基于AMSR-E土壤湿度产品的LIS同化试验

由陆面信息系统 (Land Information System, 简称LIS) 通过NOAH陆面过程模型使用集合卡尔曼滤波开展AMSR-E (Advanced Microwave Scanning Radiometer-Earth Observing System) 土壤湿度同化试验,得到2003年中国区域垂直深度为4层、水平空间分辨率为0.25°×0.25°的土壤湿度试验数据。使用农业气象观测站土壤相对湿度和国家生态系统野外科学观测研究站土壤体积含水量对试验结果进行检验,结果表明:同化过程整体上提高了陆面模型的模拟精度,草地生态系统模拟精度高于作物和森林生态系统;有效的同化过程依赖于AMSR-E土壤湿度的准确性;模拟出的土壤湿度空间分布特征与实际相符。同化试验得到的时空相对连续且精度相对准确的土壤湿度数据是气候变化和干旱监测的重要数据基础。     

Assimilation of Polarimetric Radar Data Using an Ensemble Kalman Filter for the Analysis and Forecast of Tropical Storm Ewiniar

This study explores the potential for directly assimilating polarimetric radar data (including reflectivity Z and differential reflectivity ZDR) using an ensemble Kalman filter (EnKF) based on the Weather Research and Forecasting (WRF) model to improve analysis and forecast of Tropical Storm Ewiniar (2018). Ewiniar weakened but brought about heavy rainfall over Guangdong, China after its final landfall. Two experiments are performed, one assimilating only Z and the other assimilating both Z and ZDR. Assimilation of ZDR together with Z effectively modifies hydrometeor fields, and improves the intensity, shape and position of rainbands. Forecast of 24-hour extraordinary rainfall ≥250 mm is significantly improved. Improvement can also be seen in the wind fields because of cross-variable covariance. The current study shows the possibility of applying polarimetric radar data to improve forecasting of tropical cyclones, which deserves more researches in the future.     

数据同化在海洋生态模型中的应用和研究进展

将数据同化方法引入海洋生态系统动力学模型研究,利用现有的观测数据,获得最佳的模式参数、初始场或提高状态模拟,是当前多学科交叉研究的热门领域。本文依据国内外研究进展,主要就海洋生态模型研究中所采用的变分伴随、卡尔曼滤波、模拟退火法方法进行了介绍,总结了数据同化在我国的海洋生态系统研究中的现状和发展趋势。     

Ekman模型时间变化风应力系数的伴随参数估计

本文基于时间分布参数设置,利用伴随同化方法,反演了Ekman模型中随时间变化的风应力拖曳系数,并在孪生实验和实际实验中对该方法进行了验证。在孪生实验中,研究了参数反演结果对不同影响因素的响应,包括:风速分布、风应力系数分布、风应力系数初始猜测值、风应力系数独立变量个数、观测数据误差和观测的深度。孪生实验结果验证了伴随同化方法反演Ekman模型中时变风应力系数的有效性,具体包括如下五个方面结论:1)不同风速分布下均能成功反演出不同风应力拖曳系数分布; 2)反演结果对初始猜测值较为敏感,风应力系数初始猜测值越接近给定值,反演结果越好;3)风应力系数独立点个数的选取会显著影响反演结果,合理的选择有利于提高反演效率及减小观测数据误差;4)观测误差能够影响反演结果,观测数据误差在20%以下时能取得合理的反演结果; 5)反演结果对观测数据的表层和次表层流速更为敏感,这是由Ekman流的物理性质决定的。实际实验,利用百慕大锚系试验平台的风速和流速数据,去除周期性潮流和地转流成分后得到Ekman流成分,并作为观测输入到该同化模型,反演出了适用于该区域和该时段的随时间变化的风应力系数。通过比较模拟流速和观测流速,证明利用伴随同化方法能从实测数据中反演出合理的时变风应力系数,对于海洋模型风应力系数的确定是一项有益的尝试。     

COSMIC掩星资料同化对台风“天兔”预报影响的试验

台风数值预报的关键之一在于给定准确的模式初始场。本文使用气象、电离层和气候星座观测系统(COSMIC)的卫星资料,通过资料同化技术改善数值天气预报模式的初始场,进而评估数据同化技术对台风(或热带气旋)预报的改善效果。使用我国自主研发的数值天气预报模式,对1319号台风“天兔”进行模拟试验,对温度场、湿度场、涡度散度场及假相当位温场进行诊断分析。试验结果表明,与同化常规探空资料相比,COSMIC资料的同化可减小模式预报台风路径误差,台风附近降水落区更为集中。研究评估了掩星资料同化对模式的改进作用,并能为台风预报技术和防灾减灾提供参考。     

GPS气象学研究及应用的进展与前景

概述了GPS气象学的形成、发展过程及其主要研究内容,对GPS气象学基本原理作了简单介绍。比较全面、系统地分析了国内外GPS技术在气象学研究及应用方面的现状及最新进展,展望了GPS气象学潜在的应用领域以及广阔的应用前景。     

Improvement in Background Error Covariances Using Ensemble Forecasts for Assimilation of High-Resolution Satellite Data

Satellite data obtained over synoptic data-sparse regions such as an ocean contribute toward improving the quality of the initial state of limited-area models. Background error covariances are crucial to the proper distribution of satellite-observed information in variational data assimilation. In the NMC (National Meteorological Center) method, background error covariances are underestimated over data-sparse regions such as an ocean because of small differences between different forecast times. Thus, it is necessary to reconstruct and tune the background error covariances so as to maximize the usefulness of the satellite data for the initial state of limited-area models, especially over an ocean where there is a lack of conventional data. In this study, we attempted to estimate background error covariances so as to provide adequate error statistics for data-sparse regions by using ensemble forecasts of optimal perturbations using bred vectors. The background error covariances estimated by the ensemble method reduced the overestimation of error amplitude obtained by the NMC method. By employing an appropriate horizontal length scale to exclude spurious correlations, the ensemble method produced better results than the NMC method in the assimilation of retrieved satellite data. Because the ensemble method distributes observed information over a limited local area, it would be more useful in the analysis of high-resolution satellite data. Accordingly, the performance of forecast models can be improved over the area where the satellite data are assimilated.     

基于热层电离层耦合数据同化的热层参量估计

本文采用高效集合卡尔曼滤波（EnKF）算法和背景场热层电离层理论模式NCAR-TIEGCM,开发了热层电离层数据同化系统.基于全球空地基GNSS电离层斜TEC观测、CHAMP和TIMED/GUVI热层参量观测构型设计了系列观测系统模拟实验,对热层参量进行估计.实验结果表明,（1）通过集合卡尔曼滤波算法同化电离层TEC观测能够较好地优化热层参量.（2）中性质量密度优化效果在整个同化阶段均有提升,提升百分比能达到40%.（3）积分氧氮比在同化阶段也能得到较好的优化,但在电子密度水平梯度变化剧烈区域效果较差.最后本文对中性质量密度进行了预报评估,结果表明,由于中性成分优化,在地磁平静条件下其预报时间尺度可长达24 h.

     

Reconstruction of eddies by assimilating satellite altimeter data into Princeton Ocean Model

An optimal interpolation assimilation model for satellite altimetry data is developed based on Princeton Ocean Model (POM), which is applied in a quasi-global domain, by the method of isotropic correlation between sea level anomaly (SLA) and sea temperature anomaly. The perfor- mance of this assimilation model is validated by the modeled results of SLA and the current patterns. Comparisons between modeling and satellite data show that both the magnitudes and distribution patterns of the simulated SLA are improved by assimilation. The most signiˉcant improvement is that meso-scale systems, e.g., eddies, are well reconstructed. The evolution of an eddy located in the northwest Paciˉc Ocean is traced by using the assimilation model. Model results show that during three months the eddy migrated southwestward for about 6 degrees before merging into the Kuroshio. The three dimensional structure of this eddy on 12 August 2001 is further analyzed. The strength of this warm, cyclonic eddy decreases with the increase of depth. The eddy shows di?erent horizontal patterns at di?erent layers, and the SLA and temperature ˉelds agree with each other well. This study suggests that this kind of data assimilation is economic and reliable for eddy reconstruction, and can be used as a promising technique in further studies of ocean eddies as well as other ˉne circulation structures.     

高时空分辨率三维风场在强对流天气临近预报中的融合应用研究

以业务应用为目标,开展高时、空分辨率三维风场在强对流天气临近预报中的融合应用研究。运用北京快速更新多尺度分析和预报系统集成子系统（RMAPS-IN,Rapid-refresh Multi-scale Analysis and Prediction System-Integration）,对雷达四维变分分析系统（VDRAS）30 min临近预报的高时、空分辨率三维风场作为数据源与自动气象站风场观测进行快速融合处理。结果表明,以VDRAS临近预报风场取代数值模式预报场作为融合初猜场后形成的分析结果对于风场有明显的改善:（1）长时间序列客观检验结果表明,地面10 m高风场U/V分量绝对误差分别为0.05和0.06 m/s。实时融合对未来预报的影响随着预报时效的延长,U/V分量的绝对误差不断增大。（2）对于11个强对流个例,地面10 m高风场风速均方根误差降低0.3 m/s,风向均方根误差降低13°;边界层三维风场,风速均方根误差降低0.8 m/s,风向均方根误差降低10°。平原站点融合以后风速、风向预报效果有较大改善,山区站点融合以后改善相对较小。（3）通过对2017年7月20日暴雨和7月7日雷暴大风个例的详细分析,发现融合基于雷达资料四维变分同化获得的高分辨率临近预报风场对各对流系统中的中尺度结构特征给出了更加细致准确的描述。