29 April 2009 Brian Balm Program Manager Integrating New Capabilities in Operational Space Weather Systems

2 Objective Identify Considerations and Share Lessons Learned to Better Enable Integration of New Space Wx Capabilities into Operations –New Models and Applications –New Data Sources

3 Space Weather Model Integration Model and Application Considerations Operational Requirement Opportunity for Coupling Data Timeliness Validation Approach Output Resolution/Format Information Assurance Maintenance Concept Overall Cost Target Environment Considerations Operational Implementation Sensor Data Radiation Belt Model Thermospheric Model Magnetospheric Model Heliospheric Model Ionospheric Model Models Potential Sources SCINDA SEEFS C/NOFS RADAR HFCOMM Applications Ingest / Decode Data Access Operational Models Product Generation Product Dissemination End User Applications Operations Comm Processor

4 Steps for Incorporation of Models into Ops Integration of models and applications typically follow a standard sequence of steps. The collection of lessons learned allow for risk identification and adjustments early in the process: Operational Requirement Identified Model Selection by Operational Group Final Validation of the Model (typically by independent agency) Integration and Test into Operational Environment Operational Use of Model / Application Candidate Models and Applications are Assessed

5 Space Weather Model Integration New Data Source Considerations Space Sensor Data In-situ particle UV Optical Occultation X-Ray Magnetometer Ground Sensor Data Optical Radio Ionospheric Sounding Radiation Belt Model Thermospheric Model Magnetospheric Model Heliospheric Model Ionospheric Model Models Operational Requirement Existing Data Type Data Format/Growth Data Source Duration Data Latency Data Reliability Compatibility Overall Cost Bandwidth Required Potential Sources Considerations Operational Implementation Ingest / Decode Data Access Operational Models Product Generatio n Product Dissemination End User Applications Operations Comm Processor

6 Space Model Integration Lessons Learned Consideration: Data Format –Product generators could not use native model output format--required a post- processor for translation to standard output; documentation was distributed for end users –Lesson: Provide model out put in format that is readily used Consideration: Target Environment –After initial testing, processing runs were too slow to meet ops requirements-- required trade studies to identify alternatives; model developer had to modify code to optimize performance –Lesson: Learn about timing requirements and implementation environment to ease transition to operations Consideration: Maintenance Approach –Data rights issues can result in abandoning model. Government use often involves some level of third party access; baseline control helps avoid ambiguity over data rights. –Lesson: Ensure data rights, appropriate levels of support, and a disciplined approach to configuration management are in place

7 Space Model Integration Lessons Learned Consideration: Data Timeliness –Two auroral boundary models were installed and execute side by side; one model is used as the primary forecast model because of data latency in the other model. The other model has a better depiction of the pole-ward boundary, but data latency is not sufficient for forecasting –Lesson: In initial phases of model development, consider real-time data feeds, model performance, and data latency within the concept of operations Consideration: Opportunity for Coupling –Established, documented interfaces facilitate smooth integration. Third party developers require operational interfaces. –Lesson: Deliver structured software and accommodate user requirements in interface documentation

8 Atmospheric Model Integration Lessons Learned Consideration: Operational Requirements –New models provide many new parameters of value to military and other planners, addressing previously unmet requirement –Lesson: Understanding the users’ concept of employment and capability priorities could multiply applications of model output Consideration: Validation of Results –Understanding and community acceptance of new models depends critically upon clear and convincing validation of model results –Lesson: Develop quantifiable and community accepted validation rules at the start –Lesson: Follow up with the model integrator and user to support validation in the operational environment Consideration: Opportunity for Coupling –Coupling of models speeds delivery of capability and potentially improves model performance –Lesson: Design models with likely coupling in mind, to allow easier integration with other model components and physics packages –Lesson: Modeling components that run on the same grid simplify coupling

9 Conclusion—Top Considerations –Validation: Well thought-out model validation greatly facilitates integration, testing, and operational acceptance –Data Formats: Adherence to DoD and industry engineering standards and data formats simplifies integration and reduces cost/schedule –Data Rights: Careful consideration on data rights and licensing to include configuration management is a primary element to ensure that the model can be integrated and supported after turnover to operations –Security: Elimination of security vulnerabilities during design and implementation expands number of domains where model can be used –Cost: Attention to overall costs increases likelihood of continued use by wide range of users