Evaluation of the OMI cloud pressures derived from rotational Raman scattering by comparisons with other satellite data and radiative transfer simulations

Abstract

[1] In this paper we examine differences between cloud pressures retrieved from the Ozone Monitoring Instrument (OMI) using the ultraviolet rotational Raman scattering (RRS) algorithm and those from the thermal infrared (IR) Aqua/MODIS. Several cloud data sets are currently being used in OMI trace gas retrieval algorithms including climatologies based on IR measurements and simultaneous cloud parameters derived from OMI. From a validation perspective, it is important to understand the OMI retrieved cloud parameters and how they differ with those derived from the IR. To this end, we perform radiative transfer calculations to simulate the effects of different geophysical conditions on the OMI RRS cloud pressure retrievals. We also quantify errors related to the use of the Mixed Lambert-Equivalent Reflectivity (MLER) concept as currently implemented of the OMI algorithms. Using properties from the Cloudsat radar and MODIS, we show that radiative transfer calculations support the following: (1) The MLER model is adequate for single-layer optically thick, geometrically thin clouds, but can produce significant errors in estimated cloud pressure for optically thin clouds.(2) In a two-layer cloud, the RRS algorithm may retrieve a cloud pressure that is either between the two cloud decks or even beneath the top of the lower cloud deck because of scattering between the cloud layers; the retrieved pressure depends upon the viewing geometry and the optical depth of the upper cloud deck. (3) Absorbing aerosol in and above a cloud can produce significant errors in the retrieved cloud pressure. (4) The retrieved RRS effective pressure for a deep convective cloud will be significantly higher than the physical cloud top pressure derived with thermal IR.Citation: Vasilkov, A., J. Joiner, R. Spurr, P. K. Bhartia, P. Levelt, and G. Stephens (2008), Evaluation of the OMI cloud pressures derived from rotational Raman scattering by comparisons with other satellite data and radiative transfer simulations,

Accounting for the effects of surface BRDF on satellite cloud and trace-gas retrievals: a new approach based on geometry-dependent Lambertian equivalent reflectivity applied to OMI algorithms

“…The OMI RRS cloud algorithm is detailed in , , and Vasilkov et al (2008). OCP is derived from the high-frequency structure in the TOA reflectance caused by RRS in the atmosphere.…”

The operational cloud retrieval algorithms from TROPOMI on board Sentinel-5 Precursor

“…The first (OMI/Aura Cloud Pressure and Fraction Raman Scattering, or OMCLDRR) uses the cloud screening effect on Fraunhofer filling signatures (due to inelastic rotational Raman scattering) in the region 346-354 nm to derive effective cloud fraction and cloud optical centroid pressure (Joiner and Vasilkov, 2006;Vasilkov et al, 2008;Joiner et al, 2012). This algorithm is based on the mixed Lambertian equivalent reflectivity (MLER) assumption.…”

Characterization of ozone profiles derived from Aura TES and OMI radiances

“…Two sets of cloud products are available from OMI measurements. One set of cloud top pressure and cloud fraction was obtained using the O 2 -O 2 absorption band near 477 nm (Acarreta et al, 2004) and the other set was retrieved using the effects of rotational Raman scattering (Joiner and Vasilkov, 2006;Vasilkov et al, 2008). Having two sets of OMI cloud products is to improve the temporal coverage of OMI cloud information since cloud information might not be available due to the quality control of cloud retrievals.…”