一次湖北暴雪天气的诊断与模拟

利用NCEP GFS资料分析了2007年1月15—16日鄂东南地区降雪过程,对造成暴雪过程的天气系统发生、发展背景场进行分析。并利用中尺度数值天气模式WRF模拟了这次暴雪过程,探讨了其发生发展的机制。天气系统的背景分析表明,这次暴雪过程主要是受700 hPa西南急流和地面冷空气的共同影响而产生的,降水过程与西南急流的变化密切联系。WRF模式较好地再现了此次暴雪的过程。模拟结果表明西南急流的减弱和移出,对应着降雪的开始和停止;在西南急流的左侧,由于低层涡度的增加,使低空辐合、高空辐散,在连续性原理和动力机制约束下导致上升运动的加强是该次暴雪的形成机制。模式结果说明,产生暴雪的上升运动要远小于产生暴雨的上升运动,且在暴雪过程中,中层为上升运动,近地层和高层伴随着下沉运动。     

Evaluation of a Regional Ensemble Data Assimilation System for Typhoon Prediction

An ensemble Kalman filter(EnKF) combined with the Advanced Research Weather Research and Forecasting model(WRF) is cycled and evaluated for western North Pacific(WNP) typhoons of year 2016. Conventional in situ data, radiance observations, and tropical cyclone(TC) minimum sea level pressure(SLP) are assimilated every 6 h using an 80-member ensemble. For all TC categories, the 6-h ensemble priors from the WRF/EnKF system have an appropriate amount of variance for TC tracks but have insufficient v...     

The impact of AIRS atmospheric temperature and moisture profiles on hurricane forecasts: Ike (2008) and Irene (2011)

Atmospheric Infra Red Sounder(AIRS) measurements are a valuable supplement to current observational data, especially over the oceans where conventional data are sparse. In this study, two types of AIRS-retrieved temperature and moisture profiles, the AIRS Science Team product(Sci Sup) and the single field-of-view(SFOV) research product, were evaluated with European Centre for Medium-Range Weather Forecasts(ECMWF) analysis data over the Atlantic Ocean during Hurricane Ike(2008) and Hurricane Irene(2011). The evaluation results showed that both types of AIRS profiles agreed well with the ECMWF analysis, especially between 200 h Pa and 700 h Pa. The average standard deviation of both temperature profiles was approximately 1 K under 200 h Pa, where the mean AIRS temperature profile from the AIRS Sci Sup retrievals was slightly colder than that from the AIRS SFOV retrievals. The mean Sci Sup moisture profile was slightly drier than that from the SFOV in the mid troposphere. A series of data assimilation and forecast experiments was then conducted with the Advanced Research version of the Weather Research and Forecasting(WRF) model and its three-dimensional variational(3DVAR) data assimilation system for hurricanes Ike and Irene. The results showed an improvement in the hurricane track due to the assimilation of AIRS clear-sky temperature profiles in the hurricane environment. In terms of total precipitable water and rainfall forecasts, the hurricane moisture environment was found to be affected by the AIRS sounding assimilation.Meanwhile, improving hurricane intensity forecasts through assimilating AIRS profiles remains a challenge for further study.     

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.     

A STUDY ON THE PREDICT ABILITY OF GRAPES MODEL OVER SOUTH CHINA: COMPARISONS BY TWO INITIALIZATION CONDITIONS BETWEEN ECMWF AND NCEP

This paper aims to assess the performances of different model initialization conditions (ICs) and lateral boundary conditions between two global models (GMs), i.e., the European Centre for Medium-Range Weather Forecasts (ECMWF) and National Centers for Environmental Prediction (NCEP), on the accuracy of the Global/Regional Assimilation and Prediction System (GRAPES) forecasts for south China. A total of 3-month simulations during the rainy season were examined and a specific case of torrential rain over Guangdong Province was verified. Both ICs exhibited cold biases over south China, as well as a strong dry bias over the Pearl River Delta (PRD). In particular, the ICs from ECMWF had a stronger cold bias over the PRD region but with a more detailed structure than NCEP. In general, the NCEP provided a realistic surface temperature compared to the ECMWF over south China. Moreover, GRAPES initialized by the NCEP had better simulations of both location and intensity of precipitation than by ECMWF. The results presented in this paper could be used as a general guideline to the operational numerical weather prediction that use regional models driven by the GMs.     

Atmosphere data assimilation system for the Siberian region with the WRF-ARW model and three-dimensional variational analysis WRF 3D-Var

The system of the cyclic assimilation of data on atmospheric conditions used in the West Siberian Administration for Hydrometeorology and Environmental Monitoring is described. It is based on the WRF-ARW mesoscale atmospheric model and on the WRF 3D-Var system of the three-dimensional variational analysis of data. The system is verified when the first approximation data (6-hour forecast) and WRF-ARW forecasts with the lead time up to 24 hours are compared with the observational data. The problems of assimilation of observations from the AMSU-A and AIRS satellite instruments are considered. The effect of using AMSU-A and AIRS for the analysis in the Novosibirsk region is estimated. The experiments demonstrated that the cyclic data assimilation system operates successfully. The AMSU-A observations improve the quality of analyses and forecasts in winter. In summer the impact of satellite observations on the forecast skill scores is ambiguous. Good short-term forecasts are provided by the initial conditions obtained using the system of detailing of the NCEP large-scale analysis.     

Improving Multi-Model Ensemble Forecasts of Tropical Cyclone Intensity Using Bayesian Model Averaging

This paper proposes a method for multi-model ensemble forecasting based on Bayesian model averaging (BMA), aiming to improve the accuracy of tropical cyclone (TC) intensity forecasts, especially forecasts of minimum surface pressure at the cyclone center (P_min). The multi-model ensemble comprises three operational forecast models: the Global Forecast System (GFS) of NCEP, the Hurricane Weather Research and Forecasting (HWRF) models of NCEP, and the Integrated Forecasting System (IFS) of ECMWF. The mean of a predictive distribution is taken as the BMA forecast. In this investigation, bias correction of the minimum surface pressure was applied at each forecast lead time, and the distribution (or probability density function, PDF) of P_min was used and transformed. Based on summer season forecasts for three years, we found that the intensity errors in TC forecast from the three models varied significantly. The HWRF had a much smaller intensity error for short lead-time forecasts. To demonstrate the proposed methodology, cross validation was implemented to ensure more efficient use of the sample data and more reliable testing. Comparative analysis shows that BMA for this three-model ensemble, after bias correction and distribution transformation, provided more accurate forecasts than did the best of the ensemble members (HWRF), with a 5%–7% decrease in root-mean-square error on average. BMA also outperformed the multi-model ensemble, and it produced “predictive variance” that represented the forecast uncertainty of the member models. In a word, the BMA method used in the multi-model ensemble forecasting was successful in TC intensity forecasts, and it has the potential to be applied to routine operational forecasting.     

Early Flood Warning for Linyi Watershed by the GRAPES/XXT Model Using TIGGE Data

Early and effective flood warning is essential for reducing loss of life and economic damage.Three global ensemble weather prediction systems of the China Meteorological Administration (CMA),the Europe...     

Improved Analyses and Forecasts With AIRS Temperature Retrievals Using the Local Ensemble Transform Kalman Filter

In this paper we investigate the impact of the Atmospheric Infra-Red Sounder (AIRS) temperature retrievals on data assimilation and the resulting forecasts using the four-dimensional Local Ensemble Transform Kalman Filter (LETKF) data assimilation scheme and a reduced resolution version of the NCEP Global Forecast System (GFS). Our results indicate that the AIRS temperature retrievals have a significant and consistent positive impact in the Southern Hemispheric extratropics on both analyses and forecasts, which is found not only in the temperature field but also in other variables. In tropics and the Northern Hemispheric extratropics these impacts are smaller, but are still generally positive or neutral.     

2020年梅汛期强降水事件的预报误差来源分析

基于WRF模式（Weather Research and Forecasting Model）分析2020年超长梅汛期内11次强降水事件的预报误差来源。分别以FNL（Final Global Data Assimilation System）、TIGGE_EC（THORPEX Interactive Grand Global Ensemble from European Centre for Medium-Range Weather Forecasts）作为初始场进行预报,对比预报结果发现,TIGGE_EC初始场的预报结果普遍优于FNL,这说明初始条件的不确定性对预报结果有重要影响。进一步探究初始条件不确定性（初始误差）来源的区域（敏感区）和变量（敏感变量）发现,敏感区集中分布于降水区西侧上游,相对应的敏感变量为水汽场。分别考察动能、有效位能以及比湿能在初始误差总能量中的占比,结果表明,扰动比湿能占比最小,但敏感性试验 表明比湿场扰动对预报效果的影响最大。选取比湿场扰动对预报效果影响最大且WRF_EC具有更好预报效果的6个暴雨事件,通过HYSPLIT后向轨迹模式分别追踪其累计降水量最大值点的水汽来源及路径发现,有6个事件均有向降水区西侧上游延伸的水汽来源通道,进一步表明了敏感区的初始水汽场的准确性对暴雨预报的影响。因此降水区西侧上游的水汽场的误差是这11次梅汛期暴雨过程重要的预报误差来源,对其准确描述可有助于预报效果的提升。     

High-Resolution Hindcast of Record-Breaking Rainfall in Beijing and Impact of Topography

In this paper,a hindcast study of the record-breaking rainfall event occurring in Beijing on 21July 2012,is conducted by using the Weather Research and Forecasting(WRF)model forced by National Centers for Environmental Prediction(NCEP)Global Forecasting System(GFS)outputs,paired with an investigation of the impact of topography in this region.The results indicate that WRF can reasonably predict the salient features of orographic precipitation;the 24-h rainfall amount and spatial distribution compare reasonably well with the observations.The hindcast simulation also indicates that rainfall events can be predicted approximately 36 h ahead.When the topography is removed,the spatial distribution of rainfall changes remarkably,suggesting the importance of the topography in determining rainfall structure.These results also indicate that prediction of such city-scale heavy rainfall events would benefit from a high-resolution prediction system.     

COSMIC反演大气温湿廓线与模式客观分析场的比较

利用2009年不同季节COSMIC湿反演的大气温度和相对湿度廓线数据,分别与时、空相匹配的ECMWF(European Centre for Medium-Range Weather Forecasts,欧洲中尺度天气预报中心)、NCEP(National Centers for Environmental Prediction,美国环境预报中心)模式客观分析场和无线电探空观测数据,进行全球范围的比较分析.初步研究表明,无论夏季还是冬季,各种资料源之间相互比较的偏差和标准差分布相似,与季节无关.就温度而言,三种资料源的温度水平、垂直分布都很接近,ECMWF模式数据比NCEP不论是温度廓线还是湿度廓线都更接近COSMIC反演值.模式的水汽客观分析场在对流层基本上都比无线电探空观测值偏湿,对流层中高层在大部分海洋地区也比COSMIC反演场偏湿.COSMIC反演的相对湿度相对于无线电探空整层偏大,具有明显正偏差,在300 hPa偏差达最大值(约30%).     

不同初始场资料对台风“桑美”数值模拟的影响

以2006年超强台风"桑美"为个例,分别以GFS(全球预报系统)再分析资料和JMA(日本气象厅)区域客观再分析资料为初始场,利用中尺度模式WRF(Weather Research and Forecasting Model)对台风"桑美"进行高分辨率数值模拟,分析不同初始场资料对台风内部结构和移动路径的影响。结果表明:对于500hPa高度场和850hPa水汽、风场等预报,GFS再分析资料和JMA区域客观再分析资料均能较好地把握台风整体的流场形势。从700hPa风场、近地面风速、降水、雷达反射率、最小海平面气压和最大风速等模拟效果来看,JMA区域客观再分析资料均优于GFS再分析资料。对于台风路径的预报,前15h内JMA区域客观再分析资料较GFS再分析资料要好,而15h之后,以这两种资料作为初始场的台风预报路径与实况均有较大偏差。     

基于TIGGE多模式集合的24小时气温BMA 概率预报

利用TIGGE(THORPEX Interactive Grand Global Ensemble)单中心集合预报系统(ECMWF、United Kingdom Meteorological Office、China Meteorological Administration和NCEP)以及由此所构成的多中心模式超级集合预报系统24小时地面日均气温预报,结合淮河流域地面观测率定贝叶斯模型平均(Bayesian model averaging,BMA)参数,从而建立地面日均气温BMA概率预报模型.由此针对淮河流域进行地面日均气温BMA概率预报及其检验与评估,结果表明BMA模型比原始集合预报效果好;单中心的BMA概率预报都有较好的预报效果,其中ECMWF最好.多中心模式超级集合比单中心BMA概率预报效果更好,采用可替换原则比普通的多中心模式超级集合BMA模型计算量小,且在上述BMA集合预报系统中效果最好.它与原始集合预报相比其平均绝对误差减少近7%,其连续等级概率评分提高近10%.基于采用可替换原则的多中心模式超级集合BMA概率预报,针对研究区域提出了极端高温预警方案,这对防范高温天气有着重要意义.     

Impact of MSMR data on NCMRWF Global Data Assimilation System

Summary ?It is very essential to make use of non-conventional remotely sensed data mainly from various satellites for numerical weather prediction. IRS series of Indian satellites have been found very useful for various types of studies. Recently IRS-P4 was successfully launched and its Multi-frequency Scanning Microwave Radiometer (MSMR) sensor is giving near surface wind speed and total precipitable water content over oceanic region. An attempt has been made to assimilate directly these two geophysical parameters derived at 150 km resolution, along with other global meteorological data, in the National Center for Medium Range Weather Forecasting (NCMRWF), New Delhi Global Data Assimilation System. (GDAS). The paper describes the development work done in utilizing the surface wind speed and the total precipitable water content data in the NCMRWF operational GDAS. The basic algorithms for assimilating the MSMR data directly in the global analysis scheme have been described. The analyzed fields produced after running the six hourly GDAS cycle have been examined and various aspects of the impact of this data set on the global analysis especially for Indian oceanic region have been evaluated. Other aspects like, penalty contributions, root mean square (rms) errors of various types of data both with respect to the analysis and the background field, etc. have been examined. The impact of additional IRS-P4 data on assimilation and model simulation is found to be positive and beneficial. Present affiliation: Department of Meteorology, Florida State University, Tallahassee, Florida 32306, USA Received February 26, 2001; revised February 27, 2002     

利用AIRS卫星资料反演大气廓线Ⅰ.特征向量统计反演法

引进美国威斯康星大学的IMAPP（International MODIS／AIRS Preprocessing Package）软件包，介绍了利用高光谱分辨率大气红外探测器AIRS（Atmospheric Infrared Sounder）观测辐射值，用特征向量统计法反演大气温度、湿度等垂直廓线的算法，采用亮度温度分类和扫描角分类回归后，减小了反演误差。并将其应用到中国地区，通过与无线电探空值及欧洲中期天气预报中心ECMWF（European Center of Medium-range Weather Forecasts）客观分析场的比较，结果表明：该方法所获得的温度、水汽反演结果与探空观测及ECMWF大气廓线分布一致，且AIRS因其高光谱分辨率（即高垂直空间分辨率）显示了精细的大气结构。     

A Study on Retrieving Atmospheric Profiles from EOS/AIRS Observations

1. IntroductionThe development of global climate and weathermodels requires accurate monitoring of atmospherictemperature and moisture profiles, as well as the con-tents of trace gases and aerosols. It is quite difficultto monitor continuously these parameters on a globalscale.Until recently. AIRS (Atmospheric InfraredSounder) offers a new opportunity to improve globalmonitoring of temperature, moisture, and ozone distri-butions and changes therein. The high spectral resolu-tion (v/Δv ? 12…     

一次苏南大暴雨过程数值模拟及其对交通能见度的影响

利用0.5°×0.5°的GFS分析场数据和中尺度数值模式WRF,对2011年6月17—18日发生在江苏省南部地区覆盖沪宁、沿江及宁常高速公路的一次大暴雨天气过程进行了数值模拟和诊断分析。经过AWMS实测数据及常规气象观测资料的验证,得到如下结论:(1)WRF模式对此次大暴雨过程的模拟结果较为理想,具有良好的预报能力;(2)高低空急流的耦合是本次降水过程爆发的主要动力机制,西南低空急流为此次降水提供了水汽输送通道;(3)雨区上空的垂直螺旋度在低层为正、高层为负;(4)干空气的活动对强降水的形成具有一定的指示作用;(5)强降水是导致公路交通能见度降低的重要因素,且水平能见度与降水强度成负指数关系。     

一次粤西南暴雨过程的预报误差来源分析

本文基于WRF模式研究了2015年5月16～17日广东西南地区的一次暴雨过程的预报误差来源。首先比较了以NCEP_FNL为初始资料的WRF模式的模拟预报（记为WRF_FNL）和ECMWF（European Centre for Medium-Range Weather Forecasts）关于该次暴雨过程的确定性预报。结果表明,ECMWF具有较高的预报技巧,因此,认为ECMWF的模式和初始场都较为准确。进一步,以ECMWF的初值作为初始场,选用相同的物理参数化方案,再次用WRF模式进行预报（预报结果记为WRF_EC）。结果表明相对WRF_FNL,WRF_EC的预报结果有明显改善。这表明,初始场的改进对预报有较大的影响,初始误差是预报误差的重要来源。进一步,分析了初始误差的主要来源区域和来源变量。结果表明,南海北部湾至广西西南区域为本次暴雨预报初始误差的主要来源区域,而初始温度场和初始湿度场则为此次暴雨预报初始误差的主要来源变量。同时改进初始温度场和湿度场可以较大程度提高本次暴雨过程的预报技巧。     

Impact of single-point GPS integrated water vapor estimates on short-range WRF model forecasts over southern India

Specifying physically consistent and accurate initial conditions is one of the major challenges of numerical weather prediction (NWP) models. In this study, ground-based global positioning system (GPS) integrated water vapor (IWV) measurements available from the International Global Navigation Satellite Systems (GNSS) Service (IGS) station in Bangalore, India, are used to assess the impact of GPS data on NWP model forecasts over southern India. Two experiments are performed with and without assimilation of GPS-retrieved IWV observations during the Indian winter monsoon period (November–December, 2012) using a four-dimensional variational (4D-Var) data assimilation method. Assimilation of GPS data improved the model IWV analysis as well as the subsequent forecasts. There is a positive impact of ～10 % over Bangalore and nearby regions. The Weather Research and Forecasting (WRF) model-predicted 24-h surface temperature forecasts have also improved when compared with observations. Small but significant improvements were found in the rainfall forecasts compared to control experiments.