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Volume 9, issue 8 | Copyright

Special issue: Twenty-five years of operations of the Network for the Detection...

Atmos. Meas. Tech., 9, 4079-4101, 2016
https://doi.org/10.5194/amt-9-4079-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 25 Aug 2016

Research article | 25 Aug 2016

Proposed standardized definitions for vertical resolution and uncertainty in the NDACC lidar ozone and temperature algorithms – Part 3: Temperature uncertainty budget

Thierry Leblanc1, Robert J. Sica2, Joanna A. E. van Gijsel3, Alexander Haefele4, Guillaume Payen5, and Gianluigi Liberti6 Thierry Leblanc et al.
  • 1Jet Propulsion Laboratory, California Institute of Technology, Wrightwood, CA 92397, USA
  • 2Department of Physics and Astronomy, The University of Western Ontario, London, Canada
  • 3Royal Netherlands Meteorological Institute (KNMI), Bilthoven, the Netherlands
  • 4Meteoswiss, Payerne, Switzerland
  • 5Observatoire des Sciences de l'Univers de La Réunion, CNRS and Université de la Réunion (UMS3365), Saint Denis de la Réunion, France
  • 6ISAC-CNR, Via Fosso del Cavaliere 100, 00133, Rome, Italy

Abstract. A standardized approach for the definition, propagation, and reporting of uncertainty in the temperature lidar data products contributing to the Network for the Detection for Atmospheric Composition Change (NDACC) database is proposed. One important aspect of the proposed approach is the ability to propagate all independent uncertainty components in parallel through the data processing chain. The individual uncertainty components are then combined together at the very last stage of processing to form the temperature combined standard uncertainty.

The identified uncertainty sources comprise major components such as signal detection, saturation correction, background noise extraction, temperature tie-on at the top of the profile, and absorption by ozone if working in the visible spectrum, as well as other components such as molecular extinction, the acceleration of gravity, and the molecular mass of air, whose magnitudes depend on the instrument, data processing algorithm, and altitude range of interest.

The expression of the individual uncertainty components and their step-by-step propagation through the temperature data processing chain are thoroughly estimated, taking into account the effect of vertical filtering and the merging of multiple channels. All sources of uncertainty except detection noise imply correlated terms in the vertical dimension, which means that covariance terms must be taken into account when vertical filtering is applied and when temperature is integrated from the top of the profile. Quantitatively, the uncertainty budget is presented in a generic form (i.e., as a function of instrument performance and wavelength), so that any NDACC temperature lidar investigator can easily estimate the expected impact of individual uncertainty components in the case of their own instrument.

Using this standardized approach, an example of uncertainty budget is provided for the Jet Propulsion Laboratory (JPL) lidar at Mauna Loa Observatory, Hawai'i, which is typical of the NDACC temperature lidars transmitting at 355nm. The combined temperature uncertainty ranges between 0.1 and 1K below 60km, with detection noise, saturation correction, and molecular extinction correction being the three dominant sources of uncertainty. Above 60km and up to 10km below the top of the profile, the total uncertainty increases exponentially from 1 to 10K due to the combined effect of random noise and temperature tie-on. In the top 10km of the profile, the accuracy of the profile mainly depends on that of the tie-on temperature. All other uncertainty components remain below 0.1K throughout the entire profile (15–90km), except the background noise correction uncertainty, which peaks around 0.3–0.5K. It should be kept in mind that these quantitative estimates may be very different for other lidar instruments, depending on their altitude range and the wavelengths used.

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This article prescribes a standardized approach for the treatment of uncertainty in the backscatter temperature lidar data processing algorithms. The recommendations are designed to be used homogeneously across large atmospheric observation networks such as NDACC, and allow a clear understanding of the uncertainty budget of multiple lidar datasets for a large spectrum of middle atmospheric science applications (e.g., climatology, long-term trends, mesospheric tides, satellite validation).
This article prescribes a standardized approach for the treatment of uncertainty in the...
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