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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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
id ftzenodo:oai:zenodo.org:10569644
record_format openpolar
spelling ftzenodo:oai:zenodo.org:10569644 2024-09-09T19:23:13+00:00 Data and scripts for the RaFSIP scheme Georgakaki, Paraskevi Nenes, Athanasios 2024-01-25 https://doi.org/10.5281/zenodo.10569644 eng eng Zenodo https://doi.org/10.22541/essoar.170365383.34520011/v1 https://zenodo.org/communities/epfl https://zenodo.org/communities/forces-project https://doi.org/10.5281/zenodo.10569643 https://doi.org/10.5281/zenodo.10569644 oai:zenodo.org:10569644 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode Clouds Arctic Ice multiplication Machine learning Modeling Parameterization Cloud microphysics Random Forests info:eu-repo/semantics/other 2024 ftzenodo https://doi.org/10.5281/zenodo.1056964410.22541/essoar.170365383.34520011/v110.5281/zenodo.10569643 2024-07-26T23:32:12Z 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 Other/Unknown Material Arctic Zenodo Arctic
institution Open Polar
collection Zenodo
op_collection_id ftzenodo
language English
topic Clouds
Arctic
Ice multiplication
Machine learning
Modeling
Parameterization
Cloud microphysics
Random Forests
spellingShingle Clouds
Arctic
Ice multiplication
Machine learning
Modeling
Parameterization
Cloud microphysics
Random Forests
Georgakaki, Paraskevi
Nenes, Athanasios
Data and scripts for the RaFSIP scheme
topic_facet Clouds
Arctic
Ice multiplication
Machine learning
Modeling
Parameterization
Cloud microphysics
Random Forests
description 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
format Other/Unknown Material
author Georgakaki, Paraskevi
Nenes, Athanasios
author_facet Georgakaki, Paraskevi
Nenes, Athanasios
author_sort Georgakaki, Paraskevi
title Data and scripts for the RaFSIP scheme
title_short Data and scripts for the RaFSIP scheme
title_full Data and scripts for the RaFSIP scheme
title_fullStr Data and scripts for the RaFSIP scheme
title_full_unstemmed Data and scripts for the RaFSIP scheme
title_sort data and scripts for the rafsip scheme
publisher Zenodo
publishDate 2024
url https://doi.org/10.5281/zenodo.10569644
geographic Arctic
geographic_facet Arctic
genre Arctic
genre_facet Arctic
op_relation https://doi.org/10.22541/essoar.170365383.34520011/v1
https://zenodo.org/communities/epfl
https://zenodo.org/communities/forces-project
https://doi.org/10.5281/zenodo.10569643
https://doi.org/10.5281/zenodo.10569644
oai:zenodo.org:10569644
op_rights info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
op_doi https://doi.org/10.5281/zenodo.1056964410.22541/essoar.170365383.34520011/v110.5281/zenodo.10569643
_version_ 1809763604473315328

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