ALBERT

Description: We have developed a new technique for estimating ozone mixing ratio inside deep convective clouds. The technique uses the concept of an optical centroid cloud pressure that is indicative of the photon path inside clouds. Radiative transfer calculations based on realistic cloud vertical structure as provided by CloudSat radar data show that because deep convective clouds are optically thin near the top, photons can penetrate significantly inside the cloud. This photon penetration coupled with in-cloud scattering produces optical centroid pressures that are hundreds of hPa inside the cloud. We use the measured column ozone and the optical centroid cloud pressure derived using the effects of rotational-Raman scattering to estimate O3 mixing ratio in the upper regions of deep convective clouds. The data are obtained from the Ozone Monitoring Instrument (OMI) aboard NASA's Aura satellite. Our results show that low O3 concentrations in these clouds are a common occurrence throughout much of the tropical Pacific. Ozonesonde measurements in the tropics following convective activity also show very low concentrations of O3 in the upper troposphere. These low amounts are attributed to vertical injection of ozone poor oceanic boundary layer air during convection into the upper troposphere followed by convective outflow. Over South America and Africa, O3 mixing ratio inside deep convective clouds often exceeds 50 ppbv which is comparable to mean background (cloud-free) concentrations. These areas contain higher amounts of ozone precursors due to biomass burning and lightning. Assuming that O3 is well mixed (i.e. constant mixing ratio with height) up to the tropopause, we can estimate the stratospheric column O3 over clouds. Stratospheric column ozone derived in this manner agrees well with that retrieved independently with the Aura Microwave Limb Sounder (MLS) instrument and thus provides a consistency check of our method.

Description: We have developed a new technique for estimating ozone mixing ratio inside deep convective clouds. The technique uses the concept of an optical centroid cloud pressure that is indicative of the photon path inside clouds. Radiative transfer calculations based on realistic cloud vertical structure as provided by CloudSat radar data show that because deep convective clouds are optically thin near the top, photons can penetrate significantly inside the cloud. This photon penetration coupled with in-cloud scattering produces optical centroid pressures that are hundreds of hPa inside the cloud. We combine measured column ozone and the optical centroid cloud pressure derived using the effects of rotational-Raman scattering to estimate O3 mixing ratio in the upper regions of deep convective clouds. The data are obtained from the Ozone Monitoring Instrument (OMI) onboard NASA's Aura satellite. Our results show that low O3 concentrations in these clouds are a common occurrence throughout much of the tropical Pacific. Ozonesonde measurements in the tropics following convective activity also show very low concentrations of O3 in the upper troposphere. These low amounts are attributed to vertical injection of ozone poor oceanic boundary layer air during convection into the upper troposphere followed by convective outflow. Over South America and Africa, O3 mixing ratios inside deep convective clouds often exceed 50 ppbv which are comparable to mean background (cloud-free) amounts and are consistent with higher concentrations of injected boundary layer/lower tropospheric O3 relative to the remote Pacific. The Atlantic region in general also consists of higher amounts of O3 precursors due to both biomass burning and lightning. Assuming that O3 is well mixed (i.e., constant mixing ratio with height) up to the tropopause, we can estimate the stratospheric column O3 over clouds. Stratospheric column ozone derived in this manner agrees well with that retrieved independently with the Aura Microwave Limb Sounder (MLS) instrument and thus provides a consistency check of our method.

Description: The accuracy of total ozone computed from the Smithsonian Astrophysical Observatory (SAO) optimal estimation (OE) ozone profile algorithm (SOE) applied to the Ozone Monitoring Instrument (OMI) is assessed through comparisons with ground-based Brewer spectrometer measurements from 2005 to 2008. We also make comparisons with the three OMI operational ozone products, derived from the NASA Total Ozone Mapping Spectrometer (TOMS), KNMI Differential Optical Absorption Spectroscopy (DOAS), and KNMI OE (KOE) algorithms. Excellent agreement is observed between SAO and Brewer, with a mean difference of less than ±1% at most individual stations. The KNMI OE algorithm systematically overestimates Brewer total ozone by 2% at low/mid latitudes and 5% at high latitudes while the TOMS and DOAS algorithms underestimate it by ~1.65% on average. Standard deviations of ~1.8% are found for both SOE and TOMS, but DOAS and KOE have scatters of 2.2% and 2.6%, respectively. The stability of the SOE algorithm is found to have insignificant dependence on viewing geometry, cloud parameters, total ozone column. In comparison, the KOE differences to Brewer values are significantly correlated with solar and viewing zenith angles, with a significant deviation depending on cloud parameters and total ozone amount. The TOMS algorithm exhibits similar stability to SOE with respect to viewing geometry and total column ozone, but stronger cloud parameter dependence. The dependence of DOAS on the algorithmic variables is marginal compared to KOE, but distinct compared to the SOE and TOMS algorithms. Comparisons of All four OMI products with Brewer show no apparent long-term drift but a seasonally affected feature, especially for KOE and TOMS. The substantial differences in the KOE vs. SOE algorithm performance cannot be sufficiently explained by the use of soft calibration (in SOE) and the use of different a priori error covariance matrix, but other algorithm details cause larger fitting residuals by a factor of 2–3 for KOE.

Description: The accuracy of total ozone computed from the Smithsonian Astrophysical Observatory (SAO) optimal estimation (OE) ozone profile algorithm (SOE) applied to the Ozone Monitoring Instrument (OMI) is assessed through comparisons with ground-based Brewer spectrometer measurements from 2005 to 2008. We also compare the three OMI operational ozone products, derived from the NASA Total Ozone Mapping Spectrometer (TOMS) algorithm, the KNMI (Royal Netherlands Meteorological Institute) differential optical absorption spectroscopy (DOAS) algorithm, and KNMI's Optimal Estimation (KOE) algorithm. The best agreement is observed between SAO and Brewer, with a mean difference of within 1% at most individual stations. The KNMI OE algorithm systematically overestimates Brewer total ozone by 2% at low and mid-latitudes and 5% at high latitudes while the TOMS and DOAS algorithms underestimate it by ~1.65% on average. Standard deviations of ~1.8% are calculated for both SOE and TOMS, but DOAS and KOE have higher values of 2.2% and 2.6%, respectively. The stability of the SOE algorithm is found to have insignificant dependence on viewing geometry, cloud parameters, or total ozone column. In comparison, the KOE–Brewer differences are significantly correlated with solar and viewing zenith angles and show significant deviations depending on cloud parameters and total ozone amount. The TOMS algorithm exhibits similar stability to SOE with respect to viewing geometry and total column ozone, but has stronger cloud parameter dependence. The dependence of DOAS on observational geometry and geophysical conditions is marginal compared to KOE, but is distinct compared to the SOE and TOMS algorithms. Comparisons of all four OMI products with Brewer show no apparent long-term drift, but seasonal features are evident, especially for KOE and TOMS. The substantial differences in the KOE vs. SOE algorithm performance cannot be sufficiently explained by the use of soft calibration (in SOE) and the use of different a priori error covariance matrices; however, other algorithm details cause fitting residuals larger by a factor of 2–3 for KOE.