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NASA Biodiversity and Ecological Forecasting Team Meeting August 30, 2005

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NASA Biodiversity and Ecological Forecasting Team Meeting August 30, 2005

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  1. Terrestrial Observation and Prediction SystemDevelopment 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

  2. Turning Observations into Knowledge Products

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

  4. 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

  5. Terrestrial Observation and Prediction System Key elements: Monitoring Modeling Forecasting Scale flexibility

  6. Technology focusDistributed Agent Architecture UW PRECISE UMT TOPS Appl CMU Nat’l. Data Centers NASA ARC TOPS/IMAGEbot UWF, Tetrad IV Scripps Inst. Oceanography CO2/Climate Forecasts

  7. 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

  8. 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

  9. Global Vegetation Production Anomaly May 2005

  10. Temperature Potential Climate Limits for Plant Growth Sunlight Water Each month, our analysis identifies climate-related causes behind the predicted NPP anomalies

  11. 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

  12. Predicting fire risk Brian Bonnlander/Clark Glymour/Votava, IHMC/ARC

  13. 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

  14. Monitoring snow conditions Columbia river basin MODIS MODEL

  15. Interannual variability in snow conditions Snow Cover Area (105 km2)

  16. Collaboration with the National Park Service

  17. 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

  18. 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 meters 1000 N

  19. Interannual variability interannual climate-wine quality Nemani et al., 2001 Climate Research

  20. 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

  21. Predicted Changes in phenology in response to climatic changes Later bloom over the west after 1998

  22. Changes in start of growing season derived from satellite data

  23. Planning/Execution Agent technologies beyond TOPS Current Future

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

  25. Summary Summary 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 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 the end

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