1. A global, 2-hourly atmospheric precipitable water dataset from ground-based GPS measurements and its scientific applications Junhong (June) Wang NCAR/EOL/T II MES 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 1)To develop an analysis technique to derive atmospheric precipitable water (PW) using existing IGS tropospheric product (zenith tropospheric delay, ZTD) on a global scale; 2)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; 3)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. How does it work and Why using GPS data? All weather Continuous measurements High temporal resolution High accuracy (~1-2 mm) Long term stability Total delay = Ionosphere + dry + wet

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

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

6. The role of radiosondes observations in the climatic record is limited, in part, by sensor characteristics that vary substantially in time and space. 1.Systematic errors 2.Spatial and temporal inhomogeneity 3.Spatial sampling errors 4.Diurnal sampling errors

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

8. Comparison results 1.Systematic biases for 10 types of radiosondes 2.Day/night differences of systematic biases 3.Characteristics of systematic biases 4.Temporal inhomogeneity 5.Impacts on long-term changes 6.Diurnal sampling errors

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

10. Day/Night Differences Vaisala RS90

11. Characteristics of systematic biases

12. Temporal inhomogeneity VIZRS80H

13. Impacts on long-term changes VIZRS80H

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

15. Summary 1.Dataset: A global, 10-year, 2-hourly GPS-PW dataset is created for various scientific applications. You are welcome to use it. 2.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. 3.Day/night differences: The dry bias in Vaisala sondes has larger magnitudes during the day than at night, especially for RS90. 4.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 l Correlation coefficient of water vapor and station pressure is -0.71 l Storms with winds of 70 mph have coefficient of -0.76 From John Braun (UCAR/COSMIC)

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

18. Diurnal variations of PW

20. Error Analysis

21. Global ZPD data from International GNSS Service (IGS) ~378 stations, 1997-present, 2-hourly

22. DATA: IGS ZTD data: ~340 stations, 1997-present, 2-hourly

23. How does it work and Why use GPS data? All weather Continuous measurements High temporal resolution High accuracy (~1 mm) Long term stability Total delay = Ionosphere + dry + wet ZWD = ZPD - ZHD Output: PW =  * ZWD  = f (Tm) Input: ZPD = ZHD + ZWD

24. Day/Night Differences Day time comparison RS92 vs CFH TCSP, Costa Rica Jul 2005

25. Temporal inhomogeneity: RS80 to RS92 RS80ARS92

26. Day/Night Differences Vaisala RS90 RS80H RS90