## climetlab-mltc-surface-observation-postprocessing
A dataset plugin for climetlab for the dataset mltc-surface-observation-postprocessing-main.
Features
--------
In this README is a description of how to get the dataset provided by the python package climetlab_mltc_surface_observation_postprocessing.
## Datasets description
The dataset is designed for learning forecast errors of 2m-temperature from a weather forecasting model, using station observations as the truth. Currently there are 3 variables in the dataset. For each variable the dataset contains over 5 million datapoints covering a range of different locations (not specified).
### 1 : `field = forecast_error`
The predictand for the problem, the difference between the forecasted value of 2m-temperature and the observed value, units degrees C (or equally Kelvin).
### 2 : `field = time_of_day`
Local time of day, in decimal hours. Useful for diagnosing the diurnal cycle model bias.
### 3: `field = soil_temperature`
Model soil temperature, units degrees C.
## Using climetlab to access the data
See the [demo notebooks](https://github.com/mchantry/climetlab-mltc-surface-observation-postprocessing/tree/main/notebooks)
[](https://mybinder.org/v2/gh/mchantry/climetlab-mltc-surface-observation-postprocessing/main?urlpath=lab)
- [demo_main.ipynb](https://github.com/mchantry/climetlab-mltc-surface-observation-postprocessing/tree/main/notebooks/demo_main.ipynb)
[](https://nbviewer.jupyter.org/github/mchantry/climetlab-mltc-surface-observation-postprocessing/blob/main/notebooks/demo_main.ipynb)
[](https://colab.research.google.com/github/mchantry/climetlab-mltc-surface-observation-postprocessing/blob/main/notebooks/demo_main.ipynb)
[](https://mybinder.org/v2/gh/mchantry/climetlab-mltc-surface-observation-postprocessing/main?filepath=notebooks/demo_main.ipynb)
[<img src="https://deepnote.com/buttons/launch-in-deepnote-small.svg">](https://deepnote.com/launch?name=MyProject&url=https://github.com/mchantry/climetlab-mltc-surface-observation-postprocessing/tree/main/notebooks/demo_main.ipynb)
- TODO.
The climetlab python package allows easy access to the data with a few lines of code such as:
``` python
!pip install climetlab climetlab-mltc-surface-observation-postprocessing
import climetlab as cml
ds = cml.load_dataset("mltc-surface-observation-postprocessing", field="forecast_error")
ds.to_pandas()
```
Support and contributing
------------------------
Either open a issue on github if this is a github repository, or send an email to email@example.com.
LICENSE
-------
See the LICENSE file.
(C) Copyright 2022 European Centre for Medium-Range Weather Forecasts.
This software is licensed under the terms of the Apache Licence Version 2.0
which can be obtained at http://www.apache.org/licenses/LICENSE-2.0.
In applying this licence, ECMWF does not waive the privileges and immunities
granted to it by virtue of its status as an intergovernmental organisation
nor does it submit to any jurisdiction.
Authors
-------
Matthew Chantry, Fenwick Cooper, Zied Ben Bouallegue, Peter Dueben
See also the CONTRIBUTORS.md file.
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"description": "## climetlab-mltc-surface-observation-postprocessing\n\nA dataset plugin for climetlab for the dataset mltc-surface-observation-postprocessing-main.\n\n\nFeatures\n--------\n\nIn this README is a description of how to get the dataset provided by the python package climetlab_mltc_surface_observation_postprocessing.\n\n## Datasets description\n\nThe dataset is designed for learning forecast errors of 2m-temperature from a weather forecasting model, using station observations as the truth. Currently there are 3 variables in the dataset. For each variable the dataset contains over 5 million datapoints covering a range of different locations (not specified).\n\n### 1 : `field = forecast_error`\nThe predictand for the problem, the difference between the forecasted value of 2m-temperature and the observed value, units degrees C (or equally Kelvin).\n\n### 2 : `field = time_of_day`\nLocal time of day, in decimal hours. Useful for diagnosing the diurnal cycle model bias.\n\n### 3: `field = soil_temperature`\nModel soil temperature, units degrees C.\n\n\n## Using climetlab to access the data\n\nSee the [demo notebooks](https://github.com/mchantry/climetlab-mltc-surface-observation-postprocessing/tree/main/notebooks)\n[](https://mybinder.org/v2/gh/mchantry/climetlab-mltc-surface-observation-postprocessing/main?urlpath=lab)\n\n\n- [demo_main.ipynb](https://github.com/mchantry/climetlab-mltc-surface-observation-postprocessing/tree/main/notebooks/demo_main.ipynb)\n[](https://nbviewer.jupyter.org/github/mchantry/climetlab-mltc-surface-observation-postprocessing/blob/main/notebooks/demo_main.ipynb) \n[](https://colab.research.google.com/github/mchantry/climetlab-mltc-surface-observation-postprocessing/blob/main/notebooks/demo_main.ipynb) \n[](https://mybinder.org/v2/gh/mchantry/climetlab-mltc-surface-observation-postprocessing/main?filepath=notebooks/demo_main.ipynb)\n[<img src=\"https://deepnote.com/buttons/launch-in-deepnote-small.svg\">](https://deepnote.com/launch?name=MyProject&url=https://github.com/mchantry/climetlab-mltc-surface-observation-postprocessing/tree/main/notebooks/demo_main.ipynb)\n\n\n- TODO.\n\n\nThe climetlab python package allows easy access to the data with a few lines of code such as:\n``` python\n\n!pip install climetlab climetlab-mltc-surface-observation-postprocessing\nimport climetlab as cml\nds = cml.load_dataset(\"mltc-surface-observation-postprocessing\", field=\"forecast_error\")\nds.to_pandas()\n```\n\n\nSupport and contributing\n------------------------\n\nEither open a issue on github if this is a github repository, or send an email to email@example.com.\n\nLICENSE\n-------\n\nSee the LICENSE file.\n(C) Copyright 2022 European Centre for Medium-Range Weather Forecasts.\nThis software is licensed under the terms of the Apache Licence Version 2.0\nwhich can be obtained at http://www.apache.org/licenses/LICENSE-2.0.\nIn applying this licence, ECMWF does not waive the privileges and immunities\ngranted to it by virtue of its status as an intergovernmental organisation\nnor does it submit to any jurisdiction.\n\nAuthors\n-------\n\nMatthew Chantry, Fenwick Cooper, Zied Ben Bouallegue, Peter Dueben\n\nSee also the CONTRIBUTORS.md file.\n",
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