Progress in the Energy Closure of Eddy Covariance Systems

The sum of turbulent fluxes measured by the eddy-covariance method is often 10%~30% lower than available energy (i.e., the net radiation minus the ground heat flux). This systematic bias in the EC method is called the "EC energy closure problem" and has been one of the biggest challenges in micrometeorology. In the past decades, lots of studies have focused on this problem and have found extensive knowledge about it. In this paper, we introduced the reasons to the EC energy closure problem and systematically summarized the understandings on this problem with a discussion regarding the merits and limitations of possible correction methods. Compared with previous overview studies, our study focused on the methods and progresses on the EC energy closure problem using the Large Eddy Simulation (LES). The existing problem, e.g., the lack of high quality and high spatial density of "surface true fluxes" observations (e.g., matric observations), the failure of fully emulating the EC using the LES and how to mechanistically understand that large eddies affect turbulence structures and lead to non-closure problem, hinder the further understanding of the EC energy closure problem. Thus, in the future, focus should be put on the mechanistic understanding of how large eddies affect the non-closure problem and some dense field observations to obtain the high quality "surface true fluxes".     

Large-scale estimates of gross primary production on the Qinghai-Tibet plateau based on remote sensing data

Vegetation gross primary production (GPP) is an important variable for the carbon cycle on the Qinghai-Tibetan Plateau (QTP). Based on the measurements from 12 eddy covariance flux sites, we validated a light use efficiency model (i.e. EC-LUE) to evaluate the spatial-temporal patterns of GPP and the effect of environmental variables on QTP. In general, EC-LUE model performed well in predicting GPP at different time scale over QTP. Annual GPP over the entire QTP ranged from 575 to 703 Tg C, and showed a significantly increasing trend from 1982 to 2013. However, there were large spatial heterogeneities in long-term trends of GPP. Throughout the entire QTP, air temperature increase had a greater influence than solar radiation and precipitation (PREC) changes on productivity. Moreover, our results highlight the large uncertainties of previous GPP estimates due to insufficient parameterization and validations. When compared with GPP estimates of the EC-LUE model, most Coupled Model Intercomparison Project (CMIP5) GPP products overestimate the magnitude and increasing trends of regional GPP, which potentially impact the feedback of ecosystems to regional climate changes.     

Long‐term variability in the water budget and its controls in an oak‐dominated temperate forest

Water availability is one of the key environmental factors that control ecosystem functions in temperate forests. Changing climate is likely to alter the ecohydrology and other ecosystem processes, which affect forest structures and functions. We constructed a multi‐year water budget (2004–2010) and quantified environmental controls on an evapotranspiration (ET) in a 70‐year‐old mixed‐oak woodland forest in northwest Ohio, USA. ET was measured using the eddy‐covariance technique along with precipitation (P), soil volumetric water content (VWC), and shallow groundwater table fluctuation. Three biophysical models were constructed and validated to calculate potential ET (PET) for developing predictive monthly ET models. We found that the annual variability in ET was relatively stable and ranged from 578 mm in 2009 to 670 mm in 2010. In contrast, ET/P was more variable and ranged from 0.60 in 2006 to 0.96 in 2010. Mean annual ET/PET_FAO was 0.64, whereas the mean annual PET_FAO/P was 1.15. Annual ET/PET_FAO was relatively stable and ranged from 0.60 in 2005 to 0.72 in 2004. Soil water storage and shallow groundwater recharge during the non‐growing season were essential in supplying ET during the growing season when ET exceeded P. Spring leaf area index (LAI), summer photosynthetically active radiation, and autumn and winter air temperatures (T_a) were the most significant controls of monthly ET. Moreover, LAI regulated ET during the whole growing season and higher temperatures increased ET even during dry periods. Our empirical modelling showed that the interaction of LAI and PET explained >90% of the variability in measured ET. Altogether, we found that increases in T_a and shifts in P distribution are likely to impact forest hydrology by altering shallow groundwater fluctuations, soil water storage, and ET and, consequently, alter the ecosystem functions of temperate forests. Copyright © 2013 John Wiley & Sons, Ltd.     

Eigen-feature analysis of weighted covariance matrices for LiDAR point cloud classification

The features used in the separation of different objects are important for successful point cloud classification. Eigen-features from a covariance matrix of a point set with the sample mean are commonly used geometric features that can describe the local geometric characteristics of a point cloud and indicate whether the local geometry is linear, planar, or spherical. However, eigen-features calculated by the principal component analysis of a covariance matrix are sensitive to LiDAR data with inherent noise and incomplete shapes because of the non-robust statistical analysis. To obtain reliable eigen-features from LiDAR data and to improve classification accuracy, we introduce a method of analyzing local geometric characteristics of a point cloud by using a weighted covariance matrix with a geometric median. Each point is assigned a weight to represent its spatial contribution in the weighted principal component analysis and to estimate the geometric median which can be regarded as a localized center of a shape. In the experiments, qualitative and quantitative analyses on airborne LiDAR data and simulated point clouds show a clear improvement of the proposed method compared with the standard eigen-features. The classification accuracy is improved by 1.6–4.5% using a supervised classifier.     

An analytical study of general hyper-diffusivity and barotropic eddies

An exact solution to the barotropic potential vorticity equation is used to examine the properties of barotropic vortices under arbitrary nth-order hyper-diffusivity. Analytical expressions are derived for an eddy's lifetime, meridional drift, decay in size, and energy, as functions of the Coriolis parameter, order and magnitude of diffusivity, and the eddy's size, shape and strength. These expressions provide a simple explanation for many observed features of oceanic and atmospheric vortices. For example, the competition between the Coriolis effect and eddy strength in giving permitted eddy geometries; the bias towards a zonal anisotropy for large vortices but not for small ones; energetic preference for axisymmetry; poleward meridional drift of cyclonic vortices; and meridional speed variation depending on eddy geometry and strength.     

迭代EnSRF方案设计及在Lorenz96模式下的检验

本文利用非同步 (Asynchronous) 算法设计了一个包含迭代过程的集合平方根滤波方案 (迭代EnSRF),并在Lorenz96模式下详细对比分析了该方案和传统EnSRF方案的同化效果.与传统EnSRF方案不同,迭代EnSRF方案能够同时更新两个时次的背景场并通过迭代过程来改进分析结果.本文不仅检验了迭代EnSRF在同化不同类型观测资料时的效果,还检验了存在模式误差时该方案的同化效果,并且对同化结果的合理性进行了详细分析.试验结果表明:在完美模式下,迭代EnSRF能够显著加快同化常规观测时的收敛速度,并能够更加有效地同化非常规观测资料;在存在模式误差时,迭代EnSRF并不能有效改进分析结果;当对不准确的模式参数进行扰动后,迭代EnSRF能够更好地利用改进后的集合预报系统来提高其对部分类型观测的分析结果.进一步的分析表明,分析结果的改进主要得益于迭代EnSRF改进了背景误差协方差空间结构,并使得EnSRF的线性假设得到更好的满足.     

遥感GPP模型在高寒草甸的应用比较

随着遥感数据时空分辨率的提高,大范围实时监测总初级生产力GPP(Gross Primary Productivity)的变化成为可能。本研究收集了黑河流域阿柔冻融观测站的气象观测资料和MODIS数据,驱动VPM、TG、VI和EC-LUE4个模型估算了该站点的GPP,并应用涡动相关观测的GPP验证了模拟结果,并比较了这4个模型的模拟精度。结果表明:阿柔站2009年的涡动相关观测的GPP、NEE(Net Ecosystem Exchange)和ER(Ecosystem Respiration)分别为:804.2gC/m2/yr、129.6gC/m2/yr和673.6gC/m2/yr。该站点光合作用固定的碳有83.8%通过生态系统的呼吸作用释放到大气中。基于遥感的GPP模型能够很好地模拟高寒草甸的GPP,全年的判定系数在0.94以上,生长季的判定系数大于0.84。