JCSDA-internal/ioda-converters
Dec 2024 – Feb 2026
5 Months active
Languages Used
C++PythonBashCMake
Technical Skills
Data ConversionData ProcessingPython ScriptingScientific ComputingNetCDF HandlingSatellite Data Processing
Shih-Wei Wei
PROFILE
Shih-wei Wei
William contributed to the JCSDA-internal/ioda-converters repository by developing and enhancing aerosol optical depth (AOD) data converters for multiple satellite sensors, including MODIS, VIIRS, and PACE. He implemented robust data processing pipelines in Python and C++, focusing on temporal windowing, metadata extraction, and support for diverse retrieval methods. William improved data quality by refining masking and thinning workflows, standardizing missing value handling, and expanding compatibility with NASA and NOAA datasets. His work integrated new features such as surface qualifiers and error estimation, while maintaining rigorous testing and validation. These efforts ensured reliable, high-quality data for downstream scientific assimilation workflows.
Overall Statistics
Feature vs Bugs
71%Features
Repository Contributions
10Total
Bugs
2
Commits
10
Features
5
Lines of code
1,505
Activity Months5
Your Network
14 peopleShared Repositories
14
Work History
February 2026
1 Commits
Feb 1, 2026February 2026: Focused on reliability and data quality improvements in AOD processing within ioda-converters. Delivered a targeted bug fix that ensures masking is applied after thinning, improving accuracy and relevance of observations within the defined time window. The change enhances downstream analytics and maintains data integrity for the IODA processing pipeline.
1 Commits
Feb 1, 2026February 2026: Focused on reliability and data quality improvements in AOD processing within ioda-converters. Delivered a targeted bug fix that ensures masking is applied after thinning, improving accuracy and relevance of observations within the defined time window. The change enhances downstream analytics and maintains data integrity for the IODA processing pipeline.
February 2026
October 2025
5 Commits • 2 Features
Oct 1, 2025Monthly work summary for 2025-10 focused on delivering enhanced AOD converters in JCSDA-internal/ioda-converters, including Surface Qualifier support and expanded retrieval methods, with emphasis on data quality, sensor coverage, and downstream assimilation impact.
October 2025
5 Commits • 2 Features
Oct 1, 2025Monthly work summary for 2025-10 focused on delivering enhanced AOD converters in JCSDA-internal/ioda-converters, including Surface Qualifier support and expanded retrieval methods, with emphasis on data quality, sensor coverage, and downstream assimilation impact.
March 2025
2 Commits • 1 Features
Mar 1, 2025Concise monthly summary for 2025-03 focusing on key accomplishments, major fixes, and business impact for the ioda-converters work.
2 Commits • 1 Features
Mar 1, 2025Concise monthly summary for 2025-03 focusing on key accomplishments, major fixes, and business impact for the ioda-converters work.
March 2025
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 monthly performance summary for JCSDA-internal/ioda-converters: focused on expanding data compatibility and robustness of the VIIRS AOD converter. Delivered NASA Dark Target and Deep Blue data compatibility alongside NOAA data, with targeted refactoring and improved testing.
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 monthly performance summary for JCSDA-internal/ioda-converters: focused on expanding data compatibility and robustness of the VIIRS AOD converter. Delivered NASA Dark Target and Deep Blue data compatibility alongside NOAA data, with targeted refactoring and improved testing.
December 2024
1 Commits • 1 Features
Dec 1, 2024December 2024 monthly work summary for JCSDA-internal/ioda-converters focusing on delivering a feature-rich MODIS AOD converter update and improving data quality.
1 Commits • 1 Features
Dec 1, 2024December 2024 monthly work summary for JCSDA-internal/ioda-converters focusing on delivering a feature-rich MODIS AOD converter update and improving data quality.
December 2024
Activity
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Quality Metrics
Correctness89.0%
Maintainability80.0%
Architecture82.0%
Performance78.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
BashC++CMakePython
Technical Skills
API IntegrationAtmospheric Data ProcessingBuild SystemsC++CMakeCommand Line Interface (CLI) DevelopmentData ConversionData ProcessingIODANetCDFNetCDF HandlingPythonPython DevelopmentPython ScriptingPython programming
Repositories Contributed To
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