Evaluation of radar and automatic weather station data assimilation for a heavy rainfall event in southern China

To improve the accuracy of short-term(0–12 h) forecasts of severe weather in southern China, a real-time storm-scale forecasting system, the Hourly Assimilation and Prediction System(HAPS), has been implemented in Shenzhen, China. The forecasting system is characterized by combining the Advanced Research Weather Research and Forecasting(WRF-ARW)model and the Advanced Regional Prediction System(ARPS) three-dimensional variational data assimilation(3DVAR) package. It is capable of assimilating radar reflectivity and radial velocity data from multiple Doppler radars as well as surface automatic weather station(AWS) data. Experiments are designed to evaluate the impacts of data assimilation on quantitative precipitation forecasting(QPF) by studying a heavy rainfall event in southern China. The forecasts from these experiments are verified against radar, surface, and precipitation observations. Comparison of echo structure and accumulated precipitation suggests that radar data assimilation is useful in improving the short-term forecast by capturing the location and orientation of the band of accumulated rainfall. The assimilation of radar data improves the short-term precipitation forecast skill by up to9 hours by producing more convection. The slight but generally positive impact that surface AWS data has on the forecast of near-surface variables can last up to 6–9 hours. The assimilation of AWS observations alone has some benefit for improving the Fractions Skill Score(FSS) and bias scores; when radar data are assimilated, the additional AWS data may increase the degree of rainfall overprediction.     

风廓线雷达资料质量控制及其同化应用

为更好地同化风廓线雷达观测资料开展了相应的质量控制与同化应用研究。针对2013年5月广东地区13部风廓线雷达的观测数据,采用经验正交函数(EOF) 分析方法对其进行质量控制。相比原始观测,经过质量控制的风场提高(降低)了来自时空大(小)尺度的贡献,较好地滤除了小尺度高频脉动,也较好地保留了大尺度平均状态与局地中小尺度系统的共同影响,并且更加接近ECMWF再分析场。此外,还对质量控制后的数据进行了垂直稀疏化。分别计算了质量控制前、后风廓线雷达观测与NCEP 6 h预报场的差值,对比差值的特征发现,经过质量控制的数据的观测增量更好地满足了高斯分布与无偏假设。针对一个实际天气个例,基于GRAPES 3D-Var同化系统,分析了质量控制后的风廓线雷达资料对模式分析与预报的影响。试验表明,在循环同化过程中加入风廓线雷达资料可以更好地描述模式初始场低层风场的特征,从而对强降水的位置与强度做出更好的预报。针对2013年5月的批量试验表明,同化风廓线雷达资料使短期降水预报有明显的改善。     

C波段雷达在新疆区域数值预报系统中的应用初探

基于新疆区域数值预报系统（Desert Oasis Gobi Rapid Analysis Forecast System,下称DOGRAFS）,开展了同化C波段雷达资料对2010年10月6日发生在新疆的一次强降水过程预报结果影响的试验分析。其中设计包括不同化任何资料、同化常规资料、同化雷达反射率因子、同化雷达径向风、同时同化反射率因子和径向风五组试验,重点分析了雷达资料同化对此次天气过程降水、温度以及风速模拟效果的影响。结果表明：（1）同化雷达径向风和同时同化径向风和反射率因子相对于其他三组试验,对降水预报的TS和ETs评分更高;（2）相对于其他三组试验,同化雷达径向风和同时同化径向风和反射率因子对模式垂直方向上的温度、风速预报偏差具有一定的改善效果;（3）对于地面2 m温度和10 m风速而言,同化常规观测资料比其他四组试验预报的平均偏差和均方根误差更小;其它四组试验误差相当,差别不明显,表明同化雷达对近地面层温度和风的影响不明显。本研究旨在探索C波段雷达观测资料在新疆区域数值预报系统中的适用性,为今后雷达观测资料在业务系统中的应用提供参考。     

土壤湿度对卫星辐射率资料直接同化的影响

本研究利用WRF模式及其三维变分同化系统实现了对NOAA-16 AMSU-A微波资料的直接同化,针对2010年6月19日江西地区的一次强降水过程开展模拟与同化试验,并利用中国区域土壤湿度同化系统(CLSMDAS—China Land Soil Moisture Data Assimilation System)输出的土壤湿度值替换NCEP(National Centers for Environmental Prediction)资料中的土壤湿度,研究土壤湿度初值对辐射率资料直接同化中观测场与背景场偏差调整的影响。结果表明:采用CLSMDAS输出土壤湿度初值条件下模拟的亮温值与实际观测值更为接近,经过质量控制和偏差订正后更多的观测资料能够进入到同化系统中,说明改进的土壤湿度初值条件下观测算子的计算值得到正的调整,对低层地表通道的改进效果明显,尤其以50.3 GHz的窗区通道3的结果最为理想;针对此次强降水过程中24 h累积降水分布的模拟结果,CLSMDAS输出土壤湿度初值条件下同化AMSU-A资料,能够较为准确的把握整个雨带的走向、大雨以上级别降水的落区范围、降水中心落区及强度等。说明准确的土壤湿度初值能够改进卫星辐射率资料的同化结果,进而提高数值模式的模拟预报能力。     

GPS掩星资料三维变分同化对台风模式预报的改进试验

本文尝试了GPSRO COSMIC资料在中尺度数值模式中的应用,利用COSMIC资料受云和降水影响较小,且有高数据精度、高垂直分辨率等优点,以改善模式初始场,进而提高预报准确度。模式采用中尺度气象模式WRF V3.0.1版本及其三维变分同化系统3DVAR,利用NCEP再分析资料、GTS资料和COSMIC资料对2009年第8号台风"莫拉克"登陆台湾岛前到登陆台湾岛的过程进行了模拟试验,并对温度、露点温度、对流有效位能等要素进行了诊断分析。试验结果表明:该项试验成功将COSMIC资料同化进模式,加深对"莫拉克"热力结构特征的了解,有效改善台风降水和路径预报,其中仅屏东县单点降水预报提高600 mm左右,24 h预报路径误差提高80 km以上。同时对提高台风强度预报起到积极作用。     

Assimilating the LAI Data to the VEGAS Model Using the Local Ensemble Transform Kalman Filter: An Observing System Simulation Experiment

Information on the spatial and temporal pat- terns of surface carbon flux is crucial to understanding of source/sink mechanisms and projection of future atmospheric CO2 concentrations and climate. This study presents the construction and implementation of a terrestrial carbon cycle data assimilation system based on a dynamic vegetation and terrestrial carbon model Vegetation-Global-Atmosphere-Soil （VEGAS） with an advanced assimilation algorithm, the local ensemble transform Kalman filter （LETKF, hereafter LETKF-VEGAS）. An observing system simulation experiment （OSSE） framework was designed to evaluate the reliability of this system, and numerical experiments conducted by the OSSE using leaf area index （LAI） observations suggest that the LETKF -VEGAS can improve the estimations of leaf carbon pool and LAI significantly, with reduced root mean square errors and increased correlation coefficients with true values, as compared to a control run without assimilation. Furthermore, the LETKF-VEGAS has the potential to provide more accurate estimations of the net primary productivity （NPP） and carbon flux to atmosphere （CFta）.     

探空观测黑名单检查在变分同化系统中的应用

针对探空观测资料使用造成的某些区域GRAPES分析场存在虚假的高、低压系统问题,该文通过对比全球探空资料的位势高度观测与NCEP分析场,统计站点中观测质量较差的时次出现频数,确定探空位势高度观测黑名单。研究表明:500 hPa在印度地区、北大西洋和南极洲附近的探空位势高度观测与NCEP分析场的均方根误差在30 gpm以上的站点较多,且位势高度观测不可靠观测比率为20%以上的站点主要集中这些区域,以上观测站均列入黑名单。文中在GRAPES全球三维变分分析场的质量控制中加入探空位势高度观测黑名单检查,通过6 h分析预报循环试验表明:探空位势高度观测黑名单检查能有效提高分析场质量,GRAPES位势高度分析场在南极洲附近和印度地区有所改善。     

掩星弯角资料同化在一次暴雨过程中的应用

使用GPS掩星弯角资料和NCEP预报场资料,采用GSI(Grid-point Statistical Interpolation)变分同化系统,对同化掩星弯角资料前后的分析场以及数值预报结果进行详细比较,分析了弯角资料同化对2012年7月21日北京特大暴雨预报结果的影响。结果表明,同化掩星弯角资料对初始场中的温度、湿度产生明显影响,500 hPa和700 hPa温度调整中心量值达到0.5℃和-0.6℃,700 hPa湿度调整中心最大量值达-0.14 kg·kg-1;同化过程改变了强降水发生前流场结构,明显提高了强降水中心位置、雨带走向及范围、降水强度的预报准确性;由降水TS评分可知,200 mm以上大暴雨模拟改善效果明显,但对弱降水预报效果较差。     

Time-Expanded Sampling Approach for Ensemble Kalman Filter:Experiment Assimilation of Simulated Soundings

In the Ensemble Kalman Filter (EnKF) data assimilation-prediction system, most of the computation time is spent on the prediction runs of ensemble members. A limited or small ensemble size does reduce the computational cost, but an excessively small ensemble size usually leads to filter divergence, especially when there are model errors. In order to improve the efficiency of the EnKF data assimilation-prediction system and prevent it against filter divergence, a time-expanded sampling approach for EnKF based on the WRF (Weather Research and Forecasting) model is used to assimilate simulated sounding data. The approach samples a series of perturbed state vectors from Nb member prediction runs not only at the analysis time (as the conventional approach does) but also at equally separated time levels (time interval is △t) before and after the analysis time with M times. All the above sampled state vectors are used to construct the ensemble and compute the background covariance for the analysis, so the ensemble size is increased from Nb to Nb+2M￡Nb=(1+2M)×Nb) without increasing the number of prediction runs (it is still Nb). This reduces the computational cost. A series of experiments are conducted to investigate the impact of △t (the time interval of time-expanded sampling) and M (the maximum sampling times) on the analysis. The results show that if △t and M are properly selected, the time-expanded sampling approach achieves the similar effect to that from the conventional approach with an ensemble size of (1+2M)×Nb, but the number of prediction runs is greatly reduced.     

Using a H∞ filter assimilation procedure to estimate root zone soil water content

Root zone soil water content impacts plant water availability, land energy and water balances. Because of unknown hydrological model error, observation errors and the statistical characteristics of the errors, the widely used Kalman filter (KF) and its extensions are challenged to retrieve the root zone soil water content using the surface soil water content. If the soil hydraulic parameters are poorly estimated, the KF and its extensions fail to accurately estimate the root zone soil water. The H‐infinity filter (HF) represents a robust version of the KF. The HF is widely used in data assimilation and is superior to the KF, especially when the performance of the model is not well understood. The objective of this study is to study the impact of uncertain soil hydraulic parameters, initial soil moisture content and observation period on the ability of HF assimilation to predict in situ soil water content. In this article, we study seven cases. The results show that the soil hydraulic parameters hold a critical role in the course of assimilation. When the soil hydraulic parameters are poorly estimated, an accurate estimation of root soil water content cannot be retrieved by the HF assimilation approach. When the estimated soil hydraulic parameters are similar to actual values, the soil water content at various depths can be accurately retrieved by the HF assimilation. The HF assimilation is not very sensitive to the initial soil water content, and the impact of the initial soil water content on the assimilation scheme can be eliminated after about 5–7 days. The observation interval is important for soil water profile distribution retrieval with the HF, and the shorter the observation interval, the shorter the time required to achieve actual soil water content. However, the retrieval results are not very accurate at a depth of 100 cm. Also it is complex to determine the weighting coefficient and the error attenuation parameter in the HF assimilation. In this article, the trial‐and‐error method was used to determine the weighting coefficient and the error attenuation parameter. After the first establishment of limited range of the parameters, ‘the best parameter set’ was selected from the range of values. For the soil conditions investigated, the HF assimilation results are better than the open‐loop results. Copyright © 2010 John Wiley & Sons, Ltd.