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Aerosols are particles that are omnipresent in the atmosphere. They vary in size, shape and composition. They can be naturally occurring or might be produced artificially. However, proper classification and characterization of aerosols have been still in progress and this creates uncertainty in climatological studies. In this paper, an aerosol classification scheme has been presented based on the measurements done using a CIMEL sunphotometer in Thessaloniki, Greece from 1998 to 2017. The study has been mainly upheld by the direct measurements of Single Scattering Albedo (SSA) at 440nm and Fine Mode Fraction (FMF) at 500nm. These parameters have been used to establish testing and training datasets. Machine learning algorithms have been used to validate the classified data. Various performance metrics have been evaluated. Also, the bestfit algorithm for classifying aerosol data has been found out. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) refers to the recreation of human intelligence in machines that have been designed to think like humans and mimic their actions. AI has been used in many fields such as image processing, health care, education, and marketing. Machine Learning (ML) has been the sub-division of AI, and deep learning has been the subdivision of ML. Artificial Neural Network has been the most predominantly used deep learning technique. While implementing the ANN technique, knowing whether the implementation could have been done in hardware or software becomes necessary, which is essential to achieve the expected performance. This paper gives a survey on the available methods in which the ANN architecture has been implemented to achieve efficient output with minimal resources. It is vital to study and analyze various strategies for implementation and their functionality. This paper has also explained the advantages and disadvantages of different implementation techniques that allow selecting the most appropriate hardware and respective methodology for optimizing the hardware. [ABSTRACT FROM AUTHOR]

A model that is less sensitive to errors in the extreme small and large drop diameters, the gamma model with central moments (3, 4 and 6), is proposed to model the rain drop size distribution of Singapore. This is because, the rain rate estimated using measured drop size distribution shows that the contributions of lower drop diameters are small as compared to the central drop diameters. This is expected since the sensitivity of the Joss distrometer degrades for small drop diameters. The lower drop diameters are therefore removed from the drop size data and the gamma model is redesigned for its moments. The effects of the removal of a particular rain drop size diameter on the specific rain attenuation (in dB) and the slant-path rain attenuation calculations with forward scattering coefficients for vertical polarization are analyzed at Ku-band, Ka-band and Q-band frequencies. It is concluded that the sensitivity of the Joss distrometer although affects the rain rate estimation at low rain rates, does not affect the slant path rain attenuation on microwave links. Therefore, the small drop diameters can be ignored completely for slant path rain attenuation calculations in the tropical region of Singapore. [ABSTRACT FROM AUTHOR]