Analysis and very short range forecast of cyclone “AILA” with radar data assimilation with rapid intermittent cycle using ARPS 3DVAR and cloud analysis techniques

In this study, both reflectivity and radial velocity are assimilated into the Weather Research and Forecasting (WRF) model using ARPS 3DVAR technique and cloud analysis procedure for analysis and very short range forecast of cyclone ÁILA. Doppler weather radar (DWR) data from Kolkata radar are assimilated for numerical simulation of landfalling tropical cyclone. Results show that the structure of cyclone AILA has significantly improved when radar data is assimilated. Radar reflectivity data assimilation has strong influence on hydrometeor structures of the initial vortex and precipitation pattern and relatively less influence is observed on the wind fields. Divergence/convergence conditions over cyclone inner-core area in the low-to-middle troposphere (600–900 hPa) are significantly improved when wind data are assimilated. However, less impact is observed on the moisture field. Analysed minimum sea level pressure (SLP) is improved significantly when both reflectivity and wind data assimilated simultaneously (RAD-ZVr experiment), using ARPS 3DVAR technique. In this experiment, the centre of cyclone is relocated very close to the observed position and the system maintains its intensity for longer duration. As compared to other experiments track errors are much reduced and predicted track is very much closer to the best track in RAD-ZVr experiment. Rainfall pattern and amount of rainfall are better captured in this experiment. The study also reveals that cyclone structure, intensification, direction of movement, speed and location of cyclone are significantly improved and different stages of system are best captured when both radar reflectivity and wind data are assimilated using ARPS 3DVAR technique and cloud analysis procedure. Thus optimal impact of radar data is realized in RAD-ZVr experiment. The impact of DWR data reduces after 12 h forecast and it is due to the dominance of the flow from large-scale global forecast system model. Successful coupling of data assimilation package ARPS 3DVAR with WRF model for Indian DWR data is also demonstrated.     

Prediction of tropical systems over Indian region using mesoscale model

Summary The Advanced Regional Prediction System (ARPS) model developed at Center for Analysis and Prediction of Storms at Oklahoma State University, USA is used for simulation of monsoon depression and tropical cyclone over Indian region. The radiosonde data are included in the initial analyses and subsequently; the simulations are performed with 50km and 25km grid resolutions. Two sets of forecast experiments produced by two types of analyses (with radiosonde and without radiosonde data) are compared. It is found that predicted mean sea-level pressure of the depression becomes closer to mean sea level pressure reported in Indian Daily Weather Reports when initialized with analyses containing radiosonde data. The precipitation forecast also is improved when initialized with the analyses containing radiosonde data. The simulation of tropical cyclone with 25km grid resolution is able to simulate some subsynoptic scale features of the system.     

Impact of vortex-removal from environmental flow in cyclone track prediction using Lagrangian advection model

In the present study, a new approach is discussed to find out the residual steering flow from the high-resolution global Numerical Weather Prediction (NWP) model-forecasted wind fields, which have been used in the Lagrangian advection model to determine the track of tropical cyclones formed in the Indian Ocean. The Lagrangian advection model is newly developed model and conceptually closer to the dynamical models, which utilizes environmental steering flow and the effect due to earth’s rotation (the beta-effect) to determine the motion of cyclone. In this approach, the effect of environmental flow on the cyclone track is examined by removing the existing cyclone vortex from the steering flow which is determined by potential vorticity approach. A new approach based on vortex pattern matching has been used to identify the cyclone vortex and to remove it from the steering flow. The tracks of five tropical cyclones (viz., Nargis, Khai_Muk, Nisha, Aila and Phyan) which were formed in the North Indian basin during the period 2008–2009 have been generated by the Lagrangian advection model using the proposed scheme. The position errors were computed with respect to the Joint Typhoon Warning Center (JTWC) best track analysis positions and compared with that of without-vortex-removal scheme. The results show that the mean track errors for five cyclones are reduced by 6–35?% for 12–72?h forecast in case of vortex-removal scheme as compared to the without-vortex-removal scheme.     

多普勒天气雷达资料同化对江淮暴雨模拟的影响

利用GSI同化系统 (Gridpoint Statistical Interpolation System) 对我国多普勒天气雷达资料进行直接循环同化分析,并采用WRF-ARW 3.5.1中尺度模式对2013年我国夏季江淮流域典型暴雨过程进行模拟试验。结果表明:经过质量控制的雷达径向风、反射率因子资料经GSI同化系统同化后,可形成合理的分析增量。仅同化径向风,模拟的风场与实况更接近,模拟的降雨落区与观测雨带位置更加接近。仅同化反射率因子,对水平风场的直接调整比较小,通过水凝物含量调整,对水平和垂直风场进行调整,对降水的落区影响较小,主要影响模拟的降水强度。同时同化两种资料,能更好地反映风场特征,并改善强降水的落区和强度的模拟。模拟改善最明显是在积分12~36 h时段内,该时段有效雷达资料量较多,表明同化雷达资料对暴雨模拟确实具有正效果。     

天气雷达反射率资料订正前后在ARPS模式中的同化试验对比

以TRMM/PR反射率资料作参照,对常州、南京天气雷达反射率资料进行一致性订正,再利用中尺度模式ARPS及其数据同化系统ADAS对订正前、后的天气雷达反射率因子进行同化,模拟2013年6月25日和2010年8月24日江苏地区两次暴雨过程。两次暴雨过程均包括3组试验:控制试验和雷达资料订正前、后的同化试验。结果表明:(1)雷达反射率因子的同化很好地改进了模式初始湿度场,使降水预报在分布和强度上更接近实况;(2)雷达资料的订正进一步改进了反射率因子同化试验,并且通过调整初始湿度场和上升运动场(调整作用主要体现在前2~3 h内)改善了对降水的模拟预报,其结果证明雷达反射率因子的订正改善了雷达资料的质量。     

多普勒天气雷达资料在数值模式ARPS中的试验

利用中尺度预报模式ARPS及其资料分析系统ADAS和三维变分同化系统ARPS3DVAR,直接加入多普勒天气雷达基数据反射率因子和径向速度资料进行数值预报试验。试验包括：控制试验、反射率因子同化试验、径向速度同化试验,两种资料同时同化,多时次连续同化等试验。通过模拟2009年7月7日发生在江苏省附近一次暴雨过程,分析了多普勒天气雷达反射率因子和径向速度资料不同组合的加入对改进模式初始湿度场、风场及预报结果的影响。结果表明：同化雷达反射率因子和径向速度资料,分别对湿度场和风场有显著调整;同时同化两种资料比单独同化在2 h内更显优势;多时次连续同时同化两种资料的试验比起其他试验预报的风速更接近探空风速,预报的降水位置与雷达3 h累积降水产品较为对应,但降水量的预报仍有偏差。     

IMPROVEMENT OF NUMERICAL SIMULATION OF TYPHOON RANANIM (0414) BY USING DOPPLER RADAR DATA

Typhoon Rananim (0414) has been simulated by using the non-hydrostatic Advanced Regional Prediction System (ARPS) from Center of Analysis and Prediction of Storms (CAPS). The prediction of Rananim has generally been improved with ARPS using the new generation CINRAD Doppler radar data. Numerical experiments with or without using the radar data have shown that model initial fields with the assimilated radar radial velocity data in ARPS can change the wind field at the middle and high levels of the troposphere; fine characteristics of the tropical cyclone (TC) are introduced into the initial wind, the x component of wind speed south of the TC is increased and so is the y component west of it. They lead to improved forecasting of TC tracks for the time after landfall. The field of water vapor mixing ratio, temperature, cloud water mixing ratio and rainwater mixing ratio have also been improved by using radar reflectivity data. The model’s initial response to the introduction of hydrometeors has been increased. It is shown that horizontal model resolution has a significant impact on intensity forecasts, by greatly improving the forecasting of TC rainfall, and heavy rainstorm of the TC specially, as well as its distribution and variation with time.     

A quantitative analysis of KALPANA-1 derived water vapor winds and its impact on NWP model

The coverage of satellite derived winds over the Indian region including Indian Ocean has improved by the operation of India’s first dedicated satellite for meteorology, KALPANA-1 since 12 September 2002. Atmospheric motion vectors (AMVs) are being derived at the India Meteorological Department (IMD), New Delhi on a routine operational basis. The AMV is recognized as an important source of information for numerical weather prediction (NWP) and is particularly suited for tracking the low and middle level clouds mainly because of the good contrast in albedo between target and background, whereas the upper level moisture pattern can be better tracked by water vapor winds (WVW) using water vapor (WV) channel (5.7–7.1 μm). The WVWs proved to be a very useful wind product for predicting the future track position of cyclones, well marked low pressure areas or heavy rainfall warnings in advance and so, often these types of weather systems are steered by the upper level winds. In the present study, the quantitative as well as qualitative analyses of KALPANA-1 WVW have been carried out. The primary change introduced is making use of first guess (FG) forecast fields obtained from National Center for Environmental Prediction (NCEP) and Global Forecast System (GFS), at a resolution of 1° × 1° with T-382/L64 instead of forecasts of operational limited area model (LAM) of IMD. The overall results showed a consistent improvement after using improved FG wind fields from NCEP instead of LAM with a significantly increasing number of good qualities of KALPANA-1 derived WVWs. The quantitative error analysis has also been carried out for the validation of WVWs using collocated radiosonde observations for the period from May 2008 to December 2009 and the available mid-upper level winds derived from METEOSAT-7 data for the period from October to December 2008. The analysis shows that after modification, the RMSE and bias of KALPANA-1 WVWs have reduced considerably. Further, to assess the impact of these winds, a high resolution mesoscale model WRF 3DVAR system is used in the present study for the analysis of tropical cyclone ‘Sidr’. The results show that the wind assimilation experiments (analysis at 200 hPa) using upper level KALPANA-1 WVW have great potential for improving the NWP analysis. The impact of additional wind data in the model is found to be positive and beneficial.     

Parallelization Load Balance Strategy for a Global Grid-Point Model

The Global/Regional Assimilation and PrEdiction System (GRAPES) is a new-generation operational numerical weather prediction (NWP) model developed by the China Meteorological Administration (CMA).It is a grid-point model with a code structure different from that of spectral models used in other operational NWP centers such as the European Centre for Medium-Range Weather Forecasts (ECMWF),National Centers for Environmental Prediction (NCEP),and Japan Meteorological Agency (JMA),especially in the context of p...     

Land-surface scheme validation using the Oklahoma Atmospheric Surface-layer Instrumentation System (OASIS) and Oklahoma Mesonet data: Preliminary results

Summary The Oklahoma Atmospheric Surface-layer Instrumentation System (OASIS) is a recently-developed observational system that collects, archives, and quality controls atmospheric, surface, and soil data in real-time from 90 stations across Oklahoma. Ten of the 90 sites, termed “super sites”, are equipped with additional sonic anemometry and four-component net radiometers to provide complete observations of the surface energy balance. Oklahoma Mesonet and OASIS data are used in this study to validate the sensitivity and accuracy of a land-surface scheme within a numerical prediction model. The Advanced Regional Prediction System (ARPS) is a three-dimensional, nonhydrostatic mesoscale model developed by the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma. The land-surface model (LSM) used within ARPS is the Interactions Soil Biosphere Atmosphere (ISBA) scheme. Mesonet and OASIS data collected from the super site located in Norman, Oklahoma, are used as verification for the ISBA. Research presented in this study outlines the challenges in developing, maintaining, and using in-situ data for model validation. Such problems as instrument error, surface heterogeneity, and non-closure of the surface energy budget limit data accuracy. Preliminary results of model validation focus on the sensitivity of the soil physics within the ISBA scheme. Model sensitivity to vegetation cover, surface roughness, and soil type are investigated. Furthermore, several recent improvements to ISBA are evaluated and compared to observations. This study concludes that the sensitivity of the ISBA to a priori soil and vegetation type is detrimental for this scheme to be used in a mesoscale model without improved treatment of surface heterogeneity. Received November 18, 2001 Revised December 28, 2001     

多普勒雷达资料在中尺度模式短时预报中的应用

将国内新一代多普勒雷达基数据Lev-Ⅱ的9个仰角的原始反射率和径向风资料应用于中尺度模式ARPS(the Advanced Regional Prediction System),通过对2007年7月18日济南短时特大暴雨过程的个例研究,分析了雷达资料对初始场的改进效果及其对模拟结果的影响。结果表明,同时利用雷达反射率和径向风资料改进过的初始场能明显改善中尺度数值模式短时定量降水预报。对比风场发现,雷达径向风调整后在初始场中出现明显的中小尺度特征,能减少模式的spin-up时间。随着积分的进行,对水汽场的调节也有一定作用,但相对于风场调节不够显著。雷达反射率主要是改进初始场中的湿度参数,增加初始场中云水等含量。积分开始后,对风场调整也很显著。此次模拟,控制试验较好地预报出雷达回波的分布和水汽中心,对应的降水落区与实况也较为接近,但对雷达回波中心强度的预报略大,相应地降水量的预报也偏大。     

“碧利斯”暴雨增幅高分辨率数值模拟及诊断分析

利用中尺度非静力数值模式ARPS (Advanced Regional Prediction System),对2006年第4号强热带风暴“碧利斯”的登陆过程,尤其是登陆后的暴雨增幅过程开展了高分辨率数值模拟,并利用观测资料对模拟结果进行了验证,进一步基于该高分辨率模拟资料对该暴雨增幅过程开展了诊断分析.结果表明,ARPS模式较好地模拟再现了“碧利斯”的登陆过程以及登陆后在广东、江西、湖南三省交界处引发的暴雨增幅过程;散度垂直通量和湿位涡的大值区与暴雨增幅地区均有良好的对应关系,对强降水落区有较好指示意义,其中散度垂直通量的对应关系更好.湿位涡不仅很好地反映了与暴雨增幅相关的湿位涡分布,同时也很好地反映了“碧利斯”环流本身所对应的湿位涡分布特征.     

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。     

多普勒雷达资料对中尺度系统短期预报的改进

文中给了一种由单多普勒雷达资料反演风矢量场的变分方法 ,并由此形成预报初始场。运用美国风暴分析预报中心 ( CAPS)的改进的区域系统模式对移经美国俄克拉何马州的一个强雷暴系统进行了数值预报试验 ,结果表明 ,初始场中引入多普勒雷达观测资料后明显地改进了预报。     

数值天气预报业务模式现状与展望

20 0 4年是数值天气预报理论提出 10 0周年 ,同时也是数值天气预报业务化应用 5 0周年。经过百年的发展历程 ,数值天气预报学科有了飞跃的发展。特别是最近 10多年来 ,大气科学以及地球科学的研究进展 ,高速度、大容量的巨型计算机及网络系统的快速发展 ,更加快了数值天气预报的发展步伐。文中从模式动力框架、物理过程参数化、模式程序软件等方面对数值天气预报业务模式现状进行了简要综述 ,对存在的问题进行了探讨 ,并对数值天气预报业务模式的未来发展作了展望。当前数值天气预报业务模式发展的特点有 :(1) 2 0世纪 90年代中期以来 ,各国的全球和区域模式水平和垂直分辨率都有明显提高 ,且模式物理过程也同步进行改进 ;数值预报业务模式已进入了大规模并行计算的阶段 ;(2 )主要发达国家和中国都正在致力于研发各自的新业务数值预报模式———非静力 (多尺度 )一体化模式或非静力中尺度模式 ,部分国家的新一代天气 气候一体化数值模式已业务运行 ;(3)业务数值预报模式正在朝着不断完善的方向发展。随着模式分辨率的提高 ,云物理过程、陆面过程和湍流过程、考虑坡度 -坡向因子的辐射过程等在模式中的参数化方案 ,以及模式垂直坐标的选择越来越受重视 ,这些物理过程的描述成为业务数值模式改进的重点和     

台风试验陆地加密观测在数值天气预报中的作用

采用国家气象中心（NMC）有限区同化预报系统（HLAFS）和华北区域分析预报系统（NCAFS）的基本方案,选择“八五”期间台风试验期进行陆地测站特殊加密观测的两个台风个例作数值天气预报（NWP）试验,探讨这些资料在NWP中的作用。试验结果表明:陆地测站加密地面观测资料,对NMC的NWP系统的预报,尤其在较高分辨率情况下,具有较明显的改进作用。     

双多普勒雷达风场反演资料的同化应用试验

利用合肥、马鞍山2部多普勒雷达联合观测资料及多部多普勒雷达合成和连续调整技术（MUSCAT）,在笛卡尔坐标下反演得到三维风场。将得到的风场通过由CAPS（center for analysis and prediction of storm）研发的ARPS（advancedregional prediction systemv5．2．4）模式及数据处理系统ADAS（ARPS data analysissystem）进行同化试验。结果表明,反演风场信息可以改善短时模拟场、特别是风场的状况;但由于对湿度场等的改进较小,致使预报的3h降水量与实况有出入。     

Radar data assimilation of the GRAPES model and experimental results in a typhoon case

Constructing β-mesoscale weather systems in initial fields remains a challenging problem in a mesoscale numerical weather prediction (NWP) model. Without vertical velocity matching the β-mesoscale weather system, convection activities would be suppressed by downdraft and cooling caused by precipitating hydrometeors. In this study, a method, basing on the three-dimensional variational (3DVAR) assimilation technique, was developed to obtain reasonable structures of β-mesoscale weather systems by assimilating radar data in a next-generation NWP system named GRAPES (the Global and Regional Assimilation and Prediction System) of China. Single-point testing indicated that assimilating radial wind significantly improved the horizontal wind but had little effect on the vertical velocity, while assimilating the retrieved vertical velocity (taking Richardson's equation as the observational operator) can greatly improve the vertical motion. Experiments on a typhoon show that assimilation of the radial wind data can greatly improve the prediction of the typhoon track, and can ameliorate precipitation to some extent. Assimilating the retrieved vertical velocity and rainwater mixing ratio, and adjusting water vapor and cloud water mixing ratio in the initial fields simultaneously, can significantly improve the tropical cyclone rainfall forecast but has little effect on typhoon path. Joint assimilating these three kinds of radar data gets the best results. Taking into account the scale of different weather systems and representation of observational data, data quality control, error setting of background field and observation data are still requiring further in-depth study.     

雷达资料同化对一次飑线过程的模拟影响

雷达资料同化能够改善强对流天气的预报,但是不同的模式方案配置会得到不同的结果。本文针对中国南部2018年3月4日一次飑线过程,以全球预报模式GFS分析场和预报场为背景场,采用中尺度区域气象预报模式ARPS 3DVAR系统同化多普勒雷达径向速度,用云分析处理反射率数据,考虑同化间隔、频次、云分析中不同参数调整,采用1 h同化窗口,设计不同同化方案,最后用WRF模式进行预报,研究雷达资料同化对飑线系统触发及发展机制的影响。结果表明,同化间隔过短时,由于模式热动力变量没有平衡产生虚假回波,同化间隔过长时,系统触发和发展的特征普遍偏弱;采用12 min间隔同化得到了最好的初始场,并且同化频次越高得到的降水预报结果越好。此外,ARPS云分析能大大改善初始场,减少模式自调整时间,其中湿度调整、温度调整、雨水调整及水汽调整对系统动力过程和水凝物初始场分布都有较大的影响,而垂直速度相关参数调整影响较小。     

2006年7—9月西北太平洋热带气旋季节活动的数值模拟

利用NCEP（National Centers for Environmental Prediction）提供的1°×1°的FNL（final）资料和中尺度WRF（Weather Research and Forecasting）模式,研究了热带气旋（tropical cyclone,简记TC）动力季节预报的可能性,通过在27km的粗网格中运用张弛逼近（Nudging）技术,对2006年7-9月西北太平洋TC活动进行了92d的连续数值积分。与观测结果比较表明,WRF模式不仅较好地模拟了MJO（Madden-Julian oscillation）和准双周振荡的活动情况,而且模拟的TC频数、移动路径和强度都与实际观测结果比较接近。在嵌套的9km网格中,不仅模拟出眼墙、暖心等TC结构的主要特征和TC的西行盛行路径及登陆活动情况,而且所模拟的生成过程包括早期研究中提出的TC生成过程中的两次快速发展的过程。模拟的TC初始涡旋主要出现在季风槽中,伴随准双周振荡活动,它的第一次发展在初始涡旋中心形成强烈的对流区;经过一段时间的减弱后,在有利的大尺度形势下,涡旋中心湿水汽层迅速增厚,导致气旋的第二次强烈发展。