ALPGM (ALpine Parameterized Glacier Model) v1.1
New release with the latest version of ALPGM for the published version of the Bolibar et al. (2020) The Cryosphere paper. Model overview ALPGM is a fully parameterized glacier evolution model based on data science. Glacier-wide surface mass balance (SMB) are simulated using a deep artificial neural...
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ftzenodo:oai:zenodo.org:3609136 2023-05-15T18:32:12+02:00 ALPGM (ALpine Parameterized Glacier Model) v1.1 Jordi Bolibar 2020-01-15 https://zenodo.org/record/3609136 https://doi.org/10.5281/zenodo.3609136 unknown https://github.com/JordiBolibar/ALPGM/tree/v1.1 doi:10.5194/tc-2019-163 doi:10.5281/zenodo.3269677 https://zenodo.org/record/3609136 https://doi.org/10.5281/zenodo.3609136 oai:zenodo.org:3609136 info:eu-repo/semantics/openAccess The Cryosphere glacier evolution model glaciers glacier mass balance deep learning info:eu-repo/semantics/other software 2020 ftzenodo https://doi.org/10.5281/zenodo.360913610.5194/tc-2019-16310.5281/zenodo.3269677 2023-03-11T03:38:58Z New release with the latest version of ALPGM for the published version of the Bolibar et al. (2020) The Cryosphere paper. Model overview ALPGM is a fully parameterized glacier evolution model based on data science. Glacier-wide surface mass balance (SMB) are simulated using a deep artificial neural network (i.e. deep learning) or Lasso (i.e. regularized multilinear regression). Glacier dynamics are parameterized using glacier-specific delta-h functions (Huss et al. 2008). The model has so far been implemented with a dataset of French alpine glaciers, using climate forcings for past (SAFRAN, Durand et al. 1993) and future (ADAMONT, Verfaillie et al. 2018) periods. The machine learning SMB modelling approach is built upon widely used Python libraries (Keras, Scikit-learn and Statsmodels). Workflow ALPGM's workflow can be controlled via the alpgm_interface.py file. In this file, different settings can be configured, and each step can be run or skipped with a boolean flag. The default workflow runs as it follows: (1) First of all, the meteorological forcings are pre-processed (safran_forcings.py / adamont_forcings.py) in order to extract the necessary data closest to each glacier’s centroid. The meteorological features are stored in intermediate files in order to reduce computation times for future runs, automatically skipping this preprocessing step when the files are already generated. (2) The SMB machine learning module retrieves the pre-processed meteorological features and assembles the spatio-temporal training dataset, comprised by both climatic and topographical data. An algorithm is chosen for the SMB model, which can be loaded from a previous training or it can be trained again with the training dataset (smb_model_training.py). These model(s) are stored in intermediate files, allowing to skip this step for future runs. (3) The performances of these SMB models can be evaluated performing a leave-one-glacier-out (LOGO) cross-validation (smb_validation.py). This step can be skipped when using already ... Software The Cryosphere Zenodo |
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Open Polar |
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Zenodo |
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unknown |
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glacier evolution model glaciers glacier mass balance deep learning |
spellingShingle |
glacier evolution model glaciers glacier mass balance deep learning Jordi Bolibar ALPGM (ALpine Parameterized Glacier Model) v1.1 |
topic_facet |
glacier evolution model glaciers glacier mass balance deep learning |
description |
New release with the latest version of ALPGM for the published version of the Bolibar et al. (2020) The Cryosphere paper. Model overview ALPGM is a fully parameterized glacier evolution model based on data science. Glacier-wide surface mass balance (SMB) are simulated using a deep artificial neural network (i.e. deep learning) or Lasso (i.e. regularized multilinear regression). Glacier dynamics are parameterized using glacier-specific delta-h functions (Huss et al. 2008). The model has so far been implemented with a dataset of French alpine glaciers, using climate forcings for past (SAFRAN, Durand et al. 1993) and future (ADAMONT, Verfaillie et al. 2018) periods. The machine learning SMB modelling approach is built upon widely used Python libraries (Keras, Scikit-learn and Statsmodels). Workflow ALPGM's workflow can be controlled via the alpgm_interface.py file. In this file, different settings can be configured, and each step can be run or skipped with a boolean flag. The default workflow runs as it follows: (1) First of all, the meteorological forcings are pre-processed (safran_forcings.py / adamont_forcings.py) in order to extract the necessary data closest to each glacier’s centroid. The meteorological features are stored in intermediate files in order to reduce computation times for future runs, automatically skipping this preprocessing step when the files are already generated. (2) The SMB machine learning module retrieves the pre-processed meteorological features and assembles the spatio-temporal training dataset, comprised by both climatic and topographical data. An algorithm is chosen for the SMB model, which can be loaded from a previous training or it can be trained again with the training dataset (smb_model_training.py). These model(s) are stored in intermediate files, allowing to skip this step for future runs. (3) The performances of these SMB models can be evaluated performing a leave-one-glacier-out (LOGO) cross-validation (smb_validation.py). This step can be skipped when using already ... |
format |
Software |
author |
Jordi Bolibar |
author_facet |
Jordi Bolibar |
author_sort |
Jordi Bolibar |
title |
ALPGM (ALpine Parameterized Glacier Model) v1.1 |
title_short |
ALPGM (ALpine Parameterized Glacier Model) v1.1 |
title_full |
ALPGM (ALpine Parameterized Glacier Model) v1.1 |
title_fullStr |
ALPGM (ALpine Parameterized Glacier Model) v1.1 |
title_full_unstemmed |
ALPGM (ALpine Parameterized Glacier Model) v1.1 |
title_sort |
alpgm (alpine parameterized glacier model) v1.1 |
publishDate |
2020 |
url |
https://zenodo.org/record/3609136 https://doi.org/10.5281/zenodo.3609136 |
genre |
The Cryosphere |
genre_facet |
The Cryosphere |
op_source |
The Cryosphere |
op_relation |
https://github.com/JordiBolibar/ALPGM/tree/v1.1 doi:10.5194/tc-2019-163 doi:10.5281/zenodo.3269677 https://zenodo.org/record/3609136 https://doi.org/10.5281/zenodo.3609136 oai:zenodo.org:3609136 |
op_rights |
info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.5281/zenodo.360913610.5194/tc-2019-16310.5281/zenodo.3269677 |
_version_ |
1766216275612139520 |