English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

A standardized approach for the definition, propagation, andreporting of uncertainty in the temperature lidar data products contributingto the Network for the Detection for Atmospheric Composition Change (NDACC)database is proposed. One important aspect of the proposed approach is theability to propagate all independent uncertainty components in parallelthrough the data processing chain. The individual uncertainty components arethen combined together at the very last stage of processing to form thetemperature combined standard uncertainty.  The identified uncertainty sources comprise major components such as signaldetection, saturation correction, background noise extraction, temperaturetie-on at the top of the profile, and absorption by ozone if working in thevisible spectrum, as well as other components such as molecular extinction, theacceleration of gravity, and the molecular mass of air, whose magnitudesdepend on the instrument, data processing algorithm, and altitude range ofinterest.  The expression of the individual uncertainty components and theirstep-by-step propagation through the temperature data processing chain arethoroughly estimated, taking into account the effect of vertical filteringand the merging of multiple channels. All sources of uncertainty exceptdetection noise imply correlated terms in the vertical dimension, whichmeans that covariance terms must be taken into account when verticalfiltering is applied and when temperature is integrated from the top of theprofile. Quantitatively, the uncertainty budget is presented in a genericform (i.e., as a function of instrument performance and wavelength), so thatany NDACC temperature lidar investigator can easily estimate the expectedimpact of individual uncertainty components in the case of their owninstrument.  Using this standardized approach, an example of uncertainty budget isprovided for the Jet Propulsion Laboratory (JPL) lidar at Mauna Loa Observatory, Hawai'i, which istypical of the NDACC temperature lidars transmitting at 355 nm. The combinedtemperature uncertainty ranges between 0.1  and 1 K below 60 km, withdetection noise, saturation correction, and molecular extinction correctionbeing the three dominant sources of uncertainty. Above 60 km and up to 10 kmbelow the top of the profile, the total uncertainty increases exponentiallyfrom 1  to 10 K due to the combined effect of random noise and temperaturetie-on. In the top 10 km of the profile, the accuracy of the profile mainlydepends on that of the tie-on temperature. All other uncertainty componentsremain below 0.1 K throughout the entire profile (15–90 km), except thebackground noise correction uncertainty, which peaks around 0.3–0.5 K. Itshould be kept in mind that these quantitative estimates may be verydifferent for other lidar instruments, depending on their altitude range andthe wavelengths used

atmospheric measurement techniques

Proposed standardized definitions for vertical resolution and uncertainty inthe NDACC lidar ozone and temperature algorithms – Part 3: Temperatureuncertainty budget