Observing System Simulation of Snow Microwave Emissions Over Data Sparse Regions— Part I: Single Layer Physics
Observing System Simulation of Snow Microwave Emissions Over Data Sparse Regions— Part I: Single Layer Physics
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DOI：
10.1109/tgrs.2011.2169073
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发表时间：
2012-05
影响因子：
8.2
通讯作者：
D. Kang;A. Barros
中科院分区：
文献类型：
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作者：
D. Kang;A. Barros
这项工作的目标是制定一个框架，用于监测雪水当量（SWE）和积雪辐射特性（例如，在缺乏用于模型校准和/或数据同化的辅助数据和地面观测的偏远地区，为此，现有的陆面水文模型（LSHM）与单层（SL）雪物理耦合到微波发射模型（MEMLS）。耦合模型（MLSHM-SL）预测了各种频率和极化下的微波发射以及积雪辐射特性（例如，发射率）的基础上积雪密度，温度，雪深，和体积液态水含量模拟的水文模型与大气强迫从观测或天气预报的分析。MLSHM-SL在预报观测系统模拟（OSS）模式下进行了两个案例研究：1）俄罗斯瓦尔代积雪无线电亮度行为的多年模拟，与三个频率的扫描多通道微波辐射计（SMMR）观测进行了比较（18、21和37 GHz、V和H极化），1978-1983年; 2）比较了专用传感器微波/成像仪（SSM/I）和高级微波扫描辐射计-EOS（AMSR-1）的模拟和观测亮温。E）在2002-2003雪季期间，作为科罗拉多的冷地过程实地实验（CLPX）的一部分。在瓦尔代的情况下，该模型捕捉以及积雪的积累和融化过程中的质量平衡以及辐射行为，具有显着的最佳技能的垂直极化（10-16 K的差异，误差统计相比，水平极化），特别是在冬季1月至3月（干雪条件）。在秋季初，由于在SMMR的空间尺度上的部分积雪覆盖和雪湿度的不确定性，检测到间歇性积雪条件的较大偏差。对于CLPX的SSM/I和AMSR-E的OSS获得了类似的结果，尽管垂直和水平极化误差统计之间的差异更适度（~ 2- 4K）。AMSR-E V-pol在19和37 GHz时的误差统计较低。MLSHM-SL预测的积雪物理特性（散装雪密度和SWE）与CLPX雪坑观测在积累季节的残差小于10%的观测值。此外，MLSHM-SL模拟在完整的预测模式，从雪季开始到结束没有校准，是熟练的MEMLS与雪坑观测指定的物理属性。这表明MSLSHM-SL可以独立地用作偏远地区SWE的基于物理的估计器，并在数据同化框架中为基于卫星的雪观测的解释提供物理基础。
The objective of this work is to develop a framework for monitoring snow water equivalent (SWE) and snowpack radiometric properties (e.g., surface emissivity and reflectivity) and microwave emissions in remote regions where ancillary data and ground-based observations for model calibration and/or data assimilation are lacking. For this purpose, an existing land surface hydrology model (LSHM) with single-layer (SL) snow physics was coupled to a microwave emission model (MEMLS). The coupled model (MLSHM-SL) predicts microwave emissions at various frequencies and polarizations as well as snowpack radiometric properties (e.g., emissivity) based on snowpack density, temperature, snow depth, and volumetric liquid water content simulated by the hydrology model with atmospheric forcing obtained from either observations, or the analysis of weather forecasts. The MLSHM-SL was evaluated in prognostic observing system simulation (OSS) mode for two case-studies: 1) a multi-year simulation of snowpack radio-brightness behavior at Valdai, Russia compared against Scanning Multichannel Microwave Radiometer (SMMR) observations at three frequencies (18, 21, and 37 GHz, V, and H polarizations) over six years, 1978-1983; and 2) an intercomparison of simulated and observed brightness temperatures for the Special Sensor Microwave/Imager (SSM/I) and the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) during the 2002-2003 snow season as part of the Cold Land Processes Field Experiment (CLPX) in Colorado. In the case of Valdai, the model captures well the mass balance as well as radiometric behavior of the snowpack during both accumulation and melt, with significantly best skill for vertical polarization (10-16 K differences in error statistics as compared to horizontal polarization), particularly in the winter season January-March (dry snow conditions). Larger biases were detected for intermittent snowpack conditions at the beginning of the fall season due to uncertainty in fractional snow cover and snow wetness at the spatial scale of the SMMR. Similar results were obtained for the OSS of SSM/I and AMSR-E for CLPX, though differences between vertical and horizontal polarization error statistics are more modest (~ 2-4 K). Error statistics are lower for AMSR-E V-pol at 19 and 37 GHz. MLSHM-SL predicted snowpack physical properties (bulk snow density and SWE) compare well against CLPX snowpit observations during the accumulation season with residuals smaller than 10% of observed values. Moreover, the MLSHM-SL simulations in full prognostic mode, and without calibration from the beginning through the end of the snow season, are as skillful as MEMLS with specified physical attributes from snow pit observations. This indicates that the MSLSHM-SL can be used independently as a physically based estimator of SWE in remote regions, and in a data-assimilation framework to provide a physical basis to the interpretation of satellite-based observations of snow.