P1.5
MADIS: The Meteorological Assimilation Data Ingest System
Michael F. Barth, NOAA/ERL/FSL, Boulder, CO; and P. A. Miller and A. E. MacDonald
The Meteorological Assimilation Data Ingest System (MADIS) is dedicated toward making value-added data from the Central Facility of the NOAA Forecast Systems Laboratory (FSL) available to universities and government agencies for general use in scientific applications, and for the particular purpose of improving numerical weather prediction through support of programs designed for the development and verification of data assimilation systems.
Critical to the success of these programs is access to reliable and easy-to-use real-time and archived datasets, which are now available from FSL via either ftp or using Unidata's Local Data Manager (LDM) software. Users can subscribe to the entire MADIS dataset, or ask for only particular datasets of interest. Quality Control (QC) of MADIS observations is necessary, since considerable evidence exists that the retention of erroneous data, or the rejection of too many good data, can substantially distort forecast grids and verification results. Observations in the MADIS database are stored with a series of flags indicating the quality of the observation from a variety of perspectives (e.g. temporal consistency and spatial consistency), or more precisely, a series of flags indicating the results of various QC checks. Users of the database can then inspect the flags and decide whether or not to ingest the observation.
Also available to MADIS users is an Application Program Interface (API) that allows easy access to the data and quality control information. The API allows each user to specify station and observation types, as well as QC choices, and domain and time boundaries. With the API, the underlying format of the datasets remains completely invisible to the user, and many details of data ingest are automatically performed. Users of the MADIS API, for example, can choose to have their wind data automatically rotated to a specified grid projection, and/or choose to have mandatory and significant levels from radiosonde data interleaved, sorted by descending pressure, and corrected for hydrostatic consistency.
This paper will cover the current status of the MADIS project, including details on how to access the MADIS datasets, API, and documentation, and will also cover future plans.
Poster Session 1, Effective Assimilation of the Vast Observational Datasets Becoming Available
Monday, 14 January 2002, 3:30 PM-5:30 PM
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