Title of Presentation: Intelligent Assimilation of Satellite Data into a Forecast Model Using Sensor Web Processes and Protocols
Primary (Corresponding) Author: Helen Conover
Organization of Primary Author: University of Alabama in Huntsville
Co-Authors: H. Michael Goodman, Bradley Zavodsky, Kathryn Regner, Manil Maskey, Jessica Lu, Xiang Li, Mike Botts, and Gregoire Berthiau
Abstract: The goal of the Sensor Management Applied Research Technologies (SMART) On-Demand Modeling project is to develop and demonstrate the readiness of the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) capabilities to integrate both space-based Earth observations and forecast model output into new data acquisition and assimilation strategies. Working with NASAís Short-term Prediction Research and Transition (SPoRT) Center, the SMART team is developing a sensor web-enabled processing workflow to intelligently assimilate Atmospheric Infrared Sounding (AIRS) satellite temperature and moisture retrievals into a regional Weather Research and Forecast (WRF) model over the southeastern United States.
At SPoRT, a North American Mesoscale (NAM) forecast is used as the initial conditions for a regional WRF model run. The addition of current weather observations (such as those from AIRS) can improve the accuracy of a WRF forecast, but assimilating voluminous satellite observations into the initial conditions is computationally expensive. Modelers and IT experts on the SMART team have worked together closely to determine how sensor web-enabled data access and analysis tools can best facilitate data assimilation decisions. The SMART workflow involves mining NAM forecasts for interesting weather phenomena, then determining whether AIRS observations are coincident with the detected weather events. The assumption is that assimilating AIRS observations of anomalous weather conditions will improve the forecast. A variety of SWE protocols are used for data access and alert services, and for process chain definition.
The success of SWE in applied science systems will only be achieved through the proliferation of SWE technologies within the science community. By applying SWE protocols to a real world science exercise we are demonstrating the utility and promise of a more timely and efficient satellite data assimilation process.