Articles | Volume 15, issue 7
https://doi.org/10.5194/gmd-15-2763-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-2763-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Coupling a weather model directly to GNSS orbit determination – case studies with OpenIFS
Angel Navarro Trastoy
Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, Finland
Sebastian Strasser
Institute of Geodesy, Graz University of Technology, Graz, Austria
Lauri Tuppi
Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, Finland
Maksym Vasiuta
Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, Finland
Markku Poutanen
Finnish Geospatial Research Institute, National Land Survey of Finland, Espoo, Finland
Torsten Mayer-Gürr
Institute of Geodesy, Graz University of Technology, Graz, Austria
Heikki Järvinen
CORRESPONDING AUTHOR
Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, Finland
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Cited articles
Arnold, D., Meindl, M., Beutler, G., Dach, R., Schaer, S., Lutz, S., Prange, L., Sośnica, K., Mervart, L., and Jäggi, A.: CODE's new solar radiation pressure model for GNSS orbit determination, J. Geodesy, 89, 775–791, https://doi.org/10.1007/s00190-015-0814-4, 2015. a
Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical weather prediction, Nature, 525, 47–55, https://doi.org/10.1038/nature14956, 2015. a
Bevis, M., Businger, S., Herring, T., Rocken, C., Anthes, R., and Ware, R.: GPS Meteorology: Remote Sensing of Atmospheric Water Vapour Using the Global Positioning System, J. Geophys. Res., 97, 15787–15801, https://doi.org/10.1029/92JD01517, 1992. a
Böhm, J., Niell, A., Tregoning, P., and Schuh, H.: Global Mapping Function (GMF): A new empirical mapping function based on numerical weather model data, Geophys. Res. Lett., 33, L07304, https://doi.org/10.1029/2005GL025546, 2006. a
Chen, G. and Herring, T. A.: Effects of atmospheric azimuthal asymmetry on the analysis of space geodetic data, J. Geophys. Res.-Sol. Ea., 102, 20489–20502, https://doi.org/10.1029/97JB01739, 1997. a
CSC: CSC user guide, https://docs.csc.fi/computing/available-systems/ (last access: February 2021), 2021. a
Eresmaa, R., Järvinen, H., Niemelä, S., and Salonen, K.: Asymmetricity of ground-based GPS slant delay data, Atmos. Chem. Phys., 7, 3143–3151, https://doi.org/10.5194/acp-7-3143-2007, 2007. a
Eresmaa, R., Healy, S., Järvinen, H., and Salonen, K.: Implementation of a ray-tracing operator for ground-based GPS Slant Delay observation modeling, J. Geophys. Res.-Atmos., 113, D11114, https://doi.org/10.1029/2007JD009256, 2008a. a, b, c
Eresmaa, R., Järvinen, H., Nordman, M., Poutanen, M., and Luntama, J.-P.: Parameterization of tropospheric delay correction for mobile GNSS positioning: a case study of a cold front passage, Meteorol. Appl., 15, 447–454, https://doi.org/10.1002/met.86, 2008b. a
Eresmaa, R., Healy, S., and Tuppi, L.: Least travel time (LTT) operator, Zenodo [code], https://doi.org/10.5281/zenodo.4834412, 2021. a
Eriksson, D., MacMillan, D., and Gipson, J.: Tropospheric delay ray tracing applied in VLBI analysis, J. Geophys. Res.-Sol. Ea., 119, 9156–9170, https://doi.org/10.1002/2014JB011552, 2014. a
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. a
Hauschild, A.: Basic Observation Equations, in: Springer Handbook of Global Navigation Satellite Systems, edited by: Teunissen, P. J. and Montenbruck, O., Springer Handbooks, Springer International Publishing, Cham, 561–582, https://doi.org/10.1007/978-3-319-42928-1_19, 2017. a
Herring, T. A.: Modeling atmospheric delays in the analysis of space geodetic data, in: Proceedings of Refraction of Transatmospheric Signals in Geodesy, edited by: DeMunck, J. C. and Spoelstra, T. A. T., vol. 36 of Netherlands Geodetic Commission Series, The Hague, Netherlands, 57–164, 1992. a
Hobiger, T., Ichikawa, R., Takasu, T., Koyama, Y., and Kondo, T: Ray-traced troposphere slant delays for precise point positioning, Earth Planets Space, 60, e1–e4, https://doi.org/10.1186/BF03352809, 2008. a
Hugentobler, U. and Montenbruck, O.: Satellite Orbits and Attitude, in: Springer Handbook of Global Navigation Satellite Systems, edited by: Teunissen, P. J. and Montenbruck, O., Springer Handbooks, Springer International Publishing, Cham, 59–90, https://doi.org/10.1007/978-3-319-42928-1_3, 2017. a
Johnston, G., Riddell, A., and Hausler, G.: The International GNSS Service, in: Springer Handbook of Global Navigation Satellite Systems, edited by: Teunissen, P. J. and Montenbruck, O., Springer Handbooks, Springer International Publishing, Cham, 967–982, https://doi.org/10.1007/978-3-319-42928-1_33, 2017.
a
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. a, b
Mayer-Gürr, T., Behzadpour, S., Eicker, A., Ellmer, M., Koch,
B., Krauss, S., Pock, C., Rieser, D., Strasser, S., Suesser-Rechberger, B., Zehentner, N., and Kvas, A.: GROOPS: A software toolkit for gravity field recovery and GNSS processing, Comput. Geosci., 155, 104864,
https://doi.org/10.1016/j.cageo.2021.104864, 2021a. a, b
Mayer-Gürr, T., Behzadpour, S., Eicker, A., Ellmer, M., Koch, B., Krauss, S., Pock, C., Rieser, D., Strasser, S., Süsser-Rechberger, B., Zehentner, N., and Kvas, A.: GROOPS: A software toolkit for gravity field recovery and GNSS processing, https://github.com/groops-devs/groops (last access: March 2021), GitHub [code], 2021b. a
Nordman, M., Eresmaa, R., Poutanen, M., Järvinen, H., Koivula, H., and Luntama, J.-P.: Using numerical weather prediction model derived tropospheric slant delays in GPS processing: a case study, Geophysica, 43, 49–57, 2007. a
Nordman, M., Eresmaa, R., Boehm, J., Poutanen, M., Koivula, H., and Järvinen, H.: Effect of troposphere slant delays on regional double difference GPS processing, Earth Planets Space, 61, 845–852, 2009. a
Ollinaho, P., Carver, G. D., Lang, S. T. K., Tuppi, L., Ekblom, M., and Järvinen, H.: Ensemble prediction using a new dataset of ECMWF initial states – OpenEnsemble 1.0, Geosci. Model Dev., 14, 2143–2160, https://doi.org/10.5194/gmd-14-2143-2021, 2021. a, b
Petit, G. and Luzum, B. (Eds.): IERS Conventions (2010), IERS Technical Note 36, Verlag des Bundesamts für Kartographie und Geodäsie, Frankfurt am Main, https://www.iers.org/IERS/EN/Publications/TechnicalNotes/tn36.html (last access: February 2021), 2010. a
Rocken, C., Sokolovskiy, S., Johnson, J. M., and Hunt, D.: Improved mapping of tropospheric delays, J. Atmos. Ocean. Tech., 18, 1205–1213, 2001. a
Rodgers, C. D.: Inverse Methods for Atmospheric Sounding, Theory and Practice, 2, 256 pp., https://doi.org/10.1142/3171, 2000. a, b, c
Schönemann, E.: Analysis of GNSS raw observations in PPP solutions, Schriftenreihe der Fachrichtung Geodäsie, 42, https://tuprints.ulb.tu-darmstadt.de/3843/ (last access: February 2021), 2014. a
Schönemann, E., Becker, M., and Springer, T.: A new Approach for GNSS Analysis in a Multi-GNSS and Multi-Signal Environment, Journal of Geodetic Science, 1, 204–214, https://doi.org/10.2478/v10156-010-0023-2, 2011. a
Strasser, S., Mayer-Gürr, T., and Zehentner, N.: Processing of GNSS constellations and ground station networks using the raw observation approach, J. Geodesy, 93, 1045–1057, https://doi.org/10.1007/s00190-018-1223-2, 2019. a, b, c
Zus, F., Dick, G., Dousa, J., Heise, S., and Wickert, J.: The rapid and precise computation of GPS slant total delays and mapping factors utilizing a numerical weather model, Radio Sci., 49, 207–216, https://doi.org/10.1002/2013RS005280, 2014. a, b
Short summary
Production of satellite products relies on information from different centers. By coupling a weather model and an orbit determination solver we eliminate the dependence on one of the centers. The coupling has proven to be possible in the first stage, where no formatting has been applied to any of the models involved. This opens a window for further development and improvement to a coupling that has proven to be as good as the predecessor model.
Production of satellite products relies on information from different centers. By coupling a...