UNVEILING THE SHAPE OF CLOUDS: A CNN-POWERED MATLAB FRAMEWORK FOR CLOUD PARTICLE RECOGNITION

Abstract

Abstract - This work is crucial in understanding cloud microphysics, its effects on weather forecasting and climate modeling. It uses a 15-layer convolutional neural network (CNN) to overcome drawback of manual classification methods. The proposed algorithm improves cloud particle identification, classification efficiency and accuracy making it a more objective and scalable cloud microphysics research solution. It is likely to have a major impact on atmospheric science, particularly climate change and meteorological forecasting. A 15-layer CNN for cloud particle shape recognition, offering automated classification with enhanced accuracy and efficiency. Through diverse training data, the CNN accurately identifies cloud shapes across various atmospheric conditions, potentially improving weather forecasting and climate modeling. Its scalability and performance make it a valuable tool for atmospheric and environmental sciences. Keywords: Cloud Particle Recognition, Convolutional Neural Networks, Cloud Microphysics, Automated Classification, Atmospheric Sciences, Climate Modeling, Weather Prediction, Data Analysis.