SMOPS Blended Soil Moisture Product Improves Land Model Simulations
April 19, 2019 02:38 PM
CICS-MD Scientists Jifu Yin, Jicheng Liu, and Mitch Schull co-authored an article with STAR Scientist Xiwu (Jerry) Zhan about results from their CICS task on “Advancing the Effectiveness and Efficiency of GLDAS Assimilation of JPSS Land Data Products for NCEP NWP and Drought Monitoring Operations.” Soil moisture (SM) data from the Soil Moisture Operational Products System (SMOPS) developed by NOAA/NESDIS/STAR have been available and used by users since 2013, and the latest version 3.0 has been operationally released since 2017. In this paper, the authors compared the blended SMOPS with individual microwave satellite soil moisture data, to evaluate the benefits of assimilating blended and individual soil moisture retrievals.
Results show that SMOPS SM product presents a significant advantage in data availability in comparison with the individual SM retrievals. The figure above shows improved data availability (%) by SMOPS over (a) SMOS (Soil Moisture and Ocean Salinity satellite), (b) SMAP (Soil Moisture Active Passive satellite), (c) AMSR2 (Advanced Microwave Scanning Radiometer on the GCOM satellite) and (d) ASCAT (Advanced Scatterometer on METOP satellite).
Significant differences in data availability, climatology, and dynamic range of SM values between the bias‐corrected SMOPS and individual SM data lead to remarkable distinctions in Noah model SM simulations. The SMOPS SM product reduced the observation‐based Root Mean Square Errors and raised the correlation rates for the Enhanced Vegetation Index. These benefits resulted from better estimations for the surface soil layer. Yin, J., Zhan, X., Liu, J., & Schull, M. (2019). An intercomparison of Noah model skills with benefits of assimilating SMOPS blended and individual soil moisture retrievals. Water Resources Research, 55, in press, https://doi.org/10.1029/2018WR024326. POC: Jifu Yin (email@example.com).