Lessons Learned On Transitioning MADIS (meteorological Assimilation Data Ingest System) To Operations and Continuing To Provide A Pathway For Research To Operations Through MADIS

Abstract

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 partners 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. The end result of this partnering is a finer density, higher frequency global observational database. MADIS currently provides access to observations for over 60,000 surface stations worldwide. MADIS adds value to all observations it collects by reducing the many different formats of the data into a common format with uniform observational units and time steps and applying QC techniques to the observations to assess data validity. MADIS provides graphical displays, subscription services, and developer application interfaces for users requesting access to MADIS data and metadata. MADIS can also restrict access to data based on provider requirements. MADIS started as a research project in 2001 funded by both OAR and the NWS. January 21, 2015 MADIS achieved operational status at the NWS’ National Centers for Environmental Prediction (NCEP) Central Operations (NCO) as part of the Integrated Dissemination Program (IDP) with the MADIS archive housed at the National Environmental Satellite, Data, and Information Service (NESDIS) National Climate Data Center (NCDC). This presentation will focus on lessons learned attaining operational status and how MADIS is now a conduit or pathway for other observational systems to quickly transition to operations through MADIS.