| Name | nnja-ai JSON |
| Version |
1.0.0
JSON |
| download |
| home_page | None |
| Summary | Find and load data from the Brightband AI-ready mirror of the NOAA NASA Joint Archive (NNJA) of Observations for Earth System Reanalysis |
| upload_time | 2025-08-05 22:40:58 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | MIT License
Copyright (c) 2025 Brightband
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
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The above copyright notice and this permission notice shall be included in all
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
|
| keywords |
nnja
artificial intelligence
earth system reanalysis
data catalog
dataset
earth observation
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
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[](https://github.com/brightbandtech/nnja-ai/actions/workflows/ci.yaml)
[](https://nnja-ai.readthedocs.io/en/stable/)
[](https://pypi.org/p/nnja-ai)
[](https://doi.org/10.5281/zenodo.14633508)
---
# nnja-ai: multi-modal, AI-ready weather observations
This is the companion Python SDK to the [Brightband](https://www.brightband.com/) AI-ready reprocessing of the [NOAA NASA Joint Archive](https://psl.noaa.gov/data/nnja_obs/) (NNJA).
It is meant to serve as a helpful interface between a user and the underlying NNJA datasets (which currently consist of parquet files on [GCS](https://console.cloud.google.com/storage/browser/nnja-ai)).
The V1 release of the NNJA-AI dataset and SDK represents a major increment in availability of NNJA data, with ~50 TiB of observations made available in parquet form along with a data catalog and code examples in this SDK.
## Background
The NNJA archive project is a curated archive of Earth system data from 1979 to present.
This data represents a rich trove of observational data for use in AI weather modelling, however the archival format in which the data is originally available (BUFR) is cumbersome to work with.
In [partnership with NOAA](https://techpartnerships.noaa.gov/tpo_partnership/making-observation-data-ai-ready/), Brightband is processing that data to make it more accessible to the community.
## Data
NNJA datasets are organized by sensor/source (e.g. all-sky radiances from the GOES ABI).
The list of all NNJA datasets can be found on the [NNJA project page](https://psl.noaa.gov/data/nnja_obs/#data-sources), while the subset that is currently found in the NNJA-AI archive can be found [here](datasets.md) or by exploring the data catalog (this will be be expanding rapidly).
## Getting Started
To install this package directly from the GitHub repository, you can use the following `pip` command:
```sh
pip install git+https://github.com/brightbandtech/nnja-ai.git
```
You can find an example notebook [here](example_notebooks/basic_dataset_example.ipynb) showing the basics of opening the data catalog, finding a dataset, subsetting, and finally loading the data to pandas.
Though to get started, you can open the data catalog like so:
```python
from nnja_ai import DataCatalog
catalog = DataCatalog()
print("datasets in catalog:", catalog.list_datasets())
```
```
datasets in catalog:
['amsua-1bamua-NC021023',
'atms-atms-NC021203',
'mhs-1bmhs-NC021027',
'cris-crisf4-NC021206',
...]
```
## How to Cite
If you use this library or the Brightband reprocessed NNJA data, please cite it using the following DOI:
[](https://doi.org/10.5281/zenodo.14633508)
Additionally, please follow the citation guidance on the [NNJA project page](https://psl.noaa.gov/data/nnja_obs/#cite
).
The NNJA-AI data is distributed with the same license as the original NNJA data, [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/deed.en).
Raw data
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