The experimental data set of downwelling radiance spectra measured at the ground in clear conditions during 2013 by a Far-Infrared Fourier Transform Spectrometer at Dome-C, Antarctica, presented in Rizzi et al. (2016, https://doi.org/10.1002/2016JD025341) is used to estimate the effect of solar heating of the radiosonde humidity sensor, called dry bias. The effect is quite evident comparing residuals for the austral summer and winter clear cases and can be modeled by an increase of the water vapor concentration at all levels by about 15%. Such an estimate has become possible only after a new version of the simulation code and spectroscopic data has become available, which has substantially improved the modeling of water vapor absorption in the far infrared. The negative yearly spectral bias reported in Rizzi et al. (2016, https://doi.org/10.1002/2016JD025341) is in fact greatly reduced when compared to the same measurement data set.
Rizzi, R., Maestri, T., Arosio, C. (2018). Estimate of Radiosonde Dry Bias From Far-Infrared Measurements on the Antarctic Plateau. JOURNAL OF GEOPHYSICAL RESEARCH. ATMOSPHERES, 123(6), 3205-3211 [10.1002/2017JD027874].
Estimate of Radiosonde Dry Bias From Far-Infrared Measurements on the Antarctic Plateau
Rizzi, R.
;Maestri, T.;AROSIO, CARLO
2018
Abstract
The experimental data set of downwelling radiance spectra measured at the ground in clear conditions during 2013 by a Far-Infrared Fourier Transform Spectrometer at Dome-C, Antarctica, presented in Rizzi et al. (2016, https://doi.org/10.1002/2016JD025341) is used to estimate the effect of solar heating of the radiosonde humidity sensor, called dry bias. The effect is quite evident comparing residuals for the austral summer and winter clear cases and can be modeled by an increase of the water vapor concentration at all levels by about 15%. Such an estimate has become possible only after a new version of the simulation code and spectroscopic data has become available, which has substantially improved the modeling of water vapor absorption in the far infrared. The negative yearly spectral bias reported in Rizzi et al. (2016, https://doi.org/10.1002/2016JD025341) is in fact greatly reduced when compared to the same measurement data set.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.