Data and scripts for the RaFSIP scheme

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 Athana...

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Bibliographic Details
Main Authors: Georgakaki, Paraskevi, Nenes, Athanasios
Format: Other/Unknown Material
Language:English
Published: Zenodo 2024
Subjects:
Clouds
Arctic
Ice multiplication
Machine learning
Modeling
Parameterization
Cloud microphysics
Random Forests
Online Access:https://doi.org/10.5281/zenodo.10569644
Description
Summary: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

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