NASAs Land Information System as a Testbed for
NASA's Land Information System as a Testbed for Agency Partners and Investigators Christa D. Peters-Lidard, Ph. D. Physical Scientist and Head, Hydrological Sciences Branch NASA/ Goddard Space Flight Center(GSFC), Code 614. 3, Greenbelt, MD 20771 Christa. Peters@nasa. gov, 301 -614 -5811 Contributions: Sujay Kumar, Joseph Santanello, Jr. , David Mocko http: //lis. gsfc. nasa. gov
Outline • LIS Background • LIS Architecture &Design • Hydrologic modeling support – NLDAS Drought Example – Data Assimilation Example – Soil Parameter Estimation Example – LIS/WRF Coupled Modeling Example • Future enhancements
LIS Heritage: NLDAS and GLDAS North American LDAS 1/8 Degree Resolution Mitchell et al. , JGR, 2004 Global LDAS 1/4 Degree Resolution Rodell et al. , BAMS, 2004 Land Information System (http: //lis. gsfc. nasa. gov) Multi-Resolution Ensemble LDAS Software Framework Kumar et al. , EMS, 2006
LIS Motivation: Exploit moderate (e. g. , MODIS) and high-res (Landsat) data 25 km 1 km
LIS Vision: Land Component for Earth System Models Atmospheric Models (WRF/GCE/ GFS/GEOS) Land Surface Models (LIS) Estuary /Coastal /Ocean Models
LIS Running Modes Uncoupled or Analysis Mode Coupled or Forecast Mode Station Data Global, Regional Forecasts and (Re-)Analyses Satellite Products ESMF Land Sfc Models (Noah, Catchment, CLM, VIC, HYSSi. B) Kumar, S. V. , C. D. Peters-Lidard, J. L. Eastman and W. -K. Tao, 2008. An integrated high-resolution hydrometeorological modeling testbed using LIS and WRF. Environmental Modelling & Software, Vol. 23, 169 -181. LSM Initial Conditions WRF/ GFS/ GCE
LIS Uncoupled/Analysis Mode Inputs Topography, Soils Physics Outputs Land Surface Models Soil Moisture & Temperature Land Cover, Vegetation Properties Weather/ Climate Evaporation Water Sensible Heat Resources Flux Agriculture Meteorological Forecasts, Analyses, and/or Observations Snow Soil Moisture Temperature Applications Drought Runoff Military Ops Data Assimilation Modules Snowpack Properties Natural Hazards
LIS Architecture Kumar, S. V. , C. D. Peters-Lidard, Y. Tian, P. R. Houser, J. Geiger, S. Olden, L. Lighty, J. L. Eastman, B. Doty, P. Dirmeyer, J. Adams, K. Mitchell, E. F. Wood and J. Sheffield, 2006. Land Information System - An Interoperable Framework for High Resolution Land Surface Modeling. Environmental Modelling & Software, Vol. 21, 1402 -1415.
LIS Design • Earth System Modeling Framework (ESMF) to interoperate with other Earth system model components (e. g. , the Weather Research and Forecasting Model, WRF) • ESMF tools are also used to enable interoperability within the LIS components (e. g. , Data Assimilation, Parameter Estimation, Land Surface Models) • I/O standards – ALMA (Assistance for Land Modeling Activities) – CF (Climate and Forecasting) • I/O Formats Supported – GRIB, Net. CDF, HDF-EOS, Binary, Ascii
Hydrologic Modeling Support • • NLDAS Drought Example Data Assimilation Example Soil Parameter Estimation Example LIS/WRF Coupled Modeling Example
NLDAS Drought Monitor Example http: //ldas. gsfc. nasa. gov/drought
• Capabilities have been demonstrated for assimilating soil moisture, snow and skin temperature observations. NCEP/AFWA Noah model Figure 1: Soil Moisture Assimilation Open Loop Temperature RMSE (K) • LIS is a comprehensive system that integrates the use of various land surface models, assimilation algorithms, observational sources for users at NASA, AFWA, NOAA, USDA and other agency investigators. GMAO Catchment model Root Zone Soil Moisture Improvement • NASA/GMAO-developed capabilities for sequential data assimilation have been implemented in the NASA/HSB Land Information System (LIS) framework. Surface Soil Moisture Improvement LIS Data Assimilation Example No Bias Correction With Bias Correction Figure 2: Skin Temperature Assimilation
LIS Data Assimilation Flexibility Kumar, Sujay V. , Rolf H. Reichle, Christa D. Peters-Lidard, Randal D. Koster, Xiwu Zhan, Wade T. Crow, John B. Eylander, and Paul R. Houser, 2008: A Land Surface Data Assimilation Framework using the Land Information System: Description and Applications, In press, Advances in Water Resources, Special Issue on Remote Sensing. doi: 10. 1016/j. advwatres. 2008. 013.
LIS Soil Parameter Estimation Example LIS+SSURGO OBS LIS+PEST Peters-Lidard C. D. , D. M. Mocko, M. Garcia, J. A. Santanello Jr. , M. A. Tischler, M. S. Moran, Y. Wu (2008), Role of precipitation uncertainty in the estimation of hydrologic soil properties using remotely sensed soil moisture in a semiarid environment, Water Resour. Res. , 44, W 05 S 18, doi: 10. 1029/2007 WR 005884. Santanello, J. A. , Jr. , C. D. Peters-Lidard, M. Garcia, D. Mocko, M. Tischler, MS. Moran, and D. P. Thoma, 2007: Using Remotely-Sensed Estimates of Soil Moisture to Infer Soil Texture and Hydraulic Properties across a Semi-arid Watershed, Remote Sensing of Environment, 110(1), 79 -97, DOI=http: //dx. doi. org/10. 1016/j. rse. 2007. 02. 007.
LIS-WRF Coupled Example 1 AFWA, NASA and NCAR Joint Study
LIS-WRF Coupled Example 2: 0 -10 cm initial soil moisture (%) Eta soil moisture (1200 UTC 6 May 2004) • Much more detail in LIS (as expected) • LIS drier, especially over N. FL & S. GA • LIS slightly more moist over Everglades LIS soil moisture Difference (LIS – Eta) LIS Substantially Drier 16
LIS-WRF Coupled Example 2: Sea Breeze Evolution Difference (1800 UTC 6 May to 0300 UTC 7 May) Case, Jonathan L. , William L. Crosson, Sujay V. Kumar, William M. Lapenta, Christa D. Peters-Lidard, 2008. Impacts of High-Resolution Land Surface Initialization on Regional Sensible Weather Forecasts from the WRF Model. In press, Journal of Hydrometeorology. 17
LIS-WRF Coupled Example 2: Sea Breeze Evolution Difference (Meteogram plots at 40 J and CTY) 18
LIS Integrates Observations, Models and Applications to Maximize Impact 1. Observations 2. Modeling and Data Assimilation 3. Applications
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