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Observing System Simulation of Snow Microwave Emissions Over Data Sparse Regions— Part I: Single Layer Physics

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2 Author(s)
Do Hyuk Kang ; Pratt Sch. of Eng., Duke Univ., Durham, NC, USA ; Barros, A.P.

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 - now 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.

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Geoscience and Remote Sensing, IEEE Transactions on  (Volume:50 ,  Issue: 5 )