[1] The Dutch-Finnish Ozone Monitoring Instrument (OMI) on board NASA's EOS-Aura satellite is measuring ozone, NO 2 , and other trace gases with daily global coverage. To correct these trace gas retrievals for the presence of clouds, there are two OMI cloud products, based on different physical processes, namely, absorption by O 2 -O 2 at 477 nm (OMCLDO2) and rotational Raman scattering (RRS) in the UV (OMCLDRR). Both cloud products use a Lambertian cloud model with albedo 0.8 and contain the effective (i.e., radiometric) cloud fraction and the cloud pressure. First, the theoretical framework for the Lambertian cloud model is given and the concept of effective cloud fraction is discussed. Next, an intercomparison of the effective cloud fractions from both products is presented, as well as a comparison with MODIS cloud data. It is shown that the O 2 -O 2 and RRS effective cloud fractions correlate very well (95%) but that there is an offset of about 0.10. From MODIS geometric cloud fraction and cloud optical thickness data a MODIS effective cloud fraction was calculated. The effective cloud fractions from OMCLDO2 and MODIS show a high correlation of 92% with a very small offset (0.01). In order to guide users, a summary of the validation status of effective cloud fraction and cloud pressure from the OMCLDO2 and OMCLDRR cloud products is presented.
[1] The cloud pressures determined by three different algorithms, operating on reflectances measured by two spaceborne instruments in the ''A'' train, are compared with each other. The retrieval algorithms are based on absorption in the oxygen A-band near 765 nm, absorption by a collision induced absorption in oxygen near 477 nm, and the filling in of Fraunhofer lines by rotational Raman scattering near 350 nm. A Lambertian reflector as cloud model is assumed in the retrievals. 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 EOS-Aura. The satellites sample the same air mass within about 15 min. We compare the retrieval algorithms using synthetic spectra to give the comparison realistic baseline expectations. It appears that these cloud pressures are not the pressure of the cloud top, but of a level inside the cloud. This is corroborated by comparisons with MODIS and CloudSat data: while the top of the cloud is seen by MODIS using emitted IR radiation, both OMI and PARASOL algorithms retrieve a pressure near the midlevel of the cloud. The three cloud pressure products are compared using 1 month of data. The cloud pressures are found to show a similar behavior, with correlation coefficients larger than 0.85 between the data sets for high effective cloud fractions. The average differences in the cloud pressure are small, between 2 and 45 hPa, for the whole data set, with an RMS difference of 65 to 93 hPa. This falls within the science requirement for the OMI cloud pressure to have an accuracy of 100 hPa. For small to medium effective cloud fractions, the cloud pressure distribution found by PARASOL is very similar to that found by OMI using the O 2 -O 2 absorption. Somewhat larger differences are found for very high effective cloud fractions.
Abstract. The cloud Optical Centroid Pressure (OCP) is a satellite-derived parameter that is commonly used in tracegas retrievals to account for the effects of clouds on nearinfrared through ultraviolet radiance measurements. Fast simulators are desirable to further expand the use of cloud OCP retrievals into the operational and climate communities for applications such as data assimilation and evaluation of cloud vertical structure in general circulation models. In this paper, we develop and validate fast simulators that provide estimates of the cloud OCP given a vertical profile of optical extinction. We use a pressure-weighting scheme where the weights depend upon optical parameters of clouds and/or aerosols. A cloud weighting function is easily extracted using this formulation. We then use fast simulators to compare two different satellite cloud OCP retrievals, from the Ozone Monitoring Instrument (OMI), with estimates based on collocated cloud extinction profiles from a combination of CloudSat radar and MODIS visible radiance data. These comparisons are made over a wide range of conditions to provide a comprehensive validation of the OMI cloud OCP retrievals. We find generally good agreement between OMI cloud OCPs and those predicted by CloudSat. However, the OMI cloud OCPs from the two independent algorithms agree better with each other than either does with the estimates from CloudSat/MODIS. Differences between OMI cloud OCPs and those based on CloudSat/MODIS may result from undetected snow/ice at the surface, cloud 3-D effects, cases of low clouds obscurred by ground-clutter in CloudSat observations and by opaque high clouds in CALIPSO lidar observations, and the fact that CloudSat/CALIPSO only observes a relatively small fraction of an OMI field-of-view.
Abstract. We use the technique of Observing System Simulation Experiments (OSSEs) to quantify the impact of spaceborne carbon monoxide (CO) total column observations from the Sentinel-5 Precursor (S-5P) platform on tropospheric analyses and forecasts. We focus on Europe for the period of northern summer 2003, when there was a severe heat wave episode associated with extremely hot and dry weather conditions. We describe different elements of the OSSE: (i) the nature run (NR), i.e., the "truth"; (ii) the CO synthetic observations; (iii) the assimilation run (AR), where we assimilate the observations of interest; (iv) the control run (CR), in this study a free model run without assimilation; and (v) efforts to establish the fidelity of the OSSE results. Comparison of the results from AR and the CR, against the NR, shows that CO total column observations from S-5P provide a significant benefit (at the 99 % confidence level) at the surface, with the largest benefit occurring over land in regions far away from emission sources. Furthermore, the S-5P CO total column observations are able to capture phenomena such as the forest fires that occurred in Portugal during northern summer 2003. These results provide evidence of the benefit of S-5P observations for monitoring processes contributing to atmospheric pollution.
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