NRLMSIS  2.0 is an empirical atmospheric model that extends from the ground to the exobase and describes the average observed behavior of temperature, 8 species densities, and mass density via a parametric analytic formulation. The model inputs are location, day of year, time of day, solar activity, and geomagnetic activity. NRLMSIS 2.0 is a major, reformulated upgrade of the previous version, NRLMSISE-00. The model now couples thermospheric species densities to the entire column, via an effective mass profile that transitions each species from the fully mixed region below ~70 km altitude to the diffusively separated region above ~200 km. Other changes include the extension of atomic oxygen down to 50 km and the use of geopotential height as the internal vertical coordinate. We assimilated extensive new lower and middle atmosphere temperature, O, and H data, along with global average thermospheric mass density derived from satellite orbits, and we validated the model against independent samples of these data. In the mesosphere and below, residual biases and standard deviations are considerably lower than NRLMSISE-00. The new model is warmer in the upper troposphere and cooler in the stratosphere and mesosphere. In the thermosphere, N2 and O densities are lower in NRLMSIS 2.0; otherwise, the NRLMSISE-00 thermosphere is largely retained. Future advances in thermospheric specification will likely require new in situ mass spectrometer measurements, new techniques for species density measurement between 100 and 200 km, and the reconciliation of systematic biases among thermospheric temperature and composition datasets, including biases attributable to long-term changes.

[1] Ozone profiles in the upper mesosphere (70-100 km) retrieved from nine instruments are compared. Ozone from the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument is used as the basis of comparison. Other measurements are from the Halogen Occultation Experiment, the High Resolution Doppler Imager, the Michelson Interferometer for Passive Atmospheric Sounding, the Global Ozone Monitoring by Occultation of Stars, the Atmospheric Chemistry Experiment-Fourier Transform Spectrometer, the Solar Occultation For Ice Experiment, the Optical Spectrograph and InfraRed Imaging System, and the Superconducting Submillimeter-Wave Limb-Emission Sounder. Comparisons of each data set with SABER using coincident profiles indicate agreement in the basic vertical profile of ozone but also some systematic differences in daytime ozone. Ozone from the SABER 9.6 m channel is higher than the other measurements over the altitude range 60-80 km by 20-50%. Nighttime comparisons indicate better relative agreement (<10% difference). Taking all the data, not limited to coincidences, shows the global and seasonal distributions of ozone in the upper mesosphere from each instrument. The average maximum in ozone mixing ratio is around 90-92 km during daytime and 95 km at night. There is a maximum in ozone density at night ( 90 km) and during some hours of the day. The latitude structure of ozone has appreciable variations with season, particularly in the tropical upper mesosphere. The basic latitude-altitude structure of ozone depends on local time, even when the analysis is restricted to day-only observations.

Abstract. In this paper, we present a merged dataset of ozone profiles from several satellite instruments: SAGE II on ERBS, GOMOS, SCIAMACHY and MIPAS on Envisat, OSIRIS on Odin, ACE-FTS on SCISAT, and OMPS on Suomi-NPP. The merged dataset is created in the framework of the European Space Agency Climate Change Initiative (Ozone_cci) with the aim of analyzing stratospheric ozone trends. For the merged dataset, we used the latest versions of the original ozone datasets. The datasets from the individual instruments have been extensively validated and intercompared; only those datasets which are in good agreement, and do not exhibit significant drifts with respect to collocated ground-based observations and with respect to each other, are used for merging. The long-term SAGE-CCI-OMPS dataset is created by computation and merging of deseasonalized anomalies from individual instruments.The merged SAGE-CCI-OMPS dataset consists of deseasonalized anomalies of ozone in 10 • latitude bands from 90 • S to 90 • N and from 10 to 50 km in steps of 1 km covering the period from October 1984 to July 2016. This newly created dataset is used for evaluating ozone trends in the stratosphere through multiple linear regression. Negative ozone trends in the upper stratosphere are observed before 1997 and positive trends are found after 1997. The upper stratospheric trends are statistically significant at midlatitudes and indicate ozone recovery, as expected from the decrease of stratospheric halogens that started in the middle of the 1990s and stratospheric cooling.

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The ACE-FTS (Atmospheric Chemistry Experiment -Fourier Transform Spectrometer) instrument on board the Canadian satellite SCISAT has been observing the Earth's limb in solar occultation since its launch in 2003. Since February 2004, high resolution (0.02 cm −1) observations in the spectral region of 750-4400 cm −1 have been used to derive volume mixing ratio profiles of over 30 atmospheric trace species and over 20 atmospheric isotopologues. Although the full ACE-FTS level 2 data set is available to users in the general atmospheric community, until now no quality flags have been assigned to the data. This study describes the two-stage procedure for detecting physically unrealistic out-liers within the data set for each retrieved species, which is a fixed procedure across all species. Since the distributions of ACE-FTS data across regions (altitude/latitude/season/local time) tend to be asymmetric and multimodal, the screening process does not make use of the median absolute deviation. It makes use of volume mixing ratio probability density functions , assuming that the data, when sufficiently binned, are at most tri-modal and that these modes can be represented by the superposition of three normal, or log-normal, distributions. Quality flags have been assigned to the data based on retrieval statistical fitting error, the physically unrealis-tic outliers described in this study, and known instrumen-tal/processing errors. The quality flags defined and discussed in this study are now available for all level 2 versions 2.5 and 3.5 data and will be made available as a standard product for future versions.