Orals: Thu, 1 May | Room M1
Based on an instrumental synergy of in-situ and remote sensing measurements collected at SIRTA observatory, a peri-urban site located near Paris, and a fog conceptual model (CM), this study presents a statistical analyses of the local and synoptic processes driving the different phases (formation, evolution and dissipation) of radiation fogs (RADs) and stratus lowering fogs (STLs). The very high resolution of the co-localised BASTA cloud Radar, Ceilometer and visibilimeter, allows to estimate the occurrence of fog during the 2013-2023 period. A microwave radiometer (MWR Hatpro) and the equivalent adiabaticity by closure from the CM are used to estimate the lowest layer atmospheric stability. 191 fogs (82 RADs and 99 STLs) are well documented and divided into several categories depending on their geometry (radiation fogs), their type of dissipation (lifting or lowering cloud base height), and their time of dissipation (nocturnal or diurnal).
By associating fog types with large-scale atmospheric circulations, the result show the role of synoptic regimes on the fog evolution at SIRTA. Longer RADs and STLs are associated with strong temperature inversions driven by the Atlantic Ridge and positive North Atlantic Oscillation (NAO+) regimes. These two regimes promote the advection of warm air to the West of Europe and contribute to the diurnal variability of temperatures in the region.
The analysis of the local processes driving the different phases of fogs is conducted using the turbulent kinetic energy (TKE), the sensible heat flux (SHF) and the fog reservoir of liquid water path (RLWP) estimated by a sonic anemometer and at 30 m a.g.l, the Licor analyzers at 2 m a.g.l, and the CM, respectively. For RAD fogs, mechanical turbulence is the factor favoring the vertical development of fog making it adiabatic. Therefore, fine and very fine RADs remain in their stable phase when the TKE is less than 0.2 m2 s-2. RADs begin their transition as soon as the TKE exceeds this threshold and remains less than 0.4 m2 s-2 and the RLWP > 0 g m-2. The dissipation of thick RADs is observed when the TKE exceeds 0.4 m2 s-2 and the RLWP < 0 g m-2. This strong turbulence can be of mechanical origin associated with an advection across the site or thermal with a diurnal increase in the SHF (50 W m-2). Less SHF (25 W m-2) is needed to dissipate very thin RADs. The amount of heat required for the diurnal dissipation of RADs is proportional to their geometric and microphysical characteristics. STLs persist for a stable TKE around 0.4 m2 s-2 and dissipate by elevation of the CBH when the SHF is low (25 W m-2). Their dissipation by evaporation needs more SHF (75 w m-2). The results indicates that the instrumental synergy and the metrics used in this study allows to produce an early warning tools for the nowcasting of RAD and STL formation, evolution and dissipation.
How to cite: Dione, C., Dupont, J.-C., Haeffelin, M., and Ribaud, J.-F.: Statistical analysis of the influence of local and synoptic processes on radiation and stratus lowering fogs at SIRTA Observatory, Paris, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19911, https://doi.org/10.5194/egusphere-egu25-19911, 2025.
Clouds and their interaction with aerosol particles remain one of the largest sources of uncertainty for quantitatively describing the climate system, primarily due to the challenges in accurately representing their microphysical properties. These properties determine, for example, how a cloud interacts with short and longwave radiation or determine its lifetime, and thus, influence the local energy budget. However, microphysical properties of clouds are highly variable in space and time and are difficult to measure with high precision.
The Ground-Based Fog and Aerosol Spectrometer (GFAS, Droplet Measurement Technologies, USA) is a newly developed instrument, designed to characterize microphysical properties of clouds and aerosol particles via forward and backward light scattering. Operating in the diameter size range of 0.4 – 40 µm, the GFAS builds on the capabilities of the Fog Monitor (Droplet Measurement Technologies, USA) but is extended by backscattered light intensity and changes in the polarization of the backscattered light as measured variables. These new parameters provide information on the particle’s shape and refractive index, enabling differentiation between solid and liquid particles, such as snow crystals, dust, and droplets, while minimizing biases in particle sizing caused by a change in refractive index. Furthermore, the GFAS automatically aligns with the main wind direction to minimize sampling losses.
Laboratory tests have validated the GFAS’ ability to characterize various particle types. Nebulized droplets and solid materials, like dust and ash, were analysed, revealing distinct polarization signatures between solids and liquids at an optical diameter > 10 µm. The backscattering ratio was used to further refine size distributions by investigating particle phase functions.
Field deployment of the GFAS during the ARTofMELT 2023 expedition in the Arctic under extreme environmental conditions provided first valuable insights into the role of low-level clouds and fog during the onset of sea ice melt. Over six weeks, the GFAS captured 46 hours of in-cloud data, showing strong agreement with the Fog Monitor data. These results confirm the GFAS as a new and powerful tool for advancing cloud microphysical measurements, reducing uncertainties in particle characterization, and improving our understanding of cloud-climate interactions.
How to cite: Haberstock, L., Neuberger, A., Baumgardner, D., Hughes, D., Riipinen, I., and Zieger, P.: A new instrument to study fog and clouds: Insights from laboratory characterization and field deployment of the Ground-Based Fog and Aerosol Spectrometer (GFAS, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12141, https://doi.org/10.5194/egusphere-egu25-12141, 2025.
This study characterizes the physical processes involved in fog formation through an analysis of Integrated Water Vapor (IWV) measured via GPS, combined with in situ meteorological data, with a particular focus on atmospheric humidity. The research was conducted in the Casablanca-Nouaceur airport, Morocco, where fog events present significant challenges to air transportation by severely reducing visibility. In this study, fog is modeled as a simple function of water vapor and cloud water within the framework of a non-precipitating warm cloud based on the bulk water-continuity model, with the assumption that the atmosphere above the fog layer remains horizontally homogeneous during fog events.
The objectives were to investigate the relationship between IWV and meteorological conditions during the whole lifecycle of fog events and to evaluate the potential of IWV for detecting and classifying these events. Over a six-year period (2017–2022), 207 fog episodes were analyzed in terms of occurrence, duration, intensity, and seasonal variability. The effectiveness of IWV as an indicator for fog detection and classification was also assessed. Using a fog type classification algorithm, the results revealed that that the most prevalent fog types were advection-radiation fog and stratus-lowering fog, followed by radiation fog and, less frequently, advection fog. These episodes exhibited strong seasonal variability, with higher occurrence rates during winter and autumn, corresponding to favorable meteorological conditions such as low temperatures and high relative humidity. Fog formation typically occurred during nighttime or early morning hours, dissipating gradually after sunrise.
Analysis of IWV variations before and during fog episodes revealed a consistent increase in IWV prior to fog formation, signaling an influx of moisture conducive to condensation. During the mature fog phase, IWV remained relatively stable, while the mixing ratio in the lower atmospheric layers decreased, indicating active condensation processes. These trends varied across different fog types, highlighting the complexity of the thermodynamic interactions involved. However, attempts to classify fog types based solely on IWV—using parameters such as IWV magnitude at fog onset, its amplitude during the three hours preceding formation, and its trend—proved challenging. The results demonstrate that GPS-derived IWV is a valuable tool for detecting changes in atmospheric moisture dynamics associated with fog events. However, its capacity for classifying specific fog types is limited due to the influence of additional factors, including temperature, wind patterns, and surface atmospheric conditions, which IWV measurements alone cannot capture.
Keywords : fog; GPS IWV; visibility; fog classification; water vapour; cloud water
How to cite: Sayrou, I., El messari, A., and Bari, D.: Leveraging Integrated Water Vapor derived from GPS for fog detection and fog characteristics analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9989, https://doi.org/10.5194/egusphere-egu25-9989, 2025.
The region of “Valles Cruceños”, Bolivia, is characterized by intrinsic water scarcity and increasing pressure for food production. In the area, located between the Andean altiplano and the Bolivian lowlands, orographic fog is a phenomenon occurring all year round and represents a sustainable water source to improve farmers’ resilience to dry spells and to promote food security and sovereignty. Here, the NGOs ICO and Zabalketa, and the University of Florence realized the first-ever experience of fog collection in Bolivia, in a structured set of activities, started in 2012 with a pilot project. After that, in 2018, the first scientific study on the potential of fog collection in the area was carried out in 10 locations, with a 1-year experimental analysis made through 1-m2 fog collectors, resulting in a yearly average of 6.01 l/m2/d observed in the best location. Based on these results, a large-scale fog collection system was implemented through international funding with innovative CloudFisher collection meshes starting in 2022, with a total of over 330 m2 of mesh over 4 locations, for multiple water uses (domestic, irrigation, reforestation). In the present contribution, we documented the whole implementation process from 2012, presenting the results of fog collection rates of the new system from 2022 up to the present, including also lessons learned and the main potential development for the area's future.
References:
Castelli, G., et al. (2023). Fog as unconventional water resource: Mapping fog occurrence and fog collection potential for food security in Southern Bolivia. Journal of Arid Environments, 208, 104884. https://doi.org/10.1016/j.jaridenv.2022.104884
Zabalketa (2023). FOG WATER COLLECTION IN BOLIVIA. Experiences with Different Designs, Results and Practical Findings: https://zabalketa.org/archivos/publicaciones/FOG-WATER-BOLIVIA_eng.pdf
How to cite: Castelli, G., Cubero, G., López de Armentia, T., Osses Mc Intyre, P. E., Forzini, E., Montano, L. C., Salbitano, F., and Bresci, E.: A twelve-year living lab for the experimentation and implementation of fog collection in the Valles Crucenos Region, Bolivia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12868, https://doi.org/10.5194/egusphere-egu25-12868, 2025.
Radiation fogs characterize the wintertime climate of many mid-latitude environments, especially in orographic depressions, and exert a profound impact on visibility, surface temperatures and soil-air water vapor fluxes. Trend analysis of fog occurrence based on visibility data supports the potential impact of air pollution decline on reduced fog frequency in several areas of the world, net of the effects of climate warming (van Oldenborgh et al., 2010; Gray et al., 2019). Microphysical models indicate that aerosol-fog interactions manifest on a range of fog physical properties for which, however, we miss relevant observations over decadal time scales. Here, we present a 30-year long timeline of radiation fog liquid water content (LWC) from the Po Valley, Italy, and analyse long-term trends, concomitantly with the trends in potential meteorological and atmospheric composition drivers. In particular, we reconstructed the entire time series of cloud condensation nuclei (CCN) concentrations using a machine learning approach trained on an extended (ca. 15-year long) observation record of differential mobility particle sizer (DMPS) measurements available from the same site (Leinonen et al., 2022). Our results show a consistent decrease of fog LWC in the 90s’ and early 2000’s at a time when aerosols and CCN concentrations underwent a steep decline. By contrast, in the last ten years, aerosol concentrations stabilized while fog LWC has recovered, probably because of an increased daily temperature excursion as a feature of current climate change in the Po Valley. Although the time evolution of Po Valley fog microphysics could not be followed over the three decades, the comparison of the detailed observations performed during recent intensive field campaigns in 2021 and 2022 (Neuberger et al., 2025) with those carried out in the early studies of 1989 and 1994 with state-of-the-art instrumentation (Fuzzi et al, 1992; Wendisch et al., 1998) support the hypothesized effects of CCN on fog LWC be mediated by changes in fog droplet concentration, size distribution and deposition rates. This research provides new insights on the effects of anthropogenic activities on fog occurrence and characteristics, as well as on the mechanisms of aerosol-cloud interactions in regime of high CCN and low supersaturations.
This work was funded by the EU Horizon 2020 project FORCeS (grant no. 821205) and the Horizon Europe project CleanCloud (Grant No. 101137639).
References
Fuzzi et al., The Po Valley fog experiment 1989, Tellus, 44B, 448–468, 1992.
Gray et al., J. Geophys. Res., 124, 10.1029/2018JD029419, 2019.
Leinonen et al., Comparison of particle number size distribution trends in ground measurements and climate models, Atmos. Chem. Phys., 22., 10.5194/acp-22-12873-2022, 2022.
Neuberger et al. From Molecules to Droplets: The Fog and Aerosol Interaction Research Italy (FAIRARI) 2021/22 Campaign. Bull. Amer. Meteor. Soc., 106, E23–E50, https://doi.org/10.1175/BAMS-D-23-0166.1, 2025.
van Oldenborgh et al., On the roles of circulation and aerosols in the decline of mist and dense fog in Europe over the last 30 years, Atmos. Chem. Phys., 10, 10.5194/acp-10-4597-2010, 2010.
Wendisch et al., Drop size distribution and LWC in Po Valley fog, Contrib. Atmos. Phys., 71, 87–100, 1998.
How to cite: Decesari, S., Mansour, K., Rinaldi, M., Paglione, M., Neuberger, A., Zieger, P., Joutsensaari, J., Romakkaniemi, S., and Fuzzi, S.: Air pollution reduction and climate change drive long-term trends of fog liquid water content in the Po Valley, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15592, https://doi.org/10.5194/egusphere-egu25-15592, 2025.
Fog forecasting over highly urbanized and fog-prone regions like Delhi and the National Capital Region (NCR) in the Indo-Gangetic Plain (IGP) is challenging due to the complex interplay between land surface processes and atmospheric conditions. This research investigates the role of advanced data assimilation techniques in enhancing fog forecasting accuracy using high-resolution numerical weather prediction (NWP) models. The focus is on improving the prediction of fog lifecycle events, including onset, duration, and dissipation, by integrating land surface data and non-conventional atmospheric observations into NWP systems.
The study begins with the implementation of the High-Resolution Land Data Assimilation System (HRLDAS) to improve the initialization of critical land surface variables, such as soil moisture and soil temperature. These variables play a key role in surface energy exchanges and boundary layer dynamics. Sensitivity experiments show that incorporating fine-gridded land surface data into the Weather Research and Forecasting (WRF) model significantly improves near-surface temperature, humidity, and wind forecasts. This results in a substantial reduction in soil moisture bias and a more accurate representation of land-atmosphere interactions, leading to enhanced predictions of fog onset and dissipation in the NCR region. Building on this, the research employs cyclic assimilation of microwave radiometer (MWR) observations to improve the vertical profiles of atmospheric temperature and humidity. The combined assimilation of MWR profiles and HRLDAS-generated land surface fields enhances the boundary layer structure, which is critical for fog formation and dissipation. Results demonstrate that the assimilation of non-conventional data sources improves the spatial and temporal accuracy of fog forecasts, reducing errors in predicting fog intensity and duration. These findings emphasize the importance of high-resolution observational data and advanced assimilation techniques for capturing the microphysical and thermodynamic processes governing fog development.
This study demonstrates the importance of integrating land data assimilation and high-resolution atmospheric observations into NWP systems to enhance fog forecasting. The techniques developed address challenges posed by complex land-atmosphere interactions and can be applied to other fog-prone regions. By improving the understanding of fog dynamics, this work enables more accurate and timely forecasts, benefiting critical sectors like aviation, transportation, and public safety in regions such as the IGP.
How to cite: Parde, A. and Ghude, S.: The Role of Data Assimilation in Enhancing Warm Fog Predictions Over Delhi and NCR, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-438, https://doi.org/10.5194/egusphere-egu25-438, 2025.
„The term ‘fog’ is used when microscopic droplets reduce horizontal visibility at the Earth’s surface to less than 1 km” (https://cloudatlas.wmo.int/en/fog-compared-with-mist.html). This simple, traditional definition has proven very useful, it has been applied for decades in meteorological observations by the naked eye and – more recently – by instrument recordings. The term ‘mist’ is used “when the droplets do not reduce horizontal visibility to less than 1 km” (cloudatlas.wmo.int, see above). Both fog and mist are often associated with air pollution, specifically high aerosol load within the boundary layer, which leads to large number concentrations of small droplets. Such small hydrometeors are often non-activated. The increase of air quality (decrease of aerosol particle concentrations) worldwide leads to a decrease of fog. Yet, what are the microphysical conditions of fog in a clean atmospheric environment? Will fog form only when droplets are activated? Recently employed fog droplet size spectrometers of various manufacturers allow a deeper insight into the microphysical conditions of fog and will shed light on the role of activation of fog condensation nuclei (FCN) on the formation of fog, its interaction with air pollution, and its trends before the backdrop of climate change.
How to cite: Klemm, O. and Lin, N.-H. (.: New light on “fog”?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3435, https://doi.org/10.5194/egusphere-egu25-3435, 2025.
Similar to other types of clouds, the properties and evolution of fog are potentially sensitive to the interaction withaerosols. Assuming constant liquid water content, an increase in the aerosol concentration leads to water vapor competition, resulting in smaller droplet sizes and higher cloud droplet number concentrations. These changes can further influence the microphysical processes of fog, such as droplet sedimentation and aerosol regeneration (aerosol release upon droplet evaporation). However, substantial uncertainties in the representation of these processes pose challenges for accurately simulating fog evolution and for investigating the impact of aerosols on fog characteristics in climate models.
This study employs the large-eddy model MISU-MIT Cloud and Aerosol (MIMICA) to conduct case studies on wintertime radiation fog in the Po Valley, Italy. Observational data from the Fog and Aerosol InteRAction Research Italy (FAIRARI) campaign during the winter of 2021/22 were used as model input and simulation evaluation. Improvements of the parameterisation schemes in MIMICA were implemented, including surface forcing, warm air advection, droplet sedimentation, and aerosol hygroscopic growth. A series of one-at-a-time sensitivity experiments were performed based on the reference case.
Preliminary results indicate that the lifecycle and microphysical properties of wintertime radiation fog (e.g., droplet concentration, droplet size, and liquid water content) are sensitive to the representation of aerosol size distribution. Furthermore, an accurate description of microphysical processes is critical for capturing fog characteristics. For instance, neglecting droplet sedimentation can lead to a significant overestimation (by approximately an order of magnitude) of fog liquid water; the fog layer structure shows a significant response to different predefined parameters of droplet size distribution; aerosol hygroscopic growth plays a pivotal role in reproducing the water vapor budget and fog liquid water. We also find that an accurate representation of the meteorological conditions, such as surface temperature and humidity variations, is key for capturing the timing of fog onset and dissipation.
Financial support from the European Union’s Horizon 2020 research and innovation program (project FORCeS No 821205) and the Swedish Research council (No 2020-04158) is gratefully acknowledged.
How to cite: Ding, H., Neuberger, A., Ranjan, R., Heikkinen, L., Mansour, K., Decesari, S., Riipinen, I., Zieger, P., and M. L. Ekman, A.: The importance of aerosol and cloud microphysics on the properties and lifecycle of wintertime radiation fog in Po Valley, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12588, https://doi.org/10.5194/egusphere-egu25-12588, 2025.
Fog and low-level clouds, namely stratus, are of major concern mainly due to their
adverse effects on transportation. Visibility is drastically reduced under the
occurrence of fog conditions, thus affecting aviation and road traffic. For example,
fog causes many troubles to scheduled flights, like cancelations or delays, while
creating dangerous driving conditions, thus having significant socioeconomic impacts.
Moreover, apart from affecting transportation, fog and low-level clouds also affect the
radiation budget, specifically at the Earth’s surface. For all these reasons, the
simulation and forecasting of fog is important, especially when no observational tools,
e.g. radars, are available.
Ioannina is a middle-sized city (~120,000 inhabitants) situated in the Epirus
mountainous region in Northwestern Greece. The city is located on a plateau (basin)
with an average altitude of 500 m, surrounded by high mountains with altitudes higher
than 1500m. The airport of Ioannina lies in an area of the plateau which experiences a
high yearly number of fog events. This is mainly due to: (1) the presence of the
nearby Pamvotis lake (area 23 km 2 , average depth 4 m, maximum depth 10 m), which
locally enriches the overlying air masses with water vapour and (2) the specific
geographical and topographical characteristics of the area, which generally favour
calm (low wind speed) conditions speed and high relative humidity levels, as well as
the creation of temperature inversions. Moreover, due to the local topography and
meteorological conditions, the city frequently suffers from wintertime air pollution
episodes (smog) due to extensive biomass burning for domestic heating activities.
Despite the frequent occurrence of fog and the induced air traffic problems, there are
not available tools for forecasting fog locally. The present work aims to fill this gap
by implementing a numerical model, specifically the parameterized fog model
PAFOG along with the spectral cloud microphysics model MIFOG. The two models
will be operated and evaluated as to their ability to simulate fog under different
conditions.
The fog models will be initialized with available data from local meteorological
stations supplemented by vertically resolved reanalysis and satellite data, due to the
lack of radiosondes in the study area. Local information on aerosol particles, acting as
CCN, will be implemented as well, enabling to investigate their role for the formation
of fog. The performance of the two models will be assessed through comparisons to
available METARs from the Ioannina airport. This study is a first step towards
implementing fog models for a routine fog forecasting at the city/airport.
How to cite: Nasikas, S., Hatzianastassiou, N., Korras-Carraca, M.-B., and Bott, A.: Implementation of the parameterized and spectral fog models(PAFOG, MIFOG) for simulating fog at the Ioannina mountainouscity (Greece), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17344, https://doi.org/10.5194/egusphere-egu25-17344, 2025.
The impacts of global warming and urban heat island effects have significantly altered atmospheric conditions, leading to a noticeable decline in fog events which uplift to be low clouds. Understanding and monitoring these changes over the long term are crucial for climate studies and environmental management. However, identifying a practical and reliable method for monitoring cloud base height and fog patterns remains a challenge.
This study explores the use of the dark channel model, a computational approach originally developed for haze removal in images, to analyze cloud and fog dynamics. By leveraging the model's ability to estimate cloud base height and distinguish upslope fog from other atmospheric conditions, we provide a novel method for atmospheric monitoring. The model's performance was validated using observational data from a ceilometer, an instrument known for its precision in measuring cloud base heights. The comparison revealed a strong correlation between the model's predictions and ceilometer measurements, with a coefficient of determination (R²) of 0.85.
The results demonstrate that the dark channel model is an effective tool for long-term monitoring of cloud and fog dynamics, offering both accuracy and convenience. This approach could play a pivotal role in understanding atmospheric changes in the context of climate variability and urbanization, aiding in better management and forecasting of weather and environmental conditions.
How to cite: Lai, Y.-J., Lin, P.-H., Hsu, C.-F., and Chang, Y.-L.: Cloud and Fog Monitoring Simplified: Preliminary Validation of the Dark Channel Model with Ceilometer Observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21131, https://doi.org/10.5194/egusphere-egu25-21131, 2025.
Posters on site: Thu, 1 May, 10:45–12:30 | Hall X5
Rain, fog drip, shallow soil water and groundwater were collected for stable isotopic analysis, at a tropical seasonal rain forest site in Xshuangbanna, Southwest China. The fog drip water ranged from -30 to +27% in δD and -6.2 to +1.9% in δ18O, conforms to the equation δD=7.64δ18O+14.32, and was thought to contain water that has been evaporated and recycled terrestrial meteoric water. The rain was isotopically more depleted, and ranged from -94 to -45% in δD, and -13.2 to -6.8% in δ18O. The shallow soil water had a composition usually between those of the rain and fog drip, and was assumed to be a mixture of the two waters. However, the soil water collected in dry season appeared to contain more fog drip water than that collected in rainy season. The groundwater in both seasons had an isotopic composition similar to rainwater, suggesting that fog drip water does not play a significant role as a source of recharge for the groundwater. This groundwater was thought to be recharged solely by rainwater.
How to cite: Zhang, Y. and Liu, W. J.: Fog drip and its relation to groundwater in the tropical seasonal rain forest of Xishuangbanna, Southwest China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-445, https://doi.org/10.5194/egusphere-egu25-445, 2025.
The 3000 km long coast of Chile, characterized by a steep mountain range, interacts almost everywhere with the Southeast Pacific stratocumulus (Sc) low clouds deck, producing narrow but extensive fog banks. This long but narrow fog belt crosses diverse climates such as hyperarid (18°- 23°S), arid (23°- 28°S), semi-arid (28°- 31°S), mediterranean (31°- 34°S), and temperate (34° - 37°S), being essential for unaccountable ecosystem types and a virtual water resource to be tapped. In this work, using an extensive network of meteorological stations and standard fog collectors, combined with remote sensing observations and a numerical fog collection model, we characterize the fog water collection at multiple temporal scales, focusing on the physical conditions that allow this collection at local and larger spatial scales. Preliminary results show that the annual cycle of fog water collection along Chilean coast is closely related to the spatial variability of the thermal inversion strength across the seasons, which is the main controller of the formation of the Sc-fog cloud. Our observations taken in hyperarid climatic zones (18° - 23°S) show the highest seasonal oscillation, with peaks in winter-spring. Over arid and semi-arid climatic zones (23° - 28°S), the fog collection cycle is more constant throughout the year, showing a low seasonal oscillation. Contrary to hyperarid climatic zones, the mediterranean and temperate zones (28° - 37°) show water harvesting peaks over the summer with larger seasonal oscillation than (semi-)arid climates. The fog harvesting diurnal cycles in all climates show a decrease at midday associated with the decrease of the Sc-fog cloud presence. The maximum peaks of water collections are related to fog type being at night during advective fog events (higher liquid water content) and in the afternoon during orographic fog events (higher wind speed). In terms of water yields, these are influenced by the local conditions of the site, especially by wind speed, altitude and distance from the coast. However, the highest water collection volumes (yearly, monthly and diurnal) are found in the hyperarid and arid climatic zones (7 to 3 L m-2 d-1 in average respectively), which is consistent with the frequency of Sc-fog presence (50% in the hyper-arid and 30% in the arid). These zones are characterized by having the most fragile ecosystems and population living under a constant water scarcity. Our fog harvesting observations gathered through the largest fog monitoring network on Earth allow us for the first time to assess fog harvesting as a valuable resource in diverse climates. This network constitutes a key tool for understanding the regional and local dynamics of fog collection under a climate change context, as well as to understand its role in ecosystem conservation.
How to cite: del Río, C., Lobos-Roco, F., Espinoza, V., Keim-Vera, K., Valdivia, N., Vargas, C., and Rivera, D.: Atmospheric water collection across diverse climates along the Chilean coast: unraveling synoptic to local drivers of fog harvesting, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4880, https://doi.org/10.5194/egusphere-egu25-4880, 2025.
The Po Valley in northern Italy is an ideal laboratory to study fog-pollution interactions. The peculiar orography of the region (enclosed between the Alps and the Apennines) promotes stable meteorological conditions and radiation fog formation in wintertime. At the same time, high population density and the several agricultural and industrial activities are responsible for high levels of pollutants, among the highest in Europe. The interaction between those factors has been studied since the 1980s, however, the detailed microphysical processes behind the aerosol-fog interactions are still to be elucidated. Therefore, in winter 2021/22, the Fog and Aerosol InteRAction Research Italy (FAIRARI) campaign took place at the research station San Pietro Capofiume, in a rural area close to Bologna. Microphysical as well as chemical aerosol and fog processes from the molecular to the droplet scale were captured.
Stockholm University’s mobile atmospheric laboratory simultaneously measured the total dried aerosol and the dried fog droplets (fog residuals), which then both were analyzed with respect to their size and (re-)activation behavior. Moreover, the chemical composition of the dried aerosol particles was determined. Meteorological parameters such as horizontal wind, updraft, and visibility were measured as well as the size distribution of the fog droplets.
We will present and discuss the influence of aerosol particles on fog microphysics during FAIRARI. For example, hydrated but not activated aerosol particles contributed to more than 50% of the visibility reduction, having implications for the definition of the beginning of fog. This in turn impacted the fog describing parameters such as the effective diameter or liquid water content (LWC), which are crucial when comparing in-situ measurements to data retrieved from satellite observations and modelling predictions. During FAIRARI, if fog is defined as LWC > 0.01 g m-3, the in-fog median LWC increases by 28% (from 0.18 g m-3 to 0.23 g m-3), compared to if fog is defined by visibility < 1km. The hygroscopicity parameter κ was calculated to be around 0.36 in the ambient aerosol out of fog and about 0.47 in the fog residuals. Moreover, sensitivity tests with the large-eddy simulation model MIMICA showed that with the same amount of aerosol particles in the air, changes in the size distributions lead to significant modifications of fog microphysical properties. This work will contribute to constrain the role of aerosol parameters on fog properties and facilitate model improvements.
Financial support from the European Union’s Horizon 2020 research and innovation program (project FORCeS No 821205 and H2020-INFRAIA-2020-1 under grant agreement No 101008004) and the European Research Council (Consolidator grant INTEGRATE No 865799) is gratefully acknowledged.
How to cite: Neuberger, A., Ranjan, R., Ding, H., Decesari, S., Eckhardt, S., Ekman, A. M. L., Evangeliou, N., Haberstock, L., Mattsson, F., Mohr, C., Paglione, M., Riipinen, I., Rinaldi, M., and Zieger, P.: The influence of aerosol particles on fog microphysics during the Fog and Aerosol InteRAction Research Italy (FAIRARI) campaign 2021/22 , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11460, https://doi.org/10.5194/egusphere-egu25-11460, 2025.
The Northwest Pacific (NWP) is a region with the highest frequency of sea fog occurrence and most widely distributed fog area in the world oceans. The sea fog prone area is located in the mid-latitudes where ocean navigations are getting more and more active. Sea fog is heavy obstacles for navigation due to low visibility in fog. The techniques of sea fog forecasting in the NWP still remains unsatisfactory compared with marginal seas so far. Using observations from ICOADS and ERA5 data from 2013 to 2021, this study tried to make sea fog prediction model in the NWP based on machine learnings. The distribution characteristics of sea fog in the NWP were analyzed and compared with China offshore fog areas. Based on analysis of sea fog occurrence, 12 key factors were identified by mutual information (MI) method, including sea surface temperature (SST), surface air temperature (SAT), SST-SAT, humidity, wind speed and direction, etc. In addition, geographical coordinates (latitude and longitude information) were also taken into consideration as factors. Four machine learning models were constructed for sea fog prediction, employing resampling techniques to address the extreme imbalance between foggy and non-foggy samples. The results demonstrated a significant enhancement in model performance by resampling, especially with oversampling, and a decline in performance was noted upon the removal of the geographical coordinates. Among the four evaluated models, the eXtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and Convolutional Neural Network (CNN) exhibited similar performance metrics, with threat score (TS) scores about 0.3 and the Decision Tree (DT) showed more stable results. Regarding individual case performance, the XGBoost model outperformed the others, showing the highest agreement with the fog area range observed in satellite images. This study reveals the complexities of sea fog formation in the NWP and provides a scientific basis for sea fog prediction in vast expanded ocean areas.
How to cite: Zhang, S. and Wu, K.: Research on Sea Fog Prediction in the Northwest Pacific Using Machine Learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4971, https://doi.org/10.5194/egusphere-egu25-4971, 2025.
Sea fog often penetrates adjacent coastal areas, a process called sea fog penetration (SFP). SFP can cause traffic accidents and other economic losses. Qingdao, an international port city with a dense population, suffers from SFP originated over the Yellow Sea in the boreal spring (March-May); the process, however, is not well studied. Based on hourly observations from buoys and automatic weather stations distributed in Qingdao and its adjacent islands, we composite SFP events to reveal their spatiotemporal features and to investigate mechanisms involved. Results show that these SFP events often penetrate inland areas from southeast to northwest and last 5-8 hours at night. We further use reanalysis data to reveal that during the daytime before SFP, strong moisture advection at 925-975 hPa brings sufficient water vapor from Yellow Sea to Qingdao; the water vapor then transfers downward to the surface via background descending motion and turbulent mixing. The daytime anomalous moistening, together with the following diurnal cooling at night, saturates the surface atmosphere and hence facilitates SFP. The strength of the SFP depends on the strength of daytime anomalous moistening. Considering the moistening leads SFP by about a day, we use this relationship to predict the intensity of SFP. The accuracy of predicting SFP events could reach 50%~80%, which highlights the predictability of intensity of SFP in Qingdao.
How to cite: Shi, X.: Distribution characteristics and Mechanism of Springtime Sea Fog Penetration, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5025, https://doi.org/10.5194/egusphere-egu25-5025, 2025.
In the coastal areas of the Atacama Desert, non-rainfall water inputs (NRWI), including fog, dew, and direct vapor adsorption, sustain life in an environment where rainfall is virtually absent. These water sources provide critical moisture to the soil surface, fostering conditions for ecosystems to survive and adapt to extreme water scarcity. However, while the physical processes behind NRWI are better understood, the dynamics of each vector's events and their contributions to soil moisture remain poorly quantified.
Specifically, this research aims to characterize the dynamics of fog, dew, and direct vapor adsorption events and their contributions to soil surface moisture. A high-resolution experimental setup monitored the conditions facilitating NRWI formation—air temperature, relative humidity, surface temperature, and water vapor gradients between the air and soil pores—alongside soil surface moisture fluctuations. Our experimental design integrated a meteorological station, an infrared surface thermometer, a standard fog collector, a flat dew condenser, sensors for soil temperature, relative humidity (RH), volumetric water content (VWC), and a ground-based fog observation camera. These instruments enabled the analysis of individual NRWI vectors to investigate the timing, magnitude, and duration of events in relation to soil moisture changes.
A case study was conducted in the Las Lomitas oasis of Pan de Azúcar National Park (25°59' S, 70°36' W), situated at 731 m a.s.l. and 1.7 km from the coastline. Directly influenced by the marine boundary layer, this site provided an ideal setting to observe NRWI dynamics. Preliminary data collected between October 21 and December 5, 2024, revealed that direct vapor adsorption was the most frequently observed NRWI contributor, driving daily variations of 1–3% in VWC. Dew formation conditions were observed during 28% of the study period, primarily driven by high relative humidity (averaging 96.4%). However, dew events were short and intermittent, averaging 15 minutes, limiting their contribution to soil moisture when analyzed at the scale of individual events.
In contrast, 217 fog events were recorded, with an average duration of 3 hours and a water yield of 1.88 L/m² per event. Interestingly, only 5% of these fog events were associated with significant increases in soil moisture, with VWC rising by more than 1%. The largest fog event lasted 66.83 hours, yielding 83.58 L/m² and resulting in a 14.5% increase in VWC, the highest recorded moisture increase during the study.
The analysis shows the need to characterize and classify fog and dew events to identify strong correlations and demonstrate their impact on soil moisture. In the case of direct water vapor adsorption, it provided the most frequent moisture inputs, although in small magnitudes, that rarely contributed significantly on a daily basis. This research improves our understanding of the dynamics of NRWI events and their influence on soil surface moisture in arid environments. The findings reveal subtle mechanisms supporting coastal ecosystems and highlight their relevance for conservation and adaptation strategies in the face of climate change.
How to cite: Rivera, D., Vargas, C., Van Den Brink, L., Alfaro, F. D., and del Río, C.: Contributions of Non-rainfall water inputs to soil surface moisture in arid coastal ecosystems: A case study in Pan de Azúcar National Park (25°59' S and 70°36' O), Atacama Desert, Chile., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14315, https://doi.org/10.5194/egusphere-egu25-14315, 2025.
The Okhotsk Sea is renowned as one of the world's most foggy regions. Previous studies have identified that sea fog occurring under south winds is mainly advection fog. However, the formation mechanism of sea fog under north winds remains unclear. On August 21, 2019, the research vessel "Xiangyanghong 01" observed a phenomenon where low clouds transformed into sea fog in the waters north of the Kuril Islands under north wind . Analysis of shipborne observations, reanalysis data, and simulation results show that during this sea fog event, the upper-level atmospheric circulation over the Pacific was characterized by a cut-off low and blocking high. The stable circulation provided favorable conditions for the sea fog within the Okhotsk Sea. From August 19 to 21, influenced by east winds between tropospheric systems, the east-west mountain flow over the Kamchatka Peninsula intensified continuously. The Foehn effect on the western coast of the peninsula led to a significant increase in air temperature, enhancing the advection of warm air from land to sea, particularly between 925 hPa and 950 hPa. As this warm dry air ascended above the moist air mass over the sea, it strengthened the temperature inversion and lowered the inversion layer base, thereby promoting cloud descent and the formation of sea fog. Furthermore, under north winds, the subcloud air mass experienced evaporation of warm sea surface and cooling of cold sea surface of the eastern Okhotsk Sea. The subcloud air mass became saturated with moisture, leading the downward development of the cloud layer into sea fog.
How to cite: Jiang, Y.-C., Yi, L., and Zhang, S.-P.: Sea Fog under the North Wind with the Influence of Foehn Wind in the Okhotsk Sea, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2221, https://doi.org/10.5194/egusphere-egu25-2221, 2025.
With the changing climate, the study of fog formation is essential and needs of the hour as the nature of fog has changed due to impact of complexity of natural and anthropogenic aerosols. The chemical and physical properties of cloud condensation nuclei (CCN) significantly influence fog formation and visibility, especially in regions like India. Ample water vapor, coupled with the microphysical and thermodynamic properties of CCN, plays a vital role in the occurrence and and sustenance of fog. Weather and Climate models often struggle to simulate fog droplets accurately due to the absence of aerosol indirect effects (AIE), a key factor contributing to uncertainties in aerosol-cloud interaction (ACI). The visibility calculation, which depends on the number of cloud droplets and liquid water content (LWC) in the atmosphere, is closely tied to aerosol dynamics. The conversion of aerosols into fog droplets (Nd) requires external nuclei for water vapor condensation, a process governed by condensation or diffusional growth. The condensation process is influenced by the solute effect (Raoult's effect) and the curvature effect (Kelvin effect) and may happen over a pre-existing Aitken aerosol particle. Droplet growth is determined by the size of CCN and a droplet will always try to maintain an equilibrium state and it happens if the equilibrium supersaturation is less than the amount of water vapor available in the atmosphere. The Köhler curve illustrates the interplay between the 'curvature effect' and the 'solute effect' in droplet behaviour, showing that droplets with higher solute concentrations exhibit lower critical supersaturation values, making them easier to activate into cloud droplets. Thus, hygroscopicity (κ), a key factor in CCN activation, relates to aerosol particle contributions to fog droplet formation. This is encapsulated in the κ-Köhler theory, which combines Kelvin's and Raoult's effects to describe the activation of deliquesced aerosols into cloud droplets. Hygroscopicity is linked with the water activity and thus the thermodynamics of solution governs the aerosols liquid water and hence, better understanding of hygroscopicity is essential in numerical model for the fog and visibility prediction. The evolution of the droplet size distribution (DSD) under varying aerosol-chemical conditions remains poorly understood. To reduce uncertainties in fog forecasting, the Eulerian-Lagrangian particle-based schemes in direct numerical simulation (DNS) are utilized to study the diffusional growth of droplets. Using observational data from the Winter Fog EXperiment (WiFEX) conducted at IGI airport, New Delhi, small-scale model simulations provide valuable insights into droplet activation processes. Additionally, a novel visibility parameterization has been proposed based on the small-scale model using WiFEX observed data, which incorporates both LWC and Nd. This advancement offers a pathway to more accurate fog and visibility forecasts in numerical weather prediction models.
How to cite: Hazra, A., Bhowmik, M., Ray, A., Wagh, S., and Ghude, S. D.: Advancements in fog prediction in weather and climate model guided by DNS, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9259, https://doi.org/10.5194/egusphere-egu25-9259, 2025.
Understanding fog microphysics involves examining the size distribution, chemical composition, and hygroscopic properties of aerosols, which influence their activation as cloud condensation nuclei (CCN) and contribute to fog droplet formation. Incorporating these aerosol-driven processes into Numerical Weather Prediction (NWP) models, such as the Weather Research and Forecasting (WRF) model, has proven essential for accurately simulating fog development, sustainment, and dissipation.
India's current operational fog forecasting systems, particularly for urban regions, often lack detailed aerosol representation. The present study integrates water-friendly and carbonaceous aerosols using climatological data (2001–2007) within the Thompson-Eidhammer Aerosol-Aware Microphysics scheme in the WRF model to enhance fog forecasting for Delhi. The system incorporates improved CCN activation to capture the vertical development of fog.
To evaluate model performance, a 10-day winter spell during the 2024–25 season was selected, including hazy conditions and dense fog events. The selected period featured two hazy days with surface visibility oscillating around 1000 meters and seven fog episodes, including four dense radiation fog events and three cloud base-lowering fog occurrences. Model validation of key meteorological variables such as 2 m temperature (T2), relative humidity (RH2), and radiative fluxes (shortwave and longwave) shows strong agreement with ground-based measurements from the Winter Fog Experiment (WiFEX) at Indira Gandhi International Airport (IGI), Delhi.
Additionally, the study introduces the development of the visibility parameterization based on aerosol extinction coefficients and cloud water mixing ratios. This diagnostic approach in the WRF model effectively captures visibility degradation due to hygroscopic aerosols under hazy conditions and during the initial phases of fog formation. The findings underscore the importance of aerosol-aware modeling in urban fog forecasting and contribute to improving visibility diagnostics.
Preliminary results from this research will be presented at the EGU 2025 conference.
How to cite: Kumar, S., Parde, A. N., Peterka, A., Geresdi, I., Pakkattil, A., Govardhan, G., and Ghude, S. D.: Aerosol Based Fog Forecasting System and Visibility Parameterization For Delhi and NCR , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7865, https://doi.org/10.5194/egusphere-egu25-7865, 2025.
Severe wintertime air pollution episodes in Delhi, India, often coincide with fog events, amplifying their environmental and health impacts. This study investigates the complicated interactions between aerosols and fog during the winter season of 2023-2024, leveraging high-resolution, time-resolved measurements of non-refractory PM1 (NR-PM1) using High-Resolution Time-of-Flight Aerosol Mass Spectrometry (HR-TOF-AMS). The study highlights the temporal variability of NR-PM1 chemical composition and its transformation during fog events, focusing on processes influencing aerosol acidity, hygroscopicity, and secondary formation.
NR-PM1 mass concentrations varied widely, with organics (OA) constituting the dominant fraction (~65%), followed by nitrate, sulfate, ammonium, and chloride. Ammonium acted as the primary neutralizing agent, with an average aerosol neutralization ratio (ANR) of 0.95 ± 0.12, indicating near-neutral aerosol conditions. Significant shifts in OA composition were observed between fog and non-fog periods, with fog events promoting enhanced oxidation of organic aerosols. Elemental analysis revealed changes in the OA oxidation state. The oxygen-to-carbon (O/C) ratio increased for LV-OOA (from 0.914 to 1.016) and SV-OOA (from 0.920 to 0.838) during fog, indicating enhanced oxidation within fog droplets. The carbon oxidation state (OSc) also increased for these factors during fog, further confirming this observation.
The Positive Matrix Factorization (PMF) method identified six primary OA factors: Hydrocarbon-like OA (HOA), Nitrogen-rich Hydrocarbon OA (NHOA), Biomass Burning OA (BBOA), Solid Fuel OA (SFOA), Low-Volatile Oxygenated OA (LV-OOA), and Semi-volatile Oxygenated OA (SV-OOA). Fog events led to a ~34% increase in LV-OOA, while SV-OOA decreased correspondingly, suggesting a shift towards more oxidized, low-volatility species, reflects the significant role of fog in promoting secondary organic aerosol (SOA) formation through heterogeneous and aqueous-phase reactions.
The findings emphasize the dual role of fog as both a sink and a facilitator of aerosol transformation, with implications for regional air quality and visibility. This study provides crucial insights into aerosol evolution mechanisms during fog events, emphasizing the need for integrated observational and modeling approaches to mitigate wintertime pollution episodes in megacities like Delhi.
How to cite: Vispute, A. S., Dhangar, N. G., Gosavi, S. W., Lonkar, P., Wagh, S., Govardhan, G. R., and Ghude, S. D.: From Haze to Fog: Investigating Aerosol-Fog Interactions and Source Shifts in Wintertime Delhi, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15078, https://doi.org/10.5194/egusphere-egu25-15078, 2025.
The numerical forecast of fog is still challenging at the current stage, as many models typically show poor fog-forecasting capabilities. Model predictions of fog exhibit strong sensitivity to the selection of planetary boundary layer (PBL) parameterizations, as well as horizontal grid spacing (HGS). To address this issue, this study intercompares the fog-forecasting performance of WRF model using five PBL schemes (YSU, MYJ, MYNN, ACM2 and SH) and different HGS ranging from ten-kilometric- to hectometric-scale (15-km to 500-m). Validation against available in-situ measurements indicates that the YSU scheme produces an overall superior performance in fog forecasting, followed by MYNN and MYJ. For a certain PBL scheme, the model shows distinct forecasting capabilities in terms of different types of fog. That is, the model generally shows better performance in the forecasting of advection fog episodes, compared to radiation fog episodes. Regarding different HGS, intercomparisons of mesoscale modeling (i.e., WRF) with HGS ranging from ten-kilometric- to hectometric-scale (15-, 5-, 2.5-km and 500-m) reveal that, although simulations using finer HGS could generally better represent the spatial distribution of meteorological elements and the influence of small-scale underlying surface, the fog forecasting skills (i.e., the TS scores) does not consistently improve with the refinement of HGS. Fog forecasting at different HGS behaves differently for two types of fog days which are primarily differentiated by whether or not the dissipation of fog is substantially influenced by the background synoptic wind flow. For type-Ⅰ fog days (with no obvious impact of the background synoptic wind), differences in fog forecasting skills (TS values) among different HGS simulations are relatively smaller. Whereas for type-Ⅱ fog days (with the dissipation of fog strongly affected by the invasion of cold airflow), deviations in TS values among simulations using different HGS become more evident, with 2.5-km HGS providing better performance and the coarsest-HGS (15-km) simulation shows a noticeable degradation in the fog forecasting skills. Also note that simulation using the finest HGS (500-m) shows no superiority (or somewhat degradation) in fog forecasting skills for both of the two types of fog days. Additionally, the influence of model HGS on simulating the spatial distribution of fog is more pronounced during the formation and dissipation stage, whereas rather limited during the developing stage. On the basis of comprehensive model evaluation, we further attempt to improve the model performance under stable stratification by incorporating a wind-shear term into the mixing-length formulation for a turbulence scheme. After validation against available observations, results of the sensitivity experiments show that this modification could generally improve the representation of turbulent mixing, near-surface meteorological elements, as well as vertical boundary layer structures during stable conditions.
How to cite: Yang, J., Wu, B., Tian, M., Liu, H., and Liao, Y.: Evaluation and improvement study on the planetary boundary-layer scheme for fog-forecasting in North China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4376, https://doi.org/10.5194/egusphere-egu25-4376, 2025.
Sea fog has significant impacts on both human activities and the natural environment. Sea fog top height (SFTH) reflects the impact of sea fog on vertical space and is a crucial parameter for both Numerical Weather Prediction models and the estimation of sea fog dissipation. The existing SFTH retrieval methods based on spaceborne passive radiometers are prone to significant errors. We focus on the Yellow and Bohai Seas region and introduce a high-precision SFTH retrieval algorithm based on the peak elevation (PE) of islands in the area. In remote sensing images, islands of different PE within the sea fog regions exhibit two distinct appearances either visible or obscured by the fog. Thus, islands are automatically classified into two categories by support vector machine. The relationship between SFTH and the corresponding sea fog reflectance (SFR) in remote sensing images is established by defining a linear decision boundary in the SFR-PE space through logistic regression or support vector machine to effectively separate the two island categories. Validation experiments on twelve sea fog cases show that the proposed method exhibits higher accuracy compared to the MODIS cloud top height product and demonstrates good agreement with CALIPSO data.
How to cite: Yang, P., Tang, Y., Zhao, X., Wang, Y., and Zhou, Z.: Sea Fog Top Height Retrieval over the Yellow Sea andBohai Sea Using Island Elevation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2705, https://doi.org/10.5194/egusphere-egu25-2705, 2025.
This study analyzed the temporal and spatial distribution characteristics of jet stream in low airspace (1500 meters) and the impact to fog in Tianjin urban areas, based on the data of the boundary layer wind profiler and the 255 m Meteorological Tower during 2016-2018.
The statistical results show that: (1) As for the distribution height of wind jet stream, there are two peak heights within 1500 m, one peak is within 500-600 m, about 23% of the wind stream jet is located at the height; The other peak is located in the 200-300 m range, and about 19% of the jet stream is located at the height; and wind jet streams are more evenly distributed in other low-altitudes airspace. (2) As for the height and frequency seasonal distribution characteristics of the jet stream, the average height of the jet stream is generally located at 700-1000 m, and the seasonal variation of the jet height is not obvious, but the height of the cold season is higher than that of the warm season. The frequency of stream jet in 300-1500 m layer is the lowest in December and the highest in May. In contrast, the low-level jet within the 300 m layer occurs more frequently during the transition from warm to cold season, accounting for about 70% in autumn and winter, with the lowest frequency in June and the highest in October. (3) As for the diurnal distribution characteristics and wind direction of the jet stream, the frequency of wind stream jet at 300-1500 m layer increased at sunset, and the high frequency lasted until dawn of the next day, the character is the same for the low-altitude jet below 300 m, and the frequency during nighttime is more than 70%, far more than the frequency of wind jet at the upper layer. The prevailing jet direction is southwest, accounting for 47%; Followed by northerly wind, accounting for 17%; The southeast (90-180°) jet direction is less than 20%, while the northwest low-level jet stream least frequent. (4) The southerly jet before the fog, which was conducive to the formation of fog, transported water vapor to the fog area; while the northerly jet have bidirectional effect of leading to fog burst or dissipation, which afford to the fog area with cold air and kinetic energy.
In conclusion, Due to sparse vertical layer data, such as the FNL reanalysis data, the features of low-altitude airspace jet are rough, especially those below 300 meters. This study supplemented the understanding of the distribution characteristics of the low-altitude jet under 300 m and the effect to fog in Tianjin urban area, which is important for fog forecasting and low-altitude flight meteorological service .
How to cite: Wu, B., Ju, T., and Tian, M.: Characteristics of Jet Stream in Low-altitude Airspace and Impact to Fog in Tianjin Urban Areas, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3506, https://doi.org/10.5194/egusphere-egu25-3506, 2025.
Widespread fog is a critical weather phenomenon over the Indo-Gangetic Plain (IGP) during winter, significantly impacting transportation safety and air quality. This study investigates the contrasting mechanisms of fog formation and dissipation over an urban site (Delhi) and a rural site (Jewar, ~80 km aerial distance) using extensive ground-based observations collected during the Winter Fog Experiment (WiFEX) field campaign from December 2023 to February 2024.
Analysis of WiFEX observations at Indira Gandhi International (IGI) Airport, Delhi, and Jewar Airport identified 15 dense fog events in Delhi and 25 in Jewar. The study highlights the influence of urbanization on fog dynamics, driven by a pronounced Urban Heat Island (UHI) effect in Delhi. This urban influence results in delayed fog formation and earlier dissipation compared to Jewar. The earlier onset and prolonged persistence of fog in Jewar are linked to stronger nocturnal cooling, greater moisture availability, and minimal urban heat retention. Jewar experienced a higher frequency of radiation fog events compared to Delhi, with visibility below a few meters persisting for several hours longer. Statistical analysis revealed distinct meteorological differences between the sites: nighttime surface temperatures were consistently lower in Jewar, while relative humidity peaked higher compared to Delhi. The extended fog duration in Jewar is attributed to sustained near-surface temperature inversion and weaker boundary layer mixing, in contrast to the urban environment.
These findings underscore the critical role of urbanization in modulating fog characteristics and highlight the importance of site-specific mitigation strategies for fog forecasting and management. This research advances ongoing WiFEX efforts to enhance fog prediction capabilities across the IGP, providing valuable insights for improving regional weather forecasting and mitigating societal impacts.
How to cite: Pakkattil, A., Parde, A. N., Dhangar, N. G., Wagh, S., Lonkar, P., Kumar, S., and Ghude, S. D.: Contrasting Fog Dynamics between Urban and Rural Environments: Insights from Winter Observations over Delhi and Jewar, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15225, https://doi.org/10.5194/egusphere-egu25-15225, 2025.
In hyperarid regions, such as the Atacama Desert, fog and dew provide essential water supply to sustain unique ecosystems and drive geomorphological processes. While some studies have quantified the spatiotemporal variability of fog, it remains mostly unclear which phenomenon, i.e. fog or dew, constitutes the more important water source. However, such knowledge is crucial for a better understanding of the interplay between atmospheric, biological and geological processes. In this study, we determine fog and dew occurrence from observations provided by a network of weather stations deployed in the Atacama Desert by the Collaborative Research Center "Earth – Evolution at the Dry Limit" (https://sfb1211.uni-koeln.de/) of the German Science Foundation (DFG SFB1211). The stations are aligned in three west-to-east transects covering the coastal region, the central depression and the Andean foothills, thus, including multiple altitudinal regimes which are affected differently by the marine moisture from the nearby Southeast Pacific. Fog, dew and dry situations are distinguished according to data from a leaf wetness sensor and incoming terrestrial irradiation together with some additional constraints. Terrestrial irradiation is only available for three master stations. To obtain it for all other stations, we trained a multilayer perceptron with relative humidity, incoming solar radiation, 2m and surface temperature together with some auxiliary data as input data. For validation, the final classifications are compared to camera images available for morning and evening hours which constitute the most challenging times when fog and dew dissipate or form. Spatiotemporal variability, including diurnal and seasonal cycles of fog and dew are investigated. Furthermore, the relationship between the derived fog and dew frequencies and soil moisture variability is assessed to provide a more quantitative assessment of the moisture supply. The derived classification can be used as ground truth to build and evaluate satellite-based fog and dew retrievals in future studies.
How to cite: Böhm, C., Schween, J., May, S. M., and Crewell, S.: Spatiotemporal variability of fog and dew occurrence in the Atacama Desert - a view from a network of weather stations , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16701, https://doi.org/10.5194/egusphere-egu25-16701, 2025.
Fog and low stratus clouds (FLS) form as a result of complex interactions of atmospheric and land surface drivers and their analysis helps to improve traffic safety, solar power planning and to better understand inversion-topped boundary layer clouds in its interaction with the air quality of larger basin areas. A major factor impacting FLS occurrence and life cycle is aerosol loading, but its impact is challenging to disentangle from confounding meteorological factors and measuring both FLS occurrence and aerosol loading simultaneously is challenging.
Here we use satellite observations of FLS persistence, ERA5 reanalysis data and aerosol robotic network (AERONET) observations to disentangle the effect of meteorology and aerosol loading on FLS persistence in the Po valley in northern Italy, one of the most polluted regions in central Europe. After selecting 430 FLS events in the winter (DJF) months from 2006-2015 using a regional FLS occurrence threshold, we apply k‐means clustering to latitudinal transects of relative humidity from 550-1000hPa to identify FLS events with similar FLS formation pathways. Analyzing the average synoptic conditions for the two clusters identified shows that FLS formation in the Po valley is either based on radiative processes or moisture advection from the Mediterranean sea. Radiatively formed FLS events are more persistent, likely due to a stable boundary layer combined with a temperature inversion, whereas advective FLS events form under more dynamic conditions and are on average 2-3 hours shorter. Analysis of AERONET observations at three locations reveals that FLS persistence is significantly higher under high aerosol loading, particularly for radiatively formed FLS events. Aerosol loading further shows a clear increasing trend ahead of persistent FLS events, suggesting an accumulation of pollutants and a subsequent increase in cloud condensation nuclei prolonging FLS events through aerosol-cloud interactions.
Our results show the combined effect of meteorology, aerosol loading and geographic conditions on FLS persistence in the Po valley and underline the need for further observational studies on the effect of aerosols on the FLS life cycle over different geographic and synoptic backgrounds.
How to cite: Pauli, E., Cermak, J., Bendix, J., and Stier, P.: Satellite observations reveal higher persistence of fog in polluted conditions in the Po valley, Italy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17107, https://doi.org/10.5194/egusphere-egu25-17107, 2025.
In this study, ground-based remote sensing and in-situ measurements are combined to characterize and estimate cloud base height (CBH) development patterns of fog and low clouds (FLC). The estimated CBH is further integrated with satellite data to map fog in the Namib Desert for the first time.
The Namib Desert, characterized by its hyper-arid conditions and frequent coverage with fog or low level stratus clouds, presents an intriguing environment for the study of low-level clouds and their vertical geometry. Understanding cloud base height (CBH) dynamics in this region is crucial for improving fog detection, particularly in distinguishing fog from low clouds in satellite data. This research aims to close existing knowledge gaps by providing, for the first time, a way to spatially map fog and separating it from other low stratiform clouds by merging ground and space-based observations.
Using ground-based remote sensing, this study reveals distinct and contrasting cloud base height (CBH) seasonality between inland and coastal locations. During the diurnal fog life cycle, stratus lowering and lifting is commonly observed during the formation and dissipation phases of the fog. A robust methodology for estimating CBH through in-situ meteorological observations. Moreover, the integration of CBH estimates with satellite products facilitates spatial mapping of fog, separating it from other low clouds. In the future, this can improve the ability to estimate fog-related water and nutrient input for this unique ecosystem.
How to cite: Malik, D., Andersen, H., and Cermak, J.: Integrating Ground-Based and Satellite Observations to Map Fog in the Namib Desert, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12492, https://doi.org/10.5194/egusphere-egu25-12492, 2025.
The opening of the Arctic Ocean shipping route has significantly benefited the shipping industry. However, low visibility (Vis) in the Arctic Ocean due to fog can lead to a serious challenge during ship navigation. This study proposes a new method for analyzing the ship-based sea fog microphysical observations collected during the ice-breaker Xuelong I expedition to Arctic Ocean in 2016 and 2018, as well as the Xuelong II in 2020. The Vis calculated by droplet size distribution (DSD) using the new method aligns more closely with observed Vis. In the data analysis, both old and new methods are applied to check the droplet size distribution types: (1) double peak DSD model, which influences bulk values of microphysical parameters such as liquid water content (LWC), droplet number concentration (Nd), and effective diameters (Deff) and (2) a single peak gamma distribution model. Both DSDs obtained by two methods suggested that microphysical characteristics of sea fog adhere more closely to a Gamma-exponential size distribution rather than a Gamma distribution, with double peak values at about 6 and 22 µm. It is concluded that DSD of Arctic fog during summer months have two peak values and can affect fog microphysical properties heavily compared to a single Gamma DSD.
How to cite: Yi, L.: Microphysical Characteristics of Arctic Summer Sea Fog, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1573, https://doi.org/10.5194/egusphere-egu25-1573, 2025.
The rapid conversion of tropical rainforests into monoculture plantations of rubber (Hevea brasiliensis) in Southeast Asia (SEA) necessitates understanding of rubber tree physiology under local climatic conditions. Frequent fog immersion in the montane regions of SEA may affect the water and carbon budgets of the rubber trees and the plantation ecosystems. We studied the effect of fog on various plant physiological parameters in a mature rubber plantation in southwest China over 3 years. During the study period, an average of 141 fog events occurred every year, and the majority occurred during the dry season, when the temperature was relatively low. In addition to the low temperature, fog events were also associated with low vapor pressure deficit, atmospheric water potential, relative humidity and frequent wet-canopy conditions. We divided the dry season into cool dry (November-February) and hot dry (March-April) seasons and classified days into foggy (FG) and non-foggy (non-FG) days. During the FG days of the cool dry season, the physiological activities of the rubber trees were suppressed where carbon assimilation and evapotranspiration showed reductions of 4% and 15%, respectively, compared to the cool dry non-FG days. Importantly, the unequal declines in carbon assimilation and evapotranspiration led to enhanced crop water productivity (WPc) on cool dry FG days but insignificant WPc values were found between FG and non-FG days of the hot dry season. Our results suggest that, by regulating plant physiology, fog events during the cool dry season significantly reduce water demand and alleviate water stress for the trees through improved WPc.
How to cite: Gnanamoorthy, P., Zhang, Y., and Song, Q.: Seasonal fog enhances crop water productivity in a tropical China rubber plantation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1966, https://doi.org/10.5194/egusphere-egu25-1966, 2025.
Fog is an important phenomenon related to both atmospheric physics and chemistry, and has a significant impact on our environment. Although the meteorological factors relevant to fog formation have been extensively studied, other factors remain unexplored. Here we summarise our recent results from several studies (Hůnová et al., 2018, 2021 a, 2021 b, 2022), where we examined observed and measured long-term data from the Czech Republic (1989–2015) and Romania (1981–2017). Our focus was on the environmental factors that drive fog formation. Specifically, we examined the effects of terrain, water and forests (for Romania), and ambient air pollution (for the Czech Republic). The long-term empirical data were analysed using advanced statistical modelling GAM (generalised additive model). In terms of terrain, apart from altitude, slope and landform appeared to have a strong influence on fog formation. Forests have a significant effect on fog formation, the most significant being the forest area within 3 km of the the fog observation point, with coniferous and broad leave trees having different effects. Not surprisingly, the presence of water body in the vicinity of a fog site affects fog formation, but less so than altitude or seasonality. Freshwater and seawater show clear differences in both the seasonal profile and frequency of fog. Ambient air pollution, indicated by the daily mean concentrations of sulphur dioxide and nitrogen oxides, was the most important explanatory variable (apart from relative humidity) in modelling the probability of fog at three Centre European sites reflecting different environments (urban, rural and mountain).
References:
Hůnová I., Brabec M., Geletič J., Malý M., Dumitrescu A., 2021 a. Statistical analysis of the effects of forests on fog. STOTEN, https://doi.org/10.1016/j.scitotenv.2021.146675.
Hůnová I., Brabec M., Geletič J., Malý M., Dumitrescu A., 2022. Local fresh- and sea-water effects on fog occurrence. STOTEN, https://doi.org/10.1016/j.scitotenv.2021.150799.
Hůnová I., Brabec M., Malý M., Dumitrescu A., Geletič J., 2021 b. Terrain and its effects on fog occurrence. STOTEN, https://doi.org/10.1016/j.scitotenv.2020.144359.
Hůnová I., Brabec M., Malý M., Valeriánová A., 2018. Revisiting fog as an important constituent of the atmosphere. STOTEN, https://doi.org/10.1016/j.scitotenv.2018.04.322.
Acknowledgements:
We are grateful to the Czech Hydrometeorological Institute and Meteo Romania for providing the input data. Our study was supported by the Technological Agency of the Czech Republic (TAČR) through the project SS02030031 ARAMIS, by the Czech Hydrometeorological Institute research project ʽDlouhodobá koncepce rozvoje výzkumné organizace (DKRVO) Český hydrometeorologický ústav’ financed by the Czech Ministry of the Environment, and by the long-term strategic development financing of the Institute of Computer Science (Czech Republic RVO 67985807).
How to cite: Hunova, I., Brabec, M., Malý, M., Geletič, J., Dumitrescu, A., and Valeriánová, A.: Environmental factors affecting fog formation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3488, https://doi.org/10.5194/egusphere-egu25-3488, 2025.
We present a study of the microphysics, mesoscale and synoptic conditions of a rare radiation-fog and low-level clouds event (hereafter, FC event), and build numerical tools to forecast it. The FC event developed in the south-eastern Mediterranean region during January 3-6, 2021. The FC formed during nighttime from south to coastal areas and dissipated at morning hours leaving low-clouds only. The synoptic conditions were dominated by Red Sea Troughs at the surface without cyclonic upper air circulation, suitable for radiation fog development. The following methods were combined to analyze the event and to evaluate the feasibility of accurately numerical forecasting it: 1. in-situ measurements consisting of Forward Scattering Spectrometer Probe FSSP-100, surface meteorological stations and radiosoundings, 2. satellite-retrieved IR and visible imagery [by the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instrument (https://www.eumetsat.int/seviri) on-board of Meteosat Second Generation (MSG) satellites], 3. high resolution (1-km grid size) Weather Research and Forecast model (WRF) with Real-Time Four-Dimensional Data Assimilation (RTFDDA) model forecasts, 4. post-processing of the model forecasts with simple and machine learning (ML) algorithms. The micro-physical analysis involved measurements of droplet size distribution and visibility range, allowing the calculation of liquid-water content and effective diameter of droplets. The measured visibility range was 90 m. The droplet diameter main mode was 1-2 micrometers, followed by another one around 6 micrometers. Typical liquid-water content values were 0.01-0.025 g/m3. These measurements were in agreement with the classification of the satellite imagery as “small drops fog/low-clouds”. FC forecasting by numerical-weather-prediction models is still challenging, as microphysics parameterizations are too crude. Therefore, we developed two post-processing algorithms based on basic model-forecast variables: wind speed, dew-point temperature and relative humidity at 1000 and 975 hPa vertical levels. The first post-processing algorithm identified FC based on a combination of thresholds of the aforementioned model variables (“Thresholds Algorithm”, TA). It was verified against satellite imagery and independent in-situ observations. The second is a Gradient Boosted Tree (GBT) ML post-processing algorithm in which the aforementioned model variables served as features and satellite imagery as label in the training process. Verification of the GBT algorithm was performed by cross-validation against satellite imagery and against independent in-situ observations, too. Both, the TA and GBT algorithms proved useful to identify FC areas. The GBT algorithm over-performed the TA algorithm during early morning hours, though it overestimates FC areas during the late morning. The combination of the GBT algorithm and TA is able to remove this inaccuracy providing an optimal strategy to post-process model forecasts. While the satellite imagery cannot distinguish between surface fog and low-level clouds, the post-processed model, does show differences between the two analyzed vertical levels, providing the possibility of determining the vertical extent and level of the phenomenon whether fog or low-level clouds.
How to cite: Rostkier-Edelstein, D., Gelman, A., Kunin, P., Berkovitch, E., Sheu, R.-S., Tzadok, T., Ronen, A., and Agassi, E.: Characterization and forecast of a unique fog and low-level clouds event: microphysics measurements, mesoscale modeling and machine learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13561, https://doi.org/10.5194/egusphere-egu25-13561, 2025.
A complete understanding of the fog life cycle — defined as formation, maturity, and dissipation phases — provides a basis for better predictions of fog formation and dissipation. While satellites can observe fog and low stratus (FLS) over a large spatial extent, ground-based instruments provide more detailed vertical and temporal information about fog at specific locations.
In this study, we classify the life cycle phases of radiation fog events in autumn 2009-2015 at a ground station in Southwest Germany by combining geostationary satellite observations with ceilometer and in-situ measurements. For this, we develop a life cycle phase classification algorithm that automatically detects the start and end times of each phase based on visibility trends and thresholds. Unlike other methodologies, we define fog events not only through a visibility threshold of 1000 m but also by the processes involved during fog formation and dissipation. These processes are identified through changes in visibility trends and values and validated against backscatter patterns. Furthermore, we demonstrate that ground-based visibility effectively detects radiation fog phases, while its combination with ceilometer data has the potential to detect the life cycle phases of cloud base lowering fog events. Thus, combining these data sources is essential for effectively detecting the life cycle phases of different fog types. Additionally, we find that ground-based data performs better in phase detection at individual locations compared to a satellite-based FLS life cycle dataset. Consequently, we propose that future satellite-based FLS detection methods incorporate an assessment of the changes in the spectral signals of FLS throughout its life cycle for a more detailed phase characterization over large regions.
How to cite: Pinilla, M. L., Pauli, E., Cermak, J., and Senn, J. A.: Classification of Fog Life Cycle Phases Using Ground-based and Satellite-based Observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19827, https://doi.org/10.5194/egusphere-egu25-19827, 2025.
Posters virtual: Tue, 29 Apr, 14:00–15:45 | vPoster spot 5
EGU25-14973 | Posters virtual | VPS2
Automated Nighttime Fog Detection and Masking Using Machine Learning from Near Real-Time Satellite ObservationsTue, 29 Apr, 14:00–15:45 (CEST) | vP5.14
Fog significantly reduces visibility, impacting transportation and safety, particularly in regions like the United Arab Emirates (UAE) where it is a regular
occurrence, in particular in the winter months. This study develops a machine learning-based approach for automated fog detection and masking from near real-time observations from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument onboard the Meteosat Second Generation spacecraft to enhance fog detection and forecast. We evaluated six basic machine learning (ML) models trained with four different methods: (1) supervised training using SEVIRI pixel data and fog observations over airport stations; (2) as approach (1) but incorporating infrared channel data; (3) training with labeled fog and no-fog regions identified in SEVIRI night microphysics Red-Green-Blue (RGB) images through k-means clustering; and, (4) a fusion approach combining station-labeled data (approach 1) and k-means clustered-labeled data (approach 3). Among the models, the eXtreme Gradient Boosting (XGBoost) demonstrated slightly higher performance. Models trained on station data (approach 1) achieved a Probability of Detection (POD) of 0.73 and a False Alarm Ratio (FAR) of 0.11. For spatial fog masking, models trained on a combination of station-labeled and k-means cluster-labeled data (approach 4) performed best. Overall, the XGBoost method and the fusion approach (4) are recommended for fog detection and masking in the hyper-arid UAE. These findings demonstrate the potential for trained ML models to deliver accurate, near real-time fog detection and masking, enhancing monitoring over broad areas.
How to cite: Nelli, N. R., Francis, D., Cherif, C., Fonseca, R., and Ghedira, H.: Automated Nighttime Fog Detection and Masking Using Machine Learning from Near Real-Time Satellite Observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14973, https://doi.org/10.5194/egusphere-egu25-14973, 2025.
EGU25-3008 | Posters virtual | VPS2
Aerosol-Radiation Interaction During Dense Fog in the Indo-Gangetic Plains RegionTue, 29 Apr, 14:00–15:45 (CEST) | vP5.15
The Indo-Gangetic plains (IGP) in India are frequently affected by fog during the winter months of December, January, and February, which manifests in severe consequences for air and road traffic, thereby leading to health as well as economic losses. This region, which includes highly populated cities like the National Capital Territory of Delhi, also experiences a high concentration of aerosols during this period. While studies have indicated the importance of the role of aerosols in fog processes in the region, the role of different aspects of aerosol-radiation interaction (ARI) has not been studied in detail for the formation of fog in the region. Current numerical weather prediction models (NWP) still struggle to predict fog accurately because of the uncertainties in the representation of processes leading to fog formation, sustenance, and dissipation. The present study aims to understand the influence of aerosols and ARI on the fog over IGP with a focus on dense fog conditions using the Delhi Model with Chemistry and aerosol framework (DM-Chem1.0), which is a high-resolution (330 m) model used for operational forecasting of wintertime visibility and air quality at the National Centre for Medium-Range Weather Forecasting (NCMRWF), India. Four experiments (along with a Control experiment) were designed to analyze how both the scattering and absorbing nature of ARI influence the evolution of dense fog from temporal and spatial perspectives. Two experiments isolated the absorbing and scattering effect of aerosols, while the third excluded both these effects. The fourth experiment analyzed pristine conditions with minimal aerosol presence. The study indicated that turning off absorption had the greatest impact, significantly increasing dense fog-impacted areas and fog-associated parameters like cloud liquid water mixing ratio and cloud droplet number concentration (CDNC). Satellite data for the absorbing aerosol index also corroborated the greater contribution of absorbing aerosols in the model domain. Further, the study also indicates the importance of a realistic representation of aerosol for better model performance during daytime. The study highlights the importance of correctly representing radiative interactions in the numerical models for fog prediction. The policy measures need to focus on regulating high aerosol concentrations over IGP to mitigate the adverse effects of fog.
How to cite: Bhati, S., Anurose, T. J., Jayakumar, A., Mohandas, S., and Prasad, V. S.: Aerosol-Radiation Interaction During Dense Fog in the Indo-Gangetic Plains Region , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3008, https://doi.org/10.5194/egusphere-egu25-3008, 2025.
