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Hani Abdallah Jean-Stéphane Bailly Nicolas Baghdadi Nicolas Lemarquand 《ISPRS Journal of Photogrammetry and Remote Sensing》2011,66(6):833-844
Given that water resources are scarce and are strained by competing demands, it has become crucial to develop and improve techniques to observe the temporal and spatial variations in the inland water volume. Due to the lack of data and the heterogeneity of water level stations, remote sensing, and especially altimetry from space, appear as complementary techniques for water level monitoring. In addition to spatial resolution and sampling rates in space or time, one of the most relevant criteria for satellite altimetry on inland water is the accuracy of the elevation data. Here, the accuracy of ICESat LIDAR altimetry product is assessed over the Great Lakes in North America. The accuracy assessment method used in this paper emphasizes on autocorrelation in high temporal frequency ICESat measurements. It also considers uncertainties resulting from both in situ lake level reference data. A probabilistic upscaling process was developed. This process is based on several successive ICESat shots averaged in a spatial transect accounting for autocorrelation between successive shots. The method also applies pre-processing of the ICESat data with saturation correction of ICESat waveforms, spatial filtering to avoid measurement disturbance from the land–water transition effects on waveform saturation and data selection to avoid trends in water elevations across space. Initially this paper analyzes 237 collected ICESat transects, consistent with the available hydrometric ground stations for four of the Great Lakes. By adapting a geostatistical framework, a high frequency autocorrelation between successive shot elevation values was observed and then modeled for 45% of the 237 transects. The modeled autocorrelation was therefore used to estimate water elevations at the transect scale and the resulting uncertainty for the 117 transects without trend. This uncertainty was 8 times greater than the usual computed uncertainty, when no temporal correlation is taken into account. This temporal correlation, corresponding to approximately 11 consecutive ICESat shots, could be linked to low transmitted ICESat GLAS energy and to poor weather conditions. Assuming Gaussian uncertainties for both reference data and ICESat data upscaled at the transect scale, we derived GLAS deviations statistics by averaging the results at station and lake scales. An overall bias of −4.6 cm (underestimation) and an overall standard deviation of 11.6 cm were computed for all lakes. Results demonstrated the relevance of taking autocorrelation into account in satellite data uncertainty assesment. 相似文献
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基于高光谱Hyperion数据的线性光谱混合模型与神经网络模型的比较 总被引：1，自引：1，他引：0
混合像元问题是定量遥感中的热点问题之一,为了改进从遥感数据中提取定量信息,人们建立了各种混合光谱分解技术,其中线性光谱混合模型和神经网络模型就是两种比较成熟的方法。以陕西省横山地区的高光谱Hyperion数据为研究基础,通过最小噪声变换(MNF)、像元纯度指数(PPI)转换和RMS误差分析的迭代方法相结合提取影像中的纯净像元作为终端端元。分别运用神经网络模型和线性光谱混合模型对影像进行光谱分解,得到各个组分的分解图像。以标准植被指数(NDVI)影像为衡量标准,选取训练样本点,分别对两种模型进行回归分析,结果显示NDVI影像与线性光谱混合模型植被分解图像的判定系数(R2=0.91)要大于其与神经网络模型的判定系数(R2=0.81)。进一步分析表明在一般情况下,线性光谱混合模型具有比神经网络模型略高的分离精度,但是神经网络模型对细部信息的提取的效果要好于线性光谱混合模型,最后提出了端元均方根误差(EAR)指数,一种新的混合像元分解的思路。 相似文献
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利用全数字摄影测量得到的高精度点数据,获取不同密度的点数据,建立基于ANUDEM和TIN方法的两种DEM,比较两种DEM的高程中误差,分析数据密度、栅格尺寸与高程中误差的关系。研究结果表明,建立1∶100 00比例尺,具有高程中误差2m左右、栅格尺寸5m以下的ANUDEM,需要的点密度水平为大于7 000个/km2,而TINDEM则为8 500个/km2。同时,当栅格尺寸小于20m、数据密度介于7 000～9 400个/km2时,无论是ANUDEM还是TINDEM,高程中误差都处于一个较稳定的状态。 相似文献
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WAFS产品中GRIB资料中国区产品评估 总被引：1，自引：0，他引：1
为了给使用WAFS产品的用户提供量化参考依据，选取WAFS产品中常用区域和层次的GRIB数据，利用由国家气象中心提供的风、温客观分析网格点资料，与WAFS中同时刻的预报场产品（风、温网格点资料），用均方根误差进行数字化形式分析比较。结果表明：WAFS提供的风、温预报，通常是短时效的风、温预报比长时效的风、温预报更接近客观分析场；低层的预报比高层的预报更接近客观分析场；风的预报以v矢量的预报优于u矢量的预报；风的误差主要来源于u矢量的误差。 相似文献
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利用环渤海区域的气象站资料和NCEP/NCAR、NCEP/DOE、CFSR、ERA Interim、JRA-55共5种再分析资料,讨论了再分析资料近地面10 m平均风速场在环渤海区域的适用性问题。结果表明：JRA资料与观测站的相关系数最大,ERA资料与23站均方根误差的平均最小;再分析资料与气象站观测资料的相关系数在山东半岛和辽东半岛高于其他地区、冬半年大于夏半年。环渤海区域地面10 m平均风速场JRA和ERA两套资料的适用性较好。由于ERA-Interim的水平分辨率更高,所以在强风过程分析中确定使用ERA Interim再分析资料。 相似文献
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Present work elucidates the impact of 3DVAR data assimilation technique for the simulation of one of the heavy rainfall events reported over Kotdwara region in the North-West Himalayan (NWH) region on 4th August 2017. We have examined the impact of conventional and satellite-based radiance datasets on the simulated results with and without assimilating the observations into the Weather Research and Forecasting (WRF) model. Three experiments have been designed with 3 nested domains of variable resolutions, one without assimilation (referred as control experiment) and other two experiments after assimilating conventional and satellite radiances observations (refer as DA-OBS and DA-SAT respectively). In the present study, assimilation of surface, upper air and the satellite-based radiance observations has been carried out for the outermost domain with horizontal resolution of 9 km. Statistical analysis suggests that the correlation coefficient is high (0.55) and root mean square error (RMSE) is low (17.12) for DA-SAT experiment as compared to other two experiments. Substantial improvement in the location, pattern and intensity of extreme rainfall event is noted after assimilation of both conventional and satellite observations with respect to the observed rainfall data. However, it is noted that the assimilation of satellite radiances has greater impact in simulating better intensity of the heavy rainfall event as compared to the assimilation of conventional observations. Plausible reason behind this could be the non-availability of the conventional observations close to the extreme rainfall event affected region. 相似文献
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GRAPES中尺度模式中不同积云参数化方案预报性能对比研究 总被引：3，自引：2，他引：1
利用我国新一代中尺度数值模式GRAPES_Meso,采用KFeta和BMJ两种积云对流参数化方案,对我国2009年冬季(1月)和夏季(6—8月)天气进行批量回报试验。回报试验结果表明：在冬季,两种方案对GRAPES_Meso模式的预报性能影响差异较小。在夏季,两种方案对模式回报效果的影响表现明显。在低层BMJ方案对形势场的回报性能略优于KFeta方案,中层则是KFeta方案明显优于BMJ方案,而在高层KFeta方案略优于BMJ方案。TS评分检验表明KFeta方案对降水的预报总体上优于BMJ方案,特别是中雨到暴雨量级在华南地区KFeta方案有明显的优势。两个方案预报积云降水平均贡献率的空间分布差异主要表现在低纬度洋面上,BMJ方案的贡献率比KFeta方案大。两个方案积云降水贡献率的概率分布形态在小雨量级上都呈陡峭的“U”型分布。KFeta方案随着降水量级的增大逐渐向大贡献率偏移,特大暴雨量级时基本上是积云降水的贡献;而BMJ参数化方案则是随着降水量级的增大逐渐向小贡献率偏移,特大暴雨量级时基本上是格点降水的贡献。 相似文献
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This study was conducted under the USDA‐Conservation Effects Assessment Project (CEAP) in the Cheney Lake watershed in south‐central Kansas. The Cheney Lake watershed has been identified as ‘impaired waters’ under Section 303(d) of the Federal Clean Water Act for sediments and total phosphorus. The USDA‐CEAP seeks to quantify environmental benefits of conservation programmes on water quality by monitoring and modelling. Two of the most widely used USDA watershed‐scale models are Annualized AGricultural Non‐Point Source (AnnAGNPS) and Soil and Water Assessment Tool (SWAT). The objectives of this study were to compare hydrology, sediment, and total phosphorus simulation results from AnnAGNPS and SWAT in separate calibration and validation watersheds. Models were calibrated in Red Rock Creek watershed and validated in Goose Creek watershed, both sub‐watersheds of the Cheney Lake watershed. Forty‐five months (January 1997 to September 2000) of monthly measured flow and water quality data were used to evaluate the two models. Both models generally provided from fair to very good correlation and model efficiency for simulating surface runoff and sediment yield during calibration and validation (correlation coefficient; R2, from 0·50 to 0·89, Nash Sutcliffe efficiency index, E, from 0·47 to 0·73, root mean square error, RMSE, from 0·25 to 0·45 m3 s?1 for flow, from 158 to 312 Mg for sediment yield). Total phosphorus predictions from calibration and validation of SWAT indicated good correlation and model efficiency (R2 from 0·60 to 0·70, E from 0·63 to 0·68) while total phosphorus predictions from validation of AnnAGNPS were from unsatisfactory to very good (R2 from 0·60 to 0·77, E from ? 2·38 to 0·32). The root mean square error–observations standard deviation ratio (RSR) was estimated as excellent (from 0·08 to 0·25) for the all model simulated parameters during the calibration and validation study. The percentage bias (PBIAS) of the model simulated parameters varied from unsatisfactory to excellent (from 128 to 3). This study determined SWAT to be the most appropriate model for this watershed based on calibration and validation results. Copyright © 2008 John Wiley & Sons, Ltd. 相似文献
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Reliability Analyses of Anomalies of NCEP/NCAR Reanalyzed Wind Speed and Surface Air Temperature in Climate Change Research in China 总被引：3，自引：0，他引：3 
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下载免费PDF全文 By means of varied statistical methods,such as normalized root mean square error(RMSE),correlation analysis,empirical orthogonal function(EOF)decomposition,etc.,the reliability of the varied seasonal anomalies of NCEP/NCAR reanalyzed wind speed and surface air temperature(SAT)data frequently used in the climate change research in China is studied.Results show that RMSEs of meteorological variables are smaller in eastern China than in western China,i.e.,the reliability of NCEP/NCAR reanalysis in eastern China is better than that in western China.This could be due to effects of the topography in the reanalysis model and the disposition of"dense-in-eastern-and-sparse-in-western"of meteorological stations in China. The RMSE of anomalies of reanalyzed wind speeds decreases with increasing height,further confirming the possible impact of topography on reliability of reanalysis.Results of correlation analysis inversely correspond to those of RMSE analysis,i.e.,if the RMSE is larger,the correlation between reanalyzed and observed data is worse,and vice versa.It is found from comparing the EOF eigenvectors of anomaly of reanalyzed and observed data that if a meteorological variable has smaller RMSE,the spatial patterns of corresponding EOF eigenvectors of anomaly of reanalyzed and observed data are similar and their time coefficients are significantly correlated,and vice versa.Therefore,the similarity of EOF modes and the consistency of their time coefficients can be used to objectively assess the reliability of the reanalysis.On the whole,the reliability of the reanalyzed wind speed is better in spring,summer,and autumn,but worse in winter;and for the reanalyzed SAT,it is the best in winter and the worst in summer. 相似文献