real-time integration of remote sensing, surface meteorology, and ecological models
TRANSCRIPT
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