Advances in Meteorology

Volume 2015 (2015), Article ID 383614, 9 pages

http://dx.doi.org/10.1155/2015/383614

## Temperature Variability over the Po Valley, Italy, according to Radiosounding Data

Institute of Atmospheric Sciences and Climate (ISAC), Italian National Research Council (CNR), Via Gobetti 101, 40129 Bologna, Italy

Received 18 November 2014; Accepted 27 January 2015

Academic Editor: Sven-Erik Gryning

Copyright © 2015 Boyan Hristozov Petkov. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Temperature variations registered above the southeast part of the Po Valley, Italy, have been examined by applying the principal component analysis of radiosounding profiles recorded during the period from 1987 to 2010. Two datasets, considered to describe intra- and interannual oscillations, respectively, were extracted from the measurements data and the results show that both types of fluctuations can be projected onto four empirical orthogonal functions (EOFs), interpreted as vertical distributions of oscillation amplitudes and four uncorrelated time series that represent the evolution of corresponding EOFs. It was found that intra-annual oscillations composed of periods between 30 and 120 days, together with interannual variations of 1- to 7-year period contribute to the highest extent (about 70%) of the temperature oscillations up to 20 km, changing in both cases the phase in the tropopause region. The other three EOFs indicate prevailing weight of the oscillations in the upper troposphere-low stratosphere region and are characterised by longer periods in both types of fluctuations. The intra-annual variations can be accounted for an interaction between Madden-Julian and Arctic oscillations, while the spectral features of interannual fluctuations could be associated with those of Quasi Biennial, El Niño, and North Atlantic global oscillations.

#### 1. Introduction

The air temperature at different altitude levels is strongly affected by dynamical processes and, as a result, it is a subject of variations pertaining to large frequency diapason [1–3]. Such variations could be associated with the numerous patterns of oscillations observed in various atmospheric and oceanic parameters, like Madden-Julian oscillations (MJO) [4, 5], arctic oscillations (AO) [6, 7], quasi-biennial oscillations (QBO) [8], El Niño Southern oscillations (ENSO) [9–11], and North Atlantic oscillations (NAO) [10, 12]. Despite the fact that the major part of these variations is generated in the equatorial and tropical zones on the one hand and in Arctic on the other, they can expand to the midlatitude regions as well. For instance, it was found that ENSO episodes, which are attributed to changes of the sea surface temperature in the tropical Pacific, strongly affect extratropical atmosphere [13] and such propagation is more effective at middle latitudes in the Northern Hemisphere, where ENSO wave-like anomalies are observed up to 35–40 km altitude [11]. The above listed atmospheric oscillations consist of periods occupying comparatively large time scale starting from intraseasonal MJO that are characterised by periods between 40 and 50 days [4] and AO, presenting a very broad time spectrum composed of weekly to seasonal and longer components [14]. Further, QBO is a mode with variable period averaging approximately 28 months [8] and ENSO and NAO oscillations consisted of periods ranging from 2 to 10 years with peaks at 3 and 7 years for ENSO [10] and 2.5 and 6–10 years for NAO [12]. In addition, the interaction among these oscillations or between one of them with the annual cycle could produce amplitude modulations or variations with intermediate frequencies [8, 14–16].

Temperature is the parameter determining the meteorological conditions in the atmosphere and first of all in the troposphere that directly impacts the human life and activity. On the other hand, the temperature governs the phase transformations of the water in the atmosphere and strongly affects the chemical reactions that take place mostly in the middle and upper layers. Hence, the information about temperature variations over large temporal and spatial scales, yielded from routinely observations, has a primary importance, since the studding of such variations is closely related to the testing and improvement of climatic models [17–20]. The present study is aimed to examine the altitude-temporal features of temperature variations observed over the southeast part of the Po Valley, Italy, by analysing the radiosounding data taken for about 22 years. Such an inquiry tries to characterise the local temperature variability and to link it with the global atmospheric oscillations.

#### 2. Methodology and Data

This section shortly describes the basis of the method adopted to elaborate the radiosounding data in the present analysis and the preliminary processing of the data aiming to create a proper input for the computation procedure.

##### 2.1. Method

The principle component analysis is a powerful tool for examining the spatiotemporal variability of a scalar field presented by a physical variable [21–23]. The method requires the creation of the anomaly data matrix or the matrix containing the deviations from the average trend of the variable . The spatial distributions of the anomalies over a grid of observational points at certain time are presented as rows in , while the time series composed by measurements made at uniquely sampled times at corresponding grid points are given as columns: The anomalies are usually calculated by removing the trend from each column of the matrix , constructed similarly to but containing the measurement data instead of . The further step is to find the covariance matrix ( is the transposed matrix ), which can be presented by solving the eigenvalue problem as where are the eigenvectors of and are the corresponding eigenvalues. Since the eigenvectors are orthogonal to each other and result from field measurements data, they are named empirical orthogonal functions (EOF) and describe the spatial distribution of anomaly amplitude. Each of the values represents the weight of the eigenvector or the contribution of to the spatial distribution of . In practice, only a few of EOFs have significant weight and hence, the decomposition performed through (2) projects the variations presented by vectors in onto a space determined by orthogonal vectors . The cumulative weight determined as the sum of the first values of in the most of the real cases rapidly increases to 100% (see Figure 3) and can be determined as the value of for which becomes reasonably close to 100%.

The projection of the anomaly matrix onto th EOF is a vector named as the principal component (PC) of the corresponding EOF and characterizes the temporal EOF variations.

For the purposes of the present study, is structured so that each profile of temperature anomaly found for height levels provided by a radio sound launched at time is a row of the matrix: Thus, each column is a time series characterising the temperature anomalies at certain altitude level. In the further analysis, the EOFs and PCs have been found by means of the singular value decomposition of the anomaly matrix in (5) that is an alternative method presenting as a product of three matrices: where PCs are the columns of the matrix and EOFs are the columns of the matrix [21, 22], where is the rank of , , and in our case. The diagonal matrix contains the singular values, which are connected with the eigenvalues of the covariance matrix as . In addition, each EOF was multiplied by the associated singular value and each PC was divided by the same value, respectively, that, according to Camp et al. [23], returns the EOFs in dimensional units, Kelvin in our case. The singular value decomposition of the anomaly matrix given by (5) was performed by using the corresponding MATLAB function and the spectral analysis of the PC components was made through the Lomb-Scargle [24, 25] periodogram approach.

##### 2.2. Dataset and Preliminary Elaboration

The present study analyses the data provided by Vaisala radio sounds routinely launched at San Pietro Capofiume station (44°39′N, 11°36′E, 11 m amsl), located in the southeast part of the Po Valley, Italy, twice a day from August 1987 to March 2010. The radio sound gives the vertical distributions of the atmospheric pressure, temperature, relative humidity, and velocity and direction of the wind. Two types of radio sounds were used at the station: Vaisala RS80, until mid-September 2005 and RS92 after that, which are characterised by accuracy of the temperature measurements of ±0.2 and ±0.5 K, respectively [26]. The temperature sensor of RS80 is affected by lag error that was corrected through the procedure proposed by Tomasi et al. [26], while the sensor of RS92 does not need any corrections. Each radiosounding profile was projected by means of linear interpolation onto equally spaced 201-height-level grid starting from the station surface level, having 100 m as the second level and reaching 20 km after that with a step of 100 m. Figure 1 presents the result of this procedure applied on three temperature profiles observed at the station on different days of the examined period. Further, these profiles formed the matrix , in which they were consecutively inserted as rows and the gaps in the columns of , resulted from sporadically missing radio sound launches, were filled by means of linear interpolation to obtain sequences with a step of 12 hours. In order to eliminate the oscillations associated with micro- and mesoscales atmospheric processes considered shorter than 10 days [27], sequences, that consisted of 10-day average values of the temperature as illustrated in Figure 2(a) by the red curve, replaced the columns in . It is seen from Figure 2(a) that the temperature variations present well marked oscillations with a period close to 1 year modulated by lower frequency fluctuations. Since the amplitude of these two types of oscillations is quite different, it seems reasonable to separate them for the next analysis. For that purpose, a running average procedure with 30-day window was applied to obtain the long-period variations given by the blue curve in Figure 2(a). The difference between sequences represented by the red and blue curves is shown in Figure 2(b) and it is considered to represent the intra-annual temperature oscillations, or the oscillations characterized by periods lower than 1 year that form the anomaly matrix subject of the further analysis. Time series, presenting the long-period oscillations, were detrended by removing from each of them the corresponding trend, found through linear approximation, and the result is given by the azure curve in Figure 2(c). The spectral analysis showed that these oscillations were strongly dominated by the annual and semiannual cycles that masked the other long-period fluctuations and hence, it was decided to remove them from the data. Such a filtering was performed by extracting the variations that consisted of periods lower than 1 year, which are presented by the brown curve in Figure 2(c), from the long-period temperature oscillations given by the azure curve in the same figure. At the end, Figure 2(d) exhibits the resulting curve that represents the interannual oscillations composing the second anomaly matrix analysed in the present study.