The OH (3-1) nightglow volume emission rate retrieved from OSIRIS measurements: 2001 to 2015

The OH airglow has been used to investigate the chemistry and dynamics of the mesosphere and the lower thermosphere (MLT) for a long time. The infrared imager (IRI) aboard the Odin satellite has been recording the night-time 1.53 mu m OH (3-1) emission for more than 15 years (2001-2015), and we have recently processed the complete data set. The newly derived data products contain the volume emission rate profiles and the Gaussian-approximated layer height, thickness, peak intensity and zenith intensity, and their corresponding error estimates. In this study, we describe the retrieval steps for these data products. We also provide data screening recommendations. The monthly zonal averages depict the well-known annual oscillation and semi-annual oscillation signatures, which demonstrate the fidelity of the data set (https://doi.org/10.5281/zenodo.4746506, Li et al., 2021). The uniqueness of this Odin IRI OH long-term data set makes it valuable for studying various topics, for instance, the sudden stratospheric warming events in the polar regions and solar cycle influences on the MLT

Effect of volcanic aerosol on stratospheric NO₂ and N₂O₅ from 2002-2014 as measured by Odin-OSIRIS and Envisat-MIPAS

Following the large volcanic eruptions of Pinatubo in 1991 and El Chichón in 1982, decreases in stratospheric NO₂ associated with enhanced aerosol were observed. The Optical Spectrograph and Infrared Imaging Spectrometer (OSIRIS) measured the widespread enhancements of stratospheric aerosol following seven volcanic eruptions between 2002 and 2014, although the magnitudes of these eruptions were all much smaller than the Pinatubo and El Chichón eruptions. In order to isolate and quantify the relationship between volcanic aerosol and NO₂, NO₂ anomalies were calculated using measurements from OSIRIS and the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS). In the tropics, variability due to the quasi-biennial oscillation was subtracted from the time series. OSIRIS profile measurements indicate that the strongest anticorrelations between NO₂ and volcanic aerosol extinction were for the 5 km layer starting  ∼  3 km above the climatological mean tropopause at the given latitude. OSIRIS stratospheric NO₂ partial columns in this layer were found to be smaller than background NO₂ levels during these aerosol enhancements by up to  ∼  60 % with typical Pearson correlation coefficients of R ∼ −0. 7. MIPAS also observed decreases in NO₂ partial columns during periods affected by volcanic aerosol, with percent differences of up to  ∼  25 % relative to background levels. An even stronger anticorrelation was observed between OSIRIS aerosol optical depth and MIPAS N₂O₅ partial columns, with R ∼ −0. 9, although no link with MIPAS HNO3 was observed. The variation in OSIRIS NO₂ with increasing aerosol was found to be consistent with simulations from a photochemical box model within the estimated model uncertainty

Tomographic retrievals of ozone with the OMPS Limb Profiler: algorithm description and preliminary results

[No abstract

Model estimations of geophysical variability between satellite measurements of ozone profiles

In order to validate satellite measurements of atmospheric composition, it is necessary to understand the range of random and systematic uncertainties inherent in the measurements. On occasions where measurements from two different satellite instruments do not agree within those estimated uncertainties, a common explanation is that the difference can be assigned to geophysical variability, i.e., differences due to sampling the atmosphere at different times and locations. However, the expected geophysical variability is often left ambiguous and rarely quantified. This paper describes a case study where the geophysical variability of O3 between two satellite instruments – ACE-FTS (Atmospheric Chemistry Experiment – Fourier Transform Spectrometer) and OSIRIS (Optical Spectrograph and InfraRed Imaging System) – is estimated using simulations from climate models. This is done by sampling the models CMAM (Canadian Middle Atmosphere Model), EMAC (ECHAM/MESSy Atmospheric Chemistry), and WACCM (Whole Atmosphere Community Climate Model) throughout the upper troposphere and stratosphere at times and geolocations of coincident ACE-FTS and OSIRIS measurements. Ensemble mean values show that in the lower stratosphere, O3 geophysical variability tends to be independent of the chosen time coincidence criterion, up to within 12 h; and conversely, in the upper stratosphere geophysical variation tends to be independent of the chosen distance criterion, up to within 2000 km. It was also found that in the lower stratosphere, at altitudes where there is the greatest difference between air composition inside and outside the polar vortex, the geophysical variability in the southern polar region can be double of that in the northern polar region. This study shows that the ensemble mean estimates of geophysical variation can be used when comparing data from two satellite instruments to optimize the coincidence criteria, allowing for the use of more coincident profiles while providing an estimate of the geophysical variation within the comparison results

Validation of Ozone Profile Retrievals Derived from the OMPS LP Version 2.5 Algorithm Against Correlative Satellite Measurements

The Limb Profiler (LP) is a part of the Ozone Mapping and Profiler Suite launched on board of the Suomi NPP satellite in October 2011. The LP measures solar radiation scattered from the atmospheric limb in ultraviolet and visible spectral ranges between the surface and 80 km. These measurements of scattered solar radiances allow for the retrieval of ozone profiles from cloud tops up to 55 km. The LP started operational observations in April 2012. In this study we evaluate more than 5.5 years of ozone profile measurements from the OMPS LP processed with the new NASA GSFC version 2.5 retrieval algorithm. We provide a brief description of the key changes that had been implemented in this new algorithm, including a pointing correction, new cloud height detection, explicit aerosol correction and a reduction of the number of wavelengths used in the retrievals. The OMPS LP ozone retrievals have been compared with independent satellite profile measurements obtained from the Aura Microwave Limb Sounder (MLS), Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) and Odin Optical Spectrograph and InfraRed Imaging System (OSIRIS). We document observed biases and seasonal differences and evaluate the stability of the version 2.5 ozone record over 5.5 years. Our analysis indicates that the mean differences between LP and correlative measurements are well within required +/- 10% between 18 and 42 km. In the upper stratosphere and lower mesosphere (>43 km) LP tends to have a negative bias. We find larger biases in the lower stratosphere and upper troposphere, but LP ozone retrievals have significantly improved in version 2.5 compared to version 2 due to the implemented aerosol correction. In the northern high latitudes we observe larger biases between 20 and 32 km due to the remaining thermal sensitivity issue. Our analysis shows that LP ozone retrievals agree well with the correlative satellite observations in characterizing vertical, spatial and temporal ozone distribution associated with natural processes, like the seasonal cycle and quasi-biennial oscillations. We found a small positive drift approx. 0.5%/yr in the LP ozone record against MLS and OSIRIS that is more pronounced at altitudes above 35 km. This pattern in the relative drift is consistent with a possible 100m drift in the LP sensor pointing detected by one of our altitude-resolving methods

Estimating and Reporting Uncertainties in Remotely Sensed Atmospheric Composition and Temperature

Remote sensing of atmospheric state variables typically relies on the inverse solution of the radiative transfer equation. An adequately characterized retrieval provides information on the uncertainties of the estimated state variables as well as on how any constraint or a priori assumption affects the estimate. Reported characterization data should be intercomparable between different instruments, empirically validatable, grid-independent, usable without detailed knowledge of the instrument or retrieval technique, traceable and still have reasonable data volume. The latter may force one to work with representative rather than individual characterization data. Many errors derive from approximations and simplifications used in real-world retrieval schemes, which are reviewed in this paper, along with related error estimation schemes. The main sources of uncertainty are measurement noise, calibration errors, simplifications and idealizations in the radiative transfer model and retrieval scheme, auxiliary data errors, and uncertainties in atmospheric or instrumental parameters. Some of these errors affect the result in a random way, while others chiefly cause a bias or are of mixed character. Beyond this, it is of utmost importance to know the influence of any constraint and prior information on the solution. While different instruments or retrieval schemes may require different error estimation schemes, we provide a list of recommendations which should help to unify retrieval error reporting

Review: An overview of the Odin atmospheric mission

International audienc

Review: An overview of the Odin atmospheric mission

International audienc