Update README.md

README.md

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 # CBRAIN-CAM - a neural network climate model parameterization
 
-Author: Stephan Rasp <[email protected]>
+Author: Stephan Rasp - <[email protected]> - https://raspstephan.github.io
 
 This is my working directory for the CBRAIN-CAM project. It contains all code used to preprocess the raw climate model data, run the neural networks and analyze the results.
 
 The modified climate model code is available at https://gitlab.com/mspritch/spcam3.0-neural-net (branch: `nn_fbp_engy_ess`)
 
 ## Papers and code
 
-The first paper showing offline performance is online now: 
-P. Gentine, M. Pritchard, S. Rasp, G. Reinaudi and G. Yacalis, 2018. Could machine learning break the convection parameterization deadlock? Geophysical Research Letters. http://doi.wiley.com/10.1029/2018GL078202
+### Climate model parameterization paper
+
+The second paper with the prognostic climate simulations is available as a preprint:
+> S. Rasp, M. Pritchard and P. Gentine, 2018.
+> Deep learning to represent sub-grid processes in climate models
+> https://arxiv.org/abs/1806.04731
+
+For a snapshot of the repository as it was for the GRL paper, see release [paper2_submission](https://github.com/raspstephan/CBRAIN-CAM/releases/tag/paper2_submission). All figures for the paper were produced in [this Jupyter notebook](https://github.com/raspstephan/CBRAIN-CAM/blob/master/notebooks/presentation/paper2.ipynb)
 
-For a snapshot of the repository as it was for the GRL paper, see release [grl_submission](https://github.com/raspstephan/CBRAIN-CAM/releases/tag/grl_submission). All figures for the paper were produced in [this Jupyter notebook](https://github.com/raspstephan/CBRAIN-CAM/blob/master/notebooks/presentation/grl_paper.ipynb)
 
-The second paper is submitted and available as a preprint.
-COMING SOON.
+### GRL paper
 
-The respective release is [paper2_submission]. All analysis and plotting is done in [this Jupyter notebook](https://github.com/raspstephan/CBRAIN-CAM/blob/master/notebooks/presentation/for-my-paper.ipynb). 
+The first paper showing offline performance has been published: 
+> P. Gentine, M. Pritchard, S. Rasp, G. Reinaudi and G. Yacalis, 2018. 
+> Could machine learning break the convection parameterization deadlock? 
+> Geophysical Research Letters. http://doi.wiley.com/10.1029/2018GL078202
+
+For a snapshot of the repository as it was for the GRL paper, see release [grl_submission](https://github.com/raspstephan/CBRAIN-CAM/releases/tag/grl_submission). All figures for the paper were produced in [this Jupyter notebook](https://github.com/raspstephan/CBRAIN-CAM/blob/master/notebooks/presentation/grl_paper.ipynb)
 
 ## Repository description