Meteorological Assimilation Data Ingest System (MADIS) Research
NOAA's research, development and operational arms jointly developed MADIS starting in 2001, the office for Oceanic and Atmospheric Research (OAR) and the National Weather Service (NWS) respectively. The goal was to improve weather forecasting and numerical weather prediction by filling gaps in NOAA's observational infrastructure. To fill gaps in NOAA's observational infrastructure MADIS partnered 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' centralized data acquisition and delivery system 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 for these systems. MADIS attained operational status at NCO as part of the NWS Office of Dissemination's Integrated Dissemination Program (IDP) in January 2015.
MADIS is an enabling tool for obtaining operational access to high temporal frequency atmospheric observations. Since 2001 MADIS has been a crucial data delivery system to NWS Weather Forecast Offices and numerical weather prediction models. 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. On a daily basis, NWS forecasters utilize MADIS data to inform and refine their watches and warnings created 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 used in numerical weather prediction. 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 use:
- 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;
- Numerical Weather Prediction models data assimilation and verification.
- 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 Quality Control through improved integration and use of other observational assets.
- Reducing latency from observation to use of observation in operations.
Last updated 19 September 2019