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DISDRODB is part of an international joint effort to index, collect and homogenize drop size distribution (DSD) data from around the world.
The DISDRODB project also aims to establish a global standard for sharing disdrometer observations. Built on FAIR data principles and Climate & Forecast (CF) conventions, DISDRODB standards facilitate the processing, analysis and visualization of disdrometer data.
The software enables you to:
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Upload raw data of new disdrometer stations to the DISDRODB Decentralized Data Archive
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Download the raw disdrometer data from stations included in the DISDRODB Decentralized Data Archive
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Convert raw disdrometer data into a standard NetCDF format (DISDRODB L0 product)
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Generate standardized, homogenized, and quality-checked disdrometer measurements (DISDRODB L1 product)
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Compute empirical and model-based drop size distribution parameters and derive geophysical and polarimetric radar variables of interest (DISDRODB L2 product)
Currently, the DISDRODB Working Group is finalizing the development of the L1 and L2 scientific products. If you have ideas, algorithms, data, or expertise to share, or you want to contribute to the future DISDRODB products, do not hesitate to get in touch!!!
Join the DISDRODB Slack Workspace to meet the DISDRODB Community!
Create your own DISDRODB Local Data Archive.
The DISDRODB Metadata Archive is a collection of metadata files that describe the disdrometer stations included in DISDRODB.
To download the DISDRODB Metadata Archive, navigate to the desired directory and run:
git clone https://github.com/ltelab/DISDRODB-METADATA.gitOr download a static snapshot without using git:
disdrodb_download_metadata_archive /path/to/DISDRODB-METADATAThe disdrodb software requires to know two directories:
metadata_archive_dir: the base of your local DISDRODB Metadata Archivedata_archive_dir: the base of your local DISDRODB Data Archive
On Windows, paths must end with \DISDRODB; on macOS/Linux, they must end with /DISDRODB.
import disdrodb
metadata_archive_dir = "/<path_to>/DISDRODB-METADATA/DISDRODB"
data_archive_dir = "/<path_to>/DISDRODB"
disdrodb.define_configs(
metadata_archive_dir=metadata_archive_dir, data_archive_dir=data_archive_dir
)This creates a .config_disdrodb.yml file in your home directory (e.g., ~/.config_disdrodb.yml).
To verify the configuration, open a new Python session and run:
import disdrodb
print("Metadata Archive Directory:", disdrodb.get_metadata_archive_dir())
print("Data Archive Directory:", disdrodb.get_data_archive_dir())Or in the shell:
disdrodb_metadata_archive_directory
disdrodb_data_archive_directoryTo download all data stored into the DISDRODB Decentralized Data Archive, you just have to run the following command:
disdrodb_download_archiveTo download from a specific source (e.g., EPFL):
disdrodb_download_archive --data-sources EPFLType disdrodb_download_archive --help to see further options.
To open the local DISDRODB Data Archive directory, type:
disdrodb_open_data_archiveIf you want to convert all stations raw data into standardized netCDF4 files, run the following command in the terminal:
disdrodb_run_l0Type disdrodb_run_l0 --help to see further options.
To generate DISDRODB L1 and L2 products, run the following commands in the terminal:
disdrodb_run_l1
disdrodb_run_l2e
disdrodb_run_l2mThe software’s open_dataset function lazily opens all station files of a given product:
import disdrodb
ds = disdrodb.open_dataset(
product="L0C",
data_source="EPFL",
campaign_name="HYMEX_LTE_SOP3",
station_name="10",
)
dsThis allows you to jump directly into analyzing disdrometer data without worrying about processing steps.
To explore the DISDRODB Metadata Archive, you can type into the terminal:
disdrodb_open_metadata_archiveIf you wish to analyze the DISDRODB Metadata Archive information of all stations,
the read_metadata_archive python function returns all stations metadata information into an easy to analyze pandas.DataFrame:
import disdrodb
df = disdrodb.read_metadata_archive()
print(df)With this introduction, we just scratched the surface of the disdrodb software capabilities. To discover more about the DISDRODB products, the download and processing options, or how to contribute your own data to DISDRODB, please read the software documentation available at https://disdrodb.readthedocs.io/en/latest/.
DISDRODB can be installed via conda on Linux, Mac, and Windows. Install the package by typing the following command in the terminal:
conda install disdrodbIn case conda-forge is not set up for your system yet, see the easy to follow instructions on conda-forge.
DISDRODB can be installed also via pip on Linux, Mac, and Windows. On Windows you can install WinPython to get Python and pip running.
Then, install the DISDRODB package by typing the following command in the terminal:
pip install disdrodbTo install the latest development version via pip, see the documentation.
If you aim to contribute your data or discuss the future development of DISDRODB, we highly recommend to join the DISDRODB Slack Workspace
Feel free to also open a GitHub Issue or a GitHub Discussion specific to your questions or ideas.
You can cite the DISDRODB software by:
Gionata Ghiggi, Kim Candolfi, Régis Longchamp, Charlotte Weil, Alexis Berne (2023). ltelab/disdrodb Zenodo. https://doi.org/10.5281/zenodo.7680581
If you want to cite a specific version, have a look at the Zenodo site
The content of this repository is released under the terms of the GPL 3.0 license.
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