TY - GEN
T1 - Augmenting GNSS PPP Accuracy in South China Using Water Vapor Correction Data from WRF Assimilation Results
AU - Gong, Yangzhao
AU - Liu, Zhizhao
AU - Chan, Pak Wai
AU - Hon, Kai Kwong
N1 - Funding Information:
Acknowledgments. The support from the Key Program of the National Natural Science Foundation of China (No. 41730109) is acknowledged. The work described in this paper was also supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. PolyU 15211919 Q73B). The Emerging Frontier Area (EFA) Scheme of Research Institute for Sustainable Urban Development (RISUD) of the Hong Kong Polytechnic University (No. 1-BBWJ) is also acknowledged. The authors thank GNSS data product service platform of China Earthquake Administration for providing GNSS PWV data of the Crustal Movement Observation Network Of China (CMONOC) (http:// www.cgps.ac.cn). The National Oceanic and Atmospheric Administration (NOAA) is thanked for providing the Integrated Global Radiosonde Archive (IGRA) radiosonde data (ftp://ftp.ncdc. noaa.gov/pub/data/igra/). We thank online archives of the Crustal Dynamics Data Information System (CDDIS), NASA Goddard Space Flight Center, Greenbelt, MD, USA, for providing IGS GNSS data (https://cddis.nasa.gov/archive/gnss/data/daily/). The European Centre for Medium-Range Weather Forecasts (ECMWF) (https://cds.climate.copernicus.eu/#!/search?text= ERA5&type=dataset) is appreciated for providing the ECMWF ERA5 reanalysis data.
Funding Information:
The support from the Key Program of the National Natural Science Foundation of China (No. 41730109) is acknowledged. The work described in this paper was also supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. PolyU 15211919 Q73B). The Emerging Frontier Area (EFA) Scheme of Research Institute for Sustainable Urban Development (RISUD) of the Hong Kong Polytechnic University (No. 1-BBWJ) is also acknowledged. The authors thank GNSS data product service platform of China Earthquake Administration for providing GNSS PWV data of the Crustal Movement Observation Network Of China (CMONOC) (http:// www.cgps.ac.cn). The National Oceanic and Atmospheric Administration (NOAA) is thanked for providing the Integrated Global Radiosonde Archive (IGRA) radiosonde data (ftp://ftp.ncdc. noaa.gov/pub/data/igra/). We thank online archives of the Crustal Dynamics Data Information System (CDDIS), NASA Goddard Space Flight Center, Greenbelt, MD, USA, for providing IGS GNSS data (https://cddis.nasa.gov/archive/gnss/data/daily/). The European Centre for Medium-Range Weather Forecasts (ECMWF) (https://cds.climate.copernicus.eu/#!/search?text= ERA5&type=dataset) is appreciated for providing the ECMWF ERA5 reanalysis data.
Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2021/5
Y1 - 2021/5
N2 - Wet delay in Global Navigation Satellite System (GNSS), mainly caused by water vapor in the atmosphere, is difficult to be accurately modeled using empirical wet delay models as water vapor is highly variable in both space and time. In this paper we propose correcting the GNSS wet delay using water vapor data from Weather Research and Forecasting (WRF) model’s assimilation results. We conduct six consecutive 24-h WRF forecasts to model the three-dimension (3D) distribution of water vapor in the South China region 20° N–33° N and 108° E–123° E from 0 h UTC April 06, 2020 to 0 h UTC April 11, 2020. GNSS Precipitable Water Vapor (PWV) from 27 stations of the Crustal Movement Observation Network of China (CMONOC) and meteorological profiles from 22 radiosonde stations are assimilated into WRF model to improve the water vapor modeling performance of WRF. Totally, four WRF schemes are adopted, i.e. WRF scheme 0: WRF without water vapor data assimilation; WRF scheme 1: WRF with GNSS PWV assimilation only; WRF scheme 2: WRF with radiosonde profiles assimilation only; WRF scheme 3: WRF with both GNSS PWV and radiosonde profiles assimilation. The water vapor data from the four WRF schemes are used to augment Precise Point Positioning (PPP) by correcting GNSS wet delay at seven International GNSS Service (IGS) stations distributed in South China. The static PPP results show that using the water vapor correction data from different WRF schemes can improve PPP positioning accuracy by 29.5% to 42.3% in the vertical component of GNSS stations. In addition, the WRF-augmented PPP can shorten convergence time by 43.3% to 57.3% in the GNSS station vertical component, if using 10 cm positioning error as the convergence criterion. The kinematic PPP results show that WRF-augmented PPP can improve positioning accuracy in the vertical component by 20.0% to 33.6%.
AB - Wet delay in Global Navigation Satellite System (GNSS), mainly caused by water vapor in the atmosphere, is difficult to be accurately modeled using empirical wet delay models as water vapor is highly variable in both space and time. In this paper we propose correcting the GNSS wet delay using water vapor data from Weather Research and Forecasting (WRF) model’s assimilation results. We conduct six consecutive 24-h WRF forecasts to model the three-dimension (3D) distribution of water vapor in the South China region 20° N–33° N and 108° E–123° E from 0 h UTC April 06, 2020 to 0 h UTC April 11, 2020. GNSS Precipitable Water Vapor (PWV) from 27 stations of the Crustal Movement Observation Network of China (CMONOC) and meteorological profiles from 22 radiosonde stations are assimilated into WRF model to improve the water vapor modeling performance of WRF. Totally, four WRF schemes are adopted, i.e. WRF scheme 0: WRF without water vapor data assimilation; WRF scheme 1: WRF with GNSS PWV assimilation only; WRF scheme 2: WRF with radiosonde profiles assimilation only; WRF scheme 3: WRF with both GNSS PWV and radiosonde profiles assimilation. The water vapor data from the four WRF schemes are used to augment Precise Point Positioning (PPP) by correcting GNSS wet delay at seven International GNSS Service (IGS) stations distributed in South China. The static PPP results show that using the water vapor correction data from different WRF schemes can improve PPP positioning accuracy by 29.5% to 42.3% in the vertical component of GNSS stations. In addition, the WRF-augmented PPP can shorten convergence time by 43.3% to 57.3% in the GNSS station vertical component, if using 10 cm positioning error as the convergence criterion. The kinematic PPP results show that WRF-augmented PPP can improve positioning accuracy in the vertical component by 20.0% to 33.6%.
KW - Data assimilation
KW - Global Navigation Satellite System (GNSS)
KW - Precipitable Water Vapor (PWV)
KW - Precise Point Positioning (PPP)
KW - Weather Research and Forecasting (WRF) model
UR - http://www.scopus.com/inward/record.url?scp=85111411140&partnerID=8YFLogxK
U2 - 10.1007/978-981-16-3138-2_60
DO - 10.1007/978-981-16-3138-2_60
M3 - Conference article published in proceeding or book
AN - SCOPUS:85111411140
SN - 9789811631375
T3 - Lecture Notes in Electrical Engineering
SP - 653
EP - 670
BT - China Satellite Navigation Conference, CSNC 2021, Proceedings
A2 - Yang, Changfeng
A2 - Xie, Jun
PB - Springer Science and Business Media Deutschland GmbH
T2 - 12th China Satellite Navigation Conference, CSNC 2021
Y2 - 22 May 2021 through 25 May 2021
ER -