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1.
S. Abhilash A. K. Sahai K. Mohankumar John P. George Someshwar Das 《Pure and Applied Geophysics》2012,169(11):2047-2070
In this paper the impact of Doppler weather radar (DWR) reflectivity and radial velocity observations for the short range forecasting of a tropical storm and associated rainfall event have been examined. Doppler radar observations of a tropical storm case that occurred during 29–30 October 2006 from SHARDWR (13.6° N, 80.2° E) are assimilated in the WRF 3DVAR system. The observation operator for radar reflectivity and radial velocity is included within latest version of WRF 3DVAR system. Keeping all model physics the same, three experiments were conducted at a horizontal resolution of 30?km. In the control experiment (CTRL), NCEP Final Analysis (FNL) interpolated to the model grid was used as the initial condition for 48-h free forecast. In the second experiment (NODWR), 6-h assimilation cycles have been carried out using all conventional (radiosonde and surface data) and non-conventional (satellite) observations from the Global Telecommunication System (GTS). The third experiment (DWR) is the same as the second, except Doppler radar radial velocity and reflectivity observations are also used in the assimilation cycle. Continuous 6-h assimilation cycle employed in the WRF-3DVAR system shows positive impact on the rainfall forecast. Assimilation of DWR data creates several small scale features near the storm centre. Additional sensitivity experiments were conducted to study the individual impact of reflectivity and radial velocity in the assimilation cycle. Radar data assimilation with reflectivity alone produced large analysis response on both thermodynamical and dynamical fields. However, radial velocity assimilation impacted only on dynamical fields. Analysis increments with radar reflectivity and radial velocity produce adjustments in both dynamical and thermodynamical fields. Verification of QPF skill shows that radar data assimilation has a considerable impact on the short range precipitation forecast. Improvement of the QPF skill with radar data assimilation is more clearly seen in the heavy rainfall (for thresholds >7?mm) event than light rainfall (for thresholds of 1 and 3?mm). The spatial pattern of rainfall is well simulated by the DWR experiment and is comparable to TRMM observations. 相似文献
2.
The overall objective of this study is to improve the forecasting accuracy of the precipitation in the Singapore region by means of both rainfall forecasting and nowcasting. Numerical Weather Predication (NWP) and radar‐based rainfall nowcasting are two important sources for quantitative precipitation forecast. In this paper, an attempt to combine rainfall prediction from a high‐resolution mesoscale weather model and a radar‐based rainfall model was performed. Two rainfall forecasting methods were selected and examined: (i) the weather research and forecasting model (WRF); and (ii) a translation model (TM). The WRF model, at a high spatial resolution, was run over the domain of interest using the Global Forecast System data as initializing fields. Some heavy rainfall events were selected from data record and used to test the forecast capability of WRF and TM. Results obtained from TM and WRF were then combined together to form an ensemble rainfall forecasting model, by assigning weights of 0.7 and 0.3 weights to TM and WRF, respectively. This paper presented results from WRF and TM, and the resulting ensemble rainfall forecasting; comparisons with station data were conducted as well. It was shown that results from WRF are very useful as advisory of anticipated heavy rainfall events, whereas those from TM, which used information of rain cells already appearing on the radar screen, were more accurate for rainfall nowcasting as expected. The ensemble rainfall forecasting compares reasonably well with the station observation data. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
3.
V. Yesubabu C. V. Srinivas K. B. R. R. Hariprasad R. Baskaran 《Pure and Applied Geophysics》2014,171(8):2023-2042
In this work, the impact of assimilation of conventional and satellite remote sensing observations (Oceansat-2 winds, MODIS temperature/humidity profiles) is studied on the simulation of two tropical cyclones in the Bay of Bengal region of the Indian Ocean using a three-dimensional variational data assimilation (3DVAR) technique. The Weather Research and Forecasting (WRF)-Advanced Research WRF (ARW) mesoscale model is used to simulate the severe cyclone JAL: 5–8 November 2010 and the very severe cyclone THANE: 27–30 December 2011 with a double nested domain configuration and with a horizontal resolution of 27 × 9 km. Five numerical experiments are conducted for each cyclone. In the control run (CTL) the National Centers for Environmental Prediction global forecast system analysis and forecasts available at 50 km resolution were used for the initial and boundary conditions. In the second (VARAWS), third (VARSCAT), fourth (VARMODIS) and fifth (VARALL) experiments, the conventional surface observations, Oceansat-2 ocean surface wind vectors, temperature and humidity profiles of MODIS, and all observations were respectively used for assimilation. Results indicate meager impact with surface observations, and relatively higher impact with scatterometer wind data in the case of the JAL cyclone, and with MODIS temperature and humidity profiles in the case of THANE for the simulation of intensity and track parameters. These relative impacts are related to the area coverage of scatterometer winds and MODIS profiles in the respective storms, and are confirmed by the overall better results obtained with assimilation of all observations in both the cases. The improvements in track prediction are mainly contributed by the assimilation of scatterometer wind vector data, which reduced errors in the initial position and size of the cyclone vortices. The errors are reduced by 25, 21, 38 % in vector track position, and by 57, 36, 39 % in intensity, at 24, 48, 72 h predictions, respectively, for the two cases using assimilation of all observations. Simulated rainfall estimates indicate that while the assimilation of scatterometer wind data improves the location of the rainfall, the assimilation of MODIS profiles produces a realistic pattern and amount of rainfall, close to the observational estimates. 相似文献
4.
U. C. Mohanty A. Routray Krishna K. Osuri S. Kiran Prasad 《Pure and Applied Geophysics》2012,169(3):381-399
An attempt is made to evaluate the impact of the three dimensional variational (3DVAR) data assimilation within the Weather
Research Forecasting (WRF) modeling system to simulate two heavy rainfall events which occured on 26–27 July 2005 and 27–30
July 2006. During the 26–27 July 2005 event, the unprecedented localized intense rainfall 90–100 cm was recorded over the
northeast parts of Mumbai city; however, southern parts received only 10 cm. Model simulation with the data assimilation experiment
is reasonably well predicted for the rainfall intensity (800 mm) in 24 h and with accurate location over Mumbai agreeing with
observation. Divergence, vorticity, vertical velocity and moisture parameters are evaluated during the various stages of the
event. It is noticed that maximum convergence and vorticity during the mature stage; at the same time the vertical velocity
also follows a similar trend during the period in the assimilation experiment. Vorticity budget terms over the location of
heavy rainfall revealed that the contribution of the positive tilting term produced positive vorticity which triggered the
convection and negative contribution to vorticity from the tilting term to precede the dissipation of the system. Model simulations
from the second rain event, the off-shore trough at sea level along the west coast of India, is well represented after assimilation
of observations during day-1 and day-2 as compared to the control simulations; the orientation of the off-shore trough is
well matched with that of the observed. The intensity and spatial distribution of the rainfall has considerably improved in
the assimilation simulation. The statistical skill scores also revealed that the precipitation forecast during the period
has appreciably improved due to assimilation of observations. The results of this study indicate a positive impact of the
3DVAR assimilation on the simulation of heavy rainfall events. 相似文献
5.
Assimilation experiments are performed with the Weather Research and Forecasting (WRF) models’ three-dimensional variational
data assimilation (3D-Var) scheme to evaluate the impact of directly assimilating the Advanced Television and Infrared Observation
Satellite Operational Vertical Sounder (ATOVS) radiance, including AMSU-A, AMSU-B and HIRS, on the analysis and forecasts
of a mesoscale model over the Indian region. The present study is, to our knowledge, the first where the impact of ATOVS radiance
has been evaluated on the analysis and forecasts of a mesoscale model over the Indian region. The control (without ATOVS radiance)
as well as experimental (which assimilated ATOVS radiance) run were made for 48 h starting at 0000 UTC during the entire July
2008. The impacts of assimilating the radiances from different instruments (e.g., AMSU-A, AMSU-B and HIRS) were measured in
comparison to the control run. The assimilation experiments for July 2008 (30 cases) demonstrated a positive impact of the
assimilated ATOVS radiance on both the analysis state as well as subsequent short-range forecasts. Relative to the control
run, the moisture analysis was improved with the assimilation of AMSU-B and HIRS radiance, while AMSU-A was mainly responsible
for improved temperature analysis. The comparison of the model-predicted temperature, moisture and wind with NCEP analysis
indicated that a positive forecast impact is achieved from each of the three instruments. HIRS and AMSU-A radiance yielded
only a slight positive forecast impact, while AMSU-B radiance had the largest positive forecast impact for moisture, temperature
and wind. The comparison of model-predicted rainfall with observed rainfall indicates that ATOVS radiance, particularly AMSU-B
and HIRS, impacted the rainfall positively. This study clearly shows that the improved analysis of mid-tropospheric moisture,
due to the assimilation of AMSU-B radiances, is a key factor to improve the short-term forecast skill of a mesoscale model. 相似文献
6.
S. Abhilash Someshwar Das S. R. Kalsi M. Das Gupta K. Mohankumar John P. George S. K. Banerjee S. B. Thampi D. Pradhan 《Pure and Applied Geophysics》2007,164(8-9):1491-1509
Pre-monsoon rainfall around Kolkata (northeastern part of India) is mostly of convective origin as 80% of the seasonal rainfall
is produced by Mesoscale Convective Systems (MCS). Accurate prediction of the intensity and structure of these convective
cloud clusters becomes challenging, mostly because the convective clouds within these clusters are short lived and the inaccuracy
in the models initial state to represent the mesoscale details of the true atmospheric state. Besides the role in observing
the internal structure of the precipitating systems, Doppler Weather Radar (DWR) provides an important data source for mesoscale
and microscale weather analysis and forecasting. An attempt has been made to initialize the storm-scale numerical model using
retrieved wind fields from single Doppler radar. In the present study, Doppler wind velocities from the Kolkata Doppler weather
radar are assimilated into a mesoscale model, MM5 model using the three-dimensional variational data assimilation (3DVAR)
system for the prediction of intense convective events that occurred during 0600 UTC on 5 May and 0000 UTC on 7 May, 2005.
In order to evaluate the impact of the DWR wind data in simulating these severe storms, three experiments were carried out.
The results show that assimilation of Doppler radar wind data has a positive impact on the prediction of intensity, organization
and propagation of rain bands associated with these mesoscale convective systems. The assimilation system has to be modified
further to incorporate the radar reflectivity data so that simulation of the microphysical and thermodynamic structure of
these convective storms can be improved. 相似文献
7.
For the accurate and effective forecasting of a cyclone, it is critical to have accurate initial structure of the cyclone in numerical models. In this study, Kolkata Doppler weather radar (DWR) data were assimilated for the numerical simulation of a land-falling Tropical Cyclone Aila (2009) in the Bay of Bengal. To study the impact of radar data on very short-range forecasting of a cyclone's path, intensity and precipitation, both reflectivity and radial velocity were assimilated into the weather research and forecasting (WRF) model through the ARPS data assimilation system (ADAS) and cloud analysis procedure. Numerical experiment results indicated that radar data assimilation significantly improved the simulated structure of Cyclone Aila. Strong influences on hydrometeor structures of the initial vortex and precipitation pattern were observed when radar reflectivity data was assimilated, but a relatively small impact was observed on the wind fields at all height levels. The assimilation of radar wind data significantly improved the prediction of divergence/convergence conditions over the cyclone's inner-core area, as well as its wind field in the low-to-middle troposphere (600–900 hPa), but relatively less impact was observed on analyzed moisture field. Maximum surface wind speed produced from DWR–Vr and DWR–ZVr data assimilation experiments were very close to real-time values. The impact of radar data, after final analysis, on minimum sea level pressure was relatively less because the ADAS system does not adjust for pressure due to the lack of pressure observations, and from not using a 3DVAR balance condition that includes pressure. The greatest impact of radar data on forecasting was realized when both reflectivity and wind data (DWR–ZVr and DWR–ZVr00 experiment) were assimilated. It is concluded that after final analysis, the center of the cyclone was relocated very close to the observed position, and simulated cyclone maintained its intensity for a longer duration. Using this analysis, different stages of the cyclone are better captured, and cyclone structure, intensification, direction of movement, speed and location are significantly improved when both radar reflectivity and wind data are assimilated. As compared to other experiments, the maximum reduction in track error was noticed in the DWR–ZVr and DWR–ZVr00 experiments, and the predicted track in these experiments was very close to the observed track. In the DWR–ZVr and DWR–ZVr00 experiments, rainfall pattern and amount of rainfall forecasts were remarkably improved and were similar to the observation over West Bengal, Orissa and Jharkhand; however, the rainfall over Meghalaya and Bangladesh was missed in all the experiments. The influence of radar data reduces beyond a 12-h forecast, due to the dominance of the flow from large-scale, global forecast system models. This study also demonstrates successful coupling of the data assimilation package ADAS with the WRF model for Indian DWR data. 相似文献
8.
从GPS/MET计划开始,基于GNSS的无线电掩星技术已成为一种强大的近地空间环境探测手段.截至到目前,已经有20多颗发射的低轨道卫星带GPS掩星接收机,其中COSMIC是首个专门用于掩星探测的卫星星座.这些掩星数据被广泛应用于气象预报、气候与全球变化研究、及空间天气监测和电离层研究.由于COSMIC的成功,相关合作单位目前正积极推动COSMIC-2计划,该计划将总共有12颗卫星,于2016年与2019年各发射6颗.COSMIC-2将携带一个高级的GNSS掩星接收机,它将接受GPS与GLONASS信号,并具备接受其他可获得信号源的能力(如中国北斗定位信号),其每日观测的掩星数量将是COSMIC的4~6倍.同时COSMIC-2还将携带两个空间天气载荷,加强空间天气的监测能力.本文以COSMIC与COSMIC-2计划为主线,对掩星的发展历史、技术要点进行了简单介绍,并简要综述了COSMIC取得的部分科学成果,同时对未来包括技术发展和众多的掩星观测进行了展望. 相似文献
9.
The mesoscale Numerical Weather Prediction (NWP) model is gaining popularity among the hydrometeorological community in providing high‐resolution rainfall forecasts at the catchment scale. Although the performance of the model has been verified in capturing the physical processes of severe storm events, the modelling accuracy is negatively affected by significant errors in the initial conditions used to drive the model. Several meteorological investigations have shown that the assimilation of real‐time observations, especially the radar data can help improve the accuracy of the rainfall predictions given by mesoscale NWP models. The aim of this study is to investigate the effect of data assimilation for hydrological applications at the catchment scale. Radar reflectivity together with surface and upper‐air meteorological observations is assimilated into the Weather Research and Forecasting (WRF) model using the three‐dimensional variational data‐assimilation technique. Improvement of the rainfall accumulation and its temporal variation after data assimilation is examined for four storm events in the Brue catchment (135.2 km2) located in southwest England. The storm events are selected with different rainfall distributions in space and time. It is found that the rainfall improvement is most obvious for the events with one‐dimensional evenness in either space or time. The effect of data assimilation is even more significant in the innermost domain which has the finest spatial resolution. However, for the events with two‐dimensional unevenness of rainfall, i.e. the rainfall is concentrated in a small area and in a short time period, the effect of data assimilation is not ideal. WRF fails in capturing the whole process of the highly convective storm with densely concentrated rainfall in a small area and a short time period. A shortened assimilation time interval together with more efficient utilisation of the weather radar data might help improve the effectiveness of data assimilation in such cases. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
10.
Ashish Routray U. C. Mohanty Krishna K. Osuri S. Kiran Prasad 《Pure and Applied Geophysics》2013,170(12):2329-2350
An attempt is made to evaluate the impact of Doppler Weather Radar (DWR) radial velocity and reflectivity in Weather Research and Forecasting (WRF)-3D variational data assimilation (3DVAR) system for prediction of Bay of Bengal (BoB) monsoon depressions (MDs). Few numerical experiments are carried out to examine the individual impact of the DWR radial velocity and the reflectivity as well as collectively along with Global Telecommunication System (GTS) observations over the Indian monsoon region. The averaged 12 and 24 h forecast errors for wind, temperature and moisture at different pressure levels are analyzed. This evidently explains that the assimilation of radial velocity and reflectivity collectively enhanced the performance of the WRF-3DVAR system over the Indian region. After identifying the optimal combination of DWR data, this study has also investigated the impact of assimilation of Indian DWR radial velocity and reflectivity data on simulation of the four different summer MDs that occurred over BoB. For this study, three numerical experiments (control no assimilation, with GTS and GTS along with DWR) are carried out to evaluate the impact of DWR data on simulation of MDs. The results of the study indicate that the assimilation of DWR data has a positive impact on the prediction of the location, propagation and development of rain bands associated with the MDs. The simulated meteorological parameters and tracks of the MDs are reasonably improved after assimilation of DWR observations as compared to the other experiments. The root mean square errors (RMSE) of wind fields at different pressure levels, equitable skill score and frequency bias are significantly improved in the assimilation experiments mainly in DWR assimilation experiment for all MD cases. The mean Vector Displacement Errors (VDEs) are significantly decreased due to the assimilation of DWR observations as compared to the CNTL and 3DV_GTS experiments. The study clearly suggests that the performance of the model simulation for the intense convective system which influences the large scale monsoonal flow is significantly improved after assimilation of the Indian DWR data from even one coastal locale within the MDs track. 相似文献
11.
分别在湖北省药姑山和九宫山的山头上开展GPS掩星观测实验,成功获取山基掩星观测数据,对掩星事件进行了分析和统计.给出利用山基掩星观测数据反演大气折射指数剖面和电波弯曲角的原理和算法.利用山基GPS掩星观测模拟数据,对反演方法进行试算和检验,结果表明反演方法准确可行.将该反演方法应用于观测数据的反演,获得了观测点高度以下的大气折射率剖面,以及电波弯曲角.实验结果和原理研究表明,山基掩星观测技术是一种潜在的低层大气环境监测新技术. 相似文献
12.
Short‐term flood inundation prediction using hydrologic‐hydraulic models forced with downscaled rainfall from global NWP 
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下载免费PDF全文 A short‐term flood inundation prediction model has been formulated based on the combination of the super‐tank model, forced with downscaled rainfall from a global numerical weather prediction model, and a one‐dimensional (1D) hydraulic model. Different statistical methods for downscaled rainfall have been explored, taking into account the availability of historical data. It has been found that the full implementation of a statistical downscaling model considering physically‐based corrections to the numerical weather prediction model output for rainfall prediction performs better compared with an altitudinal correction method. The integration of the super‐tank model into the 1D hydraulic model demonstrates a minimal requirement for the calibration of rainfall–runoff and flood propagation models. Updating the model with antecedent rainfall and regular forecast renewal has enhanced the model's capabilities as a result of the data assimilation processes of the runoff and numerical weather prediction models. The results show that the predicted water levels demonstrate acceptable agreement with those measured by stream gauges and comparable to those reproduced using the actual rainfall. Moreover, the predicted flood inundation depth and extent exhibit reasonably similar tendencies to those observed in the field. However, large uncertainties are observed in the prediction results in lower, flat portions of the river basin where the hydraulic conditions are not properly analysed by the 1D flood propagation model. Copyright © 2013 John Wiley & Sons, Ltd. 相似文献
13.
Accurate information of rainfall is needed for sustainable water management and more reliable flood forecasting. The advances in mesoscale numerical weather modelling and modern computing technologies make it possible to provide rainfall simulations and forecasts at increasingly higher resolutions in space and time. However, being one of the most difficult variables to be modelled, the quality of the rainfall products from the numerical weather model remains unsatisfactory for hydrological applications. In this study, the sensitivity of the Weather Research and Forecasting (WRF) model is investigated using different domain settings and various storm types to improve the model performance of rainfall simulation. Eight 24‐h storm events are selected from the Brue catchment, southwest England, with different spatial and temporal distributions of the rainfall intensity. Five domain configuration scenarios designed with gradually changing downscaling ratios are used to run the WRF model with the ECMWF 40‐year reanalysis data for the periods of the eight events. A two‐dimensional verification scheme is proposed to evaluate the amounts and distributions of simulated rainfall in both spatial and temporal dimensions. The verification scheme consists of both categorical and continuous indices for a first‐level assessment and a more quantitative evaluation of the simulated rainfall. The results reveal a general improvement of the model performance as we downscale from the outermost to the innermost domain. Moderate downscaling ratios of 1:7, 1:5 and 1:3 are found to perform better with the WRF model in giving more reasonable results than smaller ratios. For the sensitivity study on different storm types, the model shows the best performance in reproducing the storm events with spatial and temporal evenness of the observed rainfall, whereas the type of events with highly concentrated rainfall in space and time are found to be the trickiest case for WRF to handle. Finally, the efficiencies of several variability indices are verified in categorising the storm events on the basis of the two‐dimensional rainfall evenness, which could provide a more quantitative way for the event classification that facilitates further studies. It is important that similar studies with various storm events are carried out in other catchments with different geographic and climatic conditions, so that more general error patterns can be found and further improvements can be made to the rainfall products from mesoscale numerical weather models. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
14.
15.
《Journal of Atmospheric and Solar》2000,62(12):1057-1070
Data assimilation combines atmospheric measurements with knowledge of atmospheric behavior as codified in computer models, thus producing a “best” estimate of current conditions that is consistent with both information sources. The four major challenges in data assimilation are: (1) to generate an initial state for a computer forecast that has the same mass-wind balance as the assimilating model, (2) to deal with the common problem of highly non-uniform distribution of observations, (3) to exploit the value of proxy observations (of parameters that are not carried explicitly in the model), and (4) to determine the statistical error properties of observing systems and numerical model alike so as to give each information source the proper weight. Variational data assimilation is practiced at major meteorological centers around the world. It is based upon multivariate linear regression, dating back to Gauss, and variational calculus. At the heart of the method is the minimization of a cost function, which guarantees that the analyzed fields will closely resemble both the background field (a short forecast containing a priori information about the atmospheric state) and current observations. The size of the errors in the background and the observations (the latter, arising from measurement and non-representativeness) determine how close the analysis is to each basic source of information. Three-dimensional variational (3DVAR) assimilation provides a logical framework for incorporating the error information (in the form of variances and spatial covariances) and deals directly with the problem of proxy observations. 4DVAR assimilation is an extension of 3DVAR assimilation that includes the time dimension; it attempts to find an evolution of model states that most closely matches observations taken over a time interval measured in hours. Both 3DVAR and, especially, 4DVAR assimilation require very large computing resources. Researchers are trying to find more efficient numerical solutions to these problems. Variational assimilation is applicable in the upper atmosphere, but practical implementation demands accurate modeling of the physical processes that occur at high altitudes and multiple sources of observations. 相似文献
16.
The local ensemble transform Kalman filter (LETKF) is implemented with the Weather Research and Forecasting (WRF) model, and
real observations are assimilated to assess the newly-developed WRF-LETKF system. The WRF model is a widely-used mesoscale
numerical weather prediction model, and the LETKF is an ensemble Kalman filter (EnKF) algorithm particularly efficient in
parallel computer architecture. This study aims to provide the basis of future research on mesoscale data assimilation using
the WRF-LETKF system, an additional testbed to the existing EnKF systems with the WRF model used in the previous studies.
The particular LETKF system adopted in this study is based on the system initially developed in 2004 and has been continuously
improved through theoretical studies and wide applications to many kinds of dynamical models including realistic geophysical
models. Most recent and important improvements include an adaptive covariance inflation scheme which considers the spatial
and temporal inhomogeneity of inflation parameters. Experiments show that the LETKF successfully assimilates real observations
and that adaptive inflation is advantageous. Additional experiments with various ensemble sizes show that using more ensemble
members improves the analyses consistently. 相似文献
17.
人类活动引起全球变暖,衡量全球气候变化的指标有陆地、大气和海洋温度,水汽含量等等.研究对流层底层大气温度和水汽含量变化的传统方法是用数值天气预报模型和微波声纳,尚未实现用全球均匀覆盖的数据来做精确的定量研究.和GNSS系列卫星计划比较,最近发射的COSMIC卫星气象探测数据的空间、时间以及垂直分辨率都大大提高.采用COSMIC数据可以改进和量化南极洲的大气压力模型,并综合GNSS系列卫星测量的水汽和温度剖面研究全球气候变化.用一维协方差算法估计南极洲及附近海洋的大气压、温度和湿度剖面.把COSMIC卫星密集测量期间演算得到的大气折射率和GNSS系列卫星的结果进行比较.再和独立测量数据进行比较,包括南极洲自动气象观测站资料,数值天气预报模型资料,多种测高卫星水汽资料和海洋表面温度资料以及区域GPS水汽图.上述工作将改进发展中的气象遥感技术并应用于天气预报和空间天气预报及全球气候变化研究. 相似文献
18.
An Experiment Using the High Resolution Eta and WRF Models to Forecast Heavy Precipitation over India 总被引：1，自引：0，他引：1
Y. V. Rama Rao H. R. Hatwar Ahmad Kamal Salah Y. Sudhakar 《Pure and Applied Geophysics》2007,164(8-9):1593-1615
In the present study using the Weather Research and Forecasting (WRF) and Eta models, recent heavy rainfall events that occurred
(i) over parts of Maharastra during 26 to 27 July, 2005, (ii) over coastal Tamilnadu and south coastal Andhra Pradesh during
24 to 28 October, 2005, and (iii) the tropical cyclone of 30 September to 3 October, 2004/Monsoon Depression of 2 to 5 October
2004, that developed during the withdrawal phase of the southwest monsoon season of 2004 have been investigated. Also sensitivity
experiments have been conducted with the WRF model to test the impact of microphysical and cumulus parameterization schemes
in capturing the extreme weather events. The results show that the WRF model with the microphysical process and cumulus parameterization
schemes of Ferrier et al. and Betts-Miller-Janjic was able to capture the heavy rainfall events better than the other schemes. It is also observed
that the WRF model was able to predict mesoscale rainfall more realistically in comparison to the Eta model of the same resolution. 相似文献
19.
地震预报研究的主攻方向: 动力数值预测 总被引：1，自引：0，他引：1
尽管地震预测是一个举世公认的国际性科学难题,怛在强化各种减轻地震灾害措施的同时,仍须把地震的监测和预报作为中国地震局最为重要的任务。为此,需要高举攻克地震预测难题的旗帜,打破长期以来地震预测研究徘徊不前的局面。我们必须充分认识近年来地球物理观测技术和计算机技术所取得的重大进步和发展态势,并在此基础上制定地震预测研究的发展战略。以GPS为代表的空间对地观测技术,巨型高分辨率宽频带流动地震台阵观测技术以及电磁阵列观测技术的发展趋势表明,从布网观测走向阵列观测已经成为21世纪地球物理观测研究发展的基本方向。上述高新技术和计算机数值模拟技术的发展为地震动力学研究提供了前所未有的技术基础。为此,需要积极借助数值天气预报的经验,打破经验性地震预测的局限,把研究的注意力尽快转向以动力学为基础的地震数值预报。以地震数值预报为目标的GPS阵列地壳形变连续观测,高分辨率地壳上地幔结构探测,地壳动力学,地震孕育和破裂过程的理论、模拟试验和实际观测,数据同化和计算软件的开发应成为今后研究发展的重点。现在的问题是,需要我们强化多学科,多部门的组织协调,尽早在有条件的地区开展地震动力学数值预报的科学试验和相关的理论研究。这必将极大地促进我国地震科学基础研究的发展和地震预报水平的提高。 相似文献
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本文简要阐述了山基GPS掩星数据处理过程,介绍了雾灵山(40.60.N,117.48.E,～2118 m)山基GPS掩星观测实验的基本情况,将雾灵山山基GPS掩星折射率与探空折射率进行了比较.按照山基掩星和无线电探空在空间(经纬度)上相差在2.以内、时间上相差在1.5 h以内的条件,共选出了206个山基掩星折射率廓线样本用于比较分析.山基掩星折射率与探空折射率的个例比较,表明山基掩星折射率廓线合理,且和探空折射率基本一致.统计结果显示,在2100 m 以下,山基掩星折射率与探空折射率相对偏差的平均值为-2.0%,标准偏差为7.3%. 相似文献