数字滤波方法在月尺度数值预报中的应用 I. 初始场滤波的作用

本工作将数字滤波方法用于T42L9谱模式的月预报实验。为了去除高频扰动误差的影响，保留对月预报有意义且可预报性较强的低频过程，用滤波器对观测资料的序列进行处理，得到10天以上的低频分量作为初始场进行预报。结果说明，与不进行滤波的控制实验相比，误差增长明显减慢，预报效果在10～20天时段内提高最显著。进一步分析表明，延伸预报效果随时间的变化主要是由其中低频分量的预报效果决定的。初始场滤波后预报效果的提高也主要是由于其低频分量报得更好，而在此低频背景下新产生出来的高频扰动误差也相应较小，因而总体的预报效果好于控制实验。而如果在控制实验预报后提取低频分量进行检验，则对逐日预报的改进不明显。经过初始场滤波后对1～10天至1～30天平均场的预报也有较显著的提高。对旬平均预报改进最大的是在第二个10天。对500 hPa环流形势的预报也更接近实际。本工作的结果说明，在中长期预报中如果能用适当的方法提取出低频过程的信息，则可望提高预报水平。     

数字滤波方法在月尺度数值预报中的应用：II.保留不同低频过程的效果比较

用数字滤波方法对观测资料序列进行处理，得到初始场用于Ｔ４２Ｌ９全球谱模式的月预报，以去除误差增长较快的高频扰动对低频过程的影响，并且利用多时刻的观测资料提取低频过程的信息。对冬季和夏季两个不同个例进行了实例，并比较了取不同长度的观测序列，截取不同周期的过程作为初值对预报效果的影响。结果说明，经过滤波后对低频分量和平均场的预报都有显著的改进。而且对于较长时效的预报，应保留更低频的过程（比如１０ｄ以上     

数字滤波方法在月尺度数值预报中的应用 II. 保留不同低频过程的效果比较

用数字滤波方法对观测资料序列进行处理，得到初始场用于T42L9全球谱模式的月预报，以去除误差增长较快的高频扰动对低频过程的影响，并且利用多时刻的观测资料提取低频过程的信息。对冬季和夏季两个不同个例进行了实验，并比较了取不同长度的观测序列，截取不同周期的过程作为初值对预报效果的影响。结果说明，经过滤波后对低频分量和平均场的预报都有较显著的改进。而且对于较长时效的预报，应保留更低频的过程（比如10 d以上周期）。最显著的改进是在第2旬。冬季个例经过滤波的初始场在第2旬对北太平洋阻塞形势的预报能力有较明显提高。     

基于Lorenz系统提取数值模式可预报分量的初步试验

针对数值预报模式中存在的非线性混沌特性, 从提取可预报分量的思路出发, 阐述了在数值模式中提取可预报分量的方法, 并利用Lorenz系统进行了相关数值试验。研究发现, Lorenz系统初始误差在相空间中的增长速度是不同的, 某些方向的误差增长速度较慢, 即存在对初值扰动不敏感、相对稳定的可预报分量。根据数值模式切线性误差算子的特征值演化规律, 提取出数值模式的可预报分量, 并将模式变量在其基底上进行投影变换, 建立了可预报分量数值模式。在此基础上, 研究了Lorenz系统的混沌状态、模式参数误差及外部随机噪声对提取可预报分量的影响, 发现基于可预报分量的数值模式, 具有更好的预报技巧。     

基于动力降尺度的区域集合预报初值扰动构建方法研究

利用全球集合预报系统资料(Global Ensemble Forecast System,GEFS),基于WRF中尺度模式构建了区域集合预报系统,区域集合初值的构建采用两种方案,一种是GEFS全球集合预报初值场直接动力降尺度(称为DOWN集合),另一种是提取GEFS全球集合降尺度后的扰动场,并叠加到区域数值预报系统(北京快速更新循环数值预报系统:Beijing Rapid Update Cycle System,BJ-RUC)分析场上构建集合初值场(称为D-RUC集合)。进行了批量试验,通过对比发现D-RUC集合的中小尺度扰动增长优于DOWN集合,而大尺度扰动分量的增长两者相当,说明与高分辨率分析场叠加可以促进动力降尺度扰动的中小尺度扰动分量的增长。集合预报扰动准确性检验结果显示,短预报时效内DOWN集合扰动明显低估了预报误差,在预报误差较大的位置扰动较小,而D-RUC集合能够更好地识别预报场中哪些位置预报误差较大,而哪些位置预报误差较小。集合预报检验结果表明,D-RUC方法能显著改善短时效预报效果,集合离散度有所增加、均方根误差有所减少,概率预报评分显示D-RUC集合比DOWN集合在短预报时效占优。降水个例分析结果表明D-RUC方法能显著改善短时效内的降水概率预报效果。     

用数字滤波方法进行数值模式的初始化

将数字滤波原理用于Ｔ４２Ｌ９全球谱模式的初始化。选取两个初值进行了实验。通过对模式短时间积分得到的时间序列进行滤波处理，有效地滤去了初始场中的高频重力波振荡，保留了其中有天气意义的扰动，同时对分析场的改变很小。５ｄ预报的结果说明，经过初始化后的预报较为平稳，场较为光滑。对比实验表明，绝热和非绝热初始化的效果很接近。这种方法简便有效，是一种实用的初值化方法     

运用经验正交函数分解建模制作麦播期降水定量预报

针对鹤壁市麦播期（９月２１日￣１０月３１日）降水量的高频振荡性，利用均值生成函数的延柘函数构成的序列，提取时间函数，作为主分量分析方法的时间函数场，制作鹤壁市麦播期降水定量预报方程并进行试报。     

距平模式季预报初始场滤波及时间过滤方案的试验研究

本文用地气耦合非定常距平模式对1980～1982年及1986～1988年共6年的个例进行了距平场的季（3～6月）预报试验。通过两种方法,一是在时间积分过程中改变时间过滤公式中的过滤系数β（分别取为0.05和0.1）;二是对初始场进行滤波和不滤波的比较试验来考察高频扰动及非行星波对距平场月、季预报的影响。试验表明,行星波和大气长波对距平场的月和季演变有不同的作用。长波对月以内的过程作用较大,滤掉长波使预报效果变差。而长波对季以上过程的作用较小,甚至有某种干扰性的“噪声”作用,滤掉长波使季预报的效果得到改进。     

GRAPES全球集合预报的集合卡尔曼变换初始扰动方案初步研究

初始扰动方案是集合预报研究的核心问题之一.文中根据最新提出的基于集合卡尔曼变换(ETKF)理论的集合初始扰动方案,利用模拟观测系统及其调整的观测误差与放大因子的方案,研究发展了针对中国GRAPES全球预报系统的集合初始扰动方案,建立了GRAPES全球集合预报系统.利用14个集合成员进行了近两个月的集合预报试验,重点研究了初始扰动的结构特征、扰动振幅以及扰动增长特征,分析了集合扰动初始场的质量与性能.初步试验结果表明,基于ETKF初始扰动方案的GRAPES全球集合初始扰动能够较好地反映分析误差方差的主要模态结构和扰动振幅,并具有比较合理的集合离散度.分析误差方差能够准确地反应模拟观测资料的空间分布特征.初始扰动方差近似等于预报误差方差,并对全球观测系统的空间变化具有准确的响应.集合扰动具有合适的增长率,在96 h的预报时效内可以有效地保持适当的集合离散度.52 d集合预报统计分析显示,北半球集合平均的预报质量评分相对于控制预报具有较明显的优势,副热带高压特征线的个例预报也表明GRAPES全球集合预报在短期预报时效内具有很好的预报效果.基于ETKF初始扰动方案的GRAPES全球集合预报系统显示出良好的发展前景和应用潜力.     

观测误差对GRAPES区域集合预报影响的敏感性试验

通过设计3组不同的观测误差均方差,对2012年8月1日—29日进行了基于GRAPES-M EPS(Global/Regional Assimilation and Prediction System-Mesoscale Ensemble Prediction System)的集合预报敏感性试验,研究观测误差均方差对集合预报初始扰动场结构、扰动量及垂直扰动总能量发展的影响,评估集合预报结果的差异,并分析了一次典型的江淮流域强降水个例。结果显示,模式变量扰动结构和扰动振幅对观测误差均方差较敏感,较小的观测误差均方差使得温度和风等模式变量的初始扰动量增大,扰动总能量增长更快,降水集合预报效果更优。因此在GRAPES-MEPS中,可以考虑对观测误差均方差进行适当的扰动,以体现观测误差均方差的不确定性对集合预报的影响,提高GRAPES-MEPS的集合预报技巧。     

基于集合预报产品的降尺度降水预报试验

利用降水距平百分率的降尺度预报方法和1951-2008 NCEP资料及我国降水资料,建立了降水距平百分率的预报模型,基于T106L19模式的月动力延伸集合预报结果,进行了2007-2009年3 a的预报试验和效果检验.结果表明,基于集合预报产品的统计降尺度方法对降水距平百分率的预报技巧高于模式降水的预报技巧;500 hPa月平均高度场的预报技巧直接影响到降水距平百分率的预报技巧,平均环流的预报技巧越高,降水距平百分率的预报技巧越高;无论集合成员数为多少,集合预报的结果都明显优于控制预报,随着集合成员数的增多,预报技巧呈增大的趋势;我国降水具有显著的季节性和区域性,以江淮地区的降水距平百分率预报技巧最高,华南地区的预报技巧其次.     

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.     

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.     

基于BGM的不同繁殖长度对集合预报的影响

利用T63L9全球谱模式和NCEP/NCAR再分析资料, 对BGM方法中增长模的繁殖长度对集合预报效果的影响进行研究。结果表明:与控制预报相比, 不同繁殖长度的集合预报都能使预报效果得到一定程度的改进, 特别是第4天预报以后, 改进程度随预报时效而稳步提高。三组不同繁殖长度的集合预报对控制预报的改进存在差别, 分析结果表明:繁殖长度为2 d的集合预报明显效果最差, 而繁殖3 d和4 d的集合预报差别并不明显。对集合Talagrand分布以及离散度的初步分析表明, 繁殖长度取为3 d似乎最为合理。     

两个集合预报系统对秦岭及周边降水预报性能对比

利用欧洲中期天气预报中心 (ECMWF)、美国大气环境预报中心 (NCEP) 集合预报系统 (EPS) 降水量预报资料,CMORPH (NOAA Climate Prediction Center Morphing Method) 卫星与全国3万个自动气象站降水量融合资料,基于技巧评分、ROC (relative operating characteristic) 分析等方法,对比两个集合预报系统对秦岭及周边地区的降水预报性能。结果表明:两个系统均能较好表现降水量的空间形态,对于不同量级降水,ECMWF集合预报系统0~240 h控制及扰动预报优于NCEP集合预报系统,但NCEP集合预报系统264~360 h预报时效整体表现更好; ECMWF集合预报系统0~120 h大雨集合平均优于NCEP集合预报系统,两个系统集合平均的预报技巧整体低于其控制及扰动成员预报,这种现象ECMWF集合预报系统表现更为显著; ECMWF集合预报系统降水预报概率优于NCEP集合预报系统。ROC分析显示,随着预报概率的增大,ECMWF集合预报系统在命中率略微下降的情况下,显著减小了空报率,NCEP集合预报系统则表现出高空报、高命中率。     

2010年8月环流形势的多模式超级集合预报

利用多模式超级集合预报法,以欧洲中期天气预报中心、日本气象厅、德国气象局、中国气象局和中国空军气象中心共5个决定性7 d预报产品为集合成员,对2010年8月500 hPa高度场和850 hPa温度场分别进行固定训练期和滑动训练期超级集合预报。采用均方根误差和相关系数对超级集合预报、单一模式预报和简单集合平均预报进行对比检验,同时对各预报结果的均方根误差空间分布进行对比分析。结果表明:超级集合预报在所有预报结果中最佳,且滑动集合预报对8月后期时段预报要略好于固定集合预报,两者预报效果均好于参与集合预报的各模式,也好于集合平均预报。但随着预报时效的延长,集合平均预报的优势也随之提升。从预报结果均方根误差的空间分布可知,多模式超级集合预报相比于单一模式预报效果提高的区域,500 hPa位势高度场主要位于印度半岛、印度洋、青藏高原及以西地区,而850 hPa温度场则主要位于蒙古、青藏高原、中国新疆及以西地区。     

Ensemble forecasting of tropical cyclone motion: comparisonbetween regional bred modes and random perturbations

Summary Random perturbations (RPs) and a modified version for breeding of growing modes are used with a regional baroclinic mesoscale model to perform ensemble forecasting of tropical cyclone motion. Based on a sample of six cases, similar conclusions are found as in previous barotropic modeling studies. Even after introducing a larger spatial correlation into the RPs using a multi-quadric analysis scheme, the skill of this ensemble mean track prediction is almost always lower than that of the control forecast in the cases considered. The track prediction performance of the ensemble using regional bred modes (RBMs) as perturbations has a higher average skill. At nearly all forecast intervals except less than 24 h when the initial position error still dominates, the ensemble mean tracks in all six cases are improved over the control forecast. In the 6 h–24 h range, the success rate (ratio of the cases with a forecast improvement to the total number of cases) has a value of 10/24. In the 30 h–48 h range, the success rate increases to 20/24, but drops to 18/24 in the 54 h–72 h range. A relative skill score (RSS) is used to compare the skills of the two perturbation methodologies. It is found that the average RSSs of using RBMs are significantly higher than the corresponding ones of RPs at the 99% confidence level in all three 24-h periods. Note that the above conclusion is only based on ensemble mean forecasts. All of the possibilities from an ensemble-based probabilistic track distribution are not explored in this paper. The ensemble spreads in these RBM ensembles are large enough to include the verifying tracks in all the cases considered. It is also found that the ensemble spread is well correlated with the average error in an ensemble when using RBMs, but not with the ensemble mean forecast error in both methodologies. Received February 7, 2001/Revised April 18, 2001     

华南区域高分辨率数值模式前汛期预报初步评估

基于华南地区自动站逐小时观测资料, 采用传统站点评分、邻域法等评估华南区域高分辨率数值模式(包括GRAPES_GZ_R 1 km模式和GRAPES_GZ 3 km模式)对降水、地面温度和风场等要素的预报能力。结果表明: GRAPES_GZ_R 1 km模式的降水预报技巧优于GRAPES_GZ 3 km模式, 模式预报以正偏差为主。对于不同起报时间的预报, 00时(世界时, 下同)起报的预报效果优于12时。GRAPES_GZ_R 1 km模式的TS评分是GRAPES_GZ 3 km模式的两倍以上, 对不同降水阈值的评分均较高。分数技巧评分(FSS)显示GRAPES_GZ_R 1 km模式6 h累计降水预报在0.1 mm、1 mm及5 mm以上的降水均可达到最低预报技巧尺度, 对所检验降水对象的空间位置把握能力更好。2 m气温和10 m风速检验结果表明两个模式均能较好把握广东省温度的分布特征, GRAPES_GZ_R 1 km模式对2 m气温预报结果优于GRAPES_GZ 3 km模式, 预报绝对误差更小; 两个模式对风速的预报整体偏强, 预报偏差在1~4 m/s之间, 但相比之下GRAPES_GZ 3 km模式在风场预报上表现更好。GRAPES_GZ_R 1 km模式的2 m气温和10 m风速预报偏差随降水过程存在明显波动, 强降水过后温度预报整体偏低, 风速预报偏强, 在模式产品订正、使用等需要考虑模式对主要天气系统的预报情况。总的来说, GRAPES_GZ_R 1 km模式的预报产品具有较好的参考价值。      

不同海温强迫的月动力延伸集合预报试验

利用全球谱模式T106L19和增长模繁殖法（BGM）建立了月动力延伸集合预报系统,基于气候海表面温度（SST）和预测海表面温度,设计了三组集合预报试验,一组为气候SST作为模式下边界条件的集合预报试验（CSST试验）,另一组为预测SST作为模式的下边界条件的集合预报试验（FSST试验）,第三组为前两组试验的集合预报结果之和（AVE30试验）,对两种海温强迫分别进行了48个月的试验,并对预报结果进行了检验和分析。结果表明：相对于单一的控制预报,不管是CSST试验还是FSST试验,利用BGM方法制作的初值集合预报能显著提高月平均环流的预报技巧,集合预报对PNA区域的预报技巧改进显著,特别是预测SST强迫有正的贡献;同时考虑初值和边值不确定性影响的集合预报试验（AVE30试验）,其全球预报技巧不仅高于控制预报,也分别高于FSST试验和CSST试验,这说明要提高月延伸预报技巧,必须同时考虑初值和边值的影响;大气对SST强迫的响应在模式积分10天开始显著,SST对第二旬和第三旬的作用直接影响月平均环流的预报效果,而SST对第二旬和第三旬预报的影响不仅与SST本身变化有关,还与初值有关,不同的初值其作用不同;集合预报对我国夏季月平均温度分布具有较强预报能力,采用预报海温强迫的预报结果,总体上优于气候海温强迫的结果。     

Forecast skill of the tropical intraseasonal oscillation in the NCEP GFS dynamical extended range forecasts

This study examines the forecast performance of tropical intraseasonal oscillation (ISO) in recent dynamical extended range forecast (DERF) experiments conducted with the National Centers for Environmental Prediction (NCEP) Global Forecasting System (GFS) model. The present study extends earlier work by comparing prediction skill of the northern winter ISO (Madden-Julian Oscillation) between the current and earlier experiments. Prediction skill for the northern summer ISO is also investigated. Since the boreal summer ISO exhibits northward propagation as well as eastward propagation along the equator, forecast skill for both components is computed. For the 5-year period from 1 January, 1998 through 31 December, 2002, 30-day forecasts were made once a day. Compared to the previous DERF experiment, the current model has shown some improvements in forecasting the ISO during winter season so that the skillful forecasts (anomaly correlation>0.6) for upper-level zonal wind anomaly extend from the previous shorter-than 5 days out to 7 days lead-time. A similar level of skill is seen for both northward and eastward propagation components during the summer season as in the winter case. Results also show that forecasts from extreme initial states are more skillful than those from null phases for both seasons, extending the skillful range by 3–6 days. For strong ISO convection phases, the GFS model performs better during the summer season than during the winter season. In summer forecasts, large-scale circulation and convection anomalies exhibit northward propagation during the peak phase. In contrast, the GFS model still has difficulties in sustaining ISO variability during the northern winter as in the previous DERF run. That is, the forecast does not maintain the observed eastward propagating signals associated with large-scale circulation; rather the forecast anomalies appear to be stationary at their initial location and decay with time. The NCEP Coupled Forecast System produces daily operational forecasts and its predication skill of the MJO will be reported in the future.