排序方式： 共有95条查询结果，搜索用时 15 毫秒
31.
多普勒雷达资料在冷涡强对流天气中的同化应用试验 总被引：3，自引：0，他引：3
应用WRF模式的三维变分同化系统(WRF-3DVAR),对沈阳多普勒天气雷达资料在东北冷涡暴雨个例中的同化应用进行了试验.研制了多普勒雷达资料质量控制系统,实现了对径向风和反射率因子的直接同化,不但可以反演中尺度三维气象要素场,而且可以为模式提供初始场.以天气尺度资料为背景场同化多普勒雷达资料,WRF-3DVAR可以较好地反演冷涡中尺度对流系统的三维结构,反演的地面强对流辐散气流及在对流层中层涡旋都符合中尺度系统概念模型,通过与实际地面探测资料进行了对比,风场环流基本接近,同化了雷达资料的气象要素场可为预报业务提供较好的包含中小尺度系统的实时三维分析场.通过冷涡个例同化试验,应用WRF-3DVAR同化雷达资料后,中尺度模式对对流降水的预报总体有正的影响,对强对流中的一些中小尺度雨团的预报也略有改善. 相似文献
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广州地Ⅸ的高温天气主要是受副热带高压和台风外围下沉气流的影响所致.文中采用BDA(Bogus Data Assimila-tion)方法,探讨BDA方案对广州地区台风背景条件下高温预报的改进能力.选取2005年7月中旬广州地区出现的高温天气进行研究.这是比较典型的受副热带高压和台风(海棠)共同影响造成高温的天气过程.分析有无采用BDA方案的模式初始场.结果表明:采用BDA方案同化Bogus模型可以调整台风中心位置和强度,使所得到的初始场中心位置与观测更为接近,台风强度(气压梯度力、风速)比末用Bogus的情况强,与观测值更为接近.数值模拟的结果表明,采用了BDA方案的敏感试验可以更好地预撤台风路径和台风中心强度变化,从而更好地预报高温天气,对高温区分布、日平均温度大小等的预报都有改进.文中对引起这种预报差异的原因进行了讨论,并探讨高温预报改进的可能机制.大气下沉运动的增强是高温预报改进的主要原因.敏感试验由于广州中低层大气的水汽减少,大气的下沉增强,致使天空的云量减少,对太阳短波辐射的阻挡减小,从而地面吸收热量增多,温度升高,输送给大气的感热增加,大气气温升高.采用BDA方案可以改进模式在台风"海棠"过程对广州高温的预报. 相似文献
34.
A heavy rainfall case related to Mesoscale Convective Systems (MCSs) over
the Korean Peninsula was selected to investigate the impact of radar data
assimilation on a heavy rainfall forecast. The Weather Research and
Forecasting (WRF) three-dimensional variational (3DVAR) data assimilation
system with tuning of the length scale of the background error covariance
and observation error parameters was used to assimilate radar radial
velocity and reflectivity data. The radar data used in the assimilation
experiments were preprocessed using quality-control procedures and
interpolated/thinned into Cartesian coordinates by the SPRINT/CEDRIC
packages. Sensitivity experiments were carried out in order to determine the
optimal values of the assimilation window length and the update frequency
used for the rapid update cycle and incremental analysis update experiments.
The assimilation of radar data has a positive influence on the heavy
rainfall forecast. Quantitative features of the heavy rainfall case, such as
the maximum rainfall amount and Root Mean Squared Differences (RMSDs) of
zonal/meridional wind components, were improved by tuning of the length
scale and observation error parameters. Qualitative features of the case,
such as the maximum rainfall position and time series of hourly rainfall,
were enhanced by an incremental analysis update technique. The positive
effects of the radar data assimilation and the tuning of the length scale
and observation error parameters were clearly shown by the 3DVAR increment. 相似文献
35.
四维变分同化和集合平方根滤波联合反演土壤湿度廓线的研究 总被引：1，自引：0，他引：1
提出将集合平方根滤波（EnSRF）估计的预报误差协方差用于四维变分（4DVAR）的同化方案（文中称混合四维变分同化方法,简称混合方法）来反演土壤湿度廓线,该方法由两个同化时段构成: 第一时段为EnSRF,第二时段为4DVAR,此种组合可以充分发挥每一同化方法的优势。通过同化表层土壤湿度观测反演土壤湿度廓线这一理想试验来验证方法的可行性,并与EnSRF和4DVAR的反演结果进行比较,结果表明, 混合方法反演的分析时刻土壤湿度廓线都优于EnSRF和4DVAR的结果。与此同时,为了克服小样本在估算背景场误差协方差矩阵时出现的虚假相关对反演的干扰, 提出在原有协方差矩阵中加入具有高斯指数函数成分来降低其影响;与修正前结果相比,反演的中下层（地下34～100 cm） 土壤湿度的均方根误差从0.036 cm3/cm3降到0.016 cm3/cm3, 降幅为55.6%, 更重要的是大大降低了部分深度处反演土壤湿度的误差, 如地下90 cm处误差从0.085 cm3/cm3降到0.024 cm3/cm3, 降幅达71.8%。 相似文献
36.
A three-dimensional variational ocean data assimilation system:Scheme and preliminary results 总被引：17，自引：0，他引：17
A new 3DVAR-based Ocean Variational Analysis System (OVALS) is developed. OVALS is capable of assimilating in situ sea water temperature and salinity observations and satellite altimetry data. As a component of OVALS, a new variational scheme is proposed to assimilate the sea surface height data. This scheme considers both the vertical correlation of background errors and the nonlinear temperature-salinity relationship which is derived from the generalization of the linear balance constraints to the nonlinear in the 3DVAR. By this scheme, the model temperature and salinity fields are directly adjusted from the altimetry data. Additionally, OVALS can assimilate the temperature and salinity profiles from the ARGO floats which have been implemented in recent years and some temperature and salinity data such as from expendable bathythermograph, moored ocean buoys, etc. A 21-year assimilation experiment is carried out by using OVALS and the Tropical Pacific circulation model. The results show that the assimilation system may effectively improve the estimations of temperature and salinity by assimilating all kinds of observations. Moreover, the root mean square errors of temperature and salinity in the upper depth less than 420 m reach 0.63℃ and 0.34 psu. 相似文献
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数据同化是在基于物理机制的背景模型上,融合时空不规则分布的观测数据的一种现报方法.同化能够有效弥补数据的时空局限和模型的精度偏差,使二者相互匹配从而获得更加合理可信的模拟效果.本研究利用电离层数据同化方法,针对中国及周边区域（15°N-55°N,70°E-140°E）构建了电离层总电子含量（TEC）同化现报系统.系统使用国际参考电离层（IRI）作为背景场,利用中国科学院空间环境监测网和国际GNSS服务组织（IGS）的部分地基GNSS台站数据作为观测值,并采用三维变分与Gauss-Markov卡尔曼滤波相结合的算法进行背景场和观测值的数据同化,生成覆盖中国及周边区域的电离层TEC和GPS单频接收机延迟误差的格点化准实时现报地图,并在中国科学院空间环境预报中心（http：//sepc.ac.cn/TEC_chn.php）网上发布,每15 min进行更新.该系统是我国基于同化算法的电离层现报系统之一,已用于中国及周边区域的电离层环境实时监测,可为卫星导航、雷达成像、短波通信等科学研究和工程应用提供相对及时、准确、有效的电离层TEC和误差修正信息. 相似文献
39.
Ching-Yuang Huang Ying-Hwa Kuo Shu-Ya Chen Anisetty S. K. A. V. Prasad Rao Chien-Ju Wang 《Pure and Applied Geophysics》2007,164(8-9):1577-1591
In this study, the Weather Research and Forecasting (WRF-2.0.3.1) model with three-dimensional variational data assimilation
(3DVAR) was utilized to study a heavy rainfall event along the west coast of India with and without the assimilation of GPS
occultation refractivity soundings in the monsoon period of 2002. The WRF model is a next-generation mesoscale numerical weather
prediction system designed to serve both operational forecasting and atmospheric research communities. The Global Positioning
System (GPS) radio occultation (RO) refractivity data, processed by UCAR, were obtained from the CHAMP and SAC-C missions.
This study investigates the impact of thirteen GPS occultation refractivity soundings only, as assimilated into the WRF model
with 3DVAR, on the rainfall prediction over the western coastal mountain of India. The model simulation, with the finest resolution
of 10 km, was in good agreement with rainfall observations, up to 72-h forecast. There are some subtle but important differences
in predicted rainfalls between the control run CN (without the assimilation of refractivity soundings) and G13 (with the assimilation
of thirteen GPS RO soundings). In general, the assimilation run G13 gives a better prediction in terms of both rainfall locations
and amounts at later times. The moisture increments were analyzed at the initial and forecast times to assess the impact of
GPS RO data assimilation. The results indicate that remote soundings in the forcing region could have significant impacts
on distant downstream regions. It is anticipated, based on this study, that considerably occultation soundings available from
the six-satellite constellation of FORMOSAT-3/COSMIC would have even more significant impacts on weather prediction in this
region. 相似文献
40.
Present work elucidates the impact of 3DVAR data assimilation technique for the simulation of one of the heavy rainfall events reported over Kotdwara region in the North-West Himalayan (NWH) region on 4th August 2017. We have examined the impact of conventional and satellite-based radiance datasets on the simulated results with and without assimilating the observations into the Weather Research and Forecasting (WRF) model. Three experiments have been designed with 3 nested domains of variable resolutions, one without assimilation (referred as control experiment) and other two experiments after assimilating conventional and satellite radiances observations (refer as DA-OBS and DA-SAT respectively). In the present study, assimilation of surface, upper air and the satellite-based radiance observations has been carried out for the outermost domain with horizontal resolution of 9 km. Statistical analysis suggests that the correlation coefficient is high (0.55) and root mean square error (RMSE) is low (17.12) for DA-SAT experiment as compared to other two experiments. Substantial improvement in the location, pattern and intensity of extreme rainfall event is noted after assimilation of both conventional and satellite observations with respect to the observed rainfall data. However, it is noted that the assimilation of satellite radiances has greater impact in simulating better intensity of the heavy rainfall event as compared to the assimilation of conventional observations. Plausible reason behind this could be the non-availability of the conventional observations close to the extreme rainfall event affected region. 相似文献