Dataset for "RaFSIP: Parameterizing Ice Multiplication in Models Usinga Machine Learning Approach" by Geogakaki et al. (2024)

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本仓库包含来自论文《RaFSIP: 基于机器学习方法的模式冰增殖参数化》（RaFSIP: Parameterizing ice multiplication in models using a machine learning approach），作者为Paraskevi Georgakaki与Athanasios Nenes的天气研究与预报（Weather Research and Forecasting, WRF）模式模拟所用的微物理子程序、脚本与处理后数据。 RaFSIP是一款数据驱动的参数化方案，旨在简化大尺度模式中次生冰生成（Secondary Ice Production, SIP）的表征方式。 该研究的预印本可在Authorea平台获取：https://doi.org/10.22541/essoar.170365383.34520011/v1