4.2
An Introduction to FSL's Assimilation Model Experiment (FAME)
Patricia A. Miller, NOAA/OAR/FSL, Boulder, CO; and M. F. Barth and A. E. MacDonald
FSL's Assimilation Model Experiment (FAME) is dedicated toward making value-added data from FSL's Central Facility available to universities and government agencies for the 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. FAME developers are working closely with FSL's Facility Division and UCAR's Unidata program to set up an Internet Data Delivery (IDD) system for disseminating near real-time observations using Unidata's Local Data Manager (LDM) software. The Unidata IDD allows users to "subscribe" to certain datasets and data products. IDD servers then deliver the requested data to the users' local servers via the LDM software. Ftp access to real-time and saved data will also be available. Quality Control (QC) of FAME 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 FAME 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 FAME 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 FAME 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 FAME project, details on how to access the FAME datasets and API, and future plans.
Session 4, Assimilation
Tuesday, 16 January 2001, 2:15 PM-5:45 PM
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