A Deep Convolutional Neural Network for Retrieving Tropospheric Temperature and Moisture Profiles from Refractivity over Tropical Oceans: Framework Development and Characterization

Abstract. Retrieving profiles of temperature and water vapor from atmospheric refractivity over tropical oceans constitutes an inherently underdetermined problem. Conventional one-dimensional variational methods resolve this through numerical weather prediction (NWP) priors, potentially propagating model biases into retrieved profiles and limiting the independence desirable for climate monitoring applications. We present a deep learning retrieval that substitutes learned statistical constraints for model-dependent priors. A convolutional neural network, trained on approximately 20,800 high resolution radiosonde profiles —combining reference-grade GCOS Reference Upper-Air Network (GRUAN) measurements with quality-controlled operational GCOS Upper Air Network (GUAN) and field campaign data— predicts dry refractivity and partial pressure of dry air as intermediate targets; temperature, water vapor pressure, and relative humidity are derived analytically. The model achieves water vapor pressure root-mean-squared errors of ~0.5 hPa near the surface (decreasing with height) and relative humidity errors below 6 % from 100 m to 10 km, performance comparable in magnitude to the mean measurement uncertainty of state-of-the-art radiosondes. Temperature errors are ~1.5 K near the surface, improving to ~0.5 K in the 10–15 km range where dry refractivity dominates. Evaluation against more than 27,400 geographically independent radiosonde ascents across the tropical Pacific, Indian Ocean, and Atlantic—including observations from the 1992–93 TOGA-COARE field campaign, two decades predating the training period—demonstrates robust generalization within the tropical marine atmosphere. The framework accepts only refractivity vertical structure as input, with no dependence on geographic coordinates or NWP background states. This paper establishes the retrieval framework and characterizes performance under ideal input conditions; Part 2 addresses application to satellite observations.