Real-time integration of remote Real-time integration of remote sensing, surface meteorology, sensing, surface meteorology, and ecological modelsand ecological models

Goals Goals

Provide Nowcast/Forecasts of water and carbon cycle variables for the conterminous United States with the Terrestrial Observation and Prediction System (TOPS)

Need for integration Need for integration

Integration of remote sensing, surface meteorology, and ecological models provides the best opportunity for comprehensive assessment of the state and activity of landscape processes

Disciplines are traditionally separate but can be highly complementary

Remote sensing alone … Remote sensing alone …

Useful for describing short- and long-term variation in terrestrial vegetation– Photosynthetic activity, leaf area index,

absorbed radiation– Phenological development– Land use and land cover changes

Less useful for detecting plant stress and hydrologic cycles

Surface meteorology alone ...Surface meteorology alone ...

Provides critical information needed to describe land-atmosphere interactions

Inadequate for assessment of landscape processes

Prognostic ecological models Prognostic ecological models

Simulate past and future climate scenarios

Mass-balance simulations of carbon, water, and nutrient cycles

Often do not ingest vegetation observations

Thus less useful for real-time management applications

What we need What we need

A non-prognostic ecological model ingesting real-time satellite and surface meteorology observations

TOPS OverviewTOPS Overview

1 kilometer spatial resolutionRemotely-sensed leaf area index

(LAI) Rapid Update Cycle meteorology

(RUC)Land surface model (LSM)

Terrestrial Observation and Prediction System

Land Surface Models

operating at Watershed toContinental Scales

Surface Weather Data

Temperature, Humidity, Rainand Wind

Over 3000 stations for the U.S

EOS Products

Land cover. LAI, Snow cover,Vegetation Index

1km

GOES

Incident shortwave radiation

0.5x0.5 lat/lon

Calibration

USDA Snotel NetworkUSGS Guage Network

DOE FluxnetUSDA/USFS Fuel moisture

USDA Crop yields

Gridded daily

Wee

kly

Dow

nsca

led

daily

NO

WC

AS

T

AncillaryData

SoilsTopography

Weather/ClimateForecasts

upto 10 days &Seasonal

FO

RE

CA

ST

MONITORINGSnow CoverStream flow

Soil moistureVegetation phenology

Vegetation moisture stressCrop/Range/Forest production

Fire Risk

FORECASTINGSoil MoistureStreamflow

Vegetation moisture stressVegetation phenologyVegetation production

Fire Risk

Rapid Update CycleAssimilated data

SCALING {up or down}

TOPS Concept LogicPoint Inputsstream flow ,flux tow ers

A-spatia linputs:

profiles, d istributions

INGEST

INPUTTRANSFORM

GAT ES

A :: actionP :: pass-through

TEMPORAL{hourly-to-daily}

Rem ote SensingM O DIS,AVHRR,others

C lim ato logy,Envion. vars

FORMAL FILTERS .{wgrib, others}

SPATIAL REPROJECTION

TO PSM O DEL M ANAG ER

..other integratedmodels...

LAND SURFACEMODEL

Input ModelSpecification Membrane

Analysis o f Anom olies

Know ledge Extraction

Data Reduction / Statistical Sum m aries

Output ModelSpecification Membrane

DerivedBiophysical Variab les

Derived Ind ices

PRESENT AT IO N INT ERFACES

W EBIm ages, T ab les

Event-T riggered pushto C lient S ites

SELECT IVE ARCHIVE

Near-online tape,age-po licy d isk

POST-PROCESSTRANSFORM

GAT ES

A :: actionP :: pass-through

-- A

-- A

-- A

-- A

-- A

-- A

-- P-- P

-- P

-- A

-- A

-- A -- A

-- A

-- P

-- P

-- A

-- A

-- P

-- P

-- P

-- P

-- P

-- A

-- A

-- A

22 June 2000, jm g

Remotely-sensed LAIRemotely-sensed LAI

Currently: Advanced Very High Resolution Radiometer (AVHRR)

Future: Moderate Resolution Imaging Spectroradiometer (MODIS)

Algorithm– Main: MODIS backup– Cloud contamination: historical

averages

Week of May 12 - May 18Week of May 12 - May 18

Week of May 19 - May 25Week of May 19 - May 25

Week of May 26 - June 1Week of May 26 - June 1

Week of June 2 - June 8Week of June 2 - June 8

Week of June 9 - June 15Week of June 9 - June 15

RUC-2RUC-2

Produced by the National Centers for Environmental Prediction

Hourly outputs20 kilometer resolutionAutomated scripts gather data and

process hourly values to daily valuesFuture developments will include

downscaling algorithms

Example RUC-2 meteorologyExample RUC-2 meteorology

Temp = 20 deg C DEM

Downscaling: use of lapse rates and digital elevation model to adjust temperatures within each 40 km pixel

Ecological modelEcological model

Based on BIOME-BGCNo complete carbon balanceForced with observed LAI

Results Results

Beta versionJune 18

Plant stress index: higher values indicate higher stress

Planned transformed variablesPlanned transformed variables

Accumulated stress/fire danger– incorporate lightning strike information

Anomalies/departures from normalWater deficit/irrigation requirements

Forecasts Forecasts

Six-month goal: incorporate Forecast Systems Laboratory (FSL) short- to medium-term forecasts– seven-day forecasts– one to three month climatological

forecasts

One year goal: Ingest long-lead forecasts from ECPC/NCEP.

Real-time and forecast modesReal-time and forecast modes

Must be run simultaneously– unconstrained use of forecast data

leads to catastrophic errors in hydrologic cycles

– important for regional scale climate models to accurately parameterize the land surface, especially in the Southwest

ConclusionsConclusionsReal-time management needs can be addressed

with an approach integrating remote sensing, surface meteorology, and ecological modeling

TOPS will provide real-time simulations of water and carbon cycles through a web-based interface within two months

Within six months we will add forecast simulations constrained by current conditions

System is flexible and can be adapted to variable spatial resolutions and inputs