Modeling of Nitric Oxide Infrared radiative flux in lower thermosphere: a machine learning perspective

TL;DR

This paper develops a machine learning model to accurately predict Nitric Oxide infrared radiative flux in the lower thermosphere, outperforming traditional models especially during geomagnetic storms, aiding space weather understanding.

Contribution

The study introduces an optimized ML-based predictive model for NOIRF that surpasses existing models in accuracy during extreme space weather events.

Findings

ML model shows high agreement with observations during quiet and storm conditions.

NOEMLM outperforms traditional models like TIEGCM during geomagnetic storms.

Utilizing space weather indices with ML enhances upper atmosphere studies.

Abstract

Nitric Oxide (NO) significantly impacts energy distribution and chemical processes in the mesosphere and lower thermosphere (MLT). During geomagnetic storms, a substantial influx of energy in the thermosphere leads to an increase in NO infrared emissions. Accurately predicting the radiative flux of Nitric Oxide is crucial for understanding the thermospheric energy budget, particularly during extreme space weather events. With advancements in computational techniques, machine learning (ML) has become a highly effective tool for space weather forecasting. This effort becomes even more worthwhile considering the availability of two decades of continuous NO infrared emissions measurement by TIMED/SABER along with several other key thermospheric variables. We present the scheme of development of an ML-based predictive model for Nitric Oxide Infrared Radiative Flux (NOIRF). Various ML algorithms have been tested for better predictive ability, and an optimized model (NOEMLM) has been developed for the study of NOIRF. This model is able to extract the underlying relationships between the input features and effectively predict the NOIRF. The NOEMLM predictions have very good agreements with SABER observation during quiet time as well as geomagnetic storms. In comparison with the existing TIEGCM model, NOEMLM has very good performance, especially during extreme space weather conditions. The results of this study suggest that utilizing geomagnetic and space weather indices with ML/AI can serve as superior parameters for studying the upper atmosphere, as compared to focusing on specific species having complex chemical processes and associated uncertainties in constituents. ML techniques can effectively carry out the analysis with greater ease than traditional chemical studies.