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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