Detecting and attributing drought-induced changes in catchment hydrological behaviours in a southeastern Australia catchment using a data assimilation method

It is widely recognized that multi-year drought can induce changes in catchment hydrological behaviours. However, at present, our understanding about multi-year drought-induced changes in catchment hydrological behaviours and its driving factors at the process level is still very limited. This study proposed a new approach using a data assimilation technique with a process-based hydrological model to detect multi-year drought-induced changes in catchment hydrological behaviours and to identify driving factors for the changes in an unimpaired Australian catchment (Wee Jasper) which experienced prolonged drought from 1997 to 2009. Modelling experiments demonstrated that the multi-year drought caused a significant change in the catchment rainfall-runoff relationship, indicated by significant step changes in the estimated time-variant hydrological parameters SC (indicating catchment active water storage capacity) and C (reflecting catchment evapotranspiration dynamics), whose average values increased 23.4% and 10.2%, respectively, due to drought. The change in the rainfall-runoff relationship identified by the data assimilation method is consistent with that arrived at by a statistical examination. The proposed method provides insights about the drivers of the changes in the rainfall-runoff relationship at the processes level. Increasing catchment water storage capacity and decreasing ratio of rainfall to soil moisture for supplying actual evapotranspiration during drought are the main driving factors for the catchment behaviours change in the Wee Jasper catchment in terms of model structure. And they are related to decrease in catchment groundwater level and deep soil moisture. The proposed new method can be used as an effective technique for detecting both the change of hydrological behaviours induced by prolonged drought and its driving factors at the process level.     

Spectral wave data assimilation for the prediction of waves in the North Sea

Spectral observations from pitch-and-roll buoys have been assimilated in a North Sea wave model, in order to study their impact on the wave analysis and forecast. The assimilation is based on Optimal Interpolation (OI) of a limited number of characteristic spectral parameters. In a case study, the propagation of the corrections through the model domain is followed, and it is clarified for which wave conditions the data assimilation has the largest influence on the forecast: this is especially the case for swell waves with long travel times between the assimilation site and the location where validation is carried out. A 1-year test has been carried out in which an analysis and subsequent forecast were produced four times a day. From a statistical analysis of the results a modest but systematic improvement of the 12-h forecast is found. When only swell cases are selected, the impact is more pronounced. It is argued that for shelf seas like the North Sea, more progress is to be expected from extension of the ‘conventional' observations network (buoys and wave radars) than from satellite measurements.     

同化海温观测数据研究波浪破碎对海洋上层结构的影响

首先利用考虑波浪破碎效应的Mellor-Yamada 2.5阶湍流闭合方案,探讨了海表温度(SST)对波能因子α和Charnock数β的敏感性问题。然后采用变分数据同化途径,基于Papa海洋天气站(OWS Papa Station)的上层温度观测数据,对该参数化方案中的波能因子α和Charnock数β两个参数进行了最优估计。最优估计的结果表明,当α约为167、β约为4.1×105时,价值函数达到最小值。利用上述参数的最优估计进行海温的数值模拟,可以较好地反映出海表温度的日变化和月变化过程,模拟的上混合层的温度和深度也与观测较为一致。最后利用以上参数的最优估计结果对湍动能方程进行诊断计算,研究了波浪破碎对海洋上层湍能量收支的影响。     

集合滤波和三维变分混合数据同化方法研究

发展了一种新的混合数据同化方法——基于集合滤波和三维变分的混合数据同化方法。该方法将集合调整卡尔曼滤波(ensemble adjustment Kalman filter,EAKF)得到的集合样本扰动通过一个转换矩阵的形式直接作用到背景场上,利用顺序滤波的思想得到分析场的一个扰动;然后在三维变分(three dimensional variational analysis,3D-Var)的框架下与观测数据进行拟合,从而给出分析场的最优估计。文中以Lorenz63模型为例,开展了理想数据同化试验,结果表明,相比于集合调整卡尔曼滤波,这种新的混合同化方法可以给出更好的同化结果。     

多普勒雷达资料在近海强台风模拟中的同化试验

利用中尺度数值模式(WRF),并同化了多普勒雷达反射率和径向速度资料以及非常规的观测资料,对近几年登陆于浙闽沿海的4例强台风进行了数值模拟。通过高时空分辨率的模拟结果对比分析表明:雷达资料的同化,对近海登陆台风路径和降水模拟以及中尺度降水特征都有进一步改进的效果;模拟较好的揭示了台风近中心螺旋云带中的强中尺度对流系统。通过模拟分析表明,在台风近中心的螺旋云带中,低层有一条强辐合线存在,它与实况多普勒雷达给出的低层平显(PPI)强度回波带有较好的对应关系,也与沿海地区中尺度暴雨系统紧密联系,并由此看到近海海域降水带和强对流区的存在。     

三维斜压台风模式 Ⅱ.预报试验

一种斜压多重移动套网格台网模式在国家海洋环境预报中心已被应用于海洋环境预报。本文第一部分已描述了模式方程组和数值方法。本文继续概述模式网格、变分调整初始化和预报试验结果。最外粗网格域固定,内部细网格域随台风中心轨迹移动。模式中,粗细网格变量采用双向耦合。平衡方程和方程,理想台风场和变分调整方案被用于台风模式初始化。一种简单而有效的资料同化方法,即用第6h台风报和弱约束变分原理调整初始场,被应用于提高预报结果。最后本文给出预报试验结果。预报误差统计显示本模式对台风路径预报具有相当能力,而且可以提供海面风和气压场较好的预报。本模式已经与海浪模式联结,得到满意的波高预报结果。     

资料同化在空气质量预报中的应用

随着数值模式的不断完善和观测技术的不断提高,资料同化逐渐成为能够进一步提高数值预报水平的一种有效方法。20世纪70年代,资料同化开始引入空气质量预报领域,成为当前大气环境科学研究的一个新方向。简要介绍了资料同化的含义,较详细地介绍了Kalman滤波法、四维变分同化法、牛顿松弛法的基本思想和优缺点,重点阐述了国内外资料同化在空气质量预报中的研究应用情况,最后指出资料同化应用于空气质量预报时存在的问题和今后的研究方向。     

集合Kalman滤波资料同化技术及研究现状

针对国内集合Kalman滤波资料同化领域的研究空白,对该技术的背景、理论、优势以及存在的问题做了简要描述,对目前国际上的主要研究成果做了介绍,并给出了该方法可能的发展方向。     

An experimental analysis for the impact of 3D variation assimilation of satellite data on typhoon track simulation

A series of test simulations are performed to evaluate the impact of satellite-derived meteorological data on numerical typhoon track prediction. Geostationary meteorological satellite (GMS-5) and NOAA‘ s TIROS operational vertical sounder (TOVS) observations are used in the experiments. A three-dimensional variational (3D-Var) assimilation scheme is developed to assimilate the satellite data directly into the Penn State-NCAR nonhydrostatic meteorological model (MM5). Three-dimensional objective analysis fields based on the T213 results and conventional observations are employed as the background fields of the initialization. The comparisons of the simulated typhoon tracks are carried out, which correspond respectively to assimilate different kinds of satellite data. It is found that, compared with the experiment without satellite data assimilation, the 3D-Var assimilation schemes lead to significant improvements on typhoon track prediction. Track errors reduce from approximately 25% at 24 h to approximately 30% at 48 h for 3D-Var assimilation experiments.     

应用粒子群算法的遥感信息与水稻生长模型同化技术

在研究遥感信息和水稻生长模型的同化过程中, 最小化遥感反演与生长模型(RiceGrow)输出的水稻生长 信息差值绝对值时引入了一种新的优化算法-粒子群算法(PSO), 并对比了其与模拟退火算法(SA)的优缺点; 探讨 了叶面积指数(LAI)和叶片氮积累量(LNA)分别作为同化参数时的同化效果。结果表明, PSO 无论是从同化效率还是 反演精度上都要好于SA, 粒子群优化算法是一种可靠的遥感与模型同化算法; LAI 和LNA 作为外部同化参数时各 有优势, LAI 作为同化参数可获得较准确的播期及播种量, 而LNA 作为同化参数可获得更为准确的施氮量信息。但 是LAI 作为外部同化参数时的反演结果总体要优于利用LNA 作为同化参数时的反演结果。利用试验资料对该技术 进行了测试和检验, 结果显示反演的模型初始参数的平均值与真实值的相对误差(RE)均小于2.5%, 均方根误差 (RMSE)为0.7—2.2, 产量模拟值与实测值之间的相对误差为5%左右, 模拟与实测相关指标值吻合度较高, 该同化 技术具有较好的适用性。从而为生长模型从单点扩展到区域尺度应用奠定了基础。