Joint Poster Session 4 Advances in the Application of Land Surface Observations and Land Data Assimilation Techniques

Wednesday, 10 January 2018: 3:45 PM-5:30 PM
Exhibit Hall 3 (ACC) (Austin, Texas)
Hosts: (Joint between the 32nd Conference on Hydrology; and the 22nd Conference on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface (IOAS-AOLS) )
Laura Clemente-Harding, Pennsylvania State Univ., State College, PA; Clara Draper, CIRES, NOAA ESRL, PSD, Boulder, CO; Rolf Reichle, NASA GSFC, Greenbelt, MD; Sujay V. Kumar, NASA GSFC, Hydrological Sciences Laboratory, Greenbelt, MD; Andmorgan Fisher, Engineer Research and Development Center, Geospatial Research Laboratory, Alexandria, VA; Michael Lewis, Engineer Research and Development Center, Geospatial Research Laboratory, Alexandria, VA and John B. Eylander, U.S. Army Corps of Engineers, Hanover, NH

This session encompasses advances in soil moisture observations, assimilation of soil moisture observations, the development and application of land data assimilation systems (LDAS), and new DA methods. Advances in the development and application of land data assimilation systems (LDAS), which merge ground- or satellite-based observations with land surface estimates from coupled land–atmosphere or offline land surface models are discussed within this session. Both soil moisture and non-soil-moisture characteristics provide crucial input for land surface interactions, hydrologic processes, boundary layer meteorology, mobility models, and climate. The most cutting edge methods for soil moisture sensing at various spatial and temporal scales are continually changing. Data assimilation of soil moisture and inclusion of soil state characteristics in forecast modeling present significant challenges due to the variability in observations, observation types, uncertainty associated with observations, and observational coverage. Contributions may include studies that evaluate or refine land DA methods; studies that assess the impact of the assimilation on the quality of the LDAS products, and/or apply LDAS within operational or routinely run forecasting/hindcasting systems; contributions that incorporate LDAS into coupled land–atmosphere systems are particularly encouraged; studies that explore assimilation of novel and/or multiple observation types; improvements and advances in in situ and proximal observation techniques and data processing; novel methods and applications for soil strength and soil crust investigations; satellite remote sensing observation and data processing techniques; use of high performance computing (HPC) for modeling soil state characteristics, improving data assimilation techniques; verification and validation efforts for spatiotemporally varying data using distinctly different data sources; performance of land surface models as compared to in situ, proximal, and remotely sensed soil moisture; data assimilation techniques for soil moisture. Searchable terms: soil moisture, land surface interactions, spatial and temporal variability, in situ and remote sensing. All topic areas are invited to include a description of the importance of communication to enable collaboration and the sharing of scientific knowledge, the importance of collaboration in advancing subject matter knowledge, enabling field work, and model development with respect to improving the science.

Thermal Methods to Monitor Soil Moisture in Different Study Areas
Chenyang Xu, George Mason Univ., Fairfax, VA

A Robust Method to Derive and Predict Soil-Specific Calibration Coefficients for Soil Water Content Sensors
Edward Ayres, National Ecological Observatory Network, Boulder, CO; and J. A. Roberti, H. W. Loescher, and J. Tang

Comparison of GeoWatch Soil Moisture and Cone Index Predictions with Field Measurements
Susan Frankenstein, CRREL, Hanover, NH; and S. A. Shoop and J. Stanley

Initializing Numerical Weather Prediction Models with Model-Derived and Satellite-Based Soil Moisture Data
Eli J. Dennis, NASA/GSFC, Greenbelt, MD; and J. A. Santanello and P. Lawston

Evaluating Full Physics Hydrological Model Soil Moisture Simulations with Observations
Robert J. Zamora, NOAA/OAR/ESRL, Boulder, CO; and F. Viterbo, D. Gochis, and R. Cifelli

Analysis of Land Surface States Obtained from High-Resolution LDAS Experiment Using URMA and GLDAS Products
Roshan Shrestha, IMSG and NOAA/NCEP/EMC, College Park, MD; and M. B. Ek, M. Pondeca, P. Shafran, G. DiMego, and A. M. Gibbs

Global Multi-Sensor Land Data Assimilation Using CLM and Dart
Zong-Liang Yang, Univ. of Texas at Austin, Austin, TX; and L. Zhao and P. Lin

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