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The JWT signature verification failed. Check the signing key and the algorithm.
Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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Dataset Description:

Global, storm-resolving atmospheric simulation output generated by the SCREAM (Simple Cloud-Resolving E3SM Atmosphere Model) physics model. The dataset provides high-resolution global atmospheric state fields suitable for training and evaluating machine-learning weather and climate emulators. The data was produced by an external collaborator and is publicly available at NERSC under CC-BY-4.0. This release is a reformatted, ML-ready version: files have been converted from NetCDF (.nc) to Zarr (.zarr) and subsampled. No underlying data values were altered. This dataset is ready for commercial and non-commercial use. This repository contains the [sdy2] simulation store. It is part of a three-part release; the other simulation periods are available in the companion repositories (STRATA-SCREAM-sdecadal, STRATA-SCREAM-sdy1).

Dataset Owner(s):

E3SM Project (U.S. Department of Energy)

Dataset Creation Date:

2025-09-22

Version:

v1.0 Previous Version(s): Original raw SCREAM simulation output data in .nc format: https://portal.nersc.gov/archive/home/n/ndk/www/STRATA2026

License/Terms of Use:

Creative Commons Attribution 4.0 International (CC-BY-4.0)

Intended Usage:

Training and evaluating machine-learning weather and climate emulators using high-resolution global atmospheric simulation data.

Dataset Characterization

Data Collection Method
Synthetic Labeling Method
Not Applicable

Dataset Format

Modality: geospatial, timeseries
Format: Zarr (.zarr)

Dataset Quantification

Record Count: 1,009 timesteps (10-minute temporal resolution) Spatial Grid: cubed-sphere ne1024pg2, 25,165,824 columns (~4.9 km), 32 vertical levels Feature Count: ~34 variables Simulation: F2010-SCREAMv1

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should work with their internal developer teams to ensure this dataset meets requirements for the relevant industry and use case and addresses unforeseen product misuse. Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here.

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