TY - JOUR

T1 - Short-term prediction of horizontal winds in the mesosphere and lower thermosphere over coastal Peru using a hybrid model

AU - Mauricio, Christian

AU - Suclupe, Jose

AU - Milla, Marco

AU - López de Castilla, Carlos

AU - Kuyeng, Karim

AU - Scipion, Danny

AU - Rodriguez, Rodolfo

N1 - Publisher Copyright:
Copyright © 2024 Mauricio, Suclupe, Milla, López de Castilla, Kuyeng, Scipion and Rodriguez.

PY - 2024

Y1 - 2024

N2 - 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 hybrid model performed better compared to two other models: the persistence model and the dominant harmonics model. The RMSE ranged from 10.79 to 27.04 (Formula presented.), 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 (Formula presented.) with a correlation of 0.99, while at the sixteenth step, the RMSE increased up to 30.84 (Formula presented.) with a correlation of 0.5.

AB - 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 hybrid model performed better compared to two other models: the persistence model and the dominant harmonics model. The RMSE ranged from 10.79 to 27.04 (Formula presented.), 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 (Formula presented.) with a correlation of 0.99, while at the sixteenth step, the RMSE increased up to 30.84 (Formula presented.) with a correlation of 0.5.

KW - EM

KW - LSTM

KW - MLT

KW - OPTUNA

KW - VMD

UR - http://www.scopus.com/inward/record.url?scp=85206104502&partnerID=8YFLogxK

U2 - 10.3389/fspas.2024.1442315

DO - 10.3389/fspas.2024.1442315

M3 - Article

AN - SCOPUS:85206104502

SN - 2296-987X

VL - 11

JO - Frontiers in Astronomy and Space Sciences

JF - Frontiers in Astronomy and Space Sciences

M1 - 1442315

ER -