Bow echo detection and segmentation

This is a code and data repository for a CNN-based bow echo detector meant for use on NEXRAD composite reflectivity mosaics discussed in:"A derecho climatology (2004-2021) in the United States based on machine learning identification of bow echoes" by Jianfeng Li, Andrew Geiss, Zhe Feng, L. Ruby Leung, Yun Qian, and Wenjun Cui

The repository contains the following items:

initial_training_dataset.zip -- The dataset used to train the initial version of the segmentation model. It contains 500 positive samples with hand-drawn masks and 3,350 negative samples with no masks. The samples are stored as .png files with pixel values between 0-255 linearly mapped from dBZ values between 0-50. The negative samples are stored as single-channel images while the positive samples are RGB images with the reflectivity data stored in the blue channel and the corresponding masks stored in the red channel.

pseudo_labeled_training_dataset.zip -- The larger training set used for the final version of the CNN with 1,199 masked positive cases and 1,978 negative cases. These are also stored as .png files. Here, the masks have been produced by a CNN and reviewed for accuracy by a human.

testing_dataset.zip -- 217 plots of testing cases. These are radar images of MCS's from 2010 that may or may not contain bow echoes.

test_labels.csv -- A collection of human and CNN labels for the 217 test cases.

conus_bow_echoes.zip -- NetCDF files containing CNN segmentation results for reflectivity mosaics from 2004-2021. Bow echo masks are stored as signed integers. 0 = No bow echo, -1 = bow echo not associated with a tracked MCS, >0 bow echo associated with an MCS where each unique MCS in a year is assigned a unique integer based on PyFLEXTRKR.

June_2010.mp4 -- An animation of composite reflectivity (coloring), MCS tracks (gray shading), and bow echoes (black contours) for the month of June 2010.

project_code.zip -- The collection of Python scripts used to construct, train, and apply the CNN.

cnn.h5 -- The trained Unet 3+ model. The model performs required input clipping and scaling internally and can operate directly on reflectivity data.

sample_mosaic.npz -- Input file for 'inference_demo.py'. Contains a single composite reflectivity mosaic

sample_output.png -- The plot produced by "inference_demo.py".

inference_demo.py -- A Python script that demonstrates how to apply the bow echo segmentation CNN to reflectivity data, including post-processing steps.