Automated Quality Control of AERONET-OC LWN data

Abstract Quality Control (QC) practices are a fundamental requirement for any measurement program targeting the delivery of high-quality data. In agreement with such a need, the Ocean Color component of the Aerosol Robotic Network (AERONET-OC) includes a number of QC steps ensuring the delivery of normalized water-leaving radiance LWN spectra at incremental accuracy levels identified as Level 1.0, Level 1.5 and Level 2.0. Currently, the final QC step allowing for rising Level 1.5 LWN spectra to Level 2.0 implies the execution of an expert-based procedure, which is extremely time consuming and naturally undergoes subjective decisions on dubious cases. These limitations solicited the development of an automated procedure, so called A–QCLWN, mimicking the steps supporting an expert analyst during the final QC of AERONET-OC LWN spectra. A–QCLWN applies hierarchical tests to check: i. the relativeconsistency of Level 1.5 LWN spectra (called candidates) with respect to LWN reference spectra (called prototypes) constructed using LWN spectra formerly and independently quality controlled; ii. the absence of any pronounced spectral feature in portions of the LWN candidate spectrum expected to exhibit a regular shape; and additionally, when applicable, iii. the temporal consistency of the LWN candidate spectrum with respect to close-in-time spectra as a criterion to further strengthen the quality control of data. A–QCLWN performance has been verified using LWN spectra from AERONET-OC measurement sites representative of various water types embracing oligotrophic/mesotrophic waters dominated by chlorophyll-a concentration and coastal waters exhibiting increasing levels of optical complexity. A–QCLWN has shown an acceptance rate of AERONET-OC Level 1.5 LWN candidate spectra varying between approximately 89 and 93% with agreement in the range of 88-93% with respect to the LWN spectra independently quality controlled through the expert-based procedure. The additional capability of A–QCLWN to rank the fully quality controlled LWN spectra combining weights depending on the various tests, anticipate the possibility to best support applications with diverse accuracy needs. Finally, acceptance rates of A–QCLWN for LWN prototype spectra built using Level 1.5 data, alternative to fully quality controlled Level 2.0, have shown values generally increased by less than 1%. This indicates the possibility to lessen the constraint implying the existence of reference Level 2.0 LWN data for the relative-consistency test at the expense of a fairly low reduction in accuracy.