Overview
Observatory for Gridded Hydrometeorology (OGH) is a comprehensive Python toolkit designed to automate the retrieval, preprocessing, and visualization of gridded hydrometeorology data products. Developed during my time at UW Applied Physics Laboratory, this tool enables researchers to conduct spatial-temporal exploratory analysis and intercomparison of environmental datasets.
Purpose
Environmental scientists often need to:
- Access multiple gridded datasets from different sources
- Compare data products with different formats and resolutions
- Preprocess data for hydrological models
- Visualize spatial and temporal patterns
OGH automates these workflows, saving researchers time and ensuring consistent data handling.
Key Features
- Automated Data Retrieval: Download gridded datasets from NASA MODIS, NLDAS, and other sources
- Preprocessing Pipeline: Standardize different data formats and coordinate systems
- Spatial-Temporal Analysis: Tools for exploring patterns across space and time
- Intercomparison: Compare multiple data products side-by-side
- Visualization: Generate maps, time series, and statistical summaries
Technical Stack
- Data Handling: Xarray for multi-dimensional arrays, GDAL/OGR for geospatial processing
- Formats: NetCDF, GeoTIFF, HDF
- Analysis: NumPy, Pandas for numerical operations
- Visualization: Matplotlib, Cartopy for plotting
Research Applications
NASA High Mountain Asia (HMA) Project
Used OGH to process high-resolution remote sensing imagery for:
- Glacier mass balance analysis
- Snow cover mapping
- Hydrological modeling inputs
- Multi-sensor data fusion
Critical Zone Science
Processed meteorological and hydrological data for:
- Watershed characterization
- Soil moisture analysis
- Vegetation dynamics
- Stream flow modeling
Development & Collaboration
As Environmental Data Specialist at UW-APL (2016-2018), I:
- Developed core geospatial analysis tools
- Created automated processing pipelines
- Integrated data from multiple NASA missions
- Collaborated with 12+ NASA research teams
Impact
Publication
Phuong, J., Bandaragoda, C., Istanbulluoglu, E., Beveridge, C., Strauch, R., Setiawan, L., & Mooney, S. D. (2019). Automated retrieval, preprocessing, and visualization of gridded hydrometeorology data products for spatial-temporal exploratory analysis and intercomparison. Environmental Modelling & Software, 116, 119–130.
Research Enablement
OGH enabled:
- Faster time-to-science for environmental researchers
- Consistent data processing across research teams
- Reproducible analysis workflows
- Integration with hydrological models
Key Achievements
- Successfully developed Python geospatial analysis tools used by 12 NASA research teams
- Automated intermediate-sized processing tasks (MODIS data, GeoTIFFs → PNG tiles for web apps)
- Created reusable workflows that reduced data preprocessing time from days to hours
Conference Presentations
Phuong, J., Bandaragoda, C., Setiawan, L., et al. (2018). Observatory for Gridded Hydrometeorology (OGH): a python toolkit to automate access and analysis with gridded data products. AGU Fall Meeting Abstracts.
Legacy
OGH demonstrated the value of:
- Open-source scientific tools
- Automated data workflows
- Community-developed software
- Reproducible environmental science