Evaluation of Vaisala RS-92 Radiosonde Water Vapor Dry Bias Correction Algorithms Using Long-Term ARM Datasets

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Sunday, 2 February 2014
Hall C3 (The Georgia World Congress Center )
Andrew M. Dzambo, University of Oklahoma, Norman, OK; and D. D. Turner

Handout (1.6 MB)

Vaisala RS-92 Radiosondes are the most widely used radiosondes in the world. Water vapor measurements by these radiosondes, especially in the upper troposphere, exhibit a dry bias due to heating of the RH sensor. Although Vaisala released updated software that corrects for dry bias, the correction algorithm is proprietary and only corrects for sonde launches since late 2011 (or later, depending on the time the software was installed at the launch location). Wang et al. 2013 and Miloshevich et al. 2009 have both developed correction algorithms to correct for this radiatively induced dry bias in historical data. Both correction algorithms were applied to many years worth of radiosonde data from three climatically different Atmospheric Radiation Measurement (ARM) sites: (a) Barrow, AK, (b) Lamont, OK, and (c) Manus Island, Papua New Guinea. The precipitable water vapor (PWV) integrated along the sonde's flight path is compared to the PWV measured by a microwave radiometer (MWR) that is co-located with the sonde launch. Using the difference in PWV between the sonde and the MWR, results verify that both correction algorithms reduce the mean dry bias. The Wang correction yielded a better agreement in PWV with the MWR than the Miloshevich correction. This holds true at all three sites. Finally, both Wang and Miloshevich correction algorithms, along with the Vaisala dry bias correction, are evaluated against micropulse lidar (MPL) data in detecting high altitude clouds (i.e. where RH with respect to ice is greater than 100%). The numbers of saturated and unsaturated points (with respect to ice) are compared to MPL cloud observations to verify cloud detection by the sonde. Preliminary results show that both correction algorithms detect a higher percentage of clouds above ice supersaturation. Future work includes creating distributions of relative humidity with respect to ice in different altitude regions of the ice cloud (e.g., bottom 25%, middle 50%, and upper 25%) using the different corrected radiosonde datasets.