Short-term prediction of horizontal winds in the mesosphere and lower thermosphere over coastal Peru using a hybrid model

• Autores: Mauricio, Christian, Suclupe, Jose, Milla, Marco, López de Castilla, Carlos, Kuyeng, Karim, Scipión, Danny, Rodriguez, Rodolfo
• Formato: artículo
• Fecha de Publicación: 2024
• Institución: Instituto Geofísico del Perú
• Repositorio: IGP-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.igp.gob.pe:20.500.12816/5610
• Enlace del recurso: http://hdl.handle.net/20.500.12816/5610; https://doi.org/10.3389/fspas.2024.1442315
• Nivel de acceso: acceso abierto
• Materia: MLT; EM; VMD; LSTM; OPTUNA; Equatorial Aeronomy; Space Physics; https://purl.org/pe-repo/ocde/ford#1.05.01

The mesosphere and lower thermosphere (MLT) are transitional regions between the lower and upper atmosphere. The MLT dynamics can be investigated using wind measurements conducted with meteor radars. Predicting MLT winds could help forecast ionospheric parameters, which has many implications for global communications and geo-location applications. Several literature sources have developed and compared predictive models for wind speed estimation. However, in recent years, hybrid models have been developed that significantly improve the accuracy of the estimates. These integrate time series decomposition and machine learning techniques to achieve more accurate short-term predictions. This research evaluates a hybrid model that is capable of making a short-term prediction of the horizontal winds between 80 and 95 km altitudes on the coast of Peru at two locations: Lima (12°S, 77°W) and Piura (5°S, 80°W). The model takes a window of 56 data points as input (corresponding to 7 days) and predicts 16 data points as output (corresponding to 2 days). First, the missing data problem was analyzed using the Expectation Maximization algorithm (EM). Then, variational mode decomposition (VMD) separates the components that dominate the winds. Each resulting component is processed separately in a Long short-term memory (LSTM) neural network whose hyperparameters were optimized using the Optuna tool. Then, the final prediction is the sum of the predicted components. The efficiency of the hybrid model is evaluated at different altitudes using the root mean square error (RMSE) and Spearman’s correlation (r). The RMSE ranged from 10.79 to 27.04 ms⁻¹, and the correlation ranged from 0.55 to 0.94. In addition, it is observed that the prediction quality decreases as the prediction time increases. The RMSE at the first step reached 6.04 ms⁻¹ with a correlation of 0.99, while at the sixteenth step, the RMSE increased up to 30.84 ms⁻¹ with a correlation of 0.5.