卡尔曼滤波算法在辽宁东部测雨雷达降水同化适用性分析

为提高测雨雷达在辽宁地区的适用性,文章采用卡尔曼滤波算法对辽宁东部大洋河流域测雨雷达数据进行降水同化适用性分析。分析表明:采用卡尔曼滤波算法对测雨雷达数据进行同化分析后,其面降水误差可平均降低7.93%,单站降水回归方程可通过90%显著性检验。研究成果对于测雨雷达数据在辽宁地区流域面降水监测实际应用具有参考价值。

卡尔曼滤波算法在雷达降水同化及水文模型中的应用研究

引入卡尔曼滤波算法对雷达降水数据进行同化,并将同化的雷达降水数据用于水文模型中。结果表明:在面雨量上,卡尔曼滤波算法同化的雷达降水数据和区域实测降水数据之间相对误差低于20%,绝对误差低于45mm;在单站雨量上,各雨量站实测数据和同化雷达降水数据自回归方程可通过置信度为95%的F检验;以同化的雷达降水数据作为水文模型输入,其场次洪水合格率可达到73%。研究成果对于雷达降水数据同化及在水文模型中的应用提供参考。

同化雷达估算降水率对暴雨预报的影响

选取2009年3月28日广东省广州市大暴雨过程,考察了变分校准前后Z-I关系估算雷达降水率的区别。变分校准后的降水率资料具有较高的单点精度与合理的梯度分布。降水率资料能够反映大气动力特征和水汽分布等重要信息,是模拟中小尺度系统的关键因子。基于GRAPES(Global/Regional Analysis and Prediction System)区域三维变分系统,将FSU(Florida State University)对流参数化方案作为观测算子的同化试验指出,同化降水率资料后同时增强了低层大气的辐合和高层大气的辐散,从而使整层气柱的不稳定能量增加。沙氏指数和K指数诊断分析也表明,同化降水率资料后有利于触发强对流天气。此外,低空辐合有利于水汽垂直输送,维持对流发展,改进降水模拟。逐小时数值模拟结果表明:同化校准后的雷达估算降水率不仅可以改进降水分布,而且使中尺度对流系统的发展和消亡清晰地表现出来。

用四维变分法同化自动站降水资料

用自动站降水资料作了四维变分同化试验。试验表明,由于它的加入,增加了初始场中的中尺度信息,改进了中尺度数值模式MM5的预报,增强了模拟开始阶段的降水量,改进了降水量的落区预报,减弱了模式开始阶段的“spinup”现象。试验还表明,自动站降水资料的时间变化信息,在同化时也起重要作用。

降水量的直接同化对初始场的影响

四维变分法用严格的数学方法、合乎逻辑的物理过程根据观测资料和模式本身来获得一个最优的初始场,是当前最先进的四维资料同化方法之一。作者用四维变分法对自动站降水量作直接同化,对初始场增量进行了分析。结果表明降水量的同化对初始场中热力性质的量影响相对较大,对低层的影响大于高层,大部分增量在925 hPa至850 hPa层间达最大。通过同化,北京地区对流不稳定性明显增加。对风场的增量分析表明,同化增加了一些中尺度信息。对于散度场和水汽通量散度场,同化有使低层辐合加强的趋势。通过同化,在MM5初始场上增加了原先没有的云水、雨水含量,但同化对初始时刻的垂直环流改变不明显。

Influence of assimilating ground-based microwave radiometer data into the WRF model on precipitation

Ground-based microwave radiometers profilers(MWRPs)have been used in numerical weather prediction(NWP)systems and show different impacts on forecasts.Currently,there are around hundreds of ground-based MWPRs used in weather stations over China;however,the application of MWPRs in NWP systems is rather limited.In this work,two MWRP retrieved profiles were assimilated into the Weather Research and Forecasting(WRF)model for a rainstorm event that occurred in Beijing,China.The quality of temperature and humidity profiles retrieved from the MWRP was evaluated against radiosonde observations and showed the reliability of the two MWRP products.Then,comparisons between the measurements of ground-based rain gauges and the corresponding forecasted precipitation in different periods of the rainstorm were investigated.The results showed that assimilating the two MWRPs affected the distribution and intensity of rainfall,especially in the early stage of the rainstorm.With the development of the rainstorm,adding MWRP data showed only a slight influence on the precipitation during the stable and mature period of the rainstorm,since the two MWRP observations were too limited to affect the large area of heavy rainfall.

Characteristics of diurnal variations of warm-season precipitation over Xinjiang Province in China

Aimed at improving knowledge regarding the diurnal cycle of warm-season rainfall in northwestern China,this study investigated the diurnal variations of warm-season precipitation with different durations in Xinjiang,China,using an hourly gauge–satellite merged precipitation product during 2008 to 2019.Results show noticeable diurnal variations with distinctive regional features.The primary peak is in the early evening.Rainfall events with duration less than 3 h occur more often across the whole of Xinjiang and contribute more than half of the precipitation amount over its northern and southern peripheries,while rainfall events with duration more than 7 h over the Tianshan Mountains are responsible for the primary peak in the diurnal variations of warm-season precipitation.

Assimilating Amounts of Precipitation Using a New Four-Dimensional Variational Method

Observations of accumulated precipitation are extremely valuable for effectively improving rainfall analysis and forecast. It is, however, difficult to use such observations directly through sequential assimilation methods, such as three-dimensional variational data assimilation or an Ensemble Kalman Filter. In this study, the authors illustrate a new approach that makes effective use of precipitation data to improve rainfall forecast. The new method directly obtains an optimal solution in a reduced space by fitting observations with historical time series generated by the model; it also avoids the implementation of tangent linear model and its adjoint. A lot of historical samples are produced as the ensemble of precipitation observations with the fully nonlinear forecast model. The results show that the new approach is capable of extracting information from precipitation observations to improve the analysis and forecast. This method provides comparable performance with the standard fourdimensional variational data assimilation at a much lower computational cost.

EXPERIMENTS ON ASSIMILATION OF INITIAL VALUES IN NUMERICAL PREDICTION OF A WARM-SECTOR PRECIPITATION IN SOUTH CHINA

In order to understand the impact of initial conditions upon prediction accuracy of short-term forecast and nowcast of precipitation in South China, four experiments i.e. a control, an assimilation of conventional sounding and surface data, testing with nudging rainwater data and the assimilation of radar-derived radial wind, are respectively conducted to simulate a case of warm-sector heavy rainfall that occurred over South China, by using the GRAPES_MESO model. The results show that (1) assimilating conventional surface and sounding observations helps improve the 24-h rainfall forecast in both the area and order of magnitude; (2) nudging rainwater contributes to a significant improvement of nowcast, and (3) the assimilation of radar-derived radial winds distinctly improves the 24-h rainfall forecast in both the area and order of magnitude. These results serve as significant technical reference for the study on short-term forecast and nowcast of precipitation over South China in the future.