热带风压场平衡特征及其对GRAPES系统中同化预报的影响研究Ⅱ:动力与统计混合平衡约束方案的应用

研究I的结果表明:线性平衡方程(LBE)在热带地区不适用,而进一步改进方向是削弱LBE在该区域的约束程度。本文以此为基础,在GRAPES(global/regional assimilation and prediction system)全球变分同化系统中引入动力与统计混合平衡约束方案。新方案在逐层求解LBE的基础上增加垂直方向的线性回归,回归系数随纬度和高度变化。针对背景误差协方差的分析表明,新方案可以更好的保证独立分析变量间预报误差不相关的基本要求,并大幅度减小热带地区平衡气压预报误差方差的量值和占总方差的比例。单点试验结果表明,与LBE方案相比,新方案对中、高纬影响很小,但在热带地区成功实现了风、压场分析的解耦,两者分析更为独立。并且,虽未考虑具体波动模态,但新方案给出的风、压场协相关结构与研究I的理论分析结果相近。一个月的同化循环与预报结果表明,引入新方案后,赤道外地区的同化预报效果为中性偏正,而热带地区风场的同化预报效果显著提高,LBE方案中平流层低层的风场同化预报异常被基本消除。     

Intercomparison of the performance of MM5/WRF with and without satellite data assimilation in short-range forecast applications over the Indian region

The present study is conducted to verify the short-range forecasts from mesoscale model version5 (MM5)/weather research and forecasting (WRF) model over the Indian region and to examine the impact of assimilation of quick scatterometer (QSCAT) near surface winds, spectral sensor microwave imager (SSM/I) wind speed and total precipitable water (TPW) on the forecasts by these models using their three-dimensional variational (3D-Var) data assimilation scheme for a 1-month period during July 2006. The control (without satellite data assimilation) as well as 3D-Var sensitivity experiments (with assimilating satellite data) using MM5/WRF were made for 48 h starting daily at 0000 UTC July 2006. The control run is analyzed for the intercomparison of MM5/WRF short-range forecasts and is also used as a baseline for assessing the MM5/WRF 3D-Var satellite data sensitivity experiments. As compared to the observation, the MM5 (WRF) control simulations strengthened (weakened) the cross equatorial flow over southern Arabian sea near peninsular India. The forecasts from MM5 and WRF showed a warm and moist bias at lower and upper levels with a cold bias at the middle level, which shows that the convective schemes of these models may be too active during the simulation. The forecast errors in predicted wind, temperature and humidity at different levels are lesser in WRF as compared to MM5, except the temperature prediction at lower level. The rainfall pattern and prediction skill from day 1 and day 2 forecasts by WRF is superior to MM5. The spatial distribution of forecast impact for wind, temperature, and humidity from 1-month assimilation experiments during July 2006 demonstrated that on average, for 24 and 48-h forecasts, the satellite data improved the MM5/WRF initial condition, so that model errors in predicted meteorological fields got reduced. Among the experiments, MM5/WRF wind speed prediction is most benefited from QSCAT surface wind and SSM/I TPW assimilation while temperature and humidity prediction is mostly improved due to latter. The largest improvement in MM5/WRF rainfall prediction is due to the assimilation of SSM/I TPW. The assimilation of SSM/I wind speed alone in MM5/WRF degraded the humidity and rainfall prediction. In summary the assimilation of satellite data showed similar impact on MM5/WRF prediction; largest improvement due to SSM/I TPW and degradation due to SSM/I wind speed.     

Assimilation and Simulation of Typhoon Rusa （2002） Using the WRF System

Using the recently developed Weather Research and Forecasting (WRF) 3DVAR and the WRF model, numerical experiments are conducted for the initialization and simulation of typhoon Rusa (2002).The observational data used in the WRF 3DVAR are conventional Global Telecommunications System (GTS) data and Korean Automatic Weather Station (AWS) surface observations. The Background Error Statistics (BES) via the National Meteorological Center (NMC) method has two different resolutions, that is, a 210-km horizontal grid space from the NCEP global model and a 10-km horizontal resolution from Korean operational forecasts. To improve the performance of the WRF simulation initialized from the WRF 3DVAR analyses, the scale-lengths used in the horizontal background error covariances via recursive filter are tuned in terms of the WRF 3DVAR control variables, streamfunction, velocity potential, unbalanced pressure and specific humidity. The experiments with respect to different background error statistics and different observational data indicate that the subsequent 24-h the WRF model forecasts of typhoon Rusa‘s track and precipitation are significantly impacted upon the initial fields. Assimilation of the AWS data with the tuned background error statistics obtains improved predictions of the typhoon track and its precipitation.     

温湿统计平衡约束关系对GRAPES全球湿度分析的作用

为改进GRAPES全球三维变分同化系统(GRAPES-3DVar)的湿度分析,借鉴Hólm等(2002)的思想,在背景误差协方差结构中引入湿度与温度的统计平衡约束关系。通过扣除湿度变化中与温度有关的平衡部分获取非平衡拟相对湿度,并引入非线性对称变换对其做标准化处理,将处理后的变量作为新的湿度控制变量。统计结果表明,温湿统计平衡约束主要出现在中高纬度对流层中层相对湿度大于80%的区域,与大尺度抬升凝结加热有关;新的湿度控制变量能满足无偏、高斯分布特征。单点理想观测试验结果表明,新的湿度分析具备了流依赖特征,并能有效地抑制负水汽与超饱和水汽的出现。同化循环与预报试验结果表明,新方案给出的湿度分析的偏差和均方根误差均有所减小。而针对降水预报的检验结果表明,引入新方案后的0.1-10 mm降水预报,在ETS评分没有显著降低的情况下,BIAS评分更靠近1,降水空报有所减缓。然而60-84 h的25 mm以上的降水漏报现象更为明显,表明湿度同化分析方案还有改进空间。通过引入温湿统计平衡约束关系,完善了GRAPES-3DVar分析框架,为全球湿度分析的持续改进奠定了坚实基础。      

背景误差协方差变量平衡特征及其对台风同化和预报的影响

构造合理的背景场误差协方差是做好资料同化的关键。分析了背景误差协方差中变量相关关系在台风季节和非台风季节隐含的不同动力平衡特征,并讨论其对台风同化和预报的影响。分析发现,与非台风季节相比,在台风季节温度与非平衡速度势具有更强的动力相关性,拟相对湿度与其他控制变量的相关性也更显著。这些动力相关性在背景场误差中协方差的引入,将在同化分析过程中使得观测信息可以合理地对同化分析场产生影响。台风循环同化和预报的结果验证了对变量平衡特征的分析:背景误差协方差中新平衡关系的建立,对同化和预报有较大的正面影响,尤其是相对湿度和其他控制变量相关的建立,明显改善了台风路径、强度和降水的预报效果。      

SVD-En3DVar方法同化多普勒雷达速度观测资料 I. 模拟资料试验

利用WRF模式及模式模拟的资料,开展了利用SVD-En3DVar(基于集合和SVD技术的三维变分同化方法)方法同化雷达径向速度资料的试验.由于雷达观测经常出现大面积空缺,同化时引入了一种局地化方法避免远距离虚假相关的影响.试验着重研究了不同的初始扰动样本产生方法以及不同的样本积分时间对同化结果的影响.提出了一种为预报集...     

INFLUENCE OF DIFFERENT-SCALE ERRORS INTERACTIONS ON ANALYSIS AND FORECAST OF REGIONAL NWP MODEL

In the previous study, the influences of introducing larger- and smaller-scale errors on the background error covariances estimated at the given scales were investigated, respectively. This study used the covariances obtained in the previous study in the data assimilation and model forecast system based on three-dimensional variational method and the Weather Research and Forecasting model. In this study, analyses and forecasts from this system with different covariances for a period of one month were compared, and the causes for differing results were presented. The variations of analysis increments with different-scale errors are consistent with those of variances and correlations of background errors that were reported in the previous paper. In particular, the introduction of smaller-scale errors leads to greater amplitudes in analysis increments for medium-scale wind at the heights of both high- and low-level jets. Temperature and humidity analysis increments are greater at the corresponding scales at the middle- and upper-levels. These analysis increments could improve the intensity of the jet-convection system that includes jets at different levels and the coupling between them that is associated with latent heat release. These changes in analyses will contribute to more accurate wind and temperature forecasts in the corresponding areas. When smaller-scale errors are included, humidity analysis increments are significantly enhanced at large scales and lower levels, to moisten southern analyses. Thus, dry bias can be corrected, which will improve humidity forecasts. Moreover, the inclusion of larger- (smaller-) scale errors will be beneficial for the accuracy of forecasts of heavy (light) precipitation at large (small) scales because of the amplification (diminution) of the intensity and area in precipitation forecasts.     

不同观测误差确定方法对地基GNSS-ZTD资料同化预报效果影响的对比分析

利用2016年6—8月华北—东北地区的地基全球卫星导航系统的天顶总延迟（GNSS-ZTD）观测资料、东北区域中尺度数值预报系统,以2016年6—8月的13 d强降水为例,开展基于Desroziers等（2005）理论的Des方法和传统方法进行观测误差确定的天顶总延迟资料同化对比试验研究,探讨Des方法相对于传统观测误差确定方法对天顶总延迟资料同化预报效果的影响,并以未做天顶总延迟资料同化的试验为对照试验,考察天顶总延迟资料在数值模式中的同化应用效果。结果表明:（1）Des方法得到的天顶总延迟观测误差诊断值较为合理,诊断值站点间差别较大,说明逐站进行观测误差诊断的必要性;（2）天顶总延迟资料同化使强降水的强度、落区预报性能得到提高,使温、湿、风等要素的预报与观测接近,Des方案同化分析、预报效果优于传统方案;（3）对2016年7月25日华北—东北强降水过程进行了同化预报分析,整体而言,天顶总延迟资料同化有效增强了对流层中低层初始湿度场,修正了积分初期水凝物含量与位置,进而改善了降水预报效果,修正了对照试验对辽宁东部地区强降水的明显漏报,且通过降水的反馈作用改进了温度与风场预报效果。基于Des方法逐站诊断观测误差相比传统方法得到的观测误差更为合理,因此能够提高天顶总延迟资料的同化预报效果,同化天顶总延迟资料能够提高降水及温、湿、风等气象要素的预报水平。      

Impact of surface observations on simulation of rainfall over NCR Delhi using regional background error statistics in WRF-3DVAR model

In this study, efforts are made to improve the simulation of heavy rainfall events over National Capital Region (NCR) Delhi during 2010 summer monsoon, using additional observations from automatic weather stations (AWS). Two case studies have been carried out to simulate the relative humidity, wind speed and precipitation over NCR Delhi in 48-h model integrations; one from 00UTC, August 20, 2010, and the other from 00UTC, September 12, 2010. Several AWS installed over NCR Delhi in the recent past provide valuable surface observations, which are assimilated into state-of-the-art weather research and forecasting (WRF) model using the three-dimensional variational data assimilation (3DVAR). The quality of background error statistics (BES) is a key component in successful 3DVAR data assimilation in a mesoscale model. In this study, the domain-dependent regional background error statistics (RBS) are estimated using National Meteorological Center method in the months of August and September 2010 and then compared with the global background error statistics (GBS) in the WRF model. The model simulations are analyzed and validated against AWS and radiosonde observations to quantify the impact of RBS. The root mean square differences in the spatial distributions of precipitation, relative humidity and wind speed at the surface showed significant differences between both the global and regional BES. Similar differences are also observed in the vertical distributions along the latitudinal cross section at 28.5°N. Model-simulated fields are analyzed at five different surface stations and one upper air station located in NCR Delhi. It is found that in 24-h model simulation, the RBS significantly improves the model simulations in case of precipitation, relative humidity and wind speed as compared to GBS.     

GNSS反演资料在GRAPES_Meso三维变分中的应用

为了进一步提高GRAPES_Meso的分析和预报效果,该文在GRAPES_Meso三维变分同化系统中建立了同化GNSS/RO反演的大气资料的观测算子,实现了对GNSS/RO反演的大气资料的同化应用,并通过2013年7月1个月的同化和预报试验分析了GNSS/RO反演大气资料对GRAPES_Meso模式系统分析和预报的影响。结果表明:增加了GNSS/RO反演大气资料的同化后,GRAPES_Meso位势高度场的分析误差明显减小,平均分析误差减小约8%,预报误差略有减小,平均预报误差减小约1%;湿度场的分析误差和预报误差变化不明显,常规观测资料稀少的青藏高原地区的降水预报技巧有所提高,小雨到大雨的ETS (equitable threat score) 评分提高约0.01,对全国及其他分区的降水预报技巧总体上有正效果。     

GMS-5反演湿度场的一维变分质量控制试验

应用一维变分方法对GMS5资料反演的相对湿度场进行了变分同化质量控制试验。首先具体分析了1998年6月12～29日的MM5模式12h预报湿度场、卫星反演湿度场和同化分析湿度场的误差统计量(误差平均值、误差自协方差矩阵、均方根误差等)。在此基础上,又应用一维变分方法对1998年7月20～30日的资料进行了同化试验,并以1998年7月21日00时(世界时)的结果为例,详细分析比较了3种湿度场。分析结果表明:MM5模式背景湿度场总体上低层偏湿,中上层偏干,各层面上的干湿区域区分明显;但往往湿区过湿,干区又偏干,均方根误差(RMSE)相对较大,各层面多在10以上,最高达到15.6。GMS5卫星反演湿度场总体与探空实测结果更接近,但整体平均看,各个层面基本上略偏湿,特别在中低层,RMSE为6.3～8.9。经过一维变分质量控制的湿度场基本接近卫星反演湿度场,但同化后的湿度场的RMSE在中低层要比卫星反演场的要小,RMSE下降了0.4～0.7,下降比率达到6%～9%;高层与卫星反演结果基本接近。对模式向前12h预报场而言,同化了卫星反演湿度场后,均方根误差值下降了1.4～9.0,下降比率达到17%～59%,同化后的湿度场与实际大气大大接近。     

单多普勒天气雷达反演风场高原低纬区域同化应用

为了改善低纬高原地区天气预报水平,利用WRF(Weather Research and Forecasting)模式及其变分同化系统进行雷达VAD(Velocity Azimuth Display)反演风场资料同化试验。通过设计不同的试验方案,对2009年6月30日00:00至7月1日00:00发生在云南的一次强降水过程进行数值模拟和对比分析,结果表明:同化VAD反演风场资料后对区域模式的风矢量初始场有明显影响。同化系统能把雷达反演风场信息有效地引入模式初始场,改善强降水区域的水汽输送和风场辐合强度;同化VAD反演风场资料后对区域模式累计降水预报有一定改进作用。从长时间累计降水量定量检验结果看,具体表现为25mm以上量级的降水准确率明显提高、漏报率下降,预报偏差更趋合理。不同的同化试验方案之间的模拟结果差异较大。同化频率越高、同化持续时间越长,对区域模式初始场和预报场的影响越明显。但同化持续时间不宜过长,否则可能导致系统移速过快、降水强度偏大、空报率增加等异常。     

Impact of Assimilating Radiances with the WRFDA ETKF/3DVAR Hybrid System on Prediction of Two Typhoons in 2012

The impacts of AMSU-A and IASI (Infrared Atmospheric Sounding Interferometer) radiances assimila-tion on the prediction of typhoons Vicente and Saola (2012) are studied by using the ensemble transform ...     

下投式探空资料对Debby飓风路径预报影响的数值试验

在飓风路径的数值预报中，对于初始场的要求很高，然而，由于初始资料的缺乏，经常导致路径预报的误差较大，尤其是当飓风处于远离陆地的海上时，这种误差更大，通过利用UM模式在Debby飓风活动期间，对下投式探空仪所获取探空资料，采用不同使用方案的三个时次共计10次数值试验的结论分析，给出一些有意义的 结论，即非实时资料对实时资料的有效补充，能够提高飓风路径预报精度，而在众多气象要素场中，风场和湿度场对飓风路径预报的影响更大。     

下投探空资料在台风莫拉克路径预报的应用试验

2009年8月7日中国大陆举行了首次利用机载下投式探空仪观测台风的试验,飞机在台风莫拉克与天鹅之间的云带相对稀薄区释放11个下投式探空仪。基于下投探空观测资料、常规探空资料和1°×1°分辨率的NCEP再分析资料,分析下投探空资料的可用性,并以下投探空资料初步分析了两台风间南海上空的风场、湿度场等大气特性;分别进行了有无以同化下投探空为初始场的GRAPES模式的模拟试验,以了解下投探空资料对台风莫拉克预报的影响作用。初步结论表明,台风天鹅与莫拉克之间的南海上空对流层中低层为深厚的西南气流,对流层低层及高层湿度小,中间层大;同化下投探空资料后,观测地区(下投探空点及其附近)800 hPa以下西南风减弱,以上加强,湿度中低层减小;有无同化下投探空资料的初值场差异随模式积分向下游传播,影响台风的环境场,改变了台风的引导气流:同化后500 hPa台风引导气流偏东、偏北分量加强,使台风的路径更接近实况路径,48 h台风路径预报误差比原来减少18%。     

基于DART +WACCM模式搭建的平流层同化、天气预报和气候预测模型研究

本论文基于WACCM（Whole Atmosphere Community Climate Model）模式最新版本WACCM6和DART（Data Assimilation Research TestBed）同化工具最新版本Manhattan,开发了中高层大气温度、臭氧和水汽卫星资料的同化接口,搭建了一个包含完整平流层过程的数值同化、天气预报和短期气候预测模型（此后简称模型）;本模型对2020年3～4月平流层大气变化进行了同化观测资料的模拟,并以同化试验输出的分析场作为初值,对5～6月的平流层大气进行了0～30天天气尺度预报以及31～60天短期气候尺度预测的回报试验。结果表明:本模型能较好地重现2020年3、4月北极平流层出现的大规模臭氧损耗事件随时间的演变特征,模拟结果和Microwave Limb Sounder（MLS）卫星观测结果很接近;而未进行同化的模拟试验,虽然可以模拟出北极臭氧损耗现象,但是模拟的臭氧损耗规模相比MLS卫星观测结果要低很多;利用同化试验4月末输出的分析场作为初值,预报的5月北极平流层臭氧体积混合比变化与MLS卫星观测值的差值小于0.5,预测的6月北极平流层臭氧变化只在10～30 hPa之间的区域,与观测之间的差异达到了1 ppm（ppm=10?6）。本模型不但改善了北极平流层化学成分变化的模拟,也显著地提升了北极平流层温度和环流的模拟。本模型同化模拟的3～4月、预报预测的5～6月北极平流层温度和纬向风变化与Modern-Era Retrospective analysis for Research and Applications, Version 2 （MERRA2）再分析资料结果具有很好的一致性,仅在北极平流层顶部,预报预测的温度和纬向风分别与再分析资料之间的均方根误差（RMSE）约为3 K和4 m s?1。未进行同化的试验模拟的3～4月、预报预测的5～6月北极平流层的温度和纬向风与MERRA2再分析资料之间的RMSE在大部分区域都达到6 K及5 m s?1以上。从全球范围来看,本模型对平流层中低层模拟性能改善最为显著,其预报预测结果与观测值之间的差异,比未进行同化试验的结果,减少了50%以上。     

Assimilating Best Track Minimum Sea Level Pressure Data Together with Doppler Radar Data Using an Ensemble Kalman Filter for Hurricane Ike (2008) at a Cloud-Resolving Resolution

Extending an earlier study, the best track minimum sea level pressure (MSLP) data are assimilated for landfalling Hurricane Ike (2008) using an ensemble Kalman filter (EnKF), in addition to data from two coastal ground-based Doppler radars, at a 4-km grid spacing. Treated as a sea level pressure observation, the MSLP assimilation by the EnKF enhances the hurricane warm core structure and results in a stronger and deeper analyzed vortex than that in the GFS (Global Forecast System) analysis; it also improves the subsequent 18-h hurricane intensity and track forecasts. With a 2-h total assimilation window length, the assimilation of MSLP data interpolated to 10-min intervals results in more balanced analyses with smaller subsequent forecast error growth and better intensity and track forecasts than when the data are assimilated every 60 minutes. Radar data are always assimilated at 10-min intervals. For both intensity and track forecasts, assimilating MSLP only outperforms assimilating radar reflectivity (Z) only. For intensity forecast, assimilating MSLP at 10-min intervals outperforms radar radial wind (Vr) data (assimilated at 10-min intervals), but assimilating MSLP at 60-min intervals fails to beat Vr data. For track forecast, MSLP assimilation has a slightly (noticeably) larger positive impact than Vr(Z) data. When Vr or Z is combined with MSLP, both intensity and track forecasts are improved more than the assimilation of individual observation type. When the total assimilation window length is reduced to 1h or less, the assimilation of MSLP alone even at 10-min intervals produces poorer 18-h intensity forecasts than assimilating Vr only, indicating that many assimilation cycles are needed to establish balanced analyses when MSLP data alone are assimilated; this is due to the very limited pieces of information that MSLP data provide.     

GRAPES变分同化系统中动力平衡约束的统计求解

该文在GRAPES (Global/Regional Assimilation and PrEdiction System) 模式面三维变分 (3D_Var) 框架中引入了一种描述不同控制变量之间动力平衡约束的新方案。新方案采用统计得到的流函数和模式气压变量 ( π ) 之间的回归系数代替原方案中的线性平衡方程,来表达旋转风和质量场之间的平衡关系;采用流函数和势函数之间的回归系数,补充表达了原方案中所没有的旋转风和散度风之间的平衡关系。与原方案相比,新方案算法简单,避免了垂直方向的反复插值,减少了插值误差的引入。通过随机扰动试验和单点试验可以发现,在地转关系成立较好的区域,新方案中旋转风和质量场的耦合程度与原方案接近一致;而在地转关系不适用区域,新方案可以有效减小两者的耦合程度。此外,由于新方案中添加了旋转风和散度风之间的动力平衡约束,边界层的风场分析也更加接近大气真实状况。     

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.