An automated classification method of daily circulation patterns for surface climate data downscaling based on optimized fuzzy rules

A method for automated circulation patterns (CPs) definition and classification is presented,
based on optimized fuzzy rules. The target of the method is to provide a basis (daily classified
CPs) for downscaling of the most common climate data, i.e. precipitation and temperature.
Therefore the presented classification method is objective providing CPs that explain the
dependency between the large-scale atmospheric circulation and the surface climate. Thus
the downscaling can be done by means of downscaling models with parameters depending
on the CPs. The CPs are defined using 700 hPa geo-potential field anomalies. Fuzzy rules
are described by the position of high- and low-pressure anomalies. The fuzzy rules are
obtained automatically, using an optimization for the performance of the classification. For the
precipitation, the performance of the classification is measured by rainfall frequencies and
rainfall amounts conditioned on the CPs. Thus, the task is to define wet or dry CPs. For the
temperature, the deviation from the average long-term annual cycle is used. In this way, warm
or cold CPs are identified. The presented method produces physically realistic CP definitions.
With the help of these definitions, the observed (historical) pressure fields can be classified
for twelve precipitation stations and four temperature stations inside and around the
Mesochora catchment in Greece.