Synthetic Study of Spaceborne LiDAR Snow Depth Retrieval Assimilation within the NASA Land Information System
Abstract
Snow water equivalent (SWE) is a key hydrologic variable for accurately assessing snowmelt runoff and water availability for human and ecosystem needs. However, our knowledge of the spatial and temporal distribution of SWE across the globe is limited. In an effort to better quantify SWE, a hypothetical, space-based snow LiDAR that measures snow depth is explored as part of an observing system synthetic experiment (OSSE). Namely, the OSSE conducts a synthetic twin experiment within the NASA Land Information System (LIS) to address the following research questions: 1) What is the added value to SWE estimation associated with the assimilation of LiDAR-based snow depth retrievals?; 2) What is the upper-limit of error in a hypothetical LiDAR snow depth retrieval that still adds value to the SWE estimates?; and 3) Beyond this upper-limit to error, are the SWE estimates degraded or do they remain unchanged? In the OSSE setup, the Noah land surface model (LSM) with multi-parameterization options (Noah-MP) is used as the prognostic LSM, and an ensemble Kalman filter scheme is employed for assimilation across Western Colorado. Each "twin" uses the same Noah-MP model but is forced by a different reanalysis product for the periods 01 Sep 2007 to 01 Sep 2010. Snow depth retrievals continue to add value to Noah-MP via assimilation for snow depth observation error standard deviations (herein error for simplicity) up to 0.9 m. For error greater than 0.9 m, data assimilation adds little or no value. Compared to the open-loop case without assimilation (SWE RMSE=23.4 mm), more than 20% improvements in the SWE RMSE are achieved when LiDAR retrieval errors are less than 0.3 m. When assimilating LiDAR retrievals, SWE estimates are improved during the accumulation season, and consequently, runoff estimates are also improved during the ablation season.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFM.H31I2021K
- Keywords:
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- 1655 Water cycles;
- GLOBAL CHANGEDE: 1847 Modeling;
- HYDROLOGYDE: 1855 Remote sensing;
- HYDROLOGYDE: 1910 Data assimilation;
- integration and fusion;
- INFORMATICS