On the Prediction of Tropical Cyclones over the Indian Region Using a Synthetic Vortex Scheme in a Mesoscale Model

The tropical cyclones form over the oceanic regions where conventional meteorological observations are not available. This contributes to a poor initial analysis of the cyclonic vortex and hence inadequate forecast. One way of overcoming the above problem is to modify the initial analysis by replacing the weak and ill-defined vortex in the initial analysis with a synthetic vortex having the correct size and intensity at the correct location. In this study we are investigating the effect of inclusion of a synthetic vortex based on Rankine as well as on Holland wind profiles, using NCAR-AFWA bogussing scheme for the prediction of four tropical cyclones, which formed over the Bay of Bengal during November 2002 and 2005, December 2005 and over the Arabian Sea during May 2004, using the MM5 model. Two numerical experiments are designed in this study for each of the above four cyclones. In the first experiment the model is integrated with a synthetic vortex based on Rankine wind profile while in the second experiment we utilize the Holland wind profile. For the November 2002 cyclone, in both the experiments the model is integrated from 10 November 2002 18 UTC to 12 November, 2002 12 UTC with the synthetic vortex inserted at the initial time. The results of the study for the November 2002 cyclone show that the model simulation with the Holland vortex has produced a stronger cyclone in terms of minimum sea-level pressure and maximum wind speed. Also, the results for the November 2002 cyclone with the Holland vortex showed a better longitudinal height section of the horizontal wind speed across the center of the cyclone. The track error of the cyclone for the November 2002 cyclone is less in the model simulation with the Holland vortex at the initial time and at 24 hours of forecast. The results for the November 2002 cyclone with the Rankine vortex showed greater vertical wind speed as compared to the Holland vortex. However, for the November 2002 cyclone there were no significant differences in the spatial distribution of precipitation for both the experiments. In order to provide an adequate number of case studies for a good statistical sample, the present study is extended for three additional cyclones over the Indian region. All four cyclones studied here show that the Holland vortex has produced a stronger cyclone in terms of the minimum sea-level pressure and maximum wind speed. The Holland vortex showed a better vertical structure of wind speed in the longitudinal height section at 24 hours of forecast for the November 2005 cyclone while the structure was better for the Rankine vortex for the remaining two cyclones. There were no significant differences in the spatial distribution of precipitation for the two experiments corresponding to all four cyclones. Some statistical results pertaining to all four cyclones are provided such as the average track error as well as the average difference between the observed and the model minimum sea-level pressure and the maximum wind speed. The statistical results corresponding to the average of all the four cyclones are at only a slight variance with the results corresponding to the November 2002 cyclone.     

The influence of rainfall on PR radar measurement of ocean surface wind speed and its calibration

Rain can significantly degrade the wind vector retrieval from Precipitation Radar(PR) by three mechanisms, namely, two-way rain attenuation, rain volume-backscattering, and ocean surface roughening from the rain splash effect. Here we first derive the radar equation for PR in rainy conditions. Then we use the rain attenuation model for Ku band, volume backscatter model for spherical raindrops and PR–TMI(TRMM Microwave Imager, TMI) matchup datasets from June to August in 2010 to solve the radar equation, and quantitatively analyze the influence of rainfall on PR radar measurement of ocean surface wind speed. Our results show that the significant effect of rain on radar signal is dominated by two-way rain attenuation and rain splash effect, and the effect of rain volume-backscattering is relatively the weakest, which can even be neglected in rain-weak conditions. Moreover, both the two-way rain attenuation and rain splash effect increase with the increasing of integration rain rate and incident angle. Last, we combine volume-backscattering effect and splash effect into a simple phenomenological model for rain calibration and select three typhoon cases from June to August in 2012 to verify the accuracy of this model. Before calibration, the mean difference and mean square error(MSE) between PR-observed ? 0 and wind-induced ? 0 are about 2.95 dB and 3.10 dB respectively. However, after calibration, the mean difference and MSE are reduced to 0.64 dB and 1.61 dB respectively. The model yields an accurate calibration for PR near-nadir normalized radar cross section(NRCS) in rainy conditions.     

Assimilation of Doppler Weather Radar Data in WRF Model for Simulation of Tropical Cyclone Aila

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.     

Use of multi‐platform,multi‐temporal remote‐sensing data for calibration of a distributed hydrological model: an application in the Arno basin,Italy

Images from satellite platforms are a valid aid in order to obtain distributed information about hydrological surface states and parameters needed in calibration and validation of the water balance and flood forecasting. Remotely sensed data are easily available on large areas and with a frequency compatible with land cover changes. In this paper, remotely sensed images from different types of sensor have been utilized as a support to the calibration of the distributed hydrological model MOBIDIC, currently used in the experimental system of flood forecasting of the Arno River Basin Authority. Six radar images from ERS‐2 synthetic aperture radar (SAR) sensors (three for summer 2002 and three for spring–summer 2003) have been utilized and a relationship between soil saturation indexes and backscatter coefficient from SAR images has been investigated. Analysis has been performed only on pixels with meagre or no vegetation cover, in order to legitimize the assumption that water content of the soil is the main variable that influences the backscatter coefficient. Such pixels have been obtained by considering vegetation indexes (NDVI) and land cover maps produced by optical sensors (Landsat‐ETM). In order to calibrate the soil moisture model based on information provided by SAR images, an optimization algorithm has been utilized to minimize the regression error between saturation indexes from model and SAR data and error between measured and modelled discharge flows. Utilizing this procedure, model parameters that rule soil moisture fluxes have been calibrated, obtaining not only a good match with remotely sensed data, but also an enhancement of model performance in flow prediction with respect to a previous calibration with river discharge data only. Copyright © 2006 John Wiley & Sons, Ltd.     

Wind-speed inversion from HF radar first-order backscatter signal

Land-based high-frequency (HF) radars have the unique capability of continuously monitoring ocean surface environments at ranges up to 200 km off the coast. They provide reliable data on ocean surface currents and under slightly stricter conditions can also give information on ocean waves. Although extraction of wind direction is possible, estimation of wind speed poses a challenge. Existing methods estimate wind speed indirectly from the radar derived ocean wave spectrum, which is estimated from the second-order sidebands of the radar Doppler spectrum. The latter is extracted at shorter ranges compared with the first-order signal, thus limiting the method to short distances. Given this limitation, we explore the possibility of deriving wind speed from radar first-order backscatter signal. Two new methods are developed and presented that explore the relationship between wind speed and wave generation at the Bragg frequency matching that of the radar. One of the methods utilizes the absolute energy level of the radar first-order peaks while the second method uses the directional spreading of the wind generated waves at the Bragg frequency. For both methods, artificial neural network analysis is performed to derive the interdependence of the relevant parameters with wind speed. The first method is suitable for application only at single locations where in situ data are available and the network has been trained for while the second method can also be used outside of the training location on any point within the radar coverage area. Both methods require two or more radar sites and information on the radio beam direction. The methods are verified with data collected in Fedje, Norway, and the Ligurian Sea, Italy using beam forming HF WEllen RAdar (WERA) systems operated at 27.68 and 12.5 MHz, respectively. The results show that application of either method requires wind speeds above a minimum value (lower limit). This limit is radar frequency dependent and is 2.5 and 4.0 m/s for 27.68 and 12.5 MHz, respectively. In addition, an upper limit is identified which is caused by wave energy saturation at the Bragg wave frequency. Estimation of this limit took place through an evaluation of a year long database of ocean spectra generated by a numerical model (third generation WAM). It was found to be at 9.0 and 11.0 m/s for 27.68 and 12.5 MHz, respectively. Above this saturation limit, conventional second-order methods have to be applied, which at this range of wind speed no longer suffer from low signal-to-noise ratios. For use in operational systems, a hybrid of first- and second-order methods is recommended.     

热带气旋风场模型构造及特征参数估算

探讨了利用气旋风场分布的经验模型估算热带气旋尺度(8级大风圈半径)的方法.用美国联合台风警报中心整编的2001年西北太平洋热带气旋的“最佳尺度”资料,确定了各模型的经验常数,并计算了各模型的估算精度.结果表明,“VBogus”模型能获得热带气旋(Tropical Cyclone,简称TC)尺度的较好估算.基于“VBogus”模型,通过拟合热带气旋尺度的非对称分布,构造了能描述热带气旋非对称风场的"修正VBogus"模型,并估算了该模型中各参数在不同季节和不同地理区域的取值,为热带气旋尺度变化和非对称结构机制等问题的研究和应用提供新依据.     

两种边界层参数化方案模拟热带气旋Megi(2010)路径差异的机理分析

本文利用中尺度数值模式WRF,分别采用YSU和MYJ两种边界层参数化方案对2010年超强台风Megi的移动路径进行了模拟,研究了热带气旋(TC)路径模拟对边界层方案的敏感性,并从模拟TC尺度差异所造成的影响角度揭示了模式边界层方案影响TC路径的机理.结果表明:由于两种方案对边界层垂直混合作用过程的描述不同,两个试验模拟的低层水汽垂直输送存在差异.相对于能很好模拟出Megi路径的MYJ方案,YSU方案模拟的TC外围螺旋雨带更活跃,造成TC尺度增大,引起TC中心北侧外围气压梯度和径向风速增加,使得由副高向TC中心输送更多的质量,造成副高异常减弱,从而导致由副高主导的引导气流发生改变,最终使得采用YSU方案模拟的Megi路径出现提前转向.     

Estimation of near surface soil moisture in a sloping terrain of a Himalayan watershed using ENVISAT ASAR multi‐incidence angle alternate polarisation data

Soil moisture is one of the important input variables in hydrological and water erosion models. The extraction of information on near surface soil moisture from synthetic aperture radar (SAR) is well established mostly for flat terrain and using low incidence angle single polarisation data. The ENVISAT advanced SAR (ASAR) data available in multiple incidence angles and alternate polarisation modes were investigated in this study for soil moisture estimation in sloping terrain. The test site was Sitla Rao watershed in the Lesser Himalayas of northern India. Empirical models were developed to estimate near surface soil moisture in bare agricultural fields using alternate polarisation ASAR data. Both soil moisture and surface roughness field measurements were performed during the satellite passes. Backscatter from medium incidence angle (IS‐4) and vertical‐vertical (VV) polarisation signal is correlated better with volumetric soil moisture content compared to other incidence angles. The model parameters were further improved, and soil moisture estimation was refined by combining medium incidence angle (IS4) vertical‐horizontal polarisation response as another variable along with VV polarisation response. The effect of slope on the radar backscatter was minimized by incorporating local incidence angles derived from an ASTER DEM. Copyright © 2012 John Wiley & Sons, Ltd.     

Monitoring of rice crop using ENVISAT ASAR data

Rice is the most important food source for people and is cultivated in most countries, among which China is one of the most productive. Increase of the world population and demands for economic devel-opment lead to the need of an efficient monitoring system for rice cultivation and forecasting of rice yield. Conventional methods for rice monitoring are based on ground-collected statistics, which is time consum-ing, inaccurate and expensive. Since the 1980s, satel-lite remote sensing has been c…     

TEMPORAL MONITORING OF SOIL MOISTURE USING ERS-1 SAR DATA

Multi-temporal synthetic aperture radar (SAR) imagery from the European Remote Sensing Satellite (ERS-1) was evaluated for monitoring soil moisture at the Romney Marsh test site as part of the UK SAR Calibration and Crop Backscatter Experiment. A total of 18 C-band (5.3 GHz) ERS-1 SAR images were acquired during the three day orbit and co-registered. Accurate calibration of the backscatter measurements was achieved using calibration constants derived from an analysis of corner reflector target responses. Mean backscatter measurements were recorded for each field and compared with field data on soil moisture, surface roughness and rainfall patterns. A comparison of daily and hourly rainfall and soil moisture measurements with backscatter for different cover types showed that the observed trends in backscatter are dominated by moisture effects. A high positive correlation between volumetric soil moisture in the range 10–40% was observed for bare soil fields. A much weaker positive relationship between soil moisture and backscatter was observed for grassland fields.     

Development and evaluation of a storm surge warning system in Taiwan

An operational storm surge forecasting system aimed at providing warning information for storm surges has been developed and evaluated using four typhoon events. The warning system triggered by typhoon forecasts from Taiwan Cooperative Precipitation Ensemble Forecast Experiment (TAPEX) has been executed with two storm surge forecasting scenarios with and without tides. Three numerical experiments applying different meteorological inputs have been designed to assess the impact of typhoon forcing on storm surges. One uses synthetic wind fields, and the others use realistic wind fields with and without adjustments to the initial wind fields for the background circulation. Local observations from Central Weather Bureau (CWB) weather stations and tide gauge stations are used to evaluate the wind fields and storm surges from our numerical experiments. The comparison results show that the accuracy of the storm surge forecast is dominated by the track, the intensity, and the driving flow of a typhoon. When the structure of a typhoon is disturbed by Taiwan’s topography, using meteorological inputs from real wind fields can result in a better typhoon simulation than using inputs from synthetic wind fields. The driving flow also determines the impact of topography on typhoon movement. For quickly moving typhoons, storm forcing from TAPEX is reliable when a typhoon is strong enough to be relatively unaffected by environmental flows; otherwise, storm forcing from a sophisticated typhoon initialization scheme that better simulates the typhoon and environmental flows results in a more accurate prediction of storm surges. Therefore, when a typhoon moves slowly and interacts more with the topography and environmental flows, incorporating realistic wind fields with adjustments to the initial wind fields for the background circulation in the warning system will obtain better predictions for a typhoon and its resultant storm surges.     

Will typhoon over the western North Pacific be more frequent in the Blue Arctic conditions?

How would typhoon activity over the western North Pacific change for various scenarios of future global warming?Using the model projections of the Coupled Model Intercomparison Project phase 3(CMIP 3)under the SRES A1B scenario,we generated summer(September)ice-free Arctic conditions,also referred to as Blue Arctic conditions,and then used the corresponding monthly sea surface temperature(SST)and a set of CO2concentrations to drive an AGCM model to simulate the resulting changes in background conditions affecting typhoon activity over the western North Pacific.Our results show that,during typhoon season(June to October),atmospheric and ocean circulations over the western North Pacific would be significantly different from the present circulations.Changes in the vertical shear of zonal wind and outgoing longwave radiation(OLR)in the western North Pacific are favorable for westward and northward shift,respectively,of the location of typhoon genesis.Moreover,changes in the above fields over the key area may be conducive to less frequent typhoons.In addition,the tropical cyclone genesis potential index(GPI)over the western North Pacific would decrease(increase)east(west)of 150°E(140°E).     

Impact of assimilating Taiwan’s coastal radar radial velocity on forecasting Typhoon Morakot (2009) in southeastern China using a WRF-based EnKF

This study explores for the first time the impact of assimilating radial velocity (Vr) observations from a single or multiple Taiwan’s coastal radars on tropical cyclone (TC) forecasting after landfall in the Chinese mainland by using a Weather Research and Forecasting model (WRF)-based ensemble Kalman filter (EnKF) data assimilation system. Typhoon Morakot (2009), which caused widespread damage in the southeastern coastal regions of the mainland after devastating Taiwan, was chosen as a case study. The results showed that assimilating Taiwan’s radar Vr data improved environmental field and steering flow and produced a more realistic TC position and structure in the final EnKF cycling analysis. Thus, the subsequent TC track and rainfall forecasts in southeastern China were improved. In addition, better observations of the TC inner core by Taiwan’s radar was a primary factor in improving TC rainfall forecast in the Chinese mainland.     

冷空气入侵对热带气旋发生发展的影响

本文从梯度风方程出发,证明温度梯度增强对涡度起到增强作用.因此当冷空气侵入热带气旋外围时,只要没有破坏热带气旋的暖心结构,就会引起温度梯度的增长,从而促进热带气旋的发生发展.本文采用NCAR/PSU研制的非静力中尺度模式MM5,研究北半球冷暖空气入侵在热带气旋形成和加强过程中的作用.通过研究冷暖空气对热带气旋发展影响的试验发现,冷暖空气在入侵热带气旋外围时,最主要改变的是外围的环流场.北半球冷空气的入侵将会增强热带气旋北面的北风,形成指向热带气旋中心的推力,即辐合增强,暖空气入侵减弱北面的北风,形成背向热带气旋中心的拉力,即辐合减弱.由于拉力作用,一方面把边界上由于冷空气入侵而生成的能量往热带气旋中心输送,另一方面导致温度梯度的增加.因此从天气学形势来看,在热带气旋发生发展的过程中,北方的冷高压将会增强热带气旋北面的风速,从而导致热带气旋的增强;南半球澳高的增强,将使越赤道气流增强,热带气旋南面的风速也因此增强,从而引起热带气旋的增强.     

Radar rainfall uncertainty modelling influenced by wind

Radar‐based estimates of rainfall are affected by many sources of uncertainties, which would propagate through the hydrological model when radar rainfall estimates are used as input or initial conditions. An elegant solution to quantify these uncertainties is to model the empirical relationship between radar measurements and rain gauge observations (as the ‘ground reference’). However, most current studies only use a fixed and uniform model to represent the uncertainty of radar rainfall, without consideration of its variation under different synoptic regimes. Wind is such a typical weather factor, as it not only induces error in rain gauge measurements but also causes the raindrops observed by weather radar to drift when they reach the ground. For this reason, as a first attempt, this study introduces the wind field into the uncertainty model and designs the radar rainfall uncertainty model under different wind conditions. We separate the original dataset into three subsamples according to wind speed, which are named as WDI (0–2 m/s), WDII (2–4 m/s) and WDIII (>4 m/s). The multivariate distributed ensemble generator is introduced and established for each subsample. Thirty typical events (10 at each wind range) are selected to explore the behaviours of uncertainty under different wind ranges. In each time step, 500 ensemble members are generated, and the values of 5th to 95th percentile values are used to produce the uncertainty bands. Two basic features of uncertainty bands, namely dispersion and ensemble bias, increase significantly with the growth of wind speed, demonstrating that wind speed plays a considerable role in influencing the behaviour of the uncertainty band. On the basis of these pieces of evidence, we conclude that the radar rainfall uncertainty model established under different wind conditions should be more realistic in representing the radar rainfall uncertainty. This study is only a start in incorporating synoptic regimes into rainfall uncertainty analysis, and a great deal of more effort is still needed to build a realistic and comprehensive uncertainty model for radar rainfall data. Copyright © 2014 John Wiley & Sons, Ltd.     

机载SAR探测图像中归一化雷达截面随入射角变化规律应用

机载SAR对海探测时,探测范围小和时空匹配难等局限使其无法借助风条纹和辅助资料反演海面风矢量.本文在仿真研究CMOD5.N地球物理模型参数的函数关系,实例分析机载SAR探测图像中距离向均值曲线变化规律的基础上,发现相同风向、风速条件下,CMOD5.N模型构建的标准曲线和探测图像的距离向均值曲线遵循统一的归一化雷达截面随入射角变化规律,且两者具有良好的相关性.据此,本文提出将距离向均值曲线与标准曲线逐条匹配,采用相关系数判定两者的相关程度,选择使得相关系数绝对值最大的标准曲线作为最优匹配曲线,进而直接确定风向和风速的海面风矢量反演方法.机载SAR飞行探测实验结果表明,海面风矢量反演结果与浮标观测结果的均方根误差为风向11.3°,风速0.9m·s-1,高于反演精度指标要求,原因在于该方法既避免了机载SAR探测图像中斑点噪声的影响,又不会产生局部最优解,提高了海面风矢量反演精度.     

水成物对GNOS掩星弯曲角同化的影响评估

本文在无线电掩星弯曲角射线追踪正演算子中引入水成物的影响,针对台风个例,利用FY-3c GNOS弯曲角资料的同化展开研究.通过分析水成物对掩星弯曲角正演精度的影响,指出当掩星剖面跨越一定厚度的台风区云雨大气时,多相态水成物对GNOS弯曲角正演精度的影响不可忽略.进而提出一种考虑云雨影响的掩星折射率正演算法,将掩星折射率的正演分别在晴空区和云雨区进行,在云雨区正演算子中增加多相态水成物含量对正演掩星折射率的贡献,改进了FY-3c GNOS弯曲角资料在云雨大气环境的同化方案.针对2018年24号台风个例,进行了同化的参照试验、未考虑和考虑水成物影响时GNOS弯曲角的3DVAR同化试验,考量云雨环境下的GNOS弯曲角资料同化对台风模拟的影响差异.试验结果表明,两种同化方案皆能改善台风路径预报,台风中心海平面气压模拟都能接近实际观测,台风最大风速也不同程度增大.而考虑水成物含量的影响后,资料同化能更有效缩小观测空间与背景场空间之间的偏差,同化后观测与分析的偏差更接近高斯分布,台风外围动力场和热力场环境能够得到更优的调整,使得96 h的台风路径模拟平均距离误差较不考虑水成物影响的情形减小了约14%.     

A non-parametric automatic blending methodology to estimate rainfall fields from rain gauge and radar data

Quantitative estimation of rainfall fields has been a crucial objective from early studies of the hydrological applications of weather radar. Previous studies have suggested that flow estimations are improved when radar and rain gauge data are combined to estimate input rainfall fields. This paper reports new research carried out in this field. Classical approaches for the selection and fitting of a theoretical correlogram (or semivariogram) model (needed to apply geostatistical estimators) are avoided in this study. Instead, a non-parametric technique based on FFT is used to obtain two-dimensional positive-definite correlograms directly from radar observations, dealing with both the natural anisotropy and the temporal variation of the spatial structure of the rainfall in the estimated fields. Because these correlation maps can be automatically obtained at each time step of a given rainfall event, this technique might easily be used in operational (real-time) applications. This paper describes the development of the non-parametric estimator exploiting the advantages of FFT for the automatic computation of correlograms and provides examples of its application on a case study using six rainfall events. This methodology is applied to three different alternatives to incorporate the radar information (as a secondary variable), and a comparison of performances is provided. In particular, their ability to reproduce in estimated rainfall fields (i) the rain gauge observations (in a cross-validation analysis) and (ii) the spatial patterns of radar fields are analyzed. Results seem to indicate that the methodology of kriging with external drift [KED], in combination with the technique of automatically computing 2-D spatial correlograms, provides merged rainfall fields with good agreement with rain gauges and with the most accurate approach to the spatial tendencies observed in the radar rainfall fields, when compared with other alternatives analyzed.     

ERS-2 SAR反演海洋风矢量的研究

SAR(Synthetic Aperture Radar)反演海洋风矢量是当今微波遥感领域非常有意义的前沿课题. 本文首先介绍了星载SAR估算海面风向、风速的基本原理和三种主流反演算法，接着给出反演的流程图以及重要步骤. 然后，以2002年5月7日香港地区ERS-2 SAR海洋图像为例，对经典的SWDA (SAR Wind Direction Algorithm)－谱分析方法加以改进，求得具有180°模糊度的风向，并用香港天文台气象浮标实测数据消除了风向不确定性. 最后，利用CMOD4 GMF（Geophysical Model Function，地球物理模式函数）计算得到海面上10m高的风速. 与气象浮标站实测资料相比，利用ERS-2 SAR图像获取的海面风向、风速的精度均较高. 这一结果表明：如果对SAR预先进行ADC(Analog to Digital Converter)改正以及精确校准，结合改进的SWDA和CMOD4，可以获得高精度的风矢量.     

A Study on the Impact of Observation Assimilation on the Numerical Simulation of Tropical Cyclones JAL and THANE Using 3DVAR

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.