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 ...     

Assimilation of GPM Microwave Imager Radiance for Track Prediction of Typhoon Cases with the WRF Hybrid En3DVAR System

The impact of assimilating radiance data from the advanced satellite sensor GMI(GPM microwave imager) for typhoon analyses and forecasts was investigated using both a three-dimensional variational(3DVAR) and a hybrid ensemble-3DVAR method. The interface of assimilating the radiance for the sensor GMI was established in the Weather Research and Forecasting(WRF) model. The GMI radiance data are assimilated for Typhoon Matmo(2014), Typhoon Chan-hom(2015), Typhoon Meranti(2016), and Typhoon Mangkhut(2018) in the Pacific before their landing. The results show that after assimilating the GMI radiance data under clear sky condition with the 3DVAR method, the wind,temperature, and humidity fields are effectively adjusted, leading to improved forecast skills of the typhoon track with GMI radiance assimilation. The hybrid DA method is able to further adjust the location of the typhoon systematically. The improvement of the track forecast is even more obvious for later forecast periods. In addition, water vapor and hydrometeors are enhanced to some extent, especially with the hybrid method.     

Assimilating AMSU-a radiance data with the WRF hybrid En3DVAR system for track predictions of Typhoon Megi (2010)

The impact of assimilating radiances from the Advanced Microwave Sounding Unit-A (AMSU-A) on the track prediction of Typhoon Megi (2010) was studied using the Weather Research and Forecasting (WRF) model and a hybrid ensemble three-dimensional variational (En3DVAR) data assimilation (DA) system. The influences of tuning the length scale and variance scale factors related to the static background error covariance (BEC) on the track forecast of the typhoon were studied. The results show that, in typhoon radiance data assimilation, a moderate length scale factor improves the prediction of the typhoon track. The assimilation of AMSU-A radiances using 3DVAR had a slight positive impact on track forecasts, even when the static BEC was carefully tuned to optimize its performance. When the hybrid DA was employed, the track forecast was significantly improved, especially for the sharp northward turn after crossing the Philippines, with the flow-dependent ensemble covariance. The flow-dependent BEC can be estimated by the hybrid DA and was capable of adjusting the position of the typhoon systematically. The impacts of the typhoon-specific BEC derived from ensemble forecasts were revealed by comparing the analysis increments and forecasts generated by the hybrid DA and 3DVAR. Additionally, for 24 h forecasts, the hybrid DA experiment with use of the full flow-dependent background error substantially outperformed 3DVAR in terms of the horizontal winds and temperature in the lower and mid-troposphere and for moisture at all levels.     

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.     

AMSR2辐射率资料同化对台风“山神”分析和预报的影响研究

在WRFDA-3DVar（Weather Research and Forecasting model's 3-dimensional variational data assimilation）的框架下,添加了新的探测器AMSR2（Advanced Microwave Scanning Radiometer 2）微波辐射率资料的同化模块,实现了AMSR2辐射率资料在中小尺度同化系统中的有效使用。台风"山神"（Son-Tinh）直接同化AMSR2资料的个例试验结果表明,AMSR2资料可以很好的探测出台风的形态,并且与没有同化该资料的控制试验相比,同化AMSR2辐射率资料可以有效提高模式分析场的质量,进一步提高了台风中心气压,最大风速和台风路径的预报。     

Assimilation of All-sky Geostationary Satellite Infrared Radiances for Convection-Permitting Initialization and Prediction of Hurricane Joaquin (2015)

Intensity forecasting is one of the most challenging aspects of tropical cyclone (TC) forecasting. This work examines the impact of assimilating high-resolution all-sky infrared radiance observations from geostationary satellite GOES-13 on the convection-permitting initialization and prediction of Hurricane Joaquin (2015) with an ensemble Kalman filter (EnKF) based on the Weather Research and Forecasting (WRF) model. Given that almost all operational global and regional models struggled to capture Hurricane Joaquin (2015)’s intensity, this study examines the potential in improving Joaquin’s prediction when assimilating all-sky infrared radiances from GOES-13’s water vapor channel. It is demonstrated that, after a few 3-hour cycles assimilating all-sky radiance, the WRF model was able to forecast reasonably well Joaquin’s intensity, including its rapid intensification (RI). The improvement was largely due to a more realistic initial hurricane structure with a stronger, warmer, and more compact inner-core. Ensemble forecasts were used to further explore the important physical mechanisms driving the hurricane’s RI. Results showed that the RI forecasts were greatly impacted by the initial inner-core vortex structure.     

同化IASI资料对台风“红霞”和“莫兰蒂”预报的影响研究

红外高光谱大气探测仪IASI可提供高精度的大气垂直温度和湿度信息,能够探测台风结构特征,有效弥补台风影响区域观测资料稀缺的不足。以WRFDA三维变分同化系统为基础构建IASI同化试验平台,实现McNally提出的MW云检测方法,并调整参数形成大阈值的LMW云检测方法,以超强台风“红霞”（1506）和“莫兰蒂”（1614）为试验个例,对IASI观测资料进行同化对比试验。对于台风“红霞”,MW云检测方案对于高层通道299保留的观测数目仅为大阈值LMW云检测的16.2%和WRFDA系统默认的MMR云检测的9.2%,对于底层通道921分别为3.3%和2.6%。但是MW试验分析场强度最强,获得的72 h台风路径预报最接近真实路径,路径误差最小。两个台风个例试验结果相似,表明有效的云检测过程能提高IASI资料同化分析场的准确性,同化IASI资料有利于改善台风预报技巧。      

Impact of Coastal Radar Observability on the Forecast of the Track and Rainfall of Typhoon Morakot(2009)Using WRF-based Ensemble Kalman Filter Data Assimilation

This study explored the impact of coastal radar observability on the forecast of the track and rainfall of Typhoon Morakot(2009)using a WRF-based ensemble Kalman filter(EnKF)data assimilation(DA)system.The results showed that the performance of radar EnKF DA was quite sensitive to the number of radars being assimilated and the DA timing relative to the landfall of the tropical cyclone(TC).It was found that assimilating radial velocity(Vr)data from all the four operational radars during the 6 h immediately before TC landfall was quite important for the track and rainfall forecasts after the TC made landfall.The TC track forecast error could be decreased by about 43% and the 24-h rainfall forecast skill could be almost tripled.Assimilating Vr data from a single radar outperformed the experiment without DA, though with less improvement compared to the multiple-radar DA experiment.Different forecast performances were obtained by assimilating different radars, which was closely related to the first-time wind analysis increment, the location of moisture transport, the quasi-stationary rainband, and the local convergence line.However, only assimilating Vr data when the TC was farther away from making landfall might worsen TC track and rainfall forecasts.Besides, this work also demonstrated that Vr data from multiple radars, instead of a single radar, should be used for verification to obtain a more reliable assessment of the EnKF performance.     

Assimilating All-sky Infrared Radiances from Himawari-8 Using the 3DVar Method for the Prediction of a Severe Storm over North China

Although radar observations capture storm structures with high spatiotemporal resolutions, they are limited within the storm region after the precipitation formed. Geostationary satellites data cover the gaps in the radar network prior to the formation of the precipitation for the storms and their environment. The study explores the effects of assimilating the water vapor channel radiances from Himawari-8 data with Weather Research and Forecasting model data assimilation system(WRFDA) for a severe storm case over north China. A fast cloud detection scheme for Advanced Himawari imager(AHI)radiance is enhanced in the framework of the WRFDA system initially in this study. The bias corrections, the cloud detection for the clear-sky AHI radiance, and the observation error modeling for cloudy radiance are conducted before the data assimilation. All AHI radiance observations are fully applied without any quality control for all-sky AHI radiance data assimilation. Results show that the simulated all-sky AHI radiance fits the observations better by using the cloud dependent observation error model, further improving the cloud heights. The all-sky AHI radiance assimilation adjusts all types of hydrometeor variables, especially cloud water and precipitation snow. It is proven that assimilating all-sky AHI data improves hydrometeor specifications when verified against the radar reflectivity. Consequently, the assimilation of AHI observations under the all-sky condition has an overall improved impact on both the precipitation locations and intensity compared to the experiment with only conventional and AHI clear-sky radiance data.     

Quality Assessment and Forecast Sensitivity of Global Remote Sensing Observations

The satellite-derived wind from cloud and moisture features of geostationary satellites is an important data source for numerical weather prediction(NWP) models. These datasets and global positioning system radio occultation(GPSRO)satellite radiances are assimilated in the four-dimensional variational atmospheric data assimilation system of the UKMO Unified Model in India. This study focuses on the importance of these data in the NWP system and their impact on short-term24-h forecasts. The quality of the wind observations is compared to the short-range forecast from the model background. The observation increments(observation minus background) are computed as the satellite-derived wind minus the model forecast with a 6-h lead time. The results show the model background has a large easterly wind component compared to satellite observations. The importance of each observation in the analysis is studied using an adjoint-based forecast sensitivity to observation method. The results show that at least around 50% of all types of satellite observations are beneficial. In terms of individual contribution, METEOSAT-7 shows a higher percentage of impact(nearly 50%), as compared to GEOS, MTSAT-2and METEOSAT-10, all of which have a less than 25% impact. In addition, the impact of GPSRO, infrared atmospheric sounding interferometer(IASI) and atmospheric infrared sounder(AIRS) data is calculated. The GPSRO observations have beneficial impacts up to 50 km. Over the Southern Hemisphere, the high spectral radiances from IASI and AIRS show a greater impact than over the Northern Hemisphere. The results in this study can be used for further improvements in the use of new and existing satellite observations.     

三维变分同化机载雷达资料对飓风预报的影响研究——2012年Isaac试验

随着气旋内部资料(Inner core data)在热带气旋预报中的使用,其重要性逐渐受到人们越来越多的关注。为了研究该资料中尾部机载雷达(Tail Doppler Radar,TDR)资料在业务系统中的应用效果,本文利用2012年飓风等级热带气旋Isaac期间的TDR资料,采用业务HWRF(Weather Research and Forecasting model for Hurricane)数值模式与业务GSI(Grid-point Statistical Interpolation system)三维变分同化(Three-Dimensional Variational Data Assimilation, 3DVar)系统对TDR资料进行了同化,展开了一系列预报试验,并对其效果进行了分析和研究。结果表明与HWRF的业务预报相比,GSI系统同化TDR资料后对热带气旋的路径和强度预报有明显改进;但其同化效果同时也表明业务三维变分中的静态背景误差协方差在TDR资料的应用中仍需要进一步的改进。     

Application of ATOVS Radiance-Bias Correction to Typhoon Track Prediction with Ensemble Kalman Filter Data Assimilation

In this paper, firstly, the bias between observed radiances from the Advanced TIROS-N Operational Vertical Sounder (ATOVS) and those simulated from a model first-guess are corrected. After bias correction, the observed minus calculated (O-B) radiances of most channels were reduced closer to zero, with peak values in each channel shifted towards zero, and the distribution of O-B closer to a Gaussian distribution than without bias correction. Secondly, ATOVS radiance data with and without bias correction are assimilated directly with an Ensemble Kalman Filter (EnKF) data assimilation system, which are then adopted as the initial fields in the forecast model T106L19 to simulate Typhoon Prapiroon (2006) during the period 2-4 August 2006. The prediction results show that the assimilation of ATOVS radiance data with bias correction has a significant and positive impact upon the prediction of the typhoon’s track and intensity, although the results are not perfect.     

多普勒雷达资料循环同化在台风“鲇鱼”预报中的应用

高分辨率的中尺度预报模式ARPS及其3DVAR/云分析系统,针对2010年登陆福建的超强台风“鲇鱼”,研究对流可分辨尺度下,每1h循环同化沿海新一代多普勒雷达网资料分析、研究对台风初始场和预报场的改进作用.结果表明:单独同化雷达资料可显著改善初始场中的台风内核区动力和热力结构,以及台风强度和位置,进而提高18h台风强度、路径和降水预报,但预报路径和降水分布与实况仍存在差异.在雷达资料同化基础上加入常规观测资料,对初始场中台风内核区结构改进不大.但在显著调整大尺度背景场,从而进一步减少台风路径预报误差,能准确预报出福建沿海两个强降水区域的位置和强度.总体而言,雷达资料同化主要提高台风结构分析,而常规观测资料同化主要改善环境场分析,两者有效结合使得预报结果和实况最为接近.     

Assimilation of ASCAT Sea Surface Wind Retrievals with Correlated Observation Errors

Data assimilation systems usually assume that the observation errors of wind components, i.e., u(the longitudinal component) and v(the latitudinal component), are uncorrelated. However, since wind components are derived from observations in the form of wind speed and direction(spd and dir), the observation errors of u and v are correlated. In this paper, an explicit expression of the observation errors and correlation for each pair of wind components are derived based on the law of error propagation. The new data assimilation scheme considering the correlated error of wind components is implemented in the Weather Research and Forecasting Data Assimilation(WRFDA) system. Besides, adaptive quality control(QC) is introduced to retain the information of high wind-speed observations. Results from real data experiments assimilating the Advanced Scatterometer(ASCAT) sea surface winds suggest that analyses from the new data assimilation scheme are more reasonable compared to those from the conventional one, and could improve the forecasting of Typhoon Noru.     

GRAPES区域集合预报条件性台风涡旋重定位方法研究

为了在集合预报中更合理描述台风涡旋中心定位的不确定性,采用2009—2018年中国气象局和日本气象厅台风最佳路径数据,分析台风最佳路径涡旋中心定位的不确定性特征,在此基础上设计条件性台风涡旋重定位方法（Conditional Typhoon Vortex Relocation,CTVR）,构建集合成员台风涡旋中心重定位阈值条件、台风涡旋分离数学处理及涡旋重定位等数学处理过程,利用中国气象局数值预报中心区域集合预报系统（Global/Regional Assimilation and Prediciton System-Regional Ensemble System,GRAPES-REPS）对2018年西北太平洋上的3个台风（1808号“玛莉亚”、1824号“谭美”和1825号“康妮”）进行轴对称结构和轴对称+非对称结构条件性台风涡旋重定位两种方案的集合预报试验和检验评估。结果表明:（1）中国气象局和日本气象厅台风最佳路径误差平均值为13.72 km,可视为台风涡旋中心定位不确定性的合理估计值;（2）统计检验结果和典型个例分析表明,采用轴对称结构和轴对称+非对称结构条件性台风涡旋重定位方法的台风集合预报路径误差及集合预报一致性结果比较接近;（3）条件性台风涡旋重定位方法可以有效改进GRAPES-REPS区域集合预报台风路径概率预报效果,如台风路径集合预报平均误差有所减小,集合预报一致性（路径离散度与路径均方根误差比值）增大,特别是预报初期概率预报效果改进更为显著,而预报中后期改进有限;（4）通过对“玛莉亚”台风集合预报诊断分析发现,经过条件性台风涡旋重定位后,各集合成员的台风路径误差在预报初期明显减小且路径收敛,但随着预报时效的延长台风路径逐渐发散。应用条件性台风涡旋重定位方法后,台风涡旋环流与大尺度环境场仍然比较连续协调,且台风涡旋环流外的大尺度环境场具有一致性特点,最低气压误差、最大风速误差和降水预报技巧基本不变。可见,条件性台风涡旋重定位方法的应用可以提供更准确的台风路径预报不确定性信息,帮助预报员做出更准确的预报决策。      

雷达资料同化对2015年台风彩虹数值模拟改进

基于WRF中尺度模式,采用集合卡尔曼滤波方法同化中国岸基多普勒天气雷达径向速度资料,对2015年登陆台风彩虹（1522）进行数值试验。从台风强度、路径、结构等方面验证了同化效果,并对不同区域雷达观测资料的同化敏感性进行讨论。试验结果表明:在同化窗内同化分析场台风位置误差相比未同化平均减小15 km,最多时刻减小38 km,同化资料时次越多,确定性预报路径误差越小。同化雷达资料后较好地反映出台风彩虹（1522）近海加强过程,台风中心最低气压同化分析和预报误差相比未同化最大减小超过25 hPa,台风眼的尺度、眼墙处对流非对称结构相比未同化与观测更加接近。试验还表明:台风内核100 km范围内的雷达观测对同化效果影响最大,仅同化这部分资料（约占总量的20%）各方面效果与同化全部资料相近,而仅同化100 km以外资料效果明显不及同化所有资料。仅同化台风内核雷达观测资料可以在不影响同化效果的前提下,使集合同化计算机时减小为原来的1/3,该策略可为台风实际业务预报提供一定参考。     

The Impact of Satellite Radiance Data Assimilation within a Frequently Updated Regional Forecast System Using a GSI-based Ensemble Kalman Filter

A regional ensemble Kalman filter (EnKF) data assimilation (DA) and forecast system was recently established based on the Gridpoint Statistical Interpolation (GSI) analysis system. The EnKF DA system was tested with continuous threehourly updated cycles followed by 18-h deterministic forecasts from every three-hourly ensemble mean analysis. Initial tests showed negative to neutral impacts of assimilating satellite radiance data due to the improper bias correction procedure. In this study, two bias correction schemes within the established EnKF DA system are investigated and the impact of assimilating additional polar-orbiting satellite radiance is also investigated. Two group experiments are conducted. The purpose of the first group is to evaluate the bias correction procedure. Two online bias correction methods based on GSI 3DVar and EnKF algorithms are used to assimilate AMSU-A radiance data. Results show that both variational and EnKF-based bias correction procedures effectively reduce the observation and background radiance differences, achieving positive impacts on forecasts. With proper bias correction, we assimilate full radiance observations including AMSU-A, AMSU-B, AIRS, HIRS3/4, and MHS in the second group. The relative percentage improvements(RPIs) for all forecast variables compared to those without radiance data assimilation are mostly positive, with the RPI of upper-air relative humidity being the largest. Additionally, precipitation forecasts on a downscaled 13-km grid from 40-km EnKF analyses are also improved by radiance assimilation for almost all forecast hours.     

TAMDAR Observation Assimilation in WRF 3D-Var and Its Impact on Hurricane Ike (2008) Forecast

This study evaluates the impact of atmospheric observations from the Tropospheric Airborne Meteorological Data Reporting (TAMDAR) observing system on numerical weather prediction of hurricane Ike (2008) using three-dimensional data assimilation system for the Weather Research and Forecast (WRF) model (WRF 3D-Var). The TAMDAR data assimilation capability is added to WRF 3D-Var by incorporating the TAMDAR observation operator and corresponding observation processing procedure. Two 6-h cycling data assimilation and forecast experiments are conducted. Track and intensity forecasts are verified against the best track data from the National Hurricane Center. The results show that, on average, assimilating TAMDAR observations has a positive impact on the forecasts of hurricane Ike. The TAMDAR data assimilation reduces the track errors by about 30 km for 72-h forecasts. Improvements in intensity forecasts are also seen after four 6-h data assimilation cycles. Diagnostics show that assimilation of TAMDAR data improves subtropical ridge and steering flow in regions along Ike’s track, resulting in better forecasts.     

Evaluation of long-term trends in tropical cyclone intensity forecasts

Summary The National Hurricane Center and Joint Typhoon Warning Center operational tropical cyclone intensity forecasts for the three major northern hemisphere tropical cyclone basins (Atlantic, eastern North Pacific, and western North Pacific) for the past two decades are examined for long-term trends. Results show that there has been some marginal improvement in the mean absolute error at 24 and 48 h for the Atlantic and at 72 h for the east and west Pacific. A new metric that measures the percent variance of the observed intensity changes that is reduced by the forecast (variance reduction, VR) is defined to help account for inter-annual variability in forecast difficulty. Results show that there have been significant improvements in the VR of the official forecasts in the Atlantic, and some marginal improvement in the other two basins. The VR of the intensity guidance models was also examined. The improvement in the VR is due to the implementation of advanced statistical intensity prediction models and the operational version of the GFDL hurricane model in the mid-1990s. The skill of the operational intensity forecasts for the 5-year period ending in 2005 was determined by comparing the errors to those from simple statistical models with input from climatology and persistence. The intensity forecasts had significant skill out to 96 h in the Atlantic and out to 72 h in the east and west Pacific. The intensity forecasts are also compared to the operational track forecasts. The skill was comparable at 12 h, but the track forecasts were 2 to 5 times more skillful by 72 h. The track and intensity forecast error trends for the two-decade period were also compared. Results showed that the percentage track forecast improvement was almost an order of magnitude larger than that for intensity, indicating that intensity forecasting still has much room for improvement.     

2009年夏季西太平洋台风路径和强度的多模式集成预报

基于TIGGE资料中的中国气象局、欧洲中期天气预报中心、日本气象厅和英国气象局等四个中心的2009年5月1日-8月31日台风预报资料,利用多模式集合平均、消除偏差集合平均和加权消除偏差集合平均等方法,对2009年8月1-31日预报期的西太平洋的台风路径和强度(中心气压)进行24～ 72 h预报时效的多模式集成预报,并对0907号台风“天鹅”和0908号台风“莫拉克”进行个例分析.结果表明:各中心对于不同时效的预报,预报技巧有明显差异.消除偏差集合平均与加权消除偏差集合平均显著地减小了预报误差,预报效果优于最好的单个中心预报和多模式集合平均.对于24 ～ 72 h预报,加权消除偏差集合平均方法始终表现出最好的预报性能.