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Using the Airborne Snow Observatory to Assess Remotely Sensed Snowfall Products in the California Sierra Nevada

机译:使用空中降雪天文台评估加利福尼亚内华达山脉的遥感降雪产品

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摘要

The Airborne Snow Observatory (ASO) performed two acquisitions over two mountainous basins in California on 29 January and 3 March 2017, encompassing two atmospheric river events that brought heavy snowfall to the area. These surveys produced high-resolution (50 m) maps of snow depth and snow water equivalent (SWE) that were used to estimate monthly areal snowfall accumulation. Comparison of ASO snow accumulation with point measurements showed that the ASO estimates ranged from -10 to +16% relative bias across three sites, which is likely inflated by the disagreement in areal representation of the quantities from the actual errors in these products. The aggregated SWE accumulations from ASO are then used to evaluate a suite of in situ based and remote sensing precipitation products. During the study period, Parameter-Elevation Regressions on Independent Slopes Model (PRISM) and Mountain Mapper estimates had relative bias 10% compared with ASO-based estimates of snow accumulation, but satellite and radar products largely underestimate snowfall accumulation compared to ASO (up to 50%). Despite their underestimation, satellite and radar products show correlation coefficients 0.8 with ASO snow accumulation over the selected grids at the monthly scale. Finally, we leveraged the fine-scale sampling of the spatially complete ASO products to show that by moving from 100 m to 2 km spatial scales, the perceived bias errors SWE at point locations increased by an order of magnitude, displaying a nonlinear relationship. The study demonstrates that ASO acquisitions in cold months can bring a new and effective approach to spatial evaluation of precipitation products.
机译:空中降雪观测站(ASO)于2017年1月29日和3月3日在加利福尼亚州的两个山区盆地进行了两次采集,包括两次大气河事件,使该地区降雪严重。这些调查产生了雪深和雪水当量(SWE)的高分辨率(50 m)地图,用于估算每月的区域降雪量。 ASO积雪与点测量结果的比较表明,ASO估计值在三个站点之间的相对偏差为-10%至+ 16%,这可能是由于这些产品中实际误差的面积表示形式上的分歧所致。然后,将来自ASO的SWE累积量用于评估一套基于现场的遥感降水产品。在研究期间,与基于ASO的积雪估计相比,独立斜坡模型(PRISM)和Mountain Mapper估计的参数高程回归具有相对偏差<10%,但与ASO相比,卫星和雷达产品大大低估了降雪积聚(up至50%)。尽管被低估了,但卫星和雷达产品在月尺度上与选定网格上的ASO积雪显示的相关系数> 0.8。最后,我们利用空间上完整的ASO产品的精细采样来显示,从100 m到2 km空间尺度,点位置处的感知偏置误差SWE增加了一个数量级,显示出非线性关系。该研究表明,在寒冷月份进行ASO采集可以为降水产品的空间评估带来新的有效方法。

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  • 来源
    《Water resources research》 |2018年第10期|7331-7346|共16页
  • 作者单位

    Univ Arizona, Dept Hydrol & Atmospher Sci, Tucson, AZ 85721 USA|CALTECH, Jet Prop Lab, Pasadena, CA 91109 USA;

    CALTECH, Jet Prop Lab, Pasadena, CA 91109 USA;

    CALTECH, Jet Prop Lab, Pasadena, CA 91109 USA;

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