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This 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/v1
Thanh Trung Huynh, Quoc Viet Hung Nguyen, Thành Tâm Nguyên, Trung-Dung Hoang
Athanasios Nenes, Alexis Berne, Satoshi Takahama, Georgia Sotiropoulou, Paraskevi Georgakaki, Romanos Foskinis, Kunfeng Gao, Anne-Claire Marie Billault--Roux
Andreas Mortensen, David Hernandez Escobar, Léa Deillon, Alejandra Inés Slagter, Eva Luisa Vogt, Jonathan Aristya Setyadji