The impact of unique meteorological phenomena detected by the Oklahoma Mesonet and ARS Micronet on automated quality control

To ensure quality data from a meteorological observing network, a well-desi gned quality control system is vital. Automated quality assurance (QA) soft ware developed by the Oklahoma Mesonetwork (Mesonet) provides an efficient means to sift through over 500 000 observations ingested daily from the Mes onet and from a Micronet sponsored by the Agricultural Research Service of the United States Department of Agriculture (USDA). However, some of nature 's most interesting meteorological phenomena produce data that fail many au tomated QA tests. This means perfectly good observations are flagged as err oneous. Cold air pooling, "inversion poking," mesohighs, mesolows, heat bursts, var iations in snowfall and snow cover, and microclimatic effects produced by v ariations in vegetation are meteorological phenomena that pose a problem fo r the Mesonet's automated QA tests. Despite the fact that the QA software h as been engineered for most observations of real meteorological phenomena t o pass the various tests-but is stringent enough to catch malfunctioning se nsors-erroneous flags are often placed on data during extreme events. This manuscript describes how the Mesonet's automated QA tests responded to data captured from microscale meteorological events that, in turn, were fl agged as erroneous by the tests. The Mesonet's operational plan is to catal og these extreme events in a database so QA flags can be changed manually b y expert eyes.