High-Frequency Mapping of Downward Shortwave Radiation from GOES-R Using Gradient Boosting

Abstract

This study investigates high-frequency mapping of downward shortwave radiation (DSR) at Earth's surface using the Advanced Baseline Imager (ABI) instrument mounted on Geostationary Operational Environmental Satellite -R Series (GOES-R). The existing GOES-R DSR product (DSR ABI ) offers hourly temporal resolution and spatial resolution of 0.25 o . To enhance these resolutions, we explore machine learning (ML) for DSR estimation at the native temporal resolution of GOES-R Level-2 Cloud and Moisture Imagery (CMI) product (five minutes) and its native spatial resolution of two-kilometer at nadir. We compared four common ML regression models through the Leave-One-Out Cross-Validation (LOOCV) algorithm for robust model assessment against ground measurements from AmeriFlux and SURFRAD networks. Results show that Gradient Boosting Regression (GBR) achieves the best performance (R² = 0.916, RMSE = 88.05 W m -2 ) with efficient computation compared to Long Short-Term Memory (LSTM), which exhibited similar performance. DSR estimates from the GBR model (DSR ALIVE ) outperform DSR ABI across various temporal resolutions and sky conditions. DSR ALIVE agreement with ground measurements at SURFRAD networks exhibits high accuracy at high temporal resolutions (five-minute intervals) with R² exceeding 0.85 and RMSE=122 W m -2 . We conclude that GBR offers a promising approach for high-frequency DSR mapping from GOES-R, enabling improved applications for near-real-time monitoring of terrestrial carbon and water fluxes.