4.4 Evaluation of GRUAN and DigiCORA Humidity Corrections to Vaisala RS92 Sondes in DYNAMO

Wednesday, 11 June 2014: 11:15 AM
Salon A-B (Denver Marriott Westminster)
Hungjui Yu, National Taiwan University, Taipei, Taiwan; and P. E. Ciesielski, J. Wang, R. Johnson, H. Vomel, and H. C. Kuo

The upper-air sounding network from the DYNAMO (Dynamics of the MJO) field campaign, centered over the Indian Ocean (IO), collected over 13,000 high vertical resolution soundings primarily during its intensive observing periods from October to December 2011. These sounding observations are used to study the MJO initiation over the IO, where deep convection typically originates. Since one of the primary objectives of DYNAMO was to determine the mechanisms by which the troposphere is moistened in the initiation stage of the MJO, a special effort was undertaken to create the “best possible” set of sounding humidity data. Two humidity correction methods were applied to data from Vaisala RS92 sonde which was the primary radiosonde type used during DYNAMO. The first method, developed by the GRUAN (GCOS Reference Upper-Air Network) community, is refereed as the GRUAN correction. The second, developed by Vaisala and built into the sounding ground station software, is referred as the DigiCORA correction. This study uses the sounding data collected at three sites (Gan, Manus and Nauru) to evaluate the DigiCORA and GRUAN humidity corrections. Comparisons of mean vertical profiles of relative humidity (RH) show only small differences (on the order of a few percent at any given level) between DigiCORA and GRUAN corrections. One reason for the differences is that GRUAN applies a small calibration correction of 0-3% (at T<0°C) which is not present in the DigiCORA correction. Good agreement is found between radiosonde data corrected by both methods and ground-based microwave radiometer (MWR) estimates of TPW with mean differences less than 0.3mm (< 1%). Relative to the MWR TPW estimates, both sounding corrections show a slight (1-2%) daytime dry bias and nighttime moist bias.
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