Terrestrial Observation and Prediction System Development of a Biospheric Nowcast and Forecast Capability

Ramakrishna NemaniNASA/Ames Research Center

Collaborators:

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 MeetingAugust 30, 2005

Turning Observations into Knowledge Products

With the Launch of Aura, the 1st Series of EOS is Now Complete

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

Key elements:

Monitoring

Modeling

Forecasting

Scale flexibility

Terrestrial Observation and Prediction System

Technology focusDistributed Agent Architecture

UWF,Tetrad IV

CMU

Nat’l. Data Centers

UWPRECISE

NASA ARCTOPS/IMAGEbot

UMTTOPS Appl

Scripps Inst. OceanographyCO2/Climate Forecasts

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

gridding climate data

RAWS

Modular

Unattended

Tmax / TminVPD, precipitationSolar radiationDaylength

Any userDefined grid

Jolly, nemani, Running…. 2004. Envi. Modeling and Software

Global Vegetation Production Anomaly May 2005

Potential Climate Limits for Plant Growth

Temperature

WaterSunlight

Each month, our analysis identifies climate-related

causes behind the predicted NPP anomalies

Brian Bonnlander/Clark Glymour/Votava, IHMC/ARC

Train the algorithms on all the non-arson fires during 2000-2002

Methods include:Support Vector MachinesArtificial Neural NetworksLogistic Regression

Data-driven modelsMODIS data in mapping wildland fire risk

Predicting fire risk

Brian Bonnlander/Clark Glymour/Votava, IHMC/ARC

CAL-SYNERGY1km Daily weather, satellite and model data

MaximumAir Temperature

Vegetation density Vegetation Growth Soil Moisture

Most downloaded data setUsed by USGS, CDW, NPS, BLM andWine industry

MODIS MODEL

Monitoring snow conditionsMonitoring snow conditions Columbia river basinColumbia river basin

Interannual variability in snow conditions

Sn

ow C

ove

r A

rea

(1

05

km

2)

Collaboration with the National Park Service

TOPS Irrigation Scheduling

LAI from NDVIImagery

Limited Farm-scale Soils Data

0

5

10

15

20

25

30

J F M A M J J A S O N D

0

10

20

30

40

50

60

70

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Tavg, C

ETo, mm

Ppt, mmMet Data from CIMIS

CropParamsfrom Variety

Irrigation ForecastsCrop Monitoring

Inputs Modeling Outputs

Forecast from NWS

Maintaining optimal water stress for better vintages

Vineyard Water ManagementIrrigation forecasts

Used to maintain vines at specificwater stress level to maximize

fruit quality

Forecasts integrate high-resolutionsatellite/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 wineries1000meters N

interannual climate-wine quality

Nemani et al., 2001 Climate Research

Interannual variability

Change in Spring (March-April-May) Temperature, oC

[1998-2004] - [1991-1998]

Decadal climate changes and U.S wine industry

Cooler springs after 1998

Late budbreak Slow ripening

Delayed harvest Increasing risk from frost

Predicted Changes in phenology in response to climatic changes

Later bloom over the west after 1998

Changes in start of growing season derived from satellite data

Planning/Execution Agent technologies beyond TOPS

FutureCurrent

Ecological Forecastinghttp://ecocast.arc.nasa.gov

Summary

Willem de Kooning (1904-1997)A Tree in Naples (1960)Museum of Modern Art

the end

more information at: http://ecocast.arc.nasa.gov

Unprecedented data volumes

Working with large data sets requires robust automation

Planning/Execution 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 inecological forecasts remains a challenge

Summary