Browsing by Author "Suclupe, Jose"
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Item Open Access Climatology of mesosphere and lower thermosphere diurnal tides over Jicamarca (12°S, 77°W): observations and simulations(SpringerOpen, 2023-12-14) Suclupe, Jose; Chau, Jorge L.; Conte, Federico J.; Milla, Marco; Pedatella, N. M.; Kuyeng, KarimThis work shows a 3-year climatology of the horizontal components of the solar diurnal tide, obtained from wind measurements made by a multistatic specular meteor radar (SIMONe) located in Jicamarca, Peru (12°S, 77°W). Our observations show that the meridional component is more intense than the zonal component, and that it exhibits its maxima shifted with respect to the equinox times (i.e., the largest peak occurs in August–September, and the second one in April–May). The zonal component only shows a clear maximum in August–September. This observational climatology is compared to a climatology obtained with the Whole Atmosphere Community Climate Model with thermosphere and ionosphere extension (WACCM-X). Average comparisons indicate that the model amplitudes are 50% smaller than the observed ones. The WACCM-X results are also used in combination with observed altitude profiles of the tidal phases to understand the relative contributions of migrating and non-migrating components. Based on this, we infer that the migrating diurnal tide (DW1) dominates in general, but that from June until September (November until July) the DE3 (DW2) may have a significant contribution to the zonal (meridional) component. Finally, applying wavelet analysis to the complex amplitude of the total diurnal tide, modulating periods between 5 and 80 days are observed in the SIMONe measurements and the WACCM-X model. These modulations might be associated to planetary waves and intraseasonal oscillations in the lower tropical atmosphere.Item Open Access On the Abnormally Strong Westward Phase of the Mesospheric Semiannual Oscillation at Low Latitudes During March Equinox 2023(American Geophysical Union, 2024-08-13) Suclupe, Jose; Chau, Jorge L.; Conte, J. Federico; Pedatella, Nicholas M.; Garcia, Rolando; Sato, Kaoru; Zülicke, Christoph; Lima, Lourivaldo M.; Li, Guozhu; Bhaskara Rao, S. Vijaya; Ratnam, M. Venkat; Rodriguez, Rodolfo; Scipión, DannyDifferent meteor radars at low latitudes observed abnormally strong westward mesospheric winds around the March Equinox of 2023, that is, during the first phase of the Mesospheric Semiannual Oscillation. This event was the strongest of at least the last decade (2014–2023). The westward winds reached −80 m/s at 82 km of altitude in late March, and decreased with increasing altitude and latitude. A considerable increase in the diurnal tide amplitude was also observed. The Whole Atmosphere Community Climate Model with thermosphere-ionosphere eXtension constrained to meteorological reanalysis up to ∼50 km does not capture the observed low-latitude behavior. Additionally, these strong mesospheric winds developed during the westerly phase of the Quasi-Biennial Oscillation, in accordance with the filtering mechanism of gravity waves in the stratosphere proposed in previous works. Finally, analysis of SABER temperatures strongly suggests that the breaking of the migrating diurnal tide may be the main driver of these strong winds.Item Open Access Short-term prediction of horizontal winds in the mesosphere and lower thermosphere over coastal Peru using a hybrid model(Frontiers Media, 2024-09-23) Mauricio, Christian; Suclupe, Jose; Milla, Marco; López de Castilla, Carlos; Kuyeng, Karim; Scipión, Danny; Rodriguez, RodolfoThe 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.