Improved Daytime Column-Integrated Precipitable Water Vapor from Vaisala Radiosonde Humidity Sensors

Abstract

Accurate water vapor profiles from radiosondes are essential for long-term climate prediction, weather prediction, validation of remote sensing retrievals, and other applications. The Vaisala RS80, RS90, and RS92 radiosondes are among the more commonly deployed radiosondes in the world. However, numerous investigators have shown that the daytime water vapor profiles measured by these instruments present a significant dry bias due to the solar heating of the humidity sensor. This bias in the column-integrated precipitable water vapor (PWV), along with variability due to calibration, can be removed by scaling the humidity profile to agree with the PWV retrieved from a microwave radiometer (MWR), as has been demonstrated by several previous studies. Infrared radiative closure analyses have shown that the MWR PWV does not present daytime versus nighttime differences; thus, scaling by the MWR is a possible approach for removing the daytime dry bias. However, MWR measurements are not routinely available at all radiosonde launch sites. Starting from a long-term series of sonde and MWR PWV measurements from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site, the authors have developed a simple correction to the column-integrated sonde PWV, derived from an analysis of the ratio of the MWR and sonde measurements; this correction is a function of the atmospheric transmittance as determined by the solar zenith angle, and it effectively removes the daytime dry bias at all solar zenith angles. The correction was validated by successfully applying it to an independent dataset from the ARM tropical western Pacific (TWP) site.

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

“…If the RH sensor is warmer than the ambient environment due to solar heating, then the measured RH (as computed by Vaisala's DigiCORA ® software) will be lower than its actual value. Many correction algorithms have been developed (e.g., Vömel et al, 2007b;Cady-Pereira et al, 2008;Yoneyama et al, 2008;Miloshevich et al, 2009;Wang et al, 2013) to correct for this solar radiative dry bias (SRDB). Nearly all of the aforementioned algorithms correct RH as a function of pressure, solar elevation (zenith) angle, and/or RH itself.…”

The Midlatitude Continental Convective Clouds Experiment (MC3E) sounding network: operations, processing and analysis

“…Data points that are outside of these limits are automatically flagged. The SONDEADJUST value-added product (VAP) takes the sounding that has been corrected for known biases and then uses an independent retrieval of PWV to scale the radiosonde water vapor measurements (Turner et al, 2003;Cady-Pereira et al, 2008). During MC3E, there was a microwave radiometer (Turner et al, 2007;Gaustad et al, 2011) deployed at C1.…”

The Radiative Heating in Underexplored Bands Campaigns

“…In this analysis, the PWV retrieved from a 183-GHz radiometer is used to scale the observed radiosonde humidity profile; scaling greatly reduces the uncertainty in the radiosonde humidity profile and mitigates any diurnal bias in the radiosonde observations (Turner et al 2003;Cady-Pereira et al 2008). It should be noted that the nighttime water vapor profile measurements (i.e., when the sun is below the horizon) made by the Vaisala RS-92, the type of radiosonde used in both RHUBC experiments, agree very well with chilled mirror hygrometers and have little bias (Miloshevich et al 2009).…”