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Theoretical and Applied Climatology

, Volume 123, Issue 1–2, pp 263–275 | Cite as

Precipitable water vapor and its relationship with the Standardized Precipitation Index: ground-based GPS measurements and reanalysis data

  • Isabella BordiEmail author
  • Xiuhua Zhu
  • Klaus Fraedrich
Original Paper

Abstract

Monthly means of ground-based GPS measurements of precipitable water vapor (PWV) from six stations in the USA covering the period January 2007–December 2012 are analyzed to investigate their usefulness for monitoring meteorological wet/dry spells. For this purpose, the relationship between PWV and the Standardized Precipitation Index (SPI) on 1-month timescale is investigated. The SPI time series at grid points close to the stations are computed using gridded precipitation records from the NOAA Climate Prediction Center (CPC) unified precipitation dataset (January 1948–April 2012). GPS measurements are first verified against PWV data taken from the latest ECMWF reanalysis ERA-Interim; these PWV reanalysis data, which extend back to 1979, are then used jointly with CPC precipitation to compute precipitation efficiency (PE), defined as the percentage of total water vapor content that falls onto the surface as measurable precipitation in a given time period. The overall results suggest that (i) PWV time series are dominated by the seasonal cycle with maximum values during summer months, (ii) the comparison between GPS and ERA-Interim PWV monthly data shows good agreement with differences less than 4 mm, (iii) at all stations and for almost all months, PWV is only poorly correlated with recorded precipitation and the SPI, while PE correlates highly with the SPI, providing an estimate of the water availability at a given location and useful information on wet/dry spell occurrence, and (iv) long data records would allow, for each month of the year, the identification of PE thresholds associated with different SPI classes that, in turn, have potential for forecasting meteorological wet/dry spells. Thus, it is through PE that ground-based GPS measurements appear of relevance for assessing wet/dry spells, although there is not a direct relationship between PWV and SPI.

Keywords

Standardize Precipitation Index Precipitable Water Vapor Zenith Tropospheric Delay Climate Prediction Center Precipitation Efficiency 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

PWV data from the SuomiNet GPS network have been freely retrieved from the website http://www.suominet.ucar.edu managed by the UCAR in Boulder, CO, USA. CPC US unified precipitation data are provided by the NOAA/OAR/ESRL PSD, Boulder, CO, USA, from their website at http://www.esrl.noaa.gov/psd/. ERA-Interim data have been provided by the ECMWF, Reading, UK, from their web site http://apps.ecmwf.int/datasets/. Support by the Max Planck Society is acknowledged (KF).

References

  1. Beutler G, Brockman E, Frankhauser S, Gurtner W, Johnson J, Mervart L, Rothacher M, Schaer S, Springer T, Weber R (1996) Bernese GPS software 4.0, University of BerneGoogle Scholar
  2. Bevis M, Businger S, Herring TA, Rocken C, Anthes RA, Ware RH (1992) GPS meteorology: remote sensing of atmospheric water vapor using the global positioning system. J Geophys Res 97:15787–15801CrossRefGoogle Scholar
  3. Bevis M, Businger S, Chiswell S, Herring TA, Anthes RA, Rocken C, Ware RH (1994) GPS meteorology: mapping zenith wet delays onto precipitable water. J Appl Meteorol 33:379–386CrossRefGoogle Scholar
  4. Bevis M, Alsdorf D, Kendrick E, Fortes LP, Forsberg B, Smalley R Jr, Becker J (2005) Seasonal fluctuations in the mass of the Amazon River system and Earth’s elastic response. Geophys Res Lett 32:L16308CrossRefGoogle Scholar
  5. Bock O, Guichard F, Janicot S, Lafore JP, Bouin M-N, Sultan B (2007) Multiscale analysis of precipitable water vapor over Africa from GPS data and ECMWF analyses. Geophys Res Lett 34:L09705. doi: 10.1029/2006GL028039 CrossRefGoogle Scholar
  6. Bordi I, Sutera A (2004) Drought variability and its climatic implications. Glob Plan Chang 40:115–127CrossRefGoogle Scholar
  7. Bordi I, Fraedrich K, Sutera A, Zhu X (2014a) Ground-based GPS measurements: time behavior from half-hour to years. Theor Appl Climatol 115:615–625CrossRefGoogle Scholar
  8. Bordi I, Raziei T, Pereira LS, Sutera A (2014b) Ground-based GPS measurements of precipitable water vapor and their usefulness for hydrological applications. Water Resour Manag. doi: 10.1007/s11269-014-0672-5 Google Scholar
  9. Borsa AA, Agnew DC, Cayan DR (2014) Ongoing drought-induced uplift in the western United States. Science. doi: 10.1126/science.1260279 Google Scholar
  10. Brunner FK, Gu M (1991) An improved model for the dual frequency ionospheric correction of GPS observations. Manuscr Geodaet 16:205–214Google Scholar
  11. Davis JL, Herring TA, Shapiro II, Rogers AEE, Elgered G (1985) Geodesy by radio interferometry: effects of atmospheric modeling errors on estimates of baseline length. Radio Sci 20:1593–1607CrossRefGoogle Scholar
  12. Dee DP et al (2011) ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137:553–597CrossRefGoogle Scholar
  13. Duan J, Bevis M, Fang P, Bock Y, Chiswell S, Businger S, Rocken C, Solheim F, van Hove T, Ware R, McClusky S, Herring TA, King RW (1996) GPS meteorology: direct estimation of the absolute value of precipitable water. J Appl Meteorol 35:830–838CrossRefGoogle Scholar
  14. Gaffen DJ, Robock A, Elliot WP (1992) Annual cycles of tropospheric water vapor. J Geophys Res 97:18185–18193CrossRefGoogle Scholar
  15. Guerova G, Bettems J-M, Brockmann E, Matzler C (2004) Assimilation of the GPS-derived integrated water vapour (IWV) in the MeteoSwiss numerical weather prediction model — a first experiment. Phys Chem Earth 29:177–186CrossRefGoogle Scholar
  16. Guttman NB (1999) Accepting the Standardized Precipitation Index: a calculation algorithm. J Am Water Resour Assoc 35:311–322CrossRefGoogle Scholar
  17. Hagemann S, Bengtsson L, Gendt G (2003) On the determination of atmospheric water vapor from GPS measurements. J Geophys Res 108(D21):4678. doi: 10.1029/2002JD003235 CrossRefGoogle Scholar
  18. Hayes M, Svoboda M, Wall N, Widhalm M (2011) The Lincoln declaration on drought indices: universal meteorological drought index recommended. Bull Am Meteorol Soc 92:485–488CrossRefGoogle Scholar
  19. Higgins RW, Janowiak JE, Yao Y-P (1996) A gridded hourly precipitation data base for the United States (1963–1993). NCEP/Climate Prediction Center ATLAS No. 1. US Department of Commerce, Washington, 47 ppGoogle Scholar
  20. Higgins RW, Shi W, Yarosh E, Joyce R (2000) Improved United States precipitation quality control system and analysis. NCEP/Climate Prediction Center ATLAS No. 7. US Department of Commerce, WashingtonGoogle Scholar
  21. Houghton JT (1977) The physics of atmospheres. Cambridge University Press, CambridgeGoogle Scholar
  22. Lutz JT (1977) Precipitation efficiency under varying atmospheric conditions. Geograph Bull 14:16–28Google Scholar
  23. McKee TB, Doesken NJ, Kleist J (1993) The relationship of drought frequency and duration of time scales. Eighth conference on applied climatology, American Meteorological Society, Jan 17–23, 1993, Anaheim CA, pp. 179–186Google Scholar
  24. Nakamura H, Koizumi K, Mannoji N (2004) Data assimilation of GPS precipitable water vapor into the JMA mesoscale numerical weather prediction model and its impact on rainfall forecasts. J Meteor Soc Jpn 82:441–452CrossRefGoogle Scholar
  25. Niell AE (1996) Global mapping functions for the atmosphere delay at radio wavelengths. J Geophys Res 101:3227–3246CrossRefGoogle Scholar
  26. Ortis de Galisteo JP, Bennouna Y, Toledano C, Cachorro V, Romero P, Andres MI, Torres B (2013) Analysis of the annual cycle of precipitable water vapor over Spain from 10-year homogenized series of GPS data. Q J R Meteorol Soc. doi: 10.1002/qj.2146 Google Scholar
  27. Rocken C, Van Hove T, Ware R (1997) Near real-time GPS sensing of atmospheric water vapor. Geophys Res Lett 24:3221–3224CrossRefGoogle Scholar
  28. Saastamoinen J (1972) Atmospheric correction for the troposphere and stratosphere in radio ranging satellites. In: Henriksen SW, Mancini A, Chovitz BH (eds) The use of artificial satellites for geodesy. Geophys Monogr Ser, vol. 15, pp 247–251, AGU, Washington, D.C. doi: 10.1029/GM015p0247
  29. Seco A, González PJ, Ramírez F, García R, Prieto E, Yagüe C, Fernández J (2009) GPS monitoring of the tropical storm Delta along the Canary Islands track, November 28–29, 2005. Pure Appl Geophys 166:1519–1531CrossRefGoogle Scholar
  30. Smith TL, Benjamin SG, Gutman SI, Sahm SR (2007) Short-range forecast impact from assimilation of GPS-IPW observations into the Rapid Update Cycle. Mon Weather Rev 135:2914–2930CrossRefGoogle Scholar
  31. Sui C-H, Xiaofan L, Ming-Jen Y (2007) On the definition of precipitation efficiency. J Atmos Sci 64:4506–4513CrossRefGoogle Scholar
  32. Tuller SE (1971) The world distribution of annual precipitation efficiency. J Geogr 70:219–223CrossRefGoogle Scholar
  33. Ulich BL (1980) Improved correction for millimeter-wavelength atmospheric attenuation. Astrophys Lett 21:21–28Google Scholar
  34. Vedel H, Huang X-Y, Haase J, Ge M, Calais E (2004) Impact of GPS zenith tropospheric delay data on precipitation forecasts in Mediterranean France and Spain. Geophys Res Lett 31:L02102. doi: 10.1029/2003GL017715 CrossRefGoogle Scholar
  35. Vey S, Dietrich R, Rülke A, Fritsche M (2010) Validation of precipitable water vapor within the NCEP/DOE reanalysis using global GPS observations from one decade. J Clim 23:1675–1695CrossRefGoogle Scholar
  36. Ware RH, Fulker DW, Stein SA, Anderson DN, Avery SK, Clark RD, Droegemeier KK, Kuettner JP, Minster JB, Sorooshian S (2000) SuomiNet: a real-time national GPS network for atmospheric research and education. Bull Am Meteorol Soc 81:677–694CrossRefGoogle Scholar
  37. Wells N, Goddard S, Hayes MJ (2004) A self-calibrating Palmer Drought Severity Index. J Clim 17:2335–2351CrossRefGoogle Scholar
  38. Xie P, Yatagai A, Chen M, Hayasaka T, Fukushima Y, Liu C, Yang S (2007) A gauge-based analysis of daily precipitation over East Asia. J Hydrometeorol 8:607–626CrossRefGoogle Scholar
  39. Zhang M, Lu B, Song W (1999) A method for dual-frequency ionospheric time-delay correcting using a C/A code GPS receiver. J Electron 16:66–72Google Scholar

Copyright information

© Springer-Verlag Wien 2015

Authors and Affiliations

  1. 1.Department of PhysicsSapienza University of RomeRomeItaly
  2. 2.Universität HamburgHamburgGermany
  3. 3.Max Planck Institute für MeteorologieHamburgGermany

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