The Meteorological Assimilation Data Ingest System (MADIS), developed by The National Oceanic and Atmospheric Administration’s (NOAA) Office of Atmospheric Research (OAR) and the National Weather Service (NWS), extends NOAA’s observational networks by collecting, integrating, Quality Controlling (QC), and distributing observations from NOAA and non-NOAA organizations. MADIS leverages partnerships with international agencies; federal, state, and local agencies (e.g. state Departments of Transportation); universities; volunteer networks; and the private sector (e.g. airlines, railroads) to integrate observations from their stations with those of NOAA to provide a finer density, higher frequency observational database for use by the greater meteorological community. MADIS is collecting over 60,000 surface stations as well as upper-air datasets including satellite, wind profiler, radiometer, and automated commercial aircraft observations. MADIS adds value by applying QC techniques to the observations to assess data validity and by simplifying access to the data. Access is simplified by providing easy to use interfaces to MADIS distribution services and applying standards to the underlying data sets that comprise MADIS. MADIS can also restrict access to data, based on provider needs, which allows NOAA to use the data for research and operations without impacting the provider’s business model. MADIS started as a research project in 2001 and is currently running operationally at the NWS’ National Centers of Environmental Prediction (NCEP) Central Operations (NCO) as part of NOAA’s Integrated Dissemination Program (IDP) with MADIS data archive being provided by National Environmental Satellite, Data, and Information Service (NESDIS) National Climate Data Center (NCDC). Now that MADIS is fully operational within the NWS, MADIS efforts focus on being a quick and efficient conduit for adding and improving NOAA’s observational infrastructure. This poster presentation will provide information on capabilities that were added in FY2016 and scheduled improvements to operational MADIS for FY2017 and beyond.
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