NOAA GOES-16 · Gulf Stream Region · Jan 1, 2024
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Data from NOAA GOES-16 ABI Level-2 SST product. Each frame represents one hourly observation. White areas indicate clouds or missing data (DQF masked). Hover over any pixel to see the exact temperature.
Exploring Weather Patterns Using NOAA GOES Satellite Data
How does sea surface temperature vary spatially and temporally across the Gulf Stream region over the course of a single day?
We chose a heatmap as the primary encoding because SST is a continuous spatial variable, color is the most expressive channel for communicating 2D temperature fields. The inferno colormap (purple, orange, yellow) was selected because it is colorblind accessible and intuitively conveys heat.
We considered using animation (auto-play) but preferred a slider to give users full control over pacing and comparison. We also considered a line chart of mean SST over time as an alternative, but the spatial heatmap reveals Gulf Stream boundary structure that a line chart cannot convey.
Raw GOES-16 ABI L2 SST NetCDF files were accessed from the NOAA Open Data Registry on AWS S3 (bucket: noaa-goes16). Six hourly files from January 1, 2024 were loaded using xarray and subsetted to the Gulf Stream region (x: −0.01 to 0.06 rad, y: 0.09 to 0.12 rad). Pixels with Data Quality Flag (DQF) ≠ 0 were masked as missing data. The spatial grid was downsampled by a factor of 4 in both dimensions to reduce file size while preserving spatial structure.
Edmond Yang — Writeup, Reflection, and Page Structure.
Marvell Suhali — Exploratory Analysis and Static Visualizations (mean SST heatmap, min/max time series, SST spatial average).
Liuxuhong Zhao — Data Pipeline (Python/xarray), JSON export, D3.js Interactive Visualization.
Total estimated effort: ~12 hours. The most time consuming aspect was setting up the GOES data access pipeline and resolving xarray/h5py dependency issues in the Python environment. Formatting the website also took some trial and error to achieve the wanted layout and styling.