EDUCE: WP 6 & 7
Development of an Algorithm to Detect Spikes and Distortions in UV Spectra
Charikleia Meleti, Alkis BaisAristotle University of Thessaloniki
Laboratory of Atmospheric Physics
Goals
• Independent of other measurements
• Expose anomalies above statistical noise
• Run automatically– Producing flags– Correcting spectra
• Applicable to cloud-induced distortions
Description of Procedure
• Construction of a Reference spectrum
α(λ, i): statistical coefficients to derive E(λ) from its neighboring wavelengths (SZA dependent)w(i-λ): weights inversely proportional to the difference from the central wavelength
Description of Procedure
• Construction of a Reference spectrum
α(λ, i): statistical coefficients to derive E(λ) from its neighboring wavelengths (SZA dependent)w(i-λ): weights inversely proportional to the difference from the central wavelength
Description of Procedure
• Construction of a Reference spectrum
α(λ, i): statistical coefficients to derive E(λ) from its neighboring wavelengths (SZA dependent)w(i-λ): weights inversely proportional to the difference from the central wavelength
Detection of Spikes
• Compute the ratio between E(λ) and ER(λ)
• Ratios higher than 1.5 indicate the presence of a spike
Flagging Spectral Distortions
• Compute the correlation coefficient (r2) between E(λ) and ER(λ)– Accepted spectra r2 > 0.99– Suspicious spectra 0.89 < r2 < 0.99
• Small spikes• Distortion by clouds
– Highly distorted spectra r2 < 0.89• Wavelength shifts• Spikes• Noise• Zeros