Related Links
  • MADIS Distribution Services
  • Global Data Center (GDC)
  • HADS

MADIS Research


MADIS was a joint development effort between NOAA's line offices of Oceanic and Atmospheric Research (OAR) and the National Weather Service (NWS) that started in 2001 to improve weather forecasting and numerical weather prediction by filling observational gaps in NOAA's observational infrastructure. This was achieved by partnering with non-NOAA entities to acquire environmental observations from their networks, integrate their observations with NOAA's, quality control the observations, and provide a standardized interface for delivering this information to NOAA operations and the greater meteorological community. MADIS attained operational status at NCO as part of the NWS Office of Dissemination's Integrated Dissemination Program (IDP) in January 2015.

Since 2001 MADIS has been a crucial data delivery system for NWS Weather Forecast Offices. On a daily basis, NWS forecasters utilize MADIS data to refine and improve information to protect life and property. MADIS is an essential capability allowing NOAA to collect and identify high quality observations which form the foundation of the digital analysis and verification processes. MADIS is an enabling tool for obtaining operational access to high temporal frequency atmospheric observations. Operational access to these data is essential in enhancing forecaster situational awareness, thereby extending warning lead-times and for aiding in the establishment of a `Warn-on-Forecast` capability within the NWS. MADIS's centralized data acquisition reduces the cost of operations for NWS WFOs, which would otherwise be required to individually, and often redundantly, collect observations locally. By assimilating private sector as well as government purchased observations, MADIS saves NOAA the costs of new observing systems and the cost of maintenance. MADIS enhances Numerical Weather Prediction (NWP) by improving the quality, quantity, and temporal frequency of observations available to NWS global and regional data assimilation and modeling systems.

MADIS is required by the NWS for meeting:
  • NOAA Strategic Plan;
  • NWS Strategic Plan;
  • NWS Roadmap;
  • NWS Tactical for:
    • AWIPS Data Ingest;
    • Next Generation Data Delivery;
    • NWP Data Assimilation and Verification;
    • Situational Awareness Including Real-Time Mesoscale Analysis (RTMA);
    • WFO and River Forecast Centers (RFC) Forecast and Warnings;
    • NWS applications such as Multi-Radar Multi-Sensor (MRMS) system;
Now that MADIS is operational, research will continue to provide support to maintain the operational integrity of the MADIS system and to enhance MADIS to be able to provide the services required to help build a weather ready nation in the areas of Numerical Weather Prediction, model verification, forecaster situational awareness, and improved weather information to support other NWS operational applications such as the Multi-Radar Multi-Sensor system.

Efforts directed at improved operational integrity include:
  • Continuing to leverage the strengths of systems integrated into MADIS since 2015 such as the Hydrometeorological Automated Data System (HADS).
    • Improved data integrity through real-time updates to stations and observations.
    • Extended data delivery pathways based on the capabilities of all systems transitioned to MADIS.
  • Improving the performance of the MADIS system by tuning MADIS based on operational constraints.
    • Database
    • Web services
    • Disk Input/Output
    • Memory
    • Processor
    • Bandwidth
  • Lowering operational risks.
    • Standardized MADIS data ingest format and the tools required to help providers help CIRA researchers with this effort.
    • Standardized MADIS metadata formats, automated updates, and the tools needed to help providers help CIRA researchers with this effort.
    • Automatic data delivery from source to operations by employing standards mentioned above and building tools to vet, alert, and monitor new sources.
Strategic positioning through collaboration:
  • Acquire and handle data based on strategic plans from other research efforts such as environmental data required for accurately modeling aerosol dispersion and effects on the environment and weather. Historical data is in place as well as real-time data feeds to meet strategic needs from research community.
  • Improved data integrity and quality through improved metadata collection and integration.
  • Improved QC through improved integration and use of other observational assets.
  • Improving MADIS based on technological improvements and advances in science, so pathways for these improvements are in place well before operational needs arise.

Last updated 08 May 2018