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

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.

Statistical analysis of the mesospheric inversion layers over two symmetrical tropical sites: Réunion (20.8° S, 55.5° E) and Mauna Loa (19.5° N, 155.6° W)

“…Temperature uncertainty ranges from 0.5 K in the mid-stratosphere to 2 K in the lower mesosphere, and more than 10 K in the upper mesosphere as a result of lower signal-to-noise ratios and uncertainty owing to the "a priori" temperature initialization. A full uncertainty budget for Rayleigh/Raman temperature lidars with a specific example for the Mauna Loa system is presented in Leblanc et al (2016b). At Réunion, the Rayleigh lidar system was established at the Saint-Denis campus (approximately sea level) by the Laboratoire de l'Atmosphere et des Cyclones (LACy) and was operational from 1994 to 2009.…”

“…The signals collected by the photomultipliers are sampled by multi-channel scalers (MCS) at 2 µs intervals (300 m) and saved in the raw data files every 5 to 20 min, depending on the period considered over the 1993-present time series. During processing, the temperature profiles are further integrated to a 2 h average and then vertically smoothed to yield an effective resolution ranging from 300 m in lower stratosphere to 4 km in the upper mesosphere, following the NDACC-standardized definition detailed in Leblanc et al (2016a). Temperature uncertainty ranges from 0.5 K in the mid-stratosphere to 2 K in the lower mesosphere, and more than 10 K in the upper mesosphere as a result of lower signal-to-noise ratios and uncertainty owing to the "a priori" temperature initialization.…”

A middle latitude Rayleigh-scatter lidar temperature climatology determined using an optimal estimation method

“…It is almost less than 3000 m below the 0.9 cutoff 5 height, however, it increases rapidly to 5000 m around the 0.8 cutoff line. Leblanc et al (2016b) recommended two standardized definitions for a temperature profile vertical resolution. In order to compare the retrieved temperature profiles using the OEM and the HC method, the two vertical resolution definitions given by Leblanc et al (2016b) were used to find the best bin size for the HC method so it would have an identical vertical resolution to the OEM retrieval.…”

“…Leblanc et al (2016b) recommended two standardized definitions for a temperature profile vertical resolution. In order to compare the retrieved temperature profiles using the OEM and the HC method, the two vertical resolution definitions given by Leblanc et al (2016b) were used to find the best bin size for the HC method so it would have an identical vertical resolution to the OEM retrieval. We found that 264 m co-added bins and a 3's and 5's filter gave a vertical resolution of 1008 m, close to the OEM temperature retrieval grid (1056 m).…”

“…NDACC2016 calculated the temperature uncertainty due to each parameter per 1% uncertainty. In order to compare NDACC2016 results with the PCL uncertainties using the OEM, we need to scale NDACC2016 simulations to the PCL as recommended by Leblanc et al (2016b). For example, if the temperature uncertainty due to air number density is per 1% uncertainty in NDACC2016, then we must divide NDACC2016 uncertainties by a factor of 5 because we assume an air number density uncertainty of 0.2% (recommended by NDACC2016) in the PCL forward model ( The statistical uncertainty comparison between the PCL and NDACC2016 is shown in dark blue in Fig.…”