Development and evaluation of a long-term NO2 data record from GOME-2 observations

Kurzfassung

In the framework of the EUMETSAT Satellite Application Facility on Atmospheric Composition Monitoring (AC-SAF) project, nitrogen dioxide (NO₂) column data products are generated from the Global Ozone Monitoring Experiment-2 (GOME-2) A, B and C measurements using the GOME Data Processor (GDP). In this study, an improved prototype algorithm for the retrieval of total and tropospheric NO₂ columns from GOME-2 is presented and evaluated. The prototype GOME-2 NO₂ retrieval is applied to an extended 425-497 nm wavelength fitting window, updated absorption cross sections and a linear offset correction to reduce slant column errors and increase the signal-to-noise ratio by including more NO₂ absorption structures. An improved slit function treatment and a new Level 1B Fundamental Data Record release 3 version are also applied in the NO₂ slant column retrieval, which compensates for long-term and in-orbit drifts. The stratospheric estimation is implemented using the STRatospheric Estimation Algorithm from Mainz (STREAM) with an improved latitudinal correction. The tropospheric NO₂ vertical column is converted using an updated box air mass factor look-up table and improved input datasets. The retrieved NO₂ columns from the prototype algorithm show better consistency between three different GOME-2 sensors and reduced retrieval errors for a long-term period compared to the current operational (GDP 4.8) dataset and Quality Assurance for the Essential Climate Variables (QA4ECV) products. Based on the evaluation of prototype GOME-2 NO₂ retrieval performance, we develop NO₂ climate data records (CDRs) combining the GOME-2A, B and C Level 2 orbital NO₂ products to create one merged Level 3 product from 2007 to now. As three GOME-2 satellites have different long-term variations and instrumental degradations, additional post-corrections are applied to the Level 2 dataset to compensate for bias and improve consistency. Also, gridding the datasets from different sensors for overlapping periods is improved using an advanced gridding tool to yield a consolidated Level 3 product. Finally, the constructed GOME-2 NO₂ CDRs will be used to analyse trends and interannual variabilities of NO₂ columns in different regions.