Abstract. We discuss the theoretical basis of a recently developed technique to characterize aerosols from space. We show that the interaction between aerosols and the strong molecular scattering in the near ultraviolet produces spectral variations of the backscattered radiances that can be used to separate aerosol absorption from scattering effects. This capability allows identification of several aerosol types, ranging from nonabsorbing sulfates to highly UV-absorbing mineral dust, over both land and water surfaces. Two ways of using the information contained in the near-UV radiances are discussed. In the first method, a residual quantity, which measures the departure of the observed spectral contrast from .that of a molecular atmosphere, is computed. Since clouds yield nearly zero residues, this method is a useful way of separately mapping the spatial distribution of UV-absorbing and nonabsorbing particles. To convert the residue to optical depth, the aerosol type must be known. The second method is an inversion procedure that The consequent aerosol effect on climate is usually quantified in terms of radiative forcing, i.e., the net flux change at the top of the atmosphere due solely to the direct aerosol radiative effects. Although there are uncertainties in the estimates of aerosol radiative forcing, it is generally agreed that the averaged global direct effects of anthropogenic sulfate aerosols are In spite of the difficulties inherent with satellite-based sensing, spaceborne measurements remain the most convenient method to characterize aerosol particles and determine their time and space distribution on a global basis. Currently available satellite data sets on aerosol properties do not provide a full description of the atmospheric aerosol load. The advanced very high resolution radiometer (AVHRR) aerosol data set provides information on optical depth only over the water surfaces of the Earth. The SAM and SAGE family of sensors were specifically designed to retrieve information on strato-17,099

Abstract. We present a new algorithm for the near-real time retrieval -within 3 h of the actual satellite measurement -of tropospheric NO 2 columns from the Ozone Monitoring Instrument (OMI). The retrieval is based on the combined retrieval-assimilation-modelling approach developed at KNMI for off-line tropospheric NO 2 from the GOME and SCIAMACHY satellite instruments. We have adapted the off-line system such that the required a priori informationprofile shapes and stratospheric background NO 2 -is now immediately available upon arrival (within 80 min of observation) of the OMI NO 2 slant columns and cloud data at KNMI. Slant columns for NO 2 are retrieved using differential optical absorption spectroscopy (DOAS) in the 405-465 nm range. Cloud fraction and cloud pressure are provided by a new cloud retrieval algorithm that uses the absorption of the O 2 -O 2 collision complex near 477 nm. Online availability of stratospheric slant columns and NO 2 profiles is achieved by running the TM4 chemistry transport model (CTM) forward in time based on forecast ECMWF meteo and assimilated NO 2 information from all previously observed orbits. OMI NO 2 slant columns, after correction for spurious across-track variability, show a random error for individual pixels of approximately 0.7×10 15

Abstract. We describe a new algorithm for the retrieval of nitrogen dioxide (NO2) vertical columns from nadir-viewing satellite instruments. This algorithm (SP2) is the basis for the Version 2.1 OMI This algorithm (SP2) is the basis for the Version 2.1 Ozone Monitoring Instrument (OMI) NO2 Standard Product and features a novel method for separating the stratospheric and tropospheric columns. NO2 Standard Product and features a novel method for separating the stratospheric and tropospheric columns. The approach estimates the stratospheric NO2 directly from satellite data without using stratospheric chemical transport models or assuming any global zonal wave pattern. Tropospheric NO2 columns are retrieved using air mass factors derived from high-resolution radiative transfer calculations and a monthly climatology of NO2 profile shapes. We also present details of how uncertainties in the retrieved columns are estimated. The sensitivity of the retrieval to assumptions made in the stratosphere–troposphere separation is discussed and shown to be small, in an absolute sense, for most regions. We compare daily and monthly mean global OMI NO2 retrievals using the SP2 algorithm with those of the original Version 1 Standard Product (SP1) and the Dutch DOMINO product. The SP2 retrievals yield significantly smaller summertime tropospheric columns than SP1, particularly in polluted regions, and are more consistent with validation studies. SP2 retrievals are also relatively free of modeling artifacts and negative tropospheric NO2 values. In a reanalysis of an INTEX-B validation study, we show that SP2 largely eliminates an ~20% discrepancy that existed between OMI and independent in situ springtime NO2 SP1 measurements.

No abstract

[1] We review the standard nitrogen dioxide (NO 2 ) data product (Version 1.0.), which is based on measurements made in the spectral region 415-465 nm by the Ozone Monitoring Instrument (OMI) on the NASA Earth Observing System-Aura satellite. A number of ground-and aircraft-based measurements have been used to validate the data product's three principal quantities: stratospheric, tropospheric, and total NO 2 column densities under nearly or completely cloud-free conditions. The validation of OMI NO 2 is complicated by a number of factors, the greatest of which is that the OMI observations effectively average the NO 2 over its field of view (minimum 340 km 2 ), while a ground-based instrument samples at a single point. The tropospheric NO 2 field is often very inhomogeneous, varying significantly over tens to hundreds of meters, and ranges from <10 15 cm À2 over remote, rural areas to >10 16 cm À2 over urban and industrial areas. Because of OMI's areal averaging, when validation measurements are made near NO 2 sources the OMI measurements are expected to underestimate the ground-based, and this is indeed seen. Further, we use several different instruments, both new and mature, which might give inconsistent NO 2 amounts; the correlations between nearby instruments is 0.8-0.9. Finally, many of the validation data sets are quite small and span a very short length of time; this limits the statistical conclusions that can be drawn from them. Despite these factors, good agreement is generally seen between the OMI and ground-based measurements, with OMI stratospheric NO 2 underestimated by about 14% and total and tropospheric columns underestimated by 15-30%. Typical correlations between OMI NO 2 and ground-based measurements are generally >0.6.