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1.
采用基于风条纹提取风向的方式,利用地球物理模式函数,基于Sentinel-1A数据,通过CMOD5模型反演2017年3、5、7、12月份广东省近海海域风场。将反演结果与实测数据对比,风速普遍比实测风速大,风速反演的平均绝对误差为1.98 m/s,均方根误差为2.74 m/s,相关系数为0.8。其中3、5、7月的风速较为接近,且平均绝对误差和均方根误差都＜2 m/s,而12月份平均风速＞8 m/s,实测数据与卫星过境时间不完全匹配,导致平均绝对误差和均方根误差都偏大。哨兵一（Sentinel-1A）影像反演结果整体上与实测数据相一致,验证了COMD5反演模型适用于广东省近海高分辨率海洋风场反演,可为下一步估算广东省风能资源储量提供可能。 相似文献
2.
Delay-time tomography can be either linearized or non-linear. In the case of linearized tomography, an error due to the linearization is introduced. If the tomography is performed in a non-linear fashion, the theory used is more accurate from the physical point of view, but if the data have a statistical error, a noise bias in the model is introduced due to the non-linear propagation of errors. We investigate the error propagation of a weakly non-linear delay-time tomography example using second-order perturbation theory. This enables us to compare the linearization error with the noise bias. We show explicitly that the question of whether a non-linear inversion methods leads to a better estimation of the model parameters than a linearized method is dependent on the signal-to-noisc ratio. We also show that, in cases of poor data quality, a linearized inversion method leads to a better estimation of the model parameters than a non-linear method. 相似文献
3.
气象要素空间插值方法优化 总被引：86，自引：8，他引：78
在区域水土平衡模型的研究中 ,空间插值可提供每个计算栅格的气象要素资料。本文运用反距离加权法 (IDW )和梯度距离反比法 (GIDW ) ,对 196 1～ 2 0 0 0年甘肃省及其周围85个气象站点的多年平均温度与降雨量进行了内插。交叉验证结果表明 :对于IDW和GIDW ,二者温度插值的平均绝对误差 (MAE)分别为 2 2 8℃和 0 73℃ ,平均相对误差(MRE)分别为 2 9 0 2 %和 9 4 1% ,降雨插值的MAE值依次为 5 5 2mm和 4 90mm ,MRE值分别为 19 4 3%和 17 80 % ,GIDW明显优于IDW。需要指出的是 :对于降雨 ,当其经纬度和海拔高程的复相关系数大于 0 80时 ,GIDW插值结果优于IDW ;否则相反 相似文献
4.
针对第五次国际耦合模式比较计划（CMIP5）中3个全球气候模式对中国气温季节变化模拟能力的空间差异特征进行具体分析。结果表明：BCC-CSM1.1（m）模式和GFDL-CM3模式能够再现中国气温的季节性变化,在中国东部地区模拟能力较强,平均绝对误差和均方根误差均较小,在中国西部地区模拟能力较弱,平均绝对误差和均方根误差较大。与BCC-CSM1.1（m）和GFDL-CM3模式相比,HADGEM2-ES模式再现中国地区气温季节变化的能力最弱,平均绝对误差和均方根误差在西部部分地区、内蒙古地区和东北地区较大,在华南地区南部较小。在相同模式下,日平均气温模拟效果最好,其次是日最低气温,日最高气温模拟效果最差。纬度、经度、海拔和坡度对气候模式模拟效果的影响存在模式间的差异,而坡向和地形遮蔽度对模式的模拟效果无明显影响。 相似文献
5.
Cort J. Willmott 《自然地理学》2013,34(2):184-194
Traditional methods of evaluating geographic models by statistical comparisons between observed and simulated variates are criticized. In particular, it is suggested that the correlation coefficient (r), its square and tests of their statistical significance are inadequate for such purposes. The root mean squared error (RMSE) and related measures as well as a new index of agreement (d) are alternatively presented as superior indices for making such comparisons. Arguments are made for increasing the number of digital algorithms and data plots being published. 相似文献
6.
Shuqiang Xue Jiping Liu Jinzhong Mi Chun Dong Yingyan Cheng 《International journal of geographical information science》2016,30(11):2155-2170
The calculation of surface area is meaningful for a variety of space-filling phenomena, e.g., the packing of plants or animals within an area of land. With Digital Elevation Model (DEM) data we can calculate the surface area by using a continuous surface model, such as by the Triangulated Irregular Network (TIN). However, just as the triangle-based surface area discussed in this paper, the surface area is generally biased because it is a nonlinear mapping about the DEM data which contain measurement errors. To reduce the bias in the surface area, we propose a second-order bias correction by applying nonlinear error propagation to the triangle-based surface area. This process reveals that the random errors in the DEM data result in a bias in the triangle-based surface area while the systematic errors in the DEM data can be reduced by using the height differences. The bias is theoretically given by a probability integral which can be approximated by numerical approaches including the numerical integral and the Monte Carlo method; but these approaches need a theoretical distribution assumption about the DEM measurement errors, and have a very high computational cost. In most cases, we only have variance information on the measurement errors; thus, a bias estimation based on nonlinear error propagation is proposed. Based on the second-order bias estimation proposed, the variance of the surface area can be improved immediately by removing the bias from the original variance estimation. The main results are verified by the Monte Carlo method and by the numerical integral. They show that an unbiased surface area can be obtained by removing the proposed bias estimation from the triangle-based surface area originally calculated from the DEM data. 相似文献
7.
C. J. Willmott K. Matsuura 《International journal of geographical information science》2013,27(1):89-102
Spatial cross‐validation and average‐error statistics are examined with respect to their abilities to evaluate alternate spatial interpolation methods. A simple cross‐validation methodology is described, and the relative abilities of three, dimensioned error statistics—the root‐mean‐square error (RMSE), the mean absolute error (MAE), and the mean bias error (MBE)—to describe average interpolator performance are examined. To illustrate our points, climatologically averaged weather‐station temperatures were obtained from the Global Historical Climatology Network (GHCN), Version 2, and then alternately interpolated spatially (gridded) using two spatial‐interpolation procedures. Substantial differences in the performance of our two spatial interpolators are evident in maps of the cross‐validation error fields, in the average‐error statistics, as well as in estimated land‐surface‐average air temperatures that differ by more than 2°C. The RMSE and its square, the mean‐square error (MSE), are of particular interest, because they are the most widely reported average‐error measures, and they tend to be misleading. It (RMSE) is an inappropriate measure of average error because it is a function of three characteristics of a set of errors, rather than of one (the average error). Our findings indicate that MAE and MBE are natural measures of average error and that (unlike RMSE) they are unambiguous. 相似文献
8.
Toshiro Tanimoto 《Geophysical Journal International》1985,82(1):105-123
Summary. The Backus–Gilbert method is applied to obtain the phase velocity variations on a sphere from the measured phase velocity. Narrow peak kernels, with radii of about 2000 km, are obtained for almost everywhere on the sphere. The phase velocity results are thus interpreted as an average within such regions. The most trouble comes from the antipodal peak in the resolution kernel. This is evaluated as contamination and is incorporated in the error estimation. The total error, which is a root mean square of contamination from the antipodal peak and statistical error estimated from the data covariance matrix, is about 1 per cent of the phase velocity in the average earth model, which is the Preliminary Reference Earth Model (PREM). However, there is about a factor of 2 variation of errors on the sphere. Maximum variations of phase velocity are about 3–4 per cent of the phase velocity in the average earth model, and thus there still remain anomalies which exceed estimated errors. The estimated errors correspond to one standard deviation under the assumptions of uncorrelated Gaussian distribution. For high confidence interval, they show that statistically significant anomalies are scarce for the current data set. Generally, Love-wave phase velocity maps show more resolved features than Rayleigh-wave maps and we can see, in high confidence maps, fast velocities in old oceans and old continents and slow velocities in tectonically active regions like the East Pacific Rise and various back-arc regions. 相似文献
9.
The methods PARAFAC and three-way PLS are compared with respect to their ability to predictreversed-phase retention values.Special attention is paid to simple validatory tools,the meaning and useof which are explained.The simple validatory tools consist of percentages of explained variation in the training set and thosethat can be calculated with the use of markers.These markers are special(reference)solutes,the retentionvalues of which are used to gain information about a new object for which predictions are wanted.Different validatory tools can be calculated with the use of these marker retention values:percentagesof used variation and mean sum of squared residuals after applying the model to these marker retentionvalues.The validatory tools are evaluated on their power to estimate their test set counterparts:thepercentages of explained variation in the test set and mean sum of squared prediction errors in the test set.Two different data sets from reversed-phase chromatography are used to evaluate the validatory tools.The first data set has a high signal-to-noise ratio and is measured under the same measurementconditions.The second data set has a low signal-to-noise ratio and is measured under differentmeasurement conditions.Some of the simple validatory tools seem to have relevance to their test setcounterparts,even in the case of the second data set. 相似文献
10.
《地理学报(英文版)》1991,(3)
An additive model is used to express the observed value of a sample characteristic as the sum of the truesample characteristic and a value of the data collection error,commonly known as experimental error.The data uncertainty of the experimental results(or of a survey data set)is defined as the expectedsquared error.The expected squared error may change with the sample characteristic,e.g.the errormoment could be concentration-dependent.The relationship between the error variance and the analyteconcentration may not be very distinct.In such a case the data transformation to stabilize the errormoments may not be appropriate.A step function is proposed as an alternative way to represent thesecond moment of the error.The data uncertainty is defined as the weighted average of the step valuesof the second raw moment of the error,using the appropriate proportions of the routine samples asweights.The data uncertainties associated with the different data collection stages were evaluated by usingregional soil survey data. 相似文献
11.
The marginal distributions for the magnetotelluric (MT) magnitude squared response function (and hence apparent resistivity) and phase are derived from the bivariate complex normal distribution that describes the distribution of response function estimates when the Gauss–Markov theorem is satisfied and the regression random errors are normally distributed. The distribution of the magnitude squared response function is shown to be non-central chi-squared with 2 degrees of freedom, with the non-centrality parameter given by the squared magnitude of the true MT response. The standard estimate for the magnitude squared response function is biased, with the bias proportional to the variance and hence important when the uncertainty is large. The distribution reduces to the exponential when the expected value of the MT response function is zero. The distribution for the phase is also obtained in closed form. It reduces to the uniform distribution when the squared magnitude of the true MT response function is zero or its variance is very large. The phase distribution is symmetric and becomes increasingly concentrated as the variance decreases, although it is shorter-tailed than the Gaussian. The standard estimate for phase is unbiased. Confidence limits are derived from the distributions for magnitude squared response function and phase. Using a data set taken from the 2003 Kaapvaal transect, it is shown that the bias in the apparent resistivity is small and that confidence intervals obtained using the non-parametric delta method are very close to the true values obtained from the distributions. Thus, it appears that the computationally simple delta approximation provides accurate estimates for the confidence intervals, provided that the MT response function is obtained using an estimator that bounds the influence of extreme data. 相似文献
12.
Traditionally,one form of preprocessing in multivariate calibration methods such as principal componentregression and partial least squares is mean centering the independent variables(responses)and thedependent variables(concentrations).However,upon examination of the statistical issue of errorpropagation in multivariate calibration,it was found that mean centering is not advised for some datastructures.In this paper it is shown that for response data which(i)vary linearly with concentration,(ii)have no baseline(when there is a component with a non-zero response that does not change inconcentration)and(iii)have no closure in the concentrations(for each sample the concentrations of allcomponents add to a constant,e.g.100%)it is better not to mean center the calibration data.That is,the prediction errors as evaluated by a root mean square error statistic will be smaller for a model madewith the raw data than a model made with mean-centered data.With simulated data relativeimprovements ranging from 1% to 13% were observed depending on the amount of error in thecalibration concentrations and responses. 相似文献
13.
George A. Maul 《Geophysical Journal International》1986,87(1):55-66
Summary. The 1964-70 Florida Current data of Niiler & Richardson are examined for linear correlation with observed sea-level and weather, because their data provide an independent test of similar correlations reported in Maul et al. Seventy-five values of directly measured volume transport and 67 values of surface speed from Niiler & Richardson's unevenly spaced data are correlated with available daily mean values of Miami Beach sea-level, Bimini sea-level, Bimini-Miami Beach sea-level difference, and Miami weather (barometric pressure, air temperature, and north and east components of wind speed). Statistical frequency distribution of transport and of surface speed suggest variability that is not dominated by annual and/or semiannual cycles. Volume transport is most highly correlated with Bimini minus Miami Beach sea-level difference, and surface speed is most highly correlated with inverted Miami Beach sea-level. Including certain weather variables results in statistically significant improvements in linear multivariate modelling of transport and surface speed from sea-level; the standard errors are ± 2.6 sverdrup and ±10 cms−1 respectively. Linear correlation coefficients and multivariate regression parameters from Niiler & Richardson's data are in agreement with those from Maul et al. , except that the standard error of estimating volume transport from sea-level is smaller in Maul et al. , apparently because of smaller errors in the direct measurements. 相似文献
14.
Global solar radiation(GSR) is the most direct source and form of global energy, and calculation of its quantity is highly complex due to influences of local topography and terrain inter-shielding. Digital elevation model(DEM) data as a representation of the complex terrain and multiplicity condition produces a series of topographic factors(e.g. slope, aspect, etc.). Based on 1 km resolution DEM data, meteorological observations and NOAA-AVHRR remote sensing data, a distributed model for the calculation of GSR over rugged terrain within the Yangtze River Basin has been developed. The overarching model permits calculation of astronomical solar radiation for rugged topography and comprises a distributed direct solar radiation model, a distributed diffuse radiation model and a distributed terrain reflectance radiation model. Using the developed model, a quantitative simulation of the GSR space distribution and visualization has been undertaken, with results subsequently analyzed with respect to locality and terrain. Analyses suggest that GSR magnitude is seasonally affected, while the degree of influence was found to increase in concurrence with increasing altitude. Moreover, GSR magnitude exhibited clear spatial variation with respect to the dominant local aspect; GSR values associated with the sunny southern slopes were significantly greater than those associated with shaded slopes. Error analysis indicates a mean absolute error of 12.983 MJm-2 and a mean relative error of 3.608%, while the results based on a site authentication procedure display an absolute error of 22.621 MJm-2 and a relative error of 4.626%. 相似文献
15.
Global solar radiation（GSR） is the most direct source and form of global energy, and calculation of its quantity is highly complex due to influences of local topography and terrain inter-shielding. Digital elevation model（DEM） data as a representation of the complex terrain and multiplicity condition produces a series of topographic factors（e.g. slope, aspect, etc.）. Based on 1 km resolution DEM data, meteorological observations and NOAA-AVHRR remote sensing data, a distributed model for the calculation of GSR over rugged terrain within the Yangtze River Basin has been developed. The overarching model permits calculation of astronomical solar radiation for rugged topography and comprises a distributed direct solar radiation model, a distributed diffuse radiation model and a distributed terrain reflectance radiation model. Using the developed model, a quantitative simulation of the GSR space distribution and visualization has been undertaken, with results subsequently analyzed with respect to locality and terrain. Analyses suggest that GSR magnitude is seasonally affected, while the degree of influence was found to increase in concurrence with increasing altitude. Moreover, GSR magnitude exhibited clear spatial variation with respect to the dominant local aspect; GSR values associated with the sunny southern slopes were significantly greater than those associated with shaded slopes. Error analysis indicates a mean absolute error of 12.983 MJm-2 and a mean relative error of 3.608%, while the results based on a site authentication procedure display an absolute error of 22.621 MJm-2 and a relative error of 4.626%. 相似文献
16.
J. A. Ruiz‐Arias D. Pozo‐Vázquez H. Alsamamra 《International journal of geographical information science》2013,27(8):1049-1076
Daily solar radiation estimates of four up‐to‐date solar radiation models (Solar Analyst, r.sun, SRAD and Solei‐32), based on a digital elevation model (DEM), have been evaluated and compared in a Mediterranean environment characterized by a complex topography. The models' estimates were evaluated against 40 days of radiometric data collected in 14 stations. Analyzed sky conditions ranged from completely overcast conditions to clear skies. Additionally, the role of the spatial resolution of the DEM has been evaluated through the use of two different resolutions: 20 and 100 m. Results showed that, under clear‐sky conditions, the daily solar radiation variability in the study area may be reasonably estimated with mean bias errors under 10% and root mean square error values of around 15%. On the other hand, results proved that the reliability of the estimates substantially decreases under overcast conditions for some of the solar radiation models. Regarding the role of the DEM spatial resolution, results suggested that the reliability of the estimates for complex topography areas under clear‐sky conditions improves using a higher spatial resolution. 相似文献
17.
Recent upward trends in acres irrigated have been linked to increasing near-surface moisture. Unfortunately, stations with dew point data for monitoring near-surface moisture are sparse. Thus, models that estimate dew points from more readily observed data sources are useful. Daily average dew temperatures were estimated and evaluated at 14 stations in Southwest Georgia using linear regression models and artificial neural networks (ANN). Estimation methods were drawn from simple and readily available meteorological observations, therefore only temperature and precipitation were considered as input variables. In total, three linear regression models and 27 ANN were analyzed. The two methods were evaluated using root mean square error (RMSE), mean absolute error (MAE), and other model evaluation techniques to assess the skill of the estimation methods. Both methods produced adequate estimates of daily averaged dew point temperatures, with the ANN displaying the best overall skill. The optimal performance of both models was during the warm season. Both methods had higher error associated with colder dew points, potentially due to the lack of observed values at those ranges. On average, the ANN reduced RMSE by 6.86% and MAE by 8.30% when compared to the best performing linear regression model. 相似文献
18.
Gerard B. M. Heuvelink 《International journal of geographical information science》2013,27(8):921-935
Abstract Recent developments in theory and computer software mean that it is now relatively straightforward to evaluate how attribute errors are propagated through quantitative spatial models in GIS. A major problem, however, is to estimate the errors associated with the inputs to these spatial models. A first approach is to use the root mean square error, but in many cases it is better to estimate the errors from the degree of spatial variation and the method used for mapping. It is essential to decide at an early stage whether one should use a discrete model of spatial variation (DMSV—homogeneous areas, abrupt boundaries), a continuous model (CMSV—a continuously varying regionalized variable field) or a mixture of both (MMSV—mixed model of spatial variation). Maps of predictions and prediction error standard deviations are different in all three cases, and it is crucial for error estimation which model of spatial variation is used. The choice of model has been insufficiently studied in depth, but can be based on prior information about the kinds of spatial processes and patterns that are present, or on validation results. When undetermined it is sensible to adopt the MMSV in order to bypass the rigidity of the DMSV and CMSV. These issues are explored and illustrated using data on the mean highest groundwater level in a polder area in the Netherlands. 相似文献
19.
Gary L. Powell Anthony J. Brazel Martin J. Pasqualetti 《The Professional geographer》1984,36(2):227-233
A significantly less expensive but comparatively accurate means of estimating solar radiation from satellite imagery is demonstrated. Opaque cloud cover is visually extracted from nondigitized, photographic forms of GOES satellite imagery and solar radiation is estimated using this information as input into a relatively simple solar model. Daily root mean square, mean absolute and mean bias errors were 13.2, 8.6 and 1.1 percent, respectively. Maps of the spatial distribution of solar radiation for Arizona are included. 相似文献
20.
Sonja Pravilovic Donato Malerba 《International journal of geographical information science》2018,32(1):191-212
Managing geophysical data generated by emerging spatiotemporal data sources (e.g. geosensor networks) presents a growing challenge to Geographic Information System science. The presence of correlation poses difficulties with respect to traditional spatial data analysis. This paper describes a novel spatiotemporal analytical scheme that allows us to yield a characterization of correlation in geophysical data along the spatial and temporal dimensions. We resort to a multivariate statistical model, namely CoKriging, in order to derive accurate spatiotemporal interpolation models. These predict unknown data by utilizing not only their own geosensor values at the same time, but also information from near past data. We use a window-based computation methodology that leverages the power of temporal correlation in a spatial modeling phase. This is done by also fitting the computed interpolation model to data which may change over time. In an assessment, using various geophysical data sets, we show that the presented algorithm is often able to deal with both spatial and temporal correlations. This helps to gain accuracy during the interpolation phase, compared to spatial and spatiotemporal competitors. Specifically, we evaluate the efficacy of the interpolation phase by using established machine-learning metrics (i.e. root mean squared error, Akaike information criterion and computation time). 相似文献