GRAPES全球/区域集合预报系统10年发展回顾及展望

较系统地概述了中国气象局全球/区域集合预报系统及描述模式初值和模式自身不确定性的集合预报扰动技术发展历程,回顾了GRAPES(Global/Regional Assimilation Pr Ediction System)全球集合预报的奇异向量初值扰动方法、GRAPES区域集合预报的集合变换卡尔曼滤波初值扰动方法和多尺度混合初值扰动方法、GRAPES全球/区域集合预报模式不确定性的随机物理过程倾向项扰动方法和动能后向散射随机补偿方法等研究成果,介绍了GRAPES全球/区域集合预报业务系统构建参数设置和预报性能,最后分析了GRAPES全球/区域集合预报中存在的问题,展望了未来发展方向。     

Using analysis state to construct a forecast error covariance matrix in ensemble Kalman filter assimilation

Correctly estimating the forecast error covariance matrix is a key step in any data assimilation scheme. If it is not correctly estimated, the assimilated states could be far from the true states. A popular method to address this problem is error covariance matrix inflation. That is, to multiply the forecast error covariance matrix by an appropriate factor. In this paper, analysis states are used to construct the forecast error covariance matrix and an adaptive estimation procedure associated with the error covariance matrix inflation technique is developed. The proposed assimilation scheme was tested on the Lorenz-96 model and 2D Shallow Water Equation model, both of which are associated with spatially correlated observational systems. The experiments showed that by introducing the proposed structure of the forecast error covariance matrix and applying its adaptive estimation procedure, the assimilation results were further improved.     

Streamflow Modelling: A Primer on Applications,Approaches and Challenges

This article examines the current practice of streamflow modelling, a field under development for over a century. A sample of the wide range of assessment and planning applications of streamflow models is presented. The diversity in the use of these models is mirrored in the diversity of model complexity, and modelling approaches ranging from empirical to physically based and from lumped to fully distributed are described with examples. Predictions derived from hydrological models are subject to many sources of error; these are discussed along with methods for error minimization or anticipation. Model error is generally quantified using an ensemble of forecasts meant to sample the range of predictive uncertainty. This ensemble can be used to generate reliable probabilistic forecasts of hydrological quantities if all sources of error are accounted for. To date, applications of ensemble methods in streamflow forecasting have typically focused on only one or two error sources. A challenge will be to develop ensemble streamflow forecasts that sample a wider range of predictive uncertainty.

[Traduit par la rédaction] Le présent article examine la pratique actuelle en modélisation d’écoulement fluvial, un domaine qui évolue depuis plus d'un siècle. Nous présentons un échantillon de la vaste gamme d'applications d’évaluation et de planification des modèles d’écoulement fluvial. La diversité dans l'utilisation de ces modèles est le reflet de la diversité dans la complexité des modèles, et nous décrivons à l'aide d'exemples les approches de modélisation qui peuvent être empiriques ou basées sur la physique ou encore localisées ou entièrement réparties. Plusieurs sources d'erreur peuvent affecter les prévisions issues des modèles hydrologiques; nous discutons de ces sources d'erreur de même que des méthodes de réduction ou d'anticipation des erreurs. L'erreur du modèle est généralement quantifiée à l'aide d'un ensemble de prévisions servant à échantillonner la grandeur de l'incertitude prévisionnelle. Cet ensemble peut servir à produire des prévisions probabilistes fiables des grandeurs hydrologiques si toutes les sources d'erreur sont prises en compte. Jusqu’à maintenant, les applications des méthodes d'ensemble à la prévision des écoulements fluviaux n'ont généralement tenu compte que d'une ou deux sources d'erreur. Ce sera un défi de mettre au point des prévisions d'ensemble d’écoulement fluvial qui échantillonnent un plus large éventail d'incertitude prévisionnelle.     

基于路径相似的登陆热带气旋降水之动力-统计集合预报模型

数值天气预报(NWP)过去几十年在热带气旋(TC)预报方面的最大进步是越来越准确的路径预报。对于登陆TC降水的预报,目前以数值模式为代表的技术手段预报能力还十分有限。围绕动力-统计结合之方法研究,初步发展了登陆热带气旋降水(LTP)预报的一种新方法:基于路径相似的登陆热带气旋降水之动力统计集合预报(LTP_DSEF)模型。该方法主要分为五步:TC路径预报、相似路径TC识别、其他特征相似性的判别、TC降水集合预报和最佳预报方案选择;涉及两个关键技术:TC降水分离的客观天气图分析法(OSAT)和TC路径相似面积指数(TSAI)。LTP DSEF模型对2012-2016年影响华南地区出现最大日降水量≥100 mm的21个TC的定量降水预报(QPF)试验结果显示,该模型对登陆TC过程降水的预报结果优于动力模式。登陆TC过程降水≥50 mm情况下,建模样本和独立样本平均TS评分均高于动力模式(EC、GFS、T639)相应的最好表现。对LTP_DSEF模型三个最佳方案的参数取值分析显示,起报时刻参数设定为最临近影响时刻即TC对陆地产生降水的前一天12:00 UTC、集合参数取最大值时预报效果稳定趋好。     

华南暴雨区域集合预报中不同同化方案的影响试验研究

基于全球集合预报系统(GEFS)资料,利用WRF中尺度模式及GEFS动力降尺度获取区域集合预报初值场,通过对同化后的分析场进行模式积分实现华南前汛期区域集合预报。对2019年6月10日的一次华南前汛期暴雨过程进行不同同化方案的试验:混合同化(Hybrid)、三维变分(3Dvar)、集合卡尔曼滤波(EnKF)和对比试验(Ctrl)四组试验的对比分析,探讨具有不同背景误差协方差矩阵的同化方案对区域集合预报集合扰动和集合离散随时间演变特征的影响,评估不同试验的降水模拟效果。(1) Hybrid对模式初始场有较好的改善作用,而3DVar和EnKF对初始场的改善作用不明显。(2) 对风场、温度场和湿度场,在前期预报中Hybrid的预报误差小于3DVar和EnKF,在中后期的预报中,3DVar和EnKF的预报误差得到改善,且好于Hybrid。同样,集合扰动能量,Hybrid和Ctrl在前期预报发展好于3DVar和EnKF,而在中后期的预报3DVar和EnKF好于Hybrid和Ctrl。(3) 从24 h累积降水评分中,整体上同化试验好于Ctrl,3DVar和EnKF好于Hybrid,且3DVar对大中雨级别的降水评分较好,而EnKF对暴雨以上级别的降水评分较好。(4) 对于集合统计检验分析,同化试验的AUC值都大于Ctrl的AUC值,24 h累积降水量阈值在10~100 mm的AUC值,3DVar最好;而125 mm阈值的AUC值,EnKF最好。      

Application of Partial Least Squares Regression in Multimodal Integrated Forecasting of Water Vapor and Surface Air Temperature

The use of a new multi model integration method of Partial Least Squares regression (PLS) can completely eliminate the multicollinearity features to improve multi model’s integrated forecasting results of the humidity and temperature. Based on the four centers’ ensemble forecast results, namely, the European Center for Medium-Range Weather Forecasts (ECMWF), Chinese Meteorological Administration (CMA), the Japan Meteorological Agency (JMA) and the UK Met Office (UKMO), we built a 2012 multi mode (25°~60°N, 60°~150°E) 24 ~168 hours forecast time (interval 24 hours) multi model for humidity and temperature and used the four methods, like ensemble average (BREM) for eliminating the deviation, a simple set of average (EMN), Super Ensemble (SUP) and Partial Least Squares regression (PLS) for ground temperature multi model integration. We used the Root-Mean-Square Error (RMSE) and anomaly correlation coefficient (cor) to determine the effect of more modes of integration and to predict a short course of cold. The two prediction results showed that the Partial Least Squares regression (PLS) was the best multi model integrated method, more superior than the other three single modes and compared with the other three methods, it showed better prediction performance, which has certain value and application prospect.     

Evaluation of Goddard’s LiDAR,hyperspectral, and thermal data products for mapping urban land-cover types

Goddard’s LiDAR (Light Detection And Ranging), hyperspectral and thermal (G-LiHT) airborne imager is a new system to advance concepts of data fusion for worldwide applications. A recent G-LiHT mission conducted in June 2016 over an urban area opens a new opportunity to assess the G-LiHT products for urban land-cover mapping. In this study, the G-LiHT hyperspectral and LiDAR-canopy height model (LiDAR-CHM) products were evaluated to map five broad land-cover types. A feature/decision-level fusion strategy was developed to integrate two products. Contemporary data processing techniques were applied, including object-based image analysis, machine-learning algorithms, and ensemble analysis. Evaluation focused on the capability of G-LiHT hyperspectral products compared with multispectral data with similar spatial resolution, the contribution of LiDAR-CHM, and the potential of ensemble analysis in land-cover mapping. The results showed that there was no significant difference between the application of the G-LiHT hyperspectral product and simulated Quickbird data in the classification. A synthesis of G-LiHT hyperspectral and LiDAR-CHM products achieved the best result with an overall accuracy of 96.3% and a Kappa value of 0.95 when ensemble analysis was applied. Ensemble analysis of the three classifiers not only increased the classification accuracy but also generated an uncertainty map to show regions with a robust classification as well as areas where classification errors were most likely to occur. Ensemble analysis is a promising tool for land-cover classification.     

The Super-large Ensemble Experiments of CAS FGOALS-g3

A super-large ensemble simulation dataset with 110 members has been produced by the fully coupled model FGOALS-g3 developed by researchers at the Institute of Atmospheric Physics, Chinese Academy of Sciences. This is the first dataset of large ensemble simulations with a climate system model developed by a Chinese modeling center. The simulation has the largest realizations up to now worldwide in terms of single-model initial-condition large ensembles. Each member includes a historical experiment (1850–2014) and an experiment (2015–99) under the very high greenhouse gas emissions Shared Socioeconomic Pathway scenario (SSP5-8.5). The dataset includes monthly and daily temperature, precipitation, and other variables, requiring storage of 275 TB. Additionally, the surface air temperature (SAT) and land precipitation simulated by the FGOALS-g3 super-large ensemble have been validated and projected. The ensemble can capture the response of SAT and land precipitation to external forcings well, and the internal variabilities can be quantified. The availability of more than 100 realizations will help researchers to study rare events and improve the understanding of the impact of internal variability on forced climate changes.     

台风路径集合预报的实时订正技术研究

在台风业务预报中,由于模式运行、后处理及资料传输等原因,数值模式指导产品包括集合预报都存在一定时间的滞后,若直接使用会造成数值模式或集合预报平均的预报效果降低。利用ECMWF集合预报台风路径和中央气象台(简称中央台)实时业务定位,在统计分析的基础上,提出一种业务上可用的针对单模式集合预报的台风路径实时订正技术。结果表明,该方法明显优于单模式集合预报平均和确定性预报,在对2012年的预报试验中,24、48、72、96 h的时效路径预报误差分别比集合平均提高了15%、6%、10%、8%。同时其路径误差优于目前我国业务路径预报,2012年平均24、48、72、96 h的路径误差分别减小7、7、11、10 km。     

热带气旋路径集合预报方法研究Ⅰ——正压模式结果的初步分析

选择1979～1993年间的热带气旋为试验个例,通过扰动热带气旋初始位置和初始结构,构造集合成员, 用正压原始方程模式,进行路径集合预报试验, 并初步探讨预报成员的集合方法。试验结果表明:热带气旋定位误差影响路径预报,但扰动初始位置的集合平均预报与控制试验的预报水平相接近。扰动热带气旋初始结构的集合预报试验表明,约有60 %～70 %个例的集合路径预报得到改进。此外,试验结果还表明,当环境引导气流较弱时,进行扰动热带气旋初始结构的集合预报,预报结果的改善较明显。