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Real-time integration of remote
sensing, surface meteorology, and
ecological models
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
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 …

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 ...
Provides critical information needed
to describe land-atmosphere
interactions
 Inadequate for assessment of
landscape processes

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

A non-prognostic ecological model
ingesting real-time satellite and
surface meteorology observations
TOPS Overview
1 kilometer spatial resolution
 Remotely-sensed leaf area index
(LAI)
 Rapid Update Cycle meteorology
(RUC)
 Land surface model (LSM)

Terrestrial Observation and Prediction System
EOS Products
GOES
Temperature, Humidity, Rain
and Wind
Land cover. LAI, Snow cover,
Vegetation Index
Incident shortwave radiation
Over 3000 stations for the U.S
1km
0.5x0.5 lat/lon
d
de
da
ly
Do
w
i
da
ns
ca
le
d
id
Weekly
Gr
Rapid Update Cycle
Assimilated data
ily
Surface Weather Data
Soils
Topography
operating at Watershed to
Continental Scales
Calibration
USDA Snotel Network
USGS Guage Network
DOE Fluxnet
USDA/USFS Fuel moisture
USDA Crop yields
Weather/Climate
Forecasts
upto 10 days &
Seasonal
FORECAST
Land Surface Models
NOWCAST
Ancillary
Data
MONITORING
FORECASTING
Snow Cover
Soil Moisture
Stream flow
Streamflow
Soil moisture
Vegetation moisture stress
Vegetation phenology
Vegetation phenology
Vegetation moisture stress
Vegetation production
Crop/Range/Forest production
Fire Risk
Fire Risk
TOPS Concept Logic
INPUT
TRANSFORM
GATES
Climatology,
Envion. vars
Remote Sensing
MODIS,
AVHRR,
others
-- A
-- A
-- A
-- P
-- A
A :: action
P :: pass-through
-- A
-- A
A-spatial
inputs:
profiles,
distributions
Point Inputs
streamflow,
flux towers
INGEST
FORMAL FILTERS.
{wgrib, others}
-- A
-- A
-- P
-- A
-- P
TEMPORAL
{hourly-to-daily}
-- A
-- A
SPATIAL REPROJECTION
-- P
-- A
SCALING {up or down}
TOPS
MODEL MANAGER
-- P
-- P
-- P
-- P
Input Model
Specification Membrane
LAND SURFACE
MODEL
..other integrated
models...
Output Model
Specification Membrane
Derived
Biophysical Variables
-- A
POST-PROCESS
TRANSFORM
GATES
A :: action
P :: pass-through
Derived Indices
Analysis of Anomolies
-- P
-- A
Knowledge Extraction
-- A
-- P
Data Reduction / Statistical Summaries
-- A
PRESENTATION INTERFACES
WEB
Images, Tables
Event-Triggered push
to Client Sites
SELECTIVE ARCHIVE
Near-online tape,
age-policy disk
22 June 2000, jmg
Remotely-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 18
Week of May 19 - May 25
Week of May 26 - June 1
Week of June 2 - June 8
Week of June 9 - June 15
RUC-2
Produced by the National Centers for
Environmental Prediction
 Hourly outputs
 20 kilometer resolution
 Automated scripts gather data and
process hourly values to daily values
 Future developments will include
downscaling algorithms

Example RUC-2 meteorology
Downscaling: use of lapse rates and digital
elevation model to adjust temperatures within each
40 km pixel
Temp = 20 deg C
DEM
Ecological model
Based on BIOME-BGC
 No complete carbon balance
 Forced with observed LAI

Results
Beta version
 June 18

Plant stress index: higher values indicate higher stress
Planned transformed variables

Accumulated stress/fire danger
– incorporate lightning strike information
Anomalies/departures from normal
 Water deficit/irrigation requirements

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 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
Conclusions
Real-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
