glmtools

GOES Geostationary Lightning Mapper Tools

Installation

glmtools requires Python 3.5+ and provides a conda environment.yml for the key dependencies.

See the documentation in docs/index.rst for complete installation instructions.

Description

Compatible data:

NetCDF format Level 2 data, as described in the Product Definition and Users Guide

glmtools automatically reconstitutes the parent-child relationships implicit in the L2 GLM data and adds traversal information to the dataset:

calculating the parent flash id for each event
calculating the number of groups and events in each flash
calculating the number of events in each group

xarray's dimension-aware indexing lets you quickly reduce the dataset to flashes of interest, as described below.

glmtools can restore the GLM event geometry using a built-in corner-point lookup table, which allows for gridding of the imagery at finer resolutions that accurately represent the full footprint of each event, group, and flash.

The methods are described in Bruning et al. (2019):

Bruning, E., Tillier, C. E., Edgington, S. F., Rudlosky, S. D., Zajic, J., Gravelle, C., et al. (2019). Meteorological imagery for the Geostationary Lightning Mapper. Journal of Geophysical Research: Atmospheres, 2019; 124: 14285 14309. https://doi.org/10.1029/2019JD030874

Some common tasks

Create gridded NetCDF imagery

Use the script in examples/grid/make_GLM_grids.py. See the documentation in docs/index.rst for complete instructions and example commands.

Interactively plot raw flash data

See the examples folder. basic_read_plot.ipynb is a good place to start.

Reduce the dataset to a few flashes

from glmtools.io.glm import GLMDataset
filename = 'OR_GLM-L2-LCFA_G16_s20180040537000_e20180040537200_c20180040537226.nc'
glm =  GLMDataset(filename)
flash_id_list = glm.dataset.flash_id[20:30]
smaller_dataset = glm.get_flashes(flash_id_list)

Filter out flashes geographically or by events/groups per flash

See glmtools.io.glm.GLMDataset.subset_flashes.

The logic implemented above is pretty simple, and below shows how to adapt it to find large flashes.

from glmtools.io.glm import GLMDataset
filename = 'OR_GLM-L2-LCFA_G16_s20180040537000_e20180040537200_c20180040537226.nc'
glm =  GLMDataset(filename)
fl_idx = glm.dataset['flash_area'] > 2000
flash_ids = glm.dataset[{glm.fl_dim: fl_idx}].flash_id.data
smaller_dataset = glm.get_flashes(flash_ids)
print(smaller_dataset)

deeplycloudy / glmtools

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