The aim of this study is to assess trends in the total ozone column (TOC) and the atmospheric factors influencing ozone variability at three Antarctic stations (Marambio, Troll and Concordia) in the period 2007–2023. For this purpose, ground-based TOC measurements were used supplemented by satellite observations from the Ozone Monitoring Instrument on board the NASA's Aura satellite. TOC trends were derived using a multiple linear regression model provided by the Long-term Ozone Trends and Uncertainties in the Stratosphere (LOTUS) project. The selected LOTUS model was able to explain 95–98 % of the TOC variability at all three stations. Several predictors were evaluated in the regression model, and it was found that the lower stratospheric temperature and the polar stratospheric cloud volume contribute most to the ozone variability at these stations. A statistically significant increasing trend was found at the Marambio station (3.33 DU/decade), while the statistically insignificant trends were detected at the other two stations. Using MERRA-2 reanalyses, the LOTUS model was applied to each grid point in the 40–90°S region, which effectively illustrates the spatial distribution of the impacts of individual predictors. It was found that warmer conditions in the Antarctic stratosphere in September 2019 caused TOC to be up to 100 DU higher than normal, especially over East Antarctica. The results contribute to a better understanding of regional TOC trends and the influence of atmospheric factors on TOC in the southern high latitudes, which is essential for assessing how the Antarctic ozone layer responds to changes in ozone-depleting substances under the Montreal Protocol regulations.

How to cite: Tichopad, D. and Láska, K.: Assessment of total ozone trends and their driving factors at three Antarctic stations in 2007–2023, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-500, https://doi.org/10.5194/ems2025-500, 2025.

Accurate characterization of aerosol optical and microphysical properties is crucial for understanding their role in atmospheric processes and climate. Remote sensing instruments, such as photometers and ceilometers, offer complementary information: photometers provide column-integrated aerosol observations, while ceilometers give vertically-resolved backscatter profiles. The synergy between these instruments can be enhanced through advanced inversion algorithms. In this study, we investigate the retrieval of aerosol properties using the Generalized Retrieval of Atmosphere and Surface Properties (GRASP) algorithm, as shown in Roman et al. (2018), focusing particularly on the integration of lunar photometer observations.

While solar photometry is well-established for daytime aerosol characterization, nighttime retrievals remain challenging due to the absence of sunlight. The last decade developments in lunar photometry (e.g. Barreto et al. 2016), allow for aerosol measurements using the moon as a light source. This opens new possibilities for continuous, 24-hour aerosol monitoring, especially when complemented with ceilometer measurements.

In our work, we apply the GRASP algorithm to combined datasets consisting of daytime/nighttime photometer AOD and radiances, and ceilometer backscatter profiles, collected at ACTRIS L’Aquila station in Italy. GRASP is used for the retrievals, leveraging the synergy between photometric and lidar-like observations to constrain aerosol properties such as size distribution, refractive index, volume concentration and vertical profile.

We present case studies that demonstrate the added value of incorporating lunar photometer data, especially during nighttime long-range transport episodes. Preliminary results show that the inclusion of lunar observations significantly improves the temporal coverage and consistency of aerosol retrievals, allowing for better constraints on nighttime aerosol dynamics.

This study highlights the potential of integrating lunar photometry into routine aerosol monitoring networks, as recently done in the AERONET network, particularly when used in combination with ceilometers and advanced inversion tools like GRASP. The approach supports continuous observation capabilities, improving our ability to monitor aerosol variability and support atmospheric and climate research.

• BARRETO, África, et al. The new sun-sky-lunar Cimel CE318-T multiband photometer–a comprehensive performance evaluation. Atmospheric Measurement Techniques, 2016, 9.2: 631-654.
• ROMÁN, Roberto, et al. Retrieval of aerosol profiles combining sunphotometer and ceilometer measurements in GRASP code. Atmospheric Research, 2018, 204: 161-177.

How to cite: Balotti, A., Iarlori, M., Lidori, R., Di Fabio, S., Avocone, E., and Rizi, V.: Retrieval of Aerosol Properties from Combined Sun-Lunar Photometer and Ceilometer Observations Using the GRASP Algorithm at L’Aquila site (Italy), EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-522, https://doi.org/10.5194/ems2025-522, 2025.

Detection of non-meteorological targets, such as fires, clear-air turbulence, and biological targets (e.g., insects and birds), using solid-state weather radar is crucial across various industries. These include agriculture, the construction of wind farms in areas outside bird migration corridors, and air traffic control. Each of these fields requires reliable identification of such targets to support operations, mitigate risks, and ensure safety. We have conducted a comprehensive study and in-depth analysis of the unique signatures of these non-meteorological targets using our solid-state radars, which are capable of capturing detailed polarimetric information.

In our presentation, we will explore the typical characteristics of polarimetric variables and several derived products for clear-air conditions, birds, bats, and fire plumes as measured by solid-state weather radars. We will present not only typical values and their distribution in histograms, but also the spatial variability of these polarimetric variables for each non-meteorological target type. These variations in polarimetric properties provide insight into the structure and movement of different targets. Additionally, we will highlight the differences between the various categories, allowing for a clearer distinction in classification. Special attention will also be given to the change in the values of polarimetric variables in areas where water vapor condenses on particles from fire, which creates unique radar signatures.

Particular emphasis will also be placed on the scanning strategy we’ve adopted for clear-air detection, aimed at improving the identification of clear-air echoes and turbulence. The results presented have significant potential for further application, including the development of algorithms using traditional classification methods, such as K-Means or the Random Forest Algorithm, as well as new AI-based methods. These approaches are expected to enable more effective classification and detection of non-meteorological targets, advancing radar technology and its practical use across multiple domains.

How to cite: Najman, F. and Staněk, M.: Signatures of Smoke, Clear-Air, Birds, and Insects on Solid-State Radars , EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-585, https://doi.org/10.5194/ems2025-585, 2025.

Raman lidars are widely used in research to measure the atmospheric profile of temperature and humidity. In operational meteorology, however, the technology is still emerging mostly because of its high costs, the high complexity and the difficulty of calibrating the measurements. The Raman Lidar for Meteorological Observations (RALMO) located at the Federal Office of Meteorology and Climatology MeteoSwiss in Payerne, Switzerland, measures humidity and temperature profiles continuously since 2008 demonstrating the technique’s potential for operational use. We have developed and implemented a calibration method based on the lidar’s solar background measurements allowing for daily calibrations  and independently from external references like radiosondes. We assessed the impact of RALMO observations in the MeteoSwiss operational, convective-scale ensemble data assimilation and forecasting system in two two-week summer and winter experiments revealing the potential to improve the analysis, especially in regions without other profile observations. We further compiled a climatology of tropospheric temperature, water vapor mixing ratio and relative humidity from the 15-year data set and will present first results of relative humidity trends in the troposphere above Payerne, Switzerland. 

How to cite: Haefele, A., Martucci, G., Jayaweera, V., Crezee, B., Leuenberger, D., Sica, R. J., Matthey, R., and Arpagaus, M.: 15 years of research and operations with a Raman lidar for meteorological and climatological applications, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-63, https://doi.org/10.5194/ems2025-63, 2025.

Forecasting high-impact convective events, which pose significant risks to people and property, remains a persistent challenge. This difficulty can partly be attributed to biases and errors in analysis data, which serve as initial conditions for numerical weather predictions (NWP), are often used to improve process understanding of such events from the model perspective. A systematic validation of the quality of analysis and forecast data, in particular in complex terrain, is often restricted by the limited availability of detailed observations.

Here we leverage observations from the ‘Swabian MOSES 2023’ field campaign, which took place in the Black Forest region, Southwestern Germany, during the summer 2023. Among others, the field campaign included an extended meso-scale network of 10 Doppler wind lidars, which are used to retrieve vertical profiles of the wind speed and direction.  These observations enable the characterization of the dynamic structure of the lower troposphere on the mesoscale in moderately complex terrain.

The retrieved wind profile observations, which extend from the near-surface to approximately 4 km above ground, are compared to the regional convective-scale analysis dataset. For this purpose, the ICON-D2 analysis data from Deutscher Wetterdienst, available at 2.2 km horizontal resolution and produced using the Icosahedral Nonhydrostatic (ICON) model , are used. The three months of continuous observations provide a comprehensive independent data set for validating the representation of mesoscale wind characteristics in the analysis across the Black Forest region. Overall, the convective-scale analysis captures the vertical profiles of horizontal wind well, with wind speed bias remaining on average below 0.5 m s^-1. Yet, we find differences between different measurement locations, depending to some extent on the local topography, with the largest differences in areas with more complex terrain. Moreover, the analysis tends to overestimate the zonal wind component at lower altitudes, while underestimating the meridional wind component at higher altitudes. Furthermore, the data show that the mean absolute error between the analysis and the observations is larger during rainy than during dry weather conditions, which highlights the added value of the observations, particularly during convective situations. Finally, we demonstrate the value of a high-resolution analysis dataset by comparing the observations against a range of analysis datasets, including ICON-D2, ICON-EU, and ICON-global, with spatial resolutions of 2.2 km, 6.5 km, and up to 13 km, respectively.  

How to cite: Nguyen, D., Gasch, P., and Oertel, A.: Representation of mesoscale flow characteristics in a convective-scale analysis dataset: validation using Doppler wind lidar measurements from the 'Swabian MOSES 2023' Campaign, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-418, https://doi.org/10.5194/ems2025-418, 2025.

A building-block urban meteorological observation experiment – wind (BBMEX-Wind) campaign was carried out to observe vertical profile of 3-dimensional wind speed below and above the building canopy of a street canyon at Gangnam district in Seoul City, Korea for the period of 10 to 24 May 2023. A wind lidar was installed center of the street canyon with a width of 50 m and a height of 70 m, and observed 3-dimensial wind speed and signal-to-noise ratio at around 1 Hz at 25 heights between 40 m and 350 m. Among total 15 days, 7 days were fine (cloud cover < 3/10), while 3 days were cloudy (cloud cover > 7/10). There were 4 rainy days, but total precipitation was as low as 1.5 mm. Skimming flows were observed below the building canopy when lateral winds blow, and channel flows were observed when longitudinal winds blow. In spite of low-speed winds below the building canopy, strong winds above 20 m s⁻¹ above the canopy were observed. Surface roughness length over the building block was calculated as values minimizing the sum of squared error assuming the log-wind profile in the surface layer. It was found that the surface roughness length was about 8 m during the daytime (09 to 16 LST) and about 6 m during the nighttime (22 to 04 LST). Mean values of turbulent kinetic energy (TKE) during the daytime were about 2 times larger than those during the nighttime, while median values of TKE during the daytime were 3 times larger than those during the nighttime. 

Key words: BBMEX-Wind, street canyon, surface roughness length, wind lidar

Acknowledgements: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2021R1I1A2052562) and Korea Aerospace Research Institute (KARI).

How to cite: Park, M.-S., Baek, K., Kim, S.-C., Kang, M., and Na, S.-J.: Structure of Wind and Turbulence below and above a building canopy during the BBMEX–Wind Campaign period in Seoul, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-331, https://doi.org/10.5194/ems2025-331, 2025.

How to cite: Billault–Roux, A.-C., Rüfenacht, R., Höffner, J., Mense, T., Garfias, P., Ernst, F., Baumgarten, G., Flügge, M., Hauchecorne, A., Martic, M., Munk, A., Lukoševičius, L., Haefele, A., and Strotkamp, M.: EULIAA : novel compact lidar systems for operational wind, temperature and aerosol measurements between 5 and 50 km altitude, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-131, https://doi.org/10.5194/ems2025-131, 2025.

The state-of-the-art of numerical weather prediction models continue to mature and contribute to better forecast skill at finer spatial and temporal resolutions. Our ability to generate atmospheric observations has not kept pace with these computational advancements, resulting in a “data gap”. There has been a push to develop and advance a broad spectrum of in-situ and remote-sensing atmospheric sampling technologies to address this need. This includes the need to advance and modernize conventional and mature instrumentation. In addition to providing trusted atmospheric measurements, data from these instruments can be used as a reference for newer, emerging technologies or signal-processing methods. Here we focus on recent developments in one such instrument, the rawinsonde (here simply referred to as radiosonde).,

In 2014, Sparv Embedded AB introduced a small, lightweight, and economical radiosonde called the Windsond S1 intended for use in the atmospheric boundary layer and lower free troposphere, typically below 8 km MSL. By focusing on the lower atmosphere, the S1 has been helping to fill one critical aspect of the data gap. As such, the S1 has been successfully used by numerous atmospheric researchers, within the international meteorological community, in fire-weather applications, for educational purposes, and more (e.g., Stouffer et al., 2024: J. Atmos. Oceanic Technol., 41, 1213–1228). Based on the popularity of the S1, Sparv has recently released a successor called the Windsond S2. Like its predecessor, the S2 is small, light-weight, and affordable; however, the new design offers many improvements over the S1. For example, the S2’s new battery and telemetry capabilities facilitate atmospheric soundings up to 10 km MSL. Moreover, the S2’s compact and robust design makes it better suited for launches in challenging environments. With a weight of only 9 g, the S2 requires less helium and smaller balloons than conventional radiosondes, which have typical weights of 60-80 g. As with the S1, the S2 still allows for simultaneous tracking of up to 126 radiosonde units. This feature can be used to map small spatial and temporal variations in atmospheric fields (e.g., Markowski et al., 2018: Bull. Amer. Meteorol. Soc., 99, 711-724). Such observations would also be valuable when validating improved high-resolution weather forecast models. In this presentation, we provide an overview of the Windsond S2, its technical specifications, and highlight several use case scenarios. Additionally we present and discuss S2 data collected under various meteorological conditions.

How to cite: Chilson, P. and Petersson, A.: Using small, light-weight radiosondes to enhance capacity for in-situ sampling of the lower atmosphere, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-401, https://doi.org/10.5194/ems2025-401, 2025.

Smartphones are now commonly used, with ownership ranging from 30 to 70 percent of the population in numerous countries. With GPS and pressure sensor installed, smartphones can measure ambient air pressure, and have the potential to be used as meteorological observations. Our recent studies have shown that the pressure of smartphones can be used to describe the strength and location of large-scale vortex, as well as mesoscale surface high and low during thunderstorm events. Nevertheless,  addressing the quality issues of smartphone barometric data owing to human activity and other factors remains a great challenge. Our study indicated that a machine-learning technique could significantly improve the usability of the smartphone data. Since smartphone users are mostly concentrated in urban areas, significantly higher-density pressure coverage is achieved than the conventional surface networks in large cities. However, can these high-density data improve the forecasting ability of weather-related disasters? This presentation will introduce the barometric pressure data observed by smartphones collected from Moji weather app, the bias correction process, and its applications in analysis and forecasting via case studies.

We used tropical cyclones (TCs) Lekima in 2019, Hagupit in 2020 and In-fa in 2021 as examples to conduct bias correction on labeled smartphone pressure data from the Moji Weather app. A quality control procedure was proposed utilizing random forest machine learning models. By applying this quality control approach to the selected TCs, we discovered that the performance of the method for labeled data significantly surpassed that for unlabeled data developed in a previous study, reducing the mean absolute error from 3.105 to 0.904 hPa.

The smartphone (SPO) and traditional weather stations (TWS) pressure data during a hailstorm that occurred on 30 June 2021 in Beijing, are assimilated into a one-hour frequent 3DVAR system based on WRF model, respectively. The results demonstrate that the spatial density of SPO data is tens of times larger than that of TWS. Compared with the control experiment and assimilation experiment which only use the TWS data, the assimilation experiments with SPO data show significant improvements, in terms of the cold pool and gust front. The improvement of FSS in 10 mm hourly precipitation reached to 3% to 12%. Our findings indicate that high-resolution SPO data have the potential to enhance the forecasting capabilities of mesoscale convective system.

How to cite: Zhang, Q.: Smartphone Pressure Data: Statistical Characteristics, Bias Correction, and Applications in Weather Forecasting, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-661, https://doi.org/10.5194/ems2025-661, 2025.

Accurate and timely observations remain fundamental to meteorological research and operational forecasting, yet many regions still rely on sparse or intermittent data. Whilst low-cost sensors are becoming increasingly abundant, their utility has been marred by lack of standardisation, calibration, and accuracy. This abstract introduces a concept for building a network of low-cost, crowdsource-able “all-sky” camera observation stations to address these coverage gaps and previous shortcomings. Each station would passively capture sky imagery at frequent intervals, analyse image features and upload the data to a central repository for use by the broader weather and climate community.

By integrating this imagery with existing meteorological datasets, it may be possible to resolve localised phenomena more precisely than current observing networks. Existing research suggests that ground-based visible images could enhance high-resolution numerical weather prediction (NWP) by offering near-real-time cloud fraction, cloud genus, and atmospheric motion vectors (AMVs) at unparalleled spatial granularity. Such data also hold promise for improving nowcasting systems designed to detect rapidly evolving weather features. Moreover, the resulting datasets would present novel opportunities for advanced AI/machine learning applications, such as training algorithms to classify cloud types, track convective development, monitor the climatic impact of contrails, or nowcast surface-level solar irradiance. The network could be designed such that over-the-air (OTA) software updates allow the ‘intelligence’ of the network in aggregate to improve over time or gain new abilities as they are discovered and developed.

While the station design and data-processing pipeline remain under early development, this presentation aims to share the concept, identify potential applications, and gather community input on technical and scientific challenges. In particular, the project welcomes feedback on topics such as image standardisation, data validation, and deployment strategies. Interested colleagues are encouraged to discuss potential collaborations or sign up for project updates, including an early-access waitlist for the first stations. By co-developing these capabilities with the meteorological community, we can create a robust, accessible network that extends and complements existing observational infrastructure—ultimately enhancing our collective ability to monitor and understand the atmosphere.

How to cite: Pickering, B.: Crowdsourced All-Sky Imagery for AI Nowcasting and Research, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-417, https://doi.org/10.5194/ems2025-417, 2025.

Following the growing use of AI/ML in atmospheric sciences, this presentation will address the use of machine learning techniques for product generation from atmospheric measurement data. Our Deep-Pathfinder algorithm uses solely ceilometer observations to extract the mixing layer height (MLH), indicating the change between vertical mixing of air near the surface and less turbulent air above. The concept is to represent the sensor data as an image and the MLH profile as a corresponding mask, and directly predict the mask using image segmentation techniques. This concept is generic and, in principle, it can be applied to various sensor types.

Deep-Pathfinder is based on a customised U-Net architecture with MobileNetV2 encoder for fast inference and a nighttime variable to indicate whether a stable or convective boundary layer can be expected. Model training used range-corrected signal data from Lufft CHM15k ceilometers in the Netherlands (2020–2022), supplemented with 50 days of high-resolution annotations. First, input samples were randomly cropped to 224x224 pixels, covering a 45-minute period and maximum altitude of 2240 meters. Then, the model was pre-trained on the Dutch National Supercomputer Snellius using 19.4 million samples of unlabelled data. Finally, the labelled data was used to fine-tune the model for the task of mask prediction. Performance on a test set was compared to MLH estimates from ceilometer manufacturer Lufft and the STRATfinder algorithm, showing Deep-Pathfinder followed short-term fluctuations more closely.

Existing path optimization algorithms have good temporal consistency but can typically only be evaluated after a full day of ceilometer data has been recorded. Deep-Pathfinder retains the advantages of temporal consistency by assessing MLH evolution in 45-minute samples, using the full 12 s x 10 m resolution of the ceilometer. KNMI’s MLOps team is implementing the Deep-Pathfinder algorithm to run it in near-real-time on observations of CHM15k ceilometers in the Netherlands. The upcoming monitoring phase will highlight areas where the model could be further improved, using a model lifecycle of enhancing annotations for identified shortcomings, retraining and deployment. Further, we are investigating extending our concept to a multi-class segmentation problem to create atmospheric feature masks. These developments exemplify how machine learning techniques can be deployed towards enhancing operational usage of sensor data.

How to cite: Wijnands, J., Apituley, A., Gouveia, D. A., Noteboom, J. W., Yan, M., de Haij, M., and Trani, L.: Deep-Pathfinder: Near-real-time detection of mixing layer height based on lidar remote sensing data and deep learning, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-700, https://doi.org/10.5194/ems2025-700, 2025.

The University of Oklahoma, Oklahoma State University, the US National Severe Storms Lab, and various US National Weather Service Forecast Offices have been actively engaged in enhancing the capabilities and applications of a 3D Mesonet system. This presentation will overview the extensive activities and advancements in Mesonet operations, focusing on the integration of uncrewed aircraft systems (UAS) for atmospheric profiling. The Oklahoma Mesonet system, comprised of 120 surface stations across the state, plays a crucial foundational role in real-time weather monitoring and data collection.

Through numerous experiments and field campaigns since 2014, we have deployed UAS for high-resolution vertical measurements of atmospheric parameters such as pressure, temperature, humidity, wind speed, and direction. These measurements are key for understanding the atmospheric boundary layer (ABL) and improving weather forecasting, agricultural decision-making, wildland fire management, wind energy optimization, and climate monitoring. Additionally, 3D Mesonet data have shown great promise in nowcasting, particularly in identifying low-level inversions that impact pesticide application and predicting fire behavior and smoke dispersal.

The integration of UAS with Mesonet infrastructure will allow for simultaneous data collection at multiple sites, providing new insights into the spatial heterogeneity of the ABL. This presentation will also discusses ongoing projects, including an initiative with WeatherHive and collaborations with the US National Weather Service (NWS) to enhance severe weather forecasting through real-time data ingest and analysis.

Furthermore, this presentation will highlight the need to develop robust and reliable UAV systems equipped with precision landing, automatic charging, and risk mitigation measures for unattended operations. A prototype built by the Advanced Radar Research Center at the University of Oklahoma demonstrated the capability to detect aircraft within a geofenced area, ensuring safe and efficient data collection.

Overall, the advancements in 3D Mesonet technology and its applications underscore the importance of continuous innovation in atmospheric research and its practical implications for various sectors.

How to cite: Fiebrich, C., Avery, A., Bell, T., Cann, M., Fox, M., Hocker, J., Jacob, J., Luttrell, C., Piltz, S., Smith, E., and Walker, J.: Transforming Weather Monitoring: Oklahoma's Advances in 3D Mesonet Systems, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-568, https://doi.org/10.5194/ems2025-568, 2025.

The Alpine-Adriatic Meteorological Society (A-AMS), in collaboration with various organizations and institutions active in the Alpe-Adria region, has launched the 'Julian Alps Meteo-Lab' project with the aim of advancing scientific knowledge of this distinctive sector of the Alps. To this end, A-AMS promotes and supports studies related to the meteorology, climate, paleoclimate, and cryosphere of the area.
The Julian Alps and Prealps — parts of which lie within the Julian Prealps Natural Park (Italy), designated a UNESCO MAB Reserve in 2019, and the Triglav National Park (Slovenia) — constitute the Alpine region with the highest mean annual precipitation. Moreover, the intensity of individual rain or snow events is often significantly greater than the average severity of similar events across Europe.
Furthermore, in the Julian Alps, several small glacial bodies remain active at much lower elevations than in other parts of the Alps due to a combination of local and regional factors. Moreover, the area is affected by pronounced karst development, which predisposes the region to a high density of subsurface cavities, many of which host perennial ice deposits.
These observation sites, along with others planned for the future, will be made available online to enable systematic monitoring of the area's meteorological and climatic conditions. Particular emphasis will be placed on making data accessible to the public, not only for scientific purposes but also to support tourism and the local economy. Additionally, the Julian Alps Meteo-Lab was established with an educational mission aimed at promoting courses, seminars, summer schools, and training events to engage both enthusiasts and non-specialists in the scientific activities related to the project.

How to cite: Colucci, R. R.: Julian Alps Meteo-Lab: A Scientific and Educational Network for Alpine Climate and Cryosphere Studies, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-501, https://doi.org/10.5194/ems2025-501, 2025.