nasa's land information system as a hydrometeorological testbed for agency partners and...
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NASA's Land Information System as a Hydrometeorological Testbed for Agency Partners and Investigators
Christa D. Peters-Lidard, Ph.D.Physical Scientist and
Head, Hydrological Sciences BranchNASA/ Goddard Space Flight Center(GSFC), Code 614.3,
Greenbelt, MD 20771Christa.Peters@nasa.gov, 301-614-5811
Contributions: Sujay Kumar, Rolf Reichle, Matt Rodell, Joseph Santanello, Jr., David Mocko, and many others…
http://lis.gsfc.nasa.gov
• LIS Background• LIS Architecture & Design• Hydrometeorologic modeling support
– LIS transition for AFWA/AGRMET– LIS transition for NOAA/NCEP/GLDAS– NLDAS Drought Example – Data Assimilation Examples– Soil Parameter Estimation Example– LIS/WRF Coupled Modeling Example
• Future enhancements
Outline
Motivation: ObservationsMotivation: Observations
Surface soil moisture (SMMR, TRMM, AMSR-E, SMOS, Aquarius, SMAP)
Snow water equivalent
(AMSR-E, SSM/I, SCLP)
Land surface data for research and applications:Comprehensive view of land surface water/energy/carbon cycle.Learn about processes, characterize errors, improve models. Enhance weather and climate forecast skill.Develop improved flood prediction and drought monitoring capability.…
Land surface temperature (MODIS, AVHRR,GOES,… )
Water surface elevation (SWOT)
Snow cover fraction (MODIS, VIIRS, MIS)
Terrestrial water storage (GRACE)Ensemble-based land data assimilation system
Precipitation (TRMM, GPM)
Vegetation/Carbon (AVHRR, MODIS, DESDynI,
ICESat-II, HyspIRI, LIST, ASCENDS )
Radiation (CERES, CLARREO )
25km
5km 1km
LIS Motivation: Exploit moderate (e.g., MODIS) and high-res (Landsat) data
NNorth American orth American LDASLDAS1/8 Degree Resolution1/8 Degree ResolutionMitchell et al., JGR, 2004Mitchell et al., JGR, 2004
GGlobal lobal LDASLDAS1/4 Degree Resolution1/4 Degree Resolution
LLandand I Informationnformation S System (http://lis.gsfc.nasa.gov)ystem (http://lis.gsfc.nasa.gov)
Multi-Resolution Ensemble LDAS Software FrameworkMulti-Resolution Ensemble LDAS Software Framework
LIS Heritage: NLDAS and GLDAS
Rodell et al., BAMS, 2004Rodell et al., BAMS, 2004
Kumar et al., EMS, 2006Kumar et al., EMS, 2006
Land Information System (LIS)Lead: Christa Peters-Lidard (614.3)
• Award-winning, modular, high-performance software• Multiple land surface models• GEOS-5 land assimilation modules• Used and co-developed by NOAA/NCEP, AFWA, JCSDA, and many others
GEOS-5 ($ by NASA Modeling, Analysis & Prediction Program)Lead (for land assimilation): Rolf Reichle (610.1)
• Comprehensive atmos./ocean/land modeling & assimilation system • Quasi-operational weather and seasonal forecasts• MERRA reanalysis• Development of ensemble-based land assimilation
Global & North American Land Data Assimilation Systems (GLDAS, NLDAS) Leads: Matt Rodell/David Mocko (614.3)
• Project for land assimilation research and applications• Data archive at GES-DISC• Uses LIS software• Contributes to GEOS-5 seasonal forecast initialization
Land Data Assimilation at NASA/GSFC
Land Surface Models(LIS)
Estuary/Coastal/Ocean Models
Atmospheric Models
(WRF/GCE/GFS/GEOS)
LIS Vision: Land Component for Earth System Models
LIS Running Modes
LSM Initial Conditions
WRF/GFS/GCE
Land Sfc Models(Noah, Catchment,
CLM, VIC, HYSSiB)
Coupled orForecast Mode
Uncoupled or Analysis Mode
Global, RegionalForecasts and (Re-)Analyses
Station Data
Satellite Products
ESMF
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.
Topography,Soils
Land Cover, Vegetation Properties
Meteorological Forecasts,
Analyses, and/or Observations
Snow Soil MoistureTemperature
Land Surface Models
Data Assimilation Modules
Soil Moisture &
Temperature
EvaporationSensible Heat
Flux
Runoff
SnowpackProperties
Inputs OutputsPhysics Applications
LIS Uncoupled/Analysis Mode
Weather/Climate
Water Resources
Agriculture
Drought
Military Ops
Natural Hazards
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 Architecture
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, NetCDF, HDF-EOS, Binary, Ascii
• LIS transition for AFWA/AGRMET
• LIS transition for NOAA/NCEP/GLDAS
• NLDAS Drought Example
• Data Assimilation Examples
• Soil Parameter Estimation Example
• LIS/WRF Coupled Modeling Example
Hydrometeorological Modeling Activities
LIS Development and Tech Transition Projects Funded by AFWA Since FY05
1. LIS Benchmarking as Next Generation AGRMET2. LIS EnKF Design and Implementation3. AGRMET Precipitation Enhancements (Joint
w/OSU, C. Daly/W. Gibson)4. LIS/WRF Coupling (Joint w/NCAR, F. Chen)5. Combined MODIS SCA- AMSR-E SWE Product6. LIS Assimilation Enhancements: MODIS SCA,
MODIS LST, and JCSDA CRTM (Joint w/NCEP, JCSDA, K. Mitchell)
7. LIS/AGRMET IOC-February, 2009
AFWA/AGRMET Background
AFWA 5-Year Vision
LSM Physics(Noah)
GFS+WRF=NEMS
Coupled orForecast Mode
Uncoupled or Analysis Mode
Global, Regional
Forecasts and
(Re-)Analyses
Station Data
Satellite Products
ESMF
JCSDA LIS-GFS-CRTM System Concept
Satellite Radiances(CRTM)
LIS
NLDAS Drought Monitor Example
http://ldas.gsfc.nasa.gov/drought
LIS Data Assimilation Examples
•NASA/GMAO-developed capabilities for sequential data assimilation have been implemented in the NASA/HSB Land Information System (LIS) framework.
•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.
Figure 1: Soil Moisture Assimilation
Figure 2: Skin Temperature Assimilation
GMAO Catchment model NCEP/AFWA Noah model
Ro
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Open Loop
With Bias Correction
No Bias Correction
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.2008.01.013.
Soil moisture assimilationSoil moisture assimilation
• Assimilation product agrees better with ground data than satellite or model alone.• Modest increase may be close to maximum possible with imperfect in situ data. • Use data assimilation for generation of SMAP “Level 4” product.
Skill (anomaly time series correlation coeff. with in situ data with 95% confidence interval)
N Satellite Model Assim.
Surface soil moisture 23 .38±.02 .43±.02 .50±.02
Root zone soil moisture 22 n/a .40±.02 .46±.02
Soil moisture [m3/m3]
Assimilate AMSR-E surface soil moisture (2002-06) into NASA Catchment model
Validate with USDA SCAN stations(only 23 of 103 suitable for validation)
Reichle et al. (2007) J Geophys Res, doi:10.1029/2006JD008033.
Soil-Moisture-Active-Passive (SMAP) Soil-Moisture-Active-Passive (SMAP) mission designmission design
Results• Assimilation of (even poor) soil moisture retrievals adds skill (relative to model product). • Published AMSR-E and SMMR assimilation products consistent with expected skill levels.
Skill (R) of retrievals (surface soil moisture)
Skill improvement of assimilation over model (ΔR)(root zone soil moisture)
AMSR-E (Δ): ΔR=0.06
SMMR (□): ΔR=0.03
Q: How uncertain can retrievals be and still add useful information in the assimilation system? A: Synthetic data assimilation experiments.
Skill measured in terms of R (=anomaly time series correlation coefficient against synthetic truth).
Each plus sign indicates result of one 19-year assimilation integration over Red-Arkansas domain. S
kil
l (R
) o
f m
od
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(ro
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oil
mo
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Reichle et al. (2008) Geophys Res Lett, doi:10.1029/2007GL031986.
CLM
Noah
Catch
Mosaic
CLM
Noah
Catch
Mosaic
Normalized ROOT ZONE soil moisture improvement from assimilation of surface soil moisture
Catchment or MOSAIC “truth” easier to estimate than Noah or CLM “truth”.
Catchment and Mosaic work better for assimilation than Noah or CLM.
Catch Mos Noa CLMCatch 0.71 0.54 0.36 0.38 0.50Mos 0.55 0.69 0.31 0.33 0.47Noa 0.43 0.43 0.36 0.26 0.37CLM 0.11 0.21 0.10 0.45 0.22
0.45 0.47 0.28 0.36 0.39
Mod
el
NIC rzmcSynthetic observations from
Avg
Avg
Stronger coupling between surface and root zone provides more “efficient” assimilation of surface observations.
How does land model formulation impact assimilation estimates of root zone soil moisture?
Kumar et al. (2008) J. Hydromet., submitted.
Multi-model soil moisture assimilationMulti-model soil moisture assimilation
Assimilation disaggregates GRACE data into snow, soil moisture, and groundwater.
Assimilation estimates of groundwater better than model estimates.
Assimilation
No assimilationValidation against observed groundwater:
RMSE = 18.5 mm
R2 = 0.49
Assimilation of GRACE terrestrial water storage (TWS)
Zaitchik, Rodell, and Reichle (2008) J. Hydrometeorol., doi:10.1175/2007JHM951.1
RMSE = 23.5 mm
R2 = 0.35
Zaitchik and Rodell, J. Hydromet., doi:10.1175/2008JHM1042.1, in press.
Mongolia (n=32)Mongolia (n=32)
West Coast (n=59)West Coast (n=59)
Sep-05 Jan-06 May-06 Sep-06 Jan-07 May-07
High Plains (n=103)High Plains (n=103)
Southwest (n=28)
―Open―Push―Pull• In situ
Sep-05 Jan-06 May-06 Sep-06 Jan-07 May-07
sno
w w
ater
eq
uiv
alen
t, m
m
Advanced rule-based MODIS snow cover Advanced rule-based MODIS snow cover assimilationassimilation
Forward-looking “pull” algorithm (smoother): • Assess MODIS snow cover 24-72 hours ahead• Adjust air temperature (rain v. snowfall, snow melting v. frozen)
control run
irrigation run
observations
Innovative algorithm models irrigation based on MODIS data, crop type, time of year, soil dryness, and common irrigation practices improved model fluxes.
Difference (%) in evapotranspiration between irrigation and control runs, Aug-Sep 2003
MODIS-derived intensity of irrigation
Daily Max surface temperature [41.7N 97.875W]
290
300
310
320
8/11/03 8/18/03 8/25/03 9/1/03 9/8/03
Ozdogan and Gutman (2008) Remote Sens EnvironOzdogan, Rodell, and Kato (2008) J Hydrometeorol, in preparation
SimulatingSimulating irrigation based on irrigation based on MODIS observationsMODIS observations
Max surface temperature (K) (irrigated site)
Reported by USGS
Simulating irrigation based on Simulating irrigation based on MODIS observationsMODIS observations
6.04.83.62.41.20.0
cubic km
2003 county irrigation totals
Modeled in this study
Ozdogan, Rodell, and Kato (2008), J Hydrometeorol, in preparation
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, W05S18, doi:10.1029/2007WR005884.
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 Soil Parameter Estimation Example
Optimized vs. Measured Soil Texture
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Site 1 Site 2 Site 3 Site 4 Site 5 Site 6 Site 7 Site 8
%
MetFlux
PEST
LIS+PEST
OBS
LIS+SSURGO
LIS-WRF Coupled Example 1AFWA, NASA and NCAR Joint Study
27
LIS-WRF Coupled Example 2:LIS-WRF Coupled Example 2:0-10 cm initial soil moisture (%)0-10 cm initial soil moisture (%)(1200 UTC 6 May 2004)(1200 UTC 6 May 2004)
Eta soil moisture LIS soil moisture
Difference (LIS – Eta)
LIS SubstantiallyDrier
• Much more detail in LIS (as expected)
• LIS drier, especially over N. FL & S. GA
• LIS slightly more moist over Everglades
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LIS-WRF Coupled Example 2: LIS-WRF Coupled Example 2: Sea Breeze Evolution DifferenceSea Breeze Evolution Difference
(1800 UTC 6 May to 0300 UTC 7 May)(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.
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LIS-WRF Coupled Example 2:LIS-WRF Coupled Example 2:Sea Breeze Evolution DifferenceSea Breeze Evolution Difference
(Meteogram plots at 40J and CTY)(Meteogram plots at 40J and CTY)
Land data assimilationLand data assimilation
Surface soil moisture (SMMR, TRMM, AMSR-E, SMOS, Aquarius, SMAP)
Snow water equivalent
(AMSR-E, SSM/I, SCLP)
Land surface data for research and applications:Investigate land surface water/energy/carbon cycle.Learn about processes, characterize errors, improve models. Enhance weather and climate forecast skill.Develop improved flood prediction and drought monitoring capability.…
Land surface temperature (MODIS, AVHRR,GOES,… )
Water surface elevation (SWOT)
Snow cover fraction (MODIS, VIIRS, MIS)
Terrestrial water storage (GRACE)Ensemble-based land data assimilation system
Precipitation (TRMM, GPM)
Vegetation/Carbon (AVHRR, MODIS, DESDynI,
ICESat-II, HyspIRI, LIST, ASCENDS )
Radiation (CERES, CLARREO )
SUMMARY
• Abundance of land surface satellite observations offers new perspectives on the global water, energy, and carbon cycle.
• Assimilation products better than model or satellite data.
• Obs. can be extrapolated and downscaled (space & time).
• Key applications: forecast initialization, monitoring of current conditions (e.g. drought), process understanding, ...
PLANS
• Prepare for new NASA sensors that offer high-res. precipitation, soil moisture, snow, water surface elevation, …
• Assimilation system contributes to mission design & products.
• As land surface models evolve, model parameters will become model states (e.g. dynamic vegetation models – 614.4 & GISS).
• Multi-variate “Integrated Earth System Analysis” (atmosphere + ocean + land)
•Case JL, Crosson WL, Kumar SV, Lapenta WM, Peters-Lidard CD (2008) Impacts of High-Resolution Land Surface Initialization on Regional Sensible Weather Forecasts from the WRF Model. J Hydrometeorol, doi:10.1175/2008JHM990.1, in press.•Crow WT, Reichle RH (2008) Adaptive filtering techniques for land surface data assimilation. Wat Resour Res, in press. •De Lannoy GJM, Reichle RH, Houser PR, Pauwels VRN, Verhoest NEC (2007) Correcting for Forecast Bias in Soil Moisture Assimilation with the Ensemble Kalman Filter. Wat Resour Res 43:W09410, doi:10.1029/2006WR005449. •Kumar SV, Reichle RH, Peters-Lidard CD, Koster RD, Zhan X, Crow WT, Eylander JB, Houser PR (2008a) A Land Surface Data Assimilation Framework using the Land Information System: Description and Applications. Adv Water Resour, doi:10.1016/j.advwatres.2008.01.013, in press.•Kumar SV, Peters-Lidard C, Tian Y, Reichle RH, Alonge C, Geiger J, Eylander J, Houser PR (2008b) An integrated hydrologic modeling and data assimilation framework enabled by the Land Information System (LIS). IEEE Computer, submitted. •Kumar SV, Reichle RH, Koster RD, Crow WT, Peters-Lidard CD (2008c) Role of subsurface physics in the assimilation of surface soil moisture observations. J. Hydromt, submitted.•Ozdogan M, Gutman G (2008) A new methodology to map irrigated areas using multi-temporal MODIS and ancillary data: An application example in the continental US, Remote Sens Environ 112:3520-3537.•Ozdogan M, Rodell M, Kato H (2008) Impact of irrigation on LDAS predicted states and hydrological fluxes, J Hydrometeorol, in preparation.•Reichle RH, Koster RD (2003) Assessing the impact of horizontal error correlations in background fields on soil moisture estimation. J Hydrometeorol 4(6):1229-1242.•Reichle RH, Koster RD (2004) Bias reduction in short records of satellite soil moisture. Geophys Res Lett 31:L19501, doi:10.1029/2004GL020938.•Reichle RH, Koster RD (2005) Global assimilation of satellite surface soil moisture retrievals into the NASA Catchment land surface model. Geophys Res Lett 32(2):L02404, doi:10.1029/2004GL021700.•Reichle RH, McLaughlin D, Entekhabi D (2002a) Hydrologic data assimilation with the Ensemble Kalman filter. Mon Weather Rev 130(1):103-114.•Reichle RH, Walker JP, Koster RD, Houser PR (2002b) Extended versus Ensemble Kalman filtering for land data assimilation. J Hydrometeorol 3(6):728-740.•Reichle RH, Koster RD, Liu P, Mahanama SPP, Njoku EG, Owe M (2007) Comparison and assimilation of global soil moisture retrievals from AMSR-E and SMMR. J Geophys Res 112:D09108, doi:10.1029/2006JD008033.•Reichle RH, Crow WT, Koster RD, Sharif H, Mahanama SPP (2008a) The contribution of soil moisture retrievals to land data assimilation products. Geophys Res Lett 35:L01404, doi:10.1029/2007GL031986.•Reichle RH, Crow WT, Keppenne CL (2008b) An adaptive ensemble Kalman filter for soil moisture data assimilation. Wat Resour Res, doi:10.1029/2007WR006357, in press.•Reichle RH, Bosilovich MG, Crow WT, Koster RD, Kumar SV, Mahanama SPP, Zaitchik BF (2008c) Recent Advances in Land Data Assimilation at the NASA Global Modeling and Assimilation Office, In: Data Assimilation for Atmospheric, Oceanic, and Hydrologic Applications, Seon Ki Park (ed), Springer, New York, NY, in press. •Rodell M, Houser PR (2004) Updating a land surface model with MODIS-derived snow cover. J Hydrometeorol 5:1064-1075.•Rodell M, Houser PR, Jambor U, Gottschalck J, Mitchell K, Meng C-J, Arsenault K, Cosgrove B, Radakovich J, Bosilovich M, Entin JK, Walker JP, Toll DL (2004) The Global Land Data Assimilation System. Bull Amer Meteorol Soc 85:381-394, doi:10.1175/BAMS-85-3-381.•Zaitchik BF, Rodell M, Reichle RH (2008) Assimilation of GRACE terrestrial water storage data into a land surface model: Results for the Mississippi River basin. J Hydrometeorol, in press.•Zaitchik BF, Rodell M (2008) Forward-looking Assimilation of MODIS-derived Snow Covered Area into a Land Surface Mode. J Hydrometeorol, doi:10.1175/2008JHM1042.1, in press.
ReferencesReferences
2. Modeling and Data Assimilation 3. Applications
1. Observations
LIS Integrates Observations, Models and Applications to Maximize Impact
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