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A spatial consistency test for surface observations from mesoscale meteorological networks
Author(s) -
Lussana Cristian,
Uboldi Francesco,
Salvati Marta R.
Publication year - 2010
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.622
Subject(s) - consistency (knowledge bases) , mesoscale meteorology , confusion , computer science , bayesian probability , exploit , scale (ratio) , meteorology , data mining , statistics , mathematics , geography , artificial intelligence , cartography , psychology , computer security , psychoanalysis
A spatial consistency test (SCT) is applied to temperature observations of a high‐resolution meteorological network composed of automatic surface weather stations. The SCT's purpose is twofold: preventing gross errors (GEs) from entering automatic numerical elaborations and returning a spatial consistency flag to an external quality‐control system. The algorithm is based on Bayesian concepts and exploits the existing objective analysis scheme by comparing each observed value with the corresponding cross‐validation (CV) analysis value. Local data density is automatically taken into account to allow a less restrictive test for isolated stations that provide precious information on poorly observed areas. Thresholds and parameters are estimated statistically for large datasets, thus eliminating any subjective and ad hoc tuning. Misjudgment rates are estimated for both missed and false rejections. Special attention is devoted to the problem of large representativity errors which, being dependent on a prescribed scale, do require multiple cross‐checks to avoid confusion with proper GEs. Copyright © 2010 Royal Meteorological Society