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Data of the SuperIce project

This repository contained an updated description of the data produced in the SuperIce project led by the NERSC and supported by ESA Φ-lab

More info on the project: (link to the website)[https://nansencenter.github.io/superice-nersc/]

Download instruction

Data are stored on the https://archive.sigma2.no/ archive.

tree of the data

The data is stored under the following paths:

model/
├── features/
└── predictions/

observations/
├── features/
└── predictions/

Description of subsets

1. model/features

File format: YYYYMMDD.nc (YYYY: year, MM: month, DD: day in the month)

Time Period: From 2020 to 2023, only the freezing season (18 Oct. to 15 Apr.)

Contains pairs of low-resolution/high-resolution fields from the NeXtSIM simulation. The dataset generation is described in this document. The fields are listed here

Dimensions y = 1086 ; x = 1308
Variable Dimensions Description
time - Time in days since 2022-01-12 00:00:00
longitude (y, x) Longitude in degrees east
latitude (y, x) Latitude in degrees north
sic (y, x) Sea Ice Concentration
sic_reprocessed (y, x) Reprocessed Sea Ice Concentration
sit (y, x) Sea Ice Thickness
sit_reprocessed (y, x) Reprocessed Sea Ice Thickness
divergence (y, x) Divergence of sea ice motion
divergence_reprocessed (y, x) Reprocessed Divergence of sea ice motion
shear (y, x) Shear of sea ice motion
shear_reprocessed (y, x) Reprocessed Shear of sea ice motion

2. model/predictions

File format: sit_hrai_YYYYMMDD.nc (YYYY: year, MM: month, DD: day in the month)

Time Period: From 2020 to 2023, only the freezing season (18 Oct. to 15 Apr.) matching dates in model/features

Contains ensemble (size n=30) of high-resolution fields generated by the diffusion model from features contained in model/features. The AI algorithm is described in this document.

The fields are listed here

Dimensions y = 1086 ; x = 1308 ; n = 30
Variable Dimensions Description
time - Time in days since 2022-01-12 00:00:00
longitude (y, x) Longitude in degrees east
latitude (y, x) Latitude in degrees north
sit_ai (n, y, x) Sea Ice Thickness generated by AI

The members of the ensemble are spanning the possible high-resolution samples from a unique set of low-resolution features.

3. observations/features

File format (python numpy files): sic_sit_e1_e2_YYYYMMDD.npz (YYYY: year, MM: month, DD: day in the month)

Time Period: From 2020 to 2023, only the freezing season (18 Oct. to 15 Apr.)

Contains pairs of low-resolution fields derived from satellite observations. The longitude/latitude grid is the same as for the model files.

The dataset generation is described in this document.

The fields are listed here:

Variable Dimensions Description
sic (1086, 1308) Sea Ice Concentration (OSI SAF)
sit ( 1086, 1308) Sea Ice Thickness (CS2SMOS)
divergence (1086, 1308) Divergence of sea ice motion
shear (1086, 1308) Shear of sea ice motion (OSI SAF)

2. observations/predictions

File format: sit_hrai_YYYYMMDD.nc (YYYY: year, MM: month, DD: day in the month)

Time Period: From 2020 to 2021, only the freezing season (18 Oct. to 15 Apr.).

Contains ensemble (size n=30) of high-resolution fields generated by the diffusion model from features contained in observations/features. The AI algorithm is described in this document.

The fields are listed here

Dimensions y = 1086 ; x = 1308 ; n = 30
Variable Dimensions Description
time - Time in days since 2022-01-12 00:00:00
longitude (y, x) Longitude in degrees east
latitude (y, x) Latitude in degrees north
sit_ai (n, y, x) Sea Ice Thickness generated by AI

The members of the ensemble are spanning the possible high-resolution samples from a unique set of low-resolution observed features.