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Contactless optical hygrometry in LACIS-T: supplementary data

Version 1.0

Nowak, Jakub L.; Grosz, Robert; Frey, Wiebke; Niedermeier, Dennis; Mijas, Jędrzej; Malinowski, Szymon P.; Ort, Linda; Schmalfuß, Silvio; Stratmann, Frank; Voigtländer, Jens; Stacewicz, Tadeusz, 2022, "Contactless optical hygrometry in LACIS-T: supplementary data", https://doi.org/10.18150/08X1HO, RepOD, V1

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Description

The dataset contains the results of the experiments perfomed with the Fast InfraRed Hygrometer (FIRH) at the Turbulent Leipzig Aerosol Cloud Interaction Simulator (LACIS-T) which are presented in the figures in the article "Contactless optical hygrometry in LACIS-T" submitted to Atmospheric Measurement Techniques.

FIRH is an open-path optical sensor developed for high-resolution measurements of small-scale humidity fluctuations in turbulent atmospheric flows. The device employs open-path tunable diode laser absorption spectroscopy at the wavelengths near 1364.6896 nm line.

LACIS-T is a unique turbulent moist-air wind tunnel established to study cloud physical processes and the interactions between cloud microphysics and turbulence under a wide range of well-defined reproducible conditions resembling warm, mixed-phase and cold clouds.

Within the study described in the related article, FIRH was adapted to perform contactless measurements of humidity and its rapid fluctuations in turbulence inside the measurement section of LACIS-T where two air streams of different properties are mixed. The configuration of the setup allows for scanning from outside the tunnel at various positions without the need for repeated optics adjustments.

The application of the developed correction methods to account for self-broadening of the absorption line, interference in the glass windows and parasitic absorption in the ambient air outside the tunnel provided an agreement between FIRH and reference dew-point mirror hygrometer across the wide range of water vapor concentration.

High temporal resolution of FIRH allowed for studying turbulent fluctuations in the course of intensive mixing of two air streams which had the same mean velocity but differed in temperature and humidity. The obtained results complement the previous characterizations of turbulent velocity and temperature fields in LACIS-T.

(2022-07)
Subject
Earth and Environmental Sciences; Physics
Keyword

humidity, hygrometry, absorption spectroscopy, self-broadening, window interference, parasitic absorption, atmospheric measurements, turbulence, turbulent mixing, cloud microphysics, wind tunnel, dew-point mirror, FIRH, Fast InfraRed Hygrometer, LACIS-T, Turbulent Leipzig Aerosol Cloud Interaction Simulator

Related Publication

Nowak, J. L., Grosz, R., Frey, W., Niedermeier, D., Mijas, J., Malinowski, S. P., Ort, L., Schmalfuß, S., Stratmann, F., Voigtländer, J., and Stacewicz, T.: Contactless optical hygrometry in LACIS-T, Atmos. Meas. Tech. Discuss. [preprint], in review, 2022. https://amt.copernicus.org/preprints/amt-2022-79/ doi: 10.5194/amt-2022-79

CC BY - Creative Commons Attribution 4.0 CC BY - Creative Commons Attribution 4.0

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1 to 7 of 7 Files
fig06_L_eff_trans_spec.csv
Comma Separated Values - 32.8 KB - Jul 11, 2022 - 14 Downloads
MD5: 1c93a85b77f655f2f5d24e44d95be4cf
License: CC BY - Creative Commons Attribution 4.0
Fig. 6. Effective transmission spectra.
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fig07_COMP.tab
Tabular Data - Original format: Comma Separated Values - 962 B - Jul 11, 2022 - 20 Downloads
License: CC BY - Creative Commons Attribution 4.0
8 Variables, 11 Observations - UNF:6:fdDtyVA0ILG80DKyO+wd1A==
Fig. 7. Comparison of FIRH with DPM.
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fig08_SCAN_L_mean.tab
Tabular Data - Original format: Comma Separated Values - 2.7 KB - Jul 11, 2022 - 19 Downloads
License: CC BY - Creative Commons Attribution 4.0
28 Variables, 13 Observations - UNF:6:eEWqhUKSQBW8fh2Vow4UZg==
Fig. 8. Turbulent mixing of the two streams differing in thermodynamic properties.
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fig09_SCAN_L_ACF.tab
Tabular Data - Original format: Comma Separated Values - 1.2 MB - Jul 11, 2022 - 17 Downloads
License: CC BY - Creative Commons Attribution 4.0
56 Variables, 4001 Observations - UNF:6:Q6M4/yZ6OPl/EfmICegdlw==
Fig. 9. Autocorrelation functions of the timeseries n(t) recorded at various positions x in the SCAN-L experiments.
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fig10_SCAN_L_PSD.tab
Tabular Data - Original format: Comma Separated Values - 444.7 KB - Jul 11, 2022 - 19 Downloads
License: CC BY - Creative Commons Attribution 4.0
28 Variables, 2043 Observations - UNF:6:uCKpRBOV6XGLQ7gmJZ9eAg==
Fig. 10. Power spectral densities of the timeseries n(t) recorded at various positions x in the SCAN-L experiments.
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fig11_SCAN_S_3_ACF.tab
Tabular Data - Original format: Comma Separated Values - 59.7 KB - Jul 11, 2022 - 16 Downloads
License: CC BY - Creative Commons Attribution 4.0
4 Variables, 2043 Observations - UNF:6:ogmcALeRbpC5ui45iSuyZQ==
Fig. 11. Autocorrelation functions of the timeseries n(t) recorded at various positions y in the SCAN-S-3 experiment.
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fig12_SCAN_S_3_PSD.tab
Tabular Data - Original format: Comma Separated Values - 73.4 KB - Jul 11, 2022 - 19 Downloads
License: CC BY - Creative Commons Attribution 4.0
4 Variables, 2043 Observations - UNF:6:hOYHWvH3R+Z1dY8cKGP/bg==
Fig. 12. Power spectral densities of the timeseries n(t) recorded at various positions y in the SCAN-S-3 experiment.
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