深圳单点气温的时间滞后集合预报研究

利用欧洲中心TL799 L91模式在深圳的单点气温预报数据和深圳本地的气温观测数据,对比分析了时间滞后集合预报方法与传统确定性预报的预报能力.结果表明:(1)TL799 L91 模式提供的确定性预报对于深圳单点气温有一定预报能力,它的预报误差总体上有随预报提前量增加而增加的趋势,但最近时次的确定性预报并不一定是最佳的预报;(2)时间滞后集合预报总体上优于确定性预报,而且参与集合的成员数量越多则预报准确率大体上越高.所以,时间滞后集合预报可在一定程度上改进单点气温的预报质量,是充分利用更早起报时刻数值预报价值的一种有效途径.     

太平洋年代际振荡的年代际预测方法

太平洋年代际振荡（PDO）是北太平洋海表温度年代际变率的主模态。由于太平洋年代际振荡对区域乃至全球气候的显著影响,其合理的预测结果可以带来多方面收益。然而,针对太平洋年代际振荡及其有关的海表温度的年代际预测,目前气候模式的预测水平还十分有限,因此,提出了一个新的增量方法。一系列的验证结果表明,增量方法可以有效预测太平洋年代际振荡,其中包括成功预测出其振荡的年代际转折。增量方法的预测过程主要包括3个步骤:（1）采用5 a滑动平均得到太平洋年代际振荡的年代际变率;（2）利用3 a增量形式的预测因子构建预测模型,预测3 a增量的太平洋年代际振荡（DI_PDO）;（3）将预测得到的DI_PDO加上3 a前的观测PDO,得到最后预测的PDO。增量方法亦可以应用到气候系统年代际内部变率的其他模态（如:北大西洋年代际振荡）和其他气候变量的年代际预测（如:海表温度）。      

四个耦合模式ENSO后报试验的“春季预报障碍”

用CliPAS计划中3个气候模式和中国科学院大气物理研究所耦合模式FGOALS-g短期气候异常回报试验结果,将动力和统计方法相结合,考察了1982—2003年厄尔尼诺/拉尼娜事件发展期和衰减期海表温度春季可预报性障碍现象。结果表明,所考察的耦合模式对ENSO事件预报的误差发展存在明显的季节依赖性,最大误差增长通常发生在春季,发生显著的可预报性障碍现象。进一步分析发现厄尔尼诺事件和拉尼娜事件在发展期的季节预报障碍现象比衰减期明显,以厄尔尼诺事件发展期春季可预报性障碍现象最为显著,拉尼娜事件衰减期季节预报障碍现象不显著。研究还发现,预报误差的增长在ENSO事件冷暖位相具有显著的非对称性,发展期暖位相预报误差强于冷位相,而衰减期冷位相的预报误差比暖位相大。通过回归分析,诊断了海-气相互作用的强度,发现耦合系统在春季最不稳定,使预报误差最易在春季发展,从而导致可预报性障碍。     

11个S2S模式对MJO预报效果的评估分析

利用次季节一季节预报研究计划(Subseasonal to Seasonal Prediction Project,S2S)的多模式产品集,系统评估了产品集中11个模式对MJO的实际预报技巧.如果以距平相关系数ACC为0.5作为有效预报技巧的阈值,S2S各模式的MJO实际预报时效为8～32 d.S2S各模式预报普遍低估...     

国内外雾预报技术研究进展

大雾是引起低能见度的主要天气现象.提高雾的预报技术水平是确保交通安全的重要措施.从统计和数值预报两个方面,回顾了过去几十年来国内外在雾预报技术上的主要研究进展,并总结了各种方法的特点及存在的缺陷.在某些情形下,新统计方法的应用提高了雾的预报准确率,但仍然无法摆脱统计方法本身的缺陷.相比较而言,数值模式在大雾预报方面具有更广泛的应用和更大的潜力.在目前的计算机水平下,使用高分辨率的一维雾模式与中尺度天气模式相结合的方法,在一定程度上可以提高雾的预报准确率,该方法在大雾易发区的机场及高速公路沿线具有重要的应用价值.随着计算机能力的不断提高,包含大雾形成和演变的各种复杂过程、具有先进资料同化过程的高分辨率三维雾模式,以及集合数值预报系统将是未来的发展方向.     

Drought prediction

Drought prediction has been an age-old problem, but in more recent times the magnitude of the Sahelian drought has brought into focus the need to improve the techniques for predicting such droughts with some measure of accuracy. Prom the present state of knowledge, drought prediction is difficult, if not impossible. Two major approaches appear to be prominent in the search for appropriate techniques. These include the use of teleconnections and the development of numerical models.Essentially, time-lagged teleconnections rely on the assumption that changes induced in any one area of the world may produce changes elsewhere on a world-wide scale, though time-lagged. The 1972-73 El Nino off the west coast of South America is a good case in point. The development of numerical models which allow the incorporation of not only climatic factors but also man's impact on the natural environment, offers some ray of hope that the general circulation models (GCMs) may help to improve the techniques of drought prediction.     

中国气候预测研究与业务发展的回顾

天气预报是指一周内至两周时间尺度的气象预报,而月季及以上时间尺度的预报则属于气候预测范畴。中国的气候预测起步很早,无论在研究工作中还是在业务应用上都取得了显著成就。文中扼要回顾了这些研究和业务发展成就,重点包括:对于季风和梅雨、寒潮的早期认知和后期研究发现、早期气候预测业务发展概况、动力气候预测的早期探索、动力-统计气候预测方法的研制和应用、气候预测模式的发展以及初始化和多模式集合预测、东亚气候系统变异的全方位探索、气候预测范畴的不断拓展和气候预测研究的不断创新。也对未来气候预测研究和业务发展提出了几个重大挑战性课题,涉及不同时间尺度气候变异过程之间的相互作用、季节内至年代际气候预测、气候系统模式及初始化、动力-统计相结合的气候预测方法等方面。      

从东北玉米冷害预测模型展望农业气象灾害预测技术的发展

以东北玉米冷害预测为例,介绍了近年发展的基于热量指数预测的热量年型统计预测模型和基于玉米生长模式的机理性预测模型,比较了两类模型在预报原理、预报对象、预报时效及准确率等方面的各自差别和利弊,探讨了两类模型取长补短优势互补的可能方法,展望了农业气象灾害预测预警技术发展的若干问题。     

Monthly prediction of tropical cyclone activity over the South China Sea using the FGOALS-f2 ensemble prediction system

本研究基于新一代FGOALS-f2动力集合预测系统35年(1981-2015年)的热带气旋历史回报试验对南海台风季(7-11月)热带气旋活动超前10天的月预测技巧进行评估,并对2020年南海台风季热带气旋活动进行了实时月预测尝试.结果表明:FGOALS-f2能较好地预测南海热带气旋路径密度演变特征,预测的热带气旋生成个...     

台风路径集合预报试验

基于T63L9模式,利用BGM法进行了台风路径的集合预报试验,并对集合预报中的一个关键技术—繁殖长度进行了研究。结果表明:相对于控制预报,利用增长模繁殖法制作的集合预报对台风路径预报的技巧水平有了很大提高。集合预报中采用不同的繁殖长度对台风路径预报效果有一定影响。繁殖长度取2 d和3 d集合预报效果相对于控制预报都有很大提高。繁殖长度取为2 d时,集合离散度较小,取为3 d时较合理,取为4 d时,离散度较大。对台风路径的预报采用集合选择平均后,繁殖2 d和繁殖3 d的集合预报系统的预报效果都优于控制预报。从综合集合预报效果分析,繁殖长度取为3 d更合适。当集合预报中繁殖长度取为3 d时,在预报的前3天,台风路径集合预报误差基本维持在100 km,第4天误差也只接近300 km。相对于控制预报,集合预报的改进率在预报的2～4 d基本超过了60%,甚至可达到70%。     

MJO prediction in the NCEP Climate Forecast System version 2

The Madden–Julian Oscillation (MJO) is the primary mode of tropical intraseasonal climate variability and has significant modulation of global climate variations and attendant societal impacts. Advancing prediction of the MJO using state of the art observational data and modeling systems is thus a necessary goal for improving global intraseasonal climate prediction. MJO prediction is assessed in the NOAA Climate Forecast System version 2 (CFSv2) based on its hindcasts initialized daily for 1999–2010. The analysis focuses on MJO indices taken as the principal components of the two leading EOFs of combined 15°S–15°N average of 200-hPa zonal wind, 850-hPa zonal wind and outgoing longwave radiation at the top of the atmosphere. The CFSv2 has useful MJO prediction skill out to 20 days at which the bivariate anomaly correlation coefficient (ACC) drops to 0.5 and root-mean-square error (RMSE) increases to the level of the prediction with climatology. The prediction skill also shows a seasonal variation with the lowest ACC during the boreal summer and highest ACC during boreal winter. The prediction skills are evaluated according to the target as well as initial phases. Within the lead time of 10 days the ACC is generally greater than 0.8 and RMSE is less than 1 for all initial and target phases. At longer lead time, the model shows lower skills for predicting enhanced convection over the Maritime Continent and from the eastern Pacific to western Indian Ocean. The prediction skills are relatively higher for target phases when enhanced convection is in the central Indian Ocean and the central Pacific. While the MJO prediction skills are improved in CFSv2 compared to its previous version, systematic errors still exist in the CFSv2 in the maintenance and propagation of the MJO including (1) the MJO amplitude in the CFSv2 drops dramatically at the beginning of the prediction and remains weaker than the observed during the target period and (2) the propagation in the CFSv2 is too slow. Reducing these errors will be necessary for further improvement of the MJO prediction.     

人工智能技术气候预测应用简介

近年来,随着人工智能技术在多个领域大数据分析中的应用,许多研究工作者尝试将地学研究与人工智能跨学科结合,取得了很多新的进展,推动了地球科学的发展。其中气候预测与人类生活以及防灾减灾等息息相关,准确的气候预测至关重要。本文简要总结了人工智能技术在气候预测应用方面的研究进展,包括资料同化、模式参数化、求解偏微分方程、构建统计预测模型、改进数值模式产品释用等领域。这些研究证明了利用人工智能提高气候预测技巧的可能性和适用性,可以极大地节省计算成本和时间。然而人工智能应用也存在诸多挑战,例如数据集的构建、模型的适用性和物理可解释性等问题,对这些难点问题的研究和攻克,可以让人工智能在大数据时代中更好地补充传统地球科学方法,产生更多有益的效应,极大地改进气候预测水平。     

基于小波分析的客观预报方法在智能网格高低温预报中的应用

基于2017—2018年中国气象局高分辨率数值预报产品、甘肃实时城镇预报产品和国家级地面观测站数据,利用小波分析、滑动训练、最优融合等技术,研发出甘肃省智能网格高低温客观订正产品。检验分析表明:城镇预报产品、滑动训练订正产品、最优融合产品3种订正产品对CMA预报均有订正能力,3种客观订正产品的最高气温订正能力强于最低气温订正能力;滑动训练法与最优融合法产生的高低温订正产品,在系统误差明显地区(甘南、陇南等)的预报结果要好于模式客观预报,而高低温城镇预报产品在气温局地性强或者模式客观预报能力差的区域有优势;最优融合预报方法生成的高低温产品预报能力略高于滑动训练订正产品且与现有预报员制作城镇预报产品基本持平,初步具备了替代主观预报的能力。     

酒泉地区区域性短期霜冻预报的着眼点

霜冻是酒泉地区主要的气象灾害之一。本文通过对环流分型、预报指标筛选、预报量值确定三个方面的探索 ,提出了短期霜冻预报的着眼点 ,由此建立的预报模式 ,使短期霜冻预报质量明显提高     

Examination of multi-perturbation methods for ensemble prediction of the MJO during boreal summer

The impact of initialization and perturbation methods on the ensemble prediction of the boreal summer intraseasonal oscillation was investigated using 20-year hindcast predictions of a coupled general circulation model. The three perturbation methods used in the present study are the lagged-averaged forecast (LAF) method, the breeding method, and the empirical singular vector (ESV) method. Hindcast experiments were performed with a prediction interval of 10 days for extended boreal summer (May–October) seasons over a 20 year period. The empirical orthogonal function (EOF) eigenvectors of the initial perturbations depend on the individual perturbation method used. The leading EOF eigenvectors of the LAF perturbations exhibit large variances in the extratropics. Bred vectors with a breeding interval of 3 days represent the local unstable mode moving northward and eastward over the Indian and western Pacific region, and the leading EOF modes of the ESV perturbations represent planetary-scale eastward moving perturbations over the tropics. By combining the three perturbation methods, a multi-perturbation (MP) ensemble prediction system for the intraseasonal time scale was constructed, and the effectiveness of the MP prediction system for the Madden and Julian oscillation (MJO) prediction was examined in the present study. The MJO prediction skills of the individual perturbation methods are all similar; however, the MP‐based prediction has a higher level of correlation skill for predicting the real-time multivariate MJO indices compared to those of the other individual perturbation methods. The predictability of the intraseasonal oscillation is sensitive to the MJO amplitude and to the location of the dominant convective anomaly in the initial state. The improvement in the skill of the MP prediction system is more effective during periods of weak MJO activity.     

季度预测研究回顾

对月平均大气环流预报试验、季度预测和中国汛期降水预测进行了总结。结果表明气候预测的对象必须是要素的时间平均场。利用数值模拟进行气候预测是今后的主要发展方向,而季度预测技巧的提高依赖于对物理参数化和物理机制的研究。最后,讨论了季平均气温和季总降水的可预报性问题,即时效性和准确率。     

Comparison of prediction performance using statistical postprocessing methods

As the 2018 Winter Olympics are to be held in Pyeongchang, both general weather information on Pyeongchang and specific weather information on this region, which can affect game operation and athletic performance, are required. An ensemble prediction system has been applied to provide more accurate weather information, but it has bias and dispersion due to the limitations and uncertainty of its model. In this study, homogeneous and nonhomogeneous regression models as well as Bayesian model averaging (BMA) were used to reduce the bias and dispersion existing in ensemble prediction and to provide probabilistic forecast. Prior to applying the prediction methods, reliability of the ensemble forecasts was tested by using a rank histogram and a residualquantile-quantile plot to identify the ensemble forecasts and the corresponding verifications. The ensemble forecasts had a consistent positive bias, indicating over-forecasting, and were under-dispersed. To correct such biases, statistical post-processing methods were applied using fixed and sliding windows. The prediction skills of methods were compared by using the mean absolute error, root mean square error, continuous ranked probability score, and continuous ranked probability skill score. Under the fixed window, BMA exhibited better prediction skill than the other methods in most observation station. Under the sliding window, on the other hand, homogeneous and non-homogeneous regression models with positive regression coefficients exhibited better prediction skill than BMA. In particular, the homogeneous regression model with positive regression coefficients exhibited the best prediction skill.     

Validation of the experimental hindcasts produced by the GloSea4 seasonal prediction system

Using 14 year (1996–2009) ensemble hindcast runs produced with the Global Seasonal Forecasting System version 4 (GloSea4), this study evaluates the spatial and temporal structure of the hindcast climatology and the prediction skill of major climate variability. A special focus is on the fidelity of the system to reproduce and to forecast phenomena that are closely related to the East Asian climate. Overall the GloSea4 system exhibits realistic representations of the basic climate even though a few model deficiencies are identified in the sea surface temperature and precipitation. In particular, the capability of GloSea4 to capture the seasonal migration of rain belt associated with Changma implies a good potential for the Asian summer monsoon prediction. It is found that GloSea4 is as skillful as other state-of-the-art seasonal prediction systems in forecasting climate variability including the El-Nino/southern oscillation (ENSO), the East Asian summer monsoon, the Arctic Oscillation (AO), and the Madden-Julian Oscillation (MJO). The results presented in this study will provide benchmark evaluation for next seasonal prediction systems to be developed at the Korea Meteorological Administration.     

Soil moisture prediction over the Australian continent

Summary This paper describes an attempt to model soil moisture over the Australian continent with an integrated system of dynamic models and a Geographic Information System (GIS) data base. A land surface scheme with improved treatment of soil hydrological processes is described. The non-linear relationships between soil hydraulic conductivity, matric potential and soil moisture are derived from the Broadbridge and White soil model. For a single location, the prediction of the scheme is in good agreement with the measurements of the Hydrological and Atmospheric Pilot Experiment (HAPEX). High resolution atmospheric and geographic data are used in soil moisture prediction over the Australian continent. The importance of reliable land surface parameters is emphasized and details are given for deriving the parameters from a GIS. Predicted soil moisture patterns over the Australian continent in summer, with a 50 km spatial resolution, are found to be closely related to the distribution of soil types, apart from isolated areas and times under the influence of precipitation. This is consistent with the notion that the Australian continent in summer is generally under water stress. In contrast, predicted soil temperatures are more closely related to radiation patterns and changes in atmospheric circulation. The simulation can provide details of soil moisture evolution both in space and time, that are very useful for studies of land use sustainability, such as plant growth modelling and soil erosion prediction.With 12 Figures