Real-time nowcast of a cloudburst and a thunderstorm event with assimilation of Doppler weather radar data

Extreme weather events such as cloudburst and thunderstorms are great threat to life and property. It is a great challenge for the forecasters to nowcast such hazardous extreme weather events. Mesoscale model (ARPS) with real-time assimilation of DWR data has been operationally implemented in India Meteorological Department (IMD) for real-time nowcast of weather over Indian region. Three-dimensional variational (ARPS3DVAR) technique and cloud analysis procedure are utilized for real-time data assimilation in the model. The assimilation is performed as a sequence of intermittent cycles and complete process (starting from reception, processing and assimilation of DWR data, running of ARPS model and Web site updation) takes less than 20 minutes. Thus, real-time nowcast for next 3 h from ARPS model is available within 20 minutes of corresponding hour. Cloudburst event of September 15, 2011, and thunderstorm event of October 22, 2010, are considered to demonstrate the capability of ARPS model to nowcast the extreme weather events in real time over Indian region. Results show that in both the cases, ARPS3DVAR and cloud analysis technique are able to extract hydrometeors from radar data which are transported to upper levels by the strong upward motion resulting in the distribution of hydrometeors at various isobaric levels. Dynamic and thermodynamic structures of cloudburst and thunderstorm are also well simulated. Thus, significant improvement in the initial condition is noticed. In the case of cloudburst event, the model is able to capture the sudden collisions of two or more clouds during 09–10 UTC. Rainfall predicted by the model during cloudburst event is over 100 mm which is very close to the observed rainfall (117 mm). The model is able to predict the cloudburst with slight errors in time and space. Real-time nowcast of thunderstorm shows that movement, horizontal extension, and north–south orientation of thunderstorm are well captured during first hour and deteriorate thereafter. The amount of rainfall predicted by the model during thunderstorm closely matches with observation with slight errors in the location of rainfall area. The temporal and spatial information predicted by ARPS model about the sudden collision/merger and broken up of convective cells, intensification, weakening, and maintaining intensity of convective cells has added value to a human forecast.     

Assimilation of Indian Doppler Weather Radar observations for simulation of mesoscale features of a land-falling cyclone

In this paper, impact of Indian Doppler Weather Radar (DWR) data, i.e., reflectivity (Z), radial velocity (Vr) data individually and in combination has been examined for simulation of mesoscale features of a land-falling cyclone with Advance Regional Prediction System (ARPS) Model at 9-km horizontal resolution. The radial velocity and reflectivity observations from DWR station, Chennai (lat. 13.0°N and long. 80.0°E), are assimilated using the ARPS Data Assimilation System (ADAS) and cloud analysis scheme of the model. The case selected for this study is the Bay of Bengal tropical cyclone NISHA of 27–28 November 2008. The study shows that the ARPS model with the assimilation of radial wind and reflectivity observations of DWR, Chennai, could simulate mesoscale characteristics, such as number of cells, spiral rain band structure, location of the center and strengthening of the lower tropospheric winds associated with the land-falling cyclone NISHA. The evolution of 850 hPa wind field super-imposed vorticity reveals that the forecast is improved in terms of the magnitude and direction of lower tropospheric wind, time, and location of cyclone in the experiment when both radial wind and reflectivity observations are used. With the assimilation of both radial wind and reflectivity observations, model could reproduce the rainfall pattern in a more realistic way. The results of this study are found to be very promising toward improving the short-range mesoscale forecasts.     

On the initialization of tropical cyclones with a three dimensional variational analysis

A method of initializing tropical cyclones in high-resolution numerical models is developed by modifying a data assimilation system, the NRL atmospheric variational data assimilation system (NAVDAS), which was designed for general mesoscale weather prediction using a three-dimensional variational (3DVAR) analysis with intermittent updates. The method includes the following three upgrades to overcome difficulties resulting from tropical cyclone initialization with the NAVDAS analysis. First, synthetic observation soundings are generated on 9 vertical levels at 49 points for strong storms (v _max?>?23.1?m?s^?1) and 41 points for weak storms around each cyclone center to supplement the observations used by the analysis. Secondly, a vortex relocation method for nested grids is developed to correct the cyclone position in the background fields of the analysis for each nested mesh. Lastly, the 3DVAR analysis is modified to gradually reduce the horizontal length scale and geostrophic coupling constraint near the center of a tropical cyclone for minimizing the problems introduced by improper covariances and coupling constraint used in the analysis. The synthetic observations significantly improve the intensity and structure of the analysis and the track forecast. The vortex relocation significantly improves the first guess background, avoiding the large analysis corrections that would be needed to correct cyclone position, and reducing the imbalance introduced by such large analysis increments. The modifications to the analysis length scale and geostrophic coupling constraint successfully improve the inner core analysis, providing a tighter circulation, and reducing the underestimate of the mass field gradient. Among the three upgrades, the vortex relocation provides the largest improvement to the tropical cyclone initialization and forecast.     

Numerical simulation of cyclonic storms FANOOS, NARGIS with assimilation of conventional and satellite observations using 3-DVAR

In this work, the impact of assimilation of conventional and satellite data is studied on the prediction of two cyclonic storms in the Bay of Bengal using the three-dimensional variational data assimilation (3D-VAR) technique. The FANOOS cyclone (December 6?C10, 2005) and the very severe cyclone NARGIS (April 28?CMay 2, 2008) were simulated with a double-nested weather research and forecasting (WRF-ARW) model at a horizontal resolution of 9?km. Three numerical experiments were performed using the WRF model. The back ground error covariance matrix for 3DVAR over the Indian region was generated by running the model for a 30-day period in November 2007. In the control run (CTL), the National Centers for Environmental Prediction (NCEP) global forecast system analysis at 0.5° resolution was used for the initial and boundary conditions. In the second experiment called the VARCON, the conventional surface and upper air observations were used for assimilation. In the third experiment (VARQSCAT), the ocean surface wind vectors from quick scatterometer (QSCAT) were used for assimilation. The CTL and VARCON experiments have produced higher intensity in terms of sea level pressure, winds and vorticity fields but with higher track errors. Assimilation of conventional observations has meager positive impact on the intensity and has led to negative impact on simulated storm tracks. The QSCAT vector winds have given positive impact on the simulations of intensity and track positions of the two storms, the impact is found to be relatively higher for the moderate intense cyclone FANOOS as compared to very severe cyclone NARGIS.     

Assimilation of Doppler weather radar observations in a mesoscale model for the prediction of rainfall associated with mesoscale convective systems

Obtaining an accurate initial state is recognized as one of the biggest challenges in accurate model prediction of convective events. This work is the first attempt in utilizing the India Meteorological Department (IMD) Doppler radar data in a numerical model for the prediction of mesoscale convective complexes around Chennai and Kolkata. Three strong convective events both over Chennai and Kolkata have been considered for the present study. The simulation experiments have been carried out using fifth-generation Pennsylvania State University-National Center for Atmospheric Research (PSU-NCAR) mesoscale model (MM5) version 3.5.6. The variational data assimilation approach is one of the most promising tools available for directly assimilating the mesoscale observations in order to improve the initial state. The horizontal wind derived from the DWR has been used alongwith other conventional and non-conventional data in the assimilation system. The preliminary results from the three dimensional variational (3DVAR) experiments are encouraging. The simulated rainfall has also been compared with that derived from the Tropical Rainfall Measuring Mission (TRMM) satellite. The encouraging result from this study can be the basis for further investigation of the direct assimilation of radar reflectivity data in 3DVAR system. The present study indicates that Doppler radar data assimilation improves the initial field and enhances the Quantitative Precipitation Forecasting (QPF) skill.     

Impact of assimilation of ATOVS temperature and humidity and SSM/I total precipitable water on the simulation of a monsoon depression

The present study explored the effect of assimilation of Advanced TIROS Vertical Sounder (ATOVS) temperature and humidity profiles and Spectral sensor microwave imager (SSM/I) total precipitable water (TPW) on the simulation of a monsoon depression which formed over the Arabian Sea during September 2005 using the Weather Research and Forecast model. The three-dimensional variational (3DVAR) data assimilation technique has been employed for the purpose of assimilation of satellite observations. Statistical scores like “equitable threat score,” “bias score,” “forecast impact,” and “improvement parameter” have been used to examine the impact of the above-mentioned satellite observations on the numerical simulation of a monsoon depression. The diagnostics of this study include verification of the vertical structure of depression, in terms of temperature anomaly profiles and relative vorticity profiles with observations/analysis. Additional diagnostics of the study include the analysis of the heat budget and moisture budget. Such budget studies have been performed to provide information on the role of cumulus convection associated with the depression. The results of this study show direct and good evidence of the impact of the assimilation of the satellite observations using 3DVAR on the dynamical and thermodynamical features of a monsoon depression along with the effect of inclusion of satellite observation on the spatial pattern of the simulated precipitation associated with the depression. The “forecast impact” parameter calculated for the wind speed provides good evidence of the positive impact of the assimilation of ATOVS temperature and humidity profiles and SSM/I TPW on the model simulation, with the assimilation of the ATOVS profiles showing better impact in terms of a more positive value of the “forecast impact” parameter. The results of the study also indicate the improvement of the forecast skill in terms of “equitable threat score” and “bias score” due to the assimilation of satellite observation.     

利用WRF3D-Var同化多普勒雷达反演风场试验研究

为了将C波段雷达风场资料更好地应用于数值预报模式中,利用两步变分法反演多普勒雷达风场资料,并处理成标准的常规探空资料,以WRF模式及其三维变分同化系统为平台,针对2013年6月19日发生在天水的一次强暴雨过程进行同化雷达反演风的试验研究.试验结果表明:同化雷达反演风场后,对降水预报的改进能维持12h,尤其同化雷达反演风场后3~9h效果非常显著;0~3h作用不是很明显;9~12h预报具有一定的正作用.另外,循环同化比同化一次效果好,但并不是同化次数越多越好.因此,同化C波段雷达反演风场后,对降水预报具有一定的正作用.     

云分析预报方法研究进展

云作为地球大气系统的重要组成部分,不仅影响着气候变化和天气系统的发展演变,还与航空活动密切相关,一直以来是空军和民航部门非常关注的气象要素之一。在云探测、资料同化和反演方法发展的基础上,从实际业务保障和数值模式发展需求出发,综述国内外云分析、预报方法和云分析预报系统开发的研究成果,分析各类方法的优势和不足,明确国内外研究的主要差距,并探讨国内未来研究的方向。云分析方法中,探空对云廓线识别较好,卫星可见光和红外资料在云顶信息反演方面优势明显,多普勒雷达能够获取对流层中层和底层的云信息,而毫米波雷达能够很好地反映云三维结构信息,发展潜力巨大。云预报方法中,传统的统计和诊断方法发展较为成熟,而考虑了大气温湿和云微物理状况的大气辐射传输模式正演模拟云顶亮温的方法是未来的发展趋势。加强云探测技术,综合利用云分析预报方法,借鉴国外先进云分析预报系统的设计理念,积极开发我国自主的云分析预报系统,推动天气预报、航空气象保障和数值预报模式的发展将会是我国云研究的重要方面。     

Impact of physical parameterization schemes on numerical simulation of super cyclone Gonu

The objective of this study is to investigate in detail the sensitivity of cumulus, planetary boundary layer and explicit cloud microphysics parameterization schemes on intensity and track forecast of super cyclone Gonu (2007) using the Pennsylvania State University-National Center for Atmospheric Research Fifth-Generation Mesoscale Model (MM5). Three sets of sensitivity experiments (totally 11 experiments) are conducted to examine the impact of each of the aforementioned parameterization schemes on the storm’s track and intensity forecast. Convective parameterization schemes (CPS) include Grell (Gr), Betts–Miller (BM) and updated Kain–Fritsch (KF2); planetary boundary layer (PBL) schemes include Burk–Thompson (BT), Eta Mellor–Yamada (MY) and the Medium-Range Forecast (MRF); and cloud microphysics parameterization schemes (MPS) comprise Warm Rain (WR), Simple Ice (SI), Mixed Phase (MP), Goddard Graupel (GG), Reisner Graupel (RG) and Schultz (Sc). The model configuration for CPS and PBL experiments includes two nested domains (90- and 30-km resolution), and for MPS experiments includes three nested domains (90-, 30- and 10-km grid resolution). It is found that the forecast track and intensity of the cyclone are most sensitive to CPS compared to other physical parameterization schemes (i.e., PBL and MPS). The simulated cyclone with Gr scheme has the least forecast track error, and KF2 scheme has highest intensity. From the results, influence of cumulus convection on steering flow of the cyclone is evident. It appears that combined effect of midlatitude trough interaction, strength of the anticyclone and intensity of the storm in each of these model forecasts are responsible for the differences in respective track forecast of the cyclone. The PBL group of experiments has less influence on the track forecast of the cyclone compared to CPS. However, we do note a considerable variation in intensity forecast due to variations in PBL schemes. The MY scheme produced reasonably better forecast within the group with a sustained warm core and better surface wind fields. Finally, results from MPS set of experiments demonstrate that explicit moisture schemes have profound impact on cyclone intensity and moderate impact on cyclone track forecast. The storm produced from WR scheme is the most intensive in the group and closer to the observed strength. The possible reason attributed for this intensification is the combined effect of reduction in cooling tendencies within the storm core due to the absence of melting process and reduction of water loading in the model due to absence of frozen hydrometeors in the WR scheme. We also note a good correlation between evolution of frozen condensate and storm intensification rate among these experiments. It appears that the Sc scheme has some systematic bias and because of that we note a substantial reduction in the rain water formation in the simulated storm when compared to others within the group. In general, it is noted that all the sensitivity experiments have a tendency to unrealistically intensify the storm at the later part of the integration phase.     

Simulation of very severe cyclone Mala over Bay of Bengal with HWRF modeling system

Tropical cyclone is one of the most devastating weather phenomena all over the world. The Environmental Modeling Center (EMC) of the National Center for Environmental Prediction (NCEP) has developed a sophisticated mesoscale model known as Hurricane Weather Research and Forecasting (HWRF) system for tropical cyclone studies. The state-of-the-art HWRF model (atmospheric component) has been used in simulating most of the features our present study of a very severe tropical cyclone ??Mala??, which developed on April 26 over the Bay of Bengal and crossed the Arakan coast of Myanmar on April 29, 2006. The initial and lateral boundary conditions are obtained from Global Forecast System (GFS) analysis and forecast fields of the NCEP, respectively. The performance of the model is evaluated with simulation of cyclone Mala with six different initial conditions at an interval of 12?h each from 00 UTC 25 April 2006 to 12 UTC 27 April 2006. The best result in terms of track and intensity forecast as obtained from different initial conditions is further investigated for large-scale fields and structure of the cyclone. For this purpose, a number of important predicted fields?? viz. central pressure/pressure drop, winds, precipitation, etc. are verified against observations/verification analysis. Also, some of the simulated diagnostic fields such as relative vorticity, pressure vertical velocity, heat fluxes, precipitation rate, and moisture convergences are investigated for understanding of the characteristics of the cyclone in more detail. The vector displacement errors in track forecasts are calculated with the estimated best track provided by the India Meteorological Department (IMD). The results indicate that the model is able to capture most of the features of cyclone Mala with reasonable accuracy.     

Impact of additional surface observation network on short range weather forecast during summer monsoon 2008 over Indian subcontinent

The three dimensional variational data assimilation scheme (3D-Var) is employed in the recently developed Weather Research and Forecasting (WRF) model. Assimilation experiments have been conducted to assess the impact of Indian Space Research Organisation’s (ISRO) Automatic Weather Stations (AWS) surface observations (temperature and moisture) on the short range forecast over the Indian region. In this study, two experiments, CNT (without AWS observations) and EXP (with AWS observations) were made for 24-h forecast starting daily at 0000 UTC during July 2008. The impact of assimilation of AWS surface observations were assessed in comparison to the CNT experiment. The spatial distribution of the improvement parameter for temperature, relative humidity and wind speed from one month assimilation experiments demonstrated that for 24-h forecast, AWS observations provide valuable information. Assimilation of AWS observed temperature and relative humidity improved the analysis as well as 24-h forecast. The rainfall prediction has been improved due to the assimilation of AWS data, with the largest improvement seen over the Western Ghat and eastern India.     

Real-time prediction of a severe cyclone ��Jal�� over Bay of Bengal using a high-resolution mesoscale model WRF (ARW)

Real-time predictions for the JAL severe cyclone formed in November 2010 over Bay of Bengal using a high-resolution Weather Research and Forecasting (WRF ARW) mesoscale model are presented. The predictions are evaluated with different initial conditions and assimilation of observations. The model is configured with two-way interactive nested domains and with fine resolution of 9?km for the region covering the Bay of Bengal. Simulations are performed with NCEP GFS 0.5° analysis and forecasts for initial/boundary conditions. To examine the impact of initial conditions on the forecasts, eleven real-time numerical experiments are conducted with model integration starting at 00, 06, 12, 18 UTC 4 Nov, 5?Nov and 00, 06, 12 UTC 6 Nov and all ending at 00 UTC 8 Nov. Results indicated that experiments starting prior to 18 UTC 04 Nov produced faster moving cyclones with higher intensity relative to the IMD estimates. The experiments with initial time at 18 UTC 04 Nov, 00 UTC 05 Nov and with integration length of 78?h and 72?h produced best prediction comparable with IMD estimates of the cyclone track and intensity parameters. To study the impact of observational assimilation on the model predictions FDDA, grid nudging is performed separately using (1) land-based automated weather stations (FDDAAWS), (2) MODIS temperature and humidity profiles (FDDAMODIS), and (3) ASCAT and OCEANSAT wind vectors (FDDAASCAT). These experiments reduced the pre-deepening period of the storm by 12?h and produced an early intensification. While the assimilation of AWS data has shown meagre impact on intensity, the assimilation of scatterometer winds produced an intermittent drop in intensity in the peak stage. The experiments FDDAMODIS and FDDAQSCAT produced minimum error in track and intensity estimates for a 90-h prediction of the storm.     

A comprehensive study of surface and upper-air characteristics over two stations on the west coast of India during the occurrence of a cyclonic storm

While qualitative information from meteorological satellites has long been recognized as critical for monitoring weather events such as tropical cyclone activity, quantitative data are required to improve the numerical prediction of these events. In this paper, the sea surface winds from QuikSCAT, cloud motion vectors and water vapor winds from KALPANA-1 are assimilated using three-dimensional variational assimilation technique within Weather Research Forecasting (WRF) modeling system. Further, the sensitivity experiments are also carried out using the available cumulus convective parameterizations in WRF modeling system. The model performance is evaluated using available observations, and both qualitative and quantitative analyses are carried out while analyzing the surface and upper-air characteristics over Mumbai (previously Bombay) and Goa during the occurrence of the tropical cyclone PHYAN at the west coast of Indian subcontinent. The model-predicted surface and upper-air characteristics show improvements in most of the situations with the use of the satellite-derived winds from QuikSCAT and KALPANA-1. Some of the model results are also found to be better in sensitivity experiments using cumulus convection schemes as compared to the CONTROL simulation.     

Impact of Doppler weather radar data on thunderstorm simulation during STORM pilot phase—2009

This study assesses the impact of Doppler weather radar (DWR) data (reflectivity and radial wind) assimilation on the simulation of severe thunderstorms (STS) events over the Indian monsoon region. Two different events that occurred during the Severe Thunderstorms Observations and Regional Modeling (STORM) pilot phase in 2009 were simulated. Numerical experiments—3DV (assimilation of DWR observations) and CNTL (without data assimilation)—were conducted using the three-dimensional variational data assimilation technique with the Advanced Research Weather Research and Forecasting model (WRF-ARW). The results show that consistent with prior studies the 3DV experiment, initialized by assimilation of DWR observations, performed better than the CNTL experiment over the Indian region. The enhanced performance was a result of improved representation and simulation of wind and moisture fields in the boundary layer at the initial time in the model. Assimilating DWR data caused higher moisture incursion and increased instability, which led to stronger convective activity in the simulations. Overall, the dynamic and thermodynamic features of the two thunderstorms were consistently better simulated after ingesting DWR data, as compared to the CNTL simulation. In the 3DV experiment, higher instability was observed in the analyses of thermodynamic indices and equivalent potential temperature (θ _e) fields. Maximum convergence during the mature stage was also noted, consistent with maximum vertical velocities in the assimilation experiment (3DV). In addition, simulated hydrometeor (water vapor mixing ratio, cloud water mixing ratio, and rain water mixing ratio) structures improved with the 3DV experiment, compared to that of CNTL. From the higher equitable threat scores, it is evident that the assimilation of DWR data enhanced the skill in rainfall prediction associated with the STS over the Indian monsoon region. These results add to the body of evidence now which provide consistent and notable improvements in the mesoscale model results over the Indian monsoon region after assimilating DWR fields.     

Impact of 3D Var GSI-ENKF hybrid data assimilation system

The hybrid two-way coupled 3DEnsVar assimilation system was tested with the NCMRWF global data assimilation forecasting system. At present, this system consists of T574L64 deterministic model and the grid-point statistical interpolation analysis scheme. In this experiment, the analysis system is modified with a two-way coupling with an 80 member Ensemble Kalman Filter of T254L64 resolution and runs are carried out in parallel to the operational system for the Indian summer monsoon season (June–September) for the year 2015 to study its impact. Both the assimilation systems are based on NCEP GFS system. It is found that hybrid assimilation marginally improved the quality of the forecasts of all variables over the deterministic 3D Var system, in terms of statistical skill scores and also in terms of circulation features. The impact of the hybrid system in prediction of extreme rainfall and cyclone track is discussed.     

Prediction of severe tropical cyclones over the Bay of Bengal during 2007–2010 using high-resolution mesoscale model

In this paper, the performance of a high-resolution mesoscale model for the prediction of severe tropical cyclones over the Bay of Bengal during 2007?C2010 (Sidr, Nargis, Aila, and Laila) is discussed. The advanced Weather Research Forecast (WRF) modeling system (ARW core) is used with a combination of Yonsei University PBL schemes, Kain-Fritsch cumulus parameterization, and Ferrier cloud microphysics schemes for the simulations. The initial and boundary conditions for the simulations are derived from global operational analysis and forecast products of the National Center for Environmental Prediction-Global Forecast System (NCEP-GFS) available at 1°lon/lat resolution. The simulation results of the extreme weather parameters such as heavy rainfall, strong wind and track of those four severe cyclones, are critically evaluated and discussed by comparing with the Joint Typhoon Warning Center (JTWC) estimated values. The simulations of the cyclones reveal that the cyclone track, intensity, and time of landfall are reasonably well simulated by the model. The mean track error at the time of landfall of the cyclone is 98?km, in which the minimum error was found to be for the cyclone Nargis (22?km) and maximum error for the cyclone Laila (304?km). The landfall time of all the cyclones is also fairly simulated by the model. The distribution and intensity of rainfall are well simulated by the model as well and were comparable with the TRMM estimates.     

Overview of Researches on All-Sky Satellite Microwave Data Variational Assimilation

As the important components of the earth’s atmospheric system, cloud and precipitation strongly affect the global hydrology and energy cycles through the interaction of solar and infrared radiation with cloud droplets and the release of latent heat in precipitation development. The microwave observations in cloudy and rainy conditions have a large amount of information closely related to the development of weather systems, especially the severe weather systems like typhoon and rainstorm. Nevertheless, satellite microwave observations are usually only assimilated in clear-sky above the ocean and their cloud and precipitation content is discarded. Over the past two decades, several Numerical Weather Prediction (NWP) centers have gradually developed the “all-sky” approach to make use of the cloud- and precipitation-affected microwave radiances. It’s been proved that the all-sky assimilation can be used to improve the first guessed mass, wind, humidity, cloud and precipitation through the tracer effect. For providing an investigated reference for the future research of all-weather assimilation in domestic numerical weather forecast, this paper reviewed the all-sky assimilation methods using microwave observation data, analyzed the advantages and disadvantages of each method, and discussed the key technical problems and the existing difficulties and challenges in this field. With the development and application of the new generation of NWP model in China, advancing the domestic research of all-weather data assimilation technology will bring more scientific and practical benefits in the future.     

黑龙江省两次温带气旋暴雪过程对比分析

利用多种资料对黑龙江省两次由江淮气旋和蒙古气旋合并引发的暴雪过程的水汽、热动力条件和中尺度特征进行了对比分析。结果表明：（1）两次暴雪过程都发生在北支槽和短波槽合并、北支槽北部有冷涡的背景下,850 hPa上低涡合并促使江淮气旋和蒙古气旋合并;气旋合并后,低空急流为降雪提供了充足水汽,强暖平流使气旋爆发性发展,导致降雪加强。（2）两次降雪过程都表现出逗点云系的合并发展,"1211 "暴雪过程中高层形成涡旋偏西,700 hPa低涡东部偏南风引导气旋北上西折,低空急流和地形共同作用使暖湿空气强烈辐合上升,产生对流云,暴雪发生在A类逗点云系的头部,降雪强度大,范围广;"1412"暴雪过程高空槽低涡位置偏东,700 hPa低涡东部西南风始终引导气旋向东北方向移动,近地面层具有冷垫,暴雪主要发生在B类气旋逗点云系头部西侧中低云团中,降雪范围和强度较"1211"过程小。（3）低层（0.3 km）冷空气侵入和中高层（5.5 km）转为偏北风对判断降雪开始和结束有很好的表征意义。（4）冷涡前部强高压脊使冷涡移动缓慢,从而延长了降水的持续时间,气旋移动路径与高压脊伸展方向密切相关。     

Impact of AWS observations in WRF-3DVAR data assimilation system: a case study on abnormal warming condition in Odisha

An abnormal warming condition with 3?C5?°C rise in temperature above its normal value was observed in the Indian state of Odisha during 12?C16 November 2009. This study aims at examining the impact of additional weather observations obtained from the automatic weather stations (AWS) installed in the recent past on the numerical simulation of such abnormal warming. AWS observations, such as temperature at 2?m (T2m), dew point temperature at 2?m (Td2m), wind vector at 10?m (speed and direction), and sea level pressure (SLP) have been assimilated into the state-of-the-art Weather Research and Forecasting (WRF) model using the three-dimensional variational data assimilation (3DVAR). Six sets of experiments have been conducted here. There is no data assimilation in the control experiment, whereas AWS and radiosonde observations have been assimilated in rest of the five experiments. The model integrations have been made for 72?h in each experiment starting from 0000 UTC November 12 to 0000 UTC November 15, 2009. Assimilation experiments have also been performed to assess the impact of individual surface parameters on the model simulations. Impact of AWS observations on model simulation has been examined with reference to the control simulation and quantified in terms of root-mean-square error and forecast skill score for temperature, sea level pressure, and relative humidity at three selected stations Bonaigarh, Brahmagiri, and Nuapada in Odisha. Results indicate improvements in the surface air temperature and SLP simulations in the timescale of 72?h at all the three stations due to additional weather data assimilation into the model. Improvements in simulation are significant up to 24?h. The assimilation of additional wind fields significantly improved the temperature simulation at all the three stations. The simulated SLP has also improved significantly due to the assimilation of surface temperature and moisture.