In the climate change era, it is fundamental to monitor the availability of water resources. One of the possible causes for a change in the water availability is related to variations in the meteorological conditions. To track this change, ground-based observations are one of the commonly used measurements. However, these datasets might include both extreme but realistic values and erroneous information. A necessary but not trivial preliminary process for exploiting the observations is to filter the former while retailing the latter. The Station Observation Outlier fiNder (SOON) is a highly innovative algorithm, that identifies errors in large dataflows. SOON can be used on historical datasets as well as in real-time dataflows. A first prototype has been tested on 8 years (2007-2014) of hourly data recorded by about 10000 stations around Europe, which includes 7 meteorological variables: temperature, dewpoint temperature, pressure, precipitation, wind speed, wind gusts, and cloudiness. The dataset belongs to the Ubimet archive and has been provided within the EDI incubator programme.
SOON: The Station Observation Outlier finder
E. Arnone;
2019-01-01
Abstract
In the climate change era, it is fundamental to monitor the availability of water resources. One of the possible causes for a change in the water availability is related to variations in the meteorological conditions. To track this change, ground-based observations are one of the commonly used measurements. However, these datasets might include both extreme but realistic values and erroneous information. A necessary but not trivial preliminary process for exploiting the observations is to filter the former while retailing the latter. The Station Observation Outlier fiNder (SOON) is a highly innovative algorithm, that identifies errors in large dataflows. SOON can be used on historical datasets as well as in real-time dataflows. A first prototype has been tested on 8 years (2007-2014) of hourly data recorded by about 10000 stations around Europe, which includes 7 meteorological variables: temperature, dewpoint temperature, pressure, precipitation, wind speed, wind gusts, and cloudiness. The dataset belongs to the Ubimet archive and has been provided within the EDI incubator programme.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
