Analysis of SARAL/AltiKa Wind and Wave over Indian Ocean and its Real-time Application in Wave Forecasting System at ISRO

The current study aims to analyze the wind and wave parameters over Indian Ocean region obtained from first Ka –band altimeter AltiKa onboard SARAL, a collaborative mission of Indian Space Research Organization (ISRO) and Centre National d'Etudes Spatiales (CNES), France. It also demonstrates a real time application of SARAL data by assimilating the wave height in a wave model operational at the Space Applications Centre, ISRO. State-of–the art coastal wave model Simulating Wave Near shore (SWAN) is used for this purpose. The well-tested optimal interpolation technique is adopted for assimilation. Before proceeding to the assimilation per se, SARAL/AltiKa Wind and Significant Wave Height (SWH) have been validated using in- situ observations and WAVEWATCH III model. Apart from assessment of wind and wave data quality, this also served the purpose of providing error covariance to be used in assimilation. Supremacy of the assimilation run over parallel control run without assimilation has been judged by comparing the results with buoy observations at Indian National Centre for Ocean Information System (INCOIS). The statistics of validation of the assimilation run has been found to be extremely encouraging and interesting.     

四维变分同化技术在风暴潮数值模拟中的应用

利用区域海洋模式ROMS(Regional Ocean Modelling System)及其四维变分同化模块,建立了具有资料同化能力的东中国海风暴潮数值模式,通过将海洋站水位观测资料同化到风暴潮模式中,提高了模式对风暴潮的模拟精度。四维变分同化技术能够在整个同化时间窗口保持动力协调,使模拟结果在该时间窗口内最大程度的靠近观测,同时,得到了最优预报初始场。利用该模式,对两次温带风暴潮过程进行了数值模拟,结果表明:在同化窗口内,同化对模拟精度有明显的提高;结束同化之后,得到的最优预报初始场对临近预报精度也有一定提高。     

天津港邻近海域精密潮汐模型的构建

基于POM模式的"blending"同化法,构建了天津港及附近海域分辨率为1'×1'、13个主分潮的精密潮汐模型。构建过程中重点关注水深数据格网化与潮汐模型的精度评估。水深数据格网化过程基于尽量扩大覆盖范围、分辨率与水深数据相匹配以及保持岸线形状基本不变等原则。潮汐数值模拟过程采用分批同化T/P点与验潮站,实现对潮汐模型精度的可靠评估。经估计,在天津港附近的9个主分潮RSS在10cm内。     

海洋资料同化对气候季节-年际预测技巧及初始场的影响试验

集合卡尔曼滤波(Ensemble Kalman filter, EnKF)是一种国内外广泛使用的海洋资料同化方案, 用集合成员的状态集合表征模式的背景误差协方差, 结合观测误差协方差, 计算卡尔曼增益矩阵, 有效地将观测信息添加到模式初始场中。由于季节、年际预测很大程度上受到初始场的影响, 因此资料同化可以提高模式的预测性能。本文在NUIST-CFS1.0预测系统逐日SST nudging的初始化方案上, 利用EnKF在每个月末将全场(full field)海表温度(sea surface temperature, SST)、温盐廓线(in-situ temperature and salinity profiles, T-S profiles)以及卫星观测海平面高度异常(sea level anomalies, SLA)观测资料同化到模式初始场中, 对比分析了无海洋资料同化以及加入同化后初始场的区别、加入海洋资料同化后模式提前1~24个月预测性能的差异以及对于厄尔尼诺-南方涛动(El Niño-southern oscillation, ENSO)预测技巧的影响。结果表明, 加入海洋资料同化能有效地改进初始场, 并且呈现随深度增加初始场改进越显著的特征。加入同化后, 对全球SST、次表层海水温度的平均预测技巧均有一定的提高, 也表现出随深度增加预测技巧改进越明显的特征。但加入海洋资料同化后, 模式对ENSO的预测技巧有所下降, 可能是由于模式误差的存在, 使得同化后的预测初始场从接近观测的状态又逐渐恢复到与模式动力相匹配的状态, 加剧了赤道太平洋冷舌偏西、中东部偏暖的气候平均态漂移。     

Numerical simulation and data assimilation of the water-energy cycle over semiarid northeastern China

The default fractional vegetation cover and terrain height were replaced by the estimated fractional vegetation cover, which was calculated by the Normalized Difference Vegetation Index(NDVI) of Earth Observing System Moderate-Resolution Imaging Spectroradiometer(EOS-MODIS) and the Digital Elevation Model of the Shuttle Radar Topography Mission(SRTM) system. The near-surface meteorological elements over northeastern China were assimilated into the three-dimensional variational data assimilation system(3DVar) module in the Weather Research and Forecasting(WRF) model. The structure and daily variations of air temperature, humidity, wind and energy fields over northeastern China were simulated using the WRF model. Four groups of numerical experiments were performed, and the simulation results were analyzed of latent heat flux, sensible heat flux, and their relationships with changes in the surface energy flux due to soil moisture and precipitation over different surfaces. The simulations were compared with observations of the stations Tongyu, Naiman, Jinzhou, and Miyun from June to August, 2009. The results showed that the WRF model achieves high-quality simulations of the diurnal characteristics of the surface layer temperature, wind direction, net radiation, sensible heat flux, and latent heat flux over semiarid northeastern China in the summer. The simulated near-surface temperature, relative humidity, and wind speed were improved in the data assimilation case(Case 2) compared with control case(Case 1). The simulated sensible heat fluxes and surface heat fluxes were improved by the land surface parameterization case(Case 3) and the combined case(Case 4). The simulated temporal variations in soil moisture over the northeastern arid areas agree well with observations in Case 4, but the simulated precipitation should be improved in the WRF model. This study could improve the land surface parameters by utilizing remote sensing data and could further improve atmospheric elements with a data assimilation system. This work provides an effective attempt at combining multi-source data with different spatial and temporal scales into numerical simulations. The assimilation datasets generated by this work can be applied to research on climate change and environmental monitoring of arid lands, as well as research on the formation and stability of climate over semiarid areas.     

Data assimilation for distributed hydrological catchment modeling via ensemble Kalman filter

Catchment scale hydrological models are critical decision support tools for water resources management and environment remediation. However, the reliability of hydrological models is inevitably affected by limited measurements and imperfect models. Data assimilation techniques combine complementary information from measurements and models to enhance the model reliability and reduce predictive uncertainties. As a sequential data assimilation technique, the ensemble Kalman filter (EnKF) has been extensively studied in the earth sciences for assimilating in-situ measurements and remote sensing data. Although the EnKF has been demonstrated in land surface data assimilations, there are no systematic studies to investigate its performance in distributed modeling with high dimensional states and parameters. In this paper, we present an assessment on the EnKF with state augmentation for combined state-parameter estimation on the basis of a physical-based hydrological model, Soil and Water Assessment Tool (SWAT). Through synthetic simulation experiments, the capability of the EnKF is demonstrated by assimilating the runoff and other measurements, and its sensitivities are analyzed with respect to the error specification, the initial realization and the ensemble size. It is found that the EnKF provides an efficient approach for obtaining a set of acceptable model parameters and satisfactory runoff, soil water content and evapotranspiration estimations. The EnKF performance could be improved after augmenting with other complementary data, such as soil water content and evapotranspiration from remote sensing retrieval. Sensitivity studies demonstrate the importance of consistent error specification and the potential with small ensemble size in the data assimilation system.     

排水砂槽渗流试验与Signorini型变分不等式方法验证

排水是岩土体及工程构筑物渗流控制的主要措施之一,其实质是通过在渗流域内形成潜在溢出边界或低水位边界而实现渗流控制的。在排水渗控条件下,渗流场往往具有强烈的边界非线性特征,Signorini型变分不等式方法从理论上为稳定和非稳定排水渗流问题提供了有效的分析方法,但其实际效果还需要得到试验的验证。通过开展含5个排水廊道的排水砂槽模型试验,研究了复杂排水条件下砂土渗流的基本规律,并通过试验数据与数值计算成果的对比分析,论证了Signorini型变分不等式方法的有效性和正确性。试验结果表明,在稳定渗流条件下,排水砂槽上游侧3个排水廊道对渗流控制起主导作用,而下游侧2个廊道则失去排水功能,数值计算与试验成果吻合较好;在非稳定渗流条件下,受测压管精度、砂样均匀性和毛细效应的影响,数值计算与试验成果存在一定偏差,但也较好地揭示了复杂排水条件下砂槽中的非稳定渗流特征。排水砂槽试验结果验证了Signorini型变分不等式方法的有效性和正确性,为复杂排水条件下岩土体及工程构筑物的渗控结构优化设计提供了有效的分析手段。     

粒度资料数据同化方法初探

本文从资料处理和应用等方面,对粒度数据的同化方法进行初步探讨,建立了粒度数据同化分类标准。从目前研究情况来看,可对不同分析精度粒度数据、不同空间分布范围数据和不同分析方法的数据进行数据同化。不同精度粒度数据同化方法的对比分析结果表明Spline插值效果较为理想。为了避免出现龙格现象,减小误差,将Spline插值和线性插值结合进行插值效果会更好。     

资料同化中的伴随方法及其在海洋中的应用

较系统地综述了资料同化中各种方法的分类,并着重说明了伴随方法的基本原理和具体实现步骤。并总结了伴随方法在海洋中的应用现状,并指出由于观测资料特别是遥感资料的日益丰富,伴随方法将有广泛地应用前景。     

Machine learning predictions of passive microwave brightness temperature over snow-covered land using the special sensor microwave imager (SSM/I)

Abstract

Two different forms of machine learning – an artificial neural network (ANN) and a support vector machine (SVM) – are used to estimate passive microwave (PMW) brightness temperatures (Tb) as observed by the special sensor microwave imager (SSM/I) satellite sensor over snow- covered land in North America. Both techniques reasonably reproduce unbiased estimates of SSM/I observations at 19.35 and 37.0 GHz for both vertically- and horizontally-polarized channels. When compared against SSM/I observations not used during training, domain-averaged statistics from 1 September 1987 to 1 September 2002 yielded a root mean squared error (RMSE) of less than 9 K for all frequency and polarization combinations examined in this study. Even though both ML techniques reasonably reproduced SSM/I Tb observations, the SVM outperformed the ANN because the SVM: (1) better captured the high-frequency (i.e. day-to-day) temporal characteristics in the Tb observations across the majority of the study domain, (2) better reproduced the spatial variability as a function of snow classification, and (3) yielded greater sensitivity to snow-related input variables during the estimation of PMW Tb. These findings reinforce previous research of SVM-based estimation of PMW Tb employing observations from the advanced microwave scanning radiometer.