Characterization and correction of OMPS nadir mapper measurements for ozone profile retrievals

Abstract

Abstract. This paper verifies and corrects the Ozone Mapping and Profiler Suite (OMPS) nadir mapper (NM) level 1B v2.0 measurements with the aim of producing accurate ozone profile retrievals using an optimal-estimation-based inversion method to fit measurements in the spectral range 302.5-340 nm. The evaluation of available slit functions demonstrates that preflight-measured slit functions represent OMPS measurements well compared to derived Gaussian slit functions. Our initial OMPS fitting residuals contain significant wavelength and cross-track-dependent biases, resulting in serious cross-track striping errors in the tropospheric ozone retrievals. To eliminate the systematic component of the fitting residuals, we apply "soft calibration" to OMPS radiances. With the soft calibration the amplitude of fitting residuals decreases from ∼ 1 to 0.2 % over low and middle latitudes, and thereby the consistency of tropospheric ozone retrievals between OMPS and the Ozone Monitoring Instrument (OMI) is substantially improved. A common mode correction is also implemented for additional radiometric calibration; it improves retrievals especially at high latitudes where the amplitude of fitting residuals decreases by a factor of ∼ 2. We estimate the noise floor error of OMPS measurements from standard deviations of the fitting residuals. The derived error in the Huggins band (∼ 0.1 %) is twice the OMPS L1B measurement error. OMPS noise floor errors constrain our retrievals better, leading to improving information content of ozone and reducing fitting residuals. The final precision of the fitting residuals is less than 0.1 % in the low and middle latitudes, with ∼ 1 degrees of freedom for signal for the tropospheric ozone, meeting the general requirements for successful tropospheric ozone retrievals.

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

“…Figure 1 is a schematic diagram of the ozone profile algorithm. With the input of satellite measurements, the slit function is parameterized through cross-correlation between satellite irradiance and a high-resolution solar reference spectrum to be used for wavelength calibration and for high-resolution cross section convolution (Sun et al, 2017;Bak et al, 2019); a normalized Gaussian distribution is assumed to derive analytic slit functions for OMI. To remove the systematic errors between measured and calculated radiances, "soft calibration" is applied to measured radiances and then the logarithms of sun-normalized radiances are calculated as measurement vectors (Liu et al, 2010a;Cai et al, 2012;Bak et al, 2017).…”

Cross-verification of simulated GEMS tropospheric ozone retrievals and ozonesonde measurements over Northeast Asia

“…The development of the GEMS ozone profile algorithm builds on heritages of the Smithsonian Astrophysical Observatory (SAO) ozone profile algorithm which was originally developed for GOME (Liu et al, 2005), continuously adapted for its successors such as OMI (Liu et al, 2010a), GOME/2 (Cai et al, 2012), and OMPS (Bak et al, 2017). In addition, the SAO algorithm will be implemented to retrieve TEMPO ozone profiles (Chance et al, 2013;Zoogman et al, 2017).…”

“…The described OE-fitting solution can be written, together with cost function χ : (Bak et al, 2013a). and high-resolution solar reference spectrum to be used for wavelength calibration and for highresolution cross section convolution (Sun et al, 2017;Bak et al, 2017); normalized Gaussian distribution is assumed to derive analytic slit function for OMI. To remove the systematic errors between measured and calculated radiances, "soft-calibration" is applied to measured radiances and then the logarithm of sun-normalized radiances is calculated as a measurement vector (Liu et al, 2010a;Cai et al, 2012;Bak et al, 2017).…”

“…and high-resolution solar reference spectrum to be used for wavelength calibration and for highresolution cross section convolution (Sun et al, 2017;Bak et al, 2017); normalized Gaussian distribution is assumed to derive analytic slit function for OMI. To remove the systematic errors between measured and calculated radiances, "soft-calibration" is applied to measured radiances and then the logarithm of sun-normalized radiances is calculated as a measurement vector (Liu et al, 2010a;Cai et al, 2012;Bak et al, 2017). Measurement covariance matrix is constructed as a diagonal matrix with each component taken from the square of the measurement errors as measurement errors are (Huang et al, 2017).…”

A physics-based approach to oversample multi-satellite, multi-species observations to a common grid

“…Global distributions of atmospheric species that play critical roles 5 in atmospheric chemistry and air pollution, such as ozone (e.g., Bak et al, 2017), NO 2 (e.g., Krotkov et al, 2017), SO 2 (e.g., Li et al, 2017a), formaldehyde (HCHO, e.g., González Abad et al, 2015), glyoxal (CHOCHO, e.g., Chan Miller et al, 2014), and BrO (e.g., Suleiman et al, 2018), have been retrieved from the backscattered solar UV-visible spectra observed by generations of polar-orbiting satellite sensors, including GOME , SCIAMACHY (Bovensmann et al, 1999), OMI (Levelt et al, 2018), GOME-2 (Munro et al, 2016), OMPS (Rodriguez et al, 2003), and TROPOMI (Veefkind et al,10 2012). A constellation of geostationary satellites will provide hourly measurements of these species over North America, Europe, and Asia in the near future (Zoogman et al, 2017).…”