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@@ -153,7 +153,7 @@ The experiments described in the GMD paper rely on the ERA5 dataset from which 1
 To obtain the complete input datasets, two approaches are possible:
 
 1) The raw ERA5 reanalysis data can be downloaded from the [ECMWF MARS archive](https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation#ERA5:datadocumentation-DataorganisationandhowtodownloadERA5). Once access is granted, the data can be retrieved with the provided retrieval script by specifying a spatial resolution of 0.3° (see keyword `GRID` as described [here](https://confluence.ecmwf.int/pages/viewpage.action?pageId=123799065)). Different meteorological variables can be selected via the key `param` (see [here](https://confluence.ecmwf.int/pages/viewpage.action?pageId=149335858)) which also supports request based on short names. A comprehensive overview is provided by the [parameter database](https://apps.ecmwf.int/codes/grib/param-db/). In the experiments of this study, the variables `2t`, `tcc` and `t` have been downloaded, where the latter has been interpolated onto the 850 hPa pressure level with [CDO](https://code.mpimet.mpg.de/projects/cdo/embedded/index.html#x1-7060002.12.10). For further information on the ERA5 dataset, please consult the [documentation](https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation).
-2) The extracted and preprocessed data (see [below]()) used in the [manuscript](https://doi.org/10.5194/gmd-2021-430) can also be downloaded via [datapub](https://datapub.fz-juelich.de/esde/esde-nfs/online_publication/2mT_by_DL/). This allows the user to start directly with training the video prediction models, but fixes the possible applications to our conducted experiments. Further details on the  preprocessed data is outlined in the corresponding [README](https://datapub.fz-juelich.de/esde/esde-nfs/online_publication/2mT_by_DL/ReadMe.md).
+2) The extracted and preprocessed data used in the [manuscript](https://doi.org/10.5194/gmd-2021-430) can also be downloaded via [datapub](https://datapub.fz-juelich.de/esde/esde-nfs/online_publication/2mT_by_DL/). This allows the user to start directly with training the video prediction models, but fixes the possible applications to our conducted experiments. Further details on the  preprocessed data is outlined in the corresponding [README](https://datapub.fz-juelich.de/esde/esde-nfs/online_publication/2mT_by_DL/ReadMe.md).
 
 ### Toy dataset