Data Products

OSIRIS limb scattered sunlight spectra are used to retrieve vertical profiles of stratospheric aerosol extinction at 750 nm. All the spectra obtained with a single vertical scan of the spectrograph line of sight are used together to retrieve an extinction profile with approximately 1.5 km vertical resolution.  Hydrated sulphuric acid particle microphysics, including a size distribution for typical background aerosol, are assumed to calculate the scattering cross section and phase functions required to retrieve the aerosol extinction.  Fundamentals of the retrieval algorithm are detailed in Bourassa et al., 2012.

The current version of the stratospheric aerosol extinction is version 7.2. The user guide and examples are here.  See details in Rieger et al., 2019. This version uses multiple wavelengths to reduce measurement geometry biases and improve extinction retrieval in the upper troposphere and lower stratosphere. The Chen et al., 2016, cloud detection algorithm has been adapted for the OSIRIS wavelength range for improved cloud screening and polar stratospheric cloud detection.

A new retrieval algorithm for OSIRIS (Optical Spectrograph and Infrared Imager System) nitrogen dioxide (NO₂) profiles has been validated. The algorithm relies on spectral fitting to obtain slant column densities of NO₂, followed by inversion using an algebraic reconstruction technique and the SaskTran spherical radiative transfer model (RTM) to obtain vertical profiles of local number density. The validation covers different latitudes (tropical to polar), years (2002–2012), all seasons (winter, spring, summer, and autumn), different concentrations of nitrogen dioxide (from denoxified polar vortex to polar summer), a range of solar zenith angles (68.6–90.5°), and altitudes between 10.5 and 39 km, thereby covering the full retrieval range of a typical OSIRIS NO₂ profile. The use of a larger spectral fitting window than used in previous retrievals reduces retrieval uncertainties and the scatter in the retrieved profiles due to noisy radiances. Improvements are also demonstrated through the validation in terms of bias reduction at 15–17 km relative to the OSIRIS operational v3.0 algorithm. The diurnal variation of NO₂ along the line of sight is included in a fully spherical multiple scattering RTM for the first time. Using this forward model with built-in photochemistry, the scatter of the differences relative to the correlative balloon NO₂ profile data is reduced. 

The NO₂ data is available by following the directions under Download and navigating to the directory /OSIRIS/Level2/no2/

A 7+ year (2001–2008) data set of stratospheric BrO profiles measured by the Optical Spectrograph and Infra‐Red Imager System (OSIRIS) instrument, a UV‐visible spectrometer measuring limb‐scattered sunlight from the Odin satellite, is presented. Zonal mean radiance spectra are computed for each day and inverted to yield effective daily zonal mean BrO profiles from 16 to 36 km. A detailed description of the retrieval methodology and error analysis is presented. Single‐profile precision and effective resolution are found to be about 30% and 3–5 km, respectively, throughout much of the retrieval range. Individual profile and monthly mean comparisons with ground‐based, balloon, and satellite instruments are found to agree to about 30%. A BrO climatology is presented, and its morphology and correlation with NO₂ is consistent with our current understanding of bromine chemistry. Monthly mean Br_y maps are derived. Two methods of calculating total Br_y in the stratosphere are used and suggest (21.0 ± 5.0) pptv with a contribution from very short lived substances of (5.0 ± 5.0) pptv, consistent with other recent estimates.

Our Level 2 Ozone Product is provided in the HARMonized dataset of OZone profiles (HARMOZ) format described in Sofieva et al, ESSD 2013. Likewise, the Level 2 Aerosol and NO₂ Products are provided in a format similar to the HARMOZ format. The format takes advantage of netCDF software developed by UCAR/Unidata.

See the OSIRIS Level 2 User Guide for details about the file format and to see examples on manipulating the data using Python.

Ozone from the SAGE II (1984-2005), OSIRIS (2001-present), and SAGE III/ISS (2017-present) missions have been merged into a monthly dataset with 10 degree latitude bins. Prior to merging, individual ozone profiles were filtered by the altitude of the first or second lapse rate tropopause. Two versions of the dataset are available. In one version a sampling correction based on MERRA-2 ozone is applied to OSIRIS and SAGE III/ISS ozone profiles to effectively shift the measurements to the middle of each month and latitude bin. A second version of the dataset is provided without this sampling correction.

This merged O₃ record is available by following the directions under Download and navigating to the directory /Merged_Datasets/o3/SAGEII-OSIRIS-SAGEIII_ISS/

NO_x from OSIRIS was merged with NO_x from SAGE II to create a monthly mean time series, spanning from 1984 to 2018. For each instrument the NO_x was derived from the retrieved NO₂ and shifted to a local solar time of 12:00 pm using the PRATMO photochemical box model. The datasets were then debiased and merged by taking advantage of the 4-year overlap between the measurement periods. 

Merged NO_x from OSIRIS and SAGE II is available by following the directions under Download and navigating to the directory /Merged_Datasets/nox/

Stratospheric aerosol extinction climatology spanning from 2002 until May 2019. This product was constructed from the OSIRIS version 7 aerosol product. Extinction has been cloud-cleared as best as possible, but may underestimate extinction in fresh volcanic or wildfire plumes.

Climatology Grid:

• Latitude: 5 degree
• Longitude: 30 degree
• Time: Daily sampling with 5 day rolling average applied
• Altitude: 1 km

The climatology is available by following the directions under Download and navigating to the directory /OSIRIS/Level3/aerosol/7.0/

The OSIRIS Level 1 InfraRed Imager System data product is only available for distribution by special request. If you would like more information on the OSIRIS Level 1 data, please contact Taran Warnock.

A sample of the OSIRIS IR data is available here.

Setup

These samples depend on xarray, numpy, scipy, and matplotlib. You will need the following imported into python to run the sample code below:

Spectrograph Data

If you have not already done so, download the sample OSIRIS Level 1 spectrograph data.

For example, you can access the data in orbit 54300 (the sample file) via:

To plot a spectra (replacing pixel number with wavelength):

To plot a scan of spectrograph data:
>>> spect = xr.open_dataset('~/Downloads/OSIRIS_L1_Spectrograph_054300_2011-02-06_v2-0.nc')
>>> spect.where(spec.scan_number_time_dim == 54300021, drop=True).swap_dims({'time': 'tangent_altitude'}).sortby('tangent_altitude').radiance.plot(y='tangent_altitude', norm=LogNorm(1e9, 1e14))

IR Data

If you have not already done so, download the sample OSIRIS Level 1 IR data. This data a sample orbit of channel 3 data for the IR instrument. It contains a single orbit of data with a daytime section in the middle and night time on either end. From left to right, the solar conditions are night, dusk, day, dawn, night. The zigzag motion of the data is due to the scanning nature of the spacecraft. That is, the satellite's attitude nods back and forth so that the instrument line-of-sight goes up and down the limb of the atmosphere. This allows the spectrograph (with its single line-of-sight) to scan the atmosphere from top to bottom. The IR instrument, on the other hand, has over 100 pixels that collect radiance data over ~100km span in the limb.

Access the IR data in orbit 36573 (the sample file) via:

To plot the radiance data for the unmasked pixels. (noting the zigzag pattern in the data):

By interpolating to the attitude data contained within the file, we can view this data with respect to altitude and not pixel number:

>>> alts = np.arange(40000., 95000., 250.)
>>> ir_altitude = []
>>> for (data, alt) in zip(ir.data.transpose('time', ...), ir.altitude.transpose('time', ...)):
>>>     f = interp1d(alt, data, bounds_error=False)
>>>     ir_altitude.append(f(alts))
>>> ir_altitude = xr.DataArray(ir_altitude, coords=[ir.time, alts], dims=['time', 'altitude'])
>>> ir_altitude.plot(x='time', y='altitude', norm=LogNorm(), vmin=1e9, vmax=1e13, figsize=(15, 6))


Similarly the error estimate can be loaded and plotted via:

>>> alts = np.arange(40000., 95000., 250.)
>>> error_altitude = []
>>> for (error, alt) in zip(ir.error.transpose('time', ...), ir.altitude.transpose('time', ...)):
>>>     f = interp1d(alt, error, bounds_error=False)
>>>     error_altitude.append(f(alts))
>>> error_altitude = xr.DataArray(error_altitude, coords=[ir.time, alts], dims=['time', 'altitude'])
>>> error_altitude.plot(x='time', y='altitude', norm=LogNorm(), vmin=1e9, vmax=1e13, figsize=(15, 6))