MADIS Quality Control

Overview

MADIS observations are quality-controlled at ESRL/GSD and these results are available to the user, since considerable evidence exists that the retention of erroneous data, or the rejection of too many good data, can substantially distort data assimilation grids and verification results. Observations in the ESRL/GSD 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 quality control (QC) checks. MADIS users and their applications can then inspect the flags and decide whether or not to use the observation.

The QC procedures are, for the most part, provided by the NWS Techniques Specification Package (TSP) 88-21-R2 (1994).

Two categories of QC checks, static and dynamic, are described in the TSP for a variety of observation types, including most of the observations available in the different MADIS datasets. The static QC checks are single-station, single-time checks which, as such, are unaware of the previous and current meteorological or hydrologic situation described by other observations and grids. Checks falling into this category include validity, internal consistency, and vertical consistency. Although useful for locating extreme outliers in the observational database, the static checks can have difficulty with statistically reasonable, but invalid data. To address these difficulties, the TSP also describes dynamic checks which refine the QC information by taking advantage of other available hydrometeorological information. Examples of dynamic QC checks include position consistency, temporal consistency, and spatial consistency.

The TSP also describes single-character "data descriptors" for each observation, which are intended to provide an overall opinion of the quality of the observation by combining the information from the various QC checks. Algorithms used to compute the data descriptor are a function of the types of QC checks applied to the observation, and the sophistication of those checks. Level 1 QC checks are considered the least sophisticated, level 3 the most sophisticated checks.

QC Processing and Data Structures

Last updated 28 November 2005.
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