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  • View in gallery

    ERA-Interim model domain (individual grid points are represented by very small gray pluses), ERA-Interim orography in meters above sea level (shaded color scale), location of sounding stations (circles), and subdomains (black polygons). Filled red circles indicate stations for which individual scatterplots will be shown later.

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    Comparison of sounding observations and ERA-Interim proximity soundings (1 027 559 cases) for (top left)–(bottom right) selected parameters. The gray line denotes a one-to-one ratio. The red line denotes the best fit line. Value in the top-left corner denotes correlation.

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    Box-and-whisker plots representing monthly variability of (left) MIXR and (right) LR85 for sounding observations (blue) and ERA-Interim proximity soundings (red): (top)–(bottom) western Europe, central Europe and Balkans, eastern Europe, and southern Europe. The median is denoted as a horizontal line inside the box, the edges of the box represent the 25th and 75th percentiles, and whiskers represent the 10th and 90th percentiles.

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    (top) Mean annual values of MIXR for ERA-Interim and (bottom) sounding locations for (left) the cold season (October–March) and (right) the warm season (April–September). A delicate smoothing was applied for better clarity.

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    As in Fig. 4, but for LR85.

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    As in Fig. 3, but for ML WMAX and MU WMAX. Values in parentheses at top left (right) denote ML CAPE (MU CAPE). Only cases with nonzero ML WMAX and MU WMAX are used.

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    As in Fig. 4, but for ML CAPE.

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    As in Fig. 3, but for DLS and LLS.

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    As in Fig. 4, but for DLS.

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    As in Fig. 4, but for LLS.

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    As in Fig. 3, but for ML WMAXSHEAR and ML WINIT. Values in parentheses denote ML CIN. Only cases with nonzero ML WINIT and ML WMAXSHEAR are used.

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    As in Fig. 4, but for ML WMAXSHEAR.

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    As in Fig. 4, but for ML CIN.

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    As in Fig. 3, but for ML LCL.

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    As in Fig. 4, but for ML LCL.

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    Scatterplot of DLS and ML WMAX for chosen sounding sites in the WE domain. Lines and points represent 60-day moving average for ERA-Interim (red) and sounding observations (green). Darker points represent the 15th day of month numbered respectively for January, April, July, and October. Black points represent days in which mean 60-day moving average ML CIN < −50 J kg−1.

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    As in Fig. 16, but for the CE&B domain.

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    As in Fig. 16, but for the EE domain.

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    As in Fig. 16, but for the SE domain.

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    As in Fig. 4, but for the mean annual number of days with potential thunderstorm (ML CAPE > 100 J kg−1 and ML CIN > −50 J kg−1).

  • View in gallery

    As in Fig. 4, but for the mean annual number of days with potential severe thunderstorm (ML CAPE > 100 J kg−1, ML CIN > −50 J kg−1 and ML WMAXSHEAR > 300 m2 s−2).

  • View in gallery

    (left) Mean annual number of days with potential thunderstorm (ML CAPE > 100 J kg−1 and ML CIN > −50 J kg−1) per station given a particular domain: WE (green), CE&B (red), EE (blue), and SE (orange). (right) As at (left), but for potential severe thunderstorm (ML CAPE > 100 J kg−1, ML CIN > −50 J kg−1, and ML WMAXSHEAR > 300 m2 s−2). Solid (dashed) line indicate estimates based on sounding observations (ERA-Interim).

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Climatological Aspects of Convective Parameters over Europe: A Comparison of ERA-Interim and Sounding Data

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  • 1 Department of Climatology, Institute of Physical Geography and Environmental Planning, Adam Mickiewicz University, Poznań, and Skywarn Poland, Warsaw, Poland
  • | 2 NOAA/National Severe Storms Laboratory, and School of Meteorology, University of Oklahoma, Norman, Oklahoma
  • | 3 Department of Climatology, Institute of Physical Geography and Environmental Planning, Adam Mickiewicz University, Poznań, Poland
  • | 4 Department of Computer Science, Cracow University of Technology, Krakow, and Skywarn Poland, Warsaw, Poland
  • | 5 Department of Meteorology and Climatology, Faculty of Geographical Sciences, University of Łódź, Łódź, Poland
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Abstract

We compare over 1 million sounding measurements with ERA-Interim reanalysis for the 38-yr period from 1979 to 2016. The large dataset allows us to provide an improved insight into the spatial and temporal distributions of the prerequisites of deep moist convection across Europe. In addition, ERA-Interim is also evaluated. ERA-Interim estimates parameters describing boundary layer moisture and midtropospheric lapse rates well, with correlation coefficients of 0.94. Mixed-layer CAPE is, on average, underestimated by the reanalysis while the most unstable CAPE is overestimated. Vertical shear parameters in the reanalysis are better correlated with observations than CAPE, but are underestimated by approximately 1–2 m s−1. The underestimation decreases as the depth of the shear layer increases. Compared to radiosonde observations, instability in ERA-Interim is overestimated in southern Europe and underestimated over eastern Europe. High values of instability are observed from May to September, out of phase with the climatological pattern of wind shear, which peaks in winter. From September to April, favorable conditions for thunderstorms occur mainly over southern and western Europe with the peak location and higher frequency shifting to central and eastern Europe from May to August. For southern Europe, the annual cycle peaks in September with high values of inhibition suppressing thunderstorm activity in July and August. The area with the highest annual number of days with environmental conditions favorable for thunderstorms extends from Italy and Austria eastward through the Carpathians and Balkans.

Denotes content that is immediately available upon publication as open access.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Mateusz Taszarek, mateusz.taszarek@amu.edu.pl.

Abstract

We compare over 1 million sounding measurements with ERA-Interim reanalysis for the 38-yr period from 1979 to 2016. The large dataset allows us to provide an improved insight into the spatial and temporal distributions of the prerequisites of deep moist convection across Europe. In addition, ERA-Interim is also evaluated. ERA-Interim estimates parameters describing boundary layer moisture and midtropospheric lapse rates well, with correlation coefficients of 0.94. Mixed-layer CAPE is, on average, underestimated by the reanalysis while the most unstable CAPE is overestimated. Vertical shear parameters in the reanalysis are better correlated with observations than CAPE, but are underestimated by approximately 1–2 m s−1. The underestimation decreases as the depth of the shear layer increases. Compared to radiosonde observations, instability in ERA-Interim is overestimated in southern Europe and underestimated over eastern Europe. High values of instability are observed from May to September, out of phase with the climatological pattern of wind shear, which peaks in winter. From September to April, favorable conditions for thunderstorms occur mainly over southern and western Europe with the peak location and higher frequency shifting to central and eastern Europe from May to August. For southern Europe, the annual cycle peaks in September with high values of inhibition suppressing thunderstorm activity in July and August. The area with the highest annual number of days with environmental conditions favorable for thunderstorms extends from Italy and Austria eastward through the Carpathians and Balkans.

Denotes content that is immediately available upon publication as open access.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Mateusz Taszarek, mateusz.taszarek@amu.edu.pl.

1. Introduction

Around 9000 severe thunderstorm incidents causing 100 fatalities and 500 injuries are reported each year in Europe according to the European Severe Weather Database (ESWD; Dotzek et al. 2009). Unfortunately, geographical bias toward densely populated areas and errors in the databases make it difficult to determine the true coverage of severe thunderstorms (Groenemeijer et al. 2017). Lightning is detected more objectively (Betz et al. 2009; Pohjola and Mäkelä 2013; Anderson and Klugmann 2014; Poelman et al. 2016), but the networks still suffer from spatial inhomogeneities and short record lengths. To overcome these issues, many researchers have addressed the idea of using covariates in the form of convective parameters (thunderstorm ingredients; Doswell et al. 1996) that reflect environmental conditions favorable for thunderstorms. The relationship between these parameters and thunderstorm occurrence allows them to be applied as a proxy for the probability of different weather events occurring (Allen and Karoly 2014). Understanding the climatological aspects of convective parameters provides an estimate of where and when the corresponding events are the most likely to occur, subject to the quality of the relationship between the covariates and the events.

Numerous studies using proximity soundings (radiosonde or numerical model profiles taken close to particular phenomena in space and time) assessed which parameters can be regarded as valuable thunderstorm predictors (covariates). The occurrence of thunderstorms is strongly dependent on convective available potential energy (CAPE), with values of roughly 100–200 J kg−1 discriminating between lightning and nonlightning events (Craven and Brooks 2004; Kaltenböck et al. 2009; Kolendowicz et al. 2017; Taszarek et al. 2017). Basing on European records, Westermayer et al. (2017) suggested that the probability for lightning is highest when CAPE exceeds 400 J kg−1 and convective inhibition (CIN) is no lower than −50 J kg−1. They highlighted that relative humidity in the low to midtroposphere has a major influence on storm occurrence with low relative humidity strongly suppressing thunderstorms.

The presence of sufficient instability ensures that convective updrafts, once initiated, can become strong. Weisman and Klemp (1982) noted that vertical wind shear promotes organization and longevity of these updrafts. The establishment of dynamically forced vertical pressure gradients can significantly enhance the strength of the updraft, especially in supercell thunderstorms. Using soundings from a global reanalysis dataset, Brooks et al. (2003) found that environments with high instability along with high vertical wind shear were associated with thunderstorms producing severe weather. The notion that the probability of convective hazards is a function of thermodynamic instability and deep layer shear (DLS) was also confirmed in many other studies (Rasmussen and Blanchard 1998; Craven and Brooks 2004; Brooks 2009, 2013; Allen et al. 2011; Allen and Karoly 2014; Púčik et al. 2015; Taszarek et al. 2017). The height of the lifting condensation level (LCL), along with low-level shear (LLS) and storm-relative helicity (SRH; Davies and Johns 1993), were found to be good predictors for tornadic supercells and for estimating tornado intensity (e.g., Thompson et al. 2003; Groenemeijer and van Delden 2007; Grams et al. 2012; Taszarek and Kolendowicz 2013). In addition, Grünwald and Brooks (2011) indicated that tornadoes in Europe usually form with lower LCL and CAPE than those occurring in the United States.

The climatological distribution of convective parameters may not be useful in making a forecast on a particular day, but it can help in understanding the baseline probability of a particular event at different locations (Brooks et al. 2007). The annual cycles of convective parameters at a variety of locations in Europe indicate that conditions supportive of severe thunderstorms are the most frequent during spring and summer (Brooks et al. 2007). The highest mean CAPE is observed in the southern and southeastern parts of Europe during summer, and the western part during winter (Riemann-Campe et al. 2009). Brooks et al. (2003) used data from a global reanalysis dataset to develop covariate discriminants that identify an increased probability of an environment to produce severe thunderstorms and tornadoes. They estimated the frequency of such environments for the whole world based on relationships developed from data in the United States. For Europe, they concluded that the southern part has the greatest frequency of significant severe thunderstorm environments. The preferred areas for severe thunderstorms in the ERA-40 reanalysis (Uppala et al. 2005) were also found along a zonal belt over southern and central Europe by Romero et al. (2007).

In recent years, a growing number of studies have analyzed long-term trends in convective parameters to define how changing climate affects the occurrence of severe thunderstorms. An increase of low-level moisture over most parts of Europe from 1979 to 2012 was seen in the reanalysis (Pistotnik et al. 2016). Steep vertical temperature gradients became less frequent in northwestern Europe and more frequent in southeastern Europe where CAPE increased as well. Future changes of severe thunderstorm environments in Europe based on climate model simulations have been addressed by Marsh et al. (2009). A small increase in favorable severe environments was observed for most locations resulting from an increase in the joint occurrence of high CAPE and high DLS situations. Púčik et al. (2017) found that the simultaneous occurrence of latent instability, strong DLS, and model precipitation is simulated to increase by up to 100% in representative concentration pathway 8.5 (RCP8.5)1 and by 30%–50% in the RCP4.5 scenario by the end of the century in central and eastern Europe. An increase in the number of severe thunderstorm environments in the RCP8.5 scenario was also noted by Viceto et al. (2017) for the Iberian Peninsula.

The majority of the climatologies discussed above were based on reanalyses. Reanalysis products are generated by the assimilation of observational data (e.g., surface observations, satellite information, and radiosondes) over a given period of time. The goal of reanalysis is to provide a climatological snapshot of conditions that are as close to reality as possible (Thorne and Vose 2010). Data assimilation entails the incorporation of observations into a background field to produce an initial condition (Mooney et al. 2011). Given the inherent uncertainties in the forecast model, input data, and data assimilation, it is important to assess the quality of these reanalyses (Hodges et al. 2011). This was done for basic atmospheric parameters (e.g., temperature and dewpoint) in the numerous studies (e.g., Tian et al. 2010; Mooney et al. 2011; Szczypta et al. 2011; Bao and Zhang 2013; de Leeuw et al. 2015; Guo et al. 2016; Duruisseau et al. 2017), but only little attention has been paid to convective parameters (e.g., thermodynamic instability). Although Gensini et al. (2014) performed such an analysis for United States and Allen and Karoly (2014) for Australia, no efforts were made for Europe. Because convective parameters include information from basic quantities, such as temperature, moisture, and winds, at a variety of levels, they are a good test of the quality of a reanalysis.

In this paper, we compare over 1 million sounding measurements with the ERA-Interim reanalysis for the 38-yr period from 1979 to 2016. The main focus is on ingredients (predictors) supporting development of thunderstorms and influencing their intensity. We look at the spatial and temporal distribution of these parameters for both sounding and reanalysis data in order to gain a better understanding of the evolution of the atmosphere supporting thunderstorms. In addition, ERA-Interim is evaluated.

2. Dataset and methodology

a. Radiosonde data

The radiosonde measurements were derived from the sounding database of the University of Wyoming. For the years 1979–2016 all available measurements for 1200 UTC (1100 LT in western Europe and 1500 LT in far eastern Europe) were downloaded from 132 stations over Europe (~1.3 million soundings). We focused only on 1200 UTC because this time best represents the typical preconvective storm environment, as indicated by peak cloud-to-ground lightning activity between 1400 and 1600 UTC (Antonescu and Burcea 2010; Wapler 2013; Virts et al. 2013; Mäkelä et al. 2014; Taszarek et al. 2015; Poelman et al. 2016). The highest frequency of severe thunderstorm phenomena such as tornadoes, large hail, or severe wind gusts is also estimated to peak in Europe between 1500 and 1800 UTC (Groenemeijer and Kühne 2014; Taszarek and Brooks 2015; Punge and Kunz 2016, Celiński-Mysław and Palarz 2017).

After quality control (discussed later), the final number of stations dropped to 119 with over 1 million soundings evenly distributed throughout the year (Table 1). For statistical purposes, we clustered the stations into four subdomains representing different regions of Europe, each with distinctive climate conditions. Those regions are western Europe (WE), which is under the influence of the Atlantic Ocean and humid oceanic climate, central Europe and the Balkans (CE&B), located in the transitional zone between an oceanic and continental climate, eastern Europe (EE), representing a continental climate, and southern Europe (SE), consisting of stations under the influence of the Mediterranean Sea (Fig. 1).

Table 1.

Number of 1200 UTC soundings used in the study given months and domains for years 1979–2016.

Table 1.
Fig. 1.
Fig. 1.

ERA-Interim model domain (individual grid points are represented by very small gray pluses), ERA-Interim orography in meters above sea level (shaded color scale), location of sounding stations (circles), and subdomains (black polygons). Filled red circles indicate stations for which individual scatterplots will be shown later.

Citation: Journal of Climate 31, 11; 10.1175/JCLI-D-17-0596.1

b. Reanalysis data

The European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim; Dee et al. 2011) was used for the period 1979–2016. The dataset used has 0.75° horizontal grid spacing with 29 pressure levels from 1000 to 50 hPa. An additional 30th level is the surface with temperature and dewpoint for 2 m above ground level (AGL) and U and V wind vectors for 10 m AGL (Table 2). The research domain extends from 31.5° to 75°N (59 grid points) and from 27°W to 53.25°E (108 grid points), giving 6372 grid points (Fig. 1). For each sounding site, we picked the nearest (by geographical distance) grid point from the reanalysis, and produced a pseudo-sounding from the reanalysis to compare with the observational sounding. A similar method was used previously by Thompson et al. (2003), Allen et al. (2011), and Gensini et al. (2014).

Table 2.

Description of model grid for ERA-Interim.

Table 2.

c. Convective parameters

Convective parameters for both sounding and reanalysis data were processed using a sounding analysis program developed by Szuster (2016). For each profile, pressure, height, temperature, dewpoint, and U and V winds were interpolated in the vertical and the parameters that have shown value in predicting thunderstorms and their intensities were calculated (Table 3). For mixed-layer (ML) calculations, we used the layer from 0 to 500 m AGL. In addition, a virtual temperature correction was included (Doswell and Rasmussen 1994). To compute SRH and estimate storm motion, we applied the ID method (Bunkers et al. 2000).

Table 3.

Parameters used in the study.

Table 3.

We defined cold (October–March) and warm (April–September) seasons. This division was based on lightning activity over Europe, which is the highest from April to September (Anderson and Klugmann 2014). With our interest in convection, this seemed an appropriate choice. For the annual cycle of parameters associated with CAPE or CIN we included only nonzero CAPE cases. This approach was also applied in the previous studies (Rasmussen and Blanchard 1998; Brooks et al. 2003; Craven and Brooks 2004; Brooks 2009; Taszarek et al. 2017) and was intended to focus mainly on unstable environments.

d. Quality control assumptions

Given the initial database of 1.3 million soundings, a considerable number contained errors of various kinds, including incomplete profiles and invalid or unrealistic values. To deal with these, as a first step, we excluded levels with incomplete measurements (e.g., temperature was available but dewpoints were missing). Then, all soundings with measurements not reaching 6 km AGL or containing fewer than 10 pressure levels were discarded. Next, temperature and wind vertical gradients were checked, and we discarded soundings with obvious errors or unrealistic values, which we took as midtropospheric lapse rates > 9 K km−1, low-tropospheric lapse rates > 11 K km−1, DLS > 70 m s−1, MLS > 45 m s−1, LLS > 35 m s−1, MIXR > 20 g kg−1, PW > 65 mm, MU CAPE > 8000 J kg−1, and ML CAPE > 6000 J kg−1. Since one of our motivations was to investigate the annual cycle, we excluded all stations with fewer than 100 soundings measurements available in any month. We also excluded stations if the difference between the month with the fewest and most soundings was more than 3% of available data for the station to avoid biases toward particular months when calculating climatological statistics. The number of soundings per station that passed quality control assumptions is listed in the appendix.

e. Limitations of the study

An important limitation to this study is the changing quantity and quality of radiosonde measurements over time (especially humidity), which impact derived variables such as CAPE and CIN (Guichard et al. 2000; Cady-Pereira et al. 2008), as well as the availability of measurements, which in our database ranges from 4000 to 10 000 soundings per site. Although we are aware of these issues, we believe that quality control assumptions and the large sample size should limit their negative influence when focusing on large-scale climatological patterns.

Another limitation is related to the pseudosoundings generated from ERA-Interim. Given the resolution of the reanalysis (Fig. 1, Table 2), soundings located over sharp boundaries such as mountains or coastal areas may be not well represented in the model data. In some cases, it is possible that the methodology of choosing the closest grid point to the station location may not be representative of the observations. Variations in the depiction of the boundary layer (Brooks et al. 2003; Thompson et al. 2003; Allen et al. 2011) and limited vertical resolution of the reanalysis (30 levels) compared to an average of 47 levels per observed sounding may also result in differences between model data and observations. On the other hand, the reanalysis can be more representative than station data with fewer vertical levels on average, as is often seen in EE and SE stations (see the appendix).

It is also possible that the model formulation may introduce problems. As an example, given the horizontal resolution of ERA-Interim, a convective parameterization is necessary. This may lead to errors in the vertical profile of temperature and moisture as a result of the approximations associated with the parameterization, and subsequent errors in CAPE. Similarly, quantities associated with low levels may be impacted by the presence of boundary layer parameterization schemes. It is beyond the scope of this paper to diagnose the source of the errors, but it provides a caveat on the results.

3. Results

a. Correlation and mean errors

We will use the sounding observations as a baseline for “truth” to compare the reanalysis data (although we are aware that observations also contain errors). The mean errors from ERA-Interim are within 0.15 g kg−1 for MIXR, 0.11 K km−1 for LR85, and 0.47 mm for PW, all close to the ranges for radiosonde accuracy (Table 4). These parameters are also quite well correlated with correlation coefficients greater than 0.9 (Fig. 2). Larger differences are observed for CAPE parameters. Among different parcel types, ERA-Interim overestimates MU CAPE on average by 24.6%, and SB CAPE by 33.1% (Table 4). It is notable that the highest overestimation is observed over SE (46.9% and 53.5%, respectively), while an underestimation (−14.1% and −2.1%, respectively) is seen in EE. Conversely, ML CAPE is underestimated in ERA-Interim on average by 13.6%. The highest ratio of underestimation is observed again over EE and exceeds 36%. Scatterplots show a large spread, most likely related to the high spatial variability of CAPE. CAPE can change significantly from a few hundred to even few thousand joules per kilogram over the distance of several kilometers, so that a “point to point” sounding and reanalysis comparison can show large errors as a result of the gradients. Although the reanalysis does not provide exactly the same values in the same locations as the observations, the correlation coefficients for MU and SB CAPE are approximately 0.7 (Fig. 2). A lower correlation (0.55) is observed with ML CAPE, which indicates that the boundary may not be depicted well in ERA-Interim, especially over EE. The boundary layer parameterization is a likely candidate for such issues.

Table 4.

Mean errors and mean percentage errors from ERA-Interim for all analyzed parameters given various domains.

Table 4.
Fig. 2.
Fig. 2.

Comparison of sounding observations and ERA-Interim proximity soundings (1 027 559 cases) for (top left)–(bottom right) selected parameters. The gray line denotes a one-to-one ratio. The red line denotes the best fit line. Value in the top-left corner denotes correlation.

Citation: Journal of Climate 31, 11; 10.1175/JCLI-D-17-0596.1

A comparison of shear values indicates that ERA-Interim underestimates them by approximately 1–2 m s−1 (Table 4). The highest ratio of underestimation is with LLS (27%), decreasing with increasing depth (15% for MLS and 7% for DLS). Scatterplots support the improved estimation with height, with the correlation coefficient for LLS, MLS, and DLS being 0.74, 0.83, and 0.92 (Fig. 2). SE shows the largest underestimation of LLS (33.7%) while it has the smallest underestimation of DLS (5.5%). Again, the boundary layer is a likely culprit. SRH is, on average, underestimated in ERA-Interim by 12% and no significant regional differences are seen. The SRH in ERA-Interim is in better agreement with observations when values are higher than 200–250 m2 s−2 (Fig. 2).

ML WMAXSHEAR (a composite parameter of instability and shear; Table 3), which is regarded as a good severe thunderstorm predictor, is also underestimated in the reanalysis data on average by 15%. This result is heavily dependent on the choice of parcel type. We use ML version for consistency with previous studies (Brooks et al. 2007; Marsh et al. 2009; Brooks 2013; Allen and Karoly 2014; Taszarek et al. 2017). Regional analysis of this parameter indicates that it is underestimated most in EE (32.5%) and CE&B (15.5%) in ERA-Interim. The correlation coefficient of 0.6 shows a slightly better relationship than ML CAPE.

ML CIN was overestimated on average by 25.7%, which means that convective inhibition was weaker in ERA-Interim than in reality. The use of a limited number of pressure levels in the reanalysis influences the computation of CIN and leads to weaker convective inhibition in general. The largest differences were observed over WE (38.6%) with the smallest over SE (19.1%). The LCL computed with the use of ML parcel is underestimated by ERA-Interim on average by 8% (around 90 m). The largest errors (12.3%) are observed over SE while the lowest are over CE&B (6.4%). The correlation coefficient between reanalysis and observation is 0.85 (Fig. 2).

b. Low-level moisture and lapse rates

Boundary layer moisture and temperature lapse rates are often regarded as basic ingredients for thermodynamic instability. MIXR usually peaks during summer under strong diurnal heating and evapotranspiration. The peak is earliest in EE (July) and latest in SE (August) (Fig. 3). During the cold season, the lowest values are observed over EE while the highest are over SE and WE. High values are observed all year in the Mediterranean basin (Fig. 4). Areas with elevated terrain (Spain, Turkey, and the Alps) are characterized by lower MIXR on average. Results regarding the spatial distribution and annual cycle of PW (not shown) are similar.

Fig. 3.
Fig. 3.

Box-and-whisker plots representing monthly variability of (left) MIXR and (right) LR85 for sounding observations (blue) and ERA-Interim proximity soundings (red): (top)–(bottom) western Europe, central Europe and Balkans, eastern Europe, and southern Europe. The median is denoted as a horizontal line inside the box, the edges of the box represent the 25th and 75th percentiles, and whiskers represent the 10th and 90th percentiles.

Citation: Journal of Climate 31, 11; 10.1175/JCLI-D-17-0596.1

Fig. 4.
Fig. 4.

(top) Mean annual values of MIXR for ERA-Interim and (bottom) sounding locations for (left) the cold season (October–March) and (right) the warm season (April–September). A delicate smoothing was applied for better clarity.

Citation: Journal of Climate 31, 11; 10.1175/JCLI-D-17-0596.1

LR85 shows a less pronounced annual cycle with peak values in the spring in all regions except the SE, which has high lapse rates through August (Fig. 3). The highest values are observed over the area surrounding the Mediterranean Sea (Fig. 5). The largest amplitude of the annual cycle of LR85 is observed over EE, which during the cold season has modest lapse rates. Conversely, there are weak annual cycles over WE and SE. The highest values of LR85 are usually observed in the early summer when strong diurnal heating has begun in the boundary layer but cool air masses still remain in the midlevels. This is less pronounced (but still visible) over SE and WE, which are heated all year by the warm waters of the Mediterranean Sea and Atlantic Ocean. The influence of the Gulf Stream during wintertime results in enhanced lapse rates in the corridor from Iceland up to the Norwegian Sea (Fig. 5). Although LR85 derived from ERA-Interim seems to be quite well correlated with observations, a slight overestimation is apparent, especially during the cool season. The exception is EE, which has an almost perfect fit between reanalysis and observational data.

Fig. 5.
Fig. 5.

As in Fig. 4, but for LR85.

Citation: Journal of Climate 31, 11; 10.1175/JCLI-D-17-0596.1

c. Instability

The parcel theory estimate of vertical velocity (WMAX; Table 3) associated with a value of CAPE has been used to describe the potential instability of the atmosphere and has some advantages in presentation in comparison to CAPE (Brooks 2013). Physically, there is no difference between looking at WMAX and CAPE, but the display naturally compresses the effect of extremely large values of CAPE and allows us to see more details in the range of low values. The warm season is characterized by large values of instability with the timing of the peak slightly later in SE than other regions (Fig. 6). The SE has the highest peak of instability and largest amplitude of the annual cycle, with WE having the smallest peak and amplitude of the cycle. ML instability is underestimated in EE, particularly in the cool season. MU instability is overestimated by the reanalysis in the SE, particularly in the autumn, making the annual cycle more asymmetric. It is worth to highlight that except for EE, ML CAPE in ERA-Interim has a good agreement with observations for the summer season, but tends to be lower in the winter when shallower less well-mixed boundary layers are common. A spatial maximum of mean ML CAPE is observed over south-central Mediterranean Sea during the cold season, and central Italy during the warm season (Fig. 7). Observational data indicate also high mean values over northeastern Spain, the Carpathians, and southwestern Russia.

Fig. 6.
Fig. 6.

As in Fig. 3, but for ML WMAX and MU WMAX. Values in parentheses at top left (right) denote ML CAPE (MU CAPE). Only cases with nonzero ML WMAX and MU WMAX are used.

Citation: Journal of Climate 31, 11; 10.1175/JCLI-D-17-0596.1

Fig. 7.
Fig. 7.

As in Fig. 4, but for ML CAPE.

Citation: Journal of Climate 31, 11; 10.1175/JCLI-D-17-0596.1

d. Vertical wind shear

Vertical wind shear is an important parameter that influences storm organization and the potential to produce severe weather. It is directly related to synoptic-scale features such as the jet stream. Jets are strongest during the wintertime, and this is when the highest values of wind shear are observed (Fig. 8). The general climatological pattern is anticorrelated with instability. Both DLS and LLS peak in winter throughout Europe and the annual minimum is during the warm season. Low values extend somewhat later into September in the SE, where the annual cycle is weaker than in other regions. The highest annual amplitude is in EE.

Fig. 8.
Fig. 8.

As in Fig. 3, but for DLS and LLS.

Citation: Journal of Climate 31, 11; 10.1175/JCLI-D-17-0596.1

Shear values are usually underestimated in the reanalysis (Fig. 8), consistent with previous work (Zwiers and Kharin 1998; Brooks et al. 2003; Allen et al. 2011; Duruisseau et al. 2017). A larger underestimation is found in LLS than deeper layers, especially evident during summertime when the 25th percentile of observational data is at the median of the reanalysis. Despite the underestimate, the pattern of the annual cycle is similar in the observations and reanalysis.

In the spatial analysis, the highest values of DLS in ERA-Interim during the cold season are observed over the British Isles, Norway, and the Alps while the minimum falls on the western Mediterranean Sea (Fig. 9). During the warm season, DLS is considerably lower. A regional minimum is observed over the Ukraine. The spatial distribution of LLS indicates higher values over northern Europe and lower values over SE (Fig. 10). It is also notable that shear is higher over the land surface than water. This is probably due to higher friction over land, which contributes to slowing down the surface winds, and thus enhancing overall shear magnitude for a given wind aloft. The highest climatological observed values of LLS are found from northern Germany to the British Isles throughout the year. The reanalysis underestimates both DLS and LLS over much of Europe but there is qualitative agreement in the spatial and temporal patterns with observations.

Fig. 9.
Fig. 9.

As in Fig. 4, but for DLS.

Citation: Journal of Climate 31, 11; 10.1175/JCLI-D-17-0596.1

Fig. 10.
Fig. 10.

As in Fig. 4, but for LLS.

Citation: Journal of Climate 31, 11; 10.1175/JCLI-D-17-0596.1

e. WMAXSHEAR and convective inhibition

WMAXSHEAR as the product of WMAX and DLS can be calculated for any thermodynamic parcel. In comparison to WMAX or DLS alone, the combination leads to smaller differences between cold and warm seasons. This is evident over WE, which has a weak annual cycle in ML WMAXSHEAR (Fig. 11). Severe thunderstorm potential over WE is roughly the same throughout the year, based on this parameter, but during wintertime it is mostly driven by high shear, whereas during summertime it is driven more by instability. Larger annual cycles are observed over EE and CE&B. As with CAPE, peak values are observed in the summer. Over SE, relatively high values are observed throughout the year. The peak in annual cycle is 1–2 months later than over CE&B. Both reanalysis and sounding data indicate that the highest warm season means of ML WMAXSHEAR are found from Italy through the western Mediterranean Sea, with a second corridor from the western Ukraine to Bulgaria (Fig. 12). During the cold season, ML WMAXSHEAR peaks over the central Mediterranean Sea and the waters west of Norway, driven by the high shear, and relatively warm water.

Fig. 11.
Fig. 11.

As in Fig. 3, but for ML WMAXSHEAR and ML WINIT. Values in parentheses denote ML CIN. Only cases with nonzero ML WINIT and ML WMAXSHEAR are used.

Citation: Journal of Climate 31, 11; 10.1175/JCLI-D-17-0596.1

Fig. 12.
Fig. 12.

As in Fig. 4, but for ML WMAXSHEAR.

Citation: Journal of Climate 31, 11; 10.1175/JCLI-D-17-0596.1

Regardless of high ML WMAXSHEAR indicating the potential for severe weather, no storm will form if convective initiation does not take place. For this purpose, we analyze the convective inhibition (ML CIN) and the theoretical parcel vertical velocity to overcome CIN (ML WINIT; Westermayer et al. 2017) for initiation. The annual cycle of ML CIN is similar to ML CAPE, with the most negative values of ML CIN during the summer, although the reanalysis has more positive values than observations (Fig. 11). As suggested by Westermayer et al. (2017), the probability for a thunderstorm sharply decreases when CIN drops below −50 J kg−1. Such values are in general between the 75th and 90th percentile of summertime distributions over WE, SE, and CE&B (Fig. 11). This suggests that the influence of ML CIN on the storm activity in these regions is not very strong. Conversely, large negative values of ML CIN are observed over SE, with summertime values frequently below −50 J kg−1. The most negative values are in the western and central Mediterranean Sea (Fig. 13), indicating that although high ML WMAXSHEAR is frequently seen there (Fig. 12), much of the potential convective activity is suppressed by the CIN. This so-called severe weather efficiency (Brooks 2009) has also been noted by Groenmeijer et al. (2017), who found that the fraction of severe thunderstorms compared to days with potential for a severe thunderstorm (CAPE × DLS > 10 000 m3 s−3) is much higher over continental Europe (mostly between 0.6 and 0.8) than over the central Mediterranean Sea (below 0.2).

Fig. 13.
Fig. 13.

As in Fig. 4, but for ML CIN.

Citation: Journal of Climate 31, 11; 10.1175/JCLI-D-17-0596.1

f. Lifted condensation level

There are differences in ML LCL among various regions. The annual cycle of ML LCL has the lowest amplitudes over WE and the highest over EE (Fig. 14). There are consistently high values in the warm season in all regions, with WE having the lowest ML LCL on average. The annual cycle for SE shows a nearly monotonic increase from January to July, followed by a decrease for the rest of the year. The highest values during the warm season are observed over the regions just north of the Mediterranean Sea and over the eastern Ukraine and southwestern Russia (Fig. 15). The smallest values are over northwestern Europe. The reanalysis represents ML LCL well during the warm season over all regions except SE, but tends to underestimate it during the cold months. As with instability, the largest underestimation of ML LCL is seen over EE. Over SE, ML LCL is underestimated throughout the year.

Fig. 14.
Fig. 14.

As in Fig. 3, but for ML LCL.

Citation: Journal of Climate 31, 11; 10.1175/JCLI-D-17-0596.1

Fig. 15.
Fig. 15.

As in Fig. 4, but for ML LCL.

Citation: Journal of Climate 31, 11; 10.1175/JCLI-D-17-0596.1

g. Annual cycles at individual stations

Apart from the general climatological overview, it is worthwhile to look at individual locations to provide more details on the annual cycle of shear and instability. To do so, we have chosen six stations for each region approximately evenly distributed in space and containing roughly 10 000 observations each (Fig. 1; also, see the appendix). Following Brooks et al. (2007), we computed a 60-day running mean of ML WMAX and DLS for each location.

Stations located on the western coast under the strong influence of Atlantic Ocean (La Coruna, Brest, and Stavanger) show a weak annual cycle of instability but large changes in vertical wind shear (Fig. 16). Stations located farther to the east (Bordeaux, Trappes, and De Bilt) have a similar shear pattern to western stations, but have higher instability during summertime. The reanalysis represents observations relatively well in Bordeaux and De Bilt but tends to underestimate instability over Brest and Stavanger. Shear is most notably underestimated over Brest, Trappes, and La Coruna.

Fig. 16.
Fig. 16.

Scatterplot of DLS and ML WMAX for chosen sounding sites in the WE domain. Lines and points represent 60-day moving average for ERA-Interim (red) and sounding observations (green). Darker points represent the 15th day of month numbered respectively for January, April, July, and October. Black points represent days in which mean 60-day moving average ML CIN < −50 J kg−1.

Citation: Journal of Climate 31, 11; 10.1175/JCLI-D-17-0596.1

The annual cycles in CE&B resemble the shape of a bow with substantial changes of both shear and instability (Fig. 17). Although all the stations show similar patterns, higher values of ML WMAX are observed in the southern part of domain (Udine, Budapest, and Bucharest). Shear decreases and instability increases from January to July, with the pattern reversing in the rest of the year. This is most obvious over the northern part of the domain (Stuttgart, Prague, and Legionowo), whereas in the southern part of the domain shear during the warm season is relatively constant with changes only in the instability. It is also worth noting that considerable values of ML CIN (which contribute to convective suppression) are observed in July over Udine and Bucharest (Fig. 17). Comparing reanalysis and observations, it can be noted that shear is underestimated at all locations. The accuracy of the reanalysis instability varies with the location, which may be a result of a complex European orography not being well represented in the reanalysis.

Fig. 17.
Fig. 17.

As in Fig. 16, but for the CE&B domain.

Citation: Journal of Climate 31, 11; 10.1175/JCLI-D-17-0596.1

A similar pattern is observed in EE. The main difference is that the amplitudes of the annual cycles of both shear and instability are larger (Fig. 18). As in the previous domains, shear in the reanalysis is underestimated in each location, as is instability. Over some locations (Moscow, Kiev, Rostov, and Saratov), the underestimation of ML WMAX in summer is considerable. This is of particular interest because all these stations are located in the plains without complex orography.

Fig. 18.
Fig. 18.

As in Fig. 16, but for the EE domain.

Citation: Journal of Climate 31, 11; 10.1175/JCLI-D-17-0596.1

Unlike the other regions, most of the stations in SE have a weak annual cycle of shear and large changes in instability (Fig. 19). In the summer and autumn, ML CIN is considerable and limits the development of convection. In contrast to other locations, the reanalysis tends to overestimate instability, particularly in the western parts of the domain (Murcia, Ajaccio, and Cagliari). These locations are in coastal regions or surrounded by complex terrain. Athens has a very different annual cycle than the rest of stations in SE. Small changes in instability and large changes in shear are more similar to WE than to Murcia located roughly on the same latitude.

Fig. 19.
Fig. 19.

As in Fig. 16, but for the SE domain.

Citation: Journal of Climate 31, 11; 10.1175/JCLI-D-17-0596.1

h. Frequency of thunderstorm and severe thunderstorm environments

Since our main interest in this study is ingredients for deep moist convection, we estimate the number of days per year with conditions favoring severe thunderstorms by using instability and shear parameters. A similar approach was used in many previous studies (Brooks et al. 2003; Trapp et al. 2007, 2009; Marsh et al. 2007, 2009; Diffenbaugh et al. 2013; Allen et al. 2014; Allen and Karoly 2014; Seeley and Romps 2015; Púčik et al. 2017). We define a potential thunderstorm day if ML CAPE > 100 J kg−1 and ML CIN > −50 J kg−1 (Table 5), based on estimates of thunderstorm events in Europe with their accompanying atmospheric conditions (Kaltenböck et al. 2009; Westermayer et al. 2017; Kolendowicz et al. 2017; Taszarek et al. 2017). The ML CIN term is used to exclude cases with inhibition strong enough to suppress deep moist convection (Sander 2011; Gensini and Ashley 2011; Diffenbaugh et al. 2013). A day with a potential severe thunderstorm adds WMAXSHEAR > 300 m2 s−2 (Taszarek et el. 2017). However, we are aware that no discriminator will perfectly distinguish between severe and nonsevere thunderstorm environments (Doswell and Schultz 2006), and thus any such analysis will always be subject to a probabilistic interpretation (Allen and Karoly 2014).

Table 5.

Definition of potential thunderstorm and potential severe thunderstorm day.

Table 5.

During the cold season, conditions conducive to thunderstorm development are mainly over the Mediterranean area, especially its central part, with a secondary maximum along the western coast of Europe (Fig. 20). During the warm season, convective activity moves inland. In the reanalysis, the most frequent thunderstorm environments occur over Italy, Austria, and the Balkan Peninsula, with a second region of high frequency from Germany to western Russia. In the observations, peak values occur over the Balkan Peninsula and EE.

Fig. 20.
Fig. 20.

As in Fig. 4, but for the mean annual number of days with potential thunderstorm (ML CAPE > 100 J kg−1 and ML CIN > −50 J kg−1).

Citation: Journal of Climate 31, 11; 10.1175/JCLI-D-17-0596.1

Potential severe thunderstorms follow a similar pattern to potential thunderstorm but with one-third to one-half the frequency. In the reanalysis, more than 15 days per warm season occur in the Balkan Peninsula and a corridor from Italy to Germany (Fig. 21). In the observations, the Carpathians and most of EE have the highest frequency. During the cold season, severe thunderstorm environments are observed most often over the central Mediterranean Sea.

Fig. 21.
Fig. 21.

As in Fig. 4, but for the mean annual number of days with potential severe thunderstorm (ML CAPE > 100 J kg−1, ML CIN > −50 J kg−1 and ML WMAXSHEAR > 300 m2 s−2).

Citation: Journal of Climate 31, 11; 10.1175/JCLI-D-17-0596.1

From September to April the most frequent conditions supportive of both thunderstorms and severe thunderstorms are mainly over SE and WE (Fig. 22). From May to August, the main storm activity moves to EE and CE&B. For SE, the peak frequency is in September whereas in July and August thunderstorm activity is suppressed due to considerable CIN. It is again evident that the reanalysis underestimates thunderstorm environments over EE (Fig. 22).

Fig. 22.
Fig. 22.

(left) Mean annual number of days with potential thunderstorm (ML CAPE > 100 J kg−1 and ML CIN > −50 J kg−1) per station given a particular domain: WE (green), CE&B (red), EE (blue), and SE (orange). (right) As at (left), but for potential severe thunderstorm (ML CAPE > 100 J kg−1, ML CIN > −50 J kg−1, and ML WMAXSHEAR > 300 m2 s−2). Solid (dashed) line indicate estimates based on sounding observations (ERA-Interim).

Citation: Journal of Climate 31, 11; 10.1175/JCLI-D-17-0596.1

4. Comparison with previous studies

Caution must be taken when looking at fields involving strong vertical gradients, which the reanalysis has difficulties with (Brooks 2009). Allen and Karoly (2014) suggested that sharp horizontal boundaries such as coastal regions or topographic boundaries may expose poor spatial resolution of the reanalysis. We suspect that some of the errors we found may be associated with strongly diversified orography and the coastline of Europe.

The performance of the reanalysis in reproducing observations has been analyzed in many previous studies. Our work supports the result that kinematic parameters are better represented in contrast to thermodynamic indices in a variety of reanalyses (Lee 2002; Niall and Walsh 2005; Thompson et al. 2003, 2007; Allen and Karoly 2014; Gensini et al. 2014). Zwiers and Kharin (1998) pointed out that low-level winds in the NCEP–NCAR reanalysis are underestimated. A systematic underestimation of the wind speed in ERA-Interim was also indicated by Duruisseau et al. (2017). Both Brooks et al. (2003) and Allen et al. (2011) pointed out that LLS is underestimated in the reanalysis and suggested that this may be due to its poor vertical resolution being not able to capture important gradients.

Our findings concerning the climatological aspects of CAPE and CIN are in agreement with Siedlecki (2009), who analyzed individual sounding stations, and Riemann-Campe et al. (2009), who used ERA-40 reanalysis. The highest means of CAPE over the Mediterranean Sea, the Balkan Peninsula, and southwestern Spain were also noted by Romero et al. (2007) and Pistotnik et al. (2016).

The notion of high (low) instability being associated with low (high) shear was also pointed out by Brooks et al. (2007). In general, our study agree with their results that there is more variability in convective parameters in a cross section from north to south in central Europe compared to one from east to west, mostly because of the increase in moisture from north to south. In addition, in comparison to the United States, the lapse rates and mixing ratios in Europe are lower, reflecting the absence of source areas comparable to the Rocky Mountains (lapse rates) or Gulf of Mexico (low-level moisture). As a result, high values of CAPE are much less likely in Europe than North America, as also seen in Brooks et al. (2003).

The European lightning climatology has a peak lightning density over northern Italy and the Balkan Peninsula (Anderson and Klugmann 2014), which our estimates of days with potential thunderstorms supports. We also find a similar patterns in the annual cycle of thunderstorms. Our results regarding the spatial distribution of severe thunderstorm environments agree with Groenemeijer et al. (2017), indicating a peak frequency in northern Italy. Contrary to the ERA-Interim results presented here, Púčik et al. (2017) found local maxima of severe thunderstorm environments over southern France and northeastern Spain using European Coordinated Regional Downscaling Experiment (EURO-CORDEX) data (Jacob et al. 2014).

5. Summary and concluding remarks

In this study, over 1 million sounding measurements was compared with ERA-Interim reanalysis for the 38-yr period from 1979 to 2016. The large dataset allowed us to provide an improved insight into the spatial and temporal distributions of the prerequisites of deep moist convection across Europe. In addition, ERA-Interim was evaluated. Our findings are consistent with results from previous studies of thunderstorm environments in Europe (Brooks et al. 2003; Brooks et al. 2007; Romero et al. 2007; Siedlecki 2009; Brooks 2009; Pistotnik et al. 2016; Kolendowicz et al. 2017; Púčik et al. 2017; Taszarek et al. 2017; Groenemeijer et al. 2017) but expand on that work because of the large sample size.

ERA-Interim estimates basic parameters describing moisture (MIXR) and lapse rates (LR85) well with correlation coefficients of 0.94. Bulk instability parameters, such as CAPE, are not represented as well. ML CAPE is, on average, underestimated while MU CAPE is overestimated, indicating possible problems with the boundary layer representation. Instability in ERA-Interim is overestimated in southern Europe and underestimated over eastern Europe. Vertical shear parameters are better correlated than CAPE but are underestimated by approximately 1–2 m s−1. The relative amount of underestimation decreases from LLS to deeper shear layers.

Enhanced values of instability are observed from May to September, out of phase with the climatological pattern of wind shear, which peaks in winter. From September to April, favorable conditions for thunderstorms occur mainly over southern and western Europe, with the peak location and higher frequency shifting to central and eastern Europe from May to August. For southern Europe, the annual cycle peaks in September with high values of inhibition suppressing thunderstorm activity in July and August. The area with the highest annual number of days with environmental conditions favorable for thunderstorms in the reanalysis extends from Italy and Austria eastward through the Carpathians and Balkans.

Although most of our results are not qualitatively surprising, we have developed quantitative assessments, leading to more precise descriptions of climatological aspects of ingredients for deep moist convection in Europe. Our results may be used as a background for future studies on thunderstorm environments in Europe, and also can be a valuable source of information for various groups such as weather forecasters and insurance companies. Differences that were found between ERA-Interim and observational data indicate that researchers doing work based solely on the reanalysis data should be cautious when drawing conclusions regarding instability and low-level shear parameters. Issues with these near-ground parameters may be indicative of the challenges in representing the boundary and gradients within it. A similar caveat relates to areas with strong horizontal gradients in topography, which may lead to orographically induced mesoscale circulations that modify the environment around the mountain ranges and modulate latent instability and shear being poorly represented in the reanalysis (Púčik et al. 2017). However, as shown here, many errors in ERA-Interim may not affect the qualitative interpretation of results, but could lead to quantitative differences if reanalysis-based thresholds are applied directly to observed soundings. Severe thunderstorm discriminators (Brooks et al. 2003; Trapp et al. 2009; Allen et al. 2011) or composite parameters (Thompson et al. 2003; Sherburn and Parker 2014) designed with certain models and domains should be applied to other datasets and geographical locations with caution because they may not perform quantitatively in the same way. Researchers using reanalysis datasets to analyze convective variables should consider examining the errors in parameters before applying the results to other models or observations. Future studies will continue to address this problem and more comparisons between model and observational data will be available to assess how well various numerical models represent the observed environment.

Acknowledgments

We appreciate valuable comments of Pieter Groenemeijer, John Allen, and anonymous reviewers who helped to improve this study. This research was supported by the grant of the Polish National Science Centre (Project No. 2014/13/N/ST10/01708). The lead author was supported by the doctoral scholarship of the National Science Centre (Project No. 2015/16/T/ST10/00373) and the Foundation for Polish Science (FNP). The reanalysis and sounding computations were performed in Poznan Supercomputing and Networking Center (Grant 331).

APPENDIX

List of Sounding Stations

The observations used, average number of pressure levels per measurement, and years of operation are shown in Table A1.

Table A1.

List of sounding stations (by WMO ID) including number of 1200 UTC sounding observations used in the study, average number of pressure levels per measurement, and years of operation.

Table A1.

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1

For further details see van Vuuren et al. (2011).

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