diff --git a/README.md b/README.md
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--- a/README.md
+++ b/README.md
@@ -8,7 +8,7 @@
 
 `snowlaps` is a Python package built on a deep learning emulator of
 the two-stream radiative transfer model
-[biosnicar](https://biosnicar.vercel.app/). It was originally
+[biosnicar v2.1](https://github.com/jmcook1186/biosnicar-py). It was originally
 developed to study the impact of different Light Absorbing Particles
 (LAPs) on snow spectral albedo as part of a [research
 project](https://doi.org/10.5194/egusphere-2024-2583,) in Southern
diff --git a/README.org b/README.org
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-[[https://www.repostatus.org/badges/latest/wip.svg][https://www.repostatus.org/badges/latest/wip.svg]]
-[[https://www.gnu.org/licenses/gpl-3.0][https://img.shields.io/badge/License-GPLv3-blue.svg]]
-[[https://github.com/openosmia/snowlaps-emulator/actions][file:https://github.com/openosmia/snowlaps-emulator/workflows/CI/badge.svg]]
-[[https://github.com/psf/black][https://img.shields.io/badge/code%20style-black-000000.svg]]
-
-* snowlaps ❄️🦠🏔️
-
-=snowlaps= is a Python package built on a deep learning emulator of
-the two-stream radiative transfer model [[https://biosnicar.vercel.app/][biosnicar]]. It was originally
-developed to study the impact of different Light Absorbing Particles
-(LAPs) on snow spectral albedo as part of a [[https://doi.org/10.5194/egusphere-2024-2583][research project]] in
-Southern Norway. Now, =snowlaps= has grown into a library with two
-main use cases.
-
-- *forward mode*: predict snow spectral albedo from prescribed surface
-  properties as a fast alternative to [[https://biosnicar.vercel.app/][biosnicar]].
-- *inverse mode*: infer surface properties from snow spectral albedo
-  observations.
-
-* Installation
-
-=snowlaps= can be installed via the command line with [[https://docs.conda.io/en/latest/][conda]] and [[https://pip.pypa.io/en/stable/][pip]]:
-
-#+begin_src shell :results verbatim
-
-# clone repository in the folder of your choice
-git clone git@github.com:openosmia/snowlaps.git
-
-# move into snowlaps directory
-cd snowlaps
-
-# create conda environment
-conda env create -f environment.yml
-
-# activate conda environment
-conda activate snowlaps
-
-# install snowlaps
-pip install -e .
-
-#+end_src
-
-Installation can be sped up using the fast cross-platform package
-manager [[https://mamba.readthedocs.io/en/latest/][mamba]] (reimplementation of the conda package manager in C++),
-simply use =mamba= instead of =conda= in the instructions above.
-
-* Usage
-
-=snowlaps= can be used directly as a Python package or interactively
-via a Streamlit app.
-
-** Running the code
-
-Example scripts are provided in [[https://github.com/openosmia/snowlaps/tree/main/examples][snowlaps/examples]].
-
-- *example 1*: forward run of the snowlaps emulator
-- *example 2*: inversion of hyperspectral albedo measurements
-- *example 3*: comparison of snowlaps and biosnicar predictions
-
-** Using the app
-
-The Streamlit app can be run locally via the terminal:
-
-#+begin_src shell :results verbatim
-
-# move into snowlaps directory
-cd snowlaps
-
-# start Streamlit app on http://localhost:8501
-./start_app.sh
-
-#+end_src
-
-
-[[./data/assets/app_forward.png]]
-
-* Contributions
-
-Any contribution to =snowlaps= is welcome! Feel free to add new issues, open pull requests or ask questions in the discussion forum.
-
-* Citation
-
-If you use this code, please cite the associated publication:
-
-Chevrollier, L.-A., Wehrlé, A., Cook, J. M., Pirk, N., Benning, L. G., Anesio, A. M., and Tranter, M.: Separating the albedo reducing effect of different light absorbing particles on snow using deep learning, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-2583, 2024.
diff --git a/examples/snowlaps_ex03_compare_with_biosnicar.py b/examples/snowlaps_ex03_compare_with_biosnicar.py
index 7119543..d6720a1 100644
--- a/examples/snowlaps_ex03_compare_with_biosnicar.py
+++ b/examples/snowlaps_ex03_compare_with_biosnicar.py
@@ -3,7 +3,7 @@
 """
 
 NOTE: this script requires an installation of the main branch of the biosnicar
-model (https://github.com/jmcook1186/biosnicar-py/tree/master)
+model v2.1 (https://github.com/jmcook1186/biosnicar-py/tree/master)
 
 """