Advanced Weather Interactive Processing System

Advanced Weather Interactive Processing System
Name	Advanced Weather Interactive Processing System
Developer	National Weather Service / National Oceanic and Atmospheric Administration
Initial release	1990s
Latest release version	AWIPS II
Programming language	Java (programming language), C (programming language), Python (programming language)
Operating system	Red Hat Enterprise Linux, Linux
License	Proprietary

Advanced Weather Interactive Processing System The Advanced Weather Interactive Processing System is a meteorological command, control, analysis, and display environment used by operational forecasting centers. It integrates radar, satellite, surface, and model data into a unified workstation to support forecasters at agencies such as the National Weather Service, National Oceanic and Atmospheric Administration, and regional forecast offices. The platform has been used in coordination with organizations including the Federal Aviation Administration, Department of Defense, and international partners like World Meteorological Organization.

Overview

AWIPS provides interactive visualization, analysis, and dissemination capabilities that connect products from programs such as Geostationary Operational Environmental Satellite and Joint Polar Satellite System missions with model guidance from Global Forecast System and North American Mesoscale models. Forecasters use AWIPS to ingest feeds from networks including Next-Generation Radar (NEXRAD), NEXRAD Doppler Weather Radar, and surface networks like Automated Surface Observing System. The system supports tactical decision-making for events such as Hurricane Katrina, Superstorm Sandy, and 2008 Iowa floods through linkage with agencies including the Federal Emergency Management Agency and United States Geological Survey. AWIPS interoperates with archival databases like National Centers for Environmental Prediction repositories and collaborates with research programs such as National Severe Storms Laboratory.

History and Development

AWIPS originated as a modernization effort in response to needs identified by the National Weather Service and technical studies involving MIT Lincoln Laboratory and contractors such as Raytheon Technologies and IBM. The initial deployment in the 1990s replaced legacy systems developed during eras influenced by projects at NOAA Central Library and partnerships with Environmental Research Laboratories. Major milestones include integration of NEXRAD in the 1990s, incorporation of GOES imagery with the GOES-R Series transition, and a comprehensive rewrite leading to AWIPS II in the 2010s. Program management saw involvement from Office of the Federal Coordinator for Meteorology and acquisitions overseen by General Services Administration contracting frameworks. Development cycles referenced standards from Open Geospatial Consortium and collaborations with academic groups such as Massachusetts Institute of Technology, University of Oklahoma, and Penn State University.

System Architecture and Components

AWIPS architecture comprises server farms, message brokers, data distribution services, client workstations, and visualization modules. Central components include the Data Distribution Service linked to Common Operating Picture implementations, the Interoperability Layer developed with Apache Software Foundation toolkits, and graphical clients built on toolkits used by Unidata and UCAR. Hardware vendors have included Dell Technologies and Hewlett-Packard Enterprise, while middleware leverages Solaris-era concepts ported to Linux distributions such as Red Hat Enterprise Linux. Key subsystems manage ingest from Doppler radar networks, product generation integrated with Storm Prediction Center decision support, and dissemination to stakeholders like the National Hurricane Center and Hydrometeorological Prediction Center.

Data Sources and Processing Techniques

AWIPS ingests data streams from satellites including GOES-16, Himawari series, and polar-orbiting platforms such as Suomi NPP. Radar sources include WSR-88D networks and cooperative networks like TDWR. Surface observations arrive from ASOS and AWOS stations, while upper-air inputs use radiosonde data coordinated with National Centers for Environmental Prediction. Numerical model inputs include outputs from GFS, NAM, ECMWF (European Centre for Medium-Range Weather Forecasts), and ensemble systems such as the Global Ensemble Forecast System. Processing techniques incorporate quality control algorithms developed in collaboration with National Severe Storms Laboratory and statistical post-processing methods informed by NOAA Research. Visualization uses composite reflectivity, mesoanalysis, and probabilistic products supported by image processing libraries influenced by work at NASA Goddard Space Flight Center.

Operational Use and Applications

Operational forecasters deploy AWIPS for short-term severe weather warnings issued by Storm Prediction Center and local Warning Decision Teams, for tropical cyclone tracking coordinated with National Hurricane Center, and for winter operations connected to NWS Weather Forecast Offices. Aviation meteorologists at Federal Aviation Administration centers use AWIPS-derived products for terminal area forecasts and convective advisories. Hydrologic specialists coordinate river forecasts with USACE and USGS using AWIPS-integrated precipitation estimates. Emergency managers at FEMA and state-level emergency operations centers depend on AWIPS outputs during incidents like Hurricane Maria and Midwest derecho. Research-to-operations transitions from NOAA Research and universities feed new algorithms into AWIPS workflows.

Performance, Accuracy, and Limitations

AWIPS performance depends on data latency, network throughput managed by entities like Internet2, and server provisioning provided by contractors including Leidos and Raytheon. Accuracy of derived products is influenced by upstream model biases such as documented differences between GFS and ECMWF, sensor calibration from sources like NEXRAD refurbishment programs, and algorithmic assumptions evaluated by National Centers for Environmental Prediction verification teams. Limitations include handling of extreme data volumes during multi-hazard events, integration lag for new satellite channels introduced by GOES-R Series, and challenges in scaling legacy database schemas for big-data analytics pursued by groups like NOAA Big Data Program.

Future Developments and Upgrades

Planned upgrades focus on cloud migration partnerships with providers referenced in federal initiatives, adoption of microservices architectures influenced by Linux Foundation projects, and enhanced machine learning integration driven by collaborations with Argonne National Laboratory, Los Alamos National Laboratory, and academic centers such as University of Colorado Boulder. Future AWIPS capabilities aim to support higher-resolution numerical guidance from next-generation systems like FV3 and tighter integration with decision support tools used by National Weather Service partners. Continued interoperability efforts involve standards coordination with Open Geospatial Consortium and data sharing with commercial platforms utilized by Airlines for America and international meteorological agencies under World Meteorological Organization frameworks.

Category:Meteorological software