RPC Preliminary Design Review (5/17/06) 1 Evaluation of the NASA Land Information System using the Rapid Prototyping Capabilities Valentine Anantharaj

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


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


Federal Partner and Decision Support Needs


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


About NASA Applied Sciences …

• NASA's vision is "to improve life here" and our mission is "to understand and protect our home planet".


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.


• Assessment, decision support, management, and conservation natural resources:

Partner Activities

irrigationwater supplynutrientsanimal wastepestssalinitywater quality

key driverskey drivers WaterSoilsWaterSoils


Identified Partner Need

• Routine analysis soil moisture over the continental needs

watersoilssunweatherclimatevegetationterrain

watersoilssunweatherclimatevegetationterrain

observe, model, assimilateobserve, model, assimilate


Identified Decision Support Tool


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


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


Relevant NASA Capabilities, Data, and Research Results


NASA Assets and Capabilities


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


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


Land Surface Model Physics

(Houser)


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)


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


Soil Moisture Assimilation:Soil Moisture Assimilation: WWalnut alnut GGulchulch

Model

Model with 4DDA

Observation

Tombstone, AZ

0% 20%

Houser et al., 1998


NASA Land Information System (LIS)


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


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


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


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


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


Purpose of RPC Experiments for LIS Evaluations


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



• 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



• 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



• 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


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


LIS Evaluation Activities


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


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


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


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


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


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


RPC PMW Evaluation Package

• Help integrate any available evaluation tools and packages

• Provide expert advice to PMW development team


Deliverables

• Evaluation report(s)• Peer-reviewed journal article(s)• ISS recommendation(s)


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


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.


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 (?)


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


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


Discussions?


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

Documents

RPC Preliminary Design Review (5/17/06) 1 Evaluation of the NASA Land Information System using the Rapid Prototyping Capabilities Valentine Anantharaj