Skip to main content
Log in

Precipitable water vapor fusion: an approach based on spherical cap harmonic analysis and Helmert variance component estimation

  • Original Article
  • Published:
Journal of Geodesy Aims and scope Submit manuscript

Abstract

Precipitable water vapor (PWV) is an important parameter in Earth’s atmosphere, and its spatiotemporal variations influence Earth’s energy transfer and weather changes. PWV can be monitored and retrieved by plenty of techniques with varying spatiotemporal resolutions, accuracies, and systematic biases. In this study, we fused PWVs from global navigation satellite system (GNSS), moderate resolution imaging spectroradiometer (MODIS), and European Centre for Medium‐Range Weather Forecasts ERA-5 reanalysis to generate PWV maps with combined resolution and accuracy. Before fusing the data, we apply a bias correction to remove the systematic biases among the three datasets. Then, the fusion is performed by an approach based on the spherical cap harmonic (SCH) analysis and the Helmert variance component estimation (HVCE). The core idea is that the SCH model represents the combined PWV field on the sphere, and the HVCE determines the weights of different datasets. We generate more than 300 combined PWV maps in North America in 2018, which are then validated by a different set of GNSS PWV. Results show that the combined PWVs have mean bias of 0.2 mm, standard deviation of 1.9 mm, and root mean square error of 2.0 mm. The fused PWVs present better accuracy than the MODIS and ERA-5 PWVs. In addition, our proposed approach effectively suppressed regional biases among different datasets. The fused PWV exhibits a better and unified accuracy compared with the MODIS and ERA-5 PWVs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Data availability

The GNSS PWV data used in this paper can be freely accessed at https://www.suominet.ucar.edu/data/pwvConusHourly/. The MODIS PWV data can be accessed at https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/61/. The ECMWF ERA5 PWV data can be accessed at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form. The codes associated with the spherical cap harmonic analysis are available upon request to Dr. Bao Zhang (sggzb@whu.edu.cn).

References

  • Alshawaf F, Fersch B, Hinz S, Kunstmann H, Mayer M, Meyer FJ (2015) Water vapor mapping by fusing InSAR and GNSS remote sensing data and atmospheric simulations. Hydrol Earth Syst Sci 19(12):4747–4764

    Google Scholar 

  • American Meteorological Society (AMS) (2000) Glossary of meteorology, 2nd edn, Boston

  • Askne J, Nordius H (1987) Estimation of tropospheric delay for microwaves from surface weather data. Radio Sci 22(3):379–386

    Google Scholar 

  • 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 Atmos 97(D14):15787–15801

    Google Scholar 

  • 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(3):379–386

    Google Scholar 

  • Bock O, Keil C, Richard E, Flamant C, Bouin MN (2005) Validation of precipitable water from ECMWF model analyses with GPS and radiosonde data during the MAP SOP. Q J R Meteorol Soc 131(612):3013–3036

    Google Scholar 

  • Chang L, Gao G, Jin S, He X, Xiao R, Guo L (2015) Calibration and evaluation of precipitable water vapor from MODIS infrared observations at night. IEEE Trans Geosci Remote Sens 53(5):2612–2620

    Google Scholar 

  • Chen SH, Zhao Z, Haase JS, Chen A, Vandenberghe F (2008) A study of the characteristics and assimilation of retrieved MODIS total precipitable water data in severe weather simulations. Mon Weather Rev 136(9):3608–3628

    Google Scholar 

  • Dach R, Hugentobler U, Fridez P, Meindl M (2007) Bernese GPS software version 5.0, vol 640. Astronomical Institute, University of Bern, Bern, p 114

    Google Scholar 

  • 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(6):1593–1607

    Google Scholar 

  • De Franceschi G, De Santis A, Pau S (1994) Ionospheric mapping by regional spherical harmonic analysis: new developments. Adv Space Res 14(12):61–64

    Google Scholar 

  • De Santis A (1991) Translated origin spherical cap harmonic analysis. Geophys J Int 106(1):253–263

    Google Scholar 

  • De Santis A, Torta JM (1997) Spherical cap harmonic analysis: a comment on its proper use for local gravity field representation. J Geod 71(9):526–532

    Google Scholar 

  • Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G, Bauer DP, Bechtold P (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137(656):553–597

    Google Scholar 

  • European Centre for Medium-Range Weather Forecasts (2017) ERA5 reanalysis. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory (updated monthly). https://doi.org/10.5065/D6X34W69

  • Gao B (2015) MODIS atmosphere L2 water vapor product. NASA MODIS Adaptive Processing System, Goddard Space Flight Center, USA, http://dx.doi.org/10.5067/MODIS/MOD05_L2.006

  • Gao BC, Kaufman YJ (1998) The MODIS near-IR water vapor algorithm. Algorithm Theoretical Basis Document, ATBD-MOD, p 5

    Google Scholar 

  • Grafarend EW (1984) Variance-covariance components estimation, theoretical results and geodetic applications. In: 16th European meeting of statisticians

  • Haines GV (1985) Spherical cap harmonic analysis. J Geophys Res Solid Earth 90(B3):2583–2591

    Google Scholar 

  • Haines GV (1988) Computer programs for spherical cap harmonic analysis of potential and general fields. Comput Geosci 14(4):413–447

    Google Scholar 

  • Haines GV, Torta JM (1994) Determination of equivalent current sources from spherical cap harmonic models of geomagnetic field variations. Geophys J Int 118(3):499–514

    Google Scholar 

  • Hubanks P (2017) MODIS Atmosphere QA Plan for Collection 061, version 9. NASA Goddard Space Flight Center

  • Hwang C (1991) Orthogonal functions over the oceans and applications to the determination of orbit error, geoid and sea surface topography from satellite altimetry. Doctoral dissertation, The Ohio State University

  • Hwang C, Chen SK (1997) Fully normalized spherical cap harmonics: application to the analysis of sea-level data from TOPEX/POSEIDON and ERS-1. Geophys J Int 129(2):450–460

    Google Scholar 

  • Jarvis A, Reuter HI, Nelson A, Guevara E (2008) Hole-filled seamless SRTM data V4, International Centre for Tropical Agriculture (CIAT). http://srtm.csi.cgiar.org

  • King MD, Kaufman YJ, Menzel WP, Tanre D (1992) Remote sensing of cloud, aerosol, and water vapor properties from the Moderate Resolution Imaging Spectrometer (MODIS). IEEE Trans Geosci Remote Sens 30(1):2–27

    Google Scholar 

  • King MD, Menzel WP, Kaufman YJ, Tanré D, Gao BC, Platnick S, Ackerman SA, Remer LA, Pincus R, Hubanks PA (2003) Cloud and aerosol properties, precipitable water, and profiles of temperature and water vapor from MODIS. IEEE Trans Geosci Remote Sens 41(2):442–458

    Google Scholar 

  • Koch KR, Kusche J (2002) Regularization of geopotential determination from satellite data by variance components. J Geod 76(5):259–268

    Google Scholar 

  • Lee SW, Kouba J, Schutz B, Kim DH, Lee YJ (2013) Monitoring precipitable water vapor in real-time using global navigation satellite systems. J Geod 87(10–12):923–934

    Google Scholar 

  • Li Z (2004) Production of regional 1 km × 1 km water vapor fields through the integration of GPS and MODIS data. http://eprints.gla.ac.uk/48158/

  • Li J, Chao D, Ning J (1995) Spherical cap harmonic expansion for local gravity field representation. Manuscr Geod 20(4):265

    Google Scholar 

  • Li Z, Muller JP, Cross P (2003) Comparison of precipitable water vapor derived from radiosonde, GPS, and Moderate-Resolution Imaging Spectroradiometer measurements. J Geophys Res Atmos 108(D20)

  • Li Z, Muller JP, Cross P, Fielding EJ (2005) Interferometric synthetic aperture radar (InSAR) atmospheric correction: GPS, Moderate Resolution Imaging Spectroradiometer (MODIS), and InSAR integration. J Geophys Res Solid Earth 110(B3)

  • Li X, Zus F, Lu C, Dick G, Ning T, Ge M, Wickert J, Schuh H (2015a) Retrieving of atmospheric parameters from multi-GNSS in real time: validation with water vapor radiometer and numerical weather model. J Geophys Res Atmos 120(14):7189–7204

    Google Scholar 

  • Li X, Dick G, Lu C, Ge M, Nilsson T, Ning T, Wickert J, Schuh H (2015b) Multi-GNSS meteorology: real-time retrieving of atmospheric water vapor from BeiDou, Galileo, GLONASS, and GPS observations. IEEE Trans Geosci Remote Sens 53(12):6385–6393

    Google Scholar 

  • Liu J, Chen R, Wang Z, Zhang H (2011) Spherical cap harmonic model for mapping and predicting regional TEC. GPS Solut 15(2):109–119

    Google Scholar 

  • Niell AE, Coster AJ, Solheim FS, Mendes VB, Toor PC, Langley RB, Upham CA (2001) Comparison of measurements of atmospheric wet delay by radiosonde, water vapor radiometer, GPS, and VLBI. J Atmos Ocean Technol 18(6):830–850

    Google Scholar 

  • Parkinson CL (2003) Aqua: an Earth-observing satellite mission to examine water and other climate variables. IEEE Trans Geosci Remote Sens 41(2):173–183

    Google Scholar 

  • Prasad AK, Singh RP (2009) Validation of MODIS Terra, AIRS, NCEP/DOE AMIP-II Reanalysis-2, and AERONET Sun photometer derived integrated precipitable water vapor using ground-based GPS receivers over India. J Geophys Res Atmos 114(D5)

  • Reuter HI, Nelson A, Jarvis A (2007) An evaluation of void-filling interpolation methods for SRTM data. Int J Geogr Inf Sci 21(9):983–1008

    Google Scholar 

  • Rocken C, Ware R, Van Hove T, Solheim F, Alber C, Johnson J, Bevis M, Businger S (1993) Sensing atmospheric water vapor with the Global Positioning System. Geophys Res Lett 20(23):2631–2634

    Google Scholar 

  • Rocken C, Van Hove T, Ware R (1997) Near real-time GPS sensing of atmospheric water vapor. Geophys Res Lett 24(24):3221–3224

    Google Scholar 

  • Roman J, Knuteson R, August T, Hultberg T, Ackerman S, Revercomb H (2016) A global assessment of NASA AIRS v6 and EUMETSAT IASI v6 precipitable water vapor using ground-based GPS SuomiNet stations. J Geophys Res Atmos 121(15):8925–8948

    Google Scholar 

  • Shi F, Xin J, Yang L, Cong Z, Liu R, Ma Y, Wang Y, Lu X, Zhao L (2018) The first validation of the precipitable water vapor of multisensor satellites over the typical regions in China. Remote Sens Environ 206:107–122

    Google Scholar 

  • Thébault E, Mandea M, Schott JJ (2006) Modeling the lithospheric magnetic field over France by means of revised spherical cap harmonic analysis (R-SCHA). J Geophys Res Solid Earth 111(B5)

    Google Scholar 

  • Tregoning P, Boers R, O’Brien D, Hendy M (1998) Accuracy of absolute precipitable water vapor estimates from GPS observations. J Geophys Res Atmos 103(D22):28701–28710

    Google Scholar 

  • 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(4):677–694

    Google Scholar 

  • Xu P, Shen Y, Fukuda Y, Liu Y (2006) Variance component estimation in linear inverse ill-posed models. J Geod 80(2):69–81

    Google Scholar 

  • Xu C, Ding K, Cai J, Grafarend EW (2009) Methods of determining weight scaling factors for geodetic–geophysical joint inversion. J Geodyn 47(1):39–46

    Google Scholar 

  • Yao Y, Zhang B, Xu C, Yan F (2014) Improved one/multi-parameter models that consider seasonal and geographic variations for estimating weighted mean temperature in ground-based GPS meteorology. J Geod 88(3):273–282

    Google Scholar 

  • Yuan Y, Zhang K, Rohm W, Choy S, Norman R, Wang CS (2014) Real-time retrieval of precipitable water vapor from GPS precise point positioning. J Geophys Res Atmos 119(16):10044–10057

    Google Scholar 

  • Zhang B, Fan Q, Yao Y, Xu C, Li X (2017) An improved tomography approach based on adaptive smoothing and ground meteorological observations. Remote Sens 9(9):886

    Google Scholar 

Download references

Acknowledgements

We thank University Corporation for Atmospheric Research (UCAR) for providing the GNSS PWV data, National Aeronautics and Space Administration (NASA) for providing the MODIS PWV products, and the European Centre for Medium-Range Weather Forecasts (ECMWF) for providing the ERA5 reanalysis data. This work is supported by the National Natural Science Foundation of China (41704004).

Author information

Authors and Affiliations

Authors

Contributions

BZ and YY together designed the research and proposed the solutions; BZ performed the research and wrote the paper. YY revised the paper; LX and XX helped process and analyze data.

Corresponding author

Correspondence to Yibin Yao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, B., Yao, Y., Xin, L. et al. Precipitable water vapor fusion: an approach based on spherical cap harmonic analysis and Helmert variance component estimation. J Geod 93, 2605–2620 (2019). https://doi.org/10.1007/s00190-019-01322-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00190-019-01322-1

Keywords

Navigation