forecast_clarify
Local water temperature (3m) weekly forecasts based on local statistics (seasonal cycle, week-to-week persistence) from NorKyst800 (2006 - 2022).
Forecasts can be created for all BarentsWatch Sites. Model parameters at these sights are derived from the closest grid points in the high-resolution ocean model ROMS, run in the NorKyst800 set-up (described in Asplin et al., 2020).
Project based on the cookiecutter science project template.
1 maintainer
Metadata
1 maintainer
Release files for forecast-clarify 0.1.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| forecast_clarify-0.1.1.tar.gz | 18.5 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| forecast_clarify-0.1.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 37.0 MB
Release files / forecast_clarify-0.1.1.tar.gz
| Download URL | forecast_clarify-0.1.1.tar.gz |
|---|---|
| Size | 18.5 MB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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twine/4.0.1 CPython/3.9.15
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Release files / forecast_clarify-0.1.1-py3-none-any.whl
| Download URL | forecast_clarify-0.1.1-py3-none-any.whl |
|---|---|
| Size | 18.5 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
7ef9fb7d88353746bce8724411b30fb1fbc301f837ee3036aee73b104288705d
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BLAKE2b-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.1 CPython/3.9.15
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