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

6. Technology focus Distributed Agent Architecture UWF, Tetrad IV CMU Nat’l. Data Centers UW PRECISE NASA ARC TOPS/IMAGEbot UMT TOPS Appl 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 Modular Unattended Tmax / Tmin VPD, precipitation Solar radiation Daylength Any user Defined grid Jolly, nemani, Running…. 2004. Envi. Modeling and Software

9. Global Vegetation Production Anomaly May 2005

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

11. Brian Bonnlander/Clark Glymour/Votava, IHMC/ARC Train the algorithms on all the non-arson fires during 2000-2002 Methods include: Support Vector Machines Artificial Neural Networks Logistic Regression Data-driven models MODIS data in mapping wildland fire risk

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

13. CAL-SYNERGY 1km Daily weather, satellite and model data Maximum Air Temperature Vegetation densityVegetation GrowthSoil Moisture Most downloaded data set Used by USGS, CDW, NPS, BLM and Wine industry

14. MODISMODEL Monitoring snow conditions Columbia river basin Columbia river basin

15. Interannual variability in snow conditions Snow Cover Area (10 5 km 2 )

16. Collaboration with the National Park Service

17. TOPS Irrigation Scheduling LAI from NDVI Imagery Limited Farm-scale Soils Data Met Data from CIMIS Crop Params from Variety Irrigation Forecasts Crop Monitoring InputsModelingOutputs Forecast from NWS Maintaining optimal water stress for better vintages

18. Vineyard Water Management Irrigation 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 1000meters N

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

20. Change in Spring (March-April-May) Temperature, o C [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

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 Future Current

24. Ecological Forecasting http://ecocast.arc.nasa.gov

25. 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 in ecological forecasts remains a challenge Summary