rpc preliminary design review (5/17/06) 1 evaluation of the nasa land information system using the...
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RPC Preliminary Design Review (5/17/06)1
Evaluation of the NASA Land Information System using the Rapid Prototyping
Capabilities
Valentine Anantharaj* on behalf of LIS Evaluation Team
Paul Houser†, Christa Peters-Lidard**, Georgy Mostovoy*, LIS Team**
* Mississippi State university - GeoResources Institute† George Mason University
** NASA Goddard Space Flight Center
RPC Preliminary Design Review (5/17/06)2
Outline
1. The identified federal partner and their decision support needs; and how NASA can meet those needs
2. Land data assimilation at NASA
3. Purpose and goals of RPC experiment
4. Proposed LIS evaluation activities
5. Expected societal benefits
6. LIS evaluation team
7. Risk Analysis
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Federal Partner and Decision Support Needs
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About our federal partner: USDA NRCS …
• NRCS Vision:– “Harmony between people in the land”
• Future Goal:– “A globally recognized source for top quality spatial
snow, water, climate, and hydrologic network of information and technology”
• Mission– Natural resource conservation
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About NASA Applied Sciences …
• NASA's vision is "to improve life here" and our mission is "to understand and protect our home planet".
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About NASA Applied Sciences …
• NASA's vision is "to improve life here" and our mission is "to understand and protect our home planet". Applications extend the NASA vision and mission by enabling and facilitating the assimilation of Earth observations and prediction outputs into decision support tools. The purpose is to enhance the performance of the decision support resources to serve society through Earth exploration from space.
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• Assessment, decision support, management, and conservation natural resources:
Partner Activities
irrigationwater supplynutrientsanimal wastepestssalinitywater quality
key driverskey drivers WaterSoilsWaterSoils
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Identified Partner Need
• Routine analysis soil moisture over the continental needs
watersoilssunweatherclimatevegetationterrain
watersoilssunweatherclimatevegetationterrain
observe, model, assimilateobserve, model, assimilate
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Identified Decision Support Tool
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USDA-NRCS SCAN DST
• 115 stations across the United States• real time and near real time observations• meteor burst technology for communications• primarily near agricultural regions
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SCAN DST: A National Priority
• National Research Council recommended priority
• NASA and USDA-NRCS are principal partners in National Integrated Drought Information System
• NIDIS is envisioned to be a “drought early warning system”
• Identified by USGEO as significant component of NIDIS
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Relevant NASA Capabilities, Data, and Research Results
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NASA Assets and Capabilities
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NASA Data Products Useful for LISClass Observation Technique Example Platform Temporal Spatial
Land Parameters
Leaf area and greenness optical/IR AVHRR, MODIS, NPOESS weekly 1km
Albedo optical/IR MODIS, NPOESS weekly 1km
Emissivity optical/IR MODIS, NPOESS weekly 1km
Vegetation structure Lidar ICESAT, ESSP lidar mission weekly-monthly 100m
Topography in-situ survey, radar GTOPO30, SRTM episodic 30m–1km
Land Forcings Wind profile radar
Air Humidity and temperature IR, MW TOVS, GOES, AVHRR, MODIS, AMSR hourly-weekly 5 km
Near- surface radiation optical/IR GOES, MODIS, CERES, ERBS, etc. hourly-weekly 1km
Precipitation microwave/IR TRMM, GPM, SSMI, GEO-IR, etc. hourly-monthly 10km
Land States Temperature IR, in-situ IR-GEO, MODIS, AVHRR, TOVS hourly-monthly 10m-4km
Thermal anomalies IR, NIR, optical AVHRR, MODIS, TRMM daily-weekly 250m–1km
Snow cover and water optical, microwave SSMI, TM, MODIS, AMSR, AVHRR, etc. weekly-monthly 1km
Freeze/thaw radar Quickscat, HYDROS, IceSAT, CryoSAT weekly 3km
Total water storage gravity GRACE monthly 1000km
Soil moisture active/passive microwave SSMI, AMSR, HYDROS, SMOS, etc. 3-30 day 10-100 km
Land Fluxes Evapotranspiration optical/IR, in-situ MODIS, GOES hourly-weekly 10m-4km
Solar radiation optical, IR MODIS, GOES, CERES, ERBS hourly-monthly
Longwave radiation optical, IR MODIS, GOES hourly-monthly 10m-4km
Sensible heat flux IR MODIS, ASTER, GOES hourly-monthly 10m-4km
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Land Data Assimilation
• Energy and moisture fluxes, at different spatial and temporal scales, at the land-atmosphere interface is modulated by the land surface conditions
• Hence, accurate initialization is critical• Remotely sensed and in-situ data provide key initial
conditions• Land models provide information about land-surface
state, essential for improving environmental assessment and prediction and decision support
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Land Surface Model Physics
(Houser)
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An Integrated Framework forLand Data Assimilation System
ApplicationsInputs OutputsPhysics
Topography,Soils
WaterSupply &Demand,
Agriculture,Hydro-ElectricPower,
EndangeredSpecies,
Water Quality
ImprovedShort Term
&Long TermPredictions
Land Cover and Vegetation (MODIS, AMSR,TRMM, SRTM)
Meteorology Modeled &
Observed (TRMM, GOES, Station)
Observed Land States(Snow, ET, Soil Moisture, Water,
Carbon, etc.)
Land Surface Models (LSM)Physical Process Models
Noah, CLM, VIC, SiB2, Mosaic, Catchment, etc.
Data Assimilation Modules(EnKF, EKF)Rule-based
Water Fluxes: Runoff
Surface States:
Moisture, Carbon, Ts
Energy Fluxes:Le & H
Biogeo-chemistry:
Carbon, Nitrogen, etc.
(Peters-Lidard, Houser, Kumar, Tian, Geiger)
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Land Surface Data Assimilation
Soil Moisture AssimilationSoil Moisture Assimilation
Observation
Assimilation with Bias Correction
AssimilationNo Assimilation
Skin Temperature AssimilationSkin Temperature Assimilation
SSM/I Snow ObservationSSM/I Snow Observation
Snow Water AssimilationSnow Water Assimilation
xt dynamics physics x
Theory DevelopmentTheory Development
Model I
ntegra
tion
Data
Insertion of Data into the Model
Obs Model4DDAImproved products,
predictions, understanding
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Soil Moisture Assimilation:Soil Moisture Assimilation: WWalnut alnut GGulchulch
Model
Model with 4DDA
Observation
Tombstone, AZ
0% 20%
Houser et al., 1998
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NASA Land Information System (LIS)
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LIS: Introduction
• A high-resolution land surface modeling and data assimilation system based on the successful Global Land Data Assimilation System
• High-performance parallel computing software that enables near-real time modeling at the scale of Earth Observing System-era observations (1 km2)
• Incorporates innovative and sophisticated data management system with Internet technologies
• High-quality software technology with a strong, expanding user base
• Based on community standards such as ESMF and ALMA
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LIS: History and Current Status
• Funded by NASA Earth Science Technology Office (ESTO) Computational Technologies Project (CT) CAN for “Grand Challenge Applications” in 2002
• Award Winning Technology• LIS team from 3 GSFC branches and UMBC GEST
center• 4 major software releases• Over 150,000 lines of C/Fortran90 code• Over 150 registered users from over 30 countries• A Linux cluster with 200 nodes built as test bed
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LIS
External
Internal
Memory Wallclock time CPU time (MB) (minutes) (minutes)
LDAS 3169 116.7 115.8LIS 313 22 21.8
reduction factor 10.12 5.3 5.3
25km
1km
5km
LIS: Scalability
200 Node “LIS” ClusterOptimized I/O, GDS Servers
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Interoperability with standards:• The Earth System Modeling Framework (ESMF)• Assistance for Land Modeling Activities (ALMA)
LISOcean Models
Atmos. Models
12-Hours Ahead Atmospheric Model
Forecasts
WithLIS
WithoutLIS
ObservedRainfall
WRF-LIS Coupled Modeling Capabilities
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Noah Domain Averaged Daily Average RMSE Values Versus Repeat Time for 30m-5010m resolutions
0
10
20
30
40
50
60
0 5 10 15 20 25 30
Repeat Time (Days)
RM
SE
(m
m s
no
w)
30
90
990
5010
Hypothetical Error
Requirement
3 7
Snow Water Equivalent Retrieval Error vs. Revisit Time
HorizontalResolution
(m)
OSSE Capabilities of LIS – Mission Planning
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Purpose of RPC Experiments for LIS Evaluations
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Purpose of RPC Experiments …
• Goal 1:– Evaluate LIS capabilities and NASA data to support
the enhancement USDA-NRCS SCAN decision support tool
• Approach:– Evaluate LIS and identified NASA data by performing
Observation Sensitivity Experiments (OSE)– Derive physically consistent soil moisture maps at a
range of spatial resolutions from 25x25 km2 to 1x1 km2
– Quantify uncertainties at all scales
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Purpose of RPC Experiments …
• Goal 2:– Evaluate LIS capabilities and NASA data to support
the extension USDA-NRCS SCAN network• Approach:
– Evaluate LIS and identified NASA data by performing Observing System Simulation Experiments (OSSE)
– For a region of interest, define and evaluate network optimization approaches by identifying network configurations for maximum impact
– Quantify uncertainties
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Purpose of RPC Experiments …
• Goal 3:– Evaluate the model coupling capabilities of LIS
• Approach:– Evaluate the ESMF coupling capabilities for a couple
WRF-LIS configuration– For a region of interest, perform case studies for one or
more meteorological events– Quantify and qualify impacts of coupling
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Purpose of RPC Experiments …
• Goal 4:– Evaluate and demonstrate the usefulness of LIS as a
customizable and cross-cutting resource that can be readily adopted, proto-typed, and deployed
• Approach:– Demonstrate the usefulness of LIS by providing LIS
derived data to one or more MRC funded ISS projects– Document the lessons learned for future enhancements
of LIS
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Purpose of RPC Experiments
• Goal 5:– Help enhance the RPC Performance Metric
Workbench• Approach:
– Help define evaluation metrics– Share existing and new evaluation tools and software– Provide expert consultations to the PMW development
team– Integrate relevant PMW capabilities into LIS V&V
package
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LIS Evaluation Activities
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LIS Evaluation Activities
1. LIS Performance Analysis
2. Observation Sensitivity Experiments (OSE)
3. Observing System Sensitivity Experiments (OSSE)
4. WRF-LIS Model Coupling Evaluation Experiments
5. Cross-cutting Demonstrations
6. PMW Evaluation Package
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LIS Performance Analysis(technical overhead & supporting activities)
• Computational and storage requirements• Reliability and security• Identify optimal LSM configuration for RPC
experiments• Data management activities• Elaboration of partner and NASA risks• Evaluation of the incorporation of identified
NASA data into LIS (MODIS: LAI, NDVI, albedo)• LIS experiments integration into RPC
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Observation Sensitivity Experiments for SCAN Enhancement
• Evaluation of data assimilation techniques– EKF, EnKF
• Data assimilation (land state)– Soil moisture
• Soil moisture stations• AMSR-E
– Temperature• MODIS LST
• Sensitivity studies• Outcomes: soil moisture analysis product, uncertainty
characterization
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OSSEs for SCAN Extension
• Identify region for pilot study• “Truth” run(s)• Open loop control run(s)• Selective data assimilation run(s)• Network optimization analysis• Network prioritization• Outcomes: evaluation report, identified ISS
pathway
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WRF-LIS Coupling Experiments
• Identification of case studies• Consultations with SPoRT and NOAA• WRF-LIS runs:
– Uncoupled WRF– WRF with LIS forcing using Noah LSM– WRF with soil moisture assimilated LIS forcing– Coupled WRF-LIS run(s)
• Outcomes: evaluation report, quantitative and qualitative assessment
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Cross-Cutting Demonstrations
• Evaluate LIS capabilities in conjunction with MRC ISS project on assessment of forest fire risk potential
• Region of study to focus on MS Gulf coast and other forests in MS
• Evaluate the utility of USFS station data
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RPC PMW Evaluation Package
• Help integrate any available evaluation tools and packages
• Provide expert advice to PMW development team
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Deliverables
• Evaluation report(s)• Peer-reviewed journal article(s)• ISS recommendation(s)
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Risk Analysis
• System incompatibilities• Security issues• Intellectual property• Quality, availability, suitability of data
– AMSR-E, in-situ data– Validation data
• Technical and scientific issues– Data assimilation, inconsistencies
• Personnel, political, funding risks
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Anticipated Societal Benefits
1. provides critical information to support drought monitoring and mitigation
2. provides critical information for predicting droughts based on weather and climate predictions
3. supports irrigation water management4. supports fire risk assessment5. supports water supply forecasting and NWS flood forecasting6. supplies a critical missing component to assist with snow, climate
and associated hydrometeorological data analysis7. supports climate change assessment8. enables water quality monitoring9. supports a wide variety of natural resource management & research
activities such as NASA remote sensing activities of soil moisture and ARS watershed studies.
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LIS Evaluation Team & Collaborators
• LIS Team:– Valentine Anantharaj and Georgy Mostovoy (MSU
GRI)– Paul Houser (GMU CREW)– Christa Peters-Lidard (NASA GSFC HSB)– LIS Team at GSFC
• Collaborators– USDA NRCS– NASA MSFC SPoRT (?) and NOAA– NASA GMAO (?)
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Data
RPC EVALUATIONS, V&V
USDA-NRCS SCAN DST
Observations
VALUE & BENEFITS
EARTH SYSTEM MODELS
EARTH OBSERVATIONS
Predictions
*Next Generation Missions
• Atmosphere: ground-based measurements (WMO, ARM/CART), MPE (NOAA), NEXRAD, AMSR-E, GOES, MODIS, GPM, TRMM, GOES-R, PERSIANN, NPP, NPOESS
• Land : SCAN (USDA), Mesonet, Producer-Cooperator
• NASA Land Information System (Noah, Catchment LSM)
• Forcing: [G|N]LDAS (NASA) using [G|E]DAS & CMAP (NCEP), AGRMET (AFWA), ECMWF, NCAR FNL, NARR, RUC
• Data Assimilation: EKF, EnKF
• Supercomputing: Project Columbia (NASA), HPC2 (MSU)
RPC Evaluation of NASA Land Information System for Application in USDA-NRCS SCAN DST
• air & land surface temperature, humidity, winds
• precipitation
• soil moisture, SST
• radiation, aerosol
• Soil moisture maps from 12x12 km2 to 1x1 km2
• Sensitivity analyses using OSEs
• AMSR-E data assimilation
• OSSEs to help optimize DST network
• NOAA NIDIS
• Coupled WRF-LIS modeling applications
• Global/Regional scale model products (1 – 50km)
• soil moisture maps
• Longer-term outlooks
•Mitigate drought effects
•Support flood analysis
•Producer decision making – planting, production, disease, pest management
•Crop yield modeling and monitoring
•Improved forecasting of severe weather and climate outlook
•Ecological forecasting
• Policy decisions
Partners
Uncertainty Analysis and Scientific Rigor
RPC Preliminary Design Review (5/17/06)45
Estimated Level of Effort
Tasks NASA (FTE)
GMU (FTE)
GSFC Contractor
(FTE)
MRC (FTE)
Performance Analysis 0.05 - 0.5 0.5
OSE / Data Assimilation 0.1 0.1 0.1 0.25
OSSE 0.1 0.1 0.1 0.25
ESMF-coupled WRF-LIS 0.1 - 0.1 0.25
Cross-cutting Demonstrations
0.05 - 0.1 0.1
PMW Support - 0.1 0.25 0.25
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Discussions?
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Data
RPC EVALUATIONS, V&V
DECISION SUPPORT TOOLS
Observations
VALUE & BENEFITS
EARTH SYSTEM MODELS
EARTH OBSERVATIONS
Predictions
*Next Generation Missions
• Atmosphere: ground-based measurements (WMO, ARM/CART), MPE (NOAA), NEXRAD, AMSR-E, GOES, MODIS, GPM, TRMM, Cloudsat, GOES-R, PERSIANN, NPOESS
• Land : SCAN (USDA), Mesonet
• NASA Land Information System (Noah, CLM, Vic)
• Forcing: [G|N]LDAS (NASA) using [G|E]DAS & CMAP (NCEP), AGRMET (AFWA), ECMWF; NCAR FNL
• Regional Models: WRF; RAMS-AROMA, COAMPS, and RUC (for evaluations)
• Supercomputing: Project Columbia (NASA), HPC2 (MSU)
RPC Evaluation of NASA Land Information System for Cross-Cutting Applications
• air & land surface temperature, humidity, winds
• precipitation
• soil moisture, SST
• radiation, aerosol
• DHS NARAC/IMAAC at LLNL
• USDA USFS Firelab
• USDA FAS PECAD
• USDA NRCS SCAN & NIDIS
• NSSTC SERVIR
• Coupled WRF-LIS modeling applications
• EPA BASINS
• USBR RiverWare
• Global/Regional scale model products (1 – 50km)
• analysis, hourly & daily forecasts
• Longer-term outlooks
•Atmospheric assessment of emissions and air pollution
•Fire risk modeling
•Crop yield modeling and monitoring
•Improved forecasting of severe weather
•Ecological forecasting
• Policy decisions
Potential Partners
Uncertainty Analysis and Scientific Rigor