Projects - Alfonso Ladino-Rincon

Here are some of the key research projects and open-source contributions I’ve been involved with:

Research Projects¶

raw2zarr: Cloud-Optimized Weather Radar Data¶

Converting weather radar data to cloud-optimized Zarr format

A Python package for converting raw weather radar data (NEXRAD, Colombian radar network) into cloud-optimized Zarr format following FAIR principles. This project enables:

Fast, parallel access to large radar datasets
Integration with Xarray and Dask for scalable analysis
Automated quality control and metadata standardization
Publishing to cloud storage for community access

Technologies: Python, Zarr, Xarray, Dask, AWS S3

xradar: Weather Radar Toolkit¶

Core contributor to the open-source radar data toolkit

xradar is a community-driven Python package for reading, processing, and analyzing weather radar data. My contributions focus on:

Implementing standardized I/O for multiple radar formats
Developing data model specifications
Writing documentation and tutorials
Supporting users in the atmospheric science community

Technologies: Python, Xarray, NetCDF, CfRadial

GPM Validation Networks¶

Ground-based validation of satellite precipitation estimates

Developing automated processing pipelines for validating NASA’s Global Precipitation Measurement (GPM) mission products using:

Ground-based weather radar networks
Rain gauge observations
Disdrometer measurements

This work improves our understanding of satellite precipitation retrieval algorithms and their uncertainties.

Technologies: Python, NumPy, Pandas, TRMM/GPM data products

Neural Networks for Precipitation Microphysics¶

Machine learning for raindrop size distribution retrieval

Developing deep learning approaches to retrieve precipitation microphysics from GPM dual-frequency radar observations:

Physics-informed neural network architectures
Training on TRMM/GPM radar and disdrometer data
Uncertainty quantification for operational use

Technologies: Python, PyTorch, TensorFlow, Jupyter

Open Source Contributions¶

Open Radar Science Community¶

Building an open, collaborative radar meteorology community

Active participant in the Open Radar Science community, contributing to:

Documentation and tutorials (Radar Cookbook)
Community workshops and training events
Software development best practices
FAIR data principles for radar data

Recent Activities:

Co-organized ERAD 2024 Open Radar Science short course
Presented at SciPy 2024 on FAIR radar data workflows
Contributed to Project Pythia Radar Cookbook

Py-ART¶

Python ARM Radar Toolkit

Contributing bug fixes, documentation improvements, and feature enhancements to one of the most widely-used radar processing libraries in atmospheric science.

Colombian Radar Data Archive¶

Making weather radar data FAIR and accessible

Transforming 15+ TB of Colombian weather radar data into cloud-optimized, community-accessible datasets:

Historical radar archive (2000-present)
Automated quality control pipelines
Cloud storage with public access
Comprehensive metadata following CF conventions

Impact: Enabling climate studies, machine learning research, and operational improvements

Field Campaigns¶

CAMP2Ex: Cloud, Aerosol and Monsoon Processes Philippines Experiment¶

NASA field campaign participant (2019)

Participated in the CAMP2Ex field campaign investigating cloud-aerosol interactions and monsoon processes over the Philippines:

Ground-based radar observations
Coordination with airborne measurements
Data quality control and processing
Precipitation microphysics analysis

Datasets & Data Products¶

I have created and maintain several research-grade datasets:

Colombian Radar Archive (Zarr): 15+ TB of quality-controlled radar data (2000-present)
GPM Validation Database: Matched satellite-ground observations for South America
Disdrometer Dataset: Rain microphysics observations from multiple campaigns

All datasets follow FAIR principles and are available through open repositories (Zenodo, AWS Open Data).