معرفی شرکت ها
climetlab-eumetnet-postprocessing-benchmark-0.1.9
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
A plugin for climetlab to retrieve the Eumetnet postprocessing benchmark dataset.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | OS Independent |
| نام فایل | climetlab-eumetnet-postprocessing-benchmark-0.1.9 |
| نام | climetlab-eumetnet-postprocessing-benchmark |
| نسخه کتابخانه | 0.1.9 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Jonathan Demaeyer |
| ایمیل نویسنده | jodemaey@meteo.be |
| آدرس صفحه اصلی | https://github.com/Climdyn/climetlab-eumetnet-postprocessing-benchmark |
| آدرس اینترنتی | https://pypi.org/project/climetlab-eumetnet-postprocessing-benchmark/ |
| مجوز | BSD-3-Clause License |
# The Eumetnet postprocessing benchmark dataset Climetlab plugin
[](https://badge.fury.io/py/climetlab-eumetnet-postprocessing-benchmark)
[](https://pypi.org/project/climetlab-eumetnet-postprocessing-benchmark/)
[](https://github.com/Climdyn/climetlab-eumetnet-postprocessing-benchmark/actions/workflows/check-and-publish.yml)
A plugin for [climetlab](https://github.com/ecmwf/climetlab) to retrieve the Eumetnet postprocessing benchmark dataset.
Ease the download of the dataset time-aligned forecasts, reforecasts (hindcasts) and observations ([ERA5 reanalysis](https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5)).
> **Warning:** The current development stage is **Alpha**.
>
## Using climetlab to access the data
See the [demo notebooks](https://github.com/Climdyn/climetlab-eumetnet-postprocessing-benchmark/tree/main/notebooks)
[](https://mybinder.org/v2/gh/Climdyn/climetlab-eumetnet-postprocessing-benchmark/main?urlpath=lab)
- [demo_training_data_forecasts.ipynb](https://github.com/Climdyn/climetlab-eumetnet-postprocessing-benchmark/tree/main/notebooks/demo_training_data_forecasts.ipynb)
[](https://nbviewer.jupyter.org/github/Climdyn/climetlab-eumetnet-postprocessing-benchmark/blob/main/notebooks/demo_training_data_forecasts.ipynb)
[](https://colab.research.google.com/github/Climdyn/climetlab-eumetnet-postprocessing-benchmark/blob/main/notebooks/demo_training_data_forecasts.ipynb)
[](https://mybinder.org/v2/gh/Climdyn/climetlab-eumetnet-postprocessing-benchmark/main?filepath=notebooks/demo_training_data_forecasts.ipynb)
[<img src="https://deepnote.com/buttons/launch-in-deepnote-small.svg">](https://deepnote.com/launch?name=MyProject&url=https://github.com/Climdyn/climetlab-eumetnet-postprocessing-benchmark/tree/main/notebooks/demo_training_data_forecasts.ipynb)
- [demo_ensemble_forecasts.ipynb](https://github.com/Climdyn/climetlab-eumetnet-postprocessing-benchmark/tree/main/notebooks/demo_ensemble_forecasts.ipynb)
[](https://nbviewer.jupyter.org/github/Climdyn/climetlab-eumetnet-postprocessing-benchmark/blob/main/notebooks/demo_ensemble_forecasts.ipynb)
[](https://colab.research.google.com/github/Climdyn/climetlab-eumetnet-postprocessing-benchmark/blob/main/notebooks/demo_ensemble_forecasts.ipynb)
[](https://mybinder.org/v2/gh/Climdyn/climetlab-eumetnet-postprocessing-benchmark/main?filepath=notebooks/demo_ensemble_forecasts.ipynb)
[<img src="https://deepnote.com/buttons/launch-in-deepnote-small.svg">](https://deepnote.com/launch?name=MyProject&url=https://github.com/Climdyn/climetlab-eumetnet-postprocessing-benchmark/tree/main/notebooks/demo_ensemble_forecasts.ipynb)
The climetlab python package allows easy access to the data with a few lines of code such as:
``` python
# Uncomment the line below if climetlab and the plugin are not yet installed
#!pip install climetlab climetlab-eumetnet-postprocessing-benchmark
import climetlab as cml
ds = cml.load_dataset('eumetnet-postprocessing-benchmark-training-data-gridded-forecasts-surface', "2017-12-02", "2t", "highres")
fcs = ds.to_xarray()
```
which download the deterministic (high-resolution) forecasts for the 2 metres temperature.
Once obtained, the corresponding observations (if available) can be retrieved in the [xarray](http://xarray.pydata.org/en/stable/index.html) format by using the `get_observations_as_xarray` method:
``` python
obs = ds.get_observations_as_xarray()
```
## Datasets description
There are two main datasets:
## 1 - Gridded Data
* The Eumetnet postprocessing benchmark dataset contains [ECMWF](https://www.ecmwf.int/) ensemble and deterministic forecasts over a large portion of Europe, from 36 to 67° in latitude and from -6 to 17° of longitude,
and covers the years 2017-2018.
* It also contains the corresponding ERA5 reanalysis for the purpose of providing observations for the benchmark.
* For some dates, it contains also reforecasts that covers 20 years of past forecasts recomputed with the most recent model version.
* All the forecasts and reforecasts provided are the noon ECMWF runs.
* The ensemble forecasts and reforecsts also contain by default the control run.
* The gridded data resolution is 0.25° x 0.25° which corresponds roughly to 25 kilometers.
* **Please note that you can presently only retrieve one forecast date** for each `climetlab.load_dataset` call.
There are 5 gridded sub-datasets:
### 1.1 - Extreme Forecast Index
All the [Extreme Forecast Index](https://www.ecmwf.int/assets/elearning/efi/efi1/story_html5.html) (EFI) variables can be obtained for each forecast date.
It includes:
TODO: add links for the efi
| Parameter name | ECMWF key | Remarks |
|------------------------------------------------------------------------------------------------|------------|---------------------------------|
| [2 metre temperature efi](https://apps.ecmwf.int/codes/grib/param-db/?id=132167) | 2ti | |
| [10 metre wind speed efi](https://apps.ecmwf.int/codes/grib/param-db/?id=132165) | 10wsi | |
| [10 metre wind gust efi](https://apps.ecmwf.int/codes/grib/param-db/?id=132049) | 10fgi | |
| [cape efi](https://apps.ecmwf.int/codes/grib/param-db/?id=132059) | capei | |
| [cape shear efi](https://apps.ecmwf.int/codes/grib/param-db/?id=132044) | capesi | |
| [Maximum temperature at 2m efi](https://apps.ecmwf.int/codes/grib/param-db/?id=132201) | mx2ti | |
| [Minimum temperature at 2m efi](https://apps.ecmwf.int/codes/grib/param-db/?id=132202) | mn2ti | |
| [Snowfall efi](https://apps.ecmwf.int/codes/grib/param-db/?id=132144) | sfi | |
| [Total precipitation efi](https://apps.ecmwf.int/codes/grib/param-db/?id=132228) | tpi | |
The EFI are available for the model step range (in hours) 0-24, 24-48, 48-72, 72-96, 96-120, 120-144 and 144-168.
**Usage:** The EFI variables can be retrieved by calling
``` python
ds = cml.load_dataset('eumetnet-postprocessing-benchmark-training-data-gridded-forecasts-efi', date, parameter)
ds.to_xarray()
```
where the `date` argument is a string with a single date, and the `parameter` argument is a string or a list of string with the
ECMWF keys described above. Setting `'all'` as `parameter` download all the EFI parameters.
**Example:**
``` python
ds = cml.load_dataset('eumetnet-postprocessing-benchmark-training-data-gridded-forecasts-efi', "2017-12-02", "2ti")
ds.to_xarray()
```
**Remark:**
By definition, observations are not available for Extreme Forecast Indices (EFI).
### 1.2 - Surface variable forecasts
The surface variables can be obtained for each forecast date, both for the ensemble (51 members) and deterministic runs.
It includes:
| Parameter name | ECMWF key | Remarks |
|------------------------------------------------------------------------------------------------|------------|---------------------------------|
| [2 metre temperature](https://apps.ecmwf.int/codes/grib/param-db/?id=167) | 2t | |
| [10 metre U wind component](https://apps.ecmwf.int/codes/grib/param-db/?id=165) | 10u | |
| [10 metre V wind component](https://apps.ecmwf.int/codes/grib/param-db/?id=166) | 10v | |
| [Total cloud cover](https://apps.ecmwf.int/codes/grib/param-db/?id=164) | tcc | |
| [100 metre U wind component anomaly](https://apps.ecmwf.int/codes/grib/param-db/?id=171006) | 100ua | Observations not available |
| [100 metre V wind component anomaly](https://apps.ecmwf.int/codes/grib/param-db/?id=171007) | 100va | Observations not available |
| [Convective available potential energy](https://apps.ecmwf.int/codes/grib/param-db/?id=59) | cape | |
| [Soil temperature level 1](https://apps.ecmwf.int/codes/grib/param-db/?id=139) | stl1 | |
| [Total column water](https://apps.ecmwf.int/codes/grib/param-db/?id=136) | tcw | |
| [Total column water vapour](https://apps.ecmwf.int/codes/grib/param-db/?id=137) | tcwv | |
| [Volumetric soil water layer 1](https://apps.ecmwf.int/codes/grib/param-db/?id=39) | swvl1 | |
| [Snow depth](https://apps.ecmwf.int/codes/grib/param-db/?id=141) | sd | |
| [Convective inhibition](https://apps.ecmwf.int/codes/grib/param-db/?id=228001) | cin | Observations not available |
| [Visibility](https://apps.ecmwf.int/codes/grib/param-db/?id=3020) | vis | Observations not available |
Some missing observations will become available later.
The forecasts are available for the model steps (in hours) 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21,
22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90,
93, 96, 99, 102, 105, 108, 111, 114, 117, 120, 123, 126, 129, 132, 135, 138, 141, 144, 150, 156, 162, 168, 174, 180, 186, 192, 198,
204, 210, 216, 222, 228, 234 and 240.
All the steps are automatically retrieved.
**Usage:** The surface variables forecasts can be retrieved by calling
``` python
ds = cml.load_dataset('eumetnet-postprocessing-benchmark-training-data-gridded-forecasts-surface', date, parameter, kind)
ds.to_xarray()
```
where the `date` argument is a string with a single date, and the `parameter` argument is a string or a list of string with the
ECMWF keys described above. Setting `'all'` as `parameter` download all the surface parameters. The `kind` argument allows to select
the deterministic or ensemble forecasts, by setting it to `'highres'` or `'ensemble'`.
**Example:**
``` python
ds = cml.load_dataset('eumetnet-postprocessing-benchmark-training-data-gridded-forecasts-surface', "2017-12-02", "sd", "highres")
ds.to_xarray()
```
### 1.3 - Pressure level variable forecasts
The variables on pressure level can be obtained for each forecast date, both for the ensemble (51 members) and deterministic runs.
It includes:
| Parameter name | Level | ECMWF key | Remarks |
|--------------------------------------------------------------------------------|----------------|------------|---------------------------------|
| [Temperature](https://apps.ecmwf.int/codes/grib/param-db/?id=130) | 850 | t | |
| [U component of wind](https://apps.ecmwf.int/codes/grib/param-db/?id=131) | 700 | u | |
| [V component of wind](https://apps.ecmwf.int/codes/grib/param-db/?id=132) | 700 | v | |
| [Geopotential](https://apps.ecmwf.int/codes/grib/param-db/?id=129) | 500 | z | |
| [Specific humidity](https://apps.ecmwf.int/codes/grib/param-db/?id=133) | 700 | q | |
| [Relative humidity](https://apps.ecmwf.int/codes/grib/param-db/?id=157) | 850 | r | |
The forecasts are available for the same model steps as the surface variables above.
**Usage:** The pressure level variables forecasts can be retrieved by calling
``` python
ds = cml.load_dataset('eumetnet-postprocessing-benchmark-training-data-gridded-forecasts-pressure', date, parameter, level, kind)
ds.to_xarray()
```
where the `date` argument is a string with a single date, and the `parameter` argument is a string or a list of string with the
ECMWF keys described above. Setting `'all'` as `parameter` download all the parameters at the given pressure level.
The `level` argument is the pressure level, as a string or an integer. The `kind` argument allows to select
the deterministic or ensemble forecasts, by setting it to `'highres'` or `'ensemble'`.
**Example:**
``` python
ds = cml.load_dataset('eumetnet-postprocessing-benchmark-training-data-gridded-forecasts-pressure', "2017-12-02", "z", 500, "highres")
ds.to_xarray()
```
### 1.4 - Postprocessed surface variable forecasts
Postprocessed surface variables can be obtained for each forecast date, both for the ensemble (51 members) and deterministic runs.
A postprocessed variable is either accumulated, averaged or filtered.
It includes:
| Parameter name | ECMWF key | Remarks |
|------------------------------------------------------------------------------------------------|------------|---------------------------------|
| [Total precipitation](https://apps.ecmwf.int/codes/grib/param-db/?id=228) | tp | |
| [Surface sensible heat flux](https://apps.ecmwf.int/codes/grib/param-db/?id=146) | sshf | |
| [Surface latent heat flux](https://apps.ecmwf.int/codes/grib/param-db/?id=147) | slhf | |
| [Surface net solar radiation](https://apps.ecmwf.int/codes/grib/param-db/?id=176) | ssr | |
| [Surface net thermal radiation](https://apps.ecmwf.int/codes/grib/param-db/?id=177) | str | |
| [Convective precipitation](https://apps.ecmwf.int/codes/grib/param-db/?id=143) | cp | |
| [Maximum temperature at 2 metres](https://apps.ecmwf.int/codes/grib/param-db/?id=121) | mx2t6 | |
| [Minimum temperature at 2 metres](https://apps.ecmwf.int/codes/grib/param-db/?id=122) | mn2t6 | |
| [Surface solar radiation downwards](https://apps.ecmwf.int/codes/grib/param-db/?id=169) | ssrd | |
| [Surface thermal radiation downwards](https://apps.ecmwf.int/codes/grib/param-db/?id=175) | strd | |
| [10 metre wind gust](https://apps.ecmwf.int/codes/grib/param-db/?id=123) | 10fg6 | |
All these variables are accumulated or filtered over the last 6 hours preceding a given forecast timestamp.
Therefore, the forecasts are available for the model steps (in hours) 6, 12, 18, 24, 30, 36, 42, 48, 54, 60, 66, 72, 78, 84, 90, 96, 102, 108,
114, 120, 126, 132, 138, 144, 150, 156, 162, 168, 174, 180, 186, 192, 198, 204, 210, 216, 222, 228, 234 and 240.
All the steps are automatically retrieved.
**Usage:** The surface variables forecasts can be retrieved by calling
``` python
ds = cml.load_dataset('eumetnet-postprocessing-benchmark-training-data-gridded-forecasts-surface-postprocessed', date, parameter, kind)
ds.to_xarray()
```
where the `date` argument is a string with a single date, and the `parameter` argument is a string or a list of string with the
ECMWF keys described above. The `kind` argument allows to select
the deterministic or ensemble forecasts, by setting it to `'highres'` or `'ensemble'`.
> **Remark:** For technical reason, most fields cannot be retrieved along the others and must be downloaded alone.
> E.g. a request with `parameter=['tp', 'mx2t6']` will fail while one with `parameter='tp'` will succeed.
**Example:**
``` python
ds = cml.load_dataset('eumetnet-postprocessing-benchmark-training-data-gridded-forecasts-surface-postprocessed', "2017-12-02", "mx2t6", "highres")
ds.to_xarray()
```
### 1.5 - Surface variable reforecasts
The surface variables for the ensemble reforecasts (11 members) can be obtained for each reforecast date.
All the variables described at the point **1.2** above are available.
The reforecasts are available for the model steps (in hours) 0, 6, 12, 18, 24, 30, 36, 42, 48, 54, 60, 66, 72, 78, 84, 90, 96, 102, 108,
114, 120, 126, 132, 138, 144, 150, 156, 162, 168, 174, 180, 186, 192, 198, 204, 210, 216, 222, 228, 234 and 240.
All the steps are automatically retrieved.
> **Remark:** The ECMWF reforecasts are only available Mondays and Thursdays. Providing any other date will fail.
**Usage:** The surface variables reforecasts can be retrieved by calling
``` python
ds = cml.load_dataset('eumetnet-postprocessing-benchmark-training-data-gridded-reforecasts-surface', date, parameter)
ds.to_xarray()
```
where the `date` argument is a string with a single date, and the `parameter` argument is a string or a list of string with the
ECMWF keys. Setting `'all'` as `parameter` download all the surface parameters.
**Example:**
``` python
ds = cml.load_dataset('eumetnet-postprocessing-benchmark-training-data-gridded-reforecasts-surface', "2017-12-28", "sd")
ds.to_xarray()
```
### 1.6 - Pressure level variable reforecasts
The variables on pressure level for the ensemble reforecasts (11 members) can be obtained for each reforecast date
All the variables described at the point **1.3** above are available.
The reforecast are available for the same model steps as the surface variables above.
> **Remark:** The ECMWF reforecasts are only available Mondays and Thursdays. Providing any other date will fail.
**Usage:** The pressure level variables reforecasts can be retrieved by calling
``` python
ds = cml.load_dataset('eumetnet-postprocessing-benchmark-training-data-gridded-reforecasts-pressure', date, parameter, level)
ds.to_xarray()
```
where the `date` argument is a string with a single date, and the `parameter` argument is a string or a list of string with the
ECMWF keys. Setting `'all'` as `parameter` download all the parameters at the given pressure level.
The `level` argument is the pressure level, as a string or an integer.
**Example:**
``` python
ds = cml.load_dataset('eumetnet-postprocessing-benchmark-training-data-gridded-reforecasts-pressure', "2017-12-28", "z", 500)
ds.to_xarray()
```
### 1.7 - Postprocessed surface variable reforecasts
Postprocessed surface variables as described in section **1.4** can also be obtained as ensemble reforecasts (11 members).
The reforecast are available for the same model steps as the surface variables described in section **1.5**.
> **Remark:** The ECMWF reforecasts are only available Mondays and Thursdays. Providing any other date will fail.
**Usage:** The surface variables forecasts can be retrieved by calling
``` python
ds = cml.load_dataset('eumetnet-postprocessing-benchmark-training-data-gridded-reforecasts-surface-postprocessed', date, parameter)
ds.to_xarray()
```
where the `date` argument is a string with a single date, and the `parameter` argument is a string or a list of string with the
ECMWF keys.
> **Remark:** For technical reason, most fields cannot be retrieved along the others and must be downloaded alone.
> E.g. a request with `parameter=['tp', 'mx2t6']` will fail while one with `parameter='tp'` will succeed.
**Example:**
``` python
ds = cml.load_dataset('eumetnet-postprocessing-benchmark-training-data-gridded-reforecasts-surface-postprocessed', "2017-12-28", "mx2t6")
ds.to_xarray()
```
### 1.8 - Static fields
Various static fields associated to the forecast grid can be obtained, with the purpose of serving as predictors for the postprocessing.
> **Remark:** For consistency with the rest of the dataset, we use the ECMWF parameters name, terminology and units here.
> However, please note that the fields provided are from other non-ECMWF data sources evaluated at grid points.
> Currently, the main data source being used is the [Copernicus Land Monitoring Service](https://land.copernicus.eu/).
It includes:
| Parameter name | ECMWF key | Remarks |
|------------------------------------------------------------------------------------------------|------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| [Land use](https://apps.ecmwf.int/codes/grib/param-db/?id=260184) | landu | Extracted from the [CORINE 2018](https://land.copernicus.eu/pan-european/corine-land-cover) dataset. Values and associated land type differ from the ECMWF one. Please look at the "legend" entry in the metadata for more details. |
| [Model terrain height](https://apps.ecmwf.int/codes/grib/param-db/?id=260183) | mterh | Extracted from the [EU-DEM v1.1](https://land.copernicus.eu/imagery-in-situ/eu-dem) data elevation model dataset. |
**Usage:** The static fields can be retrieved by calling
``` python
ds = cml.load_dataset('eumetnet-postprocessing-benchmark-training-data-gridded-static-fields', parameter)
ds.to_xarray()
```
where the `parameter` argument is a string with one of the ECMWF keys described above.
It is only possible to download one static field per call.
**Example:**
``` python
ds = cml.load_dataset('eumetnet-postprocessing-benchmark-training-data-gridded-static-fields', 'mterh')
ds.to_xarray()
```
## 2 - Stations Data
Not yet provided.
## Major ECMWF model changes
TODO
Support and contributing
------------------------
Please open a [issue on github](https://github.com/Climdyn/climetlab-eumetnet-postprocessing-benchmark/issues).
LICENSE
-------
See the [LICENSE](https://github.com/Climdyn/climetlab-eumetnet-postprocessing-benchmark/blob/main/LICENSE) file for the code, and the [DATA_LICENSE](https://github.com/Climdyn/climetlab-eumetnet-postprocessing-benchmark/blob/main/DATA_LICENSE) for the data.
Authors
-------
See the [CONTRIBUTORS.md](https://github.com/Climdyn/climetlab-eumetnet-postprocessing-benchmark/blob/main/CONTRIBUTORS.md) file.
نحوه نصب
نصب پکیج whl climetlab-eumetnet-postprocessing-benchmark-0.1.9:
pip install climetlab-eumetnet-postprocessing-benchmark-0.1.9.whl
نصب پکیج tar.gz climetlab-eumetnet-postprocessing-benchmark-0.1.9:
pip install climetlab-eumetnet-postprocessing-benchmark-0.1.9.tar.gz