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. A parameterization for the collision–coalescence process ispresented based on the methodology of basis functions. The whole dropspectrum is depicted as a linear combination of two lognormal distributionfunctions, leaving no parameters fixed. This basis function parameterizationavoids the classification of drops in artificial categories such as cloudwater (cloud droplets) or rainwater (raindrops). The total momenttendencies are predicted using a machine learning approach, in which onedeep neural network was trained for each of the total moment ordersinvolved. The neural networks were trained and validated using randomlygenerated data over a wide range of parameters employed by theparameterization. An analysis of the predicted total moment errors wasperformed, aimed to establish the accuracy of the parameterization atreproducing the integrated distribution moments representative of physicalvariables. The applied machine learning approach shows a good accuracy level when compared to the output of an explicit collision–coalescence model.

geoscientific model development

Parameterization of the collision–coalescence process using series of basis functions: COLNETv1.0.0 model development using a machine learning approach