Aerosol-type retrieval and uncertainty quantification from OMI data

Abstract

Abstract. We discuss uncertainty quantification for aerosoltype selection in satellite-based atmospheric aerosol retrieval. The retrieval procedure uses precalculated aerosol microphysical models stored in look-up tables (LUTs) and top-of-atmosphere (TOA) spectral reflectance measurements to solve the aerosol characteristics. The forward model approximations cause systematic differences between the modelled and observed reflectance. Acknowledging this model discrepancy as a source of uncertainty allows us to produce more realistic uncertainty estimates and assists the selection of the most appropriate LUTs for each individual retrieval. This paper focuses on the aerosol microphysical model selection and characterisation of uncertainty in the retrieved aerosol type and aerosol optical depth (AOD). The concept of model evidence is used as a tool for model comparison. The method is based on Bayesian inference approach, in which all uncertainties are described as a posterior probability distribution. When there is no single best-matching aerosol microphysical model, we use a statistical technique based on Bayesian model averaging to combine AOD posterior probability densities of the best-fitting models to obtain an averaged AOD estimate. We also determine the shared evidence of the best-matching models of a certain main aerosol type in order to quantify how plausible it is that it represents the underlying atmospheric aerosol conditions. The developed method is applied to Ozone Monitoring Instrument (OMI) measurements using a multiwavelength approach for retrieving the aerosol type and AOD estimate with uncertainty quantification for cloud-free over-land pixels. Several larger pixel set areas were studied in order to investigate the robustness of the developed method. We evaluated the retrieved AOD by comparison with ground-based measurements at example sites. We found that the uncertainty of AOD expressed by posterior probability distribution reflects the difficulty in model selection. The posterior probability distribution can provide a comprehensive characterisation of the uncertainty in this kind of problem for aerosoltype selection. As a result, the proposed method can account for the model error and also include the model selection uncertainty in the total uncertainty budget.

Collocation mismatch uncertainties in satellite aerosol retrieval validation

“…ADV algorithm provides a per-pixel AOD uncertainty estimate based on the propagation of the assumed 5 % uncertainty in the measured reflectance through the retrieval . This uncertainty estimate does not include sampling and smoothing uncertainties, uncertainties related to the selection of the best-fit aerosol model (Kauppi et al, 2017), or uncertainties related to the cloud screening. In this work we study the additional collocation mismatch uncertainty related to the validation against AERONET.…”

Response to Anonymous Referee #2

“…The ADV algorithm is originally based on the work by Veefkind and de Leeuw (1998), and the current version is described by Kolmonen et al (2016). The algorithm uses the AATSR stereo view to remove the surface reflectance contribution from the TOA reflectance and retrieves the best-fit aerosol model and AOD value using inversion techniques.…”

Response to Anonymous Referee #3

“…The postprocessing is based on thresholds on the local AOD variability (standard deviation of AOD) and the number of neighboring cloud-free pixels in a 3 × 3 pixels area (Sogacheva et al, 2017). ADV algorithm provides a per-pixel AOD uncertainty estimate based on the propagation of the assumed 5 % uncertainty in the measured reflectance through the retrieval (Kolmonen et al, 2016). This uncertainty estimate does not include sampling and smoothing uncertainties, uncertainties related to the selection of the best-fit aerosol model (Kauppi et al, 2017), or uncertainties related to the cloud screening.…”

“…ADV algorithm provides a per-pixel AOD uncertainty estimate based on the propagation of the assumed 5 % uncertainty in the measured reflectance through the retrieval (Kolmonen et al, 2016). This uncertainty estimate does not include sampling and smoothing uncertainties, uncertainties related to the selection of the best-fit aerosol model (Kauppi et al, 2017), or uncertainties related to the cloud screening. In this work we study the additional collocation mismatch uncertainty related to the validation against AERONET.…”

“…In this work we study systematically the effect of the sampling parameters used in satellite AOD validation and the related standard deviations. Our focus is on the AATSR (Advanced Along-Track Scanning Radiometer) data and the ADV (AATSR dual-view) algorithm (Kolmonen et al, 2016), but we also apply the methods to MODIS data to reveal possible instrument specific effects.…”