Datasets for Machine Learning Shortwave Radiative Transfer Author - Henry Schneiderman, henry@pittdata.comPlease contact me for any questions or feedback Input reanalysis data downloaded from ECMWF's Copernicus Atmospheric Monitoring Service. Each atmospheric column contains the following input variables: mu - Cosine of solar zenith anglealbedo - Surface albedois_valid_zenith_angle - Indicates if daylight is presentVertical profiles (60 layers): Temperature Pressure, Change in Pressure, H2O (vapor, liquid, solid), O3, CO2, O2, N2O, CH4 The ecRad emulator (Hogan and Bozzo, 2018) generated the following output profiles at the layer interfaces for input each atmospheric column: flux_down_direct, flux_down_diffuse, flux_down_direct_clear_sky, flux_down_diffuse_clear_sky, flux_up_diffuse, flux_up_clear_sky All data is sampled at 5,120 global locations The training dataset uses input from 2008 sampled at three-hour intervals within every fourth day The validation dataset uses input from 2008 sampled at three-hour intervals within every 28th day offset two days from the training set to avoid duplication Testing datasets use input from 2009, 2015, and 2020. Each of these samples data at three-hour intervals within every 28th day. For more information see:Henry Schneiderman. "An Open Box Physics-Based Neural Network for Shortwave Radiative Transfer." Submitted to Artificial Intelligence for the Earth Systems.