Circulation type classifications for temperature and precipitation stratification in Italy

Abstract

During the last few decades, weather circulation type classifications (CTCs) have been widely used to gain insight into processes at the synoptic scale, but also for studying the relationship between atmospheric circulation and surface climate variability. This study aims to evaluate the best performing CTCs based on COST733 software for the stratification of daily ground-level precipitation and surface air temperature across Italy by means of four statistical metrics. Six classification methods belonging to the four COST733 groups (threshold-based, PCA-based, leader algorithms and optimization algorithms) were investigated on 32 and 26 data time series derived from Italian weather stations for daily mean temperature and daily mean precipitation, respectively. CTCs were computed using gridded mean sea level pressure and geopotential height at 500 hPa derived from the NCEP Reanalysis 2 dataset between 1979 and 2015 and tested on three different numbers of classes (8/9, 11/12 and 18 circulation types). Evaluation metrics showed an evident seasonal variability and high-spatial heterogeneity reflecting the geographical complexity of the Italian territory. The study points out that the best classification, both for temperature and precipitation, is strongly dependent on the classification variable (mean sea level pressure and geopotential height at 500 hPa) showing relevant differences between surface temperature and precipitation. A low number of circulation types (8/9) resulted as the most appropriate grouping for the Italian domain and the Principal Component Transversal and Simulated Annealing were the best performing classification procedures for ground-level precipitation and temperature stratification, respectively.

A weekly spatio‐temporal distribution of drought events over the Po Plain (North Italy) in the last five decades

“…The valley is crossed by a number of tributaries running down from the Alps in the north and from the Apennines in the south. As reported by the Koppen classification, the Po Valley has a humid subtropical climate (Cfa), with the conformation of the plain causing a high level of relative humidity, increasing from south to east (Vallorani et al, 2018). Winters are cool, and the mean temperature recorded during the 1971-2010 reference period ranges between 1 C and 4 C. Temperature inversion is common during the cold period when the height of the Planetary Boundary Layer (PBL) rarely exceeds 450 m. Summers are hot, and temperatures range between 22 C and 25 C (1971( , Bigi et al, 2012.…”

A new framework for probabilistic seasonal forecasts based on circulation type classifications and driven by an ensemble global model

“…Text files of the two circulation type classifications (pct9 and san9) and the centroid values of MSLP and 500HGT (Fig. 2) are available to the following DOI (https://doi.org/10.5281/zenodo.1321007, Vallorani and Messeri, 2018). The pct9 and san9 classifications were selected through a sensitivity analysis detailed in a specific study (https://doi.org/10.1002/joc.5219, Vallorani et al, 2017).…”

“…Then such classifications were compared through the use of proper statistical indexes in order to assess the stratification of the ground-level precipitation and the surface air temperature across Italian peninsula. The PCT (Principal Component Transversal) and the SAN (Simulated Annealing) methods with 9 classes computed on MSLP and 500HGT (Vallorani et al, 2018) were selected as the best performing classifications for precipitation and temperature respectively (see in Fig. 2 the centroid maps for PCT09 and SAN09 classifications as a result of the calibration module, where the centroids are the central value of the class/cluster).…”

Dry season circulation‐type classification applied to precipitation and temperature in the Peruvian Andes

“…A thorough review of CTCs can be found in Ramos et al (2015). Many works have addressed the relation between CTCs and rainfall (Bartoszek and Skiba, 2016;Casado et al, 2010;Jacobeit et al, 2017;Maheras et al, 2018;Raziei et al, 2013), temperature (Maheras et al, 2006;Kyselý and Huth, 2008), or both variables (Jacobeit et al, 2009;Vallorani et al, 2018). Some works analyse the impact of the CTs in different high mountain areas like the Pyrenees (Esteban et al, 2005), the Alps (Masson and Frei, 2014;Panziera et al, 2015) or the Himalayas (Zhang et al, 2018).…”