CFSv2对2018年浙江梅雨降水多尺度预测的性能评估

从梅雨预测的业务需求出发,系统开展了CFSv2模式对2018年浙江梅雨期降水预报能力的多时间尺度评估。结果发现3月1日—5月31日的起报结果整体上未能较准确地预测6月浙江大部降水偏少的趋势、仅5月31日的预测结果与实况相符;在延伸期尺度上,CFSv2预测的梅雨期总降水量较实况偏少30%左右;基于相关系数、均方根误差和新定义的综合预报技巧指数等指标分析模式的延伸期预报性能,发现对梅雨期总降水量、逐日区域平均降水量和逐日全省各站降水量的预报技巧有限,对浙江梅雨区的预报水平总体高于浙江全省。评估结果表明CFSv2预报产品表现出显著的系统性干偏差;在延伸期尺度上,随着预报时效的缩短,预报效果并非逐步提升、而是客观存在一个最佳预报时效,各起报日也分别对应着不同的最优预报时段,整体而言梅雨降水的延伸期预测可能对初值并不敏感。     

CFSv2模式产品对贵州一次区域暴雨过程的预报能力分析

基于美国国家环境预报中心提供的CFSv2未来45 d的气候模式产品资料、NCEP/NCAR再分析逐日资料和贵州省85个地面气象观测站的逐日降水资料,评估了CFSv2延伸期时段降水量预报产品对贵州省2019年6月5—9日区域暴雨过程的预报能力。结果表明,CFSv2在提前15～10 d时段对贵州区域暴雨过程的预报能力较好,对6月8日最大降水日的预报信号稳定、持续。通过500 hPa环流形势的预报场与实况场的对比分析,发现影响暴雨过程预报量级偏小的原因主要在于西太平洋副热带高压预报的强度偏小、位置偏南。     

CN05.1气象数据在流域水文模拟中的应用——以新疆开都河流域为例

为探讨中国再分析气象数据集CN05.1在流域水文模拟中的适用性潜力,以开都河流域为研究区,分别使用CN05.1数据集和传统气象站数据驱动SWAT水文模型,采用决定系数（R ²)、纳什效率系数（NSE）和相对误差（R_e）等评价指标对二者模拟效果进行对比分析,以确定CN05.1数据的适用性;最后采用两种数据订正方法对CN05.1降水数据进行了订正,并以水文模拟效果进行评价。研究结果表明:(1) CN05.1气象数据在开都河流域的水文气象模拟中具有较强的适用性;(2) 基于SWAT模型的水文模拟显示,CN05.1数据驱动的水文模拟精度高于传统气象站数据,其率定期（1995—2005年）和验证期（2006—2016年）的R ²分别为0.81和0.73,NSE分别为0.81和0.72,R_e分别为-0.97%和0.39%;(3)两种数据订正方法均能较好地再现流域径流变化过程,但基于空间关系订正法的径流模拟效果更好,R ²和NSE均在0.72以上,|R_e|＜1.7%。由此,订正后的CN05.1降水数据一方面弥补了传统气象站数据缺失的问题,另一方面补足了未订正CN05.1降水数据在径流模拟中峰值欠佳的问题。     

RCP4.5情景下长江上游流域未来气候变化及其对径流的影响

基于1961—2010年长江寸滩以上流域50个气象站的逐日观测数据和寸滩水文控制站的逐日径流数据,结合流域的地形、土地利用和土壤信息,采用HBV和SWAT水文模型,模拟了流域降水径流定量关系,并利用CCLM区域气候模式,开展了气候变化背景下,寸滩未来径流的可能演变趋势分析。结果表明:HBV和SWAT水文模型都适用于位于湿润地区的长江寸滩以上流域,月径流的模拟Nash-Sutcliffe效率系数都在0.90以上。相比较,SWAT水文模型对于枯水径流的模拟较差,HBV水文模型峰值流量的模拟高于实测。相对于基准期(1986—2005年),RCP4.5情景下,2011—2040年寸滩以上流域平均气温、最高气温、最低气温将明显增加,并呈持续上升趋势;流域降水也有一定的增加,但2030年后呈弱减少趋势。从两类水文模型对径流模拟的集合结果来看,2011—2040年年径流将上升14.2%;而径流量的概率分布尾部特征及径流分位数变化进一步表明,流域的未来峰值流量预计将有所增大。     

基于SWAT模型的岷江上游流域水文模拟与干旱评估

以地势复杂、海拔高差悬殊的岷江上游流域为研究区域,选取2013～2017年水文、气象观测、土地利用、土壤类型及DEM等数据,驱动SWAT(Soil and Water Assessment Tool)分布式水文模型,在参数敏感性分析的基础上对模型进行校正和验证,模拟岷江上游流域日径流量变化过程,并采用基于SWAT计算的...     

全球大气环流模式BCC_AGCM2.0.1对1998年夏季江淮流域强降水过程的回报试验研究

利用中国气象局北京气候中心全球大气环流模式(BCC_AGCM2.0.1) 对1998年6月24日～7月3日发生在我国江淮流域的强降水天气过程进行了回报试验。模式起报时间为1998年6月24日00时, 使用前10天NCEP-II再分析逐时温度、涡度和散度场进行预报前初始协调 (spin-up) 积分, 产生模式初值, 预报时段为1998年6月24日～7月10日, 回报试验结果表明: 模式对全球500 hPa位势高度的天气尺度演变过程具有4～7天的可预报性; BCC_AGCM2.0.1模式对中国区域的降水以及大气环流场具有3～4天的可预报性, 6月24日起报后3天内的预报降水区域位置与实况一致, 但中心强度有所差异。对起报后未来2天的5 mm和10 mm以上的降水预报能力相对较强, ETS评分值达到了0.25以上, HK评分超过了0.4, 降水区域范围预报较为准确, BIA评分趋于1.0。模式对20 mm以上的降水也具有一定的可预报性, 但模式对大于30 mm以上强降水的预报能力较差。     

基于CFSv2.0西南地区机场10～30 d延伸期预报产品设计与实现

延伸期逐日预报对于民航属于新兴气象业务。针对民航运行服务需求,根据CFSv2.0气候模式预报产品,运用双线性站点插值技术,基于5 d集合滑动平均和实况报文订正方案生成10～30 d西南地区各机场延伸期逐日最高温度、最低温度、降水量和风向风速的时序柱状曲线预报产品。选取2020年冬季双流机场和贵阳机场强降温和持续性降水过程进行预报效果检验,结果表明：机场延伸期逐日预报产品在强降温与持续性降水预报方面表现出较好的预报性能,能平均提前16 d预报强降温过程,提前20 d预报持续性降水过程。与实况数据相比,强降温和持续性降水预报的时段、强度略有偏差,但预报的变化趋势基本一致。     

淮河流域2016年汛期洪水预报试验

预报大流域降雨径流与洪水是非常复杂的预报难题。本研究建立气象水文耦合预报模型对复杂大流域的洪水预报进行预报试验。模型采用中央气象台格点化降水预报产品作为预见期内降水,驱动水文水动力学耦合模型进行洪水预报。选择新安江水文模型用于流域降雨径流模拟,基于扩散波与柱蓄和楔蓄理论建立Muskingum-Cunge水位流量演算模型进行具有行蓄洪区的复杂河系洪水预报。以淮河鲁台子站以上流域2016年汛期洪水为例,将构建的气象水文耦合预报模型进行洪水预报试验。结果表明,模型取得了较好的预报精度,应用格点化降水预报产品考虑预见期内降水预报的洪水预报对于不考虑预见期降水预报,洪水预报预见期得到一定的有效延长,对同类流域预报有一定的借鉴意义。     

气候变化影响下的流域水循环:以长江和黄河典型流域为例

在对IPCC提供的多种大气环流模式(GCMs)适用性评估的基础上,采用SDSM和ASD统计降尺度模型生成未来气候变化情景,驱动分布式水文模型VIC和SWAT,分别对长江和黄河典型流域进行分布式水文模拟,定量探讨气候变化对流域水循环的影响。结果表明,适应性评估可以有效降低GCMs选择的不确定性,统计降尺度方法能够明显改善全球气候模式降水和气温输出结果。与基准期(1961—1990年)相比,未来时期(2046—2065年和2081—2100年)长江下游太湖流域径流量呈现微弱减少趋势,但汛期东南部径流量增加显著,而黄河上游流域径流量则呈下降趋势。研究结果可为开展我国各大流域适应气候变化研究提供一定的参考依据。     

水文气象研究进展

从面向流域的定量降水估测与预报、流域水文模型、水文气象耦合预报三个方面系统介绍水文气象研究进展。研究指出,融合天气雷达、卫星遥感及实况降水等多源信息是精细化定量降水估测产品的主要发展方向;采用多模式降水预报集成技术是提高定量降水预报精度的重要途径;分布式水文模型是流域水文模型的发展方向;引入定量降水预报的水文气象耦合预报模式可以延长洪水预报预见期,水文集合预报是水文预报方法的有效解决途径,而数值预报模式与水文模型的双向耦合模式是另一重要发展方向。     

NCEP CFSv2对中国2015年夏季月尺度降水和环流预报分析

利用美国环境预报中心的第二代气候预报系统(NCEP CFSv2)提供的1982~2010年历史回报资料和2015年6~8月预报产品、NCEP CFSR再分析资料及中国地面观测降水资料,评估了NCEP CFSv2对2015年(厄尔尼诺发展年)中国夏季月降水和环流形势的预报能力,并分析了影响模式预报技巧高低的可能因子。结果表明:1)模式对降水的预报技巧较低且表现出明显的月变化(7月最高,8月次之,6月最低),但总体水平都不高。预报技巧明显依赖于提前时间的长短。2)CFSv2对影响我国夏季降水的500h Pa关键区环流异常空间模态表现出较高的预报技巧。对全东亚区域,模式基本都可提前5~9天(7月9天,6月6天,8月5天)较为准确的预报出未来一个月高度异常空间模态。3)通过对比分析发现,CFSv2环流预报中选取12个集合成员(滑动3天)可以得到较稳定的预报结果。4)在2015年夏季月尺度环流异常模态预报中,东亚全区的环流预报水平很大程度上取决于中高纬地区的预报。CFSv2对中高纬环流月预报技巧(6~8月都能从提前4天开始就基本稳定维持在较高水平)比热带地区更高更稳定。      

CFSv2在湖北省梅雨特征量延伸期预报中的应用

利用NCEP的气候预报系统第二版(CFSv2)提供的逐日降水模式资料,采用集合预报方法开展区域性夏季降水预报,使用出入梅日期均方根误差(RMSE)、准确率(ACCU),梅雨期长度均方根误差(RMSE)及梅雨雨强距平符号一致率(Pc)等3种方法评估模式资料对湖北省梅雨特征量的预报能力。结果表明:入梅预报提前13 d的ACCU可达0.5以上、RMSE小于3 d,出梅预报提前14 d的ACCU可达0.5以上、RMSE小于3 d,梅雨期长度预报提前14天的RMSE小于5 d,梅雨雨强预报提前14 d的Pc可达0.5以上。梅雨特征量总体预报时效为14 d左右,CFSv2模式资料对区域性夏季降水在梅雨延伸期时段表现出一定的预报技巧。     

LOW-FREQUENCY CIRCULATION AND EXTENDED-RANGE FORECAST IN CONNECTION WITH AUTUMN EXTREME HEAVY RAINFALL PROCESS IN HAINAN

Observed daily precipitation data from the National Meteorological Observatory in Hainan province and daily data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis-2 dataset from 1981 to 2014 are used to analyze the relationship between Hainan extreme heavy rainfall processes in autumn (referred to as EHRPs) and 10–30 d low-frequency circulation. Based on the key low-frequency signals and the NCEP Climate Forecast System Version 2 (CFSv2) model forecasting products, a dynamical-statistical method is established for the extended-range forecast of EHRPs. The results suggest that EHRPs have a close relationship with the 10–30 d low-frequency oscillation of 850 hPa zonal wind over Hainan Island and to its north, and that they basically occur during the trough phase of the low-frequency oscillation of zonal wind. The latitudinal propagation of the low-frequency wave train in the middle-high latitudes and the meridional propagation of the low-frequency wave train along the coast of East Asia contribute to the ‘north high (cold), south low (warm)’ pattern near Hainan Island, which results in the zonal wind over Hainan Island and to its north reaching its trough, consequently leading to EHRPs. Considering the link between low-frequency circulation and EHRPs, a low-frequency wave train index (LWTI) is defined and adopted to forecast EHRPs by using NCEP CFSv2 forecasting products. EHRPs are predicted to occur during peak phases of LWTI with value larger than 1 for three or more consecutive forecast days. Hindcast experiments for EHRPs in 2015–2016 indicate that EHRPs can be predicted 8–24 d in advance, with an average period of validity of 16.7 d.     

Comparison of intra-seasonal forecast of Indian summer monsoon between two versions of NCEP coupled models

The real-time forecasting of monsoon activity over India on extended range time scale (about 3 weeks) is analyzed for the monsoon season of 2012 during June to September (JJAS) by using the outputs from latest (CFSv2 [Climate Forecast System version 2]) and previous version (CFSv1 [Climate Forecast System version 1]) of NCEP coupled modeling system. The skill of monsoon rainfall forecast is found to be much better in CFSv2 than CFSv1. For the country as a whole the correlation coefficient (CC) between weekly observed and forecast rainfall departure was found to be statistically significant (99 % level) at least for 2 weeks (up to 18 days) and also having positive CC during week 3 (days 19–25) in CFSv2. The other skill scores like the mean absolute error (MAE) and the root mean square error (RMSE) also had better performance in CFSv2 compared to that of CFSv1. Over the four homogeneous regions of India the forecast skill is found to be better in CFSv2 with almost all four regions with CC significant at 95 % level up to 2 weeks, whereas the CFSv1 forecast had significant CC only over northwest India during week 1 (days 5–11) forecast. The improvement in CFSv2 was very prominent over central India and northwest India compared to other two regions. On the meteorological subdivision level (India is divided into 36 meteorological subdivisions) the percentage of correct category forecast was found to be much higher than the climatology normal forecast in CFSv2 as well as in CFSv1, with CFSv2 being 8–10 % higher in the category of correct to partially correct (one category out) forecast compared to that in CFSv1. Thus, it is concluded that the latest version of CFS coupled model has higher skill in predicting Indian monsoon rainfall on extended range time scale up to about 25 days.     

Multi-model MJO forecasting during DYNAMO/CINDY period

The present study assesses the forecast skill of the Madden–Julian Oscillation (MJO) observed during the period of DYNAMO (Dynamics of the MJO)/CINDY (Cooperative Indian Ocean Experiment on Intraseasonal Variability in Year 2011) field campaign in the GFS (NCEP Global Forecast System), CFSv2 (NCEP Climate Forecast System version 2) and UH (University of Hawaii) models, and revealed their strength and weakness in forecasting initiation and propagation of the MJO. Overall, the models forecast better the successive MJO which follows the preceding event than that with no preceding event (primary MJO). The common modeling problems include too slow eastward propagation, the Maritime Continent barrier and weak intensity. The forecasting skills of MJO major modes reach 13, 25 and 28 days, respectively, in the GFS atmosphere-only model, the CFSv2 and UH coupled models. An equal-weighted multi-model ensemble with the CFSv2 and UH models reaches 36 days. Air–sea coupling plays an important role for initiation and propagation of the MJO and largely accounts for the skill difference between the GFS and CFSv2. A series of forecasting experiments by forcing UH model with persistent, forecasted and observed daily SST further demonstrate that: (1) air–sea coupling extends MJO skill by about 1 week; (2) atmosphere-only forecasts driven by forecasted daily SST have a similar skill as the coupled forecasts, which suggests that if the high-resolution GFS is forced with CFSv2 forecasted daily SST, its forecast skill can be much higher than its current level as forced with persistent SST; (3) atmosphere-only forecasts driven by observed daily SST reaches beyond 40 days. It is also found that the MJO–TC (Tropical Cyclone) interactions have been much better represented in the UH and CFSv2 models than that in the GFS model. Both the CFSv2 and UH coupled models reasonably well capture the development of westerly wind bursts associated with November 2011 MJO and the cyclogenesis of TC05A in the Indian Ocean with a lead time of 2 weeks. However, the high-resolution GFS atmosphere-only model fails to reproduce the November MJO and the genesis of TC05A at 2 weeks’ lead. This result highlights the necessity to get MJO right in order to ensure skillful extended-range TC forecasting.     

Evaluation of summer temperature and precipitation predictions from NCEP CFSv2 retrospective forecast over China

National Centers for Environmental Prediction recently upgraded its operational seasonal forecast system to the fully coupled climate modeling system referred to as CFSv2. CFSv2 has been used to make seasonal climate forecast retrospectively between 1982 and 2009 before it became operational. In this study, we evaluate the model’s ability to predict the summer temperature and precipitation over China using the 120 9-month reforecast runs initialized between January 1 and May 26 during each year of the reforecast period. These 120 reforecast runs are evaluated as an ensemble forecast using both deterministic and probabilistic metrics. The overall forecast skill for summer temperature is high while that for summer precipitation is much lower. The ensemble mean reforecasts have reduced spatial variability of the climatology. For temperature, the reforecast bias is lead time-dependent, i.e., reforecast JJA temperature become warmer when lead time is shorter. The lead time dependent bias suggests that the initial condition of temperature is somehow biased towards a warmer condition. CFSv2 is able to predict the summer temperature anomaly in China, although there is an obvious upward trend in both the observation and the reforecast. Forecasts of summer precipitation with dynamical models like CFSv2 at the seasonal time scale and a catchment scale still remain challenge, so it is necessary to improve the model physics and parameterizations for better prediction of Asian monsoon rainfall. The probabilistic skills of temperature and precipitation are quite limited. Only the spatially averaged quantities such as averaged summer temperature over the Northeast China of CFSv2 show higher forecast skill, of which is able to discriminate between event and non-event for three categorical forecasts. The potential forecast skill shows that the above and below normal events can be better forecasted than normal events. Although the shorter the forecast lead time is, the higher deterministic prediction skill appears, the probabilistic prediction skill does not increase with decreased lead time. The ensemble size does not play a significant role in affecting the overall probabilistic forecast skill although adding more members improves the probabilistic forecast skill slightly.     

Extended-Range Forecasts of the Principal 20–30-Day Oscillation of the Circulation over East Asia During the Summer of 2002

Daily 850-hPa meridional wind fields in East Asia from March to September 2002 were used to establish a model of the principal oscillation pattern (POP). This model was then used to conduct independent extended-range forecasts of the principal temporal and spatial variations in the low-frequency meridional wind field on a time scale of 20-30 days. These variations affect the occurrence of heavy precipitation events in the lower reaches of the Yangtze River valley (LYRV). The results of 135 forecast experiments during the summer half year show that the predicted and observed anomalies are strongly correlated at a lead time of 20 days (mean correlation greater than 0.50). This strong correlation indicates that the model is capable of accurately forecasting the low-frequency variations in meridional wind that corresponded to the 3 heavy precipitation events in the LYRV during the summer of 2002. Further forecast experiments based on data from multiple years with significant 20-30-day oscillations show that these prediction modes are effective tools for forecasting the space-time evolution of the low-frequency circulation. These findings offer potential for improving the accuracy of forecasts of heavy precipitation over the LYRV at lead times of 3-4 weeks.     

Prediction of Wintertime Northern Hemisphere Blocking by the NCEP Climate Forecast System

Daily output from the hindcasts by the NCEP Climate Forecast System version 2(CFSv2) is analyzed to understand CFSv2's skill in forecasting wintertime atmospheric blocking in the Northern Hemisphere.Prediction skills of sector blocking,sector-blocking episodes,and blocking onset/decay are assessed with a focus on the Euro-Atlantic sector(20°W-45°E) and the Pacific sector(160°E-135°W).Features of associated circulation and climate patterns are also examined.The CFSv2 well captures the observed features of longitudinal distribution of blocking activity,but underestimates blocking frequency and intensity and shows a decreasing trend in blocking frequency with increasing forecast lead time.Within 14-day lead time,the Euro-Atlantic sector blocking receives a higher skill than the Pacific sector blocking.Skillful forecast(taking the hit rate of 50%as a criterion) can be obtained up to 9 days in the Euro-Atlantic sector,which is slightly longer than that in the Pacific sector(7 days).The forecast skill of sector-blocking episodes is slightly lower than that of sector blocking in both sectors,and it is slightly higher in the Euro-Atlantic sector than in the Pacific sector.Compared to block onset,the skill for block decay is lower in the Euro-Atlantic sector,slightly higher in the Pacific sector during the early three days but lower after three days in lead time.In both the Euro-Atlantic and the Pacific sectors,a local dipole pattern in 500-hPa geopotential height associated with blocking is well presented in the CFSv2 prediction,but the wave-train like pattern that is far away from the blocking sector can only maintain in the forecast of relative short lead time.The CFSv2 well reproduces the observed characteristics of local temperature and precipitation anomalies associated with blocking.     

Prediction of global patterns of dominant quasi-biweekly oscillation by the NCEP Climate Forecast System version 2

Daily output from the hindcasts by the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2) is analyzed to understand the skill of forecasting atmospheric variability on quasi-biweekly (QBW) time scale. Eight dominant quasi-biweekly oscillation (QBWO) modes identified by the extended empirical orthogonal function analysis are focused. In the CFSv2, QBW variability exhibits a significant weakening tendency with lead time for all seasons. For most QBWO modes, the variance drops to only 50 % of the initial value at lead time of 11–15 days. QBW variability has better prediction skill in the winter hemisphere than in the summer hemisphere. Skillful forecast can reach about 10–15 days for most modes but those in the winter hemisphere have better forecast skills. Among the eight QBWO modes, the North Pacific mode and the South Pacific (SP) mode have the highest forecast skills while the Asia–Pacific mode and the Central American mode have the lowest skills. For the Asia–Pacific and Central American modes, the forecasted QBWO phase shows an obvious eastward shift with increase in lead time compared to observations, indicating a smaller propagating speed. However, the predicted feature for the SP mode is more realistic. Air–sea coupling on the QBW time scale is perhaps responsible for the different prediction skills for different QBWO modes. In addition, most QBWO modes have better forecasting skills in El Niño years than in La Niña years. Different dynamical mechanisms for various QBWO modes may be partially responsible for the differences in prediction skill among different QBWO modes.