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# Comparative analysis of ERA5 and Raman lidar-derived moisture profiles in the framework of the WaLiNeAs field campaigns

Frédéric Laly, Patrick Chazette

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Frédéric Laly, Patrick Chazette. Comparative analysis of ERA5 and Raman lidar-derived moisture profiles in the framework of the WaLiNeAs field campaigns. Quarterly Journal of the Royal Meteorological Society, 2025, 10.1002/qj.5044. hal-05223241

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RESEARCH ARTICLE

# Comparative analysis of ERA5 and Raman lidar-derived moisture profiles in the framework of the WaLiNeAs field campaigns

Frédéric Laly<fnref n="1" />,<fnref n="2" /> ID | Patrick Chazette<fnref n="1" />

**Correspondence**

Frédéric Laly, LSCE/IPSL,
CNRS-CEA-UVSQ, University
Paris-Saclay, CEA Saclay, Orme des
Merisiers, Bat 714, 91190 Saint-Aubin, Gif
sur Yvette, France.

Email: frederic.laly@lsce.ipsl.fr

**Funding information**

Institut National des Sciences de
L'Univers; Centre National de la
Recherche Scientifique; Commissariat à
l'Énergie Atomique et aux Énergies
Alternatives; Agence Nationale de la
Recherche, Grant/Award Number:
ANR-20-CE04-0001

**Abstract**

Water vapour mixing ratio (WVMR) measurements were performed by three Raman lidars during the WaLiNeAs field campaigns in the western Mediterranean basin. Observations took place along the French Mediterranean coast in autumn and winter 2022–2023, and in southwestern France near Toulouse in summer 2023. These multiseasonal campaigns captured a variety of meteorological phenomena, including a dry winter, rainstorms, long-range aerosol transport, and a summer heatwave. The measured WVMR varied across the troposphere, ranging from less than 1 g·kg⁻<fnref n="1" /> to over 17 g·kg⁻<fnref n="1" /> in the planetary boundary layer, and from less than 1 g·kg⁻<fnref n="1" /> to approx. 15 g·kg⁻<fnref n="1" /> in the free troposphere. Lidar profiles have a vertical resolution of 100 m and a temporal resolution of one hour, matching the spatiotemporal resolution of the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) model outputs. The primary goal is to compare the ERA5 reanalysis with the lidar-derived WVMR profiles. Results show altitude-dependent differences in Pearson correlation coefficient (COR), mean bias (MB), and root-mean-squared deviation (RMSD), especially during high WVMR periods (>10 g·kg⁻<fnref n="1" />). Over all cases, MB ranges from 0.1 to 3 g·kg⁻<fnref n="1" />, and RMSD from 0.6 to 3.7 g·kg⁻<fnref n="1" />. COR values range from 0.16 to 0.94, with the lowest values observed during warm episodes in the free troposphere. The statistical coefficients show the differences in the behaviour of the reanalysis model over different measurement periods and altitude ranges compared to lidar. However, in all cases the reanalysis underestimates the WVMR, whatever the altitude range. This article highlights the challenges faced by models during common meteorological events characterised by significant WVMR amounts. The results provide valuable insights into the performance of operational numerical weather prediction models. It highlights the necessity to refine the representation of the WVMR in the lower troposphere by assimilating ground-based lidar measurements.

**KEYWORD**

ERA5, Raman lidar, tropospheric moisture, WaLiNeAs, water vapour mixing ratio

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This is an open access article under the terms of the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2025 The Author(s). *Quarterly Journal of the Royal Meteorological Society* published by John Wiley & Sons Ltd on behalf of Royal Meteorological Society.

<footnote n="1">¹LSCE/IPSL, CNRS-CEA-UVSQ,
University Paris-Saclay, CEA Saclay, Gif
sur Yvette, France</footnote>

<footnote n="2">²ADDAIR Company, Buc, France</footnote>
# 1 | INTRODUCTION

The western Mediterranean basin is emerging as a climate change hotspot, with a growing population of over 500 million inhabitants expected around the Mediterranean Sea by 2030 (Giorgi & Lionello, 2008; IPCC, 2022; Ruti et al., 2016). The increasing vulnerability of this area is partly due to the role of rising temperatures in amplifying atmospheric water vapour, which acts as a fuel for extreme weather events as it brings latent heat in the lower troposphere. During the autumn season, the western Mediterranean basin is subject to winds carrying humid air masses at high altitudes (between 2 and 4 km above mean sea level [a.m.s.l.]) originating from the tropical Atlantic Ocean, which pass over Gibraltar or over North Africa before crossing the Mediterranean Sea (e.g., Chazette et al., 2016; Ducrocq et al., 2008; Duffourg et al., 2016), ultimately reaching the southern French coast. These air masses mix with those coming directly from the Mediterranean Sea, which travel at low altitudes in the marine boundary layer (between 0 and 2 km a.m.s.l.). As shown by Duffourg and Ducrocq (2011), these winds can trigger the development of heavy precipitation events (HPEs). Flamant et al. (2021) have confirmed that increased moisture levels in the lower atmosphere, particularly in the first few kilometres above the ground, precede these HPEs. These events are enhanced because warmer air can hold more moisture, leading to heavier rainfalls and flash floods that cause major damages in southern France such as floods and landslides (e.g., Ricard et al., 2012). During summer, tropical air masses can also bring significant moisture contents, in conjunction with heatwaves, affecting much of western Europe. Heatwaves are becoming more frequent and severe due to increased water vapour in the atmosphere (IPCC, 2022). This amplified greenhouse effect traps heat, leading to higher temperatures and humidity levels, which can cause dangerous heat-related illnesses (Lowe et al., 2015). The south of France was strongly affected by such a phenomenon in the summer of 2023, with record temperatures. This region can also be the site of intense thunderstorms during the summer, linked to the transport of humidity from the Atlantic Ocean or the Mediterranean Sea, as was the case at the end of June 2023. Storm formations are increased as water vapour provides more energy, potentially leading to more frequent and intense storms with higher precipitation rates. These extreme events, fuelled by rising temperatures and increased water vapour, pose significant threats to human life, the environment, and the economy (IPCC, 2022). Accurate measurements of the water vapour mixing ratio (WVMR) in the lower troposphere are thus crucial for various atmospheric applications, including numerical weather prediction (NWP), climate monitoring, and air quality studies.

Water vapour data assimilated in NWP and reanalysis models are typically obtained from ground-based remote-sensing techniques, like microwave radiometry, from in-situ measurement techniques using meteorological probes, and from satellite observations such as the Infrared Atmospheric Sounding Interferometer (IASI) (Guidard et al., 2011; Hilton et al., 2009). However, these techniques have limitations in terms of vertical and/or temporal resolution, and sensitivity. Using Raman lidar measurements during the Hydrological cycle in the Mediterranean eXperiment (HyMeX), Chazette, Marnas, Totems, and Shang (2014) have shown that IASI data do not provide a good retrieval of water vapour content in the lower troposphere. This is mainly due to the vertical resolution at which the infrared spectral luminance inversions can be performed and to the maximum value of the channel weight functions used being at 2 km above the ground level. Indeed, the water vapour field is highly variable in the first two kilometres of the atmosphere. Furthermore, ground-based in-situ measurement probes have good temporal resolution and accuracy, but the measurement is made at a precise point in the atmosphere and does not allow the entire air column to be monitored. Probes used in radiosondes provide good vertical sampling but poor temporal sampling with two samples per day in operational mode. In general, meteorological probes may not measure humidity accurately at low temperatures, and they are disrupted when they pass through a cloud, even a thin one. Humidity probes also have a memory of several seconds of previous measurements. These limitations have a negligible effect on ground-based meteorological measurements but distort the vertical humidity profile during radiosoundings and must be corrected. It is worth noting that the quality of this correction is difficult to quantify. Hence, biases can exist between instruments used in different locations or at different times (Bock & Nuret, 2009; Schneider et al., 2010; Serreze et al., 2012; Wang et al., 2007). All of these measurement limitations have a major impact on the ability of models to correctly predict the location and the intensity of an extreme meteorological event. For instance, Chazette et al. (2016) have shown that there is a significant mismatch in the lower troposphere between Raman lidar measurements and the Applications of Research to Operations at Mesoscale West Mediterranean (AROME-WMED, Fourrié et al., 2015), with a root-mean-squared error between 1 and 2 g·kg⁻¹, well above the World Meteorological Organization (WMO) recommendations. Totems et al. (2019) compared WVMR data retrieved from Raman lidar measurements, obtained during the Pollution in the ARCtic System (PARCS) project in Norway, with WVMR data retrieved from the Weather Research and Forecasting (WRF) simulations at high resolution, which were mainly nudged by the outputs of the
European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA) interim model above the planetary boundary layer. They observed that the model was wet-biased below approx. 1 km a.m.s.l., with a bias of +0.4 g·kg⁻¹, and dry-biased above approx. 1 km, with a bias of about −0.3 g·kg⁻¹. In fact, a reanalysis is built up from observations and models to give the best possible representation of the various atmospheric parameters. They should therefore be highly accurate and unbiased.

Responding to this need for both vertical resolution and data accuracy, Raman lidar data provide continuous and well-resolved WVMR profiles in altitude (e.g., Ansmann et al., 1992; Mattis et al., 2002; Whiteman et al., 1992). For instance, during the Special Observing Period of the 10-year HyMeX programme (Drobin ski et al., 2014), a variety of instruments were deployed to measure atmospheric parameters in the lower troposphere (Ducrocq et al., 2014; Duffourg et al., 2018). Among these technologies, two Raman lidars (Chazette, Marnas, & Totems, 2014; Di Girolamo et al., 2020) were strategically positioned across the western Mediterranean basin to measure the WVMR. By integrating this dataset, HyMeX contributed to testing the impact of lidar data assimilation in weather forecasting models, such as the Application of Research to Operations at Mesoscale (AROME) model (Fourrié et al., 2019; Seity et al., 2011). It is worth noting that the assimilation of ground-based lidar data has already proven its relevance in air quality forecasting (Wang et al., 2013, 2014).

The HyMeX project served as the foundation for the Water Vapour Lidar Assimilation (WaLiNeAs, Flamant et al., 2021) project. This project aims to improve the ability of NWP models, such as AROME, to predict extreme events that hit south of France, like HPEs. The WaLiNeAs field measurement campaigns took place first in the autumn/winter of 2022–2023, between October 2022 and January 2023, and then in the summer of 2023, between June and September 2023 (Laly et al., 2024). These measurement campaigns covered a total of eight months in the south of France and measured a wide range of meteorological events involving very different water vapour contents.

Following the WaLiNeAs field campaigns, this paper presents a comparative study of WVMR retrieved from Raman lidar measurements and reanalysis outputs of the ECMWF Reanalysis V5 (ERA5, Hersbach et al., 2020) model. The comparison over a period of eight months of the year between the water vapour estimated by several Raman lidars and that from ERA5 reanalysis over the south of France is unprecedented. The primary objective of this research is to evaluate the statistical agreement between WVMR derived from Raman lidar measurements and simulations from the ERA5 dataset during periods

of widely varying water vapour content, particularly during extreme weather events such as heavy rainstorms or heatwaves. Section 2 of the paper presents the measurement configuration of the lidars and the ERA5 database. The weather conditions in southern France during the specific periods of winter 2022–2023 and summer 2023 are described in Section 3. The statistical results of the comparison of ERA5 reanalysis and the lidars over the different measurement periods are presented in Section 4. A conclusive discussion is given in Section 5.

## 2 | INSTRUMENT AND MODELLING

### 2.1 | Lidars

During the main field campaign of WaLiNeAs in 2022, three mobile lidars were operated by the French team of the project, namely (i) the H₂O Raman Ultraviolet Sounder second generation (HORUS-2) at Coursan (43°14′5″ N, 3°3′49″ E), (ii) the H₂O Raman Ultraviolet Sounder first generation (HORUS-1) at Le Grau-du-Roi (43°31′14″ N, 4°7′39″ E), and (iii) the Water Vapour and Aerosol Lidar (WALI; Chazette, Marnas, & Totems, 2014; Totems et al., 2021) embedded in the Mobile Atmospheric Station (MAS) (e.g., Raut & Chazette, 2009) at Cannes (43°32′29″ N, 6°57′30″ E). HORUS-2 was the lidar deployed in Toulouse (43°34′28″ N, 1°22′ 25″ E) as part of the WaLiNeAs complementary measurement period to sample meteorological events occurring in the southwest of France during summer 2023. These lidars were deployed alongside lidars from other European colleagues during the first part of the WaLiNeAs campaign: ARTHUS (Lange et al., 2019) in Ajaccio (Corsica); CONCERNING (Di Girolamo, Dionisi, et al., 2023; Di Girolamo, Franco et al., 2023) in Toulon (France); MARCO (Di Girolamo, Dionisi, et al., 2023) in Port-Saint-Louis-du Rhône (France); the lidar (lidar configuration is given in Kumar et al., 2011) of the University of Catalonia in Barcelona (Spain); the Raymetrics Raman lidar (https://raymetrics.com/) of Valencia (Spain).

Pictures and tables summarizing the performance of WALI and HORUS lidars and diagrams of the lidars deployed during WaLiNeAs are provided in Laly et al. (2024), Figures 3 and 4.

The three lidar systems use as emitter pulsed frequency-tripled Nd:YAG lasers manufactured by Lumibird Quantel, with an emission wavelength of 354.7 nm. The laser beam is expanded using beam expanders (expansion factor ×10), which allow to the meet eye safety standards (EN 60825-1) at the chimney exit. The pulse energy is 30 mJ and 100 mJ for the HORUS and
WALI lidars, respectively, while the pulse repetition rate is 20 Hz for WALI and HORUS-1, and 100 Hz for HORUS-2, allowing for a better vertical range. The reception systems are 150-mm Newtonian telescopes, feeding filter-based spectral analysers, called polychromators, via an optical fibre. This reception configuration allows full overlap, that is the overlap between the transmitted beam and the received field of view, to be achieved at 200 m above the lidar. For the acquisition system, we use PXI (PCI eXtensions for Instrumentation) technology, which incorporates 12-bit digitizers manufactured by National Instruments® (https://www.ni.com/, last accessed 04/10/2024). These digitizers operate at a speed of 200 MHz, allowing for post-digitization photon counting. All lidar characteristics, measurement periods and a map showing the distribution of lidars in the western Mediterranean basin are described in Laly et al. (2024). A summary table of the various sources of error affecting lidar measurements is given in Table 3 of this article. Calibrations of the lidars using a ground-based weather station are also presented in this article, as there was no radiosonde site near the lidar sites, except for Le Grau-du-Roi, which was approx. 50 km away from the Nîmes radiosonde site. The main characteristics of the lidars are summarised in Table 1. In this table, we have set a value of 0.4 g·kg⁻¹ for the measurement uncertainty of water vapour, which corresponds to the expected accuracy of water vapour measurement according to the WMO. We note that the range of the lidar is much smaller during the day than at night. Indeed, the sky background radiation in daytime increases the shot noise, which in turn decreases the usable measurement range.

The method used to retrieve the WVMR from the raw lidar signals is also explained in Laly et al. (2024). All lidar datasets can be downloaded free of charge from the AERIS database (https://doi.org/10.25326/537). The vertical and temporal resolution of each profile is 100 m and 15 or 30 min respectively.

## 2.2 | ERA5 reanalysis

The ECMWF provides a valuable resource for atmospheric scientists: the ERA5 reanalysis (https://cds.climate.copernicus.eu/, last accessed 17 January 2025). This fifth-generation product builds on previous iterations and incorporates a vast observational dataset (Hersbach et al., 2020). Unlike operational forecasts, the ERA5 reanalyses are produced retrospectively, using past observations and forecast outputs. This allows for a more comprehensive analysis compared to real-time forecasts. ERA5 provides hourly data on a 31 km grid with up to 137 vertical pressure levels. This detailed information covers the atmosphere from the surface to very high altitudes

(1 hPa). Although ERA5 is expected to be more accurate than its predecessors and forecast models due to its extensive observational constraints, it has some limitations. The assimilation process, though powerful, cannot completely eliminate the inherent biases in forecasts and observational data.

In our study, we used outputs of the ERA5 hourly data on pressure levels (27 levels taken) to derive the WVMR product from the specific humidity. Before comparing the lidar data with the ERA5 reanalysis, we used the geopotential altitude to convert the 27 pressure levels for each ERA-5 WVMR profile into geometric height levels for comparison with the lidar profiles. As the vertical resolution of the lidars is much better than that of the ERA5 products, we interpolated each ERA5 profile to the corresponding lidar profile before calculating the difference. We also used ERA5 products to plot synoptic maps to understand the circulation of air masses. Previous studies, such as Totems et al. (2019), have identified potential biases in modelled atmospheric water vapour for specific regions at specific times. The ongoing assessment of the accuracy of ERA5 will further strengthen its role as a cornerstone for atmospheric research.

The main characteristics of ERA5 are also given in Table 1. The uncertainty in the ERA5 estimate of the WVMR is not reported because it is difficult to estimate. These uncertainties are not constant from one region to another, depending on the amount of data available. It is therefore very difficult to make an assessment, although some estimates are given by Hersbach et al. (2020).

## 2.3 | Statistical parameters

With the aim of comparing lidar measurements with ERA5 reanalysis, we used three statistical parameters that are commonly used for comparisons (Tombette et al., 2008; Wang et al., 2014), namely: the mean bias (MB), the root-mean-squared deviation (RMSD) and the unbiased Pearson correlation coefficient (COR). The MB allows to quantify the systematic errors between ERA5 reanalysis and lidar measurements. The RMSD highlights both the systematic and random errors. The COR gives the linear relationship between ERA5 and lidar profiles, representing the degree of coherence between ERA5 and the lidar WVMR profiles.

The MB is given by the relationship:

$$MB = \frac{1}{m \cdot n} \cdot \sum_{p=1}^{m} \sum_{k=1}^{n} (r_{H_2O,lidar}(p, k) - r_{H_2O,ERA5}(p, k)), \quad (1)$$

where $r_{H_2O,lidar}$ is the WVMR measured by the lidar for the profile $p$, at the altitude $k$ in g·kg⁻¹, $r_{H_2O,ERA5}$ is the WVMR
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<table><caption>TABLE 1 Main characteristics of lidars and ERA5. Uncertainties on the WVMR for lidars are given for a clear atmosphere</caption><thead><tr><th></th><th>HORUS and WALI</th><th>ERA5</th></tr></thead><tbody><tr><td>Data type</td><td>Observation</td><td>Reanalysis</td></tr><tr><td>Horizontal resolution</td><td>-</td><td>0.25 × 0.25°</td></tr><tr><td>Vertical resolution/pressure level resolution</td><td>100 m</td><td>250 hPa (range: 200–1000 hPa)</td></tr><tr><td>Temporal resolution</td><td>1 hour</td><td></td></tr><tr><td>Altitude range (a.g.l.)</td><td><b>HORUS-2:</b> 0–2.5 km (daytime)<br>0–12 km (nighttime)<br><b>HORUS-1:</b> 0–1.5 km (daytime)<br>0–7 km (nighttime)<br><b>WALI:</b> 0–2 km (daytime)<br>0–10 km (nighttime)</td><td>0–12 km</td></tr><tr><td>Uncertainty in WVMR measurement</td><td><b>HORUS-2:</b> &lt;0.4 g·kg<sup>-1</sup> (daytime: 0–1.6 km a.m.s.l./nighttime: 0–12 km a.m.s.l.)<br>0.4–2 g·kg<sup>-1</sup> (daytime: 1.6–2.5 km a.m.s.l.)<br><b>WALI:</b> &lt;0.4 g·kg<sup>-1</sup> (daytime: 0–1.5 km a.m.s.l./nighttime: 0–10 km a.m.s.l.)<br>0.2–1 g·kg<sup>-1</sup> (daytime: 1.5–2 km a.m.s.l.)<br><b>HORUS-1:</b> &lt;0.4 g·kg<sup>-1</sup> (daytime: 0–1 km a.m.s.l./nighttime: 0–4 km a.m.s.l.)<br>0.4 g·kg<sup>-1</sup> – 1 g·kg<sup>-1</sup> (daytime: 1–1.5 km a.m.s.l./nighttime: 4–5.5 km a.m.s.l.)</td><td>-</td></tr></tbody></table>

Abbreviation: a.m.s.l. above mean sea level.

estimated by ERA5 for the profile *p*, at altitude *k* in g·kg⁻¹. The total number of profiles, with a temporal resolution of one hour per profile, is *m* and the total number of altitude intervals is *n*. Using the same notations, the RMSD is given by:

RMSD

$$ = \sqrt{\frac{1}{m \cdot n} \cdot \sum_{p=1}^{m} \sum_{k=1}^{n} (r_{H_2O,lidar}(p, k) - r_{H_2O,ERA5}(p, k))} \quad (2) $$

and the unbiased Pearson correlation coefficient (COR) by:

$$ COR = \frac{1}{(m-1) \cdot (n-1)} \sum_{p=1}^{m} \sum_{k=1}^{n} \left( \frac{r_{H_2O,lidar}(p, k) - \overline{r_{H_2O,lidar}}}{\sigma_{H_2O,lidar}} \right) \cdot \left( \frac{r_{H_2O,ERA5}(p, k) - \overline{r_{H_2O,ERA5}}}{\sigma_{H_2O,ERA5}} \right), \quad (3) $$

where $\overline{r_{H_2O,lidar}}$ is the mean of the WVMR of the lidar profile over all *p* and *k* in g·kg⁻¹, $\overline{r_{H_2O,ERA5}}$ is the mean of the WVMR of ERA5 profiles over all *p* and *k* in g·kg⁻¹, $\sigma_{H_2O,lidar}$ is the standard deviation of the lidar profiles over all *p* and *k* in g·kg⁻¹ and $\sigma_{H_2O,ERA5}$ is the standard deviation of the ERA5 profiles over all *p* and *k* in g·kg⁻¹.

## 3 | PRESENTATION OF CASE STUDIES

The WaLiNeAs field measurement campaigns took place in two different seasons, autumn/winter and summer. During these periods, the WVMR in the atmosphere was affected by contrasting meteorological situations, resulting in different amounts of water vapour content in the lower troposphere. This allows relevant comparisons to be made between lidar measurements and reanalyses. For the purposes of this study, we identified five events that caused significant variations in water vapour content throughout the troposphere. These events are described in detail below.

### 3.1 | Case study 1: Evolution of the atmosphere over the western Mediterranean basin during autumn and winter 2022–2023

According to Weather in the Mediterranean (1962), different weather patterns in the Mediterranean basin occur during the mild season between October and May and the warm season between June and September. The WaLiNeAs field campaign took place during the mild season (Flamant et al., 2021), when the air entering the basin is mainly
<figure><img src="image_5.png" /><figcaption>FIGURE 1 Distribution of the lidar sites over the Mediterranean basin during WaLiNeAs. The red crosses represent the sites managed by the French WaLiNeAs team, the yellow crosses the sites managed by the other European partners. The arrows represent the circulation of the Tramontane winds (purple arrows) and the Mistral winds (dark blue arrows). The letter T represents the city of Toulouse where HORUS-2 was deployed during the summer, the letters G-D the city of Le Grau-du-Roi where HORUS-1 was deployed and the letter C the city of Cannes where WALI was deployed [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

Arctic or polar air from the continent (Europe and/or Asia) and the Atlantic Ocean (Duffourg & Ducrocq, 2013). Nevertheless, the western Mediterranean basin can occasionally be affected by air masses from the African continent (Chazette et al., 2016; Turato et al., 2004), which have travelled from the tropical ocean over the Sahara. As they approach the basin, Arctic and polar air masses warm and moisten, but remain much colder than tropical air masses. When they approach the Mediterranean basin, tropical air masses are unstable and can easily produce heavy orographic rainfall when they are lifted over high ground. As these air masses have an easterly component, it is generally the western slopes of the mountains that are affected by this rain. The eastern slopes, on the other hand, benefit from milder weather due to the combined effect of the loss of altitude and the lower humidity of the air masses, which is due to the foehn effect as they pass over the mountains. In addition, as shown in Figure 1, the atmosphere on the French Mediterranean coast is also influenced by the circulation of the Tramontane and Mistral winds. The circulation of these winds influences the atmospheric water vapour content, which can dry or humidify the atmosphere depending on the weather conditions (Obermann et al., 2018).

During autumn and winter 2022/2023, the Mediterranean basin experienced two distinct phases, as

illustrated in Figure 2 for the city of Cannes: a wetter period from October to mid-November, followed by a drier period from mid-November to mid-January. During the wetter period, a Saharan dust event was observed over Cannes as shown in Figure 3a,b,f. It should be noted that WALI was our only functional lidar for this period. HORUS-1 was not operational until early November (Laly et al., 2024). These Saharan dusts, originating from the Great Western Erg (Figure 3e), crossed the Mediterranean Sea before reaching southern France as shown in the numerical simulations of the Copernicus Atmospheric Monitoring Service (CAMS) global reanalysis model (https://www.ecmwf.int/en/forecasts/dataset/cams-global-reanalysis, last accessed 01/17/2025) (Figure 3e). The water vapour associated with the aerosol plume was measured with WALI (Figure 3a). These dusts were transported as a result of a high-pressure system that was settled over the Great Western Erg (Figure 3c). Closer examination of Figure 3a,b reveals the presence of WVMR around $9\ \text{g}\cdot\text{kg}^{-1}$ localised alongside dust at altitudes between 3 and 5 km. This altitude range shows a significant amount of water vapour content compared to what is normally found in this layer, making this dust event particularly valuable for studying the performance of the ERA5 reanalysis both within and above the planetary boundary layer (PBL).
<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /><figcaption>FIGURE 2 Temporal evolution of the integrated water vapour content derived from ERA5 near Cannes. It corresponds to the integrated water vapour content in kg·m⁻² between the ground and 2.5 km a.m.s.l [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

The period with the lowest atmospheric water vapour content occurred after mid-November, as shown in Figure 2. Indeed, during part of this period, the western Mediterranean basin was affected by an omega-type blocking pattern (similar to the one described in Baron et al., 2022), which settled over the western Mediterranean basin (Figure 4a). Two low-pressure systems, one over the Azores Islands and the other over the Balkans, surrounded a high-pressure system. This blocking system prevented the circulation of air masses and thus the humidification of the atmosphere by air masses from the Mediterranean Sea. The interest of such a meteorological situation lies in the fact that there was very little water vapour content beyond the PBL top height, at around 1.5 km a.m.s.l. Examples of temporal evolution of WVMR profiles measured by WALI and HORUS-1 and computed by ERA5 are shown in Figure 5. A very low water vapour content is evident throughout the lower and middle troposphere, with WVMR values below 8 g·kg⁻¹ in the PBL and below 5 g·kg⁻¹ above.

## 3.2 | Case study 2: Storms over Toulouse in the summer of 2023

Toulouse is located in the Haute-Garonne department, in the Midi-Pyrenees region of France. Influenced by the Atlantic Ocean and the Mediterranean Sea, the climate

in this area is temperate. Winters are temperate in the plains and colder in the south, with hot summers in the plains, followed by very sunny autumns. Spring, on the other hand, is rainy. Toulouse can be affected by the Cers wind, which blows from the west and brings humidity from the Atlantic Ocean. The city is also subject to the Autan wind, which can bring either anticyclonic weather with dry conditions or rainy weather with heavy rain and thunderstorms. The Midi-Pyrenees region also lies at the foothills of the Pyrenees mountains and is therefore subject to the foehn effect. Hence, hot and dry winds cause fairly wide local temperature and humidity variations.

As shown in Figure 6a, June 2023 was a month with particularly heavy thunderstorm precipitation compared to other years. The cumulative rainfall during the month exceeded 1000 mm. Most of the rain fell between 9 and 14 June, as shown in Figure 6b based on ERA5 data. Over the Languedoc-Roussillon and Midi-Pyrenees regions, precipitation is usually brought to the countryside by the circulation of air masses affected by the presence of the Pyrenees and the Central Massif. In summer, deep convection processes triggered by warm surfaces and fed by the advection of moist air masses can lead to thunderstorms over the Toulouse area, as observed in June 2023.

The temporal evolution of the WVMR profiles provided by HORUS-2 and ERA5 during this period is shown in Figure 7. We note that the altitude range of the lidar was severely limited by the presence of clouds in the lower
<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /><figcaption>FIGURE 3 (a,b) The water vapour mixing ratio (WVMR) temporal evolution over Cannes, given by WALI and ERA5, respectively, between 4 October 2022 0400 UTC and 9 October 2022 0900 UTC. The red circles highlight the passage of water vapour transported with the Saharan dusts. (c,d) The ERA5 reanalysis geopotential and water vapour maps, respectively, at 850 hPa on 8 October 2022 0000 UTC. The black arrows and their lengths represent the direction and the intensity of the winds. The letter H in panel (c) represents the area with a high-pressure system. (e) Modelling of the Aerosol Optical Thickness (AOT) given at 550 nm on 8 October 2022 0000 UTC and the white arrow shows the trajectory followed by the Saharan dusts before it reached the south of France. (f) The temporal evolution of the total AOT at 550 nm with the black curve and the contribution of dusts to the AOT in yellow. Panels (e) and (f) come from the data of the Copernicus Atmospheric Monitoring Service (CAMS) global atmospheric composition forecasts (https://ads.atmosphere.copernicus.eu/datasets, last accessed 28/11/2024), which has a resolution of 0.4° × 0.4°. The white areas in panel (a) correspond to times when the lidar data cannot be used [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>
<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /><figcaption>FIGURE 3 (Continued)</figcaption></figure>

troposphere, and sometimes the lidar did not get a reading when it was raining heavily.

## 3.3 | Case study 3: Intense heatwave in August 2023

Toulouse experienced one of the hottest summers on record in 2023. August 2023 was marked by an intense heatwave that hit southwestern France between 13 and 26 August. In mid-August, a dominant high-pressure system settled over western Europe, as shown in Figure 8a. This high-pressure system suppressed rising air and cloud formation, resulting in clear skies and increased solar radiation reaching the surface. In addition, hot and humid air masses of tropical origin moved northwards into western Europe. This influx of very warm air significantly increased temperatures and brought with it a significant amount of water vapour from the Atlantic Ocean (Figure 8b).

Due to this heatwave, the temperature in Toulouse reached a record of 43°C on 23 August (Figure 9), breaking the previous record of 40.5°C set in 2003. Several cities in the western Mediterranean basin also experienced record temperatures during the same period. Figure 9 shows that the most intense part of the heatwave occurred between 18 and 25 August. In addition, the temperature did not fall below 20°C for almost two weeks between 13 August and 26 August. This period of warm temperatures was enriched in water vapour with WVMR values exceeding 15 g·kg⁻¹ (Figure 10a,b), similar to those found in the tropics. We note that the HORUS-2 lidar measured WVMR higher than 10 g·kg⁻¹ above 3 km a.m.s.l., corresponding to very high water vapour content in the free troposphere. This water vapour seems to be less well estimated by the reanalysis (Figure 10b). It should be noted that these data

represent the highest WVMR component for upcoming intercomparisons with ERA5.

## 3.4 | Water vapour content associated with long-range aerosol transport

Two large aerosol events covered the Toulouse area in summer 2023. The presence of these aerosol outbreaks was detected by the Toulouse station of the AERONET network, as shown in Figure 11. The Aerosol Optical Thickness (AOT) exceeded 1 at 387 nm, higher than its background value of approx. 0.15. The Ångström exponent was low and less than 0.4, indicating the presence of dust-like particles. As shown in Figure 12, the presence of these two outbreaks is confirmed by the CAMS numerical simulations. The event observed at the end of June 2023 was advected from Canada (Figure 12a) and trapped biomass burning aerosols, while the event at the beginning of September 2023 originated from the Western Sahara (Figure 12c) and was composed of dust aerosols. These two types of aerosol outbreaks are generally associated with high water vapour content, either because of the processes that produced them or because of changes during the lifetime of the air masses carrying them. This makes them interesting case studies to consider for model/measurement comparisons.

## 3.5 | Case study 4: Water vapour content during the event of biomass burning aerosols

The first period of aerosol advection was due to major forest fires in Canada in June 2023. Around 20 June, significant forest fires occurred in the regions of Quebec
<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

**FIGURE 4** ERA5 synoptic maps of the western Mediterranean basin on 18 December 2022 0000 UTC of (a) water vapour mixing ratio (WVMR) plotted at 850 hPa and of (b) geopotential plotted at 850 hPa. Th arrows and their lengths represent the direction and the intensity of the winds. The letters L represents zones with low-pressure systems [Colour figure can be viewed at wileyonlinelibrary.com]

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>

**FIGURE 5** (a,b) Water vapour mixing ratio (WVMR) temporal evolution over Cannes and Le Grau-du-Roi, given by WALI and HORUS-1, respectively, between 6 December 2022 0000 UTC and 12 December 2022 0000 UTC. (c,d) WVMR temporal evolution over Cannes and Le Grau-du-Roi, respectively, given by ERA5 for the same period. The white areas in panels (a,b) correspond to times when the lidar data cannot be used [Colour figure can be viewed at wileyonlinelibrary.com]

and Ontario. Many particles and water vapour were then lifted into the atmosphere by pyroconvection and transported across the Atlantic Ocean to western Europe (Figure 12a) as already reported in the literature (e.g., Chazette, Marnas, & Totems, 2014). The lower free

troposphere and even the PBL can be significantly affected by this type of air mass (e.g., Menut et al., 2018). An increase in the concentration of carbon monoxide (CO) was observed near Toulouse after 24 June, as shown in Figure 12b. A higher water vapour content was also
<figure><img src="image_17.png" /></figure>

FIGURE 6 Histograms showing (a) cumulative rainfall (mm) measured during June in Toulouse since 2015, and (b) cumulative rainfall (mm) measured during June in Toulouse during 2023. ERA5 ground precipitation data have been used [Colour figure can be viewed at wileyonlinelibrary.com]

<figure><img src="image_18.png" /></figure>

FIGURE 7 Temporal evolution of the water vapour mixing ratio (WVMR) over Toulouse between 9 June 2023 0000 UTC and 16 June 2023 0000 UTC given by (a) HORUS-2 lidar deployed at the Météo-France site in Toulouse and (b) ERA5 reanalysis. The white areas in panel (a) correspond to times when the lidar data cannot be used [Colour figure can be viewed at wileyonlinelibrary.com]
<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /></figure>

FIGURE 8 ERA5 synoptic maps of the western Mediterranean basin on 20 August 2023 1500 UTC of (a) geopotential plotted at 850 hPa and of (b) water vapour mixing ratio (WVMR) plotted at 850 hPa. The arrows and their lengths represent the direction and the intensity of the winds. The letter H represents the zone with a high-pressure system [Colour figure can be viewed at wileyonlinelibrary.com]

<figure><img src="image_21.png" /></figure>

FIGURE 9 Temporal evolution of surface temperature measured by a weather station at the Toulouse site of Météo-France (43°34′27″ N, 1°22′26″ E) in August 2023. The blue dotted lines represent the heatwave thresholds set by Météo-France for southwest France, which are 20°C during nighttime and 35°C during daytime. The red dotted line represents the previous maximum temperature ever recorded in Toulouse, which was 40.5°C on 4 August 2003 [Colour figure can be viewed at wileyonlinelibrary.com]

generally observed in the presence of biomass combustion aerosols (Pistone et al., 2021), as shown in Figure 13a,c, between 23 and 29 June. It reached 5 g·kg⁻¹ up to 9 km a.m.s.l. on 28 June. Consequently, we observed high values of WVMR in the free troposphere, making this case study a strong candidate for evaluating ERA5 numerical simulations. The values observed in the PBL are those expected for a summer period in the Toulouse area, between 8 and 12 g·kg⁻¹.

3.6 | Case study 5: Water vapour content during the event of Saharan dust aerosols

The second period of aerosol advection over Toulouse occurred at the beginning of September 2023. It is shown in Figure 11. This period was characterised by an increase in the AOT, with values greater than 1, and a decrease in the Ångström exponent, with values close to 0. These values reflect the passage of Saharan dust particles identified
<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /><figcaption>**FIGURE 10** Temporal evolution of water vapour mixing ratio (WVMR) over Toulouse between 15 August 2023 0000 UTC and 28 August 2023 0000 UTC given by (a) HORUS-2 lidar deployed at the Météo-France site in Toulouse and (b) ERA5 reanalysis model. The white areas in (a) correspond to times when the lidar data cannot be used [Colour figure can be viewed at wileyonlinelibrary.com](https://wileyonlinelibrary.com)</figcaption></figure>

as coming from the Great Western Erg, as shown in Figure 12c. In fact, the Sahara region is the world's largest source of naturally wind-blown mineral dust aerosols (Mahowald et al., 1999; Prospero et al., 2002). The dust plume passed over Toulouse between 4 and 7 September due to an Atlantic low-pressure system. Figure 12d shows that the major contribution to the AOT was due to the presence of dust aerosols. This period was also characterised by important WVMR values exceeding 10 g·kg⁻¹ above

1.5 km a.m.s.l, up to 5 km a.m.s.l. in the free troposphere. This water vapour was probably transported with the Saharan dusts, as has already been demonstrated for this type of advection (Kim et al., 2004, 2009). As shown in Figure 13b,d, the WVMR appeared to be higher in the free troposphere than in the previous case studies. The water vapour content was more variable in the PBL, but with similar maximum values. A chronogram summarizing the events studied is shown in Figure 14.
<figure><img src="image_24.png" /></figure>

<figure><img src="image_25.png" /><figcaption>FIGURE 11 Whisker boxes for the daily temporal evolution of both the Aerosol Optical Thickness (AOT, in red) and the Ångström exponent (Ang, in blue) derived from the AERONET station of Toulouse (Météo-France, 43°34′28″ N, 1°22′29″ E). Black arrows indicate the periods of high AOT values associated with significant aerosol advection. AERONET level 3 data were used for this figure [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

<figure><img src="image_26.png" /></figure>

<figure><img src="image_27.png" /></figure>

<figure><img src="image_28.png" /></figure>

<figure><img src="image_29.png" /><figcaption>FIGURE 12 (a, c) Synoptic maps of the Aerosol Optical Thickness (AOT) at 550 nm during the advection of Canadian fire particles over Western Europe at the end of June 2023 (whose path is indicated by the white arrow) and the advection of dust particles from the Great Western Erg over Western Europe at the beginning of September 2023. The letter L indicates the presence of a low-pressure system in the Atlantic Ocean during this period, which brought the particles to the south of France (white arrow). (b) Temporal evolution of the carbon monoxide (CO) concentration (in g m⁻²) over Toulouse at the end of June 2023. (d) Temporal evolution of the total AOT at 550 nm (blue line) over Toulouse at the beginning of September 2023 and the temporal evolution of the dust AOT at 550 nm (yellow line). All data come from the Copernicus Atmospheric Monitoring Service (CAMS) global atmospheric composition forecasts (https://ads.atmosphere.copernicus.eu/datasets, last accessed 11 December 2024), which has a resolution of 0.4° × 0.4° [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>
<figure><img src="image_30.png" /><figcaption>FIGURE 13 Water vapour mixing ratio (WVMR) temporal evolution over Toulouse given by HORUS-2 (a) and ERA5 (c) between 23 June 2023 0000 UTC and 30 June 2023 0000 UTC. WVMR temporal evolution over Toulouse given by HORUS-2 (b) and ERA5 (d) between 4 September 2023 0000 UTC and 7 September 2023 0000 UTC. The black ellipses mark the zones where the particles are thought to have passed, with moments when the water vapour content was abnormally high for these atmospheric zones. The white areas in (a) and (b) correspond to times when the lidar data cannot be used [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

<figure><img src="image_31.png" /><figcaption>FIGURE 14 Chronogram summarizing the chronological order of the case studies during the WaLiNeAs field campaigns. Case study 4 does not follow the chronological order because it is presented in the text as an aerosols event and therefore presented just before case study 5</figcaption></figure>

## 4 | COMPARISON BETWEEN LIDARS AND ERA5

The purpose of this section is to assess the reliability of the ERA5 reanalysis in the lower, middle and high troposphere compared to lidar vertical profiles and to highlight the potential forecast improvements that could be expected

from lidar measurements of the WVMR. The statistical parameters detailed in Section 2.3 are calculated for four altitude ranges:

* 0–1.5 km (a.m.s.l.), which includes most of the PBL during daytime and the entire nocturnal layer at night. This part of the troposphere is most affected by the
<figure><img src="image_32.png" /></figure>

<table><caption>TABLE 2 Statistical comparison between the lidar-derived water vapour mixing ratio (WVMR) and the ERA5 reanalysis over the case studies. The third column represents the mean of the WVMR given by the lidars and the ERA5 outputs. The fourth column represents the mean bias (MB) and the root-mean-squared deviation (RMSD) and the fifth the correlation coefficient (COR). Lines marked 'Not relevant' indicate that no measurements were available during the periods indicated for the lidars</caption><thead><tr><th>Statistical parameter</th><th>Altitude ranges</th><th>Mean WMVR (g·kg<sup>-1</sup>)<br>Lidar/ERA5</th><th>MB/RMSD<br>(g·kg<sup>-1</sup>)</th><th>COR</th></tr></thead><tbody><tr><td>Case study 1:</td><td>0–1.5 km (a.m.s.l.)</td><td>8.28/6.65</td><td>1.63/1.81</td><td>0.81</td></tr><tr><td>Saharan dust aerosol<br>period</td><td>1.5–3 km (a.m.s.l.)</td><td>5.21/3.86</td><td>1.35/1.39</td><td>0.90</td></tr><tr><td rowspan="2">WALI vs ERA5</td><td>3–5 km (a.m.s.l.)</td><td>3.62/2.24</td><td>1.38/1.47</td><td>0.89</td></tr><tr><td>5–8 km (a.m.s.l.)</td><td>Not relevant</td><td>Not relevant</td><td>Not relevant</td></tr><tr><td>AUTHOR: Dry period</td><td>0–1.5 km (a.m.s.l.)</td><td>5.93/5.08</td><td>0.85/1.21</td><td>0.89</td></tr><tr><td rowspan="3">WALI vs ERA5</td><td>1.5–3 km (a.m.s.l.)</td><td>3.45/2.77</td><td>0.68/1.09</td><td>0.89</td></tr><tr><td>3–5 km (a.m.s.l.)</td><td>1.96/1.31</td><td>0.65/0.81</td><td>0.92</td></tr><tr><td>5–8 km (a.m.s.l.)</td><td>1.15/0.59</td><td>0.56/0.58</td><td>0.90</td></tr><tr><td>Dry period</td><td>0–1.5 km (a.m.s.l.)</td><td>5.96/4.84</td><td>1.12/1.49</td><td>0.94</td></tr><tr><td rowspan="3">HORUS-1 vs ERA5</td><td>1.5–3 km (a.m.s.l.)</td><td>3.90/2.82</td><td>1.08/1.34</td><td>0.93</td></tr><tr><td>3–5 km (a.m.s.l.)</td><td>2.88/1.26</td><td>1.62/1.84</td><td>0.87</td></tr><tr><td>5–8 km (a.m.s.l.)</td><td>Not relevant</td><td>Not relevant</td><td>Not relevant</td></tr><tr><td>Case study 2:</td><td>0–1.5 km (a.m.s.l.)</td><td>10.93/10.27</td><td>0.66/1.28</td><td>0.61</td></tr><tr><td>Rainstorm period</td><td>1.5–3 km (a.m.s.l.)</td><td>8.41/6.36</td><td>2.05/2.29</td><td>0.65</td></tr><tr><td rowspan="2">HORUS-2 vs ERA5</td><td>3–5 km (a.m.s.l.)</td><td>4.77/3.24</td><td>1.53/2.34</td><td>0.77</td></tr><tr><td>5–8 km (a.m.s.l.)</td><td>1.68/0.86</td><td>0.82/1.57</td><td>0.74</td></tr><tr><td>Case study 3:</td><td>0–1.5 km (a.m.s.l.)</td><td>12.48/11.55</td><td>0.93/1.71</td><td>0.77</td></tr><tr><td>Heatwave period</td><td>1.5–3 km (a.m.s.l.)</td><td>9.42/7.7</td><td>1.72/2.35</td><td>0.36</td></tr><tr><td rowspan="2">HORUS-2 vs ERA5</td><td>3–5 km (a.m.s.l.)</td><td>7.28/4.31</td><td>2.97/3.43</td><td>0.72</td></tr><tr><td>5–8 km (a.m.s.l.)</td><td>3.57/1.73</td><td>1.84/2.28</td><td>0.63</td></tr><tr><td>Case study 4:</td><td>0–1.5 km (a.m.s.l.)</td><td>9.94/9.26</td><td>0.68/1.33</td><td>0.82</td></tr><tr><td>Biomass burning aerosols<br>period</td><td>1.5–3 km (a.m.s.l.)</td><td>6.20/5.40</td><td>0.80/1.77</td><td>0.79</td></tr><tr><td rowspan="2">HORUS-2 vs ERA5</td><td>3–5 km (a.m.s.l.)</td><td>3.41/2.27</td><td>1.14/1.60</td><td>0.40</td></tr><tr><td>5–8 km (a.m.s.l.)</td><td>2.50/1.20</td><td>1.30/1.45</td><td>0.92</td></tr><tr><td>Case study 5:</td><td>0–1.5 km (a.m.s.l.)</td><td>8.53/8.44</td><td>0.09/1.07</td><td>0.79</td></tr><tr><td>Saharan dust aerosol<br>period</td><td>1.5–3 km (a.m.s.l.)</td><td>7.53/6.10</td><td>1.43/1.64</td><td>0.16</td></tr><tr><td rowspan="2">HORUS-2 vs ERA5</td><td>3–5 km (a.m.s.l.)</td><td>6.21/3.25</td><td>2.96/3.73</td><td>0.51</td></tr><tr><td>5–8 km (a.m.s.l.)</td><td>2.45/1.18</td><td>1.27/1.41</td><td>0.78</td></tr></tbody></table>

surface and contains most of the water vapour in the atmosphere.

* 1.5–3 km (a.m.s.l.), which includes the transition to the free troposphere and the residual layer. It can still contain a significant amount of water vapour, especially in the summer, as the PBL can be extended to higher altitudes.

* 3–5 km (a.m.s.l.), which corresponds to the middle troposphere. There is much less water vapour than in the lower parts. However, significant amounts of water vapour can be found when moist air masses are advected.

* 5–8 km (a.m.s.l.), which is between the middle and upper troposphere. Water vapour content in this part of the troposphere is generally low (less than 1 g·kg⁻¹). However, long-range transport can affect the moisture enrichment at these altitudes.

Table 2 shows the values of the statistical parameters as defined in Section 2.3 for the different case studies identified in Section 3. Figure 15 shows scatter plots comparing lidar-derived WVMR and ERA5 for the previous altitude ranges and over periods encompassing all the case studies. The scatter plots for each case study (Figures 17 and 18 below) are then discussed in the following subsections. We
found that the relationship between lidar measurements and ERA5 simulations is linear. The slopes of the linear regressions, which are similar for each site in Figure 15, suggest a coherent behaviour of ERA5 with respect to the lidar profiles. However, ERA5 tends to underestimate the WVMR with altitude. We also observe that the point clouds become more scattered with altitude. This could be due to two main reasons: (i) the lidar signal-to-noise ratio decreases with altitude and (ii) the model does not properly account for water vapour sources and sinks. All of these results are discussed below on a case-by-case basis.

## 4.1 | Mediterranean autumn and winter case study

Table 2 shows that the correlation between the lidars and ERA5 is excellent with values around 0.9 for both sites; the RMSD values are approx. 1 g·kg⁻¹ for WALI (Cannes) and approx. 1.5 g·kg⁻¹ for HORUS-1 (Le Grau-du-Roi), and the corresponding MBs are less than 1 g·kg⁻¹ and slightly more than 1 g·kg⁻¹, respectively. We note higher MB and RMSD values for HORUS-1. A possible explanation for this could be the lower measurement performance of HORUS-1 compared to other lidars (Table 1). However, it is important to note that HORUS-1 and WALI are not in the same location, so it is difficult to quantify the impact of this last point. Another reason for the differences in the coefficients, linked to the lidar sites, is given below. The MB and RMSD values are about twice those of the dry period in December 2022 during the dust event of the wet period in autumn 2022. However, the COR remains above 0.80, showing that the ERA5 reanalysis, while underestimating the water vapour, effectively captured its temporal evolution. This is true even at altitudes where long-range transport of water vapour took place, between 1.5 and 5 km a.m.s.l.

Scatter plots showing similar linear regressions for all lidar sites and for all available data are shown in Figure 15a,b. However, the scatter is larger for Horus-1 (Figure 15b,e) Due to its location, Le Grau-du-Roi is more exposed to the Mistral and Tramontane winds (Figure 1), whereas Cannes is located in the foothills of the Alps. The circulation of these winds can affect the water vapour content of the atmosphere up to the free troposphere at an altitude of 4 km (Di Girolamo et al., 2016). As shown in Figure 16, even if the mean values of the ERA5-derived WVMRs were close for the two sites between mid-November and December 2022, there were significant differences in the distribution of the WVMR profiles. Indeed, we observe more water vapour in the free troposphere at Le Grau-du-Roi (Figure 16a) than at Cannes (Figure 16b), especially between 3 and 5 km a.m.s.l. As this water vapour was less estimated by the

ERA5 reanalysis at Le Grau-du-Roi, we note a wider spread in the bias histograms of Figure 15e at this altitude range, which is not the case at Cannes (Figure 15d). In these histograms, which are predominantly monomodal, the bias is defined by the difference between the lidar and the ERA5-derived WVMR. They are centred on similar values for the lower tropospheric layers but show differences with increasing altitude, especially in the range between 3 and 5 km a.m.s.l. This can also be explained by the circulation of air masses, which is more complex at Le Grau-du-Roi because the site is located upstream of the Rhône valley. It is important to note that the uncertainty associated with the HORUS-1 measurement may play a role, especially in the mid-troposphere.

A focus on the dust event of October 2022 and on the dry period of mid-November/December 2022 is shown in Figure 17a-c. During the desert dust event, ERA5 reproduced well the temporal evolution of water vapour content throughout the lower and middle troposphere. However, there is an average negative bias of approx. -1.5 g·kg⁻¹. This shows that water vapour sources in the aerosol plume were not properly considered. A slightly greater negative bias can be observed in the dry period, both at Cannes (Figure 17b) and Le Grau-du-Roi (Figure 17c). Nevertheless, the points are not very widely scattered, confirming an excellent correlation between the ERA5 reanalysis and the lidars. This suggests that the reanalysis correctly estimated water vapour evolution throughout the troposphere during this period, which was characterised by low water vapour content in and above the PBL.

## 4.2 | Summer case study in the Toulouse area

The meteorological context in Toulouse contrasted sharply with that of the Mediterranean basin during autumn and winter. It is associated with higher water vapour content in the lower troposphere, mainly due to specific air mass advections, which also led to an increase in both temporal and vertical variability. The HORUS-2 dataset thus provides valuable additional insight into the evolved lidar-reanalysis comparisons.

In contrast to case study 1, the correlation coefficients COR of case studies 2-5 are generally lower, as shown in Table 2. The lowest values are obtained when there is a lot of water vapour present above the PBL during the extreme temperature event of case study 3 (COR ~ 0.36) or the advection of Saharan air masses of case study 5 (COR ~ 0.16). These two situations corresponded to a warmer free troposphere, which is also associated with higher MB and RMSD, ranging from 1.27 to 2.97 g·kg⁻¹ and
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<figure><img src="image_48.png" /><figcaption>FIGURE 15 (a,b,c,g,h,i) Scatter plots between lidar measurements (x-axis) and ERA5 reanalysis (y-axis) for each case study: (a,b) for the two sites of case study 1, (c) includes all the Toulouse case studies. Panel (c) is divided according to the three months containing the case studies. (g) June, (h) August and (i) September. The colours represent data for the specific altitude ranges detailed in Section 4. For each altitude range, the trend curve is plotted with the same colour and the associated linear regression equation is provided on the graph. The numbers in parentheses indicate the number of profiles considered for the comparison in each altitude range. One point corresponds to one WVMR measurement with a temporal resolution of one hour and a vertical resolution of 100 m. (d,e,f,j,k,l) Bias histograms related to panels (a,b,c,g,h,i), respectively. The panels show bias histograms for each altitude range with the same colour criterion. The bin width of the histograms is 0.2 g·kg⁻¹. The bias is defined as the difference between the lidar WVMR and the ERA5 WVMR. The mean (m) and standard deviation (σ) are given in the legend to the histograms, using the same colour code. [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>
<figure><img src="image_49.png" /></figure>

FIGURE 16 Occurrences of water vapour mixing ratio (WVMR) by altitude retrieve from ERA5 reanalysis data between mid-November and December 2022 for (a) Le Grau-du-Roi and (b) Cannes. The black line represents the mean value of the WVMR [Colour figure can be viewed at wileyonlinelibrary.com]

<figure><img src="image_50.png" /></figure>

FIGURE 17 Scatter plots between lidar measurements and ERA5 reanalysis outputs during (a) the dust event that occurred over Cannes in October 2022 (WALI, case study 1), (b) the mid-November/December 2022 dry winter over Cannes (WALI, case study 1), and (c) the mid-November/December 2022 dry winter over Le Grau-du-Roi (HORUS-1, case study 1). In each figure, colours represent data for specific altitude ranges: blue between 0 and 1.5 km a.m.s.l., cyan between 1.5 and 3 km a.m.s.l., magenta between 3 and 5 km a.m.s.l. and green between 5 and 8 km a.m.s.l.. The numbers in parentheses indicate the number of profiles considered for each altitude range. One point corresponds to one water vapour mixing ratio (WVMR) measurement with a temporal resolution of one hour and a vertical resolution of 100 m [Colour figure can be viewed at wileyonlinelibrary.com]

1.41 to 3.73 g·kg⁻¹, respectively. On the other hand, during the whole field campaign in Toulouse, the lowest values of MB and RMSD obtained in the PBL are 0.09 g·kg⁻¹ and 1.07 g·kg⁻¹, respectively. We can therefore observe that situations with both warmer temperatures and the presence of water vapour transport can lead to less efficient reanalysis for water vapour, especially between 1.5 and 5 km a.m.s.l. Moreover, as can be seen from the scatter plot in Figure 15c, which includes all the lidar measurements over Toulouse, there is much more scatter compared to

Figure 15a,b, although the trend of the regression curves remains similar. This shows the consistent behaviour of the ERA5 reanalysis, which underestimates the WVMR for all altitude ranges, as shown in Figure 15f, where the increase of the MB with altitude is clearly highlighted.

Figure 15g-i show the scatter plots for June, August and September 2023, respectively. HORUS-2 outperforms WALI, suggesting that the observed dispersion in the scatter plots, especially between 1.5 and 5 km a.m.s.l., is likely due to reanalysis limitations in accurately simulating the
<figure><img src="image_51.png" /></figure>

<figure><img src="image_52.png" /></figure>

**FIGURE 18** Scatter plots between lidar measurements and ERA5 reanalysis outputs during (a) the rainstorm that hit Toulouse in June 2023 (HORUS-2, case study 2), (b) the heatwave period in August 2023 over Toulouse (HORUS-2, case study 3), (c) the fire aerosols advection in June 2023 over Toulouse (HORUS-2, case study 4), and (d) the dust event in September 2023 over Toulouse (HORUS-2, case study 5). In each figure, colours represent data for specific altitude ranges: blue between 0 and 1.5 km a.m.s.l., cyan between 1.5 and 3 km a.m.s.l., magenta between 3 and 5 km a.m.s.l. and green between 5 and 8 km a.m.s.l.. The numbers in parentheses indicate the number of profiles considered for each altitude range. One point corresponds to one WVMR measurement with a temporal resolution of one hour and a vertical resolution of 100 m [Colour figure can be viewed at wileyonlinelibrary.com]

WVMR. The histograms in Figure 15j–l support this conclusion by showing larger dispersion and bias in the 1.5–3 km and 3–5 km a.m.s.l. altitude ranges. These higher biases may be due to limitations in the boundary conditions of the model or inaccuracies in the representation of certain processes by the model. The shape and position of the histograms for the altitude ranges 0–1.5 km and 5–8 km a.m.s.l. are comparable to those in case study 1, showing that ERA5 is much better at estimating water vapour in the PBL and in the upper troposphere. However, there is confirmation that it is more difficult above the PBL and in the middle troposphere (between 1.5 and 5 km a.m.s.l.), which can be influenced by air masses with longer histories.

The case studies are shown on separate scatter plots in Figure 18a–d and have been extracted from previous months. As with the monthly plots, there are larger biases in the layers above the PBL. In the lower troposphere, ERA5 faithfully reproduced the lidar measurements. In contrast, the divergence in the advected layers with high water vapour content is noticeable. For case studies 2 and 4, corresponding to the period of intense precipitation and the arrival of a biomass burning plume at high altitude, the agreement between the reanalysis and the lidar measurements is better, except for the assumed main transport height of the aerosol plume between 3 and 5 km, where

COR is approx. 0.4. For the two case studies 3 and 5, the results are quite similar, with the presence of warm air and a high content of water vapour at the higher altitudes. This moisture was not well captured by the reanalysis. A wider dispersion is observed in both the 1.5–3 km and 3–5 km height ranges.

5 | CONCLUSIVE DISCUSSION

The WaLiNeAs field campaign, conducted over several seasons in southern France, provided a comprehensive dataset that allowed a thorough comparison of WVMR profiles derived from Raman lidar measurements and the ERA5 reanalysis. This comparison highlighted both the strengths and limitations of the ERA5 reanalysis, in particular under different meteorological conditions and at different altitudes ranging from ground level to 8 km a.m.s.l. The field campaign measurements spanned eight months and provided an unprecedented dataset capturing different water vapour contents throughout the troposphere. We have used three independent Raman lidars, each of which has its own validated calibration procedure. The AERIS database contains all the WVMR profiles used for comparison with the ERA5 reanalyses (https://en.aeris-data.fr/, last accessed: 11 December 2024).
In our study, we used three statistical parameters (MB, RMSD and COR) to evaluate the performance of ERA5 reanalysis against lidar measurements of WVMR. The comparison focused on case studies, each characterised by different levels of tropospheric water vapour content. These case studies highlight low water vapour levels observed during the winter of 2022–2023, and high water vapour levels associated with high temperatures or aerosol plume events during the summer of 2023. This diverse dataset provides a unique opportunity to assess the accuracy of ERA5 in estimating tropospheric water vapour, from the PBL to the free troposphere, using Raman lidar observations. In general, reanalyses tend to underestimate the WVMR regardless of altitude and season. The WVMR estimated by ERA5 are the least underestimated and best correlated with lidar WVMR data within the PBL. This is highlighted by the MB and RMSD values, which generally remain below 1 g·kg⁻¹ and 1.5 g·kg⁻¹, respectively. However, an exception is observed during the October 2022 Cannes dust event, where the reanalysis struggled to represent the complex water vapour structures in the lower troposphere, resulting in higher MB (1.63 g·kg⁻¹) and RMSD (1.81 g·kg⁻¹). Beyond the PBL, ERA5 shows limitations in accurately estimating the larger amounts of water vapour between 1.5 and 5 km a.m.s.l., that is, in the lower free troposphere. While the MB and RMSD values for the winter of 2022–2023 are comparable to those found within the PBL, notable increases in these parameters are observed in the specific summer case studies in the Toulouse area. For these cases, the MB and RMSD values often exceed 1.5 and 2 g·kg⁻¹, respectively, and sometimes significantly so. These increases occur when significant amounts of water vapour have been introduced into the free troposphere, typically associated with long-range transport of Saharan dust aerosols or elevated temperatures that raise the PBL top above 1.5 km.

These results suggest that while ERA5 performs well in the PBL, it underperforms in the estimation of water vapour transported in the free troposphere. This limitation may be due to the lower vertical resolution of the reanalysis above the PBL and the lack of high-resolution measurements in the middle and upper troposphere, where Raman lidars excel. An assessment of the discrepancies related to the spatiotemporal resolution of ERA5 is given in Appendix A below. The results for the two dust aerosol events in Cannes (October 2022) and Toulouse (September 2023) illustrate the limitations of the reanalyses. In both cases, water vapour was transported beyond the PBL, between 2 and 5 km a.m.s.l. However, the reanalyseis effectively captured the temporal variability of the water vapour for the Cannes event but failed for Toulouse. The discrepancy is likely due to differences in the transport pathways of the dust plume and the associated water vapour.

During the October 2022 event, the Saharan dust travelled directly across the Mediterranean before reaching southern France, providing a relatively straightforward trajectory for the reanalysis to capture. In contrast, during the September 2023 event, the dust plume crossed the Pyrenees before reaching southwest France. This more complex inland trajectory, coupled with the geographical location of Toulouse, likely reduced the ability of the reanalysis to accurately represent the transported water vapour.

The WaLiNeAs field campaign has demonstrated the importance of Raman lidar in providing accurate, high-resolution vertical profiles of the WVMR for a wide range of common weather situations. The integration of these lidar measurements into reanalysis models such as ERA5 can help to reduce biases and improve the accuracy of the WVMR representation, especially in the lower and middle troposphere where moisture plays a critical role in weather and climate dynamics. This study highlights the need to improve model representations of water vapour, especially in moisture-rich environments, in order to better predict the intensity and location of extreme weather events that are increasingly occurring in the context of global warming. This study was based on reanalyses, which are supposed to represent the atmosphere better than analyses or prediction models. We can therefore expect even greater discrepancies with the latter.

## ACKNOWLEDGEMENTS

We thank Cyrille Flamant for the coordination of the WaLiNeAs programme and for his help during the field measurement campaigns. We also thank Jeremy Lagarrigue and Julien Totems for their help during the WaLiNeAs field campaigns. Thanks to Météo-France for their welcome and help during the campaign in Toulouse and for providing us with various meteorological measurements of the site, including radiosonde data. Thanks also to Laurent Forges, Valentin Guillet and Geraldine Pagan for their help. Special thanks to the local authorities of Thales-Alenia-Space, the harbour master of Port Camargue and the town of Coursan for their welcome during the first part of the WaLiNeAs field campaign. This research has been supported by the Agence Nationale de la Recherche via the WaLiNeAs project (grant no. ANR-20-CE04-0001). This work was also supported by the French Institut National de l'Univers (INSU) of the Centre National de la Recherche Scientifique (CNRS) and the Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA).

## FUNDING INFORMATION

This research has been supported by the Agence Nationale de la Recherche via the WaLiNeAs project (grant no. ANR-20-CE04-0001). This work was also supported by
<figure><img src="image_53.png" /></figure>

the French Institut National de l'Univers (INSU) of the Centre National de la Recherche Scientifique (CNRS) and the Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA).

## CONFLICT OF INTEREST STATEMENT

The authors declare that they have no conflict of interest.

## DATA AVAILABILITY STATEMENT

The datasets are published open access on the AERIS database (https://en.aeris-data.fr/, last accessed 11 December 2024). Weather maps plotted using ERA5 for all WaLiNeAs periods are also available on the following website: https://metclim-lidars.aeris-data.fr/walineas/.

## ORCID

Frédéric Laly https://orcid.org/0009-0007-2164-7312

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How to cite this article: Laly, F. & Chazette, P. (2025) Comparative analysis of ERA5 and Raman lidar-derived moisture profiles in the framework of the WaLiNeAs field campaigns. *Quarterly Journal of the Royal Meteorological Society*, e5044. Available from: https://doi.org/10.1002/qj.5044

APPENDIX A. DISCREPANCIES RELATED
TO THE SPATIOTEMPORAL RESOLUTION
OF ERA5

The time resolution of ERA5 is 1 h. However, the atmospheric water vapour content can vary considerably during this period (i.e. Figure A1) and explains some of the observed discrepancies between lidar and reanalysis. Most of the water vapour is found in the PBL (between 0 and 1.5 km a.m.s.l.) and it is therefore in this altitude range that the WVMR can fluctuate the most, especially during the day when PBL mixes. By way of illustration, Figure A1 a and b show the temporal evolution of the WVMR in Cannes over 1 hour in the PBL at 1 km a.m.s.l. during the day and at night, respectively. During the day, the WVMR fluctuated by almost 1 g·kg⁻¹ (between 6 and 7 g·kg⁻¹) over 1 h, while at night it fluctuated by less than 0.1 with a mean value of around 6 g·kg⁻¹. However, in the case of very hot weather, as in the summer of 2023 in Toulouse, the PBL can extend up to 3 km in altitude and the WVMR fluctuated of 1.5 g·kg⁻¹ (from 6 to 7.5 g·kg⁻¹) in 1 hour, even at 3 km altitude, as shown in Figure A1c. Therefore, larger differences in the WVMR between lidar and reanalysis are observed when water content varies significantly over 1 h, and this increases with altitude. As explained by Hersbach et al. (2020), this can be explained by the fact that there are far more assimilable measurements available in the PBL than above it, which is related to the lack of active water vapour instruments in the middle and upper troposphere.

Another possible source of discrepancies is the com-
parison of the lidar profiles, whose geographical coordi-
nates are precise, with the ERA5 data, which have a hor-
izontal resolution of 0.25 × 0.25°. (i.e., ~28 km × 28 km).
Indeed, it would be possible for the water vapour con-
tent to vary locally over less than 28 km. Considering the
horizontal wind speed provided by the reanalysis, many
situations show air masses moving more than 28 km in 1 h.
The spatial variability of air masses could therefore influ-
ence the atmospheric water vapour content and explain
part of the discrepancies observed. To quantify such an
influence, the mean bias (MB), the root mean square dif-
ference (RMSD) and the Pearson correlation coefficient
(COR) are also used.

As shown in Figure A2, each surround pixel is first
compared with the central pixel, which includes the
lidar location, for the different altitude ranges defined in
Section 4. In a second step, a synthesis is performed, con-
sidering all the adjacent pixels. All statistical parameters
are given in Table 3. The MB does not exceed 0.1 g·kg⁻¹
except for Cannes between 0 and 1.5 km where the value
<figure><img src="image_55.png" /></figure>

<figure><img src="image_56.png" /></figure>

**FIGURE A1** Temporal evolution of the WVMR in g·kg⁻¹ given by WALI in Cannes at 1 km (a.m.s.l.) during (a) daytime on 23 December 2022, between 13:00 and 1400 UTC, (b) nighttime on 23 December 2022, between 22:00 and 2300 UTC and (c) by HORUS-2 in Toulouse at 3 km (a.m.s.l.) on 23 August 2023, between 22:00 and 2300 UTC. Temporal and vertical resolution of lidar profiles is 15 min and 100 m, respectively. The lidar data come from the NetCDF files in the AERIS database (https://doi.org/10.25326/537).

<figure><img src="image_57.png" /></figure>

**FIGURE A2** Diagram showing the principle of comparison between the central pixel (P5) and the surrounding pixels. Comparison is made between each surrounding pixel and the central pixel, and then the synthesis of all these comparisons is calculated. Each pixel measures 0.25° × 0.25°, which is the horizontal resolution of the ERA5 outputs.

is 0.14 g·kg⁻¹. RMSD values are similar for the 3 sites and range from 2.7 × 10⁻³ to 0.59 g·kg⁻¹. The RMSD increases with altitude, which can be explained by the lower resolution of the reanalysis as altitude increases, leading to more WVMR variability. However, the MB values remain well below those shown in Table 2. COR coefficients are high with values above 0.97. These previous results show that there is very little variability between the different pixels, even for wind speeds higher than 10 m s⁻¹. The RMSDis slightly higher between 3 and 8 km a.m.s.l. Hence, a higher variability of the WVMR can be expected in this height range. However, the difference in WVMR between pixels is significantly smaller than the values obtained from the comparisons between lidar and ERA5.
<figure><img src="image_58.png" /></figure>

<table><caption>TABLE 3 Statistical comparison between the ERA5 central pixel and the 8 surrounding pixels. The central pixel includes the location of the site where each lidar was installed.</caption><thead><tr><th>Statistical parameter sites</th><th></th><th>MB (g·kg<sup>-1</sup>)</th><th>RMSD (g·kg<sup>-1</sup>)</th><th>COR</th></tr></thead><tbody><tr><td rowspan="6">December Cannes</td><td>0–1.5 km (a.m.s.l.)</td><td>0.14</td><td>2.8 × 10<sup>-3</sup></td><td>0.99</td></tr><tr><td>1.5–3 km (a.m.s.l.)</td><td>-1.38 × 10<sup>-2</sup></td><td>6.3 × 10<sup>-2</sup></td><td>0.99</td></tr><tr><td>3–5 km (a.m.s.l.)</td><td>-4.9 × 10<sup>-3</sup></td><td>0.19</td><td>0.98</td></tr><tr><td>5–8 km (a.m.s.l.)</td><td>-1.9 × 10<sup>-4</sup></td><td>0.39</td><td>0.97</td></tr><tr><td></td><td></td><td>2.7 × 10<sup>-3</sup></td><td>0.99</td></tr><tr><td></td><td></td><td>6.2 × 10<sup>-2</sup></td><td>0.99</td></tr><tr><td rowspan="2">December Le Grau du Roi</td><td>0–1.5 km (a.m.s.l.)</td><td>-4.5 × 10<sup>-3</sup></td><td>0.17</td><td>0.99</td></tr><tr><td>1.5–3 km (a.m.s.l.)</td><td>-2.76 × 10<sup>-2</sup></td><td>0.28</td><td>0.98</td></tr><tr><td rowspan="5">August Toulouse</td><td>0–1.5 km (a.m.s.l.)</td><td>1.2 × 10<sup>-2</sup></td><td>9.8 × 10<sup>-3</sup></td><td>0.99</td></tr><tr><td>1.5–3 km (a.m.s.l.)</td><td>2.5 × 10<sup>-2</sup></td><td>0.13</td><td>0.99</td></tr><tr><td>3–5 km (a.m.s.l.)</td><td>5.9 × 10<sup>-3</sup></td><td>0.34</td><td>0.98</td></tr><tr><td>5–8 km (a.m.s.l.)</td><td>3.5 × 10<sup>-3</sup></td><td>0.59</td><td>0.97</td></tr></tbody></table>