Articles | Volume 32, issue 2
https://doi.org/10.5194/npg-32-89-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-32-89-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Apr 2025
Research article |
| 22 Apr 2025
| 22 Apr 2025
Statistical and neural network assessment of the climatology of fog and mist at Pula Airport in Croatia
Marko Zoldoš
Risk Management Division, Erste & Steiermärkische Bank d.d., Rijeka, 51000, Croatia
Aviation Meteorology department, Croatia Control Ltd., Velika Gorica, 10410, Croatia
Laboratory of Physical Oceanography, Institute of Oceanography and Fisheries, Split, 21000, Croatia
Jadran Jurković
Aviation Meteorology department, Croatia Control Ltd., Velika Gorica, 10410, Croatia
Frano Matić
University Department of Marine Studies, University of Split, Split, 21000, Croatia
Sandra Jambrošić
Aviation Meteorology department, Croatia Control Ltd., Velika Gorica, 10410, Croatia
Ivan Ljuština
Aviation Meteorology department, Croatia Control Ltd., Velika Gorica, 10410, Croatia
Maja Telišman Prtenjak
Department of Geophysics, Faculty of Science, University of Zagreb, Zagreb, 10000, Croatia
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Ivica Vilibić, Hrvoje Mihanović, Ivica Janeković, Cléa Denamiel, Pierre-Marie Poulain, Mirko Orlić, Natalija Dunić, Vlado Dadić, Mira Pasarić, Stipe Muslim, Riccardo Gerin, Frano Matić, Jadranka Šepić, Elena Mauri, Zoi Kokkini, Martina Tudor, Žarko Kovač, and Tomislav Džoić
Ocean Sci., 14, 237–258, https://doi.org/10.5194/os-14-237-2018, https://doi.org/10.5194/os-14-237-2018, 2018
Related subject area
Subject: Time series, machine learning, networks, stochastic processes, extreme events | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Big data and artificial intelligence
Learning extreme vegetation response to climate drivers with recurrent neural networks
Representation learning with unconditional denoising diffusion models for dynamical systems
Characterisation of Dansgaard–Oeschger events in palaeoclimate time series using the matrix profile method
Evaluation of forecasts by a global data-driven weather model with and without probabilistic post-processing at Norwegian stations
The sampling method for optimal precursors of El Niño–Southern Oscillation events
A comparison of two causal methods in the context of climate analyses
A two-fold deep-learning strategy to correct and downscale winds over mountains
Downscaling of surface wind forecasts using convolutional neural networks
Data-driven methods to estimate the committor function in conceptual ocean models
Exploring meteorological droughts' spatial patterns across Europe through complex network theory
Integrated hydrodynamic and machine learning models for compound flooding prediction in a data-scarce estuarine delta
Predicting sea surface temperatures with coupled reservoir computers
Using neural networks to improve simulations in the gray zone
The blessing of dimensionality for the analysis of climate data
Producing realistic climate data with generative adversarial networks
Identification of droughts and heatwaves in Germany with regional climate networks
Extracting statistically significant eddy signals from large Lagrangian datasets using wavelet ridge analysis, with application to the Gulf of Mexico
Ensemble-based statistical interpolation with Gaussian anamorphosis for the spatial analysis of precipitation
Applications of matrix factorization methods to climate data
Detecting dynamical anomalies in time series from different palaeoclimate proxy archives using windowed recurrence network analysis
Remember the past: a comparison of time-adaptive training schemes for non-homogeneous regression
Francesco Martinuzzi, Miguel D. Mahecha, Gustau Camps-Valls, David Montero, Tristan Williams, and Karin Mora
Nonlin. Processes Geophys., 31, 535–557, https://doi.org/10.5194/npg-31-535-2024, https://doi.org/10.5194/npg-31-535-2024, 2024
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We investigated how machine learning can forecast extreme vegetation responses to weather. Examining four models, no single one stood out as the best, though "echo state networks" showed minor advantages. Our results indicate that while these tools are able to generally model vegetation states, they face challenges under extreme conditions. This underlines the potential of artificial intelligence in ecosystem modeling, also pinpointing areas that need further research.
Tobias Sebastian Finn, Lucas Disson, Alban Farchi, Marc Bocquet, and Charlotte Durand
Nonlin. Processes Geophys., 31, 409–431, https://doi.org/10.5194/npg-31-409-2024, https://doi.org/10.5194/npg-31-409-2024, 2024
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We train neural networks as denoising diffusion models for state generation in the Lorenz 1963 system and demonstrate that they learn an internal representation of the system. We make use of this learned representation and the pre-trained model in two downstream tasks: surrogate modelling and ensemble generation. For both tasks, the diffusion model can outperform other more common approaches. Thus, we see a potential of representation learning with diffusion models for dynamical systems.
Susana Barbosa, Maria Eduarda Silva, and Denis-Didier Rousseau
Nonlin. Processes Geophys., 31, 433–447, https://doi.org/10.5194/npg-31-433-2024, https://doi.org/10.5194/npg-31-433-2024, 2024
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The characterisation of abrupt transitions in palaeoclimate records allows understanding of millennial climate variability and potential tipping points in the context of current climate change. In our study an algorithmic method, the matrix profile, is employed to characterise abrupt warmings designated as Dansgaard–Oeschger (DO) events and to identify the most similar transitions in the palaeoclimate time series.
John Bjørnar Bremnes, Thomas N. Nipen, and Ivar A. Seierstad
Nonlin. Processes Geophys., 31, 247–257, https://doi.org/10.5194/npg-31-247-2024, https://doi.org/10.5194/npg-31-247-2024, 2024
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During the last 2 years, tremendous progress has been made in global data-driven weather models trained on reanalysis data. In this study, the Pangu-Weather model is compared to several numerical weather prediction models with and without probabilistic post-processing for temperature and wind speed forecasting. The results confirm that global data-driven models are promising for operational weather forecasting and that post-processing can improve these forecasts considerably.
Bin Shi and Junjie Ma
Nonlin. Processes Geophys., 31, 165–174, https://doi.org/10.5194/npg-31-165-2024, https://doi.org/10.5194/npg-31-165-2024, 2024
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Different from traditional deterministic optimization algorithms, we implement the sampling method to compute the conditional nonlinear optimal perturbations (CNOPs) in the realistic and predictive coupled ocean–atmosphere model, which reduces the first-order information to the zeroth-order one, avoiding the high-cost computation of the gradient. The numerical performance highlights the importance of stochastic optimization algorithms to compute CNOPs and capture initial optimal precursors.
David Docquier, Giorgia Di Capua, Reik V. Donner, Carlos A. L. Pires, Amélie Simon, and Stéphane Vannitsem
Nonlin. Processes Geophys., 31, 115–136, https://doi.org/10.5194/npg-31-115-2024, https://doi.org/10.5194/npg-31-115-2024, 2024
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Identifying causes of specific processes is crucial in order to better understand our climate system. Traditionally, correlation analyses have been used to identify cause–effect relationships in climate studies. However, correlation does not imply causation, which justifies the need to use causal methods. We compare two independent causal methods and show that these are superior to classical correlation analyses. We also find some interesting differences between the two methods.
Louis Le Toumelin, Isabelle Gouttevin, Clovis Galiez, and Nora Helbig
Nonlin. Processes Geophys., 31, 75–97, https://doi.org/10.5194/npg-31-75-2024, https://doi.org/10.5194/npg-31-75-2024, 2024
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Forecasting wind fields over mountains is of high importance for several applications and particularly for understanding how wind erodes and disperses snow. Forecasters rely on operational wind forecasts over mountains, which are currently only available on kilometric scales. These forecasts can also be affected by errors of diverse origins. Here we introduce a new strategy based on artificial intelligence to correct large-scale wind forecasts in mountains and increase their spatial resolution.
Florian Dupuy, Pierre Durand, and Thierry Hedde
Nonlin. Processes Geophys., 30, 553–570, https://doi.org/10.5194/npg-30-553-2023, https://doi.org/10.5194/npg-30-553-2023, 2023
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Forecasting near-surface winds over complex terrain requires high-resolution numerical weather prediction models, which drastically increase the duration of simulations and hinder them in running on a routine basis. A faster alternative is statistical downscaling. We explore different ways of calculating near-surface wind speed and direction using artificial intelligence algorithms based on various convolutional neural networks in order to find the best approach for wind downscaling.
Valérian Jacques-Dumas, René M. van Westen, Freddy Bouchet, and Henk A. Dijkstra
Nonlin. Processes Geophys., 30, 195–216, https://doi.org/10.5194/npg-30-195-2023, https://doi.org/10.5194/npg-30-195-2023, 2023
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Computing the probability of occurrence of rare events is relevant because of their high impact but also difficult due to the lack of data. Rare event algorithms are designed for that task, but their efficiency relies on a score function that is hard to compute. We compare four methods that compute this function from data and measure their performance to assess which one would be best suited to be applied to a climate model. We find neural networks to be most robust and flexible for this task.
Domenico Giaquinto, Warner Marzocchi, and Jürgen Kurths
Nonlin. Processes Geophys., 30, 167–181, https://doi.org/10.5194/npg-30-167-2023, https://doi.org/10.5194/npg-30-167-2023, 2023
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Despite being among the most severe climate extremes, it is still challenging to assess droughts’ features for specific regions. In this paper we study meteorological droughts in Europe using concepts derived from climate network theory. By exploring the synchronization in droughts occurrences across the continent we unveil regional clusters which are individually examined to identify droughts’ geographical propagation and source–sink systems, which could potentially support droughts’ forecast.
Joko Sampurno, Valentin Vallaeys, Randy Ardianto, and Emmanuel Hanert
Nonlin. Processes Geophys., 29, 301–315, https://doi.org/10.5194/npg-29-301-2022, https://doi.org/10.5194/npg-29-301-2022, 2022
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In this study, we successfully built and evaluated machine learning models for predicting water level dynamics as a proxy for compound flooding hazards in a data-scarce delta. The issues that we tackled here are data scarcity and low computational resources for building flood forecasting models. The proposed approach is suitable for use by local water management agencies in developing countries that encounter these issues.
Benjamin Walleshauser and Erik Bollt
Nonlin. Processes Geophys., 29, 255–264, https://doi.org/10.5194/npg-29-255-2022, https://doi.org/10.5194/npg-29-255-2022, 2022
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As sea surface temperature (SST) is vital for understanding the greater climate of the Earth and is also an important variable in weather prediction, we propose a model that effectively capitalizes on the reduced complexity of machine learning models while still being able to efficiently predict over a large spatial domain. We find that it is proficient at predicting the SST at specific locations as well as over the greater domain of the Earth’s oceans.
Raphael Kriegmair, Yvonne Ruckstuhl, Stephan Rasp, and George Craig
Nonlin. Processes Geophys., 29, 171–181, https://doi.org/10.5194/npg-29-171-2022, https://doi.org/10.5194/npg-29-171-2022, 2022
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Our regional numerical weather prediction models run at kilometer-scale resolutions. Processes that occur at smaller scales not yet resolved contribute significantly to the atmospheric flow. We use a neural network (NN) to represent the unresolved part of physical process such as cumulus clouds. We test this approach on a simplified, yet representative, 1D model and find that the NN corrections vastly improve the model forecast up to a couple of days.
Bo Christiansen
Nonlin. Processes Geophys., 28, 409–422, https://doi.org/10.5194/npg-28-409-2021, https://doi.org/10.5194/npg-28-409-2021, 2021
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In geophysics we often need to analyse large samples of high-dimensional fields. Fortunately but counterintuitively, such high dimensionality can be a blessing, and we demonstrate how this allows simple analytical results to be derived. These results include estimates of correlations between sample members and how the sample mean depends on the sample size. We show that the properties of high dimensionality with success can be applied to climate fields, such as those from ensemble modelling.
Camille Besombes, Olivier Pannekoucke, Corentin Lapeyre, Benjamin Sanderson, and Olivier Thual
Nonlin. Processes Geophys., 28, 347–370, https://doi.org/10.5194/npg-28-347-2021, https://doi.org/10.5194/npg-28-347-2021, 2021
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This paper investigates the potential of a type of deep generative neural network to produce realistic weather situations when trained from the climate of a general circulation model. The generator represents the climate in a compact latent space. It is able to reproduce many aspects of the targeted multivariate distribution. Some properties of our method open new perspectives such as the exploration of the extremes close to a given state or how to connect two realistic weather states.
Gerd Schädler and Marcus Breil
Nonlin. Processes Geophys., 28, 231–245, https://doi.org/10.5194/npg-28-231-2021, https://doi.org/10.5194/npg-28-231-2021, 2021
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We used regional climate networks (RCNs) to identify past heatwaves and droughts in Germany. RCNs provide information for whole areas and can provide many details of extreme events. The RCNs were constructed on the grid of the E-OBS data set. Time series correlation was used to construct the networks. Network metrics were compared to standard extreme indices and differed considerably between normal and extreme years. The results show that RCNs can identify severe and moderate extremes.
Jonathan M. Lilly and Paula Pérez-Brunius
Nonlin. Processes Geophys., 28, 181–212, https://doi.org/10.5194/npg-28-181-2021, https://doi.org/10.5194/npg-28-181-2021, 2021
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Long-lived eddies are an important part of the ocean circulation. Here a dataset for studying eddies in the Gulf of Mexico is created through the analysis of trajectories of drifting instruments. The method involves the identification of quasi-periodic signals, characteristic of particles trapped in eddies, from the displacement records, followed by the creation of a measure of statistical significance. It is expected that this dataset will be of use to other authors studying this region.
Cristian Lussana, Thomas N. Nipen, Ivar A. Seierstad, and Christoffer A. Elo
Nonlin. Processes Geophys., 28, 61–91, https://doi.org/10.5194/npg-28-61-2021, https://doi.org/10.5194/npg-28-61-2021, 2021
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An unprecedented amount of rainfall data is available nowadays, such as ensemble model output, weather radar estimates, and in situ observations from networks of both traditional and opportunistic sensors. Nevertheless, the exact amount of precipitation, to some extent, eludes our knowledge. The objective of our study is precipitation reconstruction through the combination of numerical model outputs with observations from multiple data sources.
Dylan Harries and Terence J. O'Kane
Nonlin. Processes Geophys., 27, 453–471, https://doi.org/10.5194/npg-27-453-2020, https://doi.org/10.5194/npg-27-453-2020, 2020
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Different dimension reduction methods may produce profoundly different low-dimensional representations of multiscale systems. We perform a set of case studies to investigate these differences. When a clear scale separation is present, similar bases are obtained using all methods, but when this is not the case some methods may produce representations that are poorly suited for describing features of interest, highlighting the importance of a careful choice of method when designing analyses.
Jaqueline Lekscha and Reik V. Donner
Nonlin. Processes Geophys., 27, 261–275, https://doi.org/10.5194/npg-27-261-2020, https://doi.org/10.5194/npg-27-261-2020, 2020
Moritz N. Lang, Sebastian Lerch, Georg J. Mayr, Thorsten Simon, Reto Stauffer, and Achim Zeileis
Nonlin. Processes Geophys., 27, 23–34, https://doi.org/10.5194/npg-27-23-2020, https://doi.org/10.5194/npg-27-23-2020, 2020
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Statistical post-processing aims to increase the predictive skill of probabilistic ensemble weather forecasts by learning the statistical relation between historical pairs of observations and ensemble forecasts within a given training data set. This study compares four different training schemes and shows that including multiple years of data in the training set typically yields a more stable post-processing while it loses the ability to quickly adjust to temporal changes in the underlying data.
Cited articles
Allan, S. S., Gaddy, S. G., and Evans, J. E.: Delay causality and reduction at the New York City airports using terminal weather information systems, Lincoln Laboratory, Massachusetts Institute of Technology, Cambridge, Massachussets, USA, https://archive.ll.mit.edu/mission/aviation/publications/publication-files/atc-reports/Allan_2001_ATC-291_WW-10183.pdf (last access: 19 August 2024), 2001.
Belo-Pereira, M. and Santos, J. A.: A persistent wintertime fog episode at Lisbon airport (Portugal): performance of ECMWF and AROME models, Meteorol. Appl., 23, 353–370, https://doi.org/10.1002/met.1560, 2016.
Bendix, J.: Fog climatology of the Po Valley, Riv. Meteorol. Aeronau., 54, 25–36, 1994.
Belušić, A., Prtenjak, M. T., Güttler, I., Ban, N., Leutwyler, D., and Schär, C.: Near-surface wind variability over the broader Adriatic region: insights from an ensemble of regional climate models, Clim. Dynam., 50, 4455–4480, https://doi.org/10.1007/s00382-017-3885-5, 2018.
Belušić Vozila, A., Telišman Prtenjak, M., and Güttler, I.: A weather-type classification and its application to near-surface wind climate change projections over the Adriatic region, Atmosphere-Basel, 12, 948, https://doi.org/10.3390/atmos12080948, 2021.
Bergot, T. and Koračin, D.: Observation, simulation and predictability of fog: review and perspectives, Atmosphere-Basel, 12, 235, https://doi.org/10.3390/atmos12020235, 2021.
Bonacci, O.: Analysis of mean annual temperature series in Croatia, Građevinar, 62, 781–791, https://hrcak.srce.hr/59611 (last access: 20 February 2025), 2010.
Duynkerke, P. G.: Radiation fog: A comparison of model simulation with detailed observations, Mon. Weather Rev., 119, 324–341, https://doi.org/10.1175/1520-0493(1991)119<0324:RFACOM>2.0.CO;2, 1991.
Džoić, T., Zorica, B., Matić, F., Šestanović, M., and Čikeš Keč, V.: Cataloguing environmental influences on the spatiotemporal variability of Adriatic anchovy early life stages in the eastern Adriatic Sea using an artificial neural network, Front. Mar. Sci., 9, 997937, https://doi.org/10.3389/fmars.2022.997937, 2022.
Filonczuk, M. K., Cayan, D. R., and Riddle, L. G.: Variability of marine fog along the California coast, Report 95-2, Scripps Institution of Oceanography, 102 pp., https://escholarship.org/uc/item/2kc7x97f (last access: 20 February 2025), 1995.
Fritzke, B.: A growing neural gas network learns topologies, in: Proceedings of Conference and Workshop on Neural Information Processing Systems, 28 November–3 December 1994, Denver, Colorado, USA, 625–632, https://proceedings.neurips.cc/paper/1994/file/d56b9fc4b0f1be8871f5e1c40c0067e7-Paper.pdf (last access: 20 February 2025), 1995.
Gultepe, I. and Milbrandt, J. A.: Microphysical observations and mesoscale model simulation of a warm fog case during FRAM project, Pure Appl. Geophys., 164, 1161–1178, https://doi.org/10.1007/s00024-007-0212-9, 2007.
Gultepe, I., Tardif, R., Michaelides, S. C., Cermak, I., Bott, A., Bendix, J., Müller, M. D., Pagowski, M., Hansen, B., Ellrod, G., Jacobs, W., Toth, S., and Cober, S. G.: Fog research: a review of past achievements and future perspectives, Pure Appl. Geophys., 164, 1121–1159, https://doi.org/10.1007/s00024-007-0211-x, 2007.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020a.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1979 to present, Copernicus Climate Change Service Climate Data Store [data set], https://doi.org/10.1002/qj.3803, 2020b.
Huang, B., Zhang, J., Cao, Y., Gao, X., Ma, S., and Sun, C.: Improvements of sea fog forecasting based on CMA-TYM, Front. Earth Sci., 10, 854438, https://doi.org/10.3389/feart.2022.854438, 2022.
Ju, T., Wu, B., Zhang, H., and Liu, J.: Parameterization of radiation fog-top height and methods evaluation in Tianjin, Atmosphere-Basel, 11, 480, https://doi.org/10.3390/atmos11050480, 2020.
Kawai, H., Koshiro, T., Endo, H., Arakawa, O., and Hagihara, Y.: Changes in marine fog in a warmer climate, Atmos. Sci. Lett., 17, 548–555, https://doi.org/10.1002/asl.691, 2016.
Klaić, Z. B., Pasarić, Z., and Tudor, M.: On the interplay between sea–land breezes and etesian winds over the Adriatic, J. Marine Syst., 78, 101–118, https://doi.org/10.1016/j.jmarsys.2009.01.016, 2009.
Klemm, O. and Lin, N.: What causes observed fog trends: air quality or climate change?, Aerosol Air Qual. Res., 16, 1131–1142, https://doi.org/10.4209/aaqr.2015.05.0353, 2016.
Koračin, D. and Dorman, C. E.: Marine fog: challenges and advancements in observations and forecasting, in: Springer Atmospheric Sciences Series, Springer International Publishing, Cham, 537 pp., ISBN 978-3-319-45227-2, https://doi.org/10.1007/978-3-319-45229-6, 2017.
Koračin, D., Lewis, J., Thompson, W. T., Dorman, C. E., and Businger, J. A.: Transition of stratus into fog along the California coast: observations and modeling, J. Atmos. Sci., 58, 1714–1731, https://doi.org/10.1175/1520-0469(2001)058%3C1714:TOSIFA%3E2.0.CO;2, 2001.
Kulkarni, R., Jenamani, R. K., Pithani, P., Konwar, M., Nigam, N., and Ghude, S. D.: Loss to aviation economy due to winter fog in New Delhi during the winter of 2011–2016, Atmosphere-Basel, 10, 198, https://doi.org/10.3390/atmos10040198, 2019.
Li, G., Cheng, L., Zhu, J., and Trenberth, K.: Increasing ocean stratification over the past half-century, Nat. Clim. Change, 10, 1116–1123, https://doi.org/10.1038/s41558-020-00918-2, 2020.
Li, X., Zhang, S., Koračin, D., Yi, L., and Zhang, Y.: Atmospheric conditions conducive to marine fog over the northeast Pacific in winters of 1979–'2019, Front. Earth Sci., 10, 942846, https://doi.org/10.3389/feart.2022.942846, 2022
Mariani, L.: Fog in the Po valley: Some meteo-climatic aspects, Ital. J. Agrometeorol., 3, 35–44, 2009.
Martinetz, T. and Schulten, K.: A neural-gas network learns topologies, in: Proceedings of the International Conference on Artificial Neural Networks (ICANN-91), 24–28 June 1991, Espoo, Finland, 397–402, https://www.ks.uiuc.edu/Publications/Papers/PDF/MART91B/MART91B.pdf (last access: 12 April 2025), 1991.
Matić, F., Džoić, T., Kalinić, H., Ćatipović, L., Udovičić, D., Juretić, T., Rakuljić, L., Sršen, D., and Tičina, V.: Observation of abrupt changes in the sea surface layer of the Adriatic Sea, J. Mar. Sci. Eng., 10, 848, https://doi.org/10.3390/jmse10070848, 2022.
Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., Good, S. A., Mittaz, J., Rayner, N. A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., and Donlon, C.: Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Sci. Data, 11, 326, https://doi.org/10.1038/s41597-019-0236-x, 2019.
Omazić, B., Telišman Prtenjak, M., Prša, I., Belušić Vozila, A., Vučetić, V., Karoglan, M., Karoglan Kontić, J., Prša, Ž., Anić, M., Šimon, S., and Güttler, I.: Climate change impacts on viticulture in Croatia; viticultural zoning and future potential, Int. J. Climatol., 40, 5634–5655, https://doi.org/10.1002/joc.6541, 2020.
Oztaner, Y. B. and Yilmaz, A.: An examination of fog and PM10 Relationship for Ataturk and Esenboga International Airports of Turkey, in: Volume 1,Proceedings of the 6th Atmospheric Science Symposium – ATMOS 2013, 24–26 April 2013, Istanbul, Turkey, https://www.researchgate.net/publication/262780771_An_Examination_of_Fog_and_PM10_Relationship_for_Ataturk_and_Esenboga_International_Airports_of_Turkey (last access: 12 April 2025), 2013.
Pandžić, K. and Likso, T.: Eastern Adriatic typical wind field patterns and large-scale atmospheric conditions, Int. J. Climatol., 25, 81–98, https://doi.org/10.1002/joc.1085, 2005.
Pastor, F., Valiente, J. A., and Palau, J. L.: Sea surface temperature in the Mediterranean: trends and spatial patterns, Pure Appl. Geophys., 175, 4017–4029, https://doi.org/10.1007/s00024-017-1739-z, 2018.
Pawlowicz, R.: M_Map: A mapping package for MATLAB, version 1.4 m, The University of British Columbia [code], http://www.eoas.ubc.ca/~rich/map.html (last access: September 2023), 2020.
Popović, R., Kulović, M., and Stanivuk, T.: Meteorological safety of entering eastern Adriatic ports, Trans. Marit. Sci., 3, 53–60, https://doi.org/10.7225/toms.v03.n01.006, 2014.
Šantić, D., Piwosz, K., Matić, F., Vrdoljak Tomaš, A., Arapov, J., Dean, J. L., Šolić, M., Koblížek, M., Kušpilić, G., and Šestanović, S.: Artificial neural network analysis of microbial diversity in the central and southern Adriatic Sea, Sci. Rep.-UK, 11, 1–15, https://doi.org/10.1038/s41598-021-90863-7, 2021.
Šimunić, I., Likso, T., Husnjak, S., and Bubalo Kovačić, M.: Analysis of climate elements in central and western Istria for the purpose of determining irrigation requirements of agricultural crops, Agric. Conspec. Sci., 86, 225–233, 2021.
Stipaničić, V.: Magle na zapadnoj obali istarskog poluotoka, Vijesti Pomorske meteorološke službe, 7–10, https://library.foi.hr/dbook/cas.php?B=1&item=S02101&godina=1972&broj=00001 (last access: 20 February 2025), 1972.
Stolaki, S. N., Kazadzis, S. A., Foris, D. V., and Karacostas, Th. S.: Fog characteristics at the airport of Thessaloniki, Greece, Nat. Hazards Earth Syst. Sci., 9, 1541–1549, https://doi.org/10.5194/nhess-9-1541-2009, 2009.
Tardif, R. and Rasmussen, R. M.: Event-based climatology and typology of fog in the New York City region, J. Appl. Meteorol. Clim., 46, 1141–1168, https://doi.org/10.1175/JAM2516.1, 2007.
Telišman Prtenjak, M. and Grisogono, B.: Sea–land breeze climatological characteristics along the northern Croatian Adriatic coast, Theor. Appl. Climatol., 90, 201–215, https://doi.org/10.1007/s00704-006-0286-9, 2007.
Telišman Prtenjak, M., Viher, M., and Jurković, J.: Sea–land breeze development during a summer bora event along the north-eastern Adriatic coast, Q. J. Roy. Meteor. Soc., 136, 1554–1571, https://doi.org/10.1002/qj.649, 2010.
Tešić, M. and Brozinčević, K.: Magla na istočnoj obali Jadrana, Hidrografski godišnjak 1974, 91–116, 1974.
Tojčić, I., Denamiel, C., and Vilibić, I.: Kilometer-scale trends and variability of the Adriatic present climate (1987–2017), Clim. Dynam., 61, 2521–2545, https://doi.org/10.1007/s00382-023-06700-2, 2023.
Tojčić, I., Denamiel, C., and Vilibić, I.: Kilometer-scale trends, variability, and extremes of the Adriatic far-future climate (RCP 8.5, 2070–2100), Front. Mar. Sci., 16, 907–926, https://doi.org/10.3389/fmars.2024.1329020, 2024.
Vautard, R., Yiou, P., and van Oldenborgh, G.: The decline of fog, mist and haze in Europe during the last 30 years, Nat. Geosci., 2, 115–119, https://doi.org/10.1038/ngeo414, 2009.
Veljović, K., Vujović, D., and Lazić, L.: An analysis of fog events at Belgrade International Airport, Theor. Appl. Climatol., 119, 13–24, https://doi.org/10.1007/s00704-014-1090-6, 2015.
Wang, Y., Niu, S. J., Lv, J., Lu, C., Xu, X. Q., Wang, Y., Ding, J., Zhang, H., Wang, T., and Kang, B.: A new method for distinguishing unactivated particles in cloud condensation nuclei measurements: implications for aerosol indirect effect evaluation, Geophys. Res. Lett., 46, 14185–14194, https://doi.org/10.1029/2019gl085379, 2019.
WMO: International Meteorological Vocabulary, World Meteorological Organization, Geneva, 782 pp., ISBN 978–92-63–02182-3, 1992.
Zoldoš, M. and Jurković, J.: Fog event climatology for Zagreb Airport, Croat. Meteorol. J., 51, 13–26, 2016.
Short summary
Fog can disrupt aviation by causing accidents and delays due to low visibility, yet it remains underresearched in Croatia. This study examined fog and mist at Pula Airport using 20 years of data and machine learning techniques. There is a declining trend in fog that is linked to changing weather patterns. Fog mainly occurs from October to March. These findings enhance knowledge about fog in Croatia and can improve weather forecasts, increasing safety at the airport.
Fog can disrupt aviation by causing accidents and delays due to low visibility, yet it remains...
