Climate applications of atmospheric precipitable water estimates from ground-based GPS measurements

Junhong (June) Wang

NCAR/EOL

Water vapor plays a central role in atmospheric radiation, the hydrological cycle and in understanding and predicting global climate change. Therefore, it is vital to advance the understanding of water vapor variability and change, but such advancement is hampered by inadequate observations. Observations of atmospheric water vapor have traditionally been taken through balloon-borne radiosondes. Unfortunately, the usefulness of radiosonde data in climate studies is limited, in part, by sensor characteristics that vary substantially in time and space. Several studies and reports have called for creating global water vapor datasets with sufficient accuracy and temporal resolution, and more importantly long-term stability. None of existing radiosonde, satellite or blended datasets can meet the requirements for the new water vapor datasets. Global GPS measurements of atmospheric precipitable water (PW), however, can meet all of these requirements with an accuracy of < 2 mm, and can be considered as a calibration standard to validate other measurements.

A global, ten-year (1997 - 2006), two-hourly data set of atmospheric precipitable water (PW) was produced from ground-based Global Positioning System (GPS) measurements of zenith tropospheric delay (ZTD) at approximately 370 International Global Navigation Satellite Systems (GNSS) Service (IGS) ground stations and ~200 SuomiNet stations in the U.S. The PW dataset will be updated regularly for community use. The GPS PW dataset has been used to validate global radiosonde and reanalysis PW data and study the PW diurnal cycle. This talk will focus on using the GPS PW dataset to quantify three types of systematic errors in global radiosonde PW data, including measurement biases for each of the fourteen radiosonde types along with their characteristics, long-term temporal inhomogeneity and diurnal sampling errors of once and twice daily radiosonde data. Fourteen types of radiosondes use three types of humidity sensors: capacitive polymer, carbon hygristor and goldbeater’s skin. The capacitive polymer generally shows mean dry bias of -1.19 mm (-6.8%) with larger magnitudes during the day than at night, especially for Vaisala RS90 and RS92 radiosondes. On the other hand, the carbon hygristor and goldbeater’s skin hygrometers have mean moist biases of 1.01 mm (3.4%) and 0.76 mm (5.4%), respectively. The time series of monthly mean PW differences between the radiosonde and GPS are able to detect significant changes associated with known radiosonde type changes. Such changes would have a significant impact on the long-term trend estimate of water vapor. Diurnal sampling errors of twice daily radiosonde data are generally within 2%, but can be as much as 10-15% for the once daily soundings. Other and future applications of the GPS PW dataset will be briefly presented too.