Georgakaki, ParaskeviNenes, Athanasios2024-01-262024-01-262024-01-26202410.5281/zenodo.10569644https://infoscience.epfl.ch/handle/20.500.14299/203172This repository contains microphysics routines, scripts, and processed data from the Weather Research and Forecasting (WRF) model simulations presented in the paper "RaFSIP: Parameterizing ice multiplication in models using a machine learning approach", by Paraskevi Georgakaki and Athanasios Nenes. RaFSIP is a data-driven parameterization designed to streamline the representation of Secondary Ice Production (SIP) in large-scale models. Preprint available on Authorea: https://doi.org/10.22541/essoar.170365383.34520011/v1enCloudsArcticIce multiplicationMachine learningModelingParameterizationCloud microphysicsRandom Forestsdataset281e1930-b1b9-494b-acb7-5b5549c51251