AIST Program Sensor Web Meeting Summary of Results Working Group MiddleWare 1 April 3, 2008 MW1 Model Interop MW2 System Mgmt SS Smart Sensing

April 3, Sensor Web Meeting, Working Group X Use Case Brainstorming Summary 9 presentations –SMART, Doppler Wind Lidar, Bird Flu, Wildfire Smoke, AutoChem, Severe Weather GOES-R, LISW, QuakeSIM –DESDynI mission brief Patterns –Observations influence model –Model influences observations –Observations validate model

April 3, Sensor Web Meeting, Working Group X Primary Use Case Overview Maturity Level - Mature & Developing –Mature Smart Assimilation of Satellite data into weather forecast model, M. Goodman; Obs influence models; Automated decisions whether to assimilate satellite data Bird Migration and Avian Flu, L. Di; Obs influence model & Model influence Obs; Prediction of bird migration and potential spread of Avian Flu AutoChem Atmospheric Chemistry Assimilation System, L Di, Obs influence model & Model influence Obs; Prediction of transport of pollutants Satellite and UAS fire observation inputs to smoke forecast models, S. Falke, Obs influence models; Assimilate satellite observations to improve smoke forecast Tasking new satellite and UAS observations with smoke forecasts, S. Falke; Models influence obs, understanding smoke impact on air quality with new observations Adaptive Targeting of Wind Lidar to Improve weather forecast skill, M. Seablom; Obs influence model & Model influence Obs; improve forecast skill and power modulation to extend mission life Earthquake response and forecasting, A. Donnellan; Obs influence model & Model influence Obs; improve rapid response from DESDynI Volcanoes, A. Donnellan, Obs influence model & Model influence Obs; determine the likelihood of volcanic eruptions Carbon Cycle Biomass, P. Houser; Obs influence model & Model influence Obs; improve knowledge of vegitation, biomass, and carbon cycling and changes

April 3, Sensor Web Meeting, Working Group X Additional Use Case Overviews (up to 3) Developing –Extreme event detection and tracking for targeted observing, J. Moses; Model influencing Obs, Obs influencing models; Automate decision to task location for GOES-R rapid scanning for improving sever weather forecast and warning skill –Validating smoke forecasts with satellite UAS observations, S. Falke; Obs validating models; improves smoke forecast models –Detection, tracking, and reacquisition of volcanic ash clouds, M. Burl; Obs influence models; improved height estimates and observations detecting volcanic eruption and tracking resultant ash, potentially improve hazard forecast accuracy –Predict Global Land Surface Soil Moisture, P Houser; Obs influence models; assimilate soil moisture data from SMAP (other fut. Missions) to improve global land surface predictions –Hydrology, P Houser; Obs influence models; map & monitor land surface innundation extent & change and improve land surface hydrological conditions using DESDynI

April 3, Sensor Web Meeting, Working Group X Use Case Title: –Smart assimilation of satellite data into weather forecast model POC: –Michael Goodman / Helen Conover Character: –Observations influence models Goal: Improve assimilation process of satellite data into numerical models. Assimilation of these large datasets can be computationally expensive, –Use intelligent processes to determine when/where interesting weather phenomena are expected, – Assimilate satellite observations to improve forecast accuracy. Use community standard protocols for data access and alerts.

April 3, Sensor Web Meeting, Working Group X Activity Diagram

April 3, Sensor Web Meeting, Working Group X Use Case Title: –Validating smoke forecast models with satellite, UAS and surface observations POC: –Stefan Falke Character: –Observations validate models Goal: This air quality use scenario envisions a sensor web that facilitates access, integration, and use of multi-source data for purposes of air quality assessment and forecasting. A particular emphasis is placed on the retrospective analysis of large forest fires and the validation of forecast output with satellite and unattended aerial systems (UAS) to improve numerical smoke forecast models.

April 3, Sensor Web Meeting, Working Group X Activity Flow Smoke WPS Fire Detections EO-1 Image Smoke Forecast Model Smoke Forecast NOAA HMS Smoke Obs. Smoke Product Identify Areas Of Interest Compare Model & Obs. Compare Smoke Prods. revise model revise algorith m Other Smoke Prods. task sensors Fire Proximity Analysis WPS Reconciled Fire Locs Ames UAS Obs. Influences model

April 3, Sensor Web Meeting, Working Group X Use Case Lessons Learned Generic patterns identified –Process patterns Gaps –Tighter coupling between models driving observations for mission design (carbon cycle DESDynI) –Technology influence on scientific observations (SensorWeb) –SensorWeb (technology) enablement within the future missions –Middleware - web services, portals, ontologies, etc

April 3, Sensor Web Meeting, Working Group X New Sensor Web Features, Needs Identify new features or benefits List any new AIST needs