An implementation of the optimal estimation scheme to obtain vertical ozone profiles from satellite measurements of backscattered solar ultraviolet (buv) radiation is described. This algorithm (Version 6.0) has been used to produce a 15‐year data set of global ozone profiles from Nimbus 7 SBUV, NOAA 11 SBUV/2, and Space Shuttle SSBUV instruments. A detailed discussion of the information content of the measurement is presented. Using high vertical resolution ozone profiles from the SAGE II experiment as “truth” profiles, it is shown that the buv technique can capture short‐term variabilities of ozone in 5‐km vertical layers, between 0.3 mbar and 100 mbar, with a precision of 5–15%. However, outside the 1–20 mbar range, buv‐derived results are heavily influenced by a priori assumptions. To minimize this influence, it is recommended that the studies of long‐term trends using buv data be restricted to 1–20 mbar range. Outside this range, only the column amounts of ozone between 20 mbar and surface, and above 1 mbar, can be considered relatively free of a priori assumptions.

[1] This paper is an overview of the validation of the total column ozone data products from the Ozone Monitoring Instrument (OMI) on board the NASA EOS-Aura satellite. OMI is an imaging UV/visible spectrometer that maps global ozone on a daily basis. There are two ozone products from OMI, one derived using the traditional TOMS retrieval algorithm and another derived using a Differential Optical Absorption Spectroscopy algorithm that is being developed to take advantage of the hyperspectral capabilities of OMI. Validation is primarily performed through comparison with a network of Dobson and Brewer ground stations and secondarily through campaigns conducted specifically to validate Aura. Comparison with an ensemble of 76 Northern Hemisphere ground stations shows that OMI-TOMS total column ozone averages 0.4% higher than the station average, with station-to-station standard deviation of ±0.6%. The comparison shows that the OMI-TOMS ozone was stable over the 2-year period with no evidence of drift relative to the ground network. The OMI-DOAS product is also stable but with a 1.1% offset and a seasonal variation of ±2%. During four aircraft validation campaigns using the NASA DC-8 and WB-57 aircraft, ozone above the aircraft was measured using an actinic flux instrument and compared with OMI ozone. These comparisons showed agreement within 2% over a broad range of latitude and viewing conditions. Only during the high-latitude flights did the OMI-DOAS ozone show the effects of a solar zenith angle dependent error.

The 1992 global average total ozone, measured by the Total Ozone Mapping Spectrometer (TOMS) on the Nimbus-7 satellite, was 2 to 3 percent lower than any earlier year observed by TOMS (1979 to 1991). Ozone amounts were low in a wide range of latitudes in both the Northern and Southern hemispheres, and the largest decreases were in the regions from 10 degrees S to 20 degrees S and 100N to 60 degrees N. Global ozone in 1992 is at least 1.5 percent lower than would be predicted by a statistical model that includes a linear trend and accounts for solar cycle variation and the quasi-biennial oscillation. These results are confirmed by comparisons with data from other ozone monitoring instruments: the SBUV/2 instrument on the NOAA-11 satellite, the TOMS instrument on the Russian Meteor-3 satellite, the World Standard Dobson Instrument 83, and a collection of 22 ground-based Dobson instruments.

We describe the algorithm that has been applied to develop a 41 yr time series of total ozone and ozone profiles from eight solar-backscatter UV (sbuv) instruments launched on NASA and NOAA satellites since April 1970. Although the basic algorithm is similar to the V8 algorithm that was released about a decade ago and has been in use since then at NOAA, the details of the V8 algorithm have never been published. The current version (V8.6) incorporates several changes including the use of new ozone absorption cross-sections and new ozone and cloud height climatologies. A particular emphasis in this paper is on characterizing the sources of errors that are relevant for deriving trends from monthly mean anomalies and for estimating biases between different types of ozone sensors. We show that variations in the local time of the measurement due to drifting NOAA satellite orbits can complicate the analysis of trends in the upper stratosphere. Such variations not only increase instrumental and algorithmic uncertainties but also require correction for true local time variations of ozone in the upper stratosphere and lower mesosphere for trend analysis. We find that the monthly zonal anomalies derived from the SBUV data have high precision, sufficient to track year-to-year changes in ozone over a broad range of altitudes. However, because of poor vertical resolution the data are less well suited to track short-term variability of ozone at lower altitudes

[1] The Ozone Monitoring Instrument (OMI) project team uses two total ozone retrieval algorithms in order to maintain the long-term record established with Total Ozone Mapping Spectrometer (TOMS) data as well as to improve the ozone column estimate using the hyperspectral capability of OMI. The purpose of this study is to assess where the algorithms produce comparable results and where the differences are significant. Starting with the same set of Earth reflectance data, the total ozone data used in this study have been derived using OMI-TOMS and OMI-Differential Optical Absorption Spectroscopy (DOAS) algorithms. OMI-TOMS is based on the TOMS version 8 algorithm that has been used to process TOMS data taken since November 1978. The OMI-DOAS retrieval algorithm was developed specifically for OMI. It takes advantage of the hyperspectral feature of the OMI instrument to reduce errors due to aerosols, clouds, surface, and sulfur dioxide from volcanic eruptions. The OMI-DOAS algorithm also has improved correction for cloud height. The mean differences in the ozone column derived from the two algorithms vary from 0 to 9 DU (0-3%), and their correlation coefficients vary between 0.89 and 0.99 with latitude and season. The largest differences occur in the polar regions and over clouds. Some of the differences are due to stray light, dark current, and other instrumental errors that have been corrected in the new version of the OMI radiance/irradiance data set (collection 3). Other differences are algorithmic. OMI-DOAS algorithmic errors identified through this analysis are also being corrected in collection 3 reprocessing. However, for consistency with the long-term TOMS record, OMI-TOMS collection 3 data will still be based on the TOMS V8 algorithm. Preliminary analysis shows much better agreement in the two total ozone data sets after reprocessing.