发表论著
关于数据同化的文章

Invited newsletter and book chapter
  1. Yang, K., H. Lu, and T. Koike, 2009: Microwave LDAS improves soil moisture and land flux estimates, GEWEX News, 19(3), 2-3.
    [Highlight][要点]

    This newsletter introduces the dual-pass land data assimilation system of University of Tokyo developed. Two examples at CEOP reference sites show its potential in improving soil moisture and land fluxes estimates. Additional references are Yang et al. (2007, JMSJ) and Yang et al. (2009, JHM).

    介绍了东京大学发展的双通道陆面数据同化系统,并通过两个在CEOP参考站的应用实例说明该系统对提高土壤水分和地表通量估计精度的潜力。相关研究已发表于Yang et al. (2007, JMSJ)和Yang et al. (2009, JHM)。

  2. Liang, S. and J. Qin, 2008: Data assimilation methods for land surface variable estimation. In Advances in Land Remote Sensing: System, Modeling, Inversion and applications: Springer, (ed. Liang, S.), pp. 319-339.
    [Highlight][要点]

    In this study, all kinds of assimilation algorithms based upon the optimal estimation and the optimal control theories are introduced. Their applications for data assimilation in meteorology, hydrology, ecology, and agriculture are reviewed. Moreover, research directions for the future assimilation are also discussed.

    本研究主要回顾了基于最优估计理论和最优控制理论的各种同化算法,以及其在气象、水文、生态和农业等领域中的地表过程模型同化中的应用,并对以后的同化研究发展方向进行了归纳总结。


ISI-indexed journal papers
  1. Han, M., K. Yang, J. Qin, R. Jin, Y. Ma, J. Wen, Y. Chen, L. Zhao, Lazhu, and W. Tang, 2015: An algorithm based on the standard deviation of passive microwave brightness temperatures for monitoring soil surface freeze/thaw state on the Tibetan Plateau, IEEE Trans. Geosci. Remote Sens., 53(5), 2775-2783, doi:10.11-09/TGRS.2014.2364823.
    [PDF][Highlight][要点]

    With the support of three soil moisture and temperature networks in the Tibetan Plateau, a microwave algorithm with AMSR-E data is developed for the detection of soil surface freeze/thaw state. The classification accuracy of this algorithm is more than 90% for the semi-humid and semi-arid regions, and misclassifications mainly occur at the transition period between unfrozen and frozen seasons.

    首次使用AMSR-E被动微波数据,发展了适合青藏高原地区的表层土壤冻融监测算法。该算法在青藏高原的三个土壤温、湿度观测网进行了验证,判别准确率可以达到90%以上,误分主要发生在冷暖季节过渡时期。

  2. Lu, H., K. Yang, T. Koike, L. Zhao, and J. Qin, 2015: An Improvement of the Radiative Transfer Model Component of a Land Data Assimilation System and Its Validation on Different Land Characteristics, Remote Sens., 7(5), 6358-6379, doi:10.3390/rs70506358.
    [PDF][Highlight][要点]

    The volume scattering effects of dry soil media and the surface scattering effects of rough surface are represented and coupled in a radiative transfer model (RTM). The RTM is tested through serving as the observation operator of a Land Data Assimilation System (LDAS). The LDAS results are validated at two stations with different weather and land cover conditions.

    扩展了一个新的微波辐射传输模型,已考虑干土壤的体散射和粗糙地表的表面散射。该辐射传输模型作为陆面微波数据同化系统的观测算子,在两个具有不同气候和下垫面的区域进行了验证。

  3. Zhao, L., K. Yang, J. Qin, Y. Chen, W. Tang, H. Lu, and Z. Yang, 2014: The scale-dependence of SMOS soil moisture accuracy and its improvement through land data assimilation in the central Tibetan Plateau, Remote Sens. Environ., 152, 345-355, doi:10.1016/j.rse.2014.07.005.
    [PDF][Highlight][要点]

    We evaluated SMOS L2 and L3 soil moisture products in central Tibetan Plateau. Evaluation shows the accuracy of SMOS product is scale-dependent. Large biases exist at SMOS node scale while reduced at 100-km scale. We then assimilated the 100-km averaged SMOS L2 data into a land surface model, and robust surface soil moisture estimate is achieved.

    本文以青藏高原土壤水分观测网为基础,针对 SMOS L2 和 L3 土壤水分产品进行了评估和同化实验。发现了 SMOS 土壤水分产品精度与空间尺度的关系。同时,通过同化 100-km 平均的 SMOS 土壤水分可以得到更高精度和更高时间分辨率的土壤水分产品。

  4. Qin, J., K. Yang, N. Lu, Y. Chen, L. Zhao, and M. Han, 2013: Spatial upscaling of in-situ soil moisture measurements based on MODIS-derived apparent thermal inertia, Remote Sens. Environ., 138, 1-9, doi:10.1016/j.rse.2013.07.003.
    [PDF][Highlight][要点]

    An original soil moisture upscaling algorithm has been developed by introducing MODIS-derived apparent thermal inertia (ATI). First, a functional relationship between the station-averaged soil moisture and the pixel-averaged ATI is constructed. Second, this relationship is used to calculate the representative soil moisture time series at a certain spatial scale. Last, the Bayesian linear regression is applied to obtain the upscaled area-averaged soil moisture by using in-situ measurements as independent variables. The algorithm is evaluated using a network of in-situ moisture sensors in the central Tibetan Plateau. The results are greatly encouraging.

    通过引入MODIS计算的表观热惯量,我们发展了一个全新的土壤水分空间升尺度算法。首先,建立站点平均土壤水分和所对应像元平均表观热惯量的函数关系;其次,使用此关系获取某一空间尺度的代表性土壤水分时间序列;最后,以实测站点数据为自变量,采用贝叶斯线性回归获取升尺度土壤水分。我们使用了布置在青藏高原那曲地区多尺度土壤温湿度网的数据对该算法进行了验证,结果令人鼓舞。

  5. Yang, K., J. Qin, L. Zhao, Y. Chen, W. Tang, M. Han, Lazhu, Z. Chen, N. Lu, B. Ding, H. Wu, and C. Lin, 2013: A Multi-Scale Soil Moisture and Freeze-Thaw Monitoring Network on the Third Pole, Bull. Amer. Meteor. Soc., 94(12), 1907–1916, doi:10.1175/BAMS-D-12-00203.1.
    [PDF][Highlight][要点]

    A multi-scale soil moisture and temperature monitoring network was established on the central Tibetan Plateau to support remote sensing, land hydrological modeling, and surface process studies. The network measures two state variables (soil moisture and temperature) at three spatial scales (1.0, 0.3, 0.1 degree) and four soil depths (0~5, 10, 20, and 40 cm). The data is accessible upon request.

    土壤水分是青藏高原地表-大气相互作用的主要控制因子。我们历时三年在青藏高原那曲地区建立了一个土壤水分和温度观测网。该观测网现布置有56个站点,每个站点同时监测4层的土壤温度和水分,站点平均海拔为4650米。通过合理布局,该观测网涵盖了三个典型空间尺度,分别对应GCM网格(1°)、被动微波卫星象元(0.3°)、以及雷达卫星象元(0.1°)。在此基础上,发展了独具特色的土壤水分升尺度和微波数据同化算法,为生成高原土壤水分产品奠定了基础。

  6. Zhao, L., K. Yang, J. Qin, and Y. Chen, 2013: Optimal Exploitation of AMSR-E Signals for Improving Soil Moisture Estimation Through Land Data Assimilation, IEEE Trans. Geosci. Remote Sens., 51(1), 399-410, doi:10.1109/TGRS.2012.2198483.
    [PDF][Highlight][要点]

    This study presents several sensitivity studies on the assimilating of different microwave signals into a dual-pass land data assimilation system to improve near-surface soil moisture estimation. Investigated are different polarizations, over pass time, and frequency combinations of AMSR-E brightness temperatures. Results shows that the vertically polarized, nighttime signals are the optimal choice in current system. In addition, a simple frequency-based ensamble estimation can produce more robust estimate when using different forcing data.

    基于一个双通道陆面数据同化系统,本文以AMSR-E亮温数据为例探索了同化不同极化方式、观测时间和频率组合的微波卫星信号在估计表层土壤水分中的效果。结果显示,在目前的同化系统框架下垂直极化的夜间亮温信号是最优选择。此外,基于频率组合的集合同化可以在不同驱动数据情况下获得更稳定的土壤水分估计。

  7. Lu, H., T. Koike, K. Yang, Z. Hu, X. Xu, M. Rasmy, D. Kuria, and K. Tamagawa, 2012: Improving land surface soil moisture and energy flux simulations over the Tibetan plateau by the assimilation of the microwave remote sensing data and the GCM output into a land surface model, Int. J. Appl. Earth Obs. Geoinf., 17, 43-54, doi:10.1016/j.jag.2011.09.006.
    [PDF][Highlight][要点]

    LDAS-UT output is compared with NCEP re-analysis and SiB2 simulations at two Tibetan Plateau stations. For the surface soil moisture, the LDAS simulations were superior to both NCEP and SiB2, and there was more than a one-third reduction in RMSE. This study also reveals the potential of the LDAS to improving the land surface energy and water flux simulations in ungauged and/or poorly gauged regions.

    通过在两个青藏高原观测站与NCEP再分析数据和SiB2模拟等的比较,LDAS-UT显示出更强的估计土壤水分的能力,可减小均方根误差1/3,表明LDAS在缺资料地区有提高土壤水分和地表通量模拟的潜力。

  8. Rasmy, M., T. Koike, D. Kuria, C. Mirza, and K. Yang, 2012: Development of the Coupled Atmosphere and Land Data Assimilation System (CALDAS) and its application over the Tibetan Plateau, IEEE T. Geosci. Remote Sens., 50(11), 4227-4242, doi:10.1109/TGRS.2012.2190517.
    [PDF][Highlight][要点]

    A land data assimilation scheme and a cloud microphysics data assimilation scheme were coupled with a mesoscale model. By assimilating AMSR-E data, the coupled system improves the estimates of both soil moisture and atmospheric conditions, and eventually improves predicted clouds and rainfall.

    一个陆面数据同化系统和一个云微物理同化系统被耦合在一个中尺度气象模型中。通过在耦合系统中同化AMSR-E微波数据,系统提高了对土壤水分和大气状态的估计,并最终提高了对云和降水的预测。

  9. Su, Z., J. Wen, L. Dente, R. Velde, L. Wang, Y. Ma, K. Yang, and Z. Hu, 2011: The Tibetan Plateau observatory of plateau scale soil moisture and soil temperature (Tibet-Obs) for quantifying uncertainties in coarse resolution satellite and model products, Hydrol. Earth Syst. Sci., 15(7), 2303-2316, doi:10.5194/hess-15-2303-2011.
    [PDF][Highlight][要点]

    In this paper the details of the Tibetan Plateau observatory of plateau scale soil moisture and soil temperature are reported. Analysis and comparisons with several satellite products concluded that global coarse resolution soil moisture products are useful but exhibit till now unreported uncertainties in cold and semiarid regions - use of them would be critically enhanced if uncertainties can be quantified and reduced using in-situ measurements.

    本文详细介绍了青藏高原土壤温湿观测平台三个观测网(那曲、玛曲、阿里)。观测数据和几套卫星产品的对比分析得出:全球低分辨率土壤水分数据在寒冷和半干旱区仍存在未公布的不确定性,而通过观测数据量化和减少其不确定性将使得该类产品的使用价值大大提高。

  10. Xu, T., S. Liu, S. Liang, and J. Qin, 2011: Improving Predictions of Water and Heat Fluxes by Assimilating MODIS Land Surface Temperature Products into the Common Land Model, J. Hydrometeorol., 12(2), 227-244, doi:10.1175/2010JHM1300.1.
    [PDF][Highlight][要点]

    In this study, four assimilation strategies are implemented to assimilate the MODIS land surface temperatures for estimating the sensible and latent heat fluxes: two assimilation algorithms (Ensemble Kalman Filter and SCE-UA optimization algorithm) and two control variables (soil temperature and moisture). The results indicate that the scheme of the Ensemble Kalman Filter with the soil moisture being the control variable performs best in these four strategies.

    本研究采用集合卡曼滤波最优估计算法和SCE-UA全局优化算法,分别以土壤温度和湿度为控制状态变量,同化MODIS地表温度产品,结果显示集合卡曼滤波以土壤湿度为控制状态变量的同化方案获取的地表显热和潜热通量,在四种同化方案中精度最高。

  11. Qin, J., S. Liang, K. Yang, I. Kaihotsu, R. Liu, and T. Koike, 2009: Simultaneous estimation of both soil moisture and model parameters using particle filtering method through the assimilation of microwave signal, J. Geophys. Res. Atmos., 114, D15103, doi:10.1029/2008JD011358.
    [PDF][Highlight][要点]

    In this study, the particle filter is used to couple a daily-scale land surface model with the microwave signal, realize estimation of both parameters in the model and observation operator and model state variables, and obtain the soil surface moisture content on a daily basis. This reliability of the assimilation system is verified by comparing the estimates against the in-situ measurements.

    本研究利用粒子滤波为同化算法耦合天时间尺度陆面过程模型和微波遥感信号,实现动态模型、观测算子的参数和状态变量的同时估算,获取天时间尺度的地表土壤水分含量,通过与地表面上实测数据比较,验证了该系统的可靠性。

  12. Tian, X., Z. Xie, A. Dai, C. Shi, B. Jia, F. Chen, and K. Yang, 2009: A dual-pass variational data assimilation framework for estimating soil moisture profiles from AMSR-E microwave brightness temperature, J. Geophys. Res. Atmos., 114, D16102, doi:10.1029/2008JD011600.
    [PDF][Highlight][要点]

    A dual-pass assimilation (DP-En4DVar) framework is designed to optimize the model state (volumetric soil moisture content) and model parameters simultaneously using the gridded AMSR-E satellite brightness temperature data. Experiment results show that volumetric soil moisture content can be significantly improved to be comparable with in situ observations by assimilating only daily satellite brightness temperature. Furthermore, the improvement in surface soil moisture also propagates to lower layers where no observations are available.

    设计了一个双通道四维变分数据同化体系来同时优化土壤水分和模型参数。实验结果表明:通过同化每天的卫星亮温数据(AMSR-E),可以显著提高土壤体积含水量估计水平;此外,表层土壤水分估计精度的提高还可以进一步改进深层土壤水分的估计。

  13. Yang, K., T. Koike, I. Kaihotsu, and J. Qin, 2009: Validation of a Dual-Pass Microwave Land Data Assimilation System for Estimating Surface Soil Moisture in Semiarid Regions, J. Hydrometeorol., 10(3), 780-793, doi:10.1175/2008JHM1065.1.
    [PDF][Highlight][要点]

    The LDAS uses a dual-pass assimilation algorithm, with a calibration pass to estimate major model parameters from satellite data and an assimilation pass to estimate the near-surface soil moisture. Results show that (i) the LDAS-estimated soil moistures are comparable to areal averages of in situ measurements; (ii) the satellite-based calibration does contribute to soil moisture estimations; and (iii) the LDAS produces more robust and reliable soil moisture when forcing data become worse and is less sensitive to precipitation.

    本文所用同化系统引入了一个双通道数据同化算法:一个标定通道用于优化模型参数;一个同化通道用于估计表层土壤水分。实验结果表明:(1)土壤水分估计值与区域平均的观测值相当;(2)基于卫星数据的模型参数标定对土壤水分估计有重要意义;(3)该同化系统对降雨(较小时)不敏感,而且在驱动数据质量不好的情况下表现更为稳定。

  14. Boussetta, S., T. Koike, K. Yang, T. Graf, and M. Pathmathevan, 2008: Development of a coupled land-atmosphere satellite data assimilation system for improved local atmospheric simulations, Remote Sens. Environ., 112(3), 720-734, doi:10.1016/j.rse.2007.06.002.
    [PDF][Highlight][要点]

    This study developed a coupled land-atmosphere satellite data assimilation system as a new physical downscaling approach, by coupling a mesoscale atmospheric model with a land data assimilation system (LDAS). Through the use of satellite brightness temperature, the system has shown potential ability to provide better initial surface conditions and its inputs to the atmosphere and to improve physical downscaling through regional models.

    结合中尺度大气模型和陆面数据同化,建立了一个耦合的陆气卫星数据同化系统。通过同化卫星亮温,该系统区域模式可以获得更好的地表初始边界条件和大气模式输入,同时提高物理降尺度的效果。

  15. Mirza, C., T. Koike, K. Yang, and T. Graf, 2008: Retrieval of Atmospheric Integrated Water Vapor and Cloud Liquid Water Content Over the Ocean From Satellite Data Using the 1-D-Var Ice Cloud Microphysics Data Assimilation System (IMDAS), IEEE Trans. Geosci. Remote Sens., 46(1), 119-129, doi:10.1109/TGRS.2007.907740.
    [PDF][Highlight][要点]

    This study employs a 1-D variational Ice Cloud Microphysics Data Assimilation System (IMDAS) to assimilate the satellite microwave radiometer data set of the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and retrieve integrated water vapor (IWV) and integrated cloud liquid water content (ICLWC). This new method significantly improved the performance of the cloud microphysics scheme by the intrusion of heterogeneity into the external global reanalysis data, which resultantly improved atmospheric initial conditions of numerical weather prediction (NWP) models, and the modeled microwave brightness temperatures agree well with the observations of the Wakasa Bay Experiment 2003 in Japan.

    本研究使用1维变分冰云微物理数据同化系统(IMDAS)同化AMSR-E卫星微波辐射数据,反演了总水汽含量和总云液态含水量。这种新方法通过在全球再分析数据中引入非均一性改进了云微物理方案的表现,最终改进了数值天气预报模式(NWP)的大气初始条件,并且模拟的微波亮温与2003年日本若狭湾实验观测对比有较好的一致性。

  16. Yang, K. and T. Koike, 2008: Satellite Monitoring of the Surface Water and Energy Budget in the Central Tibetan Plateau, Adv. Atmos. Sci., 25(6), 974-985, doi:10.1007/s00376-008-0974-8.
    [PDF][Highlight][要点]

    satellite data are integrated into a land data assimilation system (LDAS-UT) to estimate the soil moisture and surface energy budget on the Plateau. The results show that this satellite data-based system has a high potential for a reliable estimation of the regional surface energy budget on the Plateau.

    基于CEOP-Tibet站点数据,对GCMs结合卫星观测的天气预报能力以及降水、辐射估计进行了评估。卫星数据被引入同化系统中用于估计高原土壤水分和地表能量平衡。结果显示基于卫星观测数据的同化系统在高原区域地表能量平衡的估计中具有很大潜力。

  17. Li, A., S. Liang, A. Wang, and J. Qin, 2007: Estimating Crop Yield from Multi-temporal Satellite Data Using Multivariate Regression and Neural Network Techniques, Photogramm. Eng. Remote Sens., 73(10), 1149-1157.
    [PDF][Highlight][要点]

    In this study, both the artificial neural network and the multivariate linear regression are used to construct the mathematical relationship between the yields of corn and soybean and the NDVI derived from the AVHRR and MODIS sensors. Both the stability and reliability of these two methods are verified by comparing with the ground statistics in the Midwest and the Great Plains of America.

    本研究利用人工神经网络和多元线性回归,建立AVHRR和MODIS归一化植被指数与大豆和玉米产量之间的函数关系,并利用美国大平原和中西部地区粮食产量数据对两种方法进行比较验证,结果显示两种方法均可靠。

  18. Qin, J., S. Liang, R. Liu, H. Zhang, and B. Hu, 2007: A Weak-Constraint Based Data Assimilation Scheme for Estimating Surface Turbulent Fluxes, IEEE Geosci. Remote Sens. Lett., 4(4), 649-653, doi:10.1109/LGRS.2007.904004.
    [PDF][Highlight][要点]

    In this study, a simple land surface model is built. Then its adjoint model is constructed using the auto-differential technique and coupled with the conjugate descent algorithm to set up an assimilation system to assimilate the land surface temperature based on the weak-constraint concept. The surface latent and sensible heat flux can be retrieved through this system and furthermore they are verified by comparing with the ground measurements.

    本研究在建立地表简单陆面过程模型后,利用自动微分技术求取其伴随模型,并与共轭下降优化算法耦合,建立弱约束地表温度同化系统,实现地表显热和潜热通量的同化估算,并利用站点实测数据对其进行验证。

  19. Qin, J., R. Liu, S. Liang, H. Zhang, and B. Hu, 2007: A new method based on remote sensing observations and data assimilation for estimation of evapotranspiration over field crops, N. Z. J. Agric. Res., 50(5), 997-1004, doi:10.1080/00288230709510378.
    [PDF][Highlight][要点]

    In this study, the auto-differential technique is used to construct the adjoint model of a land surface process model and then the adjoint model is coupled with an optimization algorithm to set up an assimilation system based upon the concept of the strong constraint. The validation results indicate that the system can retrieve the ground sensible and latent heat fluxes with satisfactory accuracy by comparing with the ground measurements at a cropland station of the American flux net.

    本研究利用自动微分技术建立陆面模型的伴随模型,耦合优化算法估算模型参数和状态变量,建立基于强约束的表温度同化系统,而后利用美国通量网农田站点的实测数据对其进行了验证,结果显示该系统可以较好的估算地表显热和潜热通量。

  20. Yang, K., T. Watanabe, T. Koike, X. Li, H. Fujii, K. Tamagawa, Y. Ma, and H. Ishikawa, 2007: Auto-calibration system developed to assimilate AMSR-E data into a land surface model for estimating soil moisture and the surface energy budget, J. Meteor. Soc. Japan, 85A, 229-242, doi:10.2151/jmsj.85A.229.
    [PDF][Highlight][要点]

    This study presents a new variational land system used to assimilate AMSR-E brightness temperature of vertical polarization of 6.9 GHz and 18.7 GHz. A major feature of this system is a dual-pass assimilation technique, which can auto-calibrate model parameters in one pass and estimate the soil moisture and energy budget in the other pass. The system not only detected the effect of precipitation event that were missing in the forcing data, but also led to a significant improvement in modeling of the surface energy budget.

    本文提出的数据同化系统基于变分方法,在陆面过程模型中同化6.9GHz和18.7GHz的AMSR-E水平极化亮温,用于估计土壤水分和地表能量平衡。该系统的主要特点是引入了双通道同化技术:通道1在一个较长的时间窗口内优化模型参数;而通道2在天为周期的同化窗口内估计土壤水分。该系统不仅能分辨出驱动数据中降雨缺失的影响,而且能显著提高地表能量平衡的模拟。

  21. Qin, J., G. Yan, S. Liu, S. Liang, H. Zhang, J. Wang, and X. Li, 2006: Application of Ensemble Kalman Filter to remote sensing inversion of land surface parameters, Sci. China Ser. D, 49(6), 632-640, doi:10.1007/s11430-006-0632-x.
    [PDF][Highlight][要点]

    in this study, the parameters of semi-physical kernel BRDF model are estimated by Ensemble Kalman Filter. Then, these parameters are used to retrieve the ground surface albedo. The in-situ measurements are used to validate the inversion results, showing that this method can retrieve the ground surface with satisfactory accuracy.

    本研究利用集合卡曼滤波估算半经验半物理地表双向反射率分布模型核驱动模型的参数,而后利用此参数反演地表反照率,之后采用地表实测反射率数据对该方法进行了验证,结果显示该方法可以较好的估算地表反照率。




footer