Articles | Volume 8, issue 10
Research article
02 Oct 2015
Research article |  | 02 Oct 2015

Altitude misestimation caused by the Vaisala RS80 pressure bias and its impact on meteorological profiles

Y. Inai, M. Shiotani, M. Fujiwara, F. Hasebe, and H. Vömel

Abstract. Previous research has found that conventional radiosondes equipped with a traditional pressure sensor can be subject to a pressure bias, particularly in the stratosphere. This study examines this pressure bias and the resulting altitude misestimation, and its impact on temperature, ozone, and water vapor profiles is considered using data obtained between December 2003 and January 2010 during the Soundings of Ozone and Water in the Equatorial Region (SOWER) campaigns. The payload consisted of a radiosonde (Vaisala RS80), ozone and water vapor sondes, and a global positioning system (GPS) sensor. More than 30 soundings are used in this study. As GPS height data are thought to be highly accurate, they can be used to calculate pressure. The RS80 pressure bias in the tropical stratosphere is estimated to be −0.4 ± 0.2 hPa (1σ) between 20 and 30 km. As this pressure bias is negative throughout the stratosphere, it leads to systematic overestimation of geopotential height by 43 ± 23, 110 ± 40, and 240 ± 92 m (1σ) at 20, 25, and 30 km, respectively when it is calculated by using the hypsometric equation. Because of the altitude overestimation, we see some offsets in observation parameters having a vertical gradient such as temperature, ozone, and water vapor. Those offsets in the meteorological soundings obtained using the RS80 may have generated an artificial trend in the meteorological records when radiosondes were changed from the RS80, which had no GPS unit, to the new ones with a GPS unit. Therefore, it is important to take those offsets into account in climate change studies.

Short summary
For conventional soundings, the pressure bias of radiosonde leads to an altitude misestimation, which can lead to offsets in any meteorological profile. Therefore, we must take this issue into account to improve historical data sets.