The habits of cloud particles are a significant factor impacting microphysical processes in clouds. The accurate identification of cloud particle shapes within clouds is a fundamental requirement for calculating various cloud microphysical parameters. In this study, we established a cloud particle image dataset encompassing nine distinct habit categories, totaling 8100 images. These images were captured using three probes with varying resolutions: the Cloud Particle Imager (CPI), the Two-Dimensional Stereo Probe (2D-S), and the High-Volume Precipitation Spectrometer (HVPS). Furthermore, this study performs a comparative analysis of ten different transfer learning (TL) models based on this dataset. It was found that the VGG-16 model exhibits the highest classification accuracy, reaching 97.90%. This model also demonstrates the highest recall, precision, and F1 measure. The results indicate that the VGG-16 model can reliably classify the shapes of ice crystal particles measured by both line scan imagers (2D-S, HVPS) and an area scan imager (CPI).
The Latin American Giant Observatory (LAGO) is a ground-based extended cosmic rays observatory designed to study transient astrophysical events, the role of the atmosphere on the formation of secondary particles, and space-weather-related phenomena. With the use of a network of W…
Commercially available low-cost air quality sensors have low accuracy. The improved accuracy of low-cost PM2.5 sensors allows the use of low-cost sensor systems to reasonably investigate PM2.5 emissions from industrial activities or to accurately estimate individual exposure to P…
The application of machine learning and deep learning in air pollution management is becoming increasingly crucial, as these technologies enhance the accuracy of pollution prediction models, facilitating timely interventions and policy adjustments. They also facilitate the analys…
In this study, daily reference evapotranspiration (ET0) values for the period 2025–2030 for Lake Burdur, located in the Mediterranean climate zone and within the Burdur closed basin, were estimated using nested architecture focused on high accuracy. The ET0 target corresponds to…
Accurate forecasts of tropical cyclone (TC) track and intensity with a sufficient lead time are critical for disaster preparedness and risk mitigation. Traditional numerical weather prediction models, while fundamental to operational forecasting, often exhibit systematic errors d…
Background: Although computational models are advancing air quality prediction, achieving the desired performance or accuracy of prediction remains a gap, which impacts the implementation of machine learning (ML) air quality prediction models. Several models have been employed an…