Mitigation of bias sources for atmospheric temperature and humidity in the mobile Raman Weather and Aerosol Lidar (WALI)
<p>Lidars using vibrational and rotational Raman scattering to continuously monitor both the water vapor and temperature profiles in the low and middle troposphere offer enticing perspectives for applications in weather prediction and studies of aerosol–cloud–water vapor interactions by simult...
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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Copernicus Publications
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/c0e9142d01744f44b5dd465ccc6f0aac |
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| Sumario: | <p>Lidars using vibrational and rotational Raman scattering to continuously
monitor both the water vapor and temperature profiles in the low and middle
troposphere offer enticing perspectives for applications in weather prediction
and studies of aerosol–cloud–water vapor interactions by simultaneously deriving
relative humidity and atmospheric optical properties. Several
heavy systems exist in European laboratories, but only recently have they been
downsized and ruggedized for deployment in the field. In this paper, we
describe in detail the technical choices made during the design and
calibration of the new Raman channels for the mobile Weather and Aerosol Lidar
(WALI), going over the important sources of bias and uncertainty on the water
vapor and temperature profiles stemming from the different optical elements
of the instrument. For the first time, the impacts of interference filters and
non-common-path differences between Raman channels, and their mitigation, in particular are
investigated, using horizontal shots in a homogeneous
atmosphere. For temperature, the magnitude of the highlighted biases can be
much larger than the targeted absolute accuracy of 1 <span class="inline-formula"><sup>∘</sup>C</span>
defined by the WMO (up to 6 <span class="inline-formula"><sup>∘</sup>C</span> bias below 300 <span class="inline-formula">m</span>
range). Measurement errors are quantified using simulations and a number of
radiosoundings launched close to the laboratory. After de-biasing, the
remaining mean differences are below 0.1 <span class="inline-formula">g kg<sup>−1</sup></span> on water vapor and
1 <span class="inline-formula"><sup>∘</sup>C</span> on temperature, and rms differences are consistent with
the expected error from lidar noise, calibration uncertainty, and horizontal
inhomogeneities of the atmosphere between the lidar and radiosondes.</p> |
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