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...
| Main Authors: | , |
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| Format: | Dataset |
| Language: | English |
| Published: |
Zenodo
2024
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| Subjects: | |
| Online Access: | https://dx.doi.org/10.5281/zenodo.10569643 https://zenodo.org/doi/10.5281/zenodo.10569643 |
| _version_ | 1821823614998020096 |
|---|---|
| author | Georgakaki, Paraskevi Nenes, Athanasios |
| author_facet | Georgakaki, Paraskevi Nenes, Athanasios |
| author_sort | Georgakaki, Paraskevi |
| collection | DataCite |
| 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 |
| genre | Arctic |
| genre_facet | Arctic |
| geographic | Arctic |
| geographic_facet | Arctic |
| id | ftdatacite:10.5281/zenodo.10569643 |
| institution | Open Polar |
| language | English |
| op_collection_id | ftdatacite |
| op_doi | https://doi.org/10.5281/zenodo.1056964310.22541/essoar.170365383.34520011/v110.5281/zenodo.10569644 |
| 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 |
| publishDate | 2024 |
| publisher | Zenodo |
| record_format | openpolar |
| spelling | ftdatacite:10.5281/zenodo.10569643 2025-01-16T20:28:35+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 Arctic |
| spellingShingle | Clouds Arctic Ice multiplication Machine learning Modeling Parameterization Cloud microphysics Random Forests Georgakaki, Paraskevi Nenes, Athanasios Data and scripts for the RaFSIP scheme ... |
| title | 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_short | Data and scripts for the RaFSIP scheme ... |
| title_sort | data and scripts for the rafsip scheme ... |
| topic | Clouds Arctic Ice multiplication Machine learning Modeling Parameterization Cloud microphysics Random Forests |
| topic_facet | Clouds Arctic Ice multiplication Machine learning Modeling Parameterization Cloud microphysics Random Forests |
| url | https://dx.doi.org/10.5281/zenodo.10569643 https://zenodo.org/doi/10.5281/zenodo.10569643 |