基于卫星遥感资料的中国区域土壤湿度EnKF数据同化

土壤湿度在陆气相互作用过程中扮演着重要的角色,是气候、水文、农业、林业等研究中重要的地球物理参数之一.土壤湿度影响地面蒸散,径流、地表反射率、地表发射率以及地表感热和潜热通量,从而对气候有重要影响,它对大气的影响在全球尺度上仅次于海面温度,在陆地尺度其影响甚至超过海面温度.本文介绍了基于EnKF及陆面过程模型的中国区域陆面土壤湿度同化系统（CLSMDAS,China Land Soil Moisture Data Assimilation System）,以及该系统应用于中国区域陆面土壤湿度同化试验的结果.CLSMDAS包括以下几个部分：1）陆面模式采用美国国家大气研究中心NCAR的陆面过程模型Community Land Model Version3.0（简写为CLM3.0）;2）大气驱动场数据中的降水和地面入射太阳辐射数据来自FY2静止气象卫星每小时产品;3）陆面数据同化方法采用EnKF（Ensemble Kalman Filter）同化方法;4）观测数据包括AMSR-E卫星反演土壤湿度产品以及地面土壤湿度观测资料.利用CLSMDAS对2006年6~9月的土壤湿度同化试验结果的分析表明：陆面模式模拟和同化结果都能比较合理地反映出土壤湿度时空分布,同化的土壤湿度分布与2006年8月重庆、四川发生建国以来最严重的夏伏旱有非常好的对应关系,与发生在9月的湖北东部、广西南部等地的干旱区也有非常好的对应关系.     

基于微波亮温及集合Kalman滤波的土壤湿度同化方案

基于集合Kalman滤波及SCE-UA(shuffled complex evolution)算法发展了能够直接同化微波亮温的土壤湿度同化方案. 该方案以陆面过程模式CLM 3.0中的土壤水模型作为预报算子, 以辐射传输模型作为观测算子. 整个同化过程分为参数优化和土壤湿度同化两个阶段, 利用SCE-UA算法优化辐射传输模型中难以确定的植被光学厚度参数和地表粗糙度参数, 并利用优化参数作为观测算子的模型参数进行同化. 通过人工理想试验表明该同化方案可以明显改善表层土壤湿度的模拟精度, 并且对深层土壤湿度的模拟也有一定程度的改善; 利用AMSR-E亮温(10.65 GHz垂直极化)所进行的实际同化试验表明顶层(0~10 cm)土壤湿度同化结果与观测的均方根误差(RMSE)由模拟的0.05052减小到0.03355, 相对减小了33.6%, 而较深层(10~50 cm)平均减小了20.9%. 这些同化试验显示该同化方案的合理性.     

考虑次网格变异性和土壤冻融过程的土壤湿度同化方案

集合Kalman滤波以其简单有效的特点在陆面数据同化中广泛应用,通常作为预报模型的陆面过程模式往往要考虑模式次网格变异性和土壤冻融过程,若对此不加考虑而直接对土壤湿度进行同化可能会使得同化结果发生偏差．将双集合Kalman滤波应用于土壤湿度的同化,基于NCAR／CLM陆面过程模式建立了一个考虑次网格变异性和土壤冻融过程的土壤湿度同化方案：在同一个时间步内用状态滤波对模式网格内某片上液态水分含量进行优化,用参数滤波对该片上的固态水分含量和其他片上的液态／固态水分含量进行优化,由此考虑模式次网格变异性和土壤冻融过程的影响,从而实现对整个模式网格上土壤湿度的同化．初步的同化试验表明：其同化效果在有、无土壤冻融阶段都优于一般的不考虑次网格变异性和土壤冻融变化的同化方案;该同化方案不仅能够提高那些有直接观测信息的土壤层的土壤湿度模拟精度,还能在一定程度上改善那些没有任何观测信息的土壤层的模拟效果;另外,土壤湿度同化结果的改善还能在一定程度上提高陆面模式对于土壤温度的模拟精度．     

沙漠陆面过程参数化与模拟

沙漠地区植被稀疏、干旱少雨,其陆面物理过程具有与全球其它地区显著不同的特点.本文利用巴丹吉林沙漠观测资料,分析和计算了地表反照率、比辐射率、粗糙度和土壤热容量、热传导系数等关键陆面过程参数,建立了适合于沙漠地区的陆面过程模式DLSM (Desert Land Surface Model),并与NOAH陆面过程模式的模拟结果和观测资料进行了比较.结果表明:巴丹吉林沙漠地表反照率为0.273,比辐射率为0.950,地表粗糙度为1.55×10^-3 m,土壤热容量和热扩散系数分别为1.08×10⁶ J·m^-3·K^-1和3.34×10^-7 m²·s.辐射传输、感热输送和土壤热传导过程是影响沙漠地区地表能量平衡的主要物理过程.通过对这三种过程的准确模拟检验,DLSM能够较准确地模拟巴丹吉林沙漠地气能量交换特征;短波辐射、长波辐射和感热通量的模拟结果与观测值间的标准差分别为7.98,6.14,33.9 W·m^-2,与NOAH陆面过程模式的7.98,7.72,46.6 W·m^-2的结果接近.地表反照率是沙漠地区最重要的陆面过程参数,地表反照率增大5%,向上短波辐射通量随之增加5%,感热通量则减小2.8%.本文研究结果对丰富陆面过程参数化方案,改进全球陆面过程模式、气候模式具有参考意义.     

陆面微波比辐射率的卫星观测研究进展

复杂多变的陆地表微波比辐射率,造成陆面上星载微波观测反演大气参数较为困难,也使得许多卫星微波资料不易同化应用到数值模式,因此迫切需要提供准确可靠的陆面微波地表比辐射率信息.随着卫星观测技术的迅速发展,利用丰富的星载被动微波观测直接反演陆面微波比辐射率成为一种主要手段.国外针对星载微波成像仪和微波垂直探测器开展较为系统的陆面微波比辐射率研究,建立不同类型的地表比辐射率反演方法,开发地表比辐射率参数化方法并应用于辐射资料同化.对于卫星观测反演陆面微波比辐射率存在的问题,开展了评估分析和方法订正.国内利用卫星观测也开展了一些陆面微波比辐射率研究工作,尚需要系统、综合的提炼.对于地表特征复杂的中国地区,还需要评估认识不同陆面微波比辐射率反演方法在我国适用情况,需要增强陆面微波比辐射率数据质量的认识以及业务应用.     

城市地表潜热通量数值模拟方法研究

近年来,中尺度气象模式中城市建筑物影响的数值模拟方法日趋完善,城市地表潜热通量数值模拟方法是目前城市地气耦合研究面临的瓶颈问题之一.采用北京325 m气象塔140 m高度处的能量平衡观测资料,用陆面-单层城市冠层耦合模式进行了一年连续的离线模拟.根据模式对城市下垫面潜热通量模拟的系统性误差较大的特点,基于观测资料研究了城市下垫面潜热通量的数值模拟方法.采用观测分析与城市陆面模式相结合的方法,求出了城市中绿地的绿洲效应系数、城市不透水下垫面水份有效率的时间变化公式以及人为潜热排放的日变化曲线和四季最大值,可直接应用于城市陆面模式,有效提高了模式的整体模拟性能.该方法对其他城市地区有一定的参考意义.     

青藏高原单点陆面过程模拟试验

利用青藏铁路北麓河试验2002年6月的大气资料作为陆面模式的强迫场, 研究陆面过程模式(NCAR/LSM)在高原地区的模拟能力. 模拟结果表明, 在观测资料的强迫下, NCAR/LSM能够较好地模拟出地表特征量的变化趋势. 在LSM模式中青藏高原地区的地表植被类型描述与实况存在较大差异, 根据实际地表特征, 我们定义北麓河地区草原植被覆盖度为0.6, 叶冠高度为0.15, 位移高度为0.10. 通过对比试验发现, 修正模式参数后, 模拟的地面气温和地表温度日变化更接近于观测值, 对温度峰值模拟有较好改善. 地面通量模拟也有一定改善. 所以, 修正LSM模式中高原地区的地表及植被描述参数, 可以有效地提高陆面过程模式在青藏高原地区的模拟性能.     

一种基于数据同化优化陆面过程模式参数化方案的方法

基于数据同化优化陆面过程模式参数,从物理机制上改进和完善陆面过程模式参数化方案,提高模式的区域适应性和模拟能力,对改善大气环流模式(GCM,General Circulation Model)和区域气候模式(RCM,Regional Climate Model)模拟和气候预测能力具有重要的现实意义.本文发展了一种基于数据同化优化陆面过程模式参数化方案的方法,并以优化非饱和土壤水模型为例,发展基于非饱和土壤水模型和扩展Kalman滤波(EKF,Extended Kalman Filter)算法并结合可变下渗能力模型(VIC,Variable Infiltration Capacity Model)的土壤湿度同化方案,以月为同化窗,利用复合形混合演化算法(SCE-UA,Shuffled Complex Evolution-University of Arizona)极小化目标函数,使模拟与同化的土壤湿度按给定的目标函数度量方式达到最佳拟合,优化非饱和土壤水模型参数:饱和水力传导率、饱和土壤湿度、饱和土壤水势、Clapp和Hornberger常数,得到同化时段1986年模型参数的最优值序列,然后用其改进非饱和土壤水模型,最后利用改进后模型进行1986~1993年的数值模拟试验,评估改进后模型的模拟能力,探讨基于数据同化优化陆面过程模式土壤水参数化方案的方法与实现途径.试验结果表明:基于数据同化优化模型参数,从物理机制上改进和完善模型,提高了模型的模拟能力;优化的模型参数在时间上具有较好的移植性,适应性更强,达到了改进模型的目的,因此,本方法是合理、可行的.本文为基于数据同化优化陆面过程模式参数化方案研究提供了范例,具有较好的借鉴作用.     

中国东北半干旱区能量水分循环的同化模拟

使用地球观测系统的中分辨率成像光谱仪（EOS．MODIS）提供的归一化植被指数（NDVI）产品估算植被覆盖度和航天飞机雷达地形测绘任务（SRTM）制成的数字高程模型（DEM）数据遥感产品替换WRF模式默认的植被覆盖度和地形高度,并且利用WRF模式及其先进的三维变分同化系统（WRF．3DVar）循环同化东北半干旱区自动气象站近地面气象要素,对东北半干旱区的温度场、湿度场、风场和能量场的结构及其日变化特征进行了较为细致的模拟研究．通过4组数值模拟试验分别探讨了同化气象要素与改变模式地表参数引起的不同下垫面潜热、感热的分配关系和降水、土壤湿度变化弓f起的地表能量通量模拟效果,并利用通榆站、奈曼站、锦州站、和密云站2009年6-8月的通量观测资料与模拟结果对比检验．结果表明,WRF模式能够较好地模拟出东北半干旱区夏季的近地面温度、风向、净辐射、感热和潜热等要素的变化特征及日变化规律．同化试验（Case2）模拟的近地面气温、相对湿度、风速相比控制性试验（Case1）有所改善;陆面参数试验（Case3）和集合试验（Case4）改善了感热和地表热通量的模拟．WRF模式能较好地模拟出下垫面土壤湿度随时间变化的规律,集合试验（Case4）土壤湿度模拟结果与4个通量站观测值相比无太大差别,但降水的模拟有待改善．本研究利用卫星遥感资料改善模式下垫面陆面参数,利用气象资料同化改善近地面大气要素模拟精度,这是将各种不同空间和时间尺度的多源数据与数值模拟融合的有益尝试．此研究生成的东北地区资料同化数据集可用于气候变化、干旱监测等方面,对深入了解半干旱区气候的形成和维持机理具有重要的意义．     

北京大学陆面过程模式PKULM(Peking University Land Model)介绍及检验

陆面过程模式是气候模式和天气模式的核心组成部分之一.在土壤—植被—大气耦合模式(Soil-PlantAtmosphere Model,SPAM)的基础上,发展了新一代北京大学陆面过程模式PKULM(Peking University Land Model).本文首先介绍了PKULM的辐射传输、湍流输送、光合作用、土壤水热输送等过程的参数化方案;采用隐式迭代计算框架,发展并应用了一个快速的线性方程组求解算法,提高了模式计算稳定性;提出并使用了二分搜索算法计算气孔阻抗,避免了CLM(Community Land Model)等使用的迭代方法在干旱区不稳定的情况,提高了模式的适用性;采用水势为基础的土壤水分扩散方程,使模式能够模拟土壤饱和区的水分输送过程,为进一步与水文过程模式耦合奠定了基础;还发展了一个地表积水与径流过程的机理模型,提高了模式对地表水分平衡过程的模拟能力;最后,使用"中国西北干旱区陆—气相互作用观测试验"平凉站的资料对模式进行了检验并与NOAH(National Center for Environmental Prediction,Oregon State University,Air Force,and Hydrology Lab model)陆面过程模式的模拟结果进行了比较,结果表明PKULM能够较好地模拟西北半干旱区农田下垫面地气交换过程.     

Understanding of the Common Land Model performance for water and energy fluxes in a farmland during the growing season in Korea

The Common Land Model (CLM) is one of the most widely used land surface models (LSMs) due to the practicality of its simple parameterization scheme and its versatility in embracing a variety of field datasets. The improved assessment of land surface water and energy fluxes using CLM can be an alternative approach for understanding the complex land–atmosphere interactions in data‐limited regions. The understanding of water and energy cycles in a farmland is crucial because it is a dominant land feature in Korea and Asia. However, the applications of CLM to farmland in Korea are in paucity. The simulations of water and energy fluxes by CLM were conducted against those from the tower‐based measurements during the growing season of 2006 at the Haenam site (a farmland site) in Korea without optimization. According to the International Geosphere–Biosphere Programme (IGBP) land cover classification, a homogeneous cropland was selected initially for this study. Although the simulated soil moisture had a similar pattern to that of the observed, the former was relatively drier (at 0·1 m³ m^?3) than the latter. The simulated net radiation showed good agreement with the observed, with a root mean squared error (RMSE) of 41 W m^?2, whereas relatively large discrepancies between the simulation and observation were found in sensible (RMSE of 66 W m^?2) and latent (RMSE of 60 W m^?2) heat fluxes. On the basis of the sensitivity analysis, soil moisture was more receptive to land cover and soil texture parameterizations when compared to soil temperature and turbulent fluxes. Despite the uncertainty in the predictive capability of CLM employed without optimization, the initial performance of CLM suggests usefulness in a data‐limited heterogeneous farmland in Korea. Further studies are required to identify the controls on water and energy fluxes with an improved parameterization. Copyright © 2010 John Wiley & Sons, Ltd.     

Groundwater Monitoring Using GRACE and GLDAS Data after Downscaling Within Basaltic Aquifer System

Gravity Recovery and Climate Experiment (GRACE) satellite mission is ground-breaking information hotspot for the evaluation of groundwater storage. The present study aims at validating the sensitivity of GRACE data to groundwater storage variation within a basaltic aquifer system after its statistical downscaling on a regional scale. The basaltic aquifer system which covers 82.06% area of Maharashtra state in India, is selected as the study area. Five types of basaltic aquifer systems with varying groundwater storage capacities, based on hydrologic characteristics, have been identified within the study area. The spatial and seasonal trend analysis of observed in situ groundwater storage anomalies (ΔGWSano) computed from groundwater level data of 983 wells from the year 2002 to 2016, has been performed to analyze the variation in groundwater storages in the different basaltic aquifer system. The groundwater storage anomalies (ΔGWSDano) have been derived from GRACE Release 05 (RL05) after removing the soil moisture anomaly (ΔSMano) and canopy water storage anomaly (ΔCNOano) obtained from Global Land Data Assimilation System (GLDAS) land surface models (NOAH, MOSAIC, CLM and VIC). The artificial neural network technique has been used to downscale the GRACE and GLDAS data at a finer spatial resolution of 0.125°. The study shows that downscaled GRACE and GLDAS data at a finer spatial resolution is sensitive to seasonal groundwater storage variability in different basaltic aquifer systems and the regression coefficient R has been found satisfactory in the range of 0.696 to 0.818.     

Observed and simulated water and energy budget components at SCAN sites in the lower Mississippi Basin

Land surface models are typically constrained by one or a few observed variables, while assuming that the internal water and energy partitioning is sensitive to those observed variables and realistic enough to simulate unobserved variables. To verify these assumptions, in situ soil climate analysis network (SCAN) observations in the Lower Mississippi Basin (2002–2008) are analysed to quantify water and energy budget components and they are compared to Community Land Model (CLM3·5) simulations. The local soil texture is identified as a major indicator for water storage characteristics and the Normalized Difference Vegetation Index shows potential as a drought indicator in summer months. Both observations and simulations indicate a regime where, except in some summer months, evapotranspiration controls soil moisture. CLM simulations with different soil texture assignments show discharge sensitivity to soil moisture, but almost no impact on evapotranspiration and other energy balance components. The observed and simulated water budgets show a similar partitioning. However, the SCAN observed water balance does not close because of precipitation measurement errors, unobserved irrigation, lack of specific storage change measurements and errors in the computed actual evapotranspiration. The simulated heat flux partitioning differs from that ‘observed’, with a larger (resp. smaller) fraction of net radiation being used by latent (resp. sensible) heat flux, and unobserved freeze and thaw events. The comparison between observations and model simulations suggests that a consistent observation collection for multiple variables would be needed to constrain and improve the full set of land surface variable estimates. Copyright © 2010 John Wiley & Sons, Ltd.     

Evaluation of multiple surface soil moisture for Korean regional flux monitoring network sites: Advanced Microwave Scanning Radiometer E,land surface model,and ground measurements

In the past few decades, there have been great developments in remotely sensed soil moisture, with validation efforts using land surface models (LSMs) and ground‐based measurements, because soil moisture information is essential to understanding complex land surface–atmosphere interactions. However, the validation of remotely sensed soil moisture has been very limited because of the scarcity of the ground measurements in Korea. This study validated Advanced Microwave Scanning Radiometer E (AMSR‐E) soil moisture data with the Common Land Model (CLM), one of the most widely used LSMs, and ground‐based measurements at two Korean regional flux monitoring network sites. There was reasonable agreement regarding the different soil moisture products for monitoring temporal trends except National Snow and Ice Data Centre (NSIDC) AMSR‐E soil moisture, albeit there were essential comparison limitations by different spatial scales and soil depths. The AMSR‐E soil moisture data published by the National Aeronautics and Space Administration and Vrije Universiteit Amsterdam (VUA) showed potential to replicate temporal variability patterns (root‐mean‐square errors = 0·10–0·14 m³ m^?3 and wet BIAS = 0·09 ? 0·04 m³ m^?3) with the CLM and ground‐based measurements. However, the NSIDC AMSR‐E soil moisture was problematic because of the extremely low temporal variability and the VUA AMSR‐E soil moisture was relatively inaccurate in Gwangneung site characterized by complex geophysical conditions. Additional evaluations should be required to facilitate the use of recent and forthcoming remotely sensed soil moisture data from Soil Moisture and Ocean Salinity and Soil Moisture Active and Passive missions at representative future validation sites. Copyright © 2011 John Wiley & Sons, Ltd.     

Establishment and analysis of a High-Resolution Assimilation Dataset of the water-energy cycle in China

For better prediction and understanding of land-atmospheric interaction, in-situ observed meteorological data acquired from the China Meteorological Administration (CMA) were assimilated in the Weather Research and Forecasting (WRF) model and the monthly Green Vegetation Coverage (GVF) data, which was calculated using the Normalized Difference Vegetation Index (NDVI) of the Earth Observing System Moderate-Resolution Imaging Spectroradiometer (EOS-MODIS) and Digital Elevation Model (DEM) data of the Shuttle Radar Topography Mission (SRTM) system. Furthermore, the WRF model produced a High-Resolution Assimilation Dataset of the water-energy cycle in China (HRADC). This dataset has a horizontal resolution of 25 km for near surface meteorological data, such as air temperature, humidity, wind vectors and pressure (19 levels); soil temperature and moisture (four levels); surface temperature; downward/upward short/long radiation; 3-h latent heat flux; sensible heat flux; and ground heat flux. In this study, we 1) briefly introduce the cycling 3D-Var assimilation method and 2) compare results of meteorological elements, such as 2 m temperature and precipitation generated by the HRADC with the gridded observation data from CMA, and surface temperature and specific humidity with Global Land Data Assimilation System (GLDAS) output data from the National Aeronautics and Space Administration (NASA). We find that the simulated results of monthly 2 m temperature from HRADC is improved compared with the control simulation and has effectively reproduced the observed patterns. The simulated special distribution of ground surface temperature and specific humidity from HRADC are much closer to GLDAS outputs. The spatial distribution of root mean square errors (RMSE) and bias of 2 m temperature between observations and HRADC is reduced compared with the bias between observations and the control run. The monthly spatial distribution of surface temperature and specific humidity from HRADC is consistent with the GLDAS outputs over China. 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, and the simulated results could be used in further research on the long-term climatic effects and characteristics of the water-energy cycle over China.     

A soil water and heat transfer model including changes in soil frost and thaw fronts

Freeze-thaw processes in soils,including changes in frost and thaw fronts(FTFs),are important physical processes.The movement of FTFs affects soil hydrothermal characteristics,as well as energy and water exchanges between the land surface and the atmosphere and hydrothermal processes in the land surface.This paper reduces the issue of soil freezing and thawing to a multiple moving-boundary problem and develops a soil water and heat transfer model which considers the effects of FTF on soil hydrothermal processes.A local adaptive variable-grid method is used to discretize the model.Sensitivity tests based on the hierarchical structure of the Community Land Model(CLM)show that multiple FTFs can be continuously tracked,which overcomes the difficulties of isotherms that cannot simultaneously simulate multiple FTFs in the same soil layer.The local adaptive variable-grid method is stable and offers computational efficiency several times greater than the high-resolution case.The simulated FTF depths,soil temperatures,and soil moisture values fit well with the observed data,which further demonstrates the potential application of this simulation to the land-surface process model.     

卫星重力捕捉龙滩水库储水量变化

利用近7年的Gravity Recovery and climate Experiment(GRACE)重力卫星资料,采用改进的滑动窗去相关滤波和扇形滤波技术计算了红水河上游区域的重力变化,结果显示红水河上游区域重力场有明显的上升趋势,利用Global Land Data Assimilation Systems(GL...     

Evapotranspiration models of different complexity for multiple land cover types

A comparison between half‐hourly and daily measured and computed evapotranspiration (ET) using three models of different complexity, namely, the Priestley–Taylor (P‐T), the reference Penman–Monteith (P‐M) and the Common Land Model (CLM), was conducted using three AmeriFlux sites under different land cover and climate conditions (i.e. arid grassland, temperate forest and subhumid cropland). Using the reference P‐M model with a semiempirical soil moisture function to adjust for water‐limiting conditions yielded ET estimates in reasonable agreement with the observations [root mean square error (RMSE) of 64–87 W m^?2 for half‐hourly and RMSE of 0.5–1.9 mm day^?1 for daily] and similar to the complex Common Land Model (RMSE of 60–94 W m^?2 for half‐hourly and RMSE of 0.4–2.1 mm day^?1 for daily) at the grassland and cropland sites. However, the semiempirical soil moisture function was not applicable particularly for the P‐T model at the forest site, suggesting that adjustments to key model variables may be required when applied to diverse land covers. On the other hand, under certain land cover/environmental conditions, the use of microwave‐derived soil moisture information was found to be a reliable metric of regional moisture conditions to adjust simple ET models for water‐limited cases. Further studies are needed to evaluate the utility of the simplified methods for different landscapes. Copyright © 2011 John Wiley & Sons, Ltd.     

Improving the estimation of hydrothermal state variables in the active layer of frozen ground by assimilating <Emphasis Type="Italic">in situ</Emphasis> observations and SSM/I data

The active layer of frozen ground data assimilation system adopts the SHAW (Simulteneous Heat and Water) model as the model operator. It employs an ensemble kalman filter to fuse state variables predicted by the SHAW model with in situ observation and the SSM/I 19 GHz brightness temperature for the purpose of optimizing model hydrothermal state variables. When there is little water movement in the frozen soil during the winter season, the unfrozen water content depends primarily on soil temperature. Thus, soil temperature is the crucial state variable to be improved. In contrast, soil moisture is heavily influenced by precipitation during the summer season. The simulation accuracy of soil moisture has a strong and direct impact on the soil temperature. In this case, the crucial state variable to be improved is soil moisture. One-dimensional assimilation experiments that have been carried out at AMDO station show that land data assimilation method can improve the estimation of hydrothermal state variables in the soil by fusing model information and observation information. The reasonable model error covariance matrix plays a key role in transferring the optimized surface state information to the deep soil, and it provides improved estimations of whole soil state profiles. After assimilating the 4-cm soil temperature by in situ observation, the soil temperature RMSE (Root Mean Square Error) of each soil layer decreased by 0.96°C on average relative to the SHAW simulation. After assimilating the 4-cm soil moisture in situ observation, the soil moisture RMSE of each soil layer decreased by 0.020 m³·m⁻³. When assimilating the SSM/I 19 GHz brightness temperature, the soil temperature RMSE of each soil layer during the winter decreased by 0.76°C, while the soil moisture RMSE of each soil layer during the summer decreased by 0.018 m³·m⁻³.     

A land surface soil moisture data assimilation framework in consideration of the model subgrid-scale heterogeneity and soil water thawing and freezing

The Ensemble Kalman Filter (EnKF) is well known and widely used in land data assimilation for its high precision and simple operation. The land surface models used as the forecast operator in a land data assimilation system are usually designed to consider the model subgrid-heterogeneity and soil water thawing and freezing. To neglect their effects could lead to some errors in soil moisture assimilation. The dual EnKF method is employed in soil moisture data assimilation to build a soil moisture data as- similation framework based on the NCAR Community Land Model version 2.0 (CLM 2.0) in considera- tion of the effects of the model subgrid-heterogeneity and soil water thawing and freezing: Liquid volumetric soil moisture content in a given fraction is assimilated through the state filter process, while solid volumetric soil moisture content in the same fraction and solid/liquid volumetric soil moisture in the other fractions are optimized by the parameter filter. Preliminary experiments show that this dual EnKF-based assimilation framework can assimilate soil moisture more effectively and precisely than the usual EnKF-based assimilation framework without considering the model subgrid-scale heteroge- neity and soil water thawing and freezing. With the improvement of soil moisture simulation, the soil temperature-simulated precision can be also improved to some extent.