Analysis scripts and dataset for Zhang et. al. (2024)
Description
This archive contains post-processed data and scripts for analyses in Zhang et al. (2024) "A Machine Learning Bias Correction on Large-Scale Environment of High-Impact Weather Systems in E3SM Atmosphere Model". These data are derived from the model outputs from the simulations conducted with DOE's E3SM Atmosphere Model Version 2 (EAMv2). There are two groups of simulations. The first group consists of three model simulations were conducted with EAMv2, including one preset-day and two pseudo-global warming simulations with prescribed perturbations on sea surface temperature (SST) and sea ice concentrations (SICs). The second group contains the three same simulations that were post-processed with a machine learning bias correction model. A detailed description of the model and simulations can be found in Zhang et. al. (2024).
Files
Files
(1.7 GB)
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md5:7ccaa30e4fe4854387458e86ac1f9026
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209.4 MB | Download |
md5:ae086faf47cc288537c17b07c33d9920
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292.4 kB | Download |
md5:f329f7a11a811979573a29cf18a50556
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4.3 MB | Download |
md5:3634dbdc5b0dc98eb0de7a6654bafb2f
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387.1 kB | Download |
md5:aaa2fc74e13af89f621ca0f78a2cf41d
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1.3 GB | Download |
md5:c3c8dd4acb739e7a76bb625acd984f4c
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3.5 MB | Download |
md5:fe404fcf469bff9bde7f1ba934f684af
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11.8 MB | Download |
md5:cbc996bca2a57c49985cebc044c12a1d
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21.5 MB | Download |
md5:e43262da9de791ea61cc85cdc0349b3f
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1.1 MB | Download |
md5:72f7025dfb44bf5724984062b7d64a69
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42.0 MB | Download |
md5:e6acad5016477dfd3a990a090cc65ff9
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43.0 MB | Download |
md5:292a09f2a90ab7f3b26eac6d363d81cf
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10.7 MB | Download |
md5:3a8c1301941183fe01c40afbce8c5302
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4.8 MB | Download |
md5:c934200aea860da497741edbea646962
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35.8 MB | Download |
md5:d00647d98bb9e87f96be8b37f10025e3
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169.5 kB | Download |
md5:341bb9a71a94857c5ab23bc2526411e4
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3.7 MB | Download |
md5:db3d866a3edf2d654c9966030526ce98
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4.0 MB | Download |
md5:a6396a3e4d1731476d86c0fa8d348fd5
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66.1 MB | Download |
Additional details
Related works
- Is described by
- Journal article: 10.22541/essoar.170067232.22392274/v1 (DOI)
Dates
- Updated
-
2024-04-23