11B.5 Evaluation of Snow Modeling with Noah and Noah-MP Land Surface Models and Assimilation of the AFWA Snow Depth Products into NCEP Operational CFS/GFS System

Thursday, 11 January 2018: 11:30 AM
Room 18B (ACC) (Austin, Texas)
Jiarui Dong, NCEP, College Park, MD; and M. B. Ek, H. Wei, J. Meng, W. Zheng, S. V. Kumar, R. Reichle, D. Mocko, and C. D. Peters-Lidard

The land analysis in Climate Forecast System Reanalysis (CFSR) was conducted with the Global Land Data Assimilation System (GLDAS) running under NASA Land Information System (LIS) using the Noah LSM (Land Surface Model) to evolve land states and to compute surface fluxes. The land states are updated using a “semi-coupled” approach, where these states are generated from a parallel GLDAS driven by observed precipitation (a blend of satellite retrievals, gauge-based, and model-based at higher latitudes) and with near-surface forcing from the parent atmospheric data analysis system. However, assimilation of remotely-sensed estimates of land-surface states such as soil moisture and snowpack are not supported in the old version of LIS currently used in GLDAS. Therefore, we need to bring in the new version of LIS into NCEP operational systems. The current NASA LIS (version 7) integrates NOAA operational land surface and hydrological models (NCEP’s Noah, versions from 2.7.1 to 3.6 and the future Noah-MP), high-resolution satellite and observational data, and land DA tools. The newer versions of the Noah LSM used in operational models have a variety of enhancements compared to older versions, where the Noah-MP allows for different physics parameterization options and the choice could have large impact on physical processes underlying seasonal predictions. These impacts need to be reexamined before implemented into NCEP operational systems. A set of offline numerical experiments driven by the GFS forecast forcing have been conducted to evaluate the impact of snow modeling with daily Global Historical Climatology Network (GHCN). The existing land DA capabilities in LIS have been transitioned to support NCEP’s land surface assimilation of satellite-based soil moisture and snow observations. The statistics from LIS EnKF DA results with 20 members are better than all the other methods including AFWA SNODEP, operational GFS/GDAS product, LIS control run, and LIS DA with direct replacement.
- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner