GPS接收机仪器偏差的长期变化特性分析   总被引：3，自引：1，他引：2  

周东旭  袁运斌  李子申  刘西凤 《大地测量与地球动力学》2011,31(5):114-118

基于欧洲定轨中心2001-2009年发布的部分IGS测站接收机仪器偏差估值,分析了不同类型接收机DCB的长期变化特性.结果表明:各类接收机DCB的长期时变特征均具备一定的周期性,其中以月和年周期最为明显;接收机DCB的长期变化中还存在一定的波动,部分接收机DCB的稳定性随时间积累逐渐变差,甚至存在偏移.  相似文献   

基于FY-3C微波辐射成像仪海表温度产品的偏差订正方法研究     

廖志宏  徐宾  张雷  师春香  周自江 《气象学报》2020,78(5):840-852

提出一种针对FY-3C搭载的微波辐射成像仪（MWRI）海表温度产品的分段回归偏差订正方法,该方法通过引进气候态海表温度数据,建立与关联实测海表温度相匹配的回归模型,并通过对模型中关联变量的误差分析,选择最优样本进行分段回归,以实现对海表温度数据的重新估计。通过对MWRI海表温度数据的偏差订正试验表明,采用分段回归方法获得的订正结果无论在误差指标的空间分布还是时间序列上,都要明显优于采用传统概率密度函数偏差订正方法的结果。其中,采用概率密度函数方法订正后的海表温度产品误差标准差和均方根误差从订正前的0.9—1.0℃,减小到0.8℃左右,而采用分段回归方法获得相应的订正误差仅为0.6℃左右,订正效果有明显改善。   相似文献   

分位数映射法在RegCM4中国气温模拟订正中的应用   总被引：1，自引：0，他引：1  

韩振宇  童尧  高学杰  徐影 《气候变化研究进展》2018,14(4):331-340

将一种分位数映射法RQUANT,应用到一个区域气候模式（RegCM4）所模拟中国气温的误差订正中。从气候平均态、年际变率、极端气候及农业气候等多方面,评估了该方法对日平均气温、日最高气温和日最低气温模拟的订正效果。结果表明,该订正方法对模式模拟的日平均、日最高和最低气温气候平均态的订正效果都非常明显,中国大部分地区的订正结果与观测的偏差在±0.5℃之间。在降低极端气温指数和农业气候相关指数的模拟误差方面也有显著的效果,但对气温年际变率的订正效果有限。结合以往对降水订正的评估分析,该方法对模式模拟结果有较好的订正效果,可以应用于区域气候模式的气候变化模拟预估中,为气候变化及相关影响评估研究提供更适用和可靠的数据。  相似文献   

基于DERF2.0的月平均温度概率订正预报   总被引：2，自引：1，他引：1  

章大全  陈丽娟 《大气科学》2016,40(5):1022-1032

国家气候中心第二代月动力延伸模式回算资料的分析表明,二代模式月平均温度预报与观测实况仍然存在较大偏差,模式预报有较大改进空间。本文采用非参数百分位映射法对模式月平均温度预报进行概率订正,该方法基于模式集合平均给出的确定性预报,结合模式回算资料各集合成员计算得到的模式概率密度分布,给出确定性预报在模式概率密度分布中的百分位值,并将百分位值投影到观测资料的概率密度分布中,得到模式预报的概率订正值。对订正前后模式预报的检验评估显示,该订正方案不仅有效降低了模式预报与实况的均方根误差（RMSE）,对月平均温度距平分布的预报技巧也有所改善,不同超前时间模式预报的预测技巧评分（PS）和距平相关系数（ACC）均有提升,同时模式预报误差的大小对订正效果无明显影响。从分月的订正预报结果来看,对夏季各月的温度预测技巧的提升整体高于冬季各月。  相似文献   

大地电磁响应函数估算中改进的多道相关函数法及误差的综合评价          下载免费PDF全文

林长佑  武玉霞 《地球物理学报》1989,32(2):236-241

近年来,有关大地电磁响应函数估算中消除偏离误差、减少随机误差、评价结果质量方面已有许多研究。Lienert等（1980）提出的多道相关函数法以下式为基础:  相似文献   

Integer estimation in the presence of biases     

P. J. G. Teunissen 《Journal of Geodesy》2001,75(7-8):399-407

 Carrier phase ambiguity resolution is the key to fast and high-precision GNSS (Global Navigation Satellite System) kinematic positioning. Critical in the application of ambiguity resolution is the quality of the computed integer ambiguities. Unsuccessful ambiguity resolution, when passed unnoticed, will too often lead to unacceptable errors in the positioning results. Very high success rates are therefore required for ambiguity resolution to be reliable. Biases which are unaccounted for will lower the success rate and thus increase the chance of unsuccessful ambiguity resolution. The performance of integer ambiguity estimation in the presence of such biases is studied. Particular attention is given to integer rounding, integer bootstrapping and integer least squares. Lower and upper bounds, as well as an exact and easy-to-compute formula for the bias-affected success rate, are presented. These results will enable the evaluation of the bias robustness of ambiguity resolution. Received: 28 September 2000 / Accepted: 29 March 2001  相似文献   

A source of bias in rates of surface soil movement as estimated from marked particles     

Nel Caine 《地球表面变化过程与地形》1981,6(1):69-75

When estimated from measurements of introduced tracer particles, the rate of surface soil movement tends to be greater than the natural rate for equivalent particles on the same site. This consistent overestimation is greatest in the period following tracer introduction and leads to a measurement bias that may be as high as 300 per cent. The magnitude of the bias decreases with time, as the tracer is incorporated into the surface material, but remains detectable statistically for more than a year on some low-angle sites.  相似文献   

Bias and uncertainty in regression-calibrated models of groundwater flow in heterogeneous media     

Richard L. Cooley  Steen Christensen 《Advances in water resources》2006

Groundwater models need to account for detailed but generally unknown spatial variability (heterogeneity) of the hydrogeologic model inputs. To address this problem we replace the large, m-dimensional stochastic vector β that reflects both small and large scales of heterogeneity in the inputs by a lumped or smoothed m-dimensional approximation γθ_∗, where γ is an interpolation matrix and θ_∗ is a stochastic vector of parameters. Vector θ_∗ has small enough dimension to allow its estimation with the available data. The consequence of the replacement is that model function f(γθ_∗) written in terms of the approximate inputs is in error with respect to the same model function written in terms of β, f(β), which is assumed to be nearly exact. The difference f(β) − f(γθ_∗), termed model error, is spatially correlated, generates prediction biases, and causes standard confidence and prediction intervals to be too small. Model error is accounted for in the weighted nonlinear regression methodology developed to estimate θ_∗ and assess model uncertainties by incorporating the second-moment matrix of the model errors into the weight matrix. Techniques developed by statisticians to analyze classical nonlinear regression methods are extended to analyze the revised method. The analysis develops analytical expressions for bias terms reflecting the interaction of model nonlinearity and model error, for correction factors needed to adjust the sizes of confidence and prediction intervals for this interaction, and for correction factors needed to adjust the sizes of confidence and prediction intervals for possible use of a diagonal weight matrix in place of the correct one. If terms expressing the degree of intrinsic nonlinearity for f(β) and f(γθ_∗) are small, then most of the biases are small and the correction factors are reduced in magnitude. Biases, correction factors, and confidence and prediction intervals were obtained for a test problem for which model error is large to test robustness of the methodology. Numerical results conform with the theoretical analysis.  相似文献   

Remotely sensed estimation of forest canopy density: A comparison of the performance of four methods     

Chudamani Joshi  Jan De Leeuw  Andrew K. Skidmore  Iris C. van Duren  Henk van Oosten 《International Journal of Applied Earth Observation and Geoinformation》2006

In recent years, a number of alternative methods have been proposed to predict forest canopy density from remotely sensed data. To date, however, it remains difficult to decide which method to use, since their relative performance has never been evaluated. In this study the performance of: (1) an artificial neural network, (2) a multiple linear regression, (3) the forest canopy density mapper and (4) a maximum likelihood classification method was compared for prediction of forest canopy density using a Landsat ETM+ image. Comparison of confusion matrices revealed that the regression model performed significantly worse than the three other methods. These results were based on a z-test for comparison of weighted kappa statistics, which is an appropriate statistic for analysis of ranked categories. About 89% of the variance of the observed canopy density was explained by the artificial neural networks, which outperformed the other three methods in this respect. Moreover, the artificial neural networks gave an unbiased prediction, while other methods systematically under or over predicted forest canopy density. The choice of biased method could have a high impact on canopy density inventories.  相似文献   

Data assimilation in hydrodynamic modelling: on the treatment of non-linearity and bias   总被引：1，自引：1，他引：0  

J. V. T.?S?rensenEmail author  H.?Madsen 《Stochastic Environmental Research and Risk Assessment (SERRA)》2004,18(4):228-244

  相似文献