Session 6B Land Data Assimilation Techniques and Systems. Part II

Tuesday, 14 January 2020: 10:30 AM-12:00 PM
253A (Boston Convention and Exhibition Center)
Host: 34th Conference on Hydrology
Clara S. Draper, NOAA ESRL, PSD, CIRES, Boulder, CO
Sujay Kumar, GSFC, Greenbelt, MD; Rolf Reichle, USRA, Columbia, MD and Youlong Xia, NCEP/EMC/IMSG, College Park, MD

This session highlights advances in the development and application of land data assimilation systems (LDAS), which merge ground- or satellite-based observations with estimates from land surface models. Contributions may include studies that evaluate or refine land DA methods, and/or assess the impact of the assimilation on the quality of LDAS products.  These LDAS products may focus on the simulation of the land surface itself, or dependent processes, and could include simulation of surface hydrology, the atmosphere, drought, agriculture, and/or water resources.  Contributions focussed on transition of research to operations are particularly encouraged, as are studies that explore assimilation of novel and/or multiple observation types.

10:30 AM
SMOS Neural Network Soil Moisture Data Assimilation (Invited Presentation)
Nemesio Rodríguez-Fernández, CNRS, Toulouse, France; CESBIO, Toulouse, France; Centre d'Etudes Spatiales de la Biosphère, Toulouse, France; and P. de Rosnay, F. Aires, C. Albergel, M. Drusch, Y. Kerr, C. Prigent, S. Mecklenburg, J. Muñoz Sabater, and P. Richaume
10:45 AM
Assimilation of Vegetation Optical Depth Retrievals from Passive Microwave Radiometry
Sujay V. Kumar, NASA GSFC, Greenbelt, MD; and T. Holmes, R. de Jeu, R. Bindlish, and C. Peters-Lidard
11:00 AM
A Monte Carlo–Based Adaptive Kalman Filtering Framework for Soil Moisture Data Assimilation
Alexander Gruber, KU Leuven, Heverlee, Belgium; and G. J. M. De Lannoy and W. Crow
11:15 AM
Reduced Adjoint Variational Data Assimilation for Estimation of Soil Moisture Profile
Leila Farhadi, George Washington Univ., Washington, DC; and P. Heidari and U. Altaf
11:30 AM
Introducing a Hybrid Ensemble and Variational Data Assimilation Method for Improved Hydrologic Predictability
Hamid Moradkhani, Univ. of Alabama, Tuscaloosa, AL; and P. Abbaszadeh and K. Gavahi

11:45 AM
Hydro-DART: Ensemble Streamflow Assimilation with WRF-Hydro and the Data Assimilation Research Testbed.
Mohamad El Gharamti, NCAR, Boulder, CO; and J. McCreight, T. J. Hoar, S. Noh, and A. Rafieeinasab
- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner