Paraguay Floods

Welcome to the code repository for "Heavy rainfall in Paraguay during the 2015-2016 austral summer: causes and subseasonal-to-seasonal predictive skill", by James Doss-Gollin, Angel Munoz, Simon Mason, and Max Pasten. The code in this document will enable you to reproduce our analysis and reproduce it for your own benefit.

Code by James Doss-Gollin (2016-2018).

Goals

The key research questions that this analysis addresses are:

1. What sequences of rainfall led to intense flooding along the Paraguay and Paraná systems during NDJF 2015-16?
2. What sequences of daily circulation patterns led to the observed rainfall?
3. At sub-seasonal to seasonal timescales, what factors led to the observed rainfall and how did they enhance the probability of its occurrence?
4. What skill did models have in forecasting the rainfall?

Please see our paper to read about our scientific ideas! The below documentation will focus on how the computation is structured, which is only a small piece of the thinking we did. If you can't access our paper, please contact us!

Code Organization

Code is written in python, using freely available packages:

• numpy and scipy for numerical computation
• pandas for tabular data
• xarray for organizing gridded data with metadata
• numba to speed a few computations
• matplotlib, seaborn, and colorcet for plotting
• cartopy for mapping
• scikit-learn for EOF analysis (PCA) and clustering
• windspharm to calculate the streamfunction from wind data

To track dependencies between parameters (stored in /config), data (stored in /data), and results (stored in /figs), a Makefile is used. Some great posts such as these one by Mike Bostock, Rob Hyndman or this Software Carpentry Course go into more detail about why make is such a great tool for data anaylsis.

Using make helps us organize our code into several distinct steps:

• The first time you run the code, you need to install the package dependencies. We do this with conda, which we assume you have installed; if you want to install the packages manually with pip you may be able to do so, but it's more involved. To do this run make environment. Then run source activate pyfloods to activate the environment.
• Configure parameters of the analysis. These files are stored in config/ and control what years are studied and the regions of the analysis
• Download raw data from source (climate indices, gridded rainfall data, gridded wind data, model forecasts, etc). To do this run make get. As a general rule for gridded data, we download year by year and subset by pressure level (for reanalysis) but not by space. This means that what we download reall is "raw" data
• Process the raw data into useful "chunks" that are easy to analyze. Calculate streamfunction from the wind vectors. Aggregate the stream function and rainfall over regions relevant for plotting and calculate anomalies. Sub-set the stream function over the weather typing region and the rainfall over the Lower Paraguay River Basin. Run the weather typing algorithm. All of this is done using make process.
• Create plots and tables with make analyze.

In fact, make knows that the analysis steps depend on the processed data and so on, so once you have your environment installed you can run source activate pyfloods and then make analyze and everything will run.

Using these Codes

Please use this code under the terms of the MIT License (see LICENSE file). If you use it, please cite our paper.

If you find any errors with this code, please use the Issues tab. However, the aim of this repository is not to provide an active software library and we do not intend to modify it from this final version except to correct errors.