Optimal Estimation Retrievals and Their Uncertainties: What Every Atmospheric Scientist Should Know

Supplemental material

Maahn, M., D. D. Turner, U. Löhnert, D. J. Posselt, K. Ebell, G. G. Mace, and J. M. Comstock, 2020: Optimal Estimation Retrievals and Their Uncertainties: What Every Atmospheric Scientist Should Know. Bull. Amer. Meteor. Soc., doi:https://doi.org/10.1175/BAMS-D-19-0027.1

This repository contains examples illustrating the use of the pyOptimalEstimation library. Two Juptyter Notebooks are provided:

• Supplement A: Microwave Radiometer Temperature and Humidity Retrieval
• Supplement B: Cloud Radar Drop Size Distribution Retrieval

If you are new to Jupyter Notebooks, pelase check out the official tutorial.

How to try the examples online

You can try the examples online in your browser without any local installation on binder by following this link. Note that it takes a minute or two to launch the server and that the server shuts down when you do not use it for a couple of minutes. Changes are not saved but a modified Notebook can be downloaded via File > Download as > Notebook

How to try the examples locally

Unless you have pyOptimalEstimation already installed, it is recommended to

1. Install Anaconda. Version 3.6 or higher is recommended.
2. Download or clone this repository, e.g. with the green Code button.
3. Open a terminal and navigate to the folder of this repository
4. Install the conda environment pyoe_examples (so it won't mess with your default Python installation) with
   1. on Linux: conda env create -f environment_linux.yml
   2. on Mac OS X: conda env create -f environment_macosx.yml
5. Activate the environemnt with conda activate pyoe_examples
6. In the terminal, start the Jupyter server with jupyter notebook
7. Your browser should open automatically and you can open one of the provided ipynb files.
8. Make sure to change the kernel to pyoe_examples with Kernel > Set_Kernel