Correction of the photoelectron heating efficiency within the global ionosphere‐thermosphere model using Retrospective Cost Model Refinement

Many physics-based models are used to study and monitor the terrestrial upper atmosphere. Each of these models has internal parameterizations that introduce bias if they are not tuned for a specific set of run conditions. This study uses Retrospective Cost Model Refinement (RCMR) to remove internal model bias in the Global Ionosphere Thermosphere Model (GITM) through parameter estimation. RCMR is a low-cost method that uses the error between truth data and a biased estimate to improve the biased model. Neutral mass density measurements are used to estimate an appropriate photoelectron heating efficiency, which is shown to drive the modeled thermosphere closer to the real thermosphere. Observations from the Challenging Mini-Payload (CHAMP) satellite taken under active and quiet solar conditions show that RCMR successfully drives the GITM thermospheric mass density to the observed values, removing model bias and appropriately accounting for missing physical processes in the thermospheric heating through the photoelectron heating efficiency.