Precipitation is one of the most important variables involved in the water balance, and its variability determines the water resources of the planet. Following the definitions of regional models, precipitation can be separated into two categories: large-scale and convective precipitation. In general, convective precipitation is frequently associated with precipitation extreme events due to high intensity over a short duration. However, the simulation of these events is a well-known problem in the modelling community due to restrictions in the resolution, poor representation of complex topography, insufficient assimilated observations, forecast errors, or deficiencies in the microphysics schemes in the numerical models. In order to avoid these problems, as previously done in the literature , this paper focuses on the evaluation of the atmospheric conditions favourable for the development of convective precipitation rather than the validation of the simulation of extreme events.

The evaluation of the atmospheric conditions is typically based on the calculation of some instability indices such as the lifted index (LI) , the K index , the total totals index (TT) , or the Showalter index (S) . These conditions can be also evaluated by convective available potential energy (CAPE) or convective inhibition (CIN) . All of these variables are commonly used in the literature for these kind of studies e.g.. CAPE and CIN are based on the adiabatic lifting of a parcel, while most of the others are based on differences in the values of several variables at different pressure levels. The deep convection is caused by three ingredients: high levels of moisture in the planetary boundary layer (PBL), potential instability, and forced lifting . CAPE and CIN provide information about the first two ingredients , and both can give details about the genesis and intensity of the atmospheric convection . However, previous studies suggest that CAPE should not be used alone but should be combined with other indices. The final ingredient, which is the forced lifting, is usually caused by the orography , the convergence of horizontal moisture fluxes , or the breezes in coastal regions . Thus, the high spatial and temporal resolution is important for these kind of studies focusing on the atmospheric convection, and that is why regional simulations are needed .

The probability of occurrence of convective precipitation is not the same through the day, and previous studies support the maximum convection taking place in the afternoon and evening . According to , the preferred time in most of western Europe is between 18:00 and 24:00 UTC, with the exception of the island of Corsica, where the sea breeze usually causes convection between 06:00–12:00 UTC. In open-sea areas, the lightning activity peaks in the morning , associated with thunderstorms caused by land breezes at night . A regional study that focused over the United Kingdom suggests that the reduction of CAPE overnight is over 500 J/kg.

On the global scale, CAPE follows the spatial pattern of surface specific humidity and air temperature, which means that it increases from pole to Equator . The minimums are obtained in arid regions and over areas with cold water upwelling. Focusing on Europe, convective storms develop for lower values than the United States , and several studies have tried to determine the most active regions. Amongst them, found that the region with highest instability is located along a zonal belt over south-central Europe, particularly over the western Mediterranean Sea and the surrounding areas. This agrees with , who found that the favourable environment for thunderstorms is developed in southern Europe and that the highest number of days in such a regime is located over the Iberian Peninsula (hereafter, IP), south of the Alps, and the northern Balkans. However, found that southwestern France and the Basque Country seem to be a preferred region for the formation of severe storms that drift towards the northeast. More recent studies based on lightning data and regional climate models using higher resolution highlighted the same areas with favourable environments for thunderstorms in Europe, which are located in particular over northern Italy (Po Valley), east of the Adriatic Sea (Albania, Bosnia, and Serbia), and in the northeastern IP and southern France (near the Gulf of Lyon).

Over the IP, the seasonality of precipitation is determined by different sources of moisture due to seasonal variations of the global atmospheric circulation and contrasting climatic regions (influenced by the strong topography). Northern and western IP are mainly affected by stratiform precipitation during winter, while eastern and southern IP receive great amounts of precipitation during autumn due to convective activity . Maximum precipitation amounts over central IP are measured in early spring .

Previous studies over the IP suggest that CAPE shows a high spatio-temporal variability: the values in winter and spring over land are small due to the reduced surface temperature, and the differences between Atlantic and Mediterranean regions are remarkable during summer. According to , the mean values range from below 50 J/kg in the north to between 100 and 200 J/kg at the Mediterranean coast (some events can even reach 1000 J/kg). Similar to , also stated that CAPE is low during autumn in the Atlantic and continental regions but high in the areas surrounding the Mediterranean Sea. This seasonality was also observed for other indices such as the K index or TT, which show maximum values during summer . Observations proved that annual precipitation over eastern stations is mostly accumulated during autumn, as a result of the cumulative warming of the Mediterranean Sea due to summer insolation and later entry of very hot and humid air into the IP, while cold air is present at higher levels . Additionally, September and October are the months with the highest frequency of waterspouts and tornadoes near the Balearic Islands . Over the northwestern IP, the mean value of CAPE when hailstorms occur is 360 J/kg, while for thunderstorms it is only 259 J/kg . The dispersion of these values is really high (almost 350 J/kg over the whole sample), which is similar to that found in previous studies . The values are similar to those observed in other regions of Europe but lower than those values obtained in studies based on synoptic or lightning data for severe hailstorms (around 500 J/kg) . Due to global warming, the conditions necessary for the development of extreme precipitation events will be enhanced . The frequency and intensity of climate extremes will be magnified , projecting larger values of CAPE at the Mediterranean coast during summer and autumn .

The main objective of this paper is to evaluate the performance of two simulations created using the Weather and Research Forecasting (WRF) model (including or not including the extra 3DVAR data assimilation step) in reproducing the atmospheric conditions that can cause convective precipitation over the IP if the third ingredient (e.g. lifting) is fulfilled. We are not restricting our analysis only to convective situations, and the entire period from 2010–2014 will be considered. For the evaluation, the comparison of pseudo-soundings extracted from the model against real observations will be carried out. Additionally, the seasonal patterns of different variables commonly used to represent atmospheric instability will be studied. Moreover, this study will also help us to accurately determine the regions of the IP more prone to developing unstable thermodynamic conditions. If the condition of the forced lifting is also fulfilled, convective precipitation may occur in those areas. As shown before, atmospheric instability is a highly demanding feature in model simulations and a topic with great importance nowadays due to the large damage that extreme convective events can cause to society and of which frequency will be increased in the future. Thus, it is of great interest to diagnose the ability of particular configurations of a model to properly simulate the structure of temperature and moisture at low levels, which lead to atmospheric instability.

The novelty of this study lies in the inclusion of the data assimilation step in the downscaling experiment used for the analysis of some instability indices, as most of the previous studies are mainly based on simulations driven by boundary conditions after its initialization e.g.. To those not familiar with the data assimilation process, its main objective is to produce more reliable and accurate initial conditions for regional models. This is achieved once the effect of the assimilated observations is used to modify the fields of temperature, wind, and pressure in order to make them closer to the observations. The impact of the data assimilation is not restricted only to the location of the observations being assimilated. First, the improvements due to the analysis are propagated zonally, meridionally, and vertically to the nearby grid points of the domain by means of the background error covariance matrix . Second, after the simulation in the new cycle is performed from the initial conditions achieved through assimilation, they propagate in the next 6 h by means of advection, thus affecting areas distant from the original observations.

This paper is organized as follows: the details of the configuration of the WRF model used in both experiments are presented in Sect. 2, along with a brief outline of the methodologies used in the study. The main results are presented in Sect. 3, while they are compared against previous studies presented in the Introduction. Finally, we conclude with some remarks about our research in Sect. 4.

Taylor diagrams for the TT index calculated for each radiosonde of the IP are shown in Fig. . The box-and-whisker plots associated with the correlations (both the Pearson and Spearman) obtained for each of the 1000 time series created with the bootstrap technique are also included. According to the Taylor diagrams, the best experiment reproducing the reference values is Wyoming, followed by aiRthermo (the real measurements of temperature, mixing ratio, and pressure from the sounding were used to calculate TT with our methodology), D, and later by N. Wyoming obtains the closest values to the observations at all the stations. The results for aiRthermo are quite similar to Wyoming, except for Murcia, where D is better at reproducing the reference data. The correlations are always above 0.99 for Wyoming, 0.98 for aiRthermo, 0.97 for D, and 0.75 for N. The observed SD is really well simulated by Wyoming, aiRthermo, and D, but N underestimates it at most of the stations as it is only able to reproduce the one in Santander and A Coruna. The RMSE is below 0.6 ∘C for aiRthermo, below 1 ∘C for D, and below 2.5 ∘C for N.

The bootstrap analysis is consistent with the results obtained in the Taylor diagrams, and it shows that the Pearson correlations are always above 0.99 for Wyoming and 0.98 for aiRthermo (again, with the exception of Murcia, where they are above 0.9). The correlations are always above 0.95 for D. In the case of N, the spread of the values is much larger than for aiRthermo and D, and their median values are obtained between 0.8 and 0.9. If we change to Spearman's correlations, we can see that values are similar but with a small decrease of the values (particularly in Gibraltar, Murcia, and Madrid).

Thus, as expected, we obtain the most similar results to those calculated from IGRA (Reference) with the values from Wyoming and those calculated with the real measurements from the soundings (that is, aiRthermo). However, we can still detect small differences between the values of the datasets due to the use of measured dew point temperature in Wyoming, whilst it is computed from temperature and mixing ratio in IGRA and aiRthermo. These differences are more remarkable in Murcia. Between both WRF experiments, it is clear that the experiment including the 3DVAR data assimilation is able to outperform the standard simulation only driven by the reanalysis data at the boundaries of the domain (N). The differences between both WRF simulations are highlighted, particularly at those stations located at the Mediterranean coast (Barcelona, Murcia, and Gibraltar) and in Lisbon.

In the case of CAPE, the validation results are presented in Fig. . The best experiment reproducing the results is aiRthermo, followed by Wyoming, D, and finally by N. The correlations are at all the stations above 0.99 in aiRthermo and 0.95 for Wyoming, while for D they are above 0.9 and above 0.7 for N. A similar behaviour is observed for SD and RMSE. The largest RMSEs are obtained for Barcelona and Murcia (both in the Mediterranean region).

The bootstrap analysis shows that the highest Pearson correlations are obtained by aiRthermo and Wyoming but followed really closely by D. As for the TT index, N presents the worst performance and the largest spread. If we consider, instead, the use of Spearman's correlations, we can see that the values are similar at most of the stations, and only in A Coruna and Santander is there a strong decrease of the values.

As stated before in Sect. , the calculation of CAPE is more sensitive to subtle differences in the methodology than that of TT, and this is highlighted in the validation of these results. Even if the same data are used for the calculation of CAPE (Wyoming and aiRthermo used the same measurements as input), it is clear that small differences in the initial conditions can result in serious discrepancies between both methods as stated by . The largest RMSEs for aiRthermo can be found at Barcelona and Murcia. As for the TT index, two stations at the Mediterranean coast present the largest differences between the experiments. However, while the computation of TT from both WRF simulations produces standard deviations similar to the observed ones, the results for CAPE substantially overestimate the variance of Atlantic sites (A Coruna, Santander, and Lisbon) and Madrid or underestimate it at the Mediterranean coast (Barcelona, Murcia, and Gibraltar). In any case, it can be seen that data assimilation improves the simulation of CAPE over the IP.

Finally, the validation of CIN is presented in Fig. . As for CAPE, the best results are obtained again by aiRthermo, followed by Wyoming, D, and N (with the exception in Gibraltar, where D and N are really similar). aiRthermo obtains correlations above 0.97 in every station, followed by Wyoming, D, and N with correlations above 0.93, 0.85, and 0.65 respectively. Both WRF experiments overestimate or underestimate it depending on the station (particularly N in Lisbon, Madrid, Murcia, and Zaragoza). The RMSE is always larger for N and particularly in Murcia and Gibraltar, where the values exceed 40 J/kg.

The bootstrap analysis presents the same results as for CAPE (Fig. ). However, for Gibraltar, as shown in the Taylor diagram, both WRF experiments produce a similar Pearson correlation values during the bootstrap. If we consider, instead, Spearman's correlations, the worse performance of N is perceptible in A Coruna, Santander, Murcia, and Gibraltar. However, in Gibraltar, differences between both WRF experiments arise: WRF D obtained better correlations than WRF N as in the other stations. In contrast to previous results, the poorest correlations for CIN are obtained at stations located at the Atlantic coast as Lisbon and A Coruna.

As for CAPE, the differences between aiRthermo and Wyoming are highlighted here. This result supports the idea that small differences in the initial conditions of the lifted air parcel and the determination of the LCL due to differences in the dew point temperature can cause large differences in the values of CIN, even if the same vertical profile of temperature and mixing ratio are used for its calculation. Again, the differences between both WRF experiments are important, and the experiment including data assimilation (D) presents generally closer results to the observed ones.

The seasonal analysis of the five datasets (Reference, Wyoming, aiRthermo, N, and D) for the TT index is presented in Fig. . In this case, Wyoming, aiRthermo, and D are able to correctly simulate the reference seasonal variability of the TT index at all the stations and all the seasons. However, N tends to overestimate the values of TT in every season and for most of the stations over the IP. The difference in TT is most important during winter months as a severe overestimation by the simulation N without data assimilation can be seen. It can be tracked on the basis of Figs. S1–S3 (in the Supplement) to an improved representation of temperature at 500 hPa. As shown by Fig. S1, the winter temperature at 850 hPa is higher for D than for N, but this would lead to higher values of TT, so that it does not explain the observed discrepancies for the N simulation. For the case of dew point temperature at 850 hPa (Fig. S2), it is higher for the N simulation than for D, which leads to higher values of TT for N. Additionally, the temperature at 500 hPa is higher for D than for N, and this also leads to higher values of TT for N than for D. It is, thus, clear that the improvement in the simulation of TT during winter for the D simulation is due to an improved simulation of moisture and temperature at the low (850 hPa) and middle troposphere (500 hPa) derived from the assimilation of soundings. The same diagnostic can be done for spring, another season during which N overestimates TT in many of the soundings (A Coruna, Santander, Lisbon, and Barcelona). They are located in areas where the difference between the dew point temperature from simulation D minus the one from N is negative (see Fig. S2).

A Coruna and Santander present the largest values during winter. Higher values than in winter are observed during spring, and the maximum is recognizable in Madrid, which is the only station located over central IP. Even if the maximum is found there, the other stations also present values above 38 ∘C. During summer, central, eastern, and southern stations (Madrid, Barcelona, Zaragoza, and Murcia) are the ones presenting higher values. In that season, the Atlantic stations (A Coruna and Santander) and Gibraltar present values below 40 ∘C. The values of TT in summer at those stations are smaller than the ones in winter, which occurs mainly due to the combined effect of the high increasing values of temperature at 850 and 500 hPa (about 15 and 10∘ respectively) and the smaller increase of dew point temperature (only a few degrees) in those regions from winter to summer (see Figs. S1–S3 in the Supplement). Finally, all the stations show similar values in autumn, with the exception of Gibraltar where the values are smaller.

The seasonal analysis for CAPE is presented in Fig. , and it highlights the spatial and temporal heterogeneity of the areas where unstable air masses can be observed over the IP, as also shown by . Wyoming and aiRthermo are able to reproduce (as expected) the variability of the reference values, and D is able to capture the spread of the values at most of the stations during winter, summer, and autumn. However, both WRF experiments (particularly D) overestimate CAPE at most of the stations in spring due to the differences in the virtual temperature in lower levels compared to reference data (colder near surface and warmer near 800 hPa) and with lifted parcels for D slightly warmer than the reference ones and those for N (see Fig. S4).

The experiment without data assimilation (N) tends to overestimate CAPE in winter and to underestimate it in summer. In winter, this overestimation is caused mainly by colder conditions in the 850–750 hPa pressure levels and warmer lifted air parcels (particularly in Lisbon, A Coruna, and Santander – see Fig. S5). A detailed analysis of the vertical structure of the differences of both simulations against IGRA for virtual temperature and mixing ratio (Fig. S6) shows that the vertical structure of moisture is improved in the D simulation, thus leading to a better estimation of CAPE through the troposphere due to improved estimations of virtual temperature.

On the contrary, in summer, the underestimations of CAPE by the N simulation are caused by warmer conditions in the lower pressure levels compared to the reference, which causes the lifted parcel to cross the sounding in a lower pressure level than D, consequently underestimating CAPE (particularly for Barcelona, Murcia, and Gibraltar – see Fig. S7). A detailed analysis for Barcelona (Fig. S8) shows that there is a substantial underestimation of moisture at lowest levels by the N simulation, something which is consistent with findings in  in a verification with independent non-assimilated MODIS-integrated water vapour. This is also observed in Murcia (figure not shown). During spring and autumn, the underestimations or overestimations of N depend on the station, and a clear pattern is not observed.

The lowest values of CAPE are obtained during winter (below 50 J/kg at all the stations), and the largest ones are observed in summer (reaching 500 J/kg at some stations). However, as stated before, the distribution of CAPE is not homogeneous, and different regions are prone to higher values during each season. During winter, the three Atlantic stations (A Coruna, Santander, and Lisbon) and Gibraltar present the highest values of CAPE over the IP. In general, the values are below 50 J/kg, but some events can exceed 100 J/kg. During spring, the distribution of CAPE is quite homogeneous over the IP and only stations such as Lisbon, Madrid, or Gibraltar present slightly higher values of CAPE than the other stations. In summer, only the stations located in the eastern and southern parts of the IP present remarkable values of CAPE. Particularly, the highest CAPE values are located at the Mediterranean coast (Barcelona and Murcia). Finally, during autumn, the regions with high CAPE are extended towards the inland of the IP, such as Madrid and Zaragoza. During this season, some extreme events can reach values over 1000 J/kg over the Mediterranean coast. This feature was already observed by . All these seasonal changes in CAPE also agree with previous studies based on CAPE .

Finally, the seasonal analysis for CIN is presented in Fig. , and it highlights the stations where the inhibition is important. In general, Wyoming tends to underestimate the values of CIN at most of the stations and in every season, while aiRthermo is able to capture it. Both WRF simulations (but particularly the experiment without data assimilation) tend to underestimate the observed variability.

The values of CIN are smaller in winter and spring, and the maximum is observed in summer. During winter, CIN is higher in Gibraltar and at the Atlantic stations (Lisbon, A Coruna, and Santander) than at the other stations from the IP. However, these values are small compared to those for other seasons. During spring, the values are higher than in winter, and similar values are observed at most of the stations (around 10 J/kg), with the exception of Barcelona where the CIN reaches values of 20 J/kg. In summer, the values are higher at all the stations but particularly in those for the eastern and southern IP (Barcelona, Zaragoza, Murcia, and Gibraltar). The same regime is observed for autumn, but the values are smaller than in summer. These values during summer and autumn are in agreement with , who found CIN means above 100 J/kg in the western Mediterranean Sea and surrounding countries.

As stated before, in the final phase of this study, the same procedure for the calculation of TT, CAPE, and CIN at each station was extended to each grid point included in the IP. The mean winter and summer spatial patterns at 00:00 and 12:00 UTC were calculated for both WRF experiments. In addition, CAPE and CIN limited to those days characterized by high values of CAPE (based on the 75th percentile of CAPE at each grid point, season of the year, and time – 00:00 and 12:00 UTC) were also evaluated. These maps were added to the corresponding figures for CAPE and CIN as a third column. However, these results are only shown for the D experiment, the one that was shown to be the most accurate one according to the Taylor diagrams and seasonal box-and-whisker plots in previous results.

The spatial distribution for TT is shown in Fig. , which highlights the heterogeneity of the results. The differences between both simulations are observable but also those between day and night. Additionally, it can be seen that TT cannot be calculated in most of the mountain regions of the IP because the 850 hPa layer is near the surface or below ground.

During winter, the maps of TT show that N yields higher values than D, which is in agreement with the overestimation observed in Fig. . At 00:00 UTC, according to D, the regions where unstable air masses are observed are those at the Cantabrian coast and in the southeastern IP. Both regions are surrounded by remarkable mountainous systems such as the Cantabrian Range and the Baetic System, which can trigger convection by orographically induced lifting. For N, these areas are also extended to the rest of the IP, with the exception of the southwestern corner where the values are small. At 12:00 UTC, after solar irradiance has started heating up the land, the regions extend towards inland areas. In the experiment with data assimilation (D), most of the northern plateau presents high values of TT, and the lowest values are observed near the coastal valleys of the southwestern corner and the Mediterranean coast (like the Ebro basin or Murcia). In the case of N, the lowest values are observed mainly in the southwestern IP near the Guadalquivir valley. According to Figs. S1–S3, the lowest values of TT observed near the coastal valleys of both WRF experiments are a consequence of low dew point temperatures there mainly due to dry air.

As in Fig. , much higher TT values are obtained during summer over the IP, particularly at 12:00 UTC. At 00:00 UTC, a west–east gradient is observed in both WRF simulations. However, the values depicted at the Mediterranean coast are higher for the experiment including data assimilation (D). At 12:00 UTC, the regions with unstable air masses extend towards the central area. In this case, they are located near the Pyrenees and in the proximity of the Iberian, Central, and Baetic systems. The minimum TTs are observed in the western part of the IP but particularly near Lisbon. The intensity of the most extreme values of TT is higher in D (with data assimilation).

The maps of CAPE at 00:00 and 12:00 UTC during winter and summer are presented in Fig. , together with the mean values of CAPE that are larger than the 75th percentile at each point of the domain for the D experiment (P75 column). During winter, as shown in Fig. , the N experiment presents higher values than D. At 00:00 UTC, the patterns are really similar for both WRF experiments. The main difference between them is observed at the western Atlantic coast of the IP, where higher CAPE values are obtained for N. At 12:00 UTC, the unstable air masses are found at the western coast of the IP in both simulations, and they extend further inland than at 00:00 UTC, particularly near the Tagus and Guadalquivir rivers. Again, the values are higher for N, but the pattern is similar in both experiments. If the analysis is limited to values of CAPE beyond the third quartile during winter, the values are higher, but the spatial distribution at both 00:00 and 12:00 UTC is very similar to the average one from the D experiment.

Compared to what is observed during winter, CAPE is higher during summer for the experiment including data assimilation. This is in agreement with the station analysis shown in Fig. . At 00:00 UTC, the area with higher CAPE is observed in the northern and eastern IP but particularly near the Mediterranean coast. However, at 12:00 UTC, this area with high values (over 250 J/kg) extends towards the interior, and in the experiment including data assimilation it also covers the southern part of the Pyrenees. Additionally, high values are observed in most of the IP (except the southwestern corner for N) but particularly in the simulation including data assimilation. The patterns of CAPE obtained for both winter and summer are in agreement with those from , even if we differentiate between 00:00 and 12:00 UTC, and we studied different periods (1986–2005 in their study, 2010–2014 in ours). If the focus is set on the days characterized by CAPE higher than the third quartile (third column of Fig. ), it can be seen that, as expected, the CAPE field is intensified, but the changes in its spatial distribution are not relevant. The Mediterranean region is still the one showing the highest values of CAPE, particularly at 12:00 UTC, even though there is a general small increase of CAPE over the entire IP.

Finally, regarding CIN, the maps for the mean values at 00:00 and 12:00 UTC during winter and summer are presented in Fig. . In reverse to what we found for CAPE, CIN is usually higher at 00:00 UTC than at 12:00 UTC (with the exception of Murcia in summer at 12:00 UTC), as could be expected due to the stabilizing effect of nocturnal radiation. During winter, at 00:00 UTC, both simulations show small values over the IP, and only some high values are observed in the western and southwestern corners of the IP (and particularly for N) because of higher atmospheric stability due to surface cooling. At 12:00 UTC, the areas are confined to those coastal regions, but they also extend to the Mediterranean coast in the D experiment. For the days characterized by CAPE higher than the third quartile (rightmost column of Fig. ) in D during winter, it can be seen that the highest values of CIN appear at night at the Mediterranean coast. This area is usually where nocturnal radiation cooling is large because of a lower cloud cover, although this result is highly dependent on the dataset used for the analysis , and consequently, it should be further analysed for our simulations in the future. Conversely, during daytime, the structure of CIN for the days on which CAPE is higher than the 75th percentile does not show any remarkable features.

During summer, at 00:00 UTC, the most remarkable values are obtained in both simulations along the Ebro basin and near the Mediterranean coast. However, the CIN inland is higher for D. At 12:00 UTC, less inhibition is observed in the eastern valleys of the IP (with the exception of Murcia, which presents extremely high values of CIN). At the same time, an increase in the convective inhibition over the Guadalquivir basin is shown. The extension towards the interior is again higher for D (including data assimilation). During summer, the selection of the days with the highest values of CAPE implies again the change in the distribution of nocturnal CIN, so that large areas of the southern IP are affected by the stabilizing effect due to radiational surface cooling during the frequent clear nights beyond the Mediterranean regions, which are also apparent on the average night (00:00 UTC) map. At midday (12:00 UTC), the values of CIN for the days on which CAPE is above the 75th percentile are larger, but the spatial distribution does not change compared to the map showing the mean CIN.

Comparing the results shown in Figs.  and  (or in Figs.  and ), it seems that there is a discrepancy between the results for TT and CAPE since maximal values of these indices are not observed in the same regions. However, it must be taken into account that these results for CAPE (and CIN) are obtained from the entire series of 12-hourly values obtained during 2010–2014. Thus, Figs.  and  (also Figs.  and ) must be compared in combination with the values of TT.

Additionally, atmospheric conditions can be analysed using TT, CAPE, or CIN, but the relationship between them is not linear: the R2 between TT and CAPE is below 0.2, and the R2 between CAPE and CIN is below 0.1 for all the stations and seasons, particularly for stable or neutral atmospheres (not shown here). Thus, since the calculation of CAPE and CIN takes into consideration the vertical profile of the atmosphere until the level of neutral buoyancy, they should be considered more reliable than TT, which only takes two pressure levels into account.

Taking into consideration the information presented above, some clear patterns arise from these results. During winter, the areas with unstable air masses are located at the Atlantic coast of the IP, and the instability is larger during the afternoon as CIN is really high in those regions until 12:00 UTC. However, during summer, the unstable areas are located to the north of the Mediterranean coast. CAPE is even larger during the afternoon in those regions, but some unstable areas can also appear before 12:00 UTC, even if the inhibition is high during that period (CAPE is also high).

The main purpose of this paper is to evaluate the ability to simulate the atmospheric conditions over the IP with two high-resolution simulations created with the state-of-the-art model WRF. One of these simulations is driven by the boundary conditions provided by ERA-Interim reanalysis (N experiment), while the second one presents the same configuration but including the additional 3DVAR data assimilation step every 6 h (D experiment). Three parameters were evaluated: the TT index, CAPE, and CIN. All of them were calculated from the outputs of the model using the publicly available R package aiRthermo, also developed by the authors.

In order to validate these parameters, their values were downloaded from the University of Wyoming server for the eight radiosondes available over the IP. Additionally, temperature, mixing ratio, and height at all the available pressure levels from the radiosondes were also retrieved –in that case, also from IGRA from NOAA. These variables were used to calculate again these three parameters with aiRthermo. Comparing these new values with the ones retrieved directly from the University of Wyoming, small differences which can only be attributed to different methodologies are obtained.

The correlation coefficients according to Pearson and Spearman, SD, and RMSE show small differences between the different methods used for their computation, which are caused by the use of measured or calculated dew point temperatures in their calculation. However, these differences are more important for CAPE and CIN than for TT because they are highly dependent on the initial conditions for the calculation of the vertical integrations, while the TT index is only dependent on two discrete pressure levels. Between both WRF simulations, the most accurate results are produced by the experiment with data assimilation (D), as it is able to correctly capture their temporal evolution as shown by the correlations and RMSEs. The bootstrap analysis with resampling supports this result. Additionally, this is also in agreement with previous studies by the authors, whereby these two simulations were validated against in situ or gridded observational datasets of integrated water vapour, precipitation, evaporation, or wind over coastal areas . Particularly for integrated water vapour, a variable closely related to the parameters analysed in this study, our verification against an independent satellite-based dataset that has not been assimilated (MODIS) showed that this quantity was substantially better simulated across the whole IP for the D simulation .

The seasonal analysis carried out for TT showed really small differences between methods in every season. Between both WRF experiments, N tends to overestimate the reference variability, while D is able to capture it. In the case of CAPE and CIN, the differences between methods are larger but not as those within both WRF experiments. However, D is able to produce closer values to the reference than N, only due to the effect of data assimilation as it is the only difference between the two WRF experiments. The experiment including data assimilation is able to produce more realistic virtual temperature profiles during summer and winter, while the experiment without data assimilation produces colder or warmer conditions at the pressure levels in winter or summer respectively, along with warmer lifted air parcels in winter. The seasonal values of the indices showed that the unstable air masses are located mainly over the stations from the Atlantic coast of the IP during winter. All the stations present remarkable values during spring. However, during summer, the most remarkable values are obtained over the Mediterranean coast. These stations also showed maximum values during autumn.

All the three indices agree, highlighting the heterogeneity of the patterns observed over the IP during winter and summer and at 00:00 and 12:00 UTC. The D experiment, which is the most accurate one according to the previous analysis, shows that during winter the unstable areas are found along the entire Atlantic coast but particularly in the southwestern corner of the IP, when the instability is extended towards inland regions. During summer, this feature is reversed, and the regions most prone to unstable air masses are located at the Mediterranean coast and the Ebro basin. The convective inhibition (high values of CIN) is strong at 00:00 UTC in those regions, but that is highly reduced at 12:00 UTC. If we restrict the values of CAPE and CIN only to the days with the highest values of CAPE (above the 75th percentile of each grid point), the spatial distribution of CAPE does not change substantially, and the values are simply increased. However, in the case of CIN, the largest differences in its distribution appear at night, particularly at the Mediterranean coast during winter and in the southern IP during summer.

If we assume that convection can be triggered orographically or due to the effect of the breezes in those regions (something that is perfectly feasible as the highlighted regions are found in mountainous areas near the coasts), these patterns are in agreement with the precipitation patterns observed in previous studies over the IP . The patterns for CAPE observed during winter and summer are similar to those obtained by the regional analysis performed by . However, their values are comparable to those obtained by our experiment without data assimilation (N). In this case, the data assimilation (D) produces higher values but much more realistic than the ones from those simulations (N) without it (according to Fig. ).

Finally, no linear relationships were found between the studied parameters as the R2 is always below 0.2, independently of the station and the season. Thus, since the calculation of CAPE and CIN takes into consideration the vertical profile of the atmosphere until the level of neutral buoyancy, these two variables should be considered first to evaluate atmospheric conditions and not TT, as this index only takes two pressure levels into account (500 and 850 hPa).