海地区异常对流活动产生的波列向东北方向传播,亦对梅雨区低频峰值降水产生影响。对于低频谷值降水的大气低频演变,情况与上述基本相反。(4)东亚梅雨区降水不同位相下出现极端降水事件的概率有明显差异。梅雨区降水低频峰(谷)值位相下出现异常多(少)降水量的概率约为30%。因此,上述梅雨区降水低频振荡演变相关的大气低频振荡特征对梅雨区降水事件的延伸预报具有参考价值。

基于南海夏季风季节内振荡的降水延伸预报试验

利用代表南海夏季风季节内振荡特征的850 hPa纬向风EOF分解的前两个主成分,定义南海夏季风季节内振荡指数,并利用美国国家环境预测中心第2代气候预报系统(NCEP Climate Forecast System Version 2,NCEP/CFSv2)提供的1982 2009年逐日回算预报场计算了南海夏季风季节内振荡指数的预报值,用于我国南方地区持续性强降水的预报试验。试验结果表明:利用南海夏季风季节内振荡实时监测指数与模式直接预报降水量相结合的统计动力延伸预报方法,能够有效提高季节内降水分量的预报效果。同时,该方法能够避免末端数据损失,修正了对模式预报降水直接进行带通滤波而导致的负相关现象,并起到消除模式系统误差的作用。

2011年夏季广东季风槽暴雨与准双周振荡

采用小波分析、Lanczos时间滤波器、合成分析等方法分析了2011年夏季广东季风槽暴雨与大气低频振荡的关系。结果表明,2011年夏季降水主要存在准20 d的周期振荡,季风槽暴雨过程对应强的准双周振荡。选取与夏季降水显著相关的区域（102.5-117.5°E,20-27.5°N）平均的500 hPa高度场作为影响广东夏季降水的＂500 hPa关键区＂指数,同样选取区域（110-120°E,15-22.5°N）平均的850 hPa风场作为＂850 hPa关键区＂指数;2011年夏季500 hPa关键区与850 hPa关键区分别存在显著的准23 d、准22 d周期振荡,季风槽暴雨发生在500 hPa关键区准双周振荡的波谷、850 hPa关键区准双周振荡的波峰附近。从南海南部开始的3次低频纬向风、OLR、湿度的北传与从菲律宾以东的西太平洋向西传播的低频中心相遇,导致3次季风槽暴雨过程。利用典型个例的合成分析,对2011年6—8月广东3次季风槽暴雨的准双周振荡不同位相的大气环流场的共同演变特征进行分析,它们反映了季风槽暴雨从间歇-开始-旺盛-减弱-结束期的大气环流场演变特征,为广东季风槽暴雨的中期预报提供参考依据。

基于季风特性改进风电功率预测的研究展望

准确可靠的风电功率预测是智能电网风电消纳的关键基础之一。我国风能充沛地区大多数在季风区,利用季风在地理空间上的大范围一致性,可以显著提高风速空间相关性预测的效果,增加预测的提前时间,提高预测的准确率和可靠性。特别地,引起冬季风的蒙古高气压,与我国北部非季风区的风能充沛地区关系密切。因此,改进空间相关性预测的方法,不仅对季风区适应,还可用于我国北部非季风区冬季的风电功率预测。

MJO对我国南海冬季风异常的影响

利用长时间的逐日再分析资料和澳大利亚气象局提供的MJO指数,研究了MJO对我国南海冬季风异常的影响和过程。结果表明,随着MJO的对流中心从西印度洋进入西太平洋,南海海面风场出现偏南风-东北风-偏南风异常的振荡现象,表明南海冬季风阶段性的间断和活跃,最明显的偏南风异常和东北风异常分别位于MJO第8—2位相和第5—6位相。通过合成MJO各位相下500 h Pa东亚大槽异常和200 h Pa东亚急流异常,我们进一步证实南海冬季风活跃期(MJO第5—6位相),东亚大槽加深,高空急流加强,我国华南沿海上空的反气旋式环流异常,东南边缘的引导气流利于冷空气南下直达南海。相反,在南海冬季风间断期(MJO第8—2位相),东亚大槽和高空急流均减弱,不利于冷空气的南下。对200 h Pa垂直速度的分析表明,MJO深对流活动的东移,调整东亚局地Hadley环流异常,在高空表现为反气旋/气旋式环流异常交替发展东移,最终消失在北太平洋地区。一方面通过科氏力的作用,引起东亚高空急流的异常,进而影响东亚大槽的位置和强度,从而影响冷空气的南下;另一方面直接加剧南海海面风场异常的南北向振荡。因此,在做南海冬季风季内变化特别是大风天气过程的延伸期预报时,热带MJO活动可以作为一个重要因子考虑在内。

南海夏季风爆发的数值预报试验

分析了 １９８６年南海夏季风爆发的环流演变特征，由经向风速剖面图看出，南风首先在中南半岛迅速加强，然后向南海发展，南海夏季风爆发同孟加拉湾低压的发展密切相关。通过地形和非绝热单因子敏感性数值预报试验表明，地形作用和凝结潜热等非绝热作用对南海夏季风的爆发都是很重要的，包含有这两种作用在内的控制试验成功地预报出了南海夏季风爆发的中期演变过程，单独的地形作用或非绝热作用都不能预报出南海夏季风的爆发。

中国24h台风路径预报难点及其大尺度环流分析

主要分析了2005—2012年中国24 h台风路径预报误差较大的样本及其对应的大尺度环流特征。基于850 h Pa风场的低通滤波等分析发现:去除占总数3.9%的预报误差最大样本后,平均预报误差可以减小8.5%。这些预报误差最大的样本中有近60%呈现为移向误差较小、移速较观测慢的特点。与之相对应的大尺度环境场可分为气旋性环流、弱背景场和副热带高压西侧3类。气旋性环流包含近3/4的样本,其中又有一半受季风涡旋的影响。平均移动速度分析表明,这些台风起报时刻前后,平均移速的突然增加是预报移速较慢的主要原因,这是中国台风24 h路径预报的主要难点之一。

南海夏季风监测预测研究与业务现状评述

南海夏季风是影响中国降水的重要系统,它的爆发预示着东亚夏季环流的发展。南海夏季风爆发前后的大气环流有明显的变化,包括西太平洋副热带高压撤出南海,南海地区低层西南风盛行,高层偏东风控制等。目前南海夏季风指数的定义基本上都是利用南海局地的变量,也许从更大尺度考虑南海夏季风指数的定义会避免一些天气过程的影响。影响南海夏季风爆发的因素包含了海洋和陆地与大气的相互作用过程,气候模式(包括全球和区域)在季风区关于这方面的描述还有缺陷,而且人为因素的影响加大了气候模式对南海夏季风的模拟和预测的不确定性。