deeplycloudy/glmtools
Fork: 34 Star: 63 (更新于 2024-12-21 13:41:12)
license: BSD-3-Clause
Language: Python .
GOES-R Geostationary Lightning Mapper Tools
最后发布版本: v0.5.0 ( 2019-04-23 00:41:16)
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)
最近版本更新:(数据更新于 2024-09-10 10:06:18)
2019-04-23 00:41:16 v0.5.0
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