Sensitivity studies of a high accuracy surface modeling method

The sensitivities of the initial value and the sampling information to the accuracy of a high accuracy surface modeling(HASM) are investigated and the implementations of this new modeling method are modified and enhanced. Based on the fundamental theorem of surface theory, HASM is developed to correct the error produced in geographical information system and ecological modeling process. However, the earlier version of HASM is theoretically incomplete and its initial value must be produced by other surface modeling methods, such as spline, which limit its promotion. In other words, we must use other interpolators to drive HASM. According to the fundamental theorem of surface theory, we modify HASM, namely HASM.MOD, by adding another important nonlinear equation to make it independent of other methods and, at the same time, have a complete and solid theory foundation. Two mathematic surfaces and monthly mean temperature of 1951–2010 are used to validate the effectiveness of the new method. Experiments show that the modified version of HASM is insensitive to the selection of initial value which is particular important for HASM. We analyze the sensitivities of sampling error and sampling ratio to the simulation accuracy of HASM.MOD. It is found that sampling information plays an important role in the simulation accuracy of HASM.MOD. Another feature of the modified version of HASM is that it is theoretically perfect as it considers the third equation of the surface theory which reflects the local warping of the surface. The modified HASM may be useful with a wide range of spatial interpolation as it would no longer rely on other interpolation methods.     

动力和统计预测信息融合预测方法及对我国夏季降水预测的检验

短期气候预测中如何将气候模式和统计方法的预测结果科学、客观的集成起来,一直是非常重要的问题.本文针对动力模式和统计方法预测结果相结合的问题,引入资料同化中信息融合的思想,采用最优内插同化方法,实现了动力模式和统计季节降水预测结果的融合.检验表明,对1982-2015年我国夏季降水百分率的回报,融合预测结果与观测的平均空间相关系数可达0.44,分别较统计预测和CFSv2模式统计降尺度订正的技巧提高了0.1左右,而均方根误差较两者可以降低5%~20%.可见,该方法可以进一步提升对我国夏季降水的预测技巧,具有显著的业务应用价值.     

Terrestrial carbon cycle model-data fusion: Progress and challenges

The terrestrial carbon cycle is an important component of global biogeochemical cycling and is closely related to human well-being and sustainable development. However, large uncertainties exist in carbon cycle simulations and observations.Model-data fusion is a powerful technique that combines models and observational data to minimize the uncertainties in terrestrial carbon cycle estimation. In this paper, we comprehensively overview the sources and characteristics of the uncertainties in terrestrial carbon cycle models and observations. We present the mathematical principles of two model-data fusion methods, i.e., data assimilation and parameter estimation, both of which essentially achieve the optimal fusion of a model with observational data while considering the respective errors in the model and in the observations. Based upon reviewing the progress in carbon cycle models and observation techniques in recent years, we have highlighted the major challenges in terrestrial carbon cycle model-data fusion research, such as the "equifinality" of models, the identifiability of model parameters,the estimation of representativeness errors in surface fluxes and remote sensing observations, the potential role of the posterior probability distribution of parameters obtained from sensitivity analysis in determining the error covariance matrixes of the models, and opportunities that emerge by assimilating new remote sensing observations, such as solar-induced chlorophyll fluorescence. It is also noted that the synthesis of multisource observations into a coherent carbon data assimilation system is by no means an easy task, yet a breakthrough in this bottleneck is a prerequisite for the development of a new generation of global carbon data assimilation systems. This article also highlights the importance of carbon cycle data assimilation systems to generate reliable and physically consistent terrestrial carbon cycle reanalysis data products with high spatial resolution and longterm time series. These products are critical to the accurate estimation of carbon cycles at the global and regional scales and will help future carbon management strategies meet the goals of carbon neutrality.     

抛物线内插反应谱计算方法

为减小恢复被采信号所用的内插函数与山农要样定理要求的内插函数ha(t-n.T)之间的误差，提出了用抛物线内插－ha(t-n.T)的二阶近似代替常规的线性内插，为充分减小计算量，本文推导了计算绝对加速度反应谱的连锁公式。通过与常规方法的对比发现，抛物线反应谱算法的计算误差明显降低。在保证同样计算精度的条件下，抛物线算法较常规算法允许更低的采样率。为了将反应谱计算的相对误差控制在5％以下，抛物线算法要求有样率为奈奎斯特频率的5倍，而不是常规算法要求的10倍。     

A high-accuracy method for simulating the XCO_2 global distribution using GOSAT retrieval data

A high-accuracy surface modeling(HASM)method based on the fundamental theorem of surfaces,is developed to simulate XCO_2 surfaces using the GOSAT retrieval XCO_2 data.Two tests are designed to investigate the simulation accuracy.The first test divides the existing satellite retrieval XCO2 data into training points and testing points,and simulates the XCO2 surface using the training points while computing the simulation error using the testing points.The absolute mean error(MAE)of the testing points is 1.189 ppmv,and the corresponding values of the comparison methods,Ordinary Kriging,IDW,and Spline are1.203,1.301,and 1.355 ppmv,respectively.The second test simulates the XCO_2 surface using all the satellite retrieval points and uses the TCCON(Total Carbon Column Observing Network)site observation values as the ture values.For the six typical TCCON sites,the HASM simulation MAE is 1.688 ppmv,and the satellite retrieval MAE at the same sites is 2.147 ppmv.These results indicate that HASM can successfully simulate XCO_2 surfaces based on satellite retrieval data.     

A high-accuracy method for simulating the XCO<Subscript>2</Subscript> global distribution using GOSAT retrieval data

A high-accuracy surface modeling (HASM) method based on the fundamental theorem of surfaces, is developed to simulate XCO₂ surfaces using the GOSAT retrieval XCO₂ data. Two tests are designed to investigate the simulation accuracy. The first test divides the existing satellite retrieval XCO₂ data into training points and testing points, and simulates the XCO₂ surface using the training points while computing the simulation error using the testing points. The absolute mean error (MAE) of the testing points is 1.189 ppmv, and the corresponding values of the comparison methods, Ordinary Kriging, IDW, and Spline are 1.203, 1.301, and 1.355 ppmv, respectively. The second test simulates the XCO₂ surface using all the satellite retrieval points and uses the TCCON (Total Carbon Column Observing Network) site observation values as the ture values. For the six typical TCCON sites, the HASM simulation MAE is 1.688 ppmv, and the satellite retrieval MAE at the same sites is 2.147 ppmv. These results indicate that HASM can successfully simulate XCO₂ surfaces based on satellite retrieval data.     

Evaluation of a spatial rainfall generator for generating high resolution precipitation projections over orographically complex terrain

Space–time variability of precipitation plays a key role as driver of many environmental processes. The objective of this study is to evaluate a spatiotemporal (STG) Neyman–Scott Rectangular Pulses (NSRP) generator over orographically complex terrain for statistical downscaling of climate models. Data from 145 rain gauges over a 5760-km² area of Cyprus for 1980–2010 were used for this study. The STG was evaluated for its capacity to reproduce basic rainfall statistical properties, spatial intermittency, and extremes. The results were compared with a multi-single site NRSP generator (MSG). The STG performed well in terms of average annual rainfall (+1.5 % in comparison with the 1980–2010 observations), but does not capture spatial intermittency over the study area and extremes well. Daily events above 50 mm were underestimated by 61 %. The MSG produced a similar error (+1.1 %) in terms of average annual rainfall, while the daily extremes (>50-mm) were underestimated by 11 %. A gridding scheme based on scaling coefficients was used to interpolate the MSG data. Projections of three Regional Climate Models, downscaled by MSG, indicate a 1.5–12 % decrease in the mean annual rainfall over Cyprus for 2020–2050. Furthermore, the number of extremes (>50-mm) for the 145 stations is projected to change between ?24 and +2 % for the three models. The MSG modelling approach maintained the daily rainfall statistics at all grid cells, but cannot create spatially consistent daily precipitation maps, limiting its application to spatially disconnected applications. Further research is needed for the development of spatial non-stationary NRSP models.     

Assimilating along-track SLA data using the EnOI in an eddy resolving model of the Agulhas system

The greater Agulhas Current is one of the most energetic current systems in the global ocean. It plays a fundamental role in determining the mean state and variability of the regional marine environment, affecting its resources and ecosystem, the regional weather and the global climate on a broad range of temporal and spatial scales. In the absence of a coherent in-situ and satellite-based observing system in the region, modelling and data assimilation techniques play a crucial role in both furthering the quantitative understanding and providing better forecasts of this complicated western boundary current system. In this study, we use a regional implementation of the Hybrid Coordinate Ocean Model and assimilate along-track satellite sea level anomaly (SLA) data using the Ensemble Optimal Interpolation (EnOI) data assimilation scheme. This study lays the foundation towards the development of a regional prediction system for the greater Agulhas Current system. Comparisons to independent in-situ drifter observations show that data assimilation reduces the error compared to a free model run over a 2-year period. Mesoscale features are placed in more consistent agreement with the drifter trajectories and surface velocity errors are reduced. While the model-based forecasts of surface velocities are not as accurate as persistence forecasts derived from satellite altimeter observations, the error calculated from the drifter measurements for eddy kinetic energy is significantly lower in the assimilation system compared to the persistence forecast. While the assimilation of along-track SLA data introduces a small bias in sea surface temperatures, the representation of water mass properties and deep current velocities in the Agulhas system is improved.     

Simulating geophysical models through fractal algorithms

I present an algorithm, borrowed from the computer graphics industry, that is able to efficiently and effectively simulate pseudo‐realistic topographies and three‐dimensional geophysical models. It has been widely exploited in the movie industry for generating artificial landscapes and for simulating the surface of planets. The geophysical applications are manifold: simulation for testing inversion algorithms, interpolation, and upscaling are only some of the possibilities.     

Regionalizing mean annual flow and daily flow variability for basin‐scale sediment and nutrient modelling

River discharges vary strongly through time and space, and quantifying this variability is fundamental to understanding and modelling river processes. The river basin is increasingly being used as the unit for natural resource planning and management; to facilitate this, basin‐scale models of material supply and transport are being developed. For many basin‐scale planning activities, detailed rainfall‐runoff modelling is neither necessary nor tractable, and models that capture spatial patterns of material supply and transport averaged over decades are sufficient. Nevertheless, the data to describe the spatial variability of river discharge across large basins for use in such models are often limited, and hence models to predict river discharge at the basin scale are required. We describe models for predicting mean annual flow and a non‐dimensional measure of daily flow variability for every river reach within a drainage network. The models use sparse river gauging data, modelled grid surfaces of mean annual rainfall and mean annual potential evapotranspiration, and a network accumulation algorithm. We demonstrate the parameterization and application of the models using data for the Murrumbidgee basin, in southeast Australia, and describe the use of these predictions in modelling sediment transport through the river network. The regionalizations described contain less uncertainty, and are more sensitive to observed spatial variations in runoff, than regionalizations based on catchment area and rainfall alone. Copyright © 2006 John Wiley & Sons, Ltd.     

CATCHMENT-WIDE, SECTIONAL AND LOCAL ASPECTS IN SEDIMENT TRANSPORT MODELLING AND MONITORING

1.INTRODUCTIONOVerthelastdecadesmuchprogresshasbeenmadeconcerningsedimenttransPOrtmodellingandmonitoring.Thedifferelltiationincatchmeflt-tvide,sectionalandlocalaspectsreflectsthefactthatmanysedimenttransportandpredictionmodelsaredealingwithspecialpartsofriverSystems,mainlydifferinginscale.Overthepastyears,scaleissuesinhydrologyhaverapidlyincreasedinimportance(BLoSCHL,1996).Onalargescaletheapplicationoffractals,self-similarityanalysistolandscapeorganizationandoptimalchannelnetlvorks(O…     

基于聚焦合成的显微三维成像系统

本文利用显微镜物镜焦深范围有限,显微三维样本成像时需要一序列图像才能聚焦清晰特点,研究开发利用图像处理技术扩展显微成像焦深范围,应用改进Laplacian聚焦算子实现序列图像的融合显示,同时,提出了一种基于区域小波变换的序列显微图像融合算法并加以实现.用高斯插值算法得到三维显微图像的高度图,设计了一种网格轮廓线算法对高度索引图进行了三维重建显示和测量,并利用立体视觉原理生成了三维物体的左右视图和立体视图.应用此方法可以完成显微目标的表面三维参数的测量分析.     

Argo data assimilation in ocean general circulation model of Northwest Pacific Ocean

The Argo temperature and salinity profiles in 2005–2009 are assimilated into a coastal ocean general circulation model of the Northwest Pacific Ocean using the ensemble adjustment Kalman filter (EAKF). Three numerical tests, including the control run (CTL) (without data assimilation, which serves as the reference experiment), ensemble free run (EnFR) (without data assimilation), and EAKF experiment (with Argo data assimilation using EAKF), are carried out to examine the performance of this system. Using the restarts of different years as the initial conditions of the ensemble integrations, the ensemble spreads from EnFR and EAKF are all kept at a finite value after a sharp decreasing in the first few months because of the sensitive of the model to the initial conditions, and the reducing of the ensemble spread due to Argo data assimilation is not much. The ensemble samples obtained in this way can well represent the probabilities of the real ocean states, and no ensemble inflation is necessary for this EAKF experiment. Different experiment results are compared with satellite sea surface temperature (SST) data and the Global Temperature-Salinity Profile Program (GTSPP) data. The comparison of SST shows that modeled SST errors are reduced after data assimilation; the error reduction percentage after assimilating the Argo profiles is about 10?% on average. The comparison against the GTSPP profiles, which are independent of the Argo profiles, shows improvements in both temperature and salinity. The comparison results indicated a great error reduction in all vertical layers relative to CTL and the ensemble mean of EnFR; the maximum value for temperature and salinity reaches to 85?% and 80?%, respectively. The standard deviations of sea surface height are employed to examine the simulation ability, and it is shown that the mesoscale variability is improved after Argo data assimilation, especially in the Kuroshio extension area and along the section of 10°N. All these results suggest that this system is potentially useful for improving the simulation ability of oceanic numerical models.     

A soil moisture assimilation scheme based on the ensemble Kalman filter using microwave brightness temperature

This study presents a soil moisture assimilation scheme, which could assimilate microwave brightness temperature directly, based on the ensemble Kalman filter and the shuffled complex evolution method (SCE-UA). It uses the soil water model of the land surface model CLM3.0 as the forecast operator, and a radiative transfer model (RTM) as the observation operator in the assimilation system. The assimilation scheme is implemented in two phases: the parameter calibration phase and the pure soil moisture assimilation phase. The vegetation optical thickness and surface roughness parameters in the RTM are calibrated by SCE-UA method and the optimal parameters are used as the final model parameters of the observation operator in the assimilation phase. The ideal experiments with synthetic data indicate that this scheme could significantly improve the simulation of soil moisture at the surface layer. Furthermore, the estimation of soil moisture in the deeper layers could also be improved to a certain extent. The real assimilation experiments with AMSR-E brightness temperature at 10.65 GHz (vertical polarization) show that the root mean square error (RMSE) of soil moisture in the top layer (0–10 cm) by assimilation is 0.03355 m³ · m⁻³, which is reduced by 33.6% compared with that by simulation (0.05052 m³ · m⁻³). The mean RMSE by assimilation for the deeper layers (10–50 cm) is also reduced by 20.9%. All these experiments demonstrate the reasonability of the assimilation scheme developed in this study.     

基于改进U-Net的CMIP5全球气候模式降尺度方法及其在鄱阳湖流域的应用

利用降尺度方法对CMIP5全球气候模式进行空间降尺度并以此研究鄱阳湖流域未来气候时空变化趋势,能够为流域生态环境保护提供数据、技术和理论上的支持.通过简化原始网络结构,在网络首部添加插值层,采用反卷积算法作为上采样算法对传统U-Net网络进行改进,建立基于深度学习的气候模式空间降尺度模型(DLDM).以1965-200...     

Binary upscaling—the role of connectivity and a new formula

Although recognized as important, measures of connectivity (i.e. the existence of high-conductivity paths that increase flow and allow for early solute arrival) have not yet been incorporated into methods for upscaling hydraulic conductivities of porous media. We present and evaluate a binary upscaling formula that utilizes connectivity information. The upscaled hydraulic conductivity (K) of binary media is determined as a function of the proportions and conductivities of the two materials, the geometry of the inclusions, and the mean distance between them. The use of a phase interchange theorem renders the formula equally applicable to two-dimensional media with inclusions of low K and high K as compared with the matrix. The new upscaling formula is tested on two-dimensional binary random fields spanning a broad range of spatial correlation structures and conductivity contrasts. The computed effective conductivities are compared to what is obtained using self-consistent effective medium theory, the coated ellipsoids approximation, and to a streamline approach. It is shown that, although simple, the proposed formula performs better than available methods for binary upscaling. The use of connectivity information leads to significantly improved behavior close to the percolation threshold. The proposed upscaling formula depends exclusively on parameters that are obtainable from field investigations.     

Estimation of model errors generated by atmospheric forcings for ocean data assimilation: experiments in a regional model of the Bay of Biscay

The characterization of model errors is an essential step for effective data assimilation into open-ocean and shelf-seas models. In this paper, we propose an experimental protocol to properly estimate the error statistics generated by imperfect atmospheric forcings in a regional model of the Bay of Biscay, nested in a basin-scale North Atlantic configuration. The model used is the Hybrid Coordinate Ocean Model (HYCOM), and the experimental protocol involves Monte Carlo (or ensemble) simulations. The spatial structure of the model error is analyzed using the representer technique, which allows us to anticipate the subsequent impact in data assimilation systems. The results show that the error is essentially anisotropic and inhomogeneous, affecting mainly the model layers close to the surface. Even when the forcings errors are centered around zero, a divergence is observed between the central forecast and the mean forecast of the Monte Carlo simulations as a result of nonlinearities. The 3D structure of the representers characterizes the capacity of different types of measurement (sea level, sea surface temperature, surface velocities, subsurface temperature, and salinity) to control the circulation. Finally, data assimilation experiments demonstrate the superiority of the proposed methodology for the implementation of reduced-order Kalman filters.     

A land surface data assimilation framework using the land information system: Description and applications

The Land Information System (LIS) is an established land surface modeling framework that integrates various community land surface models, ground measurements, satellite-based observations, high performance computing and data management tools. The use of advanced software engineering principles in LIS allows interoperability of individual system components and thus enables assessment and prediction of hydrologic conditions at various spatial and temporal scales. In this work, we describe a sequential data assimilation extension of LIS that incorporates multiple observational sources, land surface models and assimilation algorithms. These capabilities are demonstrated here in a suite of experiments that use the ensemble Kalman filter (EnKF) and assimilation through direct insertion. In a soil moisture experiment, we discuss the impact of differences in modeling approaches on assimilation performance. Provided careful choice of model error parameters, we find that two entirely different hydrological modeling approaches offer comparable assimilation results. In a snow assimilation experiment, we investigate the relative merits of assimilating different types of observations (snow cover area and snow water equivalent). The experiments show that data assimilation enhancements in LIS are uniquely suited to compare the assimilation of various data types into different land surface models within a single framework. The high performance infrastructure provides adequate support for efficient data assimilation integrations of high computational granularity.     

A comparision of interpolation methods for producing digital elevation models at the field scale

Digital elevation models have been used in many applications since they came into use in the late 1950s. It is an essential tool for applications that are concerned with the Earth's surface such as hydrology, geology, cartography, geomorphology, engineering applications, landscape architecture and so on. However, there are some differences in assessing the accuracy of digital elevation models for specific applications. Different applications require different levels of accuracy from digital elevation models. In this study, the magnitudes and spatial patterning of elevation errors were therefore examined, using different interpolation methods. Measurements were performed with theodolite and levelling. Previous research has demonstrated the effects of interpolation methods and the nature of errors in digital elevation models obtained with indirect survey methods for small‐scale areas. The purpose of this study was therefore to investigate the size and spatial patterning of errors in digital elevation models obtained with direct survey methods for large‐scale areas, comparing Inverse Distance Weighting, Radial Basis Functions and Kriging interpolation methods to generate digital elevation models. The study is important because it shows how the accuracy of the digital elevation model is related to data density and the interpolation algorithm used. Cross validation, split‐sample and jack‐knifing validation methods were used to evaluate the errors. Global and local spatial auto‐correlation indices were then used to examine the error clustering. Finally, slope and curvature parameters of the area were modelled depending on the error residuals using ordinary least regression analyses. In this case, the best results were obtained using the thin plate spline algorithm. Copyright © 2009 John Wiley & Sons, Ltd.