Linearization of the effect of slit function changes for improving Ozone Monitoring Instrument ozone profile retrievals

Abstract

Abstract. We introduce a method that accounts for errors caused by the slit function in an optimal-estimation-based spectral fitting process to improve ozone profile retrievals from the Ozone Monitoring Instrument (OMI) ultraviolet measurements (270–330 nm). Previously, a slit function was parameterized as a standard Gaussian by fitting the full width at half maximum (FWHM) of the slit function from climatological OMI solar irradiances. This cannot account for the temporal variation in slit function in irradiance, the intra-orbit changes due to thermally induced change and scene inhomogeneity, and potential differences in the slit functions of irradiance and radiance measurements. As a result, radiance simulation errors may be induced due to convolving reference spectra with incorrect slit functions. To better represent the shape of the slit functions, we implement a more generic super Gaussian slit function with two free parameters (slit width and shape factor); it becomes standard Gaussian when the shape factor is fixed to be 2. The effects of errors in slit function parameters on radiance spectra, referred to as pseudo absorbers (PAs), are linearized by convolving high-resolution cross sections or simulated radiances with the partial derivatives of the slit function with respect to the slit parameters. The PAs are included in the spectral fitting scaled by fitting coefficients that are iteratively adjusted as elements of the state vector along with ozone and other fitting parameters. The fitting coefficients vary with cross-track and along-track pixels and show sensitivity to heterogeneous scenes. The PA spectrum is quite similar in the Hartley band below 310 nm for both standard and super Gaussians, but is more distinctly structured in the Huggins band above 310 nm with the use of super Gaussian slit functions. Finally, we demonstrate that some spikes of fitting residuals are slightly smoothed by accounting for the slit function errors. Comparisons with ozonesondes demonstrate noticeable improvements when using PAs for both standard and super Gaussians, especially for reducing the systematic biases in the tropics and midlatitudes (mean biases of tropospheric column ozone reduced from -1.4∼0.7 to 0.0∼0.4 DU) and reducing the standard deviations of tropospheric ozone column differences at high latitudes (by 1 DU for the super Gaussian). Including PAs also makes the retrievals consistent between standard and super Gaussians. This study corroborates the slit function differences between radiance and irradiance, demonstrating that it is important to account for such differences in the ozone profile retrievals.

Radiative transfer acceleration based on the principal component analysis and lookup table of corrections: optimization and application to UV ozone profile retrievals

“…3b). In step 3 individual calculations are interpolated into 0.05 nm intervals with the undersampling cor- Figure 2a shows the reference spectrum where Gaussian smoothing to 0.4 nm is applied to LBL calculations at the sampling rate (0.01 nm) of the ozone cross sections (Brion et al, 1993), which is used to evaluate the approximation errors related to undersampling. Figure 2b illustrates that LBL calculations are required to be performed at intervals of 0.03 nm or better.…”

“…The RT performance enhancement arises from a reduction in the number of expensive full multiple scattering (MS) calculations; the PCA scheme uses spectral binning of the wavelengths into several bins based on the similarity of their optical properties and the projection to every spectral point of these full MS calculations which are executed for a small number of PCA-derived optical states. In addition to the adaption of a PCA-based RT model for our ozone profile retrieval, we have adopted the undersampling correction from our previous implementation (Kim et al, 2013;Bak et al, 2019); this enables us to use fewer wavelengths for further speed-up without much loss of accuracy. Furthermore, we have developed a LUT-based correction to accelerate online RT simulations by starting with a lower-accuracy configuration (scalar RT with no polarization, 4 streams, 24 layers) and then correcting the accuracy to the level attainable by means of a computationally more expensive configuration (vector RT, 12 streams, 72 layers).…”

Impact of using a new ultraviolet ozone absorption cross-section dataset on OMI ozone profile retrievals

“…Prior to ACSO activities, the available ultraviolet (UV) ozone cross-sections were thoroughly reviewed by Orphal (2002Orphal ( , 2003 and as a result three datasets of ozone cross-sections were found to be in agreement of 1 %-2 % with each other, including BP 1985 (Bass and Paur, 1985), BDM 1995 (Daumont et al 1992;Brion et al, 1993;Malicet et al, 1995) and the Global Ozone Monitoring Experiment (GOME) flight model (Burrows et al, 1999) (GMFM). The BP dataset is no longer recommended for any atmospheric ozone measurements (Orphal et al, 2016) but still used to keep the long-term consistency of ground-based Dobson-Brewer total ozone records and spaceborne Total Ozone Mapping Spectrometer (TOMS)/Ozone Monitoring Instrument (OMI) total ozone records (McPeters et al, 2015).…”

“…We evaluate different sets of high-resolution ozone absorption cross-section data for use in atmospheric ozone profile measurements in the Hartley and Huggins bands with a particular focus on BDM 1995 (Daumont et al 1992;Brion et al, 1993;Malicet et al, 1995), currently used in our retrievals, and a new laboratory dataset by Birk and Wagner (2018) (BW). The BDM cross-section data have been recommended to use for retrieval of ozone profiles using spaceborne nadir-viewing backscattered ultraviolet (BUV) measurements since its improved performance was demonstrated against other cross-sections including Bass and Paur (1985) (BP) and those of Serdyuchenko et al (2014) and (SER) by the "Absorption Cross-Sections of Ozone" (ACSO) activity. The BW laboratory data were recently measured within the framework of the European Space Agency (ESA) project SEOM-IAS (Scientific Exploitation of Operational Missions -Improved Atmospheric Spectroscopy Databases) to provide an advanced absorption cross-section database.…”

Cross-evaluation of GEMS tropospheric ozone retrieval performance using OMI data and the use of an ozonesonde dataset over East Asia for validation

“…The development of geostationary ultraviolet-visible (UV-VIS) spectrometers is a new paradigm in the field of the space-based air quality monitoring. It builds on the polarorbiting instrument heritage for the last 40 years, which were initiated with the launch of a series of Total Ozone Mapping Spectrometer (TOMS) instruments starting in 1978 (Bhartia et al, 1996) and consolidated by the Global Ozone Monitoring Experiment (GOME) (ESA, 1995), the SCanning Imaging Absorption spectroMeter for Atmospheric CHartogra-phY (SCIAMACHY) (Bovensmann et al, 1999), the Ozone Monitoring Instrument (OMI) (Levelt et al, 2006), GOME-2 (EUMETSAT, 2006), the Ozone Mapping and Profiler Suite (OMPS) (Flynn et al, 2014), and the TROPOspheric Monitoring Instrument (TROPOMI) (Veefkind et al, 2012). Three geostationary air quality monitoring missions, including the Geostationary Environmental Monitoring Spectrometer (GEMS) (Bak et al, 2013a) over East Asia, TEMPO (Tropospheric Emissions: Monitoring of Pollution; Chance et al, 2013;Zoogman et al, 2017) over North America, and Sentinel-4 (Ingmann et al, 2012) over Europe, are in progress to launch in the 2019-2022 time frame, to provide unprecedented hourly measurements of aerosols and chem-ical pollutants at suburban-scale spatial resolution (∼ 10-50 km 2 ).…”