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 Athanasi...

Full description

Bibliographic Details
Main Authors: Georgakaki, Paraskevi, Nenes, Athanasios
Format: Dataset
Language:English
Published: Zenodo 2024
Subjects:
Clouds
Arctic
Ice multiplication
Machine learning
Modeling
Parameterization
Cloud microphysics
Random Forests
Online Access:https://dx.doi.org/10.5281/zenodo.10569643
https://zenodo.org/doi/10.5281/zenodo.10569643
id ftdatacite:10.5281/zenodo.10569643
record_format openpolar
spelling ftdatacite:10.5281/zenodo.10569643 2024-03-31T07:50:50+00:00 Data and scripts for the RaFSIP scheme ... Georgakaki, Paraskevi Nenes, Athanasios 2024 https://dx.doi.org/10.5281/zenodo.10569643 https://zenodo.org/doi/10.5281/zenodo.10569643 en eng Zenodo https://dx.doi.org/10.22541/essoar.170365383.34520011/v1 https://dx.doi.org/10.5281/zenodo.10569644 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 Clouds Arctic Ice multiplication Machine learning Modeling Parameterization Cloud microphysics Random Forests dataset Dataset 2024 ftdatacite https://doi.org/10.5281/zenodo.1056964310.22541/essoar.170365383.34520011/v110.5281/zenodo.10569644 2024-03-04T13:12:10Z 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 ... Dataset Arctic DataCite Metadata Store (German National Library of Science and Technology) Arctic
institution Open Polar
collection DataCite Metadata Store (German National Library of Science and Technology)
op_collection_id ftdatacite
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 Dataset
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://dx.doi.org/10.5281/zenodo.10569643
https://zenodo.org/doi/10.5281/zenodo.10569643
geographic Arctic
geographic_facet Arctic
genre Arctic
genre_facet Arctic
op_relation https://dx.doi.org/10.22541/essoar.170365383.34520011/v1
https://dx.doi.org/10.5281/zenodo.10569644
op_rights Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
cc-by-4.0
op_doi https://doi.org/10.5281/zenodo.1056964310.22541/essoar.170365383.34520011/v110.5281/zenodo.10569644
_version_ 1795029314741207040

Similar Items