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

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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

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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

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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