Abstract
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 355 nm. The combined temperature uncertainty ranges between 0.1 and 1 K below 60 km, with detection noise, saturation correction, and molecular extinction correction being the three dominant sources of uncertainty. Above 60 km and up to 10 km below the top of the profile, the total uncertainty increases exponentially from 1 to 10 K due to the combined effect of random noise and temperature tie-on. In the top 10 km of the profile, the accuracy of the profile mainly depends on that of the tie-on temperature. All other uncertainty components remain below 0.1 K throughout the entire profile (15–90 km), except the background noise correction uncertainty, which peaks around 0.3–0.5 K. 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.
Highlights
The present article is the last of three companion papers that provide a comprehensive description of recent recommendations made to the Network for Detection of Stratospheric Change (NDACC) lidar community for the standardization of vertical resolution and uncertainty in the NDACC lidar data processing algorithms
A wide range of methodologies and technologies is used for NDACC lidar instrumentation, which inherently raises the issue of consistency across the network, especially when using the lidar data to detect long-term trends, to perform intercomparisons and model or instrument validation, or when trying to ingest the data in assimilation models
Our first companion paper (Part 1) (Leblanc et al, 2016b) summarizes the recommendations made by the International Space Science Institute (ISSI) team for the use of standardized definitions of vertical resolution
Summary
The present article is the last of three companion papers that provide a comprehensive description of recent recommendations made to the Network for Detection of Stratospheric Change (NDACC) lidar community for the standardization of vertical resolution and uncertainty in the NDACC lidar data processing algorithms. Our second companion paper (Part 2) (Leblanc et al, 2016c) summarizes the definitions and approaches proposed by the ISSI team for a standardized treatment of uncertainty in the ozone differential absorption lidar (DIAL) retrievals. 3. Based on these definitions, a standardized measurement model for temperature lidars using the density integration technique is proposed in Sect. The reader should refer to the ISSI team report (Leblanc et al, 2016a) for more details on all aspects covered in the present article
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