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.     

Assessment of Data Assimilation Approaches for the Simulation of a Monsoon Depression Over the Indian Monsoon Region

A low pressure system that formed on 21 September 2006 over eastern India/Bay of Bengal intensified into a monsoon depression resulting in copious rainfall over north-eastern and central parts of India. Four numerical experiments are performed to examine the performance of assimilation schemes in simulating this monsoon depression using the Fifth Generation Mesoscale Model (MM5). Forecasts from a base simulation (with no data assimilation), a four-dimensional data assimilation (FDDA) system, a simple surface data assimilation (SDA) system coupled with FDDA, and a flux-adjusting SDA system (FASDAS) coupled with FDDA are compared with each other and with observations. The model is initialized with Global Forecast System (GFS) forecast fields starting from 19 September 2006, with assimilation being done for the first 24 hours using conventional observations, sounding and surface data of temperature and moisture from Advanced TIROS Operational Vertical Sounder satellite and surface wind data over the ocean from QuikSCAT. Forecasts are then made from these assimilated states. In general, results indicate that the FASDAS forecast provides more realistic prognostic fields as compared to the other three forecasts. When compared with other forecasts, results indicate that the FASDAS forecast yielded lower root-mean-square (r.m.s.) errors for the pressure field and improved simulations of surface/near-surface temperature, moisture, sensible and latent heat fluxes, and potential vorticity. Heat and moisture budget analyses to assess the simulation of convection revealed that the two forecasts with the surface data assimilation (SDA and FASDAS) are superior to the base and FDDA forecasts. An important conclusion is that, even though monsoon depressions are large synoptic systems, mesoscale features including rainfall are affected by surface processes. Enhanced representation of land-surface processes provides a significant improvement in the model performance even under active monsoon conditions where the synoptic forcings are expected to be dominant.     

Assimilation of the multisatellite data into the WRF model for track and intensity simulation of the Indian Ocean tropical cyclones

Weather Research and Forecasting (WRF-ARW) model and its three-dimensional variational data assimilation (3D-Var) system are used to investigate the impact of the Quick Scatterometer (QuikSCAT) near surface winds, Special Sensor Microwave/Imager (SSM/I)-derived Total Precipitable Water (TPW), and Meteosat-7-derived Atmospheric Motion Vectors (AMVs) on the track and intensity prediction of tropical cyclones over the North Indian Ocean. The case of tropical cyclone, Gonu (June 2007; Arabian Sea), is first tested and the results show significant improvements particularly due to the assimilation of QuikSCAT winds. Three other cases, cyclone Mala (April 2006; Bay of Bengal), Orissa super cyclone (October 1999; Bay of Bengal), and Very Severe Cyclonic storm (October 1999; Bay of Bengal), are then examined. The prediction of cyclone tracks improved significantly with the assimilation of QuikSCAT winds. The track improvement resulted from the relocation of the initial cyclonic vortices after the assimilation of QuikSCAT wind vectors. After the assimilation of QuikSCAT winds, the mean (for four cyclone cases) track errors for first, second, and third day forecasts are reduced to 72, 101, and 166?km, respectively, from 190, 250, and 381?km of control (without QuikSCAT winds) runs. The assimilation of QuikSCAT winds also shows positive impact on the intensity (in terms of maximum surface level winds) prediction particularly for those cyclones, which are at their initial stages of the developments at the time of data assimilation. The assimilation of SSM/I TPW has significant influence (negative and positive) on the cyclone track. In three of the four cases, the assimilation of the SSM/I TPW resulted in drying of lower troposphere over cyclonic region. This decrease of moisture in TPW assimilation experiment resulted in reduction of cyclonic intensity. In three of the four cyclones, the assimilation of Meteosat-7 AMVs shows negative impact on the track prediction.     

Observation system experiments for Typhoon Jangmi (200815) observed during T-PARC

In this study, the impact of various types of observations on the track forecast of Tropical Cyclone (TC) Jangmi (200815) is examined by using the Weather Research and Forecasting (WRF) model and the corresponding three-dimensional variational (3DVAR) data assimilation system. TC Jangmi is a recurving typhoon that is observed as part of the THORPEX Pacific Asian Regional Campaign (T-PARC). Conventional observations from the Korea Meteorological Administration (KMA) and targeted dropsonde observations from the Dropwindsonde Observations for Typhoon Surveillance near the Taiwan Region (DOTSTAR) were used for a series of observation system experiments (OSEs). We found that the assimilation of observations in oceanic areas is important to analyze environmental flows (such as the North Pacific high) and to predict the recurvature of TC Jangmi. The assimilation of targeted dropsonde observations (DROP) results in a significant impact on the track forecast. Observations of ocean surface winds (QSCAT) and satellite temperature soundings (SATEM) also contribute positively to the track forecast, especially two- to three-day forecasts. The impact of sensitivity guidance such as real-time singular vectors (SVs) was evaluated in additional experiments.     

Assimilation of HY-2A Scatterometer Ambiguous Winds Based on Feature Thinning

This paper focuses on the data assimilation methods for sea surface winds, based on the level-2B HY-2A satellite microwave scatterometer wind products. We propose a new feature thinning method, which is herein used to screen scatterometer winds while maintaining the key structure of the wind field in the process of data thinning for highresolution satellite observations. We also accomplish feeding the ambiguous wind solutions directly into the data assimilation system, thus making better use of the retrieved information while simplifying the assimilation process of the scatterometer products. A numerical simulation experiment involving Typhoon Danas shows that our method gives better results than the traditional approach. This method may be a valuable alternative for operational satellite data assimilation.     

Assimilation of <Emphasis Type="Italic">HY-2A</Emphasis> scatterometer ambiguous winds based on feature thinning

This paper focuses on the data assimilation methods for sea surface winds, based on the level-2B HY-2A satellite microwave scatterometer wind products. We propose a new feature thinning method, which is herein used to screen scatterometer winds while maintaining the key structure of the wind field in the process of data thinning for highresolution satellite observations. We also accomplish feeding the ambiguous wind solutions directly into the data assimilation system, thus making better use of the retrieved information while simplifying the assimilation process of the scatterometer products. A numerical simulation experiment involving Typhoon Danas shows that our method gives better results than the traditional approach. This method may be a valuable alternative for operational satellite data assimilation.     

FY-2C云迹风资料同化应用对台风预报的影响试验研究

针对0505号台风“海棠”, 采用WRF区域中尺度模式进行控制试验和两个同化试验, 利用WRF-3DVAR同化系统同化FY-2C红外和水汽两个通道云迹风反演产品, 同化分云迹风经质量控制和未经质量控制两组同化试验。通过三组试验分析云迹风资料对降水和风场等的预报结果的影响, 并进行24小时降水量分级Ts评分检验以及风场点对点检验。结果表明: 同化经质量控制云迹风资料可以提高降水落区和强度预报的准确度, 不同等级的Ts评分较其它试验都有较明显改进; 风场预报模拟也有所改善。增加两例台风, 使用与“海棠” 相似的处理方法进行模拟试验, 并对模拟结果24小时降水分析与检验, 得到与“海棠”类似结论。因此, 经过合理性选择的云迹风资料的加入, 有利于补充初始场中可能未包含的中尺度信息, 从而提高试验中对于降水、风场等的模拟效果, 提高WRF模式的模拟预报能力。     

Assimilating QuikSCAT Ocean Surface Winds with the Weather Research and Forecasting Model for Surface Wind-Field Simulation over the Chukchi/Beaufort Seas

To achieve a high-quality simulation of the surface wind field in the Chukchi/Beaufort Sea region, quick scatterometer (QuikSCAT) ocean surface winds were assimilated into the mesoscale Weather Research and Forecasting model by using its three-dimensional variational data assimilation system. The SeaWinds instrument on board the polar-orbiting QuikSCAT satellite is a specialized radar that measures ice-free ocean surface wind speed and direction at a horizontal resolution of 12.5 km. A total of eight assimilation case studies over two five-day periods, 1–5 October 2002 and 20–24 September 2004, were performed. The simulation results with and without the assimilation of QuikSCAT winds were then compared with QuikSCAT data available during the subsequent free-forecast period, coastal station observations, and North American Regional Reanalysis data. It was found that QuikSCAT winds are a potentially valuable resource for improving the simulation of ocean near-surface winds in the Chukchi/Beaufort Seas region. Specifically, the assimilation of QuikSCAT winds improved, (1) offshore surface winds as compared to unassimilated QuikSCAT winds, (2) sea-level pressure, planetary boundary-layer height, as well as surface heat fluxes, and (3) low-level wind fields and geopotential height. Verification against QuikSCAT data also demonstrated the temporal consistency and good quality of QuikSCAT observations.     

Simulation of heavy rainfall events over Indian monsoon region using WRF-3DVAR data assimilation system

We present the results of the impact of the 3D variational data assimilation (3DVAR) system within the Weather Research and Forecasting (WRF) model to simulate three heavy rainfall events (25–28 June 2005, 29–31 July 2004, and 7–9 August 2002) over the Indian monsoon region. For each event, two numerical experiments were performed. In the first experiment, namely the control simulation (CNTL), the low-resolution global analyses are used as the initial and boundary conditions of the model. In the second experiment (3DV-ANA), the model integration was carried out by inserting additional observations in the model’s initial conditions using the 3DVAR scheme. The 3DVAR used surface weather stations, buoy, ship, radiosonde/rawinsonde, and satellite (oceanic surface wind, cloud motion wind, and cloud top temperature) observations obtained from the India Meteorological Department (IMD). After the successful inclusion of additional observational data using the 3DVAR data assimilation technique, the resulting reanalysis was able to successfully reproduce the structure of convective organization as well as prominent synoptic features associated with the mid-tropospheric cyclones (MTC). The location and intensity of the MTC were better simulated in the 3DV-ANA as compared to the CNTL. The results demonstrate that the improved initial conditions of the mesoscale model using 3DVAR enhanced the location and amount of rainfall over the Indian monsoon region. Model verification and statistical skill were assessed with the help of available upper-air sounding data. The objective verification further highlighted the efficiency of the data assimilation system. The improvements in the 3DVAR run are uniformly better as compared to the CNTL run for all the three cases. The mesoscale 3DVAR data assimilation system is not operational in the weather forecasting centers in India and a significant finding in this study is that the assimilation of Indian conventional and non-conventional observation datasets into numerical weather forecast models can help improve the simulation accuracy of meso-convective activities over the Indian monsoon region. Results from the control experiments also highlight that weather and regional climate model simulations with coarse analysis have high uncertainty in simulating heavy rain events over the Indian monsoon region and assimilation approaches, such as the 3DVAR can help reduce this uncertainty.     

Evaluation of the assimilation of conventional and satellite-based observations in simulating heavy rainfall event using WRFDA over the North-West Himalayan region

Present work elucidates the impact of 3DVAR data assimilation technique for the simulation of one of the heavy rainfall events reported over Kotdwara region in the North-West Himalayan (NWH) region on 4th August 2017. We have examined the impact of conventional and satellite-based radiance datasets on the simulated results with and without assimilating the observations into the Weather Research and Forecasting (WRF) model. Three experiments have been designed with 3 nested domains of variable resolutions, one without assimilation (referred as control experiment) and other two experiments after assimilating conventional and satellite radiances observations (refer as DA-OBS and DA-SAT respectively). In the present study, assimilation of surface, upper air and the satellite-based radiance observations has been carried out for the outermost domain with horizontal resolution of 9 km. Statistical analysis suggests that the correlation coefficient is high (0.55) and root mean square error (RMSE) is low (17.12) for DA-SAT experiment as compared to other two experiments. Substantial improvement in the location, pattern and intensity of extreme rainfall event is noted after assimilation of both conventional and satellite observations with respect to the observed rainfall data. However, it is noted that the assimilation of satellite radiances has greater impact in simulating better intensity of the heavy rainfall event as compared to the assimilation of conventional observations. Plausible reason behind this could be the non-availability of the conventional observations close to the extreme rainfall event affected region.     

Three-dimensional variational data assimilation of WindSat ocean surface winds for the genesis and forecasting of tropical storm Henri

With available high-resolution ocean surface wind vectors retrieved from the U.S. Naval Research Laboratorys WindSat on Coriolis, the impact of these data on genesis and forecasting of tropical storm Henri is examined using the non-hydrostatic, fifth-generation mesoscale model (MM5) of Pennsylvania State University-National Center for Atmospheric Research plus its newly released three-dimensional variational data assimilation (3DVAR) system. It is shown that the assimilation of the WindSat-retrieved ocean surface wind vectors in the 3DVAR system improves the model initialization fields by introducing a stronger vortex in the lower troposphere. As a result, the model reproduces the storm formation and track reasonably close to the observations. Compared to the experiment without the WindSat surface winds, the WindSat assimilation reduced an error between the model simulated track and observations of more than 80 km and also improved the storm intensity by nearly 2 hPa. It suggests that these data could provide early detection and prediction of tropical storms or hurricanes.     

Assimilation of Doppler Radar Observations with an Ensemble Square Root Filter：A Squall Line Case Study

The effectiveness of using an Ensemble Square Root Filter（EnSRF） to assimilate real Doppler radar observations on convective scale is investigated by applying the technique to a case of squall line on 12July 2005 in midwest Shandong Province using the Weather Research and Forecasting（WRF） model.The experimental results show that：（1） The EnSRF system has the potential to initiate a squall line accurately by assimilation of real Doppler radar data.The convective-scale information has been added into the WRF model through radar data assimilation and thus the analyzed fields are improved noticeably.The model spin-up time has been shortened,and the precipitation forecast is improved accordingly.（2） Compared with the control run,the deterministic forecast initiated with the ensemble mean analysis of EnSRF produces more accurate prediction of microphysical fields.The predicted wind and thermal fields are reasonable and in accordance with the characteristics of convective storms.（3） The propagation direction of the squall line from the ensemble mean analysis is consistent with that of the observation,but the propagation speed is larger than the observed.The effective forecast period for this squall line is about 5-6 h,probably because of the nonlinear development of the convective storm.     

Improved tropical cyclone forecasts over north Indian Ocean with direct assimilation of AMSU-A radiances

The Weather Research and Forecasting (WRF-ARW) model and its three-dimensional variational data assimilation (3D-Var) system are used to investigate the impact of the Advanced Microwave Sounding Unit-A (AMSU-A) radiances on the prediction of Indian Ocean tropical cyclones. Three tropical cyclones are selected for this study: cyclone Mala (April 2006; Bay of Bengal), cyclone Gonu (June 2007; Arabian Sea), and cyclone Sidr (November 2007; Bay of Bengal). For each case, observing system experiments are designed, by producing two sets of analyses from which forecasts are initialized. Both sets of analyses contain all conventional and satellite observations operationally used, including, but not limited to, Quick Scatterometer (QuikSCAT) surface winds, Special Sensor Microwave/Imager (SSM/I) surface winds, Meteosat-derived atmospheric motion vectors (AMVs), and differ only in the exclusion (CNT) or inclusion (EXP) of AMSU-A radiances. Results show that the assimilation of AMSU-A radiances changes the large-scale thermodynamic structure of the atmosphere, and also produce a stronger warm core. These changes cause large forecast track improvements. In particular, without AMSU-A assimilation, most forecasts do not produce landfall. On the contrary, the forecasts initialized from improved EXP analyses in which AMSU-A data are included produce realistic landfall. In addition, intensity forecast is also improved. Even if the analyzed cyclone intensity is not affected by the assimilation of AMSU-A radiances, the predicted intensity improves substantially because of the development of warm cores which, through creation of stronger gradients, helps the model in producing intense low centre pressure.     

雷达热动力反演资料的高分辨率同化模拟试验

目前,北京地区的天气预报系统对局地对流性定量降水预报能力较弱,远不能满足人们生产、生活和防灾、减灾工作的需要。针对北京地区对提高0-12 h短时临近天气,尤其是夏季局地对流性降水预报能力的需求,基于中国气象局北京城市气象研究所变分多普勒雷达分析系统（VDRAS）的雷达热动力反演资料,建立了WRF模式初始化模块,采用四维资料同化（FDDA）方法,将VDRAS系统高时空分辨率三维热动力结构分析场资料同化到WRF模式中,实现了北京地区VDRAS分析场资料在WRF中尺度模式系统中的应用。通过降水个例的高分辨率同化模拟试验分析了雷达热动力反演资料同化对模式预报结果的影响。研究结果表明:雷达热动力反演资料的同化能够提高模式系统对近地面温、湿、风大气要素和降水过程的模拟能力,改善2 m比湿、降水落区、降水量级、降水时间的预报效果,减少降水漏报的现象。温度和比湿的同化比风的同化对模拟降水结果的改善更重要。虽然研究表明雷达热动力反演资料在WRF模式中的同化能够明显改善模式对选取降水个例的模拟效果,但其对模式尤其是数值业务模式系统预报效果的影响需要进一步更全面、更系统的检验,为业务化应用奠定更坚实的基础。      

卫星资料循环同化应用对区域数值预报效果影响分析

为提高卫星资料在同化系统中的利用率及验证卫星微波资料对区域数值预报效果的影响,本文以2008年8月1-31日为研究时段,利用WRF模式及其WRF-3DVAR同化模块,设计并构建了卫星微波资料的快速循环同化方案,分析循环同化方案对数值预报的改进效果.结果表明,相比于单时次同化,循环同化方案使各预报要素的相关系数在一定程度上得到改善,均方差也呈现减小的趋势.此外,对研究时段内暴雨和台风个例的具体分析显示,循环同化方案能够有效改善降水和台风路径的预报.     

Analyses of Orissa Super Cyclone using TRMM (TMI), DMSP (SSM/I) and OceanSat-I (MSMR) Derived Data

The super cyclone in October 1999 was the most intense tropical cyclone in the last century in Orissa, a coastal state in India. This state was battered for more than two days by strong winds and intense rain killing thousands of people. The main objective of this study is to examine the impact of total precipitable water content (TPWC) and surface wind speed data from sensors on board the Tropical Rainfall Measuring Mission (TRMM), Defense Meteorological Satellite Project (DMSP), and Indian Remote Sensing Satellite (OceanSat-I) satellites on the data assimilation system at NCMRWF, New Delhi during the Orissa cyclone period. Comparison of various assimilation experiments suggests that the utilization of TRMM Microwave Imager (TMI) data in the assimilation produced the best analyses. However, in all the forecasts, the storm was predicted to weaken and did not have a reasonably good track. Assimilation experiments with the other two satellite data showed the cyclone track much to the south of the observed track and also it was a weak storm. Biases in the data, when compared with each other, are evident in the analyses also. Better analyses are obtained when the satellite data are used in the originally obtained resolution than when reduced by averaging. A forecast experiment with assimilated data, utilizing the Cloud Motion Vectors (CMVs) from METEOSAT along with TMI data, produced the best forecast among all the experiments. However, the forecast quality was poor. A high-resolution data assimilation experiment was carried out to see the impact of model resolution on the analyses of the cyclone. The strength of the cyclone further increased when higher resolution TMI data were included. The study highlights the need for more satellite data over the Indian Ocean, where conventional data coverage is too poor to define the vertical structure of the atmosphere.     

Construction of vertical wind profile from satellite-derived winds for objective analysis of wind field

During summer Monex-79, a variety of observing systems viz. research ships, research aircrafts, constant pres-sure balloons and geostationary satellite etc. were deployed, besides the regular conventional observations The pur-pose of these additional systems was to make the best possible data for the studies on various aspects of monsoon cir-culation. The present study is aimed at the construction of vertical wind profile using cloud motion vectors obtained from GOES (I-O) satellite and to examine whether the constructed wind profiles improves the representation of the monsoon system, flow pattern etc. in the objective analysis. For this purpose, climatological normals of the wind field are considered as the initial guess and the objective analyses of the wind field are made with, first using only data from conventional observations over land areas, subsequently including the constructed winds from cloud motion vectors. These analyses are then compared with the standard analyses of wind field obtained from Quick Look Atlas by T. N. Krishnamurti et al. (1979).It is inferred that satellite estimated mean wind profiles show good agreement with the mean wind profiles of the research ships with RMS errors less than 5 mps below 500 hPa and less than 8 mps above 500 hPa. It is further infer-red that the inclusion of constructed winds shows a positive impact on the objective analysis and improvement is seen to be more marked in the data-sparse region of the Arabian sea. Analyses which include the constructed winds show better agreement with the standard analysis, than the analyses obtained using only conventional winds. Thus, results of our study suggest that the wind profiles constructed using cloud motion vectors are of potential use in objective analysis to depict the major circulation features over the Indian region.     

基于卫星亮温资料同化对2009年热带风暴“浪卡”的数值研究

本文利用三维变分同化系统（WRFDA）,设计了4个同化试验方案,将ATOVS卫星亮温资料直接同化到中尺度数值模式（WRF）中,研究同化ATOVS不同卫星亮温资料对2009年04号热带风暴“浪卡”数值模拟的影响。结果表明,直接同化卫星亮温资料能够改善初始场结构（大气流场、温度场）,尤其是对西太平洋反气旋系统,进而提高对热带气旋路径的模拟精度。同化不同类型的ATOVS卫星亮温资料对于热带气旋的移动路径有着不同程度的改善,其中以HIRS3和HIRS4资料同化对热带气旋移动路径改善效果最好。     

Impact of Assimilation of Radiosonde and UAV Observations from the Southern Ocean in the Polar WRF Model

Weather forecasting in the Southern Ocean and Antarctica is a challenge above all due to the rarity of observations to be assimilated in numerical weather prediction(NWP)models.As observations are expensive and logistically challenging,it is important to evaluate the benefit that additional observations could bring to NWP.Atmospheric soundings applying unmanned aerial vehicles(UAVs)have a large potential to supplement conventional radiosonde sounding observations.Here,we applied UAV and radiosonde sounding observations from an RV Polarstern cruise in the ice-covered Weddell Sea in austral winter 2013 to evaluate the impact of their assimilation in the Polar version of the Weather Research and Forecasting(Polar WRF)model.Our experiments revealed small to moderate impacts of radiosonde and UAV data assimilation.In any case,the assimilation of sounding data from both radiosondes and UAVs improved the analyses of air temperature,wind speed,and humidity at the observation site for most of the time.Further,the impact on the results of 5-day-long Polar WRF experiments was often felt over distances of at least 300 km from the observation site.All experiments succeeded in capturing the main features of the evolution of near-surface variables,but the effects of data assimilation varied between different cases.Due to the limited vertical extent of the UAV observations,the impact of their assimilation was limited to the lowermost 1?2-km layer,and assimilation of radiosonde data was more beneficial for modeled sea level pressure and near-surface wind speed.