Quality-Controlled Upper-Air Sounding Dataset for TiMREX/SoWMEX: Development and Corrections

Abstract

During the Terrain-Influenced Monsoon Rainfall Experiment (TiMREX), which coincided with Taiwan's Southwesterly Monsoon Experiment-2008 (SoWMEX-08), the upper-air sounding network over the Taiwan region was enhanced by increasing the radiosonde (''sonde'') frequency at its operational sites and by adding several additional sites (three that were land based and two that were ship based) and aircraft dropsondes. During the special observing period of TiMREX (from 15 May to 25 June 2008), 2330 radiosonde observations were successfully taken from the enhanced network. Part of the challenge of processing the data from the 13 upsonde sites is that four different sonde types (Vaisala RS80, Vaisala RS92, Meisei, and Graw) were used. Post-field phase analyses of the sonde data revealed a significant dry bias in many of the sondes-in particular, in the data from the Vaisala RS80 sondes that were used at four sites. In addition, contamination of the sonde data by the ship's structure resulted in poor-quality low-level thermodynamic data at a key oceanic site. This article examines the methods used to quality control the sonde data and, when possible, to correct them. Particular attention is given to the correction of the humidity field and its impact on various convective measures. Comparison of the corrected sonde humidity data with independent estimates shows good agreement, suggesting that the corrections were effective in removing many of the sonde humidity errors.Examining various measures of convection shows that use of the humidity-corrected sondes gives a much different perspective on the characteristics of convection during TiMREX. For example, at the RS80 sites, use of the corrected humidity data increases the mean CAPE by ;500 J kg 21 , decreases mean convective inhibition (CIN) by 80 J kg 21 , and increases the midlevel convective mass flux by greater than 70%. Ultimately, these corrections will provide more accurate moisture fields for diagnostic analyses and modeling studies.

Comparison of Vaisala radiosondes RS41 and RS92 launched over the oceans from the Arctic to the tropics

“…The correction was seldom more than 5 % RH when the RS92 temperature exceeded −60 • C. The correction was large for RS92 radiosonde values in the range 15-50 % RH and temperatures of −80 • C, with a maximum of +8.93 % RH. This pattern is similar to that of the correction table for the RS80 radiosonde in the daytime reported by Ciesielski et al (2010) (their Fig. 7b), but the values in Table 5 are much smaller.…”

“…This pattern is consistent with the results of Jauhiainen et al (2014), who indicated that the difference was largely due to the dissimilar approaches used to compensate for the heating effect of solar radiation on the humidity sensor. Similar dry biases were reported for the RS92 radiosonde with the earlier version of DigiCORA (Vömel et al, 2007;Yoneyama et al, 2008), although the dry bias was generally absent from later observations (Ciesielski et al, 2014;Yu et al, 2015) because the bias due to solar heating was removed by a correction scheme included in the v3.64 software or developed by Wang et al (2013). Figure 9 shows the relative difference of relative humidity in the daytime between the RS92 and RS41 radiosondes.…”

“…In situ aerological observations are also indispensable for validating satellitederived meteorological data (e.g., Fujita et al, 2008); for assessing long-term trends in the upper atmosphere (e.g., Thorne et al, 2005;Maturilli and Kayser, 2016); and for other meteorological research, including assimilation experiments and air-sea interaction studies (e.g., Inoue et al, 2013Inoue et al, , 2015Kawai et al, 2014). Efforts to improve the quality of radiosonde data have continued to the present time (e.g., Ciesielski et al, 2014;Bodeker et al, 2016). One consequence of the technological advancements has been the need to account for accuracy differences following radiosonde upgrades in the long-term continuous datasets (Wang et al, 2013).…”

Near real-time water vapor tomography using ground-based GPS and meteorological data: long-term experiment in Hong Kong

“…However, these perturbation factors usually do not occur in the normal atmosphere. Furthermore, although the accuracy of radiosonde data is good overall, there are still unexpected errors in RS profiles and sometimes the errors are rather big (Wang et al, 2002;Ciesielski et al, 2010). In that case, it is not appropriate to compare the tomographic solution with radio sounding.…”

Development of time-varying global gridded Ts-Tm model for precise GPS-PWV retrieval

“…Several methods have been studied for decades, such as radio sounding, water vapor radiometer, sun photometers, GPS and others (Ciesielski et al, 2010;Perez-Ramirez et al, 2014;Li et al, 2016;Campmany et al, 2010;Liu et al, 2013). Compared with traditional water vapor observations, ground-based GPS water vapor measurement has advantages in high accuracy, high spatial-temporal resolution, all-weather availability and low-cost (Pacione and Vespe, 2008;Haase et al, 2003;Lee et al, 2010;Means, 2013;Lu et al, 2015).…”