This paper considers an evolved—proven, yet forward-leaning, architecture for a significantly more agile and integrated environmental data processing, reprocessing, interaction, archival, dissemination, and utilization. Early decisions and concerns that drove this architecture included the need to flexibly accommodate changing ConOps, add new data types quickly, add new science, scale to increasing data rates and processing power, and lower sustainment costs.
There is today a robust working paradigm established with the Advanced Weather Interactive Processing System (AWIPS)—NOAA/NWS's information integration and fusion capability. This process model extends vertically, and seamlessly, from environmental sensing through the direct delivery of societal benefit. NWS, via AWIPS, is the primary source of weather forecast and warning information in the nation. AWIPS is the tested and proven “the nerve center of operations” at all 122 NWS Weather Forecast Offices (WFOs) and 13 River Forecast Centers (RFCs).
Raytheon has evolved AWIPS into a 2nd generation capability. Just as AWIPS II will support NWS decision-making, it is at the same time a platform funded by Raytheon IRAD and Government investment that can be cost effectively leveraged across all of the GEOSS and IEOS societal benefit areas. The core principles in the AWIPS II evolution were to minimize coupling, increase cohesion, minimize size of code base, maximize simplicity, and incorporate a pull-style data flow. We focused on “ilities” to drive the new AWIPS architecture—our shared architecture framework vision included six elements:
• Create a new, low-cost framework for hosting a full range of environmental services, including thick-client visualization
• Scale down framework to a small laptop and through workstations to clusters of enterprise servers without software change
• “Plug-n-play”— plug-ins can be hot deployable, or system cycled to pick up new plug-ins
• Base the framework on highly reusable design patterns that maximize reuse and have datatype independence and fast adaptability
• Open Source leveraged to maximize reuse
• “Gaming-style” interaction with the data