High-Resolution Atmospheric Water Vapor Fields by Spaceborne Geodetic Sensing, Tomographic Fusion, and Atmospheric Modeling
Final Report Abstract
Atmospheric water vapor plays an important role in Earth’s water and energy cycle. It is a very active atmospheric constituent involved in various weather processes, although it only occupies up to 4% in volume. Moreover, water vapor is the dominant greenhouse gas in Earth’s atmosphere which can intensify global warming. However, the accurate quantification of atmospheric water vapor remains a challenge due to it high spatiotemporal variability. In this project, we proposed an interdisciplinary data fusion approach for high-resolution atmospheric water vapor fields by integrating spaceborne geodetic sensing (GNSS and InSAR), tomographic fusion, and atmospheric modelling. Feasibility of this approach has been evaluated in several peer-reviewed publications). Results show that this approach enhanced the construction of dynamic atmospheric models in different seasons for the transnational Upper Rhine Graben region. In addition, an associated comprehensive multidisciplinary high-resolution atmospheric water vapor dataset is provided to the public, in which geodetic, photogrammetric, atmospheric modeling and data fusion techniques were used to obtain maps of water vapor. This multi-technology water vapor dataset is unique, and it will be useful for many kinds of studies that require high-resolution information on atmospheric water vapor, such as retrospective study of weather, evaluation of weather model, and ML for weather forecast. The IMK group (WP100) carried out atmospheric simulations and assimilated water vapor products of the project partners. Improved simulations of water vapor and precipitation are obtained in this way for all seasons. The simultaneous assimilation of different water vapor information and temperature proved to be an important factor in decisively influencing the complex interaction of variables in atmospheric models. The GIK group (WP200) obtained the GNSS IWV estimates and compared them to the WRF product developed within the project, in-situ radiosonde, and atmospheric reanalysis products. Results show that the IWV estimates are in excellent agreements with discrepancies of a few kg m-2. Moreover, we found that power-low noise is more suitable for the daily ERA5-GNSS IWV series in Europe 1994−2019 than the commonly used AR(1). This finding is important for the homogenization of the IWV time series and accurate estimation of their trends, which can be conducive to a better understanding of climate change. The IPF group (WP300) processed big stacks of Sentinel-1 data (>170scenes) to obtain the input for the tomography as well as the assimilation into the WRF model. Improvements of the state-of-the-art methods are implemented to compute absolute IWV values from the interferometric data. To resolve the rank deficiency the open-source weather model ERA5 is used to provide easy-access methods for other users. The results show that the IWV estimates are in good agreement with the GNSS results used as validation data. The ETH-IGP group (WP400) used GNSS ZTDs and InSAR ddSTDs, which are outputs of WP200 and WP300, to construct GNSS and GNSS/InSAR 2D fields of ZTDs and 3D fields of refractivities. In this project, we utilized collocation method to provide a new combination workframe for GNSS and InSAR tropospheric observations. In addition, we validated our collocated observations with GNSS processed ones and WRF based observations. In addition, we also validated, the GNSS/InSAR combinet solutions with GNSS-only and InSAR only. We found a few mm differences in terms of bias and standard deviation.
Publications
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Comparison of tropospheric path delay estimates from GNSS and space-borne SAR interferometry in alpine conditions. Remote Sensing 2019, 11(15)
Wilgan, K.; Siddique, M.A.; Strozzi, T.; Geiger, A.; Frey, O.
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High-resolution models of tropospheric delays and refractivity based on GNSS and numerical weather prediction data for alpine regions in Switzerland. Journal of Geodesy 2019, 93(6), pp. 819–835
Wilgan, K.; Geiger, A.
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Investigation of Important Aspects of GNSS/InSAR Techniques Integration for Atmospheric Water Vapor Retrieval. In proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019), Miami, FL, USA, pp.3461-3468, USA: Institute of Navigation, September 16-20, 2019
Shehaj, E.; Wilgan, K.; Geiger, A.
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(2020) Copula-based modeling of dependence structure in geodesy and GNSS applications: case study for zenith tropospheric delay in complex terrain, GPS Solutions, 2020
Mousavian, R., Lorenz, C., Hossainali, M. M., Fersch, B., Kunstmann, H.
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A collocation framework to retrieve tropospheric delays from a combination of GNSS and InSAR. Navigation 2020, 67, 823– 842
Shehaj, E.; Wilgan, K.; Frey, O.; Geiger, A.
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Total refractivity fields from GNSS tropospheric delays reconstructed with collocation methods. In proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2020, online Symposium, Waikoloa, HI, USA, pp.457-460, Sept. 26 – Oct. 2, 2020
Shehaj, E.; Geiger, A.; Moeller, G.
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Tropospheric delays derived from ground meteorological parameters: comparison between machine learning and empirical model approaches. In proceedings of European Navigation Conference (ENC) 2020, online Conference, IEEE proceedings. November 23-24, 2020
Miotti, L.; Shehaj, E.; Geiger, A.; D'Aronco, St.; Wegner, J.D.; Moeller, G.; Rothacher, M.
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Feasibility of ERA5 Integrated Water Vapor trends for climate change analysis in continental Europe: An evaluation with GPS (1994–2019) by considering statistical significance. Remote Sensing of Environment 2021, 260, 112416
Yuan, P.; Hunegnaw, A.; Alshawaf, F.; Awange, J.; Klos, A.; Teferle, F.N.; Kutterer, H.
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High-Resolution Tropospheric Refractivity Fields by Combining Machine Learning and Collocation Methods to Correct Earth Observation Data. In proceedings of International Astronautical Congress (IAC) 2021, Dubai, United Arab Emirates, October 2021
Shehaj, E.; Miotti, L.; Geiger, A.; D’Aronco, S.; Wegner, JD.; Moeller, G.; Soja, B.; Rothacher, M.
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2022. Tropospheric water vapor: A comprehensive high resolution data collection for the transnational Upper Rhine Graben region. Earth System Science Data Discussions 1–34
Fersch, B., Wagner, A., Kamm, B., Shehaj, E., Schenk, A., Yuan, P., Geiger, A., Moeller, G., Heck, B., Hinz, S., Kutterer, H., Kunstmann, H.
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Assimilation of GNSS and Synoptic Data in a Convection Permitting Limited Area Model: Improvement of Simulated tropospheric Water Vapor Content, Front. Earth Sci. - Atmospheric Science, 2022
Wagner, A.; Fersch, B.; Peng, Y.; Rummler, T.; Kunstmann, H.
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Lateral terrestrial water fluxes in the LSM of WRF-Hydro: Benefits of a 2D groundwater representation. Hydrological Processes, 36 (3), e14510. 2022
Rummler, T.; Wagner, A.; Arnault, J.; Kunstmann, H.
