Evaluations of the Characteristics of the Tropo-Strato-Mesopause Height and Temperature Variability over Bahir Dar, Ethiopia (11.60 N, 37.30 E) Using SABER

The height profile of atmospheric temperature data between 12 km and 100 km was obtained from SABER/TIMED satellite instruments during the year 2016 and used to characterize the three atmospheric pauses temporal variability of height and temperature over Bahir Dar, Ethiopia (11.60 N, 37.30 E). The daily, monthly, and frequency distributions of tropopause-stratopause-mesopause height and temperature are investigated. From the frequency distribution, we had found that of the tropopause-stratopause-mesopause height 17 km, 48 km, and 98 km with the corresponding temperature 192 K, 268 K, and 148 K. The decrement (cooling) trend lines of tropopause height 0.7 K/year and its corresponding tropopause increment temperature has been ~1.5 K/year. The stratopause and mesopause trend lines of height are insignificant and the corresponding decrement (cooling) temperatures are ~3 K/year and ~13 K/year respectively. The mean monthly maximum heights of tropopause 19 km in May with a corresponding maximum temperature of 201 K in September. The maximum stratopause height 49.5 km in February and July and its temperature 268 K and 267 K in February and April respectively. The maximum mesopause height 98 km, 95 km, 97 km in March, Jun, and November respectively, and its maximum temperature 196 K and 198 K in January and July respectively

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

Abstract The 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