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The present study designs experiments on the direct assimilation of
radial velocity and reflectivity data collected by an S-band Doppler weather
radar (CINRAD WSR-98D) at the Hefei Station and the reanalysis data produced by
the United States National Centers for Environmental Prediction using the Weather
Research and Forecasting (WRF) model, the WRF model with a three-dimensional
variational (3DVAR) data assimilation system and the WRF model with an ensemble
square root filter (EnSRF) data assimilation system. In addition, the present
study analyzes a Meiyu front heavy rainfall process that occurred in the Yangtze
-Huaihe River Basin from July 4 to July 5, 2003, through numerical simulation.
The results show the following. (1) The assimilation of the radar radial velocity
data can increase the perturbations in the low-altitude atmosphere over the heavy
rainfall region, enhance the convective activities and reduce excessive simulated
precipitation. (2) The 3DVAR assimilation method significantly adjusts the
horizontal wind field. The assimilation of the reflectivity data improves the
microphysical quantities and dynamic fields in the model. In addition, the
assimilation of the radial velocity and reflectivity data can better adjust the
wind fields and improve the intensity and location of the simulated radar echo
bands. (3) The EnSRF assimilation method can assimilate more small-scale wind
field information into the model. The assimilation of the reflectivity data alone
can relatively accurately forecast the rainfall centers. In addition, the
assimilation of the radial velocity and reflectivity data can improve the
location of the simulated radar echo bands. (4) The use of the 3DVAR and EnSRF
assimilation methods to assimilate the radar radial velocity and reflectivity
data can improve the forecast of precipitation, rain-band areal coverage and the
center location and intensity of precipitation. 相似文献
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为了考察具有多种特征尺度四阶递归滤波器在极端暴雨事件预报中的应用效果和能力,利用GRAPES三维变分同化系统,对2007年7月16—19日的川渝暴雨过程进行了模式分析。共开展了3组模拟试验：第一组试验在模式中使用单一特征尺度的一阶递归滤波;第二组试验在模式中使用由3种不同特征尺度的四阶滤波拟合而成的四阶递归滤波;第三组试验在模式中使用由3种不同特征尺度的一阶滤波拟合而成的一阶递归滤波。结果表明,具有多种特征尺度的递归滤波的预报效果要好于单一尺度的递归滤波。相比而言,由3种不同特征尺度的四阶递归滤波对预报的改进最大,不论是降水的落区还是降水的强度都有很大程度的改善,对水汽的调节更为理想。 相似文献
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利用中尺度非静力WRF(Weather Research and Forecasting)模式及其三维变分同化系统,对2007年7月淮河流域的一次强降雨过程进行多普勒雷达径向速度资料的三维变分同化试验,重点考察雷达资料的不同稀疏化方式对同化结果以及对暴雨数值模拟的影响。结果表明:同化多普勒雷达径向速度资料使得模式初始风场包含了更丰富的中尺度特征信息,有效调整了初始场的环流结构,能够改善模式对暴雨过程的模拟效果;以不同的稀疏化处理方式同化多普勒雷达径向速度资料对分析场会产生不同的影响,进而影响模式的降水预报效果,本次试验中当极坐标网格径向分辨率取10 km的时候降水过程的预报效果最好。 相似文献
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In this article, the Multi-Fractal Detrended Fluctuation Analysis (MF-DFA) method is adopted to study the temperature, i. e., the maximum temperature (Tmax), mean temperature (Tavg) and minimum (Tmin) air temperature, multifractal characteristics and their formation mechanism, in the typical temperature zones in the coastal regions in Guangdong, Jiangsu and Liaoning Provinces. Following are some terms and concepts used in the present study. Multifractality is defined as a term that characterizes the complexity and self-similarity of objects, and fractal characteristics depict the distribution of probability over the whole set caused by different local conditions or different levels in the process of evolution. Fractality strength denotes the fluctuation range of the data set, and long-range correlation (LRC) measures the stability of the climate system and the trend of climate change in the future. In this research, it is found that the internal stability and feedback mechanism of climate systems in different regions show regional differences. Furthermore, the research also proves that the Tavg, Tmax and Tmin of the above three provinces are highly multifractal. The temperature series multifractality of each province decreases in the order of temperature series multifractality of Liaoning > temperature series multifractality of Guangdong > temperature series multifractality of Jiangsu, and the corresponding long-range correlations follow the same order. It reveals that the most stable temperature series is that of Liaoning, followed by the temperature series of Guangdong, and the most unstable one is that of Jiangsu. Liaoning has the most stable climate system, and it will thus be less responsive to the future climate warming. The stability of the climate system in Jiangsu is the weakest, and its temperature fluctuation will continue to increase in the future, which will probably result in the meteorological disasters of high temperature and heat wave there. Guangdong possesses the strongest degree of multifractal strength, which indicates that its internal temperature series fluctuation is the largest among the three regions. The Tmax multifractal strength of Jiangsu is stronger than that of Liaoning, while the Tavg and Tmin multifractal strength of Jiangsu is weaker than that of Liaoning, showing that Jiangsu has a larger internal Tmax fluctuation than Liaoning does, while it has a smaller fluctuation of Tavg and Tmin than Liaoning does. Guangdong and Liaoning both show the strongest Tmin multifractal strength, followed by Tavg multifractal strength, and the weakest Tmax multifractal strength. However, Jiangsu has the strongest Tmax, followed by Tavg, and the weakest Tmin. The research findings show that these phenomena are closely related to solar radiation, monsoon strength, topography and some other factors. In addition, the multifractality of the temperature time series results from the negative power-law distribution and long-range correlation, in which the long-range correlation influence of temperature series itself plays the dominant role. With the backdrop of global climate change, this research can provide a theoretical basis for the prediction of the spatial-temporal air temperature variation in the eastern coastal areas of China and help us understand its characteristics and causes, and thus the present study will be significant for the environmental protection of coastal areas. 相似文献
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基于雷达变分同化系统(Variational Doppler Radar Analysis System,VDRAS)中雷达速度退模糊方案的思想,建立了一个雷达径向速度退模糊的方案,用于我国多普勒雷达径向速度资料的质量控制,并对方案的质量控制效果进行了检验.该方案的优点是使用模式分析场得到的参考风在每个格点独立地进行退模糊,避免了径向速度大片区域被错误地退模糊,提高了准确度.方案主要包括三步:水平梯度检验、全局退模糊和局部退模糊.利用国家气象中心全球与区域一体化的同化与数值预报系统(Global and Regional Assimilation and Prediction Enhanced System,GRAPES)快速更新循环(Rapid Update Cycle,RUC)系统提供的3h间隔的分析场资料对多个时次的雷达资料进行退模糊试验.结果表明,该方案能够有效地对模糊速度进行订正,剔除异常资料点,提高可直接用于变分同化的雷达径向风资料的质量,适合业务应用,为我国多普勒雷达资料的质量控制提供了一种新的有效方案. 相似文献
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风云三号卫星微波辐射率资料的质量控制 总被引：2，自引：2，他引：0
利用双权重算法对观测增量（卫星观测亮度温度与用辐射传输模式模拟的背景场亮度温度之差）进行质量控制,模式背景场采用的是NCEP再分析资料,观测算子使用的是RTTOV（9．3v）（thefast radiative transfer for （A）TOVS model）。质量控制分两步进行：粗检验和离群值检验,目的是剔除受地表发射率或云影响的离群资料。结果表明：质量控制后观测增量标准差显著减小,偏差接近无偏正态分布,FY-3卫星微波温度探测器辐射率数据的质量得到很大的改善,为FY-3微波温度计观测亮温资料在数值预报资料同化系统中的应用奠定基础。 相似文献
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雷达资料的3DVAR和EnSRF直接同化方法对一次暴雨预报的影响分析 总被引：7，自引：5，他引：2
利用合肥站雷达资料和NCEP再分析资料,采用WRF模式、WRF-3DVAR和WRF-En SRF同化系统设计了直接同化雷达径向风和反射率的试验,对2003年7月4—5日发生在江淮流域的一次梅雨锋暴雨过程进行数值模拟分析。结果表明:(1)同化雷达径向风资料可以增加暴雨区低层的扰动,增强对流运动,同时可以减少模拟的过量降水;(2)3DVAR同化方法对水平风场调整明显,同化反射率改进了模式中的微物理量和动力场,同时同化径向风和反射率可以更好地调整风场,改善模拟雷达回波带的强度和位置;(3)En SRF同化方法可以将更多小尺度的风场信息同化进入模式,单独同化反射率可以较准确预报出降水中心,同时同化径向风和反射率可以改进模拟雷达回波带的位置;(4)使用3DVAR和En SRF同化雷达径向风和反射率可以提高降水预报,改善雨带的范围,对降水中心位置和强度的预报也有改进。 相似文献
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基于循环神经网络的雷达回波外推算法的预报结果随时间逐渐模糊失真，同时难以预报强回波区域。针对上述问题，提出一种上下文融合和注意力机制的时空长短期记忆网络模型。该方法通过上下文融合模块充分提取雷达图像不同尺度的短期上下文信息；通过注意力模块拓宽预测单元的时间感受域，使模型感知更多的时间动态。以2019—2021年4—9月江苏省气象雷达数据为样本，通过试验对比分析，基于上下文融合和注意力机制的时空长短期记忆网络取得了更好的预测性能。在外推60min，阈值为10、20、40 dBz的条件下，临界成功指数和HSS分别达到0.7611、0.5326、0.2369和0.7335、0.5735、0.3075，有效提高了预测精度。 相似文献
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