Adaptation of the complete ensemble empirical mode decomposition with adaptive noise algotithm in lidar signal for denoising and event detection.

JUAN LUCAS BALI; LIDIA ANA OTERO; MILAGROS HERRERA; J. GÓMEZ; EDUARDO QUEL; PABLO ROBERTO RISTORI; ALEJANDRO ACQUESTA

Santos

Workshop; IX Workshop on Lidar Measurements in Latin America (IX WMLA); 2016

We analyze an adaptation of the Complete Ensemble Empirical Mode Decomposition with AdaptiveNoise (CEEMDAN) algorithm for lidar signal denoising and aerosol and cloud detection. The CEEMDAN is anempirical algorithm that is applicable to non-linear and non-stationary processes and is able to decompose the lidarsignal into a set of modes. The first decomposed modes efficiently capture much of the signal´s noise; therefore, thenoise removal process consists in the exclusion of these modes from the original signal.Event detection is achieved by studying oscillations present in the decomposed modes. We analyze these features inorder to obtain a detection criterion for aerosols which can come, for example, from the burning of biomass or fromvolcanic eruptions.The results indicate that the CEEMDAN algorithm can be successfully adapted for lidar denoising and atmosphericaerosol/cloud detection. Waveforms, phase and amplitudes are preserved in the denoising process.