Terrestrial Observation and Prediction System Development of a Biospheric Nowcast and Forecast Capability Ramakrishna Nemani NASA/Ames Research Center Collaborators: Keith Golden, Petr Votava, Michael White, Andy Michaelis, Forrest Melton, Matt

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Terrestrial Observation and Prediction System
Development of a Biospheric Nowcast and Forecast
CapabilityRamakrishna NemaniNASA/Ames Research
CenterCollaborators Keith Golden, Petr
Votava, Michael White, Andy Michaelis, Forrest
Melton, Matt Jolly, Kazuhito Itchii, Hirofumi
Hashimoto, Clark Glymour, Steve Running, Ranga
Myneni and Patricia Andrews
NASA Biodiversity and Ecological Forecasting Team
Meeting August 30, 2005
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Turning Observations into Knowledge Products
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With the Launch of Aura, the 1st Series of EOS is
Now Complete
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Goal

• Specific goal for this project is to develop a
  biospheric nowcast and forecast system useful for
  monitoring and predicting key ecosystem variables
  relevant in natural resources management

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Terrestrial Observation and Prediction System
Key elements Monitoring Modeling Forecasting
Scale flexibility
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Technology focusDistributed Agent Architecture
UW PRECISE
UMT TOPS Appl
CMU
Natl. Data Centers
NASA ARC TOPS/IMAGEbot
UWF, Tetrad IV
Scripps Inst. Oceanography CO2/Climate Forecasts
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Evaluation criteria Time and resources needed
to implement over a new geographic region
add a new sensor/new data source add a new
model adapt to a new domain Ability to
quantify improvements
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gridding climate data
RAWS
Unattended
Modular
Any user Defined grid
Tmax / Tmin VPD, precipitation Solar
radiation Daylength
Jolly, nemani, Running. 2004. Envi. Modeling and
Software
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Global Vegetation Production Anomaly
May 2005
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Potential Climate Limits for Plant Growth
Each month, our analysis identifies
climate-related causes behind the predicted NPP
anomalies
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Data-driven models MODIS data in mapping wildland
fire risk
Train the algorithms on all the non-arson fires
during 2000-2002 Methods include Support Vector
Machines Artificial Neural Networks Logistic
Regression
Brian Bonnlander/Clark Glymour/Votava, IHMC/ARC
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Predicting fire risk
Brian Bonnlander/Clark Glymour/Votava, IHMC/ARC
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CAL-SYNERGY1km Daily weather, satellite and
model data
Maximum Air Temperature
Vegetation density
Vegetation Growth
Soil Moisture
Most downloaded data set Used by USGS, CDW, NPS,
BLM and Wine industry
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Monitoring snow conditions Columbia river basin
MODIS
MODEL
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Interannual variability in snow conditions
Snow Cover Area (105 km2)
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Collaboration with the National Park Service
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Maintaining optimal water stress for better
vintages
LAI from NDVI Imagery
TOPS Irrigation Scheduling
Limited Farm-scale Soils Data
Crop Params from Variety
Met Data from CIMIS
Irrigation Forecasts Crop Monitoring
Forecast from NWS
Inputs
Modeling
Outputs
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Vineyard Water ManagementIrrigation forecasts
Used to maintain vines at specific water stress
level to maximize fruit quality Forecasts
integrate high-resolution satellite/aircraft
data, weather, soils and NWS short-term
forecasts Irrigation Forecast for week of July
27, 2005 Partners include Constellation/Mondavi,
Hess collection, Kendall Jackson and several
other small wineries
1000
meters
N
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Interannual variability
interannual climate-wine quality
Nemani et al., 2001 Climate Research
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Decadal climate changes and U.S wine industry
Cooler springs after 1998
Change in Spring (March-April-May) Temperature,
oC 1998-2004 - 1991-1998
Late budbreak Slow ripening Delayed
harvest Increasing risk from frost
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Predicted Changes in phenology in response to
climatic changes
Later bloom over the west after 1998
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Changes in start of growing season derived from
satellite data
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Planning/Execution Agent technologies beyond TOPS

• Current

Future
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Ecological Forecastinghttp//ecocast.arc.nasa.gov
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Summary
Summary
Unprecedented data volumes Working with large
data sets requires robust automation Planning/Exe
cution technologies allow integration of
distributed heterogenous
data sets TOPS is not model-centric, allowing
rapid adaptation to new domains Potential for
mimicking the weather service with ecological
forecasts of various lead times Characterizing
and communicating the uncertainty in ecological
forecasts remains a challenge
Willem de Kooning (1904-1997) A Tree in Naples
(1960) Museum of Modern Art
more information at http//ecocast.arc.nasa.gov
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