Evaluation of two Vaisala RS92 radiosonde solar radiative dry bias correction algorithms

Abstract

Abstract. Solar heating of the relative humidity (RH) probe on Vaisala RS92 radiosondes results in a large dry bias in the upper troposphere. Two different algorithms (Miloshevich et al., 2009, MILO hereafter; and Wang et al., 2013, WANG hereafter) have been designed to account for this solar radiative dry bias (SRDB). These corrections are markedly different with MILO adding up to 40 % more moisture to the original radiosonde profile than WANG; however, the impact of the two algorithms varies with height. The accuracy of these two algorithms is evaluated using three different approaches: a comparison of precipitable water vapor (PWV), downwelling radiative closure with a surface-based microwave radiometer at a high-altitude site (5.3 km m.s.l.), and upwelling radiative closure with the space-based Atmospheric Infrared Sounder (AIRS).The PWV computed from the uncorrected and corrected RH data is compared against PWV retrieved from groundbased microwave radiometers at tropical, midlatitude, and arctic sites. Although MILO generally adds more moisture to the original radiosonde profile in the upper troposphere compared to WANG, both corrections yield similar changes to the PWV, and the corrected data agree well with the groundbased retrievals.The two closure activities -done for clear-sky scenesuse the radiative transfer models MonoRTM and LBLRTM to compute radiance from the radiosonde profiles to compare against spectral observations. Both WANG-and MILOcorrected RHs are statistically better than original RH in all cases except for the driest 30 % of cases in the downwelling experiment, where both algorithms add too much water vapor to the original profile. In the upwelling experiment, the RH correction applied by the WANG vs. MILO algorithm is statistically different above 10 km for the driest 30 % of cases and above 8 km for the moistest 30 % of cases, suggesting that the MILO correction performs better than the WANG in clear-sky scenes. The cause of this statistical significance is likely explained by the fact the WANG correction also accounts for cloud cover -a condition not accounted for in the radiance closure experiments.

Intercomparison of Integrated Water Vapor Measurements at High Latitudes from Co-Located and Near-Located Instruments

“…About 50% of the corrections were smaller than 0.02 kg/m 2 . The magnitude of the differences at this site depended on the IWV and was comparable to what could be expected at midlatitude sites [50]. Although the correction may be relevant for individual launches, it had an overall small effect on the statistical distribution of the IWV.…”

“…As mentioned in Section 2.1, the radiosondes are affected by a dry bias. Although the effect is more pronounced at lower latitudes, there are some small residual effects at higher latitudes [50]. The RS data were therefore corrected using the correction proposed by Wang et al [29].…”

Characterizing relative humidity with respect to ice in midlatitude cirrus clouds as a function of atmospheric state

“…[] and Wang et al . [], both of which are height‐dependent corrections, for two reasons (results not shown) [ Dzambo et al ., ]: Both algorithms significantly improve the radiosonde's RH profile such that precipitable water vapor (PWV) derived from the Miloshevich et al . [] and Wang et al .…”

“…The primary source of the RH dry bias is from solar radiative heating of the humidity sensor [Vömel et al, 2007], while another (lesser) source of uncertainty includes errors in the calibration model [Miloshevich et al, 2009]. The temporal response time of the sensor, known as time lag, is another potential source of uncertainty but is not considered because it contributes, at most, 1-2% error in RH [Wang et al, 2013;Dzambo et al, 2016] and has a mean error of 0% [Dzambo et al, 2016]. A number of RS92 RH correction algorithms have been developed [e.g., Cady-Pereira et al, 2008;Yoneyama et al, 2008;Rowe et al, 2008;Miloshevich et al, 2009;Wang et al, 2013].…”

“…A number of RS92 RH correction algorithms have been developed [e.g., Cady-Pereira et al, 2008;Yoneyama et al, 2008;Rowe et al, 2008;Miloshevich et al, 2009;Wang et al, 2013]. We advocate the use of the correction algorithms proposed by Miloshevich et al [2009] and Wang et al [2013], both of which are height-dependent corrections, for two reasons (results not shown) [Dzambo et al, 2016]:…”