Due to a chaotic nature of the atmosphere, all weather forecasts are uncertain. The degree of uncertainty differs depending on weather situation, season and geographical location and is also dependent on characteristics of the forecast model. Data from this study shows that global warming can significantly change the uncertainty of weather forecasts. Surface air temperature and air pressure become easier to predict in a warmer climate, whereas precipitation gets harder to predict. Figures from Scher and Messori (2019).
You can download all files automatically using the software wget and the following single command.
wget -i https://bolin.su.se/data/data/scher-2019.txt
You may need to download the software wget.
The command uses wget to download all files specified in a file list. A list of all files is available at https://bolin.su.se/data/data/scher-2019.txt
Because of the large data volume (8.1 TiB, 153,404 files), downloading all files can take can take long time and requires sufficent disk space on your system.
To download a selected subset, you can edit the file list and run wget with your revised list. Alternatively, you can select files for download in the file list below.
Click on the categories below to view the files. Click on the download button to download the files shown in the list. Depending on your connection speed it may take considerable time to download the data since it is rather big. Thus, it is not recommended to use the web browser to download more than one year (366 files) per batch. Please use wget to download larger batches, as described above.
Sebastian Scher (2019) Global weather forecast data with model EC-Earth — ensemble dataset for historical and future conditions. Dataset version 1. Bolin Centre Database. https://doi.org/10.17043/scher-2019-ec-earth-1
Scher, S., & Messori, G. (2019). How Global Warming Changes the Difficulty of Synoptic Weather Forecasting. Geophysical Research Letters, 46. https://doi.org/10.1029/2018GL081856
The dataset includes 153,404 files in NetCDF format, together amounting to ca 8.1 TiB of data. The data are structured in six levels: (1) all data; (2) historical/future; (3) variable; (4) year; (5) month; (6) individual file.
Variables: 2-metre temperature (2t), Mean sea level pressure (msl), Temperature (t), Total precipitation (tp), U component of wind (u), V component of wind (v), Geopotential (z).
2D-variables (2t, msl, tp) have a grid of 160 latitudes x 320 longitudes.
3D-variables (t, u, v, z) have a grid of 240 latitudes x 480 longitudes x 3 pressure levels (850 hPa, 500 hPa, 300 hPa).
The spectral resolution of the weather forecast model is T159.
Each file contains 10 members, of which each has 11 daily snapshots valid at 12:00 UTC. The first timestep is the initial field (forecast-lead-time zero). The individual members represent different possible evolutions of the weather over the 10 days of the forecast.
Parameter units: msl [Pa], u [m/s], v [m/s], 2t and T [K], z [m²/s²], tp [m]
+4368120876741
Sebastian Scher
Department of Meteorology (MISU), Stockholm University
SE-106 91 Stockholm
Sweden
Earth science services > Models > Atmospheric general circulation models
Geographic Region > Global
Completed
English
Funded by the Department of Meteorology, Stockholm University, and by the Swedish Research Council (VR) grant no. 2016-03724
Bolin Centre Database
1
None
10.17043/scher-2019-ec-earth-1
2019-08-27 15:59:19