Detection of Multi-Layer and Vertically-Extended Clouds Using A-Train Sensors

The detection of mUltiple cloud layers using satellite observations is important for retrieval algorithms as well as climate applications. In this paper, we describe a relatively simple algorithm to detect multiple cloud layers and distinguish them from vertically-extended clouds. The algorithm can be applied to coincident passive sensors that derive both cloud-top pressure from the thermal infrared observations and an estimate of solar photon pathlength from UV, visible, or near-IR measurements. Here, we use data from the A-train afternoon constellation of satellites: cloud-top pressure, cloud optical thickness, the multi-layer flag from the Aqua MODerate-resolution Imaging Spectroradiometer (MODIS) and the optical centroid cloud pressure from the Aura Ozone Monitoring Instrument (OMI). For the first time, we use data from the CloudSat radar to evaluate the results of a multi-layer cloud detection scheme. The cloud classification algorithms applied with different passive sensor configurations compare well with each other as well as with data from CloudSat. We compute monthly mean fractions of pixels containing multi-layer and vertically-extended clouds for January and July 2007 at the OMI spatial resolution (l2kmx24km at nadir) and at the 5kmx5km MODIS resolution used for infrared cloud retrievals. There are seasonal variations in the spatial distribution of the different cloud types. The fraction of cloudy pixels containing distinct multi-layer cloud is a strong function of the pixel size. Globally averaged, these fractions are approximately 20% and 10% for OMI and MODIS, respectively. These fractions may be significantly higher or lower depending upon location. There is a much smaller resolution dependence for fractions of pixels containing vertically-extended clouds (approx.20% for OMI and slightly less for MODIS globally), suggesting larger spatial scales for these clouds. We also find higher fractions of vertically-extended clouds over land as compared with ocean, particularly in the tropics and summer hemisphere

Three Way Comparison between Two OMI/Aura and One POLDER/PARASOL Cloud Pressure Products

The cloud pressures determined by three different algorithms, operating on reflectances measured by two space-borne instruments in the "A" train, are compared with each other. The retrieval algorithms are based on absorption in the oxygen A-band near 760 nm, absorption by a collision induced absorption in oxygen near 477nm, and the filling in of Fraunhofer lines by rotational Raman scattering. The first algorithm operates on data collected by the POLDER instrument on board PARASOL, while the latter two operate on data from the OMI instrument on board Aura. The satellites sample the same air mass within about 15 minutes. Using one month of data, the cloud pressures from the three algorithms are found to show a similar behavior, with correlation coefficients larger than 0.85 between the data sets for thick clouds. The average differences in the cloud pressure are also small, between 2 and 45 hPa, for the whole data set. For optically thin to medium thick clouds, the cloud pressure the distribution found by POLDER is very similar to that found by OMI using the O2 - O2 absorption. Somewhat larger differences are found for very thick clouds, and we hypothesise that the strong absorption in the oxygen A-band causes the POLDER instrument to retrieve lower pressures for those scenes

Retrieval and validation of ozone columns derived from measurements of SCIAMACHY on Envisat

International audienceThis paper describes a new ozone column retrieval algorithm and its application to SCIAMACHY measurements. The TOSOMI algorithm is based on the Differential Optical Absorption Spectroscopy (DOAS) technique and implements several improvements over older algorithms. These improvements include aspects like (i) the explicit treatment of rotational Raman scattering, (ii) an improved air-mass factor formulation which is based on a simulation of the reflectivity spectrum and a subsequent DOAS fit of this simulated spectrum, (iii) the use of an improved ozone climatology and a column dependent air-mass factor, (iv) the use of daily varying ECMWF temperature profile analyses. The results of three validation exercises are reported. The TOSOMI columns are compared with an extensive set of ground-based observations (Brewer, Dobson) for the years 2003 and 2004. Secondly, a direct comparison for January?June 2003 with two new GOME retrievals, GDP Version 4 and TOGOMI, is presented. Third, data assimilation is used to study the dependence of the TOSOMI columns with retrieval parameters such as the viewing angle, cloud fraction and geographical location. These comparisons show a good consistency on the percent level between the GOME and SCIAMACHY algorithms. The present TOSOMI implementation (v0.32) shows an offset of about ?1.5% with respect to ground-based observations and the GOME retrievals

A Cloud-Ozone Data Product from Aura OMI and MLS Satellite Measurements

Ozone within deep convective clouds is controlled by several factors involving photochemical reactions and transport. Gas-phase photochemical reactions and heterogeneous surface chemical reactions involving ice, water particles, and aerosols inside the clouds all contribute to the distribution and net production and loss of ozone. Ozone in clouds is also dependent on convective transport that carries low troposphereboundary layer ozone and ozone precursors upward into the clouds. Characterizing ozone in thick clouds is an important step for quantifying relationships of ozone with tropospheric H2O, OH production, and cloud microphysicstransport properties. Although measuring ozone in deep convective clouds from either aircraft or balloon ozonesondes is largely impossible due to extreme meteorological conditions associated with these clouds, it is possible to estimate ozone in thick clouds using backscattered solar UV radiation measured by satellite instruments. Our study combines Aura Ozone Monitoring Instrument (OMI) and Microwave Limb Sounder (MLS) satellite measurements to generate a new research product of monthly-mean ozone concentrations in deep convective clouds between 30oS to 30oN for October 2004 April 2016. These measurements represent mean ozone concentration primarily in the upper levels of thick clouds and reveal key features of cloud ozone including: persistent low ozone concentrations in the tropical Pacific of 10 ppbv or less; concentrations of up to 60 pphv or greater over landmass regions of South America, southern Africa, Australia, and Indiaeast Asia; connections with tropical ENSO events; and intra-seasonalMadden-Julian Oscillation variability. Analysis of OMI aerosol measurements suggests a cause and effect relation between boundary layer pollution and elevated ozone inside thick clouds over land-mass regions including southern Africa and Indiaeast Asia

Sensitivity Study on Canadian Air Quality Measurements from Geostationary Orbit

Tropospheric Emissions: Monitoring of Pollution (TEMPO) is a satellite-based remote sensing air quality instrument destined for geostationary orbit over North America beginning in 2022. TEMPO will take hourly measurements with unprecedented resolution which will greatly benefit air quality forecasting, monitoring of emission sources, and health impact studies related to air quality. The field of regard of TEMPO contains a significant portion of Canada, including regions of particular interest such as major population centers and the Alberta oil sands. However, the standard retrieval algorithms that will be used to process TEMPO data do not explicitly account for some of the challenges that exist for measurements over Canada, such as pervasive snow cover, shallow lines of sight, and limited daylight hours. With the ultimate goal of creating new or optimized algorithms that address these challenges and allow Canada to take full advantage of TEMPO, standard retrieval algorithms for nitrogen dioxide and ozone have been replicated and studied. These algorithms use differential optical absorption spectroscopy (DOAS), the technique that will be used to create the standard TEMPO products, and they will serve as a baseline for comparison with future algorithms. The SASKTRAN radiative transfer framework, developed at the University of Saskatchewan, has been utilized to calculate air mass factors, key quantities in the DOAS-style retrieval, using three complementary methods which are all in agreement with each other. End-to-end retrievals modelled after cutting-edge algorithms used by modern instruments have been implemented, and they have been used to conduct a preliminary sensitivity study that quantifies the major sources of uncertainty in DOAS retrievals using synthetic TEMPO measurements

Observations over Hurricanes from the Ozone Monitoring Instrument

There is an apparent inconsistency between the total column ozone derived from the total ozone mapping spectrometer (TOMS) and aircraft observations within the eye region of tropical cyclones. The higher spectral resolution, coverage, and sampling of the ozone monitoring instrument (OMI) on NASA s Aura satellite as compared with TOMS allows for improved ozone retrievals by including estimates of cloud pressure derived simultaneously using the effects of rotational Raman scattering. The retrieved cloud pressures from OM1 are more appropriate than the climatological cloud-top pressures based on infrared measurements used in the TOMS and initial OM1 algorithms. We find that total ozone within the eye of hurricane Katrina is significantly overestimated when we use climatological cloud pressures. Using OMI-retrieved cloud pressures, total ozone in the eye is similar to that in the surrounding area. The corrected total ozone is in better agreement with aircraft measurements that imply relatively small or negligible amounts of stratospheric intrusion into the eye region of tropical cyclones

Accurate satellite-derived estimates of the tropospheric ozone impact on the global radiation budget

Estimates of the radiative forcing due to anthropogenically-produced tropospheric O3 are derived primarily from models. Here, we use tropospheric ozone and cloud data from several instruments in the A-train constellation of satellites as well as information from the GEOS-5 Data Assimilation System to accurately estimate the radiative effect of tropospheric O3 for January and July 2005. Since we cannot distinguish between natural and anthropogenic sources with the satellite data, our derived radiative effect reflects the unadjusted (instantaneous) effect of the total tropospheric O3 rather than the anthropogenic component. We improve upon previous estimates of tropospheric ozone mixing ratios from a residual approach using the NASA Earth Observing System (EOS) Aura Ozone Monitoring Instrument (OMI) and Microwave Limb Sounder (MLS) by incorporating cloud pressure information from OMI. We focus specifically on the magnitude and spatial structure of the cloud effect on both the short- and long-wave radiative budget. The estimates presented here can be used to evaluate the various aspects of model-generated radiative forcing. For example, our derived cloud impact is to reduce the radiative effect of tropospheric ozone by ~16%. This is centered within the published range of model-produced cloud effect on unadjusted ozone radiative forcing

Ozone mixing ratios inside tropical deep convective clouds from OMI satellite measurements

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 O<sub>3</sub> 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 O<sub>3</sub> 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 O<sub>3</sub> 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, O<sub>3</sub> 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 O<sub>3</sub> relative to the remote Pacific. The Atlantic region in general also consists of higher amounts of O<sub>3</sub> precursors due to both biomass burning and lightning. Assuming that O<sub>3</sub> is well mixed (i.e., constant mixing ratio with height) up to the tropopause, we can estimate the stratospheric column O<sub>3</sub> 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