Junhong (June) Wang NCAR/EOL/T II MES

1. A global, 2-hourly atmospheric precipitable water dataset from ground-based GPS measurements and its scientific applications Junhong (June) Wang NCAR/EOL/TIIMES Co-Authors: Liangying Zhang (NCAR/EOL) Acknowledgement: Aiguo Dai, Joel Van Baelen, Teresa Van Hove, Gunnar Elgered, Todd Humphreys, John Braun, Imke Durre and Dennis Shea. Thanks support from NOAA/OGP

2. Goals • To develop an analysis technique to derive atmospheric precipitable water (PW) using existing IGS tropospheric product (zenith tropospheric delay, ZTD) on a global scale; • To apply the technique to global ZTD data from 1997 to present to create a global, 2-hourly PW dataset, and make the dataset available to the public; • To use the data for various climate and weather studies: • To quantify time- and space-dependent biases in global radiosonde humidity records • To document and understand PW diurnal variations • To create a corrected global radiosonde PW dataset

3. Total delay = Ionosphere + dry + wet How does it work and Why using GPS data? • All weather • Continuous measurements • High temporal resolution • High accuracy (~1-2 mm) • Long term stability

4. Global ZPD data from International GNSS Service (IGS)~378 stations, 1997-present, 2-hourly See Wang et al. (2005, 2006)

5. Analysis Technique and Validations Input: ZPD = ZHD + ZWD ZWD = ZPD - ZHD Output: PW =  * ZWD  = f (Tm) Ps from global surface synoptic observations with adjustment Tm from NCEP/NCAR Reanalysis with horizontal and vertical interpolation (Wang et al. 2005) Wang et al. 2006 (JGR, revised) for the technique and the dataset Comparisons with radiosonde, MWR and other data

6. The role of radiosondes observations in the climatic record is limited, in part, by sensor characteristics that vary substantially in time and space. • Systematic errors • Spatial and temporal inhomogeneity • Spatial sampling errors • Diurnal sampling errors

7. Co-located GPS and radiosonde stations (< 50 km in distance and < 100 m in elevation)

8. Comparison results • Systematic biases for 10 types of radiosondes • Day/night differences of systematic biases • Characteristics of systematic biases • Temporal inhomogeneity • Impacts on long-term changes • Diurnal sampling errors

9. MRZ/Mars IM-MK3 Vaisala Systematic biases for 10 types of radiosondes

10. Day/Night Differences Vaisala RS90

11. Characteristics of systematic biases

12. VIZ RS80H Temporal inhomogeneity

13. VIZ RS80H Impacts on long-term changes

14. Diurnal sampling errors (%) of twice daily radiosonde data DJF 82% MAM 88% • U.S.A.: • (Dai et al., 2002) • < 3% for 2/day • 5-10% for 1/day JJA 92% SON 92%

15. Summary • Dataset: A global, 10-year, 2-hourly GPS-PW dataset is created for various scientific applications. You are welcome to use it. • Systematic biases: systematic biases in three widely-used radiosonde types, including dry biases in Vaisala sondes and wet biases in MRZ and IM-MK3 radiosondes. • Day/night differences: The dry bias in Vaisala sondes has larger magnitudes during the day than at night, especially for RS90. • Characteristics of systematic biases: The dry bias in Vaisala sonde increases with PW. The wet bias does not vary significantly with PW for MRZ, but prevails at PW < 30 mm for IM-MK3. • 5. Temporal inhomogeneity and impacts on long-term variations: The radiosonde type change from VIZ to Vaisala at a U.S. station was detected by the time series of PW differences between radiosonde and GPS. Such change would have significant impact on long-term trend estimate. • 6. Diurnal sampling errors: Generally within 2% twice daily soundings, but can be large for once a day

16. Hurricane Pressure and PW • Correlation coefficient of water vapor and station pressure is -0.71 • Storms with winds of 70 mph have coefficient of -0.76 From John Braun (UCAR/COSMIC)

17. Foster et al. 2003 Connections between water vapor and precipitation (Foster et al. 2000, 2003; Champollion et al. 2004)