drafted README incl. science advertisement

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 # SynSatiPy
 
-Synsat Interface to RTTOV
+- is a python package for atmospheric research
+
+- allows to derive satellite images from weather forecasts or from climate simulation
+
+SynSatiPy is a Python interface to the RTTOV software that help to input model data (e.g. from IFS or from ICON) and loads emissivity catalogues and sensor specific files.
+
+## Getting Started
+
+### Installation
+
+Before SynsatiPy can be installed **RTTOV** must be downloaded, configured and installed on your target platform. See 
+- https://nwp-saf.eumetsat.int/site/software/rttov/ or
+- https://en.wikipedia.org/wiki/RTTOV_(radiative_transfer_code)
+
+SynSatiPy can be installed via `pip`. It is recommended to install the SynSatiPy package into a separate python environment. Perhaps, the optimal way is to adjust this tutorial to your needs:
+- [SynSatiPy Installation on DKRZ Levante](docs/Installation_on_Levante.md)
+
+### Using SynSatiPy
+SynSatiPy can be import inside python scripts or jupyter notebooks. Examples are provided in the folder [Example Notebooks](docs/examples/) 
+
+### User Guide
+Further documentation is provided here: RTD (TODO).
+
+## Contributing
+Please feel invited to contributed to the SynSatiPy python package (via pull requests). Possible extension are (not exclusive):
+- implementation of further satellite sensor interfaces
+- implementation of further model input interfaces
+
+Please provide bug reports or ideas for extensions as issues!
+
+
+## Science behind SynSatiPy
+
+Earlier version of the SynSat interface were utilized for scientific studies with various research targets, for instance to
+
+- assess the realism of cloud characteristics in super high-resolution ICON simulations over Germany (https://doi.org/10.1002/qj.2947)
+
+    ![Heinze et al. (2017)](docs/images/Heinze-Fig23.jpg)
+
+- investigate SynSat uncertainties in the presence of Cirrus clouds (https://doi.org/10.1016/j.atmosres.2016.08.012)
+
+    ![Senf & Deneke (2017)](docs/images/Senf2017-Fig4.jpg)
+
+- characterize quality weather forecasts of deep convective development (https://doi.org/10.1175/MWR-D-16-0480.1)
+
+    ![Rempel et al. (2017)](docs/images/Rempel_Fig1.jpg)
+
+- evaluate simulated deep tropical convection (https://doi.org/10.1175/MWR-D-17-0378.1)
+
+    ![Senf et al. (2018)](docs/images/Senf2018-Fig7.jpg)
+
+- study convective organization over Germany (https://doi.org/10.1002/qj.3552)
+
+    ![Pscheid et al. (2019)](docs/images/Pscheid_Fig1.jpg)
+
+- support development of innovate convection parameterizations (https://doi.org/10.1175/MWR-D-20-0107.1)
+    ![Sakradzija et al. (2020)](docs/images/Sakradzija-Fig3.jpg)
+
+- study cloud radiative effects by cloud type (https://doi.org/10.1029/2020JD032667)
+    ![Senf et al. (2020)](docs/images/Senf2020-Fig1.jpg)
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