Evaluation and blending of ATMS and AMSR2 snow water equivalent retrievals over the conterminous United States

Details

• Journal Title:
  Remote Sensing of the Environment
• Personal Author:
  Gan, Yanjun ; Zhang, Yu ; Kongoli, Cezar ; Grassotti, Christopher ; Liu, Yuqiong ; Lee, Yong-Keun ; Seo, Dong-Jun
• NOAA Program & Office:
  NWS (National Weather Service) ; OWP (Office of Water Prediction) ; NESDIS (National Environmental Satellite, Data, and Information Service) ; STAR (Center for Satellite Applications and Research) ; OAR (Oceanic and Atmospheric Research) ; WPO (Weather Program Office) ; CISESS (Cooperative Institute for Satellite Earth System Studies)
• Description:
  This study first compares two different passive microwave snow water equivalent (SWE) retrievals, namely the retrieval from the Suomi National Polar-orbiting Partnership (S-NPP) Advanced Technology Microwave Sounder (ATMS) and that from the Global Change Observation Mission – Water (GCOM-W1) Advanced Microwave Scanning Radiometer 2 (AMSR2); it further creates an optimal blending mechanism that merges the two retrievals with in situ observations from the Snow Telemetry (SNOTEL) and Cooperative Observer Program (COOP) networks. The assessments of the two products are done over conterminous United States (CONUS) for the snow seasons (November–June) of the water years 2017–2019 using in situ data and the SNOw Data Assimilation System (SNODAS) SWE analysis. Both satellite products tend to underestimate SWE. Between the two, AMSR2 retrieval outperforms in terms of correlation with observations and depth of saturation, but it exhibits a distinctive, seasonally varying bias that is not seen in ATMS retrieval. The negative bias over the early snow season, as further analysis indicates, most likely stems from AMSR2 retrieval's use of a high frequency channel (i.e., 89 GHz) for shallow snow detection, while the impact of differing assumptions of snow density is marginal. The blending scheme, developed on the basis of the validation experiment, features a histogram-based bias correction as a supplement to optimal interpolation. Cross-validation suggests that interpolated station product without the satellite background broadly underperforms the blended in situ-satellite product, confirming the utility of the satellite retrievals. Furthermore, the a priori bias correction mechanism is shown to be effective in mitigating large fluctuations in bias. Finally, the bias-corrected, blended in situ-satellite product performs comparably or even favorably against SNODAS over many parts of the CONUS, with important implications for joint use of satellite and in situ observations for hydrological monitoring and forecasting.
• Keywords:
  Hydrology Remote Sensing Remote Sensing
• Source:
  Remote Sensing of the Environment 254, 112280, 2021
• DOI:
  https://doi.org/10.1016/j.rse.2020.112280
• Document Type:
  Journal Article
• Funding:
  Grant no. NA18OAR4590410
• Rights Information:
  Accepted Manuscript
• Rights Statement:
  The NOAA IR provides access to this content under the authority of the government's retained license to distribute publications and data resulting from federal funding. While users may legally access this content, the copyright owners retain rights that govern the reproduction, redistribution, and re-use of this work. The user is solely responsible for complying with applicable copyright law.
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha-512:36c62dd931d12751faef20af1f943451616c576e14a7456f973cb323100b5117cb3640d745a663299895073d6a31298bb795a57bba5be4c46de604623403f3fa
• Download URL:
  https://repository.library.noaa.gov/view/noaa/29177/noaa_29177_DS1.pdf
• File Type:
  [PDF - 15.79 MB ]