Improvement of liquid particle size distribution retrieval from dual-precipitation radar measurement using a deep neural network

Notebooks that allows to replicate results obtained in Ladino et al. (2024) deep neural network particle size distribution retrieval for liquid particles

Running the Notebooks

You can either run the notebook using Binder or on your local machine.

Running on Binder

The simplest way to interact with a Jupyter Notebook is through Binder, which enables the execution of a Jupyter Book in the cloud. You’ll be able to execute and even change the example programs. You’ll see that the code cells have no output at first, until you execute them by pressing Shift Enter. Complete details on how to interact with a live Jupyter notebook are described in Getting Started with Jupyter.

Running on Your Own Machine

If you are interested in running this material locally on your computer, you will need to follow this workflow:

1. Clone the "dnn-pds-retrieval" repository

   git clone https://github.com/aladinor/Ladino_et_al_2024_DNN_PSD_retrieval.git

2. Move into the dnn-pds-retrieval directory

   cd Ladino_et_al_2024_DNN_PSD_retrieval

3. Create and activate your conda environment from the environment.yml file

   conda env create -f environment.yml
   conda activate psd-retrievals

4. Move into the notebooks directory and start up Jupyterlab

   cd notebooks/
   jupyter lab