首页 | 本学科首页   官方微博 | 高级检索  
相似文献
 共查询到20条相似文献，搜索用时 797 毫秒
1.
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.  相似文献   

2.
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.  相似文献   

3.
Prediction of heavy rainfall events due to severe convective storms in terms of their spatial and temporal scales is a challenging task for an operational forecaster. The present study is about a record-breaking heavy rainfall event observed in Pune (18°31′N, 73°55′E) on October 4, 2010. The day witnessed highest 24-h accumulated precipitation of 181.3 mm and caused flash floods in the city. The WRF model-based real-time weather system, operating daily at Centre for Development of Advanced Computing using PARAM Yuva supercomputer showed the signature of this convective event 4-h before, but failed to capture the actual peak rainfall and its location with reference to the city’s observational network. To investigate further, five numerical experiments were conducted to check the impact of assimilation of observations in the WRF model forecast. First, a control experiment was conducted with initialization using National Centre for Environmental Prediction (NCEP)’s Global Forecast System 0.5° data, while surface observational data from NCEP Prepbufr system were assimilated in the second experiment (VARSFC). In the third experiment (VARAMV), NCEP Prepbufr atmospheric motion vectors were assimilated. Fourth experiment (VARPRO) was assimilated with conventional soundings data, and all the available NCEP Prepbufr observations were assimilated in the fifth experiment (VARALL). Model runs were compared with observations from automated weather stations (AWS), synoptic charts of Indian Meteorological Department (IMD). Comparison of 24-h accumulated rainfall with IMD AWS 24-h gridded data showed that the fifth experiment (VARALL) produced better picture of heavy rainfall, maximum up to 251 mm/day toward the southern side, 31 km away from Pune’s IMD observatory. It was noticed that the effect of soundings observations experiment (VARPRO) caused heavy precipitation of 210 mm toward the southern side 49 km away from Pune. The wind analysis at 850 and 200 hPa indicated that the surface and atmospheric motion vector observations (VARAMV) helped in shifting its peak rainfall toward Pune, IMD observatory by 18 km, though VARALL over-predicted rainfall by 60 mm than the observed.  相似文献   

4.
Western Himalayas (WH) is characterized by variable topography and heterogeneous land use. During winter, it receives enormous amount of precipitation due to eastward moving extratropical cyclones, called western disturbances (WDs), in Indian parlance. This variable altitude and orientation of orographic barriers has a complex interplay with WDs in defining precipitation over the WH. To understand such complexities, three WDs are considered to study interaction with the Himalayan orography using the advanced regional prediction system. Two simulation strategies are performed and presented??first to illustrate the impact of different initial and boundary conditions and second to illustrate the impact of different horizontal model resolution with same model configuration. In the first strategy, three different initial and boundary conditions??the National Center for Environmental Prediction?CGlobal Forecast System, USA (NCEP?CGFS) (1) analysis (2) 0000UTC forecast and the National Center for Medium Range Weather Forecast, India?CT80 spectral model (NCMRWF?CT80) (3) 0000UTC forecast??are provided to the same model configuration. In the second strategy, outputs from model simulated with NCMRWF??T80 spectral model forecast at coarser horizontal model resolution of 30?km (hereafter called Experiment I) are used as input initial and boundary conditions for simulation at finer horizontal model resolution of 10?km (hereafter called Experiment II). Though there are many other dynamical factors, but in the present study, it is shown that model-simulated precipitation is sensitive to the initial and boundary conditions. Simulations at coarse resolution could capture the weather system, but detailed spatial distribution along the orography is better illustrated at finer resolution model simulation. Also, Experiment II could simulate precipitation over different ranges of the western Himalayas depicting orographic forcings.  相似文献   

5.
Observations by Doppler weather radar are crucial for nowcasting and short-time forecasting of severe weather events as they bring in refined information of the atmosphere. However, due to the inevitable noises and non-meteorological signals, they cannot be assimilated straightforwardly into a numerical model. In the present study, assimilation of the radial component of wind velocity observed by two Doppler radars is performed in the numerical simulation of Supertyphoon Rammasun (2014) just before its landfall. After several quality-control steps, the radar-observed radial velocities are de-aliased, noise-reduced and assimilated into the model to improve initial conditions for the high-resolution simulation. Results show that only when using global background error covariance matrix can the observational increment be properly assimilated into the model, correcting large-scale background steering flow and yielding a simulated track close to the observed one. However, little improvement is found in simulating the TC core-scale structures by the assimilation of radar velocity as compared to the radar-observed flow, primarily due to the insufficient spatial resolution of the model that may lead to the incorrect representation of the TC core structure and the rejection of some core-region observations during the data assimilation procedure. Moreover, assimilation-induced asymmetries consume a certain portion of mean kinetic energy, preventing the simulated Rammasun from axisymmetrization and thus intensification as compared with the non-assimilated experiment.  相似文献   

6.
Altimeter data have been assimilated in an ocean general circulation model using the water property conserving scheme. Two runs of the model have been conducted for the year 2004. In one of the runs, altimeter data have been assimilated sequentially, while in another run, assimilation has been suppressed. Assimilation has been restricted to the tropical Indian Ocean. An assessment of the strength of the scheme has been carried out by comparing the sea surface temperature (SST), simulated in the two runs, with in situ derived as well as remotely sensed observations of the same quantity. It has been found that the assimilation exhibits a significant positive impact on the simulation of SST. The subsurface effect of the assimilation could be judged by comparing the model simulated depth of the 20°C isotherm (hereafter referred to as D20), as a proxy of the thermocline depth, with the same quantity estimated from ARGO observations. In this case also, the impact is noteworthy. Effect on the dynamics has been judged by comparison of simulated surface current with observed current at a moored buoy location, and finally the impact on model sea level forecast in a free run after assimilation has been quantified in a representative example.  相似文献   

7.
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.  相似文献   

8.
Forecasting weather parameters such as temperature and pressure with a reasonable degree of accuracy three hours ahead of the scheduled departure of an aircraft helps economic and efficient planning of aircraft operations. However, these two parameters exhibit a high degree of persistency and have nonstationary mean and variance at sub-periods (i.e. at 0000, 0300, 0600,…, 2100UTC). Hence these series have been standardised (to have mean 0 and variance 1) and thereafter seasonal differenced (lag 8) to achieve almost near stationarity. An attempt has been made to fit the standardised and seasonal differenced series of Chennai (a coastal station) and Trichy (an inland station) airport into an Auto Regressive (AR) process. The model coefficients have been estimated based on adaptive filter algorithm which uses the method of convergence by the steepest descent. The models were tested with an independent data set and diagnostic checks were made on the residual error series. An independent estimation of fractal dimension has also been made in this study to conform the number parameters used in the AR processes. The models contemplated in this study are parsimonious and can be used to forecast surface temperature and pressure.  相似文献   

9.
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.  相似文献   

10.
不同滤波算法在土壤湿度同化中的应用   总被引：1，自引：0，他引：1  
为研究不同滤波算法在土壤湿度同化中的有效性,以及土壤湿度模拟结果对模型参数的敏感性,结合简单生物圈模型SiB2,设置敏感性实验,探求土壤饱和水力传导度对土壤湿度模拟结果的影响;并在此基础上,采用集合卡尔曼滤波（EnKF）、无迹卡尔曼滤波（UKF）和无迹粒子滤波（UPF）开展土壤湿度实时同化实验。结果表明：土壤饱和水力传导度能显著影响土壤湿度模拟精度;利用EnKF、UKF、UPF同化站点观测数据,均能改善土壤湿度模拟结果;3种同化方法在不同土壤层的同化效果不同,在土壤表层,EnKF的有效性优于UKF和UPF,在根域层和土壤深层,3种滤波方法有效性在降雨前后相差较大。因此,针对性地选择同化方法,是提高土壤湿度模拟精度的有效手段。  相似文献   

11.
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.  相似文献   

12.
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.  相似文献   

13.
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.  相似文献   

14.
在全球变暖的背景下,南极已成为全球气候变化研究的热点,然而其区域内的观测站点稀疏且缺乏较长的时间序列,限制了人们对南极气候变化机制的分析与理解。Polar WRF作为目前最先进的极地区域气候模型之一,有力弥补了观测资料不足的缺陷,然而模式存在误差,在应用之前有必要对其定量评估。本文利用Polar WRF3.9.1对2004-2013年南极冰盖2m气温、10m风速和地表气压进行了数值模拟,并与28个气象站数据进行了对比分析,结果表明:模式对气温的模拟值在东南极沿岸偏低,在内陆偏高,在南极半岛既存在冷偏差也存在暖偏差,而对风速和气压的模拟整体呈高估。而从均方根误差和平均绝对误差的空间分布来看,模式对气温和气压的模拟结果在东南极沿岸的精度高于内陆和南极半岛,而风速则在内陆的精度要高于沿岸地区。但总体来说模拟效果较为理想,在2004-2013年间气温、风速、气压的模拟值的变化趋势与实测值的变化趋势相同。模式模拟的年平均2m气温和近地面气压在所有站点都通过了α=0.1的显著性检验,季节误差和月误差整体较小,且所有月份的相关系数都分别大于0.90与0.79。模式对10m风速的模拟精度要略低,部分沿岸站点的年平均误差超过了7.5m·s^(-1),但整体而言其在四季和各个月份的相关性均大于0.5且误差小于4.5m·s^(-1)。虽然Polar WRF作为天气模式,但在模拟长时间尺度的气候方面仍然表现较好。  相似文献   

15.
Snowmelt runoff is an important source of water resources in the arid mountain area. Modelling snowmelt runoff for cold regions remains a problematic aspect because of the lack of data by gauges in large basins. In order to overcome the shortage of measured data in the snowmelt runoff modelling, the temperature interpolation method would greatly help in improving the simulation accuracy and describing the snow-hydrological behaviours of the study catchments. In this study, the temperature is the principal variable used to estimate the importance of the melting of snow cover using the snowmelt runoff model. Five different temperature interpolation attempts were performed over the Kaidu River Basin for the snowmelt season of the year 2000. Three temperature inputs were taken directly from the individual weather stations in or near the study area, and the other two temperature inputs were interpolated from the three weather stations. The results indicated that the temperature estimated from different methods could result in quite a difference in runoffs in comparison with the observed ones. The simulation results using average temperature from the three stations showed good results; the simulation run with the weighted average temperature generated a lower R 2 than the average temperature of three stations and using temperature directly adopted from three individual stations gave various results. The weather stations used to perform the snowmelt runoff simulation should be located in the place which is most representative of the mountain weather conditions, and the land cover and topography that those stations represented also play an important role in the snowmelt runoff simulation.  相似文献   

16.
利用在西藏纳木错流域念青唐古拉山北坡（NQN，海拔5 400 m）和西北保吉乡（BJ，海拔4 730 m）布设的两台带有四层土壤探头自动气象站(AWS)2005—2006年冬季10个月观测数据进行了统计分析。结果表明：观测期间NQN日及月平均气温均低于BJ，但变化幅度均小于BJ，土壤冻结时间比BJ长，两处的气温梯度为0.31℃/100 m。与安多月平均气温比较，推断NQN存在高山多年冻土。NQN大气—土壤及土壤内热传输速度快于BJ；冻结期内土壤中未冻水含量在0～-2.5℃时发生跃变且与土壤温度存在较好的线性关系；相同深度处NQN土未冻水含量较小。土壤温度日变化在0～40 cm深度处较明显，40cm深度以下变化很小，未冻水含量日变化在5 cm深度较明显，20 cm以下变化微弱。利用两观测点冻结深度（Df）与冻结积温（Tg）的良好相关建立模型，NQN为：Df n= 0.0016Tg+ 1.69,R2=0.9958；BJ为：Df b= 0.002 Tg+ 1.13，R2= 0.9424，并由此推断出两观测点最大季节冻结深度分别为1.69 m和1.13 m。  相似文献   

17.
This paper proposes a new ensemble-based algorithm that assimilates the vertical rain structure retrieved from microwave radiometer and radar measurements in a regional weather forecast model, by employing a Bayesian framework. The goal of the study is to evaluate the capability of the proposed technique to improve track prediction of tropical cyclones that originate in the North Indian Ocean. For this purpose, the tropical cyclone Jal has been analyzed by the community mesoscale weather model, weather research and forecasting (WRF). The ensembles of prognostic variables such as perturbation potential temperature (θ, k), perturbation geopotential (?, m2/s2), meridional (U) and zonal velocities (V) and water vapor mixing ratio (q v , kg/kg) are generated by the empirical orthogonal function technique. An over pass of the tropical rainfall-measuring mission (TRMM) satellite occurred on 06th NOV 0730 UTC over the system, and the observations from the radiometer and radar on board the satellite(1B11 data products) are inverted using a combined in-home radiometer-radar retrieval technique to estimate the vertical rain structure, namely the cloud liquid water, cloud ice, precipitation water and precipitation ice. Each ensemble is input as a possible set of initial conditions to the WRF model from 00 UTC which was marched in time till 06th NOV 0730 UTC. The above-mentioned hydrometeors from the cloud water and rain water mixing ratios are then estimated for all the ensembles. The Bayesian filter framework technique is then used to determine the conditional probabilities of all the candidates in the ensemble by comparing the retrieved hydrometeors through measured TRMM radiances with the model simulated hydrometeors. Based on the posterior probability density function, the initial conditions at 06 00 UTC are then corrected using a linear weighted average of initial ensembles for the all prognostic variables. With these weighted average initial conditions, the WRF model has been run up to 08th Nov 06 UTC and the predictions are then compared with observations and the control run. An ensemble independence study was conducted on the basis of which, an optimum of 25 ensembles is arrived at. With the optimum ensemble size, the sensitivity of prognostic variables was also analyzed. The model simulated track when compared with that obtained with the corrected set of initial conditions gives better results than the control run. The algorithm can improve track prediction up to 35 % for a 24 h forecast and up to 12 % for a 54 h forecast.  相似文献   

18.
Measurements of the atmosphere by satellite were first collected in the 1960s. However, it was not until the mid-1990s that these observations were translated into systematic improvements of numerical weather forecasts. We present here the data and methodology of data assimilation that enabled this achievement. Data assimilation is essentially a filtering processing that exploits the (assumed) known error statistical properties of the observations and of the underlying numerical model in which data are assimilated. It is also a process which corrects the state of the numerical model with physical observations of the real world. This part relies on (assumed) known physical laws to relate meteorological quantities (such as temperature, humidity, pressure, and wind) to observables. Atmospheric data collected by satellite all come from the interaction of electromagnetic waves with the atmosphere. Satellite data assimilation has greatly supported the progress in numerical weather prediction and holds promises for climate studies, for example via reanalysis.  相似文献   

19.
Mumbai, the commercial and financial capital of India, experiences incessant annual rain episodes, mainly attributable to erratic rainfall pattern during monsoons and urban heat-island effect due to escalating urbanization, leading to increasing vulnerability to frequent flooding. After the infamous episode of 2005 Mumbai torrential rains when only two rain gauging stations existed, the governing civic body, the Municipal Corporation of Greater Mumbai (MCGM) came forward with an initiative to install 26 automatic weather stations (AWS) in June 2006 (MCGM 2007), which later increased to 60 AWS. A comprehensive statistical analysis to understand the spatio-temporal pattern of rainfall over Mumbai or any other coastal city in India has never been attempted earlier. In the current study, a thorough analysis of available rainfall data for 2006–2014 from these stations was performed; the 2013–2014 sub-hourly data from 26 AWS was found useful for further analyses due to their consistency and continuity. Correlogram cloud indicated no pattern of significant correlation when we considered the closest to the farthest gauging station from the base station; this impression was also supported by the semivariogram plots. Gini index values, a statistical measure of temporal non-uniformity, were found above 0.8 in visible majority showing an increasing trend in most gauging stations; this sufficiently led us to conclude that inconsistency in daily rainfall was gradually increasing with progress in monsoon. Interestingly, night rainfall was lesser compared to daytime rainfall. The pattern-less high spatio-temporal variation observed in Mumbai rainfall data signifies the futility of independently applying advanced statistical techniques, and thus calls for simultaneous inclusion of physics-centred models such as different meso-scale numerical weather prediction systems, particularly the Weather Research and Forecasting (WRF) model.  相似文献   

20.
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.  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号