Skip to main content
SpringerLink
Log in

Response of Meiyu process considering the temporal and spatial characteristics of GNSS PWV

  • Research
  • Published:
Theoretical and Applied Climatology Aims and scope Submit manuscript

Abstract

This study explores the spatiotemporal characteristics of Global Navigation Satellite System (GNSS) Precipitable Water Vapor (PWV) and its relationship with the Meiyu process. Using multiple sources of atmospheric PWV data and meteorological information, the study quantitatively analyzes PWV's spatiotemporal characteristics and its association with the onset and withdrawal of the Meiyu season. The research findings are as follows: (1)PWV's spatiotemporal evolution provides indications for the Meiyu season. The daily variation of water vapor content generally follows a camelback shape. Before the Meiyu season begins, PWV exhibits an upward trend with content below 40mm. After the onset of the Meiyu season, PWV gradually accumulates during the early Meiyu season with content exceeding 50mm, accompanied by rainfall. In the late Meiyu season, water vapor releases, leading to a decrease in PWV content. After the Meiyu season ends, PWV gradually declines but remains relatively high, linked to moisture transport during the Jianghuai flood season. (2)Anomaly analysis reveals that water vapor activity is highest during the Meiyu season, showing good correspondence with special Meiyu years. This provides new insights for monitoring and forecasting abnormal Meiyu events. (3)Spatially, PWV distribution during the Meiyu season exhibits a pattern of more water vapor in southern regions and less in northern areas. This pattern is influenced by the stronger atmospheric water storage capacity in low-latitude areas and the gradual weakening of monsoon water vapor during northward and westward transport.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Data availability

The datasets analyzed in the present study are available at (CORS: the data are from the Meteorological Sounding Center of China Meteorological Administration), (RS:http://weather.uwyo.edu/upperair/seasia.html), (ERA5:https://cds.climate.copernicus.eu), (the meteorological stations data are derived from the daily dataset of basic climatic elements of China’s national surface weather stations V3.0 released by the National Meteorological Data Center), (Ground Data Acquisition System is a meteorological dataset published by the National Environmental Forecasting Center of the United States).

Abbreviations

CORS :

Continuously Operating Reference Stations

RS :

Radio Sonde

ECMWF :

European Centre for Medium-Range Weather Forecasts

ZTD :

Zenith Total Delay

ZHD :

Zenith Hydrostatic Delay

ZWD :

Zenith Wet Delay

IPWV :

Integrated Precipitable Water Vapor

HYSPLIT :

Hybrid Single-Particle Lagrangian Integrated Trajectory model

References

  • Akkisetti M, Rajeevan M, Ratnam VM, Bhate JN, Naidu CV (2013) Nowcasting severe convective activity over southeast India using ground‐based microwave radiometer observations[J]. J Geophys Res: Atmos 118(1). https://doi.org/10.1029/2012JD018174

  • Allan RP, Willett KM, John VO, Trent T (2022) Global changes in water vapor 1979–2020. J Geophys Res Atmos 127:e2022JD036728

    Article  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 97:15787–15801

    Article  Google Scholar 

  • Ccoica-López KL, Pasapera-Gonzales JJ, Jimenez JC (2019) Spatio-temporal variability of the precipitable water vapor over Peru through MODIS and ERA-Interim time series. Atmos Basel 10:192

    Google Scholar 

  • Chen F, Zhang M, Wu X, Wang S, Argiriou AA, Zhou X, Chen J (2021) A Stable isotope approach for estimating the contribution of recycled moisture to precipitation in Lanzhou City, China. Water Sul 13:1783

    Article  Google Scholar 

  • Davis J, Elgered G (1998) The spatio-temporal structure of GPS water-vapor determinations. Phys Chem Earth 23:91–96

    Article  Google Scholar 

  • Ding YH, Liu JJ, Sun Y, Liu Y, He J, Song Y (2007) A study of the synoptic-climatology of the Meiyu system in East Asia [J]. Chin J Atmos Sci 31(6):1082–1101. https://doi.org/10.3878/j.issn.1006-9895.2007.06.05

    Article  Google Scholar 

  • Ding Y, Liang P, Liu Y, Zhang Y (2020) Multiscale variability of Meiyu and its prediction: a new review. J Geophys Res Atmos 25:e2019JD031496

    Article  Google Scholar 

  • Gao Q, Sun Y, You Q (2016) The northward shift of Meiyu rain belt and its possible association with rainfall intensity changes and the Pacific-Japan pattern. Dyn Atmos Oceans 76:52–62

    Article  Google Scholar 

  • Gou J, Qu S, Guan H, Shi P, Su Z, Lin Z, Liu J, Zhu J (2022) Relationship between precipitation isotopic compositions and synoptic atmospheric circulation patterns in the lower reach of the Yangtze River. J Hydrol 605:127289

    Article  Google Scholar 

  • Gratton P, Banville S, Lachapelle G, O’Keefe K (2021) Kinematic Zenith Tropospheric Delay Estimation with GNSS PPP in Mountainous Areas. Sensors Basel 21:5709

    Article  Google Scholar 

  • Hu R, Wang L (2021) Variation of high and low level circulation of Meiyu in Jiangsu Province in recent 30 years. Atmos Basel 12:1258

    Google Scholar 

  • Huang RH, Zhang ZZ, Huang G, Ren BH (1998) Characteristics of the water vapor transport in East Asian monsoon region and its difference from that in South Asian monsoon region in summer[J]. Chin J Atmos Sci 22(4):460–469. https://doi.org/10.3878/j.issn.1006-9895.1998.04.08

    Article  Google Scholar 

  • Huang L, Mo Z, Xie S, Liu L, Chen J, Kang C, Wang S (2021) Spatiotemporal characteristics of GNSS-derived precipitable water vapor during heavy rainfall events in Guilin, China. SANA. 2:1–17

    Google Scholar 

  • Jiang ZH, Ren W, Liu ZY, Yang H (2013) Analysis of water vapor transport characteristics during the Vleiyu over the Yang-tze-Huaihe River valley using the Lagrangian method. Acta Meteorol Sin 295-304

  • Jin S, Li Z, Cho J (2008) Integrated water vapor field and multiscale variations over China from GPS measurements. J Appl Me-teorol Clim 47:3008–3015

    Article  Google Scholar 

  • Jin Y, Wang Y, Jin MB (2022) Simulation of a typical Meiyu case over the Yangtze-Huai River in a record-breaking Meiyu period of 2020[C]//China High Resolution Earth Observation Conference. Singapore: Springer Nat Singap 201-215

  • Lien TY, Yeh TK, Hong JS, Hsiao TY (2022) Variations in GPS precipitable water vapor and rainfall during the 2006–2019 Mei-yu season in Taiwan. Adv Space Res 70(5):1375–1387

    Article  Google Scholar 

  • Liu J, Su Z, Liang H, Xu X, Wu P (2005) Precipitable water vapor on the Tibetan Plateau estimated by GPS, water vapor ra-diometer, radiosonde, and numerical weather prediction analysis and its impact on the radiation budget[J]. J Geophys Res Atmos 110(D17)

  • Mallick J, Talukdar S, Alsubih M, Salam R, Ahmed M, Kahla NB, Shamimuzzaman M (2021) Analysing the trend of rainfall in Asir region of Saudi Arabia using the family of Mann-Kendall tests, innovative trend analysis, and detrended fluctuation analysis. Theor Appl Climatol 143:823–841

    Article  Google Scholar 

  • Pirouzmand A, Kowsar Z, Dehghani P (2018) Atmospheric dispersion assessment of radioactive materials during severe accident conditions for Bushehr nuclear power plant using HYSPLIT code. Prog Nucl Energy 108:169–178

    Article  Google Scholar 

  • Qiang A, Wang N, Xie J, Wei J (2020) Analysis of water vapor change and precipitation conversion efficiency based on HYSPLIT backward trajectory model over the three-river headwaters region. J Coastal Res 105:6–11

    Article  Google Scholar 

  • Rong-hua J, Ning Y, Xiao-qing S, Si-jia L, Shan Y (2020) The relationship between abnormal Meiyu and medium-term scale wave perturbation energy propagation along the East Asian subtropical westerly jet. J Trop Meteorol 26:125–136

    Article  Google Scholar 

  • Sapucci LF, Machado LA, de Souza EM, Campos TB (2019) Global Positioning System precipitable water vapour (GPS-PWV) jumps before intense rain events: a potential application to nowcasting[J]. Meteorological Appl 1:26

    Google Scholar 

  • Ssenyunzi RC, Oruru B, D’ujanga FM, Realini E, Barindelli S, Tagliaferro G, von Engeln A, van de Giesen N (2020) Per-formance of ERA5 data in retrieving precipitable water vapour over East African tropical region. Adv Space Res 65:1877–1893

    Article  Google Scholar 

  • Ueta A, Sugimoto A, Iijima Y, Yabuki H, Maximov TC (2014) Contribution of transpiration to the atmospheric moisture in eastern Siberia estimated with isotopic composition of water vapour. Ecohydrology 7:197–208

    Article  Google Scholar 

  • Wang SM (2023) Research on accuracy evaluation and model establishment of ZTD/PWV based on GNSS and reanalysis[J]. Acta Geod Et Cartographica Sin 52(6):1037–1037

    Google Scholar 

  • Wang Z, Zhou X, Liu Y, Zhou D, Zhang H, Sun W (2017) Precipitable water vapor characterization in the coastal regions of China based on ground-based GPS. Adv Space Res 60:2368–2378

    Article  Google Scholar 

  • Wang H, Liu Z, Zhu J, Chen D, Qin F (2022) Spatio-temporal extraction of surface waterbody and its response of extreme climate along the Upper Huaihe River. Sustain Basel 14:3223

    Article  Google Scholar 

  • Wu HY, Zeng MJ, Zhang B, Zhou P, Wang Y (2015) Mutation and gradual variation characteristics of ground-based GPS/PWV during precipitation. J Atmos Sci 35(6):775–782

    Google Scholar 

  • Wu M, Jin S, Li Z, Cao Y, Ping F, Tang X (2021) High-precision GNSS PWV and its variation characteristics in China based on individual station meteorological data. Remote Sens 13:1296. https://doi.org/10.3390/rs13071296

    Article  Google Scholar 

  • Wu ZY, Mao Y, Lu GH, Yang Y (2014) Spatial-temporal variation and trend of meteorological variables in Jiangsu, China[C]. Proc Annu Congr Adv Eng Technol 189-194

  • Yang Y, Zhu X, Liu M (2008) Statistical analysis of atmospheric precipitable water vapour observed by GPS in Yangtze River Delta Region[J]. Plateau Meteorol (S) 27(12):150–157

    Google Scholar 

  • Yao F, Yang XQ, Liu MJ, Zhang YQ, Li H (2023) Identification of Meiyu process and spatiotemporal characteristics of different precipitation levels during the Meiyu period over the Yangtze-Huai River Basin[J]. Prog Geogr 42(1):145–160

    Article  Google Scholar 

  • Zhang Z, Duan K, Liu H, Meng Y, Chen R (2022) Spatio-temporal variation of precipitation in the Qinling Mountains from 1970 to 2100 based on CMIP6 data. Sustain Basel 14:8654

    Article  Google Scholar 

  • Zhang KF, Li HB, Wang XM, Zhu DT, He QM, Li LJ, Hu AD, Zheng NS, Li HZ (2022) Recent progresses and future prospectives of ground-based GNSS water vapor sounding[J]. Acta Geod Et Cartographica Sin 51(7):1172–1191

    Google Scholar 

  • Zhang K, Manning T, Wu S, Rohm W, Silcock D, Choy S (2015) Capturing the signature of severe weather events in Australia using GPS measurements. IEEE J Sel Top Appl Earth Obs Remote Sens 8(4):1839–1847. https://doi.org/10.1109/JSTARS.2015.2406313

    Article  Google Scholar 

  • Zhao Q, Ma X, Yao W, Liu Y, Yao Y (2019) Anomaly variation of vegetation and its influencing factors in mainland China during ENSO period. IEEE Access 8:721–734

    Article  Google Scholar 

  • Zhu M, Liu Z, Hu W (2020) Observing water vapor variability during three super typhoon events in Hong Kong based on GPS water vapor tomographic modeling technique. J Geophys Res Atmos 125:e2019JD032318

    Article  Google Scholar 

Download references

Acknowledgements

The authors are grateful to the reviewers and the editor for their valuable comments and suggestions.

Funding

This research was funded by “The National Natural Science Foundation of China, grant number 41674036; Natural Science Foundation of Jiangsu Province, grant number BK20211037.”

Author information

Authors and Affiliations

  1. School of Remote Sensing and Mapping, Nanjing University of Information Science & Technology, Nanjing, 210044, China

    Fuyang Ke, Pan Zhao, Wei Yu & Lulu Ming

  2. Wuxi Research Institute of Nanjing Information Engineering University, Wuxi, 214100, China

    Fuyang Ke

  3. The Meteorological Observation Center of China Meteorological Administration, Beijing, 100081, China

    Guan Hong

  4. School of Spatial Information and Surveying and Mapping Engineering, Anhui University of Science & Technology, Huainan, 232001, China

    Jianing Tian

Authors
  1. Fuyang Ke
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pan Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wei Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Guan Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jianing Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lulu Ming
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization, F.K. and P.Z.; methodology, P.Z.; software, P.Z.; vali-dation, F.K., P.Z.; formal analysis, F.K. and P.Z.; investigation, P.Z.; resources, F.K.; data curation, P.Z.; writing—original draft preparation, P.Z.; writing—review and editing, F.K., P.Z., W.Y., G.H., J.T., and L.M.; visualization, P.Z.; supervision, F.K.; project administration, F.K.; funding acquisition, F.K. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Fuyang Ke.

Ethics declarations

Competing interests

The authors declare no competing interests.

Informed consent

Not applicable.

Institutional Review Board Statement

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ke, F., Zhao, P., Yu, W. et al. Response of Meiyu process considering the temporal and spatial characteristics of GNSS PWV. Theor Appl Climatol 155, 1301–1319 (2024). https://doi.org/10.1007/s00704-023-04694-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00704-023-04694-9

Search

Navigation