Articles | Volume 14, issue 3
https://doi.org/10.5194/amt-14-2529-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-2529-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
31 Mar 2021
Research article |
| 31 Mar 2021
| 31 Mar 2021
A new global grid-based weighted mean temperature model considering vertical nonlinear variation
School of Environment Science and Spatial Informatics, China
University of Mining and Technology, Xuzhou 221116, China
Suqin Wu
CORRESPONDING AUTHOR
School of Environment Science and Spatial Informatics, China
University of Mining and Technology, Xuzhou 221116, China
SPACE Research Center, School of Science, RMIT University, Melbourne 3001, Australia
Kefei Zhang
School of Environment Science and Spatial Informatics, China
University of Mining and Technology, Xuzhou 221116, China
SPACE Research Center, School of Science, RMIT University, Melbourne 3001, Australia
Moufeng Wan
School of Environment Science and Spatial Informatics, China
University of Mining and Technology, Xuzhou 221116, China
Ren Wang
School of Environment Science and Spatial Informatics, China
University of Mining and Technology, Xuzhou 221116, China
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A new method has been developed to more accurately adjust atmospheric delay data for use in satellite positioning, especially in areas with large height differences. By using long-term weather data and testing with global observation stations, the new method significantly improves accuracy compared to traditional approaches. This can benefit applications such as precise positioning and weather monitoring using navigation satellite signals.
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The zenith hydrostatic delay (ZHD) derived from blind models are of low accuracy, especially in mid- and high-latitude regions. To address this issue, the ratio of the ZHD to zenith total delay (ZTD) is firstly investigated; then, based on the relationship between the ZHD and ZTD, a new ZHD model was developed using the back propagation artificial neural network (BP-ANN) method which took the ZTD as an input variable. The model outperforms blind models.
Cited articles
Askne, J. and Nordius, H.: Estimation of tropospheric delay for microwaves
from surface weather data, Radio Sci., 22, 379–386,
https://doi.org/10.1029/RS022i003p00379, 1987.
Bennitt, G. V. and Jupp, A.: Operational Assimilation of GPS Zenith Total
Delay Observations into the Met Office Numerical Weather Prediction Models,
Mon. Weather Rev., 140, 2706–2719,
https://doi.org/10.1175/MWR-D-11-00156.1, 2012.
Bevis, M.: GPS meteorology: mapping zenith wet delays onto precipitable
water, J. Appl. Meteorol., 33, 379–386,
https://doi.org/10.1175/1520-0450(1994)033<0379:GMMZWD>2.0.CO;2, 1994.
Bevis, M., Businger, S., Herring, T. A., Rocken, C., Anthes, R. A., and Ware,
R. H.: GPS meteorology: remote sensing of atmospheric water vapor using the
global positioning system, J. Geophys. Res., 97,
787–801, https://doi.org/10.1029/92jd01517, 1992.
Bianchi, C. E., Mendoza, L. P. O., Fernández, L. I., Natali, M. P., Meza, A. M., and Moirano, J. F.: Multi-year GNSS monitoring of atmospheric IWV over Central and South America for climate studies, Ann. Geophys., 34, 623–639, https://doi.org/10.5194/angeo-34-623-2016, 2016.
Böhm, J., Möller, G., Schindelegger, M., Pain, G., and Weber, R.:
Development of an improved empirical model for slant delays in the
troposphere (GPT2w), GPS Solut., 19, 433–441,
https://doi.org/10.1007/s10291-014-0403-7, 2015.
Bonafoni, S. and Biondi, R.: The usefulness of the Global Navigation
Satellite Systems (GNSS) in the analysis of precipitation events,
Atmos. Res., 167, 15–23,
https://doi.org/10.1016/j.atmosres.2015.07.011, 2016.
Calori, A., Santos, J. R., Blanco, M., Pessano, H., Llamedo, P., Alexander,
P., and de la Torre, A.: Ground-based GNSS network and integrated water vapor
mapping during the development of severe storms at the Cuyo region
(Argentina), Atmos. Res., 176/177, 267–275,
https://doi.org/10.1016/j.atmosres.2016.03.002, 2016.
Chen, B., Dai, W., Liu, Z., Wu, L., Kuang, C., and Ao, M.: Constructing a precipitable water vapor map from regional GNSS network observations without collocated meteorological data for weather forecasting, Atmos. Meas. Tech., 11, 5153–5166, https://doi.org/10.5194/amt-11-5153-2018, 2018.
Chen, Q., Song, S., Heise, S., Liou, Y.-A., Zhu, W., and Zhao, J.: Assessment
of ZTD derived from ECMWF/NCEP data with GPS ZTD over China, GPS Solut.,
15, 415–425, https://doi.org/10.1007/s10291-010-0200-x, 2011.
Choy, S., Wang, C., Zhang, K., and Kuleshov, Y.: GPS sensing of precipitable
water vapour during the March 2010 Melbourne storm, Adv. Space
Res., 52, 1688–1699, https://doi.org/10.1016/j.asr.2013.08.004,
2013.
Davis, J. L., Herring, T. A., Shapiro, I. I., Rogers, A. E. E., and Elgered,
G.: Geodesy by radio interferometry: Effects of atmospheric modeling errors
on estimates of baseline length, Radio Sci., 20, 1593–1607,
https://doi.org/10.1029/RS020i006p01593, 1985.
Decker, M., Brunke, M. A., Wang, Z., Sakaguchi, K., Zeng, X., and Bosilovich,
M. G.: Evaluation of the Reanalysis Products from GSFC, NCEP, and ECMWF
Using Flux Tower Observations, J. Climate, 25, 1916–1944,
https://doi.org/10.1175/JCLI-D-11-00004.1, 2012.
Ding, M.: A neural network model for predicting weighted mean temperature,
J. Geodesy, 92, 1187–1198,
https://doi.org/10.1007/s00190-018-1114-6, 2018.
Ding, M.: A second generation of the neural network model for predicting
weighted mean temperature, GPS Solut., 24, 61,
https://doi.org/10.1007/s10291-020-0975-3, 2020.
Ding, W., Teferle, F. N., Kazmierski, K., Laurichesse, D., and Yuan, Y.: An
evaluation of real-time troposphere estimation based on GNSS Precise Point
Positioning, J. Geophys. Res., 122, 2779–2790,
https://doi.org/10.1002/2016JD025727, 2017.
Dousa, J. and Elias, M.: An improved model for calculating tropospheric wet
delay, Geophys. Res. Lett., 41, 4389–4397,
https://doi.org/10.1002/2014GL060271, 2014.
Dousa, J. and Vaclavovic, P.: Real-time zenith tropospheric delays in
support of numerical weather prediction applications, Adv. Space
Res., 53, 1347–1358, https://doi.org/10.1016/j.asr.2014.02.021,
2014.
Douša, J., Dick, G., Kačmařík, M., Brožková, R., Zus, F., Brenot, H., Stoycheva, A., Möller, G., and Kaplon, J.: Benchmark campaign and case study episode in central Europe for development and assessment of advanced GNSS tropospheric models and products, Atmos. Meas. Tech., 9, 2989–3008, https://doi.org/10.5194/amt-9-2989-2016, 2016.
Fan, S.-J., Zang, J.-F., Peng, X.-Y., Wu, S.-Q., Liu, Y.-X., and Zhang,
K.-F.: Validation of Atmospheric Water Vapor Derived from Ship-Borne GPS
Measurements in the Chinese Bohai Sea, Terr. Atmos. Ocean. Sci., 27,
213–220, https://doi.org/10.3319/TAO.2015.11.04.01(A), 2016.
Guerova, G., Jones, J., Douša, J., Dick, G., de Haan, S., Pottiaux, E., Bock, O., Pacione, R., Elgered, G., Vedel, H., and Bender, M.: Review of the state of the art and future prospects of the ground-based GNSS meteorology in Europe, Atmos. Meas. Tech., 9, 5385–5406, https://doi.org/10.5194/amt-9-5385-2016, 2016.
He, C., Wu, S., Wang, X., Hu, A., Wang, Q., and Zhang, K.: A new voxel-based model for the determination of atmospheric weighted mean temperature in GPS atmospheric sounding, Atmos. Meas. Tech., 10, 2045–2060, https://doi.org/10.5194/amt-10-2045-2017, 2017.
He, Q., Zhang, K., Wu, S., Zhao, Q., Wang, X., Shen, Z., Li, L., Wan, M., and
Liu, X.: Real-Time GNSS-Derived PWV for Typhoon Characterizations: A Case
Study for Super Typhoon Mangkhut in Hong Kong, Remote Sens., 12, 104,
https://doi.org/10.3390/rs12010104, 2019.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1979 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS), https://doi.org/10.24381/cds.bd0915c6, 2018.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 monthly averaged data on pressure levels from 1979 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS), https://doi.org/10.24381/cds.6860a573, 2019.
Huang, L., Jiang, W., Liu, L., Chen, H., and Ye, S.: A new global grid model
for the determination of atmospheric weighted mean temperature in GPS
precipitable water vapor, J. Geodesy, 93, 159–176,
https://doi.org/10.1007/s00190-018-1148-9, 2019a.
Huang, L., Liu, L., Chen, H., and Jiang, W.: An improved atmospheric weighted
mean temperature model and its impact on GNSS precipitable water vapor
estimates for China, GPS Solut., 23, 51,
https://doi.org/10.1007/s10291-019-0843-1, 2019b.
Jiang, P., Ye, S., Lu, Y., Liu, Y., Chen, D., and Wu, Y.: Development of time-varying global gridded Ts−Tm model for precise GPS–PWV retrieval, Atmos. Meas. Tech., 12, 1233–1249, https://doi.org/10.5194/amt-12-1233-2019, 2019a.
Jiang, P., Ye, S., Lu, Y., Liu, Y., Chen, D., and Wu, Y.: Development of time-varying global gridded Ts−Tm model for precise GPS–PWV retrieval, Atmos. Meas. Tech., 12, 1233–1249, https://doi.org/10.5194/amt-12-1233-2019, 2019b.
Landskron, D. and Böhm, J.: VMF3/GPT3: refined discrete and empirical
troposphere mapping functions, J. Geodesy, 92, 349–360,
https://doi.org/10.1007/s00190-017-1066-2, 2018.
Le Marshall, J., Xiao, Y., Norman, R., Zhang, K., Rea, A., Cucurull, L.,
Seecamp, R., Steinle, P., Puri, K., Fu, E., and Le, T.: The application of
radio occultation observations for climate monitoring and numerical weather
prediction in the Australian region, Aust. Meteorol. Ocean., 62, 323–334, https://doi.org/10.22499/2.6204.010,
2012.
Le Marshall, J., Norman, R., Howard, D., Rennie, S., Moore, M., Kaplon, J.,
Xiao, Y., Zhang, K., Wang, C., Cate, A., Lehmann, P., Wang, X., Steinle, P.,
Tingwell, C., Le, T., Rohm, W., and Ren, D.: Using GNSS Data for Real-time
Moisture Analysis and Forecasting over the Australian Region I. The System,
Journal of Southern Hemisphere Earth System Science, 69, 1–21,
https://doi.org/10.22499/3.6901.009, 2019.
Li, Q., Yuan, L., Chen, P., and Jiang, Z.: Global grid-based Tm model with
vertical adjustment for GNSS precipitable water retrieval, GPS Solut.,
24, 73, https://doi.org/10.1007/s10291-020-00988-x, 2020.
Li, X., Dick, G., Lu, C., Ge, M., Nilsson, T., Ning, T., Wickert, J., and
Schuh, H.: Multi-GNSS Meteorology: Real-Time Retrieving of Atmospheric Water
Vapor from BeiDou, Galileo, GLONASS, and GPS Observations, IEEE T.
Geosci. Remote, 53, 6385–6393,
https://doi.org/10.1109/TGRS.2015.2438395, 2015.
Lu, C., Li, X., Nilsson, T., Ning, T., Heinkelmann, R., Ge, M., Glaser, S.,
and Schuh, H.: Real-time retrieval of precipitable water vapor from GPS and
BeiDou observations, J. Geodesy, 89, 843–856,
https://doi.org/10.1007/s00190-015-0818-0, 2015.
Nafisi, V., Urquhart, L., Santos, M. C., Nievinski, F. G., Böhm, J.,
Wijaya, D. D., Schuh, H., Ardalan, A. A., Hobiger, T., Ichikawa, R., Zus,
F., Wickert, J., and Gegout, P.: Comparison of ray-tracing packages for
troposphere delays, IEEE T. Geosci. Remote,
50, 469–481, https://doi.org/10.1109/TGRS.2011.2160952, 2012.
Ning, T., Wang, J., Elgered, G., Dick, G., Wickert, J., Bradke, M., Sommer, M., Querel, R., and Smale, D.: The uncertainty of the atmospheric integrated water vapour estimated from GNSS observations, Atmos. Meas. Tech., 9, 79–92, https://doi.org/10.5194/amt-9-79-2016, 2016.
Rohm, W., Yuan, Y., Biadeglgne, B., Zhang, K., and Marshall, J. L.:
Ground-based GNSS ZTD/IWV estimation system for numerical weather prediction
in challenging weather conditions, Atmos. Res., 138, 414–426,
https://doi.org/10.1016/j.atmosres.2013.11.026, 2014a.
Rohm, W., Yuan, Y., Biadeglgne, B., Zhang, K., and Le Marshall, J.:
Ground-based GNSS ZTD/IWV estimation system for numerical weather prediction
in challenging weather conditions, Atmos. Res., 138, 414–426,
https://doi.org/10.1016/j.atmosres.2013.11.026, 2014b.
Shi, J., Chaoqian, X., Jiming, G., and Yang, G.: Real-Time GPS Precise
Point Positioning-Based Precipitable Water Vapor Estimation for Rainfall
Monitoring and Forecasting, IEEE T. Geosci. Remote, 53,
3452–3459, https://doi.org/10.1109/TGRS.2014.2377041, 2015.
Sun, Z., Zhang, B., and Yao, Y.: A global model for estimating tropospheric
delay and weighted mean temperature developed with atmospheric reanalysis
data from 1979 to 2017, Remote Sens., 11, 1893,
https://doi.org/10.3390/rs11161893, 2019.
University of Wyoming: Radiosonde data, avaialable at: http://weather.uwyo.edu/upperair/sounding.html, last access: 13 March 2020.
Wang, J., Zhang, L., and Dai, A.: Global estimates of water-vapor-weighted
mean temperature of the atmosphere for GPS applications, J.
Geophys. Res.-Atmos., 110, 1–17,
https://doi.org/10.1029/2005JD006215, 2005.
Wang, J., Wu, Z., Semmling, M., Zus, F., Gerland, S., Ramatschi, M., Ge, M.,
Wickert, J., and Schuh, H.: Retrieving Precipitable Water Vapor From
Shipborne Multi-GNSS Observations, Geophys. Res. Lett., 46, 5000–5008,
https://doi.org/10.1029/2019GL082136, 2019.
Wang, X., Zhang, K., Wu, S., Fan, S., and Cheng, Y.: Water vapor-weighted
mean temperature and its impact on the determination of precipitable water
vapor and its linear trend, J. Geophys. Res.-Atmos.,
121, 833–852, https://doi.org/10.1002/2015JD024181, 2016.
Wang, X., Zhang, K., Wu, S., Li, Z., Cheng, Y., Li, L., and Yuan, H.: The
correlation between GNSS-derived precipitable water vapor and sea surface
temperature and its responses to El Niño-Southern Oscillation, Remote
Sens. Environ., 216, 1–12,
https://doi.org/10.1016/j.rse.2018.06.029, 2018.
Webb, S. R., Penna, N. T., Clarke, P. J., Webster, S., Martin, I., and
Bennitt, G. V.: Kinematic GNSS Estimation of Zenith Wet Delay over a Range
of Altitudes, J. Atmos. Ocean. Technol., 33, 3–15,
https://doi.org/10.1175/JTECH-D-14-00111.1, 2016.
Yang, F., Meng, X., Guo, J., Shi, J., An, X., He, Q., and Zhou, L.: The
Influence of different modelling factors on Global temperature and pressure
models and their performance in different zenith hydrostatic delay (ZHD)
models, Remote Sens., 12, 35, https://doi.org/10.3390/RS12010035, 2020.
Yao, Y., Zhu, S., and Yue, S. Q.: A globally applicable, season-specific
model for estimating the weighted mean temperature of the atmosphere,
J. Geodesy, 86, 1125–1135,
https://doi.org/10.1007/s00190-012-0568-1, 2012.
Yao, Y., Zhang, B., Yue, S. Q., Xu, C. Q., and Peng, W. F.: Global
empirical model for mapping zenith wet delays onto precipitable water,
J. Geodesy, 87, 439–448,
https://doi.org/10.1007/s00190-013-0617-4, 2013.
Yao, Y., Zhang, B., Xu, C., and Chen, J.: Analysis of the global Tm−Ts
correlation and establishment of the latitude-related linear model, Chinese
Sci. Bull., 59, 2340–2347,
https://doi.org/10.1007/s11434-014-0275-9, 2014a.
Yao, Y., Xu, C., Zhang, B., and Cao, N.: GTm-III: A new global empirical
model for mapping zenith wet delays onto precipitable water vapour,
Geophys. J. Int., 197, 202–212,
https://doi.org/10.1093/gji/ggu008, 2014b.
Yao, Y., Sun, Z., Xu, C., Xu, X., and Kong, J.: Extending a model for water
vapor sounding by ground-based GNSS in the vertical direction, J.
Atmos. Sol.-Terr. Phy., 179, 358–366,
https://doi.org/10.1016/j.jastp.2018.08.016, 2018.
Yuan, Y., Zhang, K., Rohm, W., Choy, S., Norman, R., and Wang, C.: Real-time
retrieval of precipitable water vapor from GPS precise point positioning,
J. Geophys. Res.-Atmos., 119, 10044–10057,
https://doi.org/10.1002/2014JD021486, 2014.
Zhang, K., Manning, T., Wu, S., Rohm, W., Silcock, D., and Choy, S.:
Capturing the Signature of Severe Weather Events in Australia Using GPS
Measurements, IEEE J. Sel. Top. Appl.,
8, 1839–1847, https://doi.org/10.1109/JSTARS.2015.2406313, 2015.
Zhou, F., Cao, X., Ge, Y., and Li, W.: Assessment of the positioning
performance and tropospheric delay retrieval with precise point positioning
using products from different analysis centers, GPS Solut., 24, 12,
https://doi.org/10.1007/s10291-019-0925-0, 2020.
Short summary
In GPS or Global navigation satellite systems (GNSS) meteorology, precipitable water vapor (PWV) at a station is obtained from a conversion of the GNSS signal zenith wet delay (ZWD) using a conversion factor which is a function of weighted mean temperature (Tm) over the site. We developed a new global grid-based empirical Tm model using ERA5 reanalysis data. The model-predicted Tm value has significance for applications needing real-time or near real-time PWV converted from GNSS signals.
In GPS or Global navigation satellite systems (GNSS) meteorology, precipitable water vapor (PWV)...
