Ground-Truthing the Sky: Evaluating Free Weather APIs Against Field Station Instruments for Citizen Science Ecological Monitoring

@techreport{hamilton2026groundtruthing, author = {Hamilton, Michael P., Ph.D.}, title = {Ground-Truthing the Sky: Evaluating Free Weather APIs Against Field Station Instruments for Citizen Science Ecological Monitoring}, institution = {Canemah Nature Laboratory}, year = {2026}, number = {CNL-TN-2026-023}, month = {february}, url = {https://canemah.org/archive/document.php?id=CNL-TN-2026-023}, abstract = {Citizen science ecological monitoring frameworks require environmental context for observation locations that lack on-site instrumentation. We evaluated three freely available weather data sources — OpenWeatherMap (real-time, ~25 km resolution), NASA POWER (reanalysis, ~50 km), and ORNL Daymet (interpolated, 1 km) — against calibrated weather stations at four field sites spanning 30 to 1,649 meters elevation across Washington, Oregon, and California. At low-elevation sites, OpenWeatherMap temperature readings agreed with station instruments within 1–2°F. At a high-elevation mountain site (James San Jacinto Mountains Reserve, 1,649 m), OpenWeatherMap and NASA POWER failed catastrophically, with temperature errors of 7°F and 24°F respectively, attributable to grid cell elevations 650+ meters below the actual station. Daymet’s 1 km grid resolved the mountain site’s elevation within 111 meters, producing climatologically realistic values. Humidity degraded with terrain complexity across all sources; wind speed was unreliable at all sites. We propose a layered data architecture for the SCOPE (Science Community Observatory for Participatory Ecology) citizen science framework and describe a follow-on experimental design to characterize the temporal structure of API bias through paired hourly collection from instrumented stations and their “virtual” API counterparts. This temporal study leverages the Dendra Science API for programmatic access to University of California Natural Reserve System (UCNRS) Campbell Scientific stations and the NASA POWER hourly temporal endpoint, enabling retrospective analysis of diurnal bias patterns across elevation gradients and weather regimes without requiring prospective data collection. We further identify the WeatherFlow Tempest community weather station network — over 63,000 stations with dense regional coverage — as a potential ground-truth resource that could transform the calibration layer from sparse personal stations to dense community networks, contingent on research access to network data through the WeatherFlow Enterprise API.} }