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

Concluding Remarks

• All spectra flagged “accepted” were actually correct (by visual inspection)

• Small uncertainties still exist at low wavelengths

• The method does not depend on absolute calibration so it can be applied to raw data