NN‐MLT Model Prediction for Low‐Latitude Region Based on Artificial Neural Network and Long‐Term SABER Observations

Abstract

AbstractThe low‐latitude mesosphere and lower thermosphere (MLT) regions are distinct and, highly turbulent transition zones in Earth's atmosphere. The scarcity of reliable measurements makes continuous monitoring of these areas challenging. Therefore, the necessity for studies focused on the MLT region cannot be overstated, as they are essential for developing effective models that meet the accuracy requirements of satellite‐based observations. The neural networks NN‐MLT model, developed using 15 years of Thermosphere, Ionosphere, and Mesosphere Energetics and Dynamics/satellite, equipped with Broadband Emission Radiometry (SABER) observed temperature data spanning from January 2006 to December 2020, employs neural network techniques. The data set was split, with 90% used for training and the remaining 10% allocated for prediction. The model's validation was tested with two other partitions (80(20) and 70(30)). The 90(10) partition, exhibiting a high correlation coefficient (R), low standard deviation (σ), and low root mean square error (RMSE), demonstrated the model's good performance. As clearly shown from statistical metrics (R, RMSE, mean, and σ) at three specific altitude levels (60, 75, and 90 km), the NN‐MLT model's performance aligns closely with the empirical model (NRLMSISE2‐0) and SABER observations. The NN‐MLT model displays a high R (0.74) and low RMSE (4.35 K) at 60 km, indicating its effective performance compared to the other two heights of 75 and 90 km. The NN‐MLT model's spatiotemporal variability in MLT temperature prediction agrees well with the SABER data at all altitudes, particularly at 60 km. While the NN‐MLT model accurately captures the seasonal variations of MLT temperature, the analysis leads to the conclusion that it consistently outperforms the empirical model and aligns closely with observations.