Embedded Files
October 31st, 2025
Our paper has been published in the Atmosphere journal #Atmosphere:
https://doi.org/10.3390/atmos16111252
Key points:
This study applies a vision–language model (VLM) to classify marine low cloud morphologies from satellite imagery.
The model provides a computationally efficient approach to integrate descriptive text prompts with image features, achieving high accuracy even with small datasets.
Keywords:
Keywords: low clouds; mesoscale cellular convection; satellite; machine learning; pattern recognition; vision-language models
January 6th, 2025
Our book chapter has been published in Geoengineering book by Wiley # #Wiley:
https://doi.org/10.1002/9781394204847.ch18
Key points:
Cirrus Cloud Thinning (CCT) as a geoengineering method: CCT aims to cool the planet by transitioning cirrus cloud type from homogeneous to heterogeneous, thereby increasing outgoing longwave radiation to space, particularly in polar regions during winter.
Challenges and Advances: Uncertainties in cirrus cloud modeling hinder the validation of CCT's feasibility, but satellite data (e.g., CALIPSO) can be used to improve cirrus cloud processes in environmental models.
Keywords:
Cirrus Cloud Thinning (CCT); geoengineering; Ice nucleation; homogeneous; heterogeneous; Outgoing longwave radiation (OLR); Cloud optical thickness; Polar regions; CALIPSO satellite; Ice-nucleating particles (INPs); Cloud radiative effect (CRE)
October 25th, 2024
Our paper has been published in Atmosphere journal #Atmosphere:
https://doi.org/10.3390/atmos15121460
Key points:
Enhanced Ice Particle Concentration and Snowfall Rates: Our simulations indicates that cloud seeding effectively increases ice particle number concentration, ice water content, and snowfall rates.
Event-Specific Variability: Our study found that the impact of cloud seeding on precipitation varies depending on initial atmospheric conditions, suggesting that the effectiveness of cloud seeding is influenced by specific background weather and environmental conditions.
Keywords:
weather modification; snow growth model; aerosol–cloud interaction; seeding-induced snowfall; cloud seeding; cloud microphysics; ice particle growth
April 30th, 2024
Our manuscript in EarthArXiv #EarthArXiv:
t
https://doi.org/10.31223/X5QM51
Key points:
- A snow growth model was developed that simulates microphysical processes of vapor deposition, aggregation, and riming.
- Lagrangian spiral descent during a flight was used to verify the model.
Keywords:
Ice cloud microphysics, cloud modeling, snow growth model, frontal clouds, cirrus clouds, mixed-phase clouds, aggregation, vapor deposition, timing, particle size distribution
October 20th, 2022
Our paper has been published in Journal of Geophysical Research #JGR #JGRA:
https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022JD037258
A brief summary:
- A Large-Eddy Simulation (LES) coupled with an aerosol model is used to study the response of clouds to initial and boundary aerosol perturbations in two marine stratocumulus-to-cumulus transition (SCT) cases.
- Although the interactive aerosol scheme within the LES adds new degrees of freedom, the results agree well with observations.
- We showed that precipitation-aerosol-scavenging feedback exists during the transition and it leads to the formation of ultra-clean layers near the inversion.
- Also, precipitation regulates the relative contributions of cloud adjustments to radiative forcing.
Google Sites
Report abuse