Articles | Volume 14, issue 12
https://doi.org/10.5194/essd-14-5287-2022
© 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/essd-14-5287-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
02 Dec 2022
Data description paper |
| 02 Dec 2022
| 02 Dec 2022
Tropospheric water vapor: a comprehensive high-resolution data collection for the transnational Upper Rhine Graben region
Campus Alpin (IMK-IFU), Karlsruhe Institute of Technology, Kreuzeckbahnstraße 19, 82467 Garmisch-Partenkirchen, Germany
Andreas Wagner
Institute of Geography (IGUA), University of Augsburg, Alter Postweg 118, 86159 Augsburg, Germany
Bettina Kamm
Institute of Photogrammetry and Remote Sensing (IPF), Karlsruhe Institute of Technology, Englerstr. 7, 76131 Karlsruhe, Germany
Endrit Shehaj
Institute of Geodesy and Photogrammetry, ETH Zurich, Robert-Gnehm-Weg 15, 8093 Zurich, Switzerland
Andreas Schenk
Institute of Photogrammetry and Remote Sensing (IPF), Karlsruhe Institute of Technology, Englerstr. 7, 76131 Karlsruhe, Germany
Peng Yuan
Geodetic Institute (GIK), Karlsruhe Institute of Technology, Englerstr. 7, 76131 Karlsruhe, Germany
Alain Geiger
Institute of Geodesy and Photogrammetry, ETH Zurich, Robert-Gnehm-Weg 15, 8093 Zurich, Switzerland
Gregor Moeller
Institute of Geodesy and Photogrammetry, ETH Zurich, Robert-Gnehm-Weg 15, 8093 Zurich, Switzerland
Bernhard Heck
Geodetic Institute (GIK), Karlsruhe Institute of Technology, Englerstr. 7, 76131 Karlsruhe, Germany
Stefan Hinz
Institute of Photogrammetry and Remote Sensing (IPF), Karlsruhe Institute of Technology, Englerstr. 7, 76131 Karlsruhe, Germany
Hansjörg Kutterer
Geodetic Institute (GIK), Karlsruhe Institute of Technology, Englerstr. 7, 76131 Karlsruhe, Germany
Harald Kunstmann
Campus Alpin (IMK-IFU), Karlsruhe Institute of Technology, Kreuzeckbahnstraße 19, 82467 Garmisch-Partenkirchen, Germany
Institute of Geography (IGUA), University of Augsburg, Alter Postweg 118, 86159 Augsburg, Germany
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M. Evers, A. Thiele, H. Hammer, E. Cadario, K. Schulz, and S. Hinz
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S. Hinz, R. Q. Feitosa, M. Weinmann, and B. Jutzi
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2021, 7–7, https://doi.org/10.5194/isprs-annals-V-1-2021-7-2021, https://doi.org/10.5194/isprs-annals-V-1-2021-7-2021, 2021
Christof Lorenz, Tanja C. Portele, Patrick Laux, and Harald Kunstmann
Earth Syst. Sci. Data, 13, 2701–2722, https://doi.org/10.5194/essd-13-2701-2021, https://doi.org/10.5194/essd-13-2701-2021, 2021
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Benjamin Fersch, Till Francke, Maik Heistermann, Martin Schrön, Veronika Döpper, Jannis Jakobi, Gabriele Baroni, Theresa Blume, Heye Bogena, Christian Budach, Tobias Gränzig, Michael Förster, Andreas Güntner, Harrie-Jan Hendricks Franssen, Mandy Kasner, Markus Köhli, Birgit Kleinschmit, Harald Kunstmann, Amol Patil, Daniel Rasche, Lena Scheiffele, Ulrich Schmidt, Sandra Szulc-Seyfried, Jannis Weimar, Steffen Zacharias, Marek Zreda, Bernd Heber, Ralf Kiese, Vladimir Mares, Hannes Mollenhauer, Ingo Völksch, and Sascha Oswald
Earth Syst. Sci. Data, 12, 2289–2309, https://doi.org/10.5194/essd-12-2289-2020, https://doi.org/10.5194/essd-12-2289-2020, 2020
N. Mazroob Semnani, M. Breunig, M. Al-Doori, A. Heck, P. Kuper, and H. Kutterer
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 485–492, https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-485-2020, https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-485-2020, 2020
S. Hinz, R. Q. Feitosa, M. Weinmann, and B. Jutzi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2020, 7–7, https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-7-2020, https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-7-2020, 2020
S. Hinz, R. Q. Feitosa, M. Weinmann, and B. Jutzi
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2020, 7–7, https://doi.org/10.5194/isprs-annals-V-1-2020-7-2020, https://doi.org/10.5194/isprs-annals-V-1-2020-7-2020, 2020
R. Ilehag, J. Leitloff, M. Weinmann, and A. Schenk
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2020, 25–32, https://doi.org/10.5194/isprs-annals-V-1-2020-25-2020, https://doi.org/10.5194/isprs-annals-V-1-2020-25-2020, 2020
M. Hermann, B. Ruf, M. Weinmann, and S. Hinz
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 357–364, https://doi.org/10.5194/isprs-annals-V-2-2020-357-2020, https://doi.org/10.5194/isprs-annals-V-2-2020-357-2020, 2020
Julius Polz, Christian Chwala, Maximilian Graf, and Harald Kunstmann
Atmos. Meas. Tech., 13, 3835–3853, https://doi.org/10.5194/amt-13-3835-2020, https://doi.org/10.5194/amt-13-3835-2020, 2020
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Commercial microwave link (CML) networks can be used to estimate path-averaged rain rates. This study evaluates the ability of convolutional neural networks to distinguish between wet and dry periods in CML time series data and the ability to transfer this detection skill to sensors not used for training. Our data set consists of several months of data from 3904 CMLs covering all of Germany. Compared to a previously used detection method, we could show a significant increase in performance.
Maximilian Graf, Christian Chwala, Julius Polz, and Harald Kunstmann
Hydrol. Earth Syst. Sci., 24, 2931–2950, https://doi.org/10.5194/hess-24-2931-2020, https://doi.org/10.5194/hess-24-2931-2020, 2020
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Commercial microwave links (CMLs), which form large parts of the backhaul from the ubiquitous cellular communication networks, can be used to estimate path-integrated rainfall rates. This study presents the processing and evaluation of the largest CML data set to date, covering the whole of Germany with almost 4000 CMLs. The CML-derived rainfall information compares well to a standard precipitation data set from the German Meteorological Service, which combines radar and rain gauge data.
Benjamin Fersch, Alfonso Senatore, Bianca Adler, Joël Arnault, Matthias Mauder, Katrin Schneider, Ingo Völksch, and Harald Kunstmann
Hydrol. Earth Syst. Sci., 24, 2457–2481, https://doi.org/10.5194/hess-24-2457-2020, https://doi.org/10.5194/hess-24-2457-2020, 2020
Marion Heublein, Patrick Erik Bradley, and Stefan Hinz
Ann. Geophys., 38, 179–189, https://doi.org/10.5194/angeo-38-179-2020, https://doi.org/10.5194/angeo-38-179-2020, 2020
Natalia Hanna, Estera Trzcina, Gregor Möller, Witold Rohm, and Robert Weber
Atmos. Meas. Tech., 12, 4829–4848, https://doi.org/10.5194/amt-12-4829-2019, https://doi.org/10.5194/amt-12-4829-2019, 2019
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In the study, the potential of GNSS tomography as an important supplementary data source for numerical weather prediction models was examined. We used two GNSS tomography models (TUW, WUELS) in different configurations. The GNSS tomography outputs were assimilated into the WRF model using a radio occultation observations operator (non-standard approach). Promising results show improvement in the weather forecasting of relative humidity and temperature during heavy-precipitation events.
Samuel Weber, Jan Beutel, Reto Da Forno, Alain Geiger, Stephan Gruber, Tonio Gsell, Andreas Hasler, Matthias Keller, Roman Lim, Philippe Limpach, Matthias Meyer, Igor Talzi, Lothar Thiele, Christian Tschudin, Andreas Vieli, Daniel Vonder Mühll, and Mustafa Yücel
Earth Syst. Sci. Data, 11, 1203–1237, https://doi.org/10.5194/essd-11-1203-2019, https://doi.org/10.5194/essd-11-1203-2019, 2019
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In this paper, we describe a unique 10-year or more data record obtained from in situ measurements in steep bedrock permafrost in an Alpine environment on the Matterhorn Hörnligrat, Zermatt, Switzerland, at 3500 m a.s.l. By documenting and sharing these data in this form, we contribute to facilitating future research based on them, e.g., in the area of analysis methodology, comparative studies, assessment of change in the environment, natural hazard warning and the development of process models.
Rebecca Mott, Andreas Wolf, Maximilian Kehl, Harald Kunstmann, Michael Warscher, and Thomas Grünewald
The Cryosphere, 13, 1247–1265, https://doi.org/10.5194/tc-13-1247-2019, https://doi.org/10.5194/tc-13-1247-2019, 2019
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The mass balance of very small glaciers is often governed by anomalous snow accumulation, winter precipitation being multiplied by snow redistribution processes, or by suppressed snow ablation driven by micrometeorological effects lowering net radiation and turbulent heat exchange. In this study we discuss the relative contribution of snow accumulation (avalanches) versus micrometeorology (katabatic flow) on the mass balance of the lowest perennial ice field of the Alps, the Ice Chapel.
Gregor Möller and Daniel Landskron
Atmos. Meas. Tech., 12, 23–34, https://doi.org/10.5194/amt-12-23-2019, https://doi.org/10.5194/amt-12-23-2019, 2019
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The paper describes a ray-tracing approach for the proper reconstruction of GNSS signal paths through the lower atmosphere, identifies possible error sources during ray tracing and provides a strategy for reducing their effect on the GNSS tomography solution, thereby contributing to a more reliable reconstruction of the 3-D water vapor distribution in the lower atmosphere from GNSS measurements.
Hugues Brenot, Witold Rohm, Michal Kačmařík, Gregor Möller, André Sá, Damian Tondaś, Lukas Rapant, Riccardo Biondi, Toby Manning, and Cédric Champollion
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2018-292, https://doi.org/10.5194/amt-2018-292, 2018
Revised manuscript not accepted
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The increasing number of navigation satellites orbiting the Earth and the continuous world wide deployment of dense networks will enable more present and future GNSS applications in the field of atmospheric monitoring. This study suggests some elements of progress in methodology to highlight the interest of ensemble tomography solution for improving the understanding of severe weather conditions, especially the initiation of the deep convection.
Ladina Steiner, Michael Meindl, Charles Fierz, and Alain Geiger
The Cryosphere, 12, 3161–3175, https://doi.org/10.5194/tc-12-3161-2018, https://doi.org/10.5194/tc-12-3161-2018, 2018
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The amount of water stored in snow cover is of high importance for flood risks, climate change, and early-warning systems. We evaluate the potential of using GPS to estimate the stored water. We use GPS antennas buried underneath the snowpack and develop a model based on the path elongation of the GPS signals while propagating through the snowpack. The method works well over full seasons, including melt periods. Results correspond within 10 % to the state-of-the-art reference data.
B. Jutzi, M. Weinmann, and S. Hinz
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1, 1–3, https://doi.org/10.5194/isprs-annals-IV-1-1-2018, https://doi.org/10.5194/isprs-annals-IV-1-1-2018, 2018
K. Chen, M. Weinmann, X. Sun, M. Yan, S. Hinz, B. Jutzi, and M. Weinmann
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1, 29–36, https://doi.org/10.5194/isprs-annals-IV-1-29-2018, https://doi.org/10.5194/isprs-annals-IV-1-29-2018, 2018
R. Gabriel, S. Keller, J. Matthes, P. Waibel, H. B. Keller, and S. Hinz
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1, 53–60, https://doi.org/10.5194/isprs-annals-IV-1-53-2018, https://doi.org/10.5194/isprs-annals-IV-1-53-2018, 2018
S. Keller, F. M. Riese, J. Stötzer, P. M. Maier, and S. Hinz
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1, 101–108, https://doi.org/10.5194/isprs-annals-IV-1-101-2018, https://doi.org/10.5194/isprs-annals-IV-1-101-2018, 2018
B. Jutzi, M. Weinmann, and S. Hinz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1, 1–3, https://doi.org/10.5194/isprs-archives-XLII-1-1-2018, https://doi.org/10.5194/isprs-archives-XLII-1-1-2018, 2018
M. Boldt, A. Thiele, K. Schulz, and S. Hinz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1, 59–64, https://doi.org/10.5194/isprs-archives-XLII-1-59-2018, https://doi.org/10.5194/isprs-archives-XLII-1-59-2018, 2018
K. Chen, M. Weinmann, X. Gao, M. Yan, S. Hinz, B. Jutzi, and M. Weinmann
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2, 65–72, https://doi.org/10.5194/isprs-annals-IV-2-65-2018, https://doi.org/10.5194/isprs-annals-IV-2-65-2018, 2018
Dominikus Heinzeller, Diarra Dieng, Gerhard Smiatek, Christiana Olusegun, Cornelia Klein, Ilse Hamann, Seyni Salack, Jan Bliefernicht, and Harald Kunstmann
Earth Syst. Sci. Data, 10, 815–835, https://doi.org/10.5194/essd-10-815-2018, https://doi.org/10.5194/essd-10-815-2018, 2018
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Climate change and population growth pose severe challenges to 21st century rural Africa. Within the framework of the West African Science Service Center on Climate Change and Adapted Land Use (WASCAL), an ensemble of high-resolution regional climate change simulations is provided to support adaptation and mitigation measures. This contribution presents the concept and design of the simulations and provides information on the format and dissemination of the available data.
R. Ilehag, M. Weinmann, A. Schenk, S. Keller, B. Jutzi, and S. Hinz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W3, 65–71, https://doi.org/10.5194/isprs-archives-XLII-3-W3-65-2017, https://doi.org/10.5194/isprs-archives-XLII-3-W3-65-2017, 2017
M. Breunig, A. Borrmann, E. Rank, S. Hinz, T. Kolbe, M. Schilcher, R.-P. Mundani, J. R. Jubierre, M. Flurl, A. Thomsen, A. Donaubauer, Y. Ji, S. Urban, S. Laun, S. Vilgertshofer, B. Willenborg, M. Menninghaus, H. Steuer, S. Wursthorn, J. Leitloff, M. Al-Doori, and N Mazroobsemnani
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4-W4, 341–352, https://doi.org/10.5194/isprs-archives-XLII-4-W4-341-2017, https://doi.org/10.5194/isprs-archives-XLII-4-W4-341-2017, 2017
M. Weinmann, M. S. Müller, M. Hillemann, N. Reydel, S. Hinz, and B. Jutzi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W6, 399–406, https://doi.org/10.5194/isprs-archives-XLII-2-W6-399-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W6-399-2017, 2017
R. Ilehag, A. Schenk, and S. Hinz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W6, 145–151, https://doi.org/10.5194/isprs-archives-XLII-2-W6-145-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W6-145-2017, 2017
Caroline Brosy, Karina Krampf, Matthias Zeeman, Benjamin Wolf, Wolfgang Junkermann, Klaus Schäfer, Stefan Emeis, and Harald Kunstmann
Atmos. Meas. Tech., 10, 2773–2784, https://doi.org/10.5194/amt-10-2773-2017, https://doi.org/10.5194/amt-10-2773-2017, 2017
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Vertical and horizontal sounding of the planetary boundary layer can be complemented by unmanned aerial vehicles (UAV). Utilizing a multicopter-type UAV spatial sampling of air and simultaneously sensing of meteorological variables is possible for the study of surface exchange processes. During stable atmospheric conditions, vertical methane gradients of about 300 ppb were found. This approach extended the vertical profile height of existing tower-based infrastructure by a factor of five.
Michal Kačmařík, Jan Douša, Galina Dick, Florian Zus, Hugues Brenot, Gregor Möller, Eric Pottiaux, Jan Kapłon, Paweł Hordyniec, Pavel Václavovic, and Laurent Morel
Atmos. Meas. Tech., 10, 2183–2208, https://doi.org/10.5194/amt-10-2183-2017, https://doi.org/10.5194/amt-10-2183-2017, 2017
Jan Schmieder, Florian Hanzer, Thomas Marke, Jakob Garvelmann, Michael Warscher, Harald Kunstmann, and Ulrich Strasser
Hydrol. Earth Syst. Sci., 20, 5015–5033, https://doi.org/10.5194/hess-20-5015-2016, https://doi.org/10.5194/hess-20-5015-2016, 2016
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We present novel research on the spatiotemporal variability of snowmelt isotopic content in a high-elevation catchment with complex terrain
to improve the isotope-based hydrograph separation method. A modelling approach was used to weight the plot-scale snowmelt isotopic content
with melt rates for the north- and south-facing slope. The investigations showed that it is important to sample at least north- and south-facing slopes,
because of distinct isotopic differences between both slopes.
Bernd Schalge, Jehan Rihani, Gabriele Baroni, Daniel Erdal, Gernot Geppert, Vincent Haefliger, Barbara Haese, Pablo Saavedra, Insa Neuweiler, Harrie-Jan Hendricks Franssen, Felix Ament, Sabine Attinger, Olaf A. Cirpka, Stefan Kollet, Harald Kunstmann, Harry Vereecken, and Clemens Simmer
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2016-557, https://doi.org/10.5194/hess-2016-557, 2016
Manuscript not accepted for further review
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In this work we show how we used a coupled atmosphere-land surface-subsurface model at highest possible resolution to create a testbed for data assimilation. The model was able to capture all important processes and interactions between the compartments as well as showing realistic statistical behavior. This proves that using a model as a virtual truth is possible and it will enable us to develop data assimilation methods where states and parameters are updated across compartment.
Jan Douša, Galina Dick, Michal Kačmařík, Radmila Brožková, Florian Zus, Hugues Brenot, Anastasia Stoycheva, Gregor Möller, and Jan Kaplon
Atmos. Meas. Tech., 9, 2989–3008, https://doi.org/10.5194/amt-9-2989-2016, https://doi.org/10.5194/amt-9-2989-2016, 2016
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GNSS products provide observations of atmospheric water vapour. Advanced tropospheric products focus on ultra-fast and high-resolution zenith total delays (ZTDs), horizontal gradients and slant delays, all suitable for rapid-cycle numerical weather prediction (NWP) and severe weather event monitoring. The GNSS4SWEC Benchmark provides a complex data set for developing and assessing these products, with initial focus on reference ZTDs and gradients derived from several NWP and dense GNSS networks.
Christian Chwala, Felix Keis, and Harald Kunstmann
Atmos. Meas. Tech., 9, 991–999, https://doi.org/10.5194/amt-9-991-2016, https://doi.org/10.5194/amt-9-991-2016, 2016
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Commercial microwave link (CML) networks, like they are used as backbone for the cell phone network, can be used to derive rainfall information. However, data availability is limited due to the lack of suitable data acquisition systems. We have developed and currently operate a custom data acquisition system for CML networks that is able to acquire the required data for a large number of CMLs in real time. This system is the basis for a future countrywide rainfall product derived from CML data.
D. Heinzeller, M. G. Duda, and H. Kunstmann
Geosci. Model Dev., 9, 77–110, https://doi.org/10.5194/gmd-9-77-2016, https://doi.org/10.5194/gmd-9-77-2016, 2016
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We present an in-depth evaluation of the Model for Prediction Across Scales (MPAS) with regards to technical aspects of performing model runs and scalability for medium-size meshes on several HPCs. We also demonstrate the model performance in terms of its capability to reproduce the dynamics of the West African monsoon and its associated precipitation in a pilot study. Finally, we conduct extreme scaling tests on a global 3km mesh with 65,536,002 horizontal grid cells on up to 458,752 cores.
F. Alshawaf, B. Fersch, S. Hinz, H. Kunstmann, M. Mayer, and F. J. Meyer
Hydrol. Earth Syst. Sci., 19, 4747–4764, https://doi.org/10.5194/hess-19-4747-2015, https://doi.org/10.5194/hess-19-4747-2015, 2015
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This work aims at deriving high spatially resolved maps of atmospheric water vapor by the fusion data from Interferometric Synthetic Aperture Radar (InSAR), Global Navigation Satellite Systems (GNSS), and the Weather Research and Forecasting (WRF) model. The data fusion approach exploits the redundant and complementary spatial properties of all data sets to provide more accurate and high-resolution maps of water vapor. The comparison with maps from MERIS shows rms values of less than 1 mm.
G. Mao, S. Vogl, P. Laux, S. Wagner, and H. Kunstmann
Hydrol. Earth Syst. Sci., 19, 1787–1806, https://doi.org/10.5194/hess-19-1787-2015, https://doi.org/10.5194/hess-19-1787-2015, 2015
A. Wagner, J. Seltmann, and H. Kunstmann
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-12-1765-2015, https://doi.org/10.5194/hessd-12-1765-2015, 2015
Manuscript not accepted for further review
R. J. van der Ent, O. A. Tuinenburg, H.-R. Knoche, H. Kunstmann, and H. H. G. Savenije
Hydrol. Earth Syst. Sci., 17, 4869–4884, https://doi.org/10.5194/hess-17-4869-2013, https://doi.org/10.5194/hess-17-4869-2013, 2013
Related subject area
Domain: ESSD – Atmosphere | Subject: Meteorology
Dataset of stable isotopes of precipitation in the Eurasian continent
A 7-year record of vertical profiles of radar measurements and precipitation estimates at Dumont d'Urville, Adélie Land, East Antarctica
Long-term monthly 0.05° terrestrial evapotranspiration dataset (1982–2018) for the Tibetan Plateau
High-resolution (1 km) all-sky net radiation over Europe enabled by the merging of land surface temperature retrievals from geostationary and polar-orbiting satellites
LGHAP v2: A global gap-free aerosol optical depth and PM2.5 concentration dataset since 2000 derived via big earth data analytics
Atmospheric and surface observations during the Saint John River Experiment on Cold Season Storms (SAJESS)
The 2023 National Offshore Wind data set (NOW-23)
Year-long buoy-based observations of the air–sea transition zone off the US west coast
The historical Greenland Climate Network (GC-Net) curated and augmented level-1 dataset
Low-level mixed-phase clouds at the high Arctic site of Ny-Ålesund: a comprehensive long-term dataset of remote sensing observations
CHESS-SCAPE: high-resolution future projections of multiple climate scenarios for the United Kingdom derived from downscaled United Kingdom Climate Projections 2018 regional climate model output
Quality-controlled meteorological datasets from SIGMA automatic weather stations in northwest Greenland, 2012–2020
A dataset of energy, water vapor, and carbon exchange observations in oasis–desert areas from 2012 to 2021 in a typical endorheic basin
Derivation and compilation of lower-atmospheric properties relating to temperature, wind, stability, moisture, and surface radiation budget over the central Arctic sea ice during MOSAiC
CLARA-A3: The third edition of the AVHRR-based CM SAF climate data record on clouds, radiation and surface albedo covering the period 1979 to 2023
Reanalysis of multi-year high-resolution X-band weather radar observations in Hamburg
An integrated and homogenized global surface solar radiation dataset and its reconstruction based on a convolutional neural network approach
IWIN: the Isfjorden Weather Information Network
Data collected using small uncrewed aircraft system during the TRacking Aerosol Convection Interactions ExpeRiment (TRACER)
A new daily gridded precipitation dataset for the Chinese mainland based on gauge observations
A 16-year global climate data record of total column water vapour generated from OMI observations in the visible blue spectral range
The EUPPBench postprocessing benchmark dataset v1.0
CHELSA-W5E5: daily 1 km meteorological forcing data for climate impact studies
Database of the Italian disdrometer network
East Asia Reanalysis System (EARS)
Data rescue of historical wind observations in Sweden since the 1920s
LegacyClimate 1.0: a dataset of pollen-based climate reconstructions from 2594 Northern Hemisphere sites covering the last 30 kyr and beyond
EURADCLIM: the European climatological high-resolution gauge-adjusted radar precipitation dataset
Radar and ground-level measurements of clouds and precipitation collected during the POPE 2020 campaign at Princess Elisabeth Antarctica
Combined wind lidar and cloud radar for high-resolution wind profiling
An enhanced integrated water vapour dataset from more than 10 000 global ground-based GPS stations in 2020
TPHiPr: a long-term (1979–2020) high-accuracy precipitation dataset (1∕30°, daily) for the Third Pole region based on high-resolution atmospheric modeling and dense observations
The AntAWS dataset: a compilation of Antarctic automatic weather station observations
HiTIC-Monthly: a monthly high spatial resolution (1 km) human thermal index collection over China during 2003–2020
A long-term 1 km monthly near-surface air temperature dataset over the Tibetan glaciers by fusion of station and satellite observations
A global dataset of daily maximum and minimum near-surface air temperature at 1 km resolution over land (2003–2020)
The hourly wind-bias-adjusted precipitation data set from the Environment and Climate Change Canada automated surface observation network (2001–2019)
Enhanced automated meteorological observations at the Canadian Arctic Weather Science (CAWS) supersites
Quality control and correction method for air temperature data from a citizen science weather station network in Leuven, Belgium
Combined high-resolution rainfall and wind data collected for 3 months on a wind farm 110 km southeast of Paris (France)
Sub-mesoscale observations of convective cold pools with a dense station network in Hamburg, Germany
Observational data from uncrewed systems over Southern Great Plains
A global dataset of spatiotemporally seamless daily mean land surface temperatures: generation, validation, and analysis
Longhu Chen, Qinqin Wang, Guofeng Zhu, Xinrui Lin, Dongdong Qiu, Yinying Jiao, Siyu Lu, Rui Li, Gaojia Meng, and Yuhao Wang
Earth Syst. Sci. Data, 16, 1543–1557, https://doi.org/10.5194/essd-16-1543-2024, https://doi.org/10.5194/essd-16-1543-2024, 2024
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We have compiled data regarding stable precipitation isotopes from 842 sampling points throughout the Eurasian continent since 1961, accumulating a total of 51 753 data records. The collected data have undergone pre-processing and statistical analysis. We also analysed the spatiotemporal distribution of stable precipitation isotopes across the Eurasian continent and their interrelationships with meteorological elements.
Valentin Wiener, Marie-Laure Roussel, Christophe Genthon, Étienne Vignon, Jacopo Grazioli, and Alexis Berne
Earth Syst. Sci. Data, 16, 821–836, https://doi.org/10.5194/essd-16-821-2024, https://doi.org/10.5194/essd-16-821-2024, 2024
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This paper presents 7 years of data from a precipitation radar deployed at the Dumont d'Urville station in East Antarctica. The main characteristics of the dataset are outlined in a short statistical study. Interannual and seasonal variability are also investigated. Then, we extensively describe the processing method to retrieve snowfall profiles from the radar data. Lastly, a brief comparison is made with two climate models as an application example of the dataset.
Ling Yuan, Xuelong Chen, Yaoming Ma, Cunbo Han, Binbin Wang, and Weiqiang Ma
Earth Syst. Sci. Data, 16, 775–801, https://doi.org/10.5194/essd-16-775-2024, https://doi.org/10.5194/essd-16-775-2024, 2024
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Accurately monitoring and understanding the spatial–temporal variability of evapotranspiration (ET) components over the Tibetan Plateau (TP) remains difficult. Here, 37 years (1982–2018) of monthly ET component data for the TP was produced, and the data are consistent with measurements. The annual average ET for the TP was about 0.93 (± 0.037) × 103 Gt yr−1. The rate of increase of the ET was around 0.96 mm yr−1. The increase in the ET can be explained by warming and wetting of the climate.
Dominik Rains, Isabel Trigo, Emanuel Dutra, Sofia Ermida, Darren Ghent, Petra Hulsman, Jose Gómez-Dans, and Diego G. Miralles
Earth Syst. Sci. Data, 16, 567–593, https://doi.org/10.5194/essd-16-567-2024, https://doi.org/10.5194/essd-16-567-2024, 2024
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Land surface temperature and surface net radiation are vital inputs for many land surface and hydrological models. However, current remote sensing datasets of these variables come mostly at coarse resolutions, and the few high-resolution datasets available have large gaps due to cloud cover. Here, we present a continuous daily product for both variables across Europe for 2018–2019 obtained by combining observations from geostationary as well as polar-orbiting satellites.
Kaixu Bai, Ke Li, Liuqing Shao, Xinran Li, Chaoshun Liu, Zhengqiang Li, Mingliang Ma, Di Han, Yibing Sun, Zhe Zheng, Ruijie Li, Ni-Bin Chang, and Jianping Guo
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-519, https://doi.org/10.5194/essd-2023-519, 2024
Revised manuscript accepted for ESSD
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A global long-term gap-free high-resolution air pollutants dataset (LGHAP v2) was generated to provide spatially contiguous AOD and PM2.5 concentration maps with daily 1-km resolution from 2000 to 2021. The LGHAP v2 dataset has good data accuracies compared against ground AOD and PM2.5 observations, which is an invaluable data base to advance aerosol-related studies and trigger multidisciplinary applications for environmental management, health risk assessment, and climate change analysis.
Hadleigh D. Thompson, Julie M. Thériault, Stephen J. Déry, Ronald E. Stewart, Dominique Boisvert, Lisa Rickard, Nicolas R. Leroux, Matteo Colli, and Vincent Vionnet
Earth Syst. Sci. Data, 15, 5785–5806, https://doi.org/10.5194/essd-15-5785-2023, https://doi.org/10.5194/essd-15-5785-2023, 2023
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The Saint John River experiment on Cold Season Storms was conducted in northwest New Brunswick, Canada, to investigate the types of precipitation that can lead to ice jams and flooding along the river. We deployed meteorological instruments, took precipitation measurements and photographs of snowflakes, and launched weather balloons. These data will help us to better understand the atmospheric conditions that can affect local communities and townships downstream during the spring melt season.
Nicola Bodini, Mike Optis, Stephanie Redfern, David Rosencrans, Alex Rybchuk, Julie K. Lundquist, Vincent Pronk, Simon Castagneri, Avi Purkayastha, Caroline Draxl, Raghavendra Krishnamurthy, Ethan Young, Billy Roberts, Evan Rosenlieb, and Walter Musial
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-490, https://doi.org/10.5194/essd-2023-490, 2023
Revised manuscript accepted for ESSD
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This article presents the 2023 National Offshore Wind data set (NOW-23), an updated resource for offshore wind information in the U.S. It replaces the Wind Integration National Dataset (WIND) Toolkit, offering improved accuracy through advanced weather prediction models. The data underwent regional tuning and validation, and can be accessed at no cost.
Raghavendra Krishnamurthy, Gabriel García Medina, Brian Gaudet, William I. Gustafson Jr., Evgueni I. Kassianov, Jinliang Liu, Rob K. Newsom, Lindsay M. Sheridan, and Alicia M. Mahon
Earth Syst. Sci. Data, 15, 5667–5699, https://doi.org/10.5194/essd-15-5667-2023, https://doi.org/10.5194/essd-15-5667-2023, 2023
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Our understanding and ability to observe and model air–sea processes has been identified as a principal limitation to our ability to predict future weather. Few observations exist offshore along the coast of California. To improve our understanding of the air–sea transition zone and support the wind energy industry, two buoys with state-of-the-art equipment were deployed for 1 year. In this article, we present details of the post-processing, algorithms, and analyses.
Baptiste Vandecrux, Jason E. Box, Andreas P. Ahlstrøm, Signe B. Andersen, Nicolas Bayou, William T. Colgan, Nicolas J. Cullen, Robert S. Fausto, Dominik Haas-Artho, Achim Heilig, Derek A. Houtz, Penelope How, Ionut Iosifescu Enescu, Nanna B. Karlsson, Rebecca Kurup Buchholz, Kenneth D. Mankoff, Daniel McGrath, Noah P. Molotch, Bianca Perren, Maiken K. Revheim, Anja Rutishauser, Kevin Sampson, Martin Schneebeli, Sandy Starkweather, Simon Steffen, Jeff Weber, Patrick J. Wright, Henry Jay Zwally, and Konrad Steffen
Earth Syst. Sci. Data, 15, 5467–5489, https://doi.org/10.5194/essd-15-5467-2023, https://doi.org/10.5194/essd-15-5467-2023, 2023
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The Greenland Climate Network (GC-Net) comprises stations that have been monitoring the weather on the Greenland Ice Sheet for over 30 years. These stations are being replaced by newer ones maintained by the Geological Survey of Denmark and Greenland (GEUS). The historical data were reprocessed to improve their quality, and key information about the weather stations has been compiled. This augmented dataset is available at https://doi.org/10.22008/FK2/VVXGUT (Steffen et al., 2022).
Giovanni Chellini, Rosa Gierens, Kerstin Ebell, Theresa Kiszler, Pavel Krobot, Alexander Myagkov, Vera Schemann, and Stefan Kneifel
Earth Syst. Sci. Data, 15, 5427–5448, https://doi.org/10.5194/essd-15-5427-2023, https://doi.org/10.5194/essd-15-5427-2023, 2023
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We present a comprehensive quality-controlled dataset of remote sensing observations of low-level mixed-phase clouds (LLMPCs) taken at the high Arctic site of Ny-Ålesund, Svalbard, Norway. LLMPCs occur frequently in the Arctic region, and substantially warm the surface. However, our understanding of microphysical processes in these clouds is incomplete. This dataset includes a comprehensive set of variables which allow for extensive investigation of such processes in LLMPCs at the site.
Emma L. Robinson, Chris Huntingford, Valyaveetil Shamsudheen Semeena, and James M. Bullock
Earth Syst. Sci. Data, 15, 5371–5401, https://doi.org/10.5194/essd-15-5371-2023, https://doi.org/10.5194/essd-15-5371-2023, 2023
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CHESS-SCAPE is a suite of high-resolution climate projections for the UK to 2080, derived from United Kingdom Climate Projections 2018 (UKCP18), designed to support climate impact modelling. It contains four realisations of four scenarios of future greenhouse gas levels (RCP2.6, 4.5, 6.0 and 8.5), with and without bias correction to historical data. The variables are available at 1 km resolution and a daily time step, with monthly, seasonal and annual means and 20-year mean-monthly time slices.
Motoshi Nishimura, Teruo Aoki, Masashi Niwano, Sumito Matoba, Tomonori Tanikawa, Tetsuhide Yamasaki, Satoru Yamaguchi, and Koji Fujita
Earth Syst. Sci. Data, 15, 5207–5226, https://doi.org/10.5194/essd-15-5207-2023, https://doi.org/10.5194/essd-15-5207-2023, 2023
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We presented the method of data quality checks and the dataset for two ground weather observations in northwest Greenland. We found that the warm and clear weather conditions in the 2015, 2019, and 2020 summers caused the snowmelt and the decline in surface reflectance of solar radiation at a low-elevated site (SIGMA-B; 944 m), but those were not seen at the high-elevated site (SIGMA-A; 1490 m). We hope that our data management method and findings will help climate scientists.
Shaomin Liu, Ziwei Xu, Tao Che, Xin Li, Tongren Xu, Zhiguo Ren, Yang Zhang, Junlei Tan, Lisheng Song, Ji Zhou, Zhongli Zhu, Xiaofan Yang, Rui Liu, and Yanfei Ma
Earth Syst. Sci. Data, 15, 4959–4981, https://doi.org/10.5194/essd-15-4959-2023, https://doi.org/10.5194/essd-15-4959-2023, 2023
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We present a suite of observational datasets from artificial and natural oases–desert systems that consist of long-term turbulent flux and auxiliary data, including hydrometeorological, vegetation, and soil parameters, from 2012 to 2021. We confirm that the 10-year, long-term dataset presented in this study is of high quality with few missing data, and we believe that the data will support ecological security and sustainable development in oasis–desert areas.
Gina C. Jozef, Robert Klingel, John J. Cassano, Björn Maronga, Gijs de Boer, Sandro Dahlke, and Christopher J. Cox
Earth Syst. Sci. Data, 15, 4983–4995, https://doi.org/10.5194/essd-15-4983-2023, https://doi.org/10.5194/essd-15-4983-2023, 2023
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Observations from the MOSAiC expedition relating to lower-atmospheric temperature, wind, stability, moisture, and surface radiation budget from radiosondes, a meteorological tower, radiation station, and ceilometer were compiled to create a dataset which describes the thermodynamic and kinematic state of the central Arctic lower atmosphere between October 2019 and September 2020. This paper describes the methods used to develop this lower-atmospheric properties dataset.
Karl-Göran Karlsson, Martin Stengel, Jan Fokke Meirink, Aku Riihelä, Jörg Trentmann, Tom Akkermans, Diana Stein, Abhay Devasthale, Salomon Eliasson, Erik Johansson, Nina Håkansson, Irina Solodovnik, Nikos Benas, Nicolas Clerbaux, Nathalie Selbach, Marc Schröder, and Rainer Hollmann
Earth Syst. Sci. Data, 15, 4901–4926, https://doi.org/10.5194/essd-15-4901-2023, https://doi.org/10.5194/essd-15-4901-2023, 2023
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This paper presents a global climate data record on cloud parameters, radiation at the surface and at the top of atmosphere, and surface albedo. The temporal coverage is 1979–2020 (42 years) and the data record is also continuously updated until present time. Thus, more than four decades of climate parameters are provided. Based on CLARA-A3, studies on distribution of clouds and radiation parameters can be made and, especially, investigations of climate trends and evaluation of climate models.
Finn Burgemeister, Marco Clemens, and Felix Ament
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-300, https://doi.org/10.5194/essd-2023-300, 2023
Revised manuscript accepted for ESSD
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Knowledge of small-scale rainfall variability is needed for hydro-meteorological applications in urban areas. Therefore, we present an open-access data set covering reanalysed radar reflectivities and rainfall estimates measured by a weather radar at high spatio-temporal resolution in the urban environment of Hamburg between 2013 and 2021. We describe the data reanalysis, outline the measurement’s performance for long time periods, and discuss open issues and limitations of the data set.
Boyang Jiao, Yucheng Su, Qingxiang Li, Veronica Manara, and Martin Wild
Earth Syst. Sci. Data, 15, 4519–4535, https://doi.org/10.5194/essd-15-4519-2023, https://doi.org/10.5194/essd-15-4519-2023, 2023
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This paper develops an observational integrated and homogenized global-terrestrial (except for Antarctica) SSRIH station. This is interpolated into a 5° × 5° SSRIH grid and reconstructed into a long-term (1955–2018) global land (except for Antarctica) 5° × 2.5° SSR anomaly dataset (SSRIH20CR) by an improved partial convolutional neural network deep-learning method. SSRIH20CR yields trends of −1.276 W m−2 per decade over the dimming period and 0.697 W m−2 per decade over the brightening period.
Lukas Frank, Marius Opsanger Jonassen, Teresa Remes, Florina Roana Schalamon, and Agnes Stenlund
Earth Syst. Sci. Data, 15, 4219–4234, https://doi.org/10.5194/essd-15-4219-2023, https://doi.org/10.5194/essd-15-4219-2023, 2023
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The Isfjorden Weather Information Network (IWIN) provides continuous meteorological near-surface observations from Isfjorden in Svalbard. The network combines permanent automatic weather stations on lighthouses along the coast line with mobile stations on board small tourist cruise ships regularly trafficking the fjord during spring to autumn. All data are available online in near-real time. Besides their scientific value, IWIN data crucially enhance the safety of field activities in the region.
Francesca Lappin, Gijs de Boer, Petra Klein, Jonathan Hamilton, Michelle Spencer, Radiance Calmer, Antonio R. Segales, Michael Rhodes, Tyler M. Bell, Justin Buchli, Kelsey Britt, Elizabeth Asher, Isaac Medina, Brian Butterworth, Leia Otterstatter, Madison Ritsch, Bryony Puxley, Angelina Miller, Arianna Jordan, Ceu Gomez-Faulk, Elizabeth Smith, Steve Borenstein, Troy Thornberry, Brian Argrow, and Elizabeth Pillar-Little
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-371, https://doi.org/10.5194/essd-2023-371, 2023
Revised manuscript accepted for ESSD
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This article provides an overview of the lower atmospheric dataset collected by two uncrewed aerial systems near the Gulf of Mexico coastline south of Houston, TX, USA, as a part of the TRACER campaign. The data were collected through boundary layer transitions, sea breeze circulations, and in the pre- and near-storm environment to understand how these processes influence the coastal environment.
Jingya Han, Chiyuan Miao, Jiaojiao Gou, Haiyan Zheng, Qi Zhang, and Xiaoying Guo
Earth Syst. Sci. Data, 15, 3147–3161, https://doi.org/10.5194/essd-15-3147-2023, https://doi.org/10.5194/essd-15-3147-2023, 2023
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Constructing a high-quality, long-term daily precipitation dataset is essential to current hydrometeorology research. This study aims to construct a long-term daily precipitation dataset with different spatial resolutions based on 2839 gauge observations. The constructed precipitation dataset shows reliable quality compared with the other available precipitation products and is expected to facilitate the advancement of drought monitoring, flood forecasting, and hydrological modeling.
Christian Borger, Steffen Beirle, and Thomas Wagner
Earth Syst. Sci. Data, 15, 3023–3049, https://doi.org/10.5194/essd-15-3023-2023, https://doi.org/10.5194/essd-15-3023-2023, 2023
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This study presents a long-term data set of monthly mean total column water vapour (TCWV) based on measurements of the Ozone Monitoring Instrument (OMI) covering the time range from January 2005 to December 2020. We describe how the TCWV values are retrieved from UV–Vis satellite spectra and demonstrate that the OMI TCWV data set is in good agreement with various different reference data sets. Moreover, we also show that it fulfills typical stability requirements for climate data records.
Jonathan Demaeyer, Jonas Bhend, Sebastian Lerch, Cristina Primo, Bert Van Schaeybroeck, Aitor Atencia, Zied Ben Bouallègue, Jieyu Chen, Markus Dabernig, Gavin Evans, Jana Faganeli Pucer, Ben Hooper, Nina Horat, David Jobst, Janko Merše, Peter Mlakar, Annette Möller, Olivier Mestre, Maxime Taillardat, and Stéphane Vannitsem
Earth Syst. Sci. Data, 15, 2635–2653, https://doi.org/10.5194/essd-15-2635-2023, https://doi.org/10.5194/essd-15-2635-2023, 2023
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A benchmark dataset is proposed to compare different statistical postprocessing methods used in forecasting centers to properly calibrate ensemble weather forecasts. This dataset is based on ensemble forecasts covering a portion of central Europe and includes the corresponding observations. Examples on how to download and use the data are provided, a set of evaluation methods is proposed, and a first benchmark of several methods for the correction of 2 m temperature forecasts is performed.
Dirk Nikolaus Karger, Stefan Lange, Chantal Hari, Christopher P. O. Reyer, Olaf Conrad, Niklaus E. Zimmermann, and Katja Frieler
Earth Syst. Sci. Data, 15, 2445–2464, https://doi.org/10.5194/essd-15-2445-2023, https://doi.org/10.5194/essd-15-2445-2023, 2023
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We present the first 1 km, daily, global climate dataset for climate impact studies. We show that the high-resolution data have a decreased bias and higher correlation with measurements from meteorological stations than coarser data. The dataset will be of value for a wide range of climate change impact studies both at global and regional level that benefit from using a consistent global dataset.
Elisa Adirosi, Federico Porcù, Mario Montopoli, Luca Baldini, Alessandro Bracci, Vincenzo Capozzi, Clizia Annella, Giorgio Budillon, Edoardo Bucchignani, Alessandra Lucia Zollo, Orietta Cazzuli, Giulio Camisani, Renzo Bechini, Roberto Cremonini, Andrea Antonini, Alberto Ortolani, Samantha Melani, Paolo Valisa, and Simone Scapin
Earth Syst. Sci. Data, 15, 2417–2429, https://doi.org/10.5194/essd-15-2417-2023, https://doi.org/10.5194/essd-15-2417-2023, 2023
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The paper describes the database of 1 min drop size distribution (DSD) of atmospheric precipitation collected by the Italian disdrometer network over the last 10 years. These data are useful for several applications that range from climatological, meteorological and hydrological uses to telecommunications, agriculture and conservation of cultural heritage exposed to precipitation. Descriptions of the processing and of the database organization, along with some examples, are provided.
Jinfang Yin, Xudong Liang, Yanxin Xie, Feng Li, Kaixi Hu, Lijuan Cao, Feng Chen, Haibo Zou, Feng Zhu, Xin Sun, Jianjun Xu, Geli Wang, Ying Zhao, and Juanjuan Liu
Earth Syst. Sci. Data, 15, 2329–2346, https://doi.org/10.5194/essd-15-2329-2023, https://doi.org/10.5194/essd-15-2329-2023, 2023
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A collection of regional reanalysis datasets has been produced. However, little attention has been paid to East Asia, and there are no long-term, physically consistent regional reanalysis data available. The East Asia Reanalysis System was developed using the WRF model and GSI data assimilation system. A 39-year (1980–2018) reanalysis dataset is available for the East Asia region, at a high temporal (of 3 h) and spatial resolution (of 12 km), for mesoscale weather and regional climate studies.
John Erik Engström, Lennart Wern, Sverker Hellström, Erik Kjellström, Chunlüe Zhou, Deliang Chen, and Cesar Azorin-Molina
Earth Syst. Sci. Data, 15, 2259–2277, https://doi.org/10.5194/essd-15-2259-2023, https://doi.org/10.5194/essd-15-2259-2023, 2023
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Newly digitized wind speed observations provide data from the time period from around 1920 to the present, enveloping one full century of wind measurements. The results of this work enable the investigation of the historical variability and trends in surface wind speed in Sweden for
the last century.
Ulrike Herzschuh, Thomas Böhmer, Chenzhi Li, Manuel Chevalier, Raphaël Hébert, Anne Dallmeyer, Xianyong Cao, Nancy H. Bigelow, Larisa Nazarova, Elena Y. Novenko, Jungjae Park, Odile Peyron, Natalia A. Rudaya, Frank Schlütz, Lyudmila S. Shumilovskikh, Pavel E. Tarasov, Yongbo Wang, Ruilin Wen, Qinghai Xu, and Zhuo Zheng
Earth Syst. Sci. Data, 15, 2235–2258, https://doi.org/10.5194/essd-15-2235-2023, https://doi.org/10.5194/essd-15-2235-2023, 2023
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Climate reconstruction from proxy data can help evaluate climate models. We present pollen-based reconstructions of mean July temperature, mean annual temperature, and annual precipitation from 2594 pollen records from the Northern Hemisphere, using three reconstruction methods (WA-PLS, WA-PLS_tailored, and MAT). Since no global or hemispheric synthesis of quantitative precipitation changes are available for the Holocene so far, this dataset will be of great value to the geoscientific community.
Aart Overeem, Else van den Besselaar, Gerard van der Schrier, Jan Fokke Meirink, Emiel van der Plas, and Hidde Leijnse
Earth Syst. Sci. Data, 15, 1441–1464, https://doi.org/10.5194/essd-15-1441-2023, https://doi.org/10.5194/essd-15-1441-2023, 2023
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EURADCLIM is a new precipitation dataset covering a large part of Europe. It is based on weather radar data to provide local precipitation information every hour and combined with rain gauge data to obtain good precipitation estimates. EURADCLIM provides a much better reference for validation of weather model output and satellite precipitation datasets. It also allows for climate monitoring and better evaluation of extreme precipitation events and their impact (landslides, flooding).
Alfonso Ferrone and Alexis Berne
Earth Syst. Sci. Data, 15, 1115–1132, https://doi.org/10.5194/essd-15-1115-2023, https://doi.org/10.5194/essd-15-1115-2023, 2023
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This article presents the datasets collected between November 2019 and February 2020 in the vicinity of the Belgian research base Princess Elisabeth Antarctica. Five meteorological radars, a multi-angle snowflake camera, three weather stations, and two radiometers have been deployed at five sites, up to a maximum distance of 30 km from the base. Their varied locations allow the study of spatial variability in snowfall and its interaction with the complex terrain in the region.
José Dias Neto, Louise Nuijens, Christine Unal, and Steven Knoop
Earth Syst. Sci. Data, 15, 769–789, https://doi.org/10.5194/essd-15-769-2023, https://doi.org/10.5194/essd-15-769-2023, 2023
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This paper describes a dataset from a novel experimental setup to retrieve wind speed and direction profiles, combining cloud radars and wind lidar. This setup allows retrieving profiles from near the surface to the top of clouds. The field campaign occurred in Cabauw, the Netherlands, between September 13th and October 3rd 2021. This paper also provides examples of applications of this dataset (e.g. studying atmospheric turbulence, validating numerical atmospheric models).
Peng Yuan, Geoffrey Blewitt, Corné Kreemer, William C. Hammond, Donald Argus, Xungang Yin, Roeland Van Malderen, Michael Mayer, Weiping Jiang, Joseph Awange, and Hansjörg Kutterer
Earth Syst. Sci. Data, 15, 723–743, https://doi.org/10.5194/essd-15-723-2023, https://doi.org/10.5194/essd-15-723-2023, 2023
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We developed a 5 min global integrated water vapour (IWV) product from 12 552 ground-based GPS stations in 2020. It contains more than 1 billion IWV estimates. The dataset is an enhanced version of the existing operational GPS IWV dataset from the Nevada Geodetic Laboratory. The enhancement is reached by using accurate meteorological information from ERA5 for the GPS IWV retrieval with a significantly higher spatiotemporal resolution. The dataset is recommended for high-accuracy applications.
Yaozhi Jiang, Kun Yang, Youcun Qi, Xu Zhou, Jie He, Hui Lu, Xin Li, Yingying Chen, Xiaodong Li, Bingrong Zhou, Ali Mamtimin, Changkun Shao, Xiaogang Ma, Jiaxin Tian, and Jianhong Zhou
Earth Syst. Sci. Data, 15, 621–638, https://doi.org/10.5194/essd-15-621-2023, https://doi.org/10.5194/essd-15-621-2023, 2023
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Our work produces a long-term (1979–2020) high-resolution (1/30°, daily) precipitation dataset for the Third Pole (TP) region by merging an advanced atmospheric simulation with high-density rain gauge (more than 9000) observations. Validation shows that the produced dataset performs better than the currently widely used precipitation datasets in the TP. This dataset can be used for hydrological, meteorological and ecological studies in the TP.
Yetang Wang, Xueying Zhang, Wentao Ning, Matthew A. Lazzara, Minghu Ding, Carleen H. Reijmer, Paul C. J. P. Smeets, Paolo Grigioni, Petra Heil, Elizabeth R. Thomas, David Mikolajczyk, Lee J. Welhouse, Linda M. Keller, Zhaosheng Zhai, Yuqi Sun, and Shugui Hou
Earth Syst. Sci. Data, 15, 411–429, https://doi.org/10.5194/essd-15-411-2023, https://doi.org/10.5194/essd-15-411-2023, 2023
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Here we construct a new database of Antarctic automatic weather station (AWS) meteorological records, which is quality-controlled by restrictive criteria. This dataset compiled all available Antarctic AWS observations, and its resolutions are 3-hourly, daily and monthly, which is very useful for quantifying spatiotemporal variability in weather conditions. Furthermore, this compilation will be used to estimate the performance of the regional climate models or meteorological reanalysis products.
Hui Zhang, Ming Luo, Yongquan Zhao, Lijie Lin, Erjia Ge, Yuanjian Yang, Guicai Ning, Jing Cong, Zhaoliang Zeng, Ke Gui, Jing Li, Ting On Chan, Xiang Li, Sijia Wu, Peng Wang, and Xiaoyu Wang
Earth Syst. Sci. Data, 15, 359–381, https://doi.org/10.5194/essd-15-359-2023, https://doi.org/10.5194/essd-15-359-2023, 2023
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We generate the first monthly high-resolution (1 km) human thermal index collection (HiTIC-Monthly) in China over 2003–2020, in which 12 human-perceived temperature indices are generated by LightGBM. The HiTIC-Monthly dataset has a high accuracy (R2 = 0.996, RMSE = 0.693 °C, MAE = 0.512 °C) and describes explicit spatial variations for fine-scale studies. It is freely available at https://zenodo.org/record/6895533 and https://data.tpdc.ac.cn/disallow/036e67b7-7a3a-4229-956f-40b8cd11871d.
Jun Qin, Weihao Pan, Min He, Ning Lu, Ling Yao, Hou Jiang, and Chenghu Zhou
Earth Syst. Sci. Data, 15, 331–344, https://doi.org/10.5194/essd-15-331-2023, https://doi.org/10.5194/essd-15-331-2023, 2023
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To enrich a glacial surface air temperature (SAT) product of a long time series, an ensemble learning model is constructed to estimate monthly SATs from satellite land surface temperatures at a spatial resolution of 1 km, and long-term glacial SATs from 1961 to 2020 are reconstructed using a Bayesian linear regression. This product reveals the overall warming trend and the spatial heterogeneity of warming on TP glaciers and helps to monitor glacier warming, analyze glacier evolution, etc.
Tao Zhang, Yuyu Zhou, Kaiguang Zhao, Zhengyuan Zhu, Gang Chen, Jia Hu, and Li Wang
Earth Syst. Sci. Data, 14, 5637–5649, https://doi.org/10.5194/essd-14-5637-2022, https://doi.org/10.5194/essd-14-5637-2022, 2022
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We generated a global 1 km daily maximum and minimum near-surface air temperature (Tmax and Tmin) dataset (2003–2020) using a novel statistical model. The average root mean square errors ranged from 1.20 to 2.44 °C for Tmax and 1.69 to 2.39 °C for Tmin. The gridded global air temperature dataset is of great use in a variety of studies such as the urban heat island phenomenon, hydrological modeling, and epidemic forecasting.
Craig D. Smith, Eva Mekis, Megan Hartwell, and Amber Ross
Earth Syst. Sci. Data, 14, 5253–5265, https://doi.org/10.5194/essd-14-5253-2022, https://doi.org/10.5194/essd-14-5253-2022, 2022
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It is well understood that precipitation gauges underestimate the measurement of solid precipitation (snow) as a result of systematic bias caused by wind. Relationships between the wind speed and gauge catch efficiency of solid precipitation have been previously established and are applied to the hourly precipitation measurements made between 2001 and 2019 in the automated Environment and Climate Change Canada observation network. The adjusted data are available for download and use.
Zen Mariani, Laura Huang, Robert Crawford, Jean-Pierre Blanchet, Shannon Hicks-Jalali, Eva Mekis, Ludovick Pelletier, Peter Rodriguez, and Kevin Strawbridge
Earth Syst. Sci. Data, 14, 4995–5017, https://doi.org/10.5194/essd-14-4995-2022, https://doi.org/10.5194/essd-14-4995-2022, 2022
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Environment and Climate Change Canada (ECCC) commissioned two supersites in Iqaluit (64°N, 69°W) and Whitehorse (61°N, 135°W) to provide new and enhanced automated and continuous altitude-resolved meteorological observations as part of the Canadian Arctic Weather Science (CAWS) project. These observations are being used to test new technologies, provide recommendations to the optimal Arctic observing system, and evaluate and improve the performance of numerical weather forecast systems.
Eva Beele, Maarten Reyniers, Raf Aerts, and Ben Somers
Earth Syst. Sci. Data, 14, 4681–4717, https://doi.org/10.5194/essd-14-4681-2022, https://doi.org/10.5194/essd-14-4681-2022, 2022
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This paper presents crowdsourced data from the Leuven.cool network, a citizen science network of around 100 low-cost weather stations distributed across Leuven, Belgium. The temperature data have undergone a quality control (QC) and correction procedure. The procedure consists of three levels that remove implausible measurements while also correcting for between-station and station-specific temperature biases.
Auguste Gires, Jerry Jose, Ioulia Tchiguirinskaia, and Daniel Schertzer
Earth Syst. Sci. Data, 14, 3807–3819, https://doi.org/10.5194/essd-14-3807-2022, https://doi.org/10.5194/essd-14-3807-2022, 2022
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The Hydrology Meteorology and Complexity laboratory of École des Ponts ParisTech (https://hmco.enpc.fr) has made a data set of high-resolution atmospheric measurements (rainfall, wind, temperature, pressure, and humidity) available. It comes from a campaign carried out on a meteorological mast located on a wind farm in the framework of the Rainfall Wind Turbine or Turbulence project (RW-Turb; supported by the French National Research Agency – ANR-19-CE05-0022).
Bastian Kirsch, Cathy Hohenegger, Daniel Klocke, Rainer Senke, Michael Offermann, and Felix Ament
Earth Syst. Sci. Data, 14, 3531–3548, https://doi.org/10.5194/essd-14-3531-2022, https://doi.org/10.5194/essd-14-3531-2022, 2022
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Conventional observation networks are too coarse to resolve the horizontal structure of kilometer-scale atmospheric processes. We present the FESST@HH field experiment that took place in Hamburg (Germany) during summer 2020 and featured a dense network of 103 custom-built, low-cost weather stations. The data set is capable of providing new insights into the structure of convective cold pools and the nocturnal urban heat island and variations of local temperature fluctuations.
Fan Mei, Mikhail S. Pekour, Darielle Dexheimer, Gijs de Boer, RaeAnn Cook, Jason Tomlinson, Beat Schmid, Lexie A. Goldberger, Rob Newsom, and Jerome D. Fast
Earth Syst. Sci. Data, 14, 3423–3438, https://doi.org/10.5194/essd-14-3423-2022, https://doi.org/10.5194/essd-14-3423-2022, 2022
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This work focuses on an expanding number of data sets observed using ARM TBS (133 flights) and UAS (seven flights) platforms by the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility. These data streams provide new perspectives on spatial variability of atmospheric and surface parameters, helping to address critical science questions in Earth system science research, such as the aerosol–cloud interaction in the boundary layer.
Falu Hong, Wenfeng Zhan, Frank-M. Göttsche, Zihan Liu, Pan Dong, Huyan Fu, Fan Huang, and Xiaodong Zhang
Earth Syst. Sci. Data, 14, 3091–3113, https://doi.org/10.5194/essd-14-3091-2022, https://doi.org/10.5194/essd-14-3091-2022, 2022
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Daily mean land surface temperature (LST) acquired from satellite thermal sensors is crucial for various applications such as global and regional climate change analysis. This study proposed a framework to generate global spatiotemporally seamless daily mean LST products (2003–2019). Validations show that the products outperform the traditional method with satisfying accuracy. Our further analysis reveals that the LST-based global land surface warming rate is 0.029 K yr−1 from 2003 to 2019.
Cited articles
Almansa, A. F., Cuevas, E., Barreto, Á., Torres, B., García,
O. E., García, R. D., Velasco-Merino, C., Cachorro, V. E.,
Berjón, A., Mallorquín, M., López, C., Ramos, R.,
Guirado-Fuentes, C., Negrillo, R., and de Frutos, Á. M.: Column
Integrated Water Vapor and Aerosol Load Characterization with the New
ZEN-R52 Radiometer, Remote Sensing, 12, 1424, https://doi.org/10.3390/rs12091424,
2020. a
Alshawaf, F., Zus, F., Balidakis, K., Deng, Z., Hoseini, M., Dick, G., and
Wickert, J.: On the statistical significance of climatic trends estimated
from GPS tropospheric time series, J. Geophys. Res.-Atmos., 123, 10–967, https://doi.org/10.1029/2018JD028703, 2018. a
Altamimi, Z., Rebischung, P., Métivier, L., and Collilieux, X.: ITRF2014: A
new release of the International Terrestrial Reference Frame modeling
nonlinear station motions, J. Geophys. Res.-Sol. Ea., 121,
6109–6131, https://doi.org/10.1002/2016JB013098, 2016. a
Andersson, E., Hólm, E., Bauer, P., Beljaars, A., Kelly, G. A., McNally,
A. P., Simmons, A. J., Thépaut, J.-N., and Tompkins, A. M.: Analysis
and forecast impact of the main humidity observing systems, Q. J. Roy. Meteorol. Soc., 133, 1473–1485, https://doi.org/10.1002/qj.112, 2007. a
Barker, D., Huang, W., Guo, Y., and Bourgeois, A.: A Three-demiensional Variational (3DVAR) Data Assimilation System for Use With MM5 (No. NCAR/TN-453+STR), University Corporation for Atmospheric Research, 73 pp., https://doi.org/10.5065/D6CF9N1J, 2003. a, b
Barker, D., Huang, W., Guo, Y., Bourgeois, A., and Xiao, A.: A
Three-Dimensional Variational Data Assimilation System for MM5:
Implementation and Initial Results, Mon. Weather Rev.,
132, 897–914, https://doi.org/10.1175/1520-0493(2004)132<0897:ATVDAS>2.0.CO;2, 2004. a, b, c
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, 15787–15801, https://doi.org/10.1029/92JD01517,
1992. a, b
Boehm, J., Werl, B., and Schuh, H.: Troposphere mapping functions for GPS and
very long baseline interferometry from European Centre for Medium-Range
Weather Forecasts operational analysis data, J. Geophys. Res.-Sol. Ea., 111, B02406, https://doi.org/10.1029/2005JB003629, 2006. a
Boniface, K., Ducrocq, V., Jaubert, G., Yan, X., Brousseau, P., Masson, F., Champollion, C., Chéry, J., and Doerflinger, E.: Impact of high-resolution data assimilation of GPS zenith delay on Mediterranean heavy rainfall forecasting, Ann. Geophys., 27, 2739–2753, https://doi.org/10.5194/angeo-27-2739-2009, 2009. a
Chen, G. and Herring, T.: Effects of atmospheric azimuthal asymmetry on the
analysis of space geodetic data, J. Geophys. Res.-Sol. Ea., 102, 20489–20502, 1997. a
Copernicus: Copernicus Sentinel data [2015–2019],
https://scihub.copernicus.eu/, last access: 22 October
2020. a
Davis, J., Herring, T., Shapiro, I., Rogers, A., 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. a, b, c, d
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi,
S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P.,
Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C.,
Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B.,
Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M.,
Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park,
B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and
Vitart, F.: The ERA-Interim reanalysis: configuration and performance of
the data assimilation system, Q. J. Roy. Meteor.
Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011. a
Divakarla, M. G., Barnet, C. D., Goldberg, M. D., McMillin, L. M., Maddy, E.,
Wolf, W., Zhou, L., and Liu, X.: Validation of Atmospheric Infrared Sounder
temperature and water vapor retrievals with matched radiosonde measurements
and forecasts, J. Geophys. Res., 111, D09S15,
https://doi.org/10.1029/2005JD006116, 2006. a
Dudhia, J.: Numericall study of convection observed during the Winterl
Monsoonl Experimentl using a mesoscale two-dimensional model, J. Atmos.
Sci., 46, 3077–3107, https://doi.org/10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2, 1989. a
DWD: DWD synoptic data, https://www.dwd.de/EN/climate_environment/cdc/cdc_node_en.html (last access: 28 November 2022),
https://opendata.dwd.de/climate_environment/CDC/observations_germany,
last access: 4 December 2020. a
Efron, B.: Bootstrap Methods: Another Look at the Jackknife, Ann.
Stat., 7, 1–26, https://doi.org/10.1214/aos/1176344552, 1979. a
Feltz, W. F., Smith, W. L., Howell, H. B., Knuteson, R. O., Woolf, H., and
Revercomb, H. E.: Near-Continuous Profiling of Temperature, Moisture, and
Atmospheric Stability Using the Atmospheric Emitted Radiance Interferometer
(AERI), J. Appl. Meteorol., 42, 584–597,
https://doi.org/10.1175/1520-0450(2003)042<0584:npotma>2.0.co;2, 2003. a
Ferretti, A., Prati, C., and Rocca, F.: Permanent scatterers in SAR
interferometry, IEEE T. Geosci. Remote, 39,
8–20, https://doi.org/10.1109/36.898661, 2001. a
Fersch, B., Senatore, A., Adler, B., Arnault, J., Mauder, M., Schneider, K., Völksch, I., and Kunstmann, H.: High-resolution fully coupled atmospheric–hydrological modeling: a cross-compartment regional water and energy cycle evaluation, Hydrol. Earth Syst. Sci., 24, 2457–2481, https://doi.org/10.5194/hess-24-2457-2020, 2020. a
Fersch, B., Kamm, B., Shehaj, E., Wagner, A., Yuan, P., Möller, G., Schenk,
A., Geiger, A., Hinz, S., Kutterer, H., and Kunstmann, H.: A comprehensive
high resolution data collection for tropospheric water vapor assessment for
the Upper Rhine Graben, Germany, PANGAEA [data set],
https://doi.org/10.1594/PANGAEA.936447, 2021. a, b, c
Furumoto, J., Kurimoto, K., and Tsuda, T.: Continuous Observations of Humidity
Profiles with the MU Radar–RASS Combined with GPS and Radiosonde
Measurements, J. Atmos. Ocean. Tech., 20, 23–41,
https://doi.org/10.1175/1520-0426(2003)020<0023:COOHPW>2.0.CO;2, 2003. a
Gallus, W. A. and Segal, M.: Impact of Improved Initialization of Mesoscale
Features on Convective System Rainfall in 10-km Eta Simulations, Weather
Forecast., 16, 680–696,
https://doi.org/10.1175/1520-0434(2001)016<0680:ioiiom>2.0.co;2, 2001. a
Giannaros, C., Kotroni, V., Lagouvardos, K., Giannaros, T. M., and Pikridas,
C.: Assessing the Impact of GNSS ZTD Data Assimilation into the WRF
Modeling System during High-Impact Rainfall Events over Greece, Remote
Sensing, 12, 383, https://doi.org/10.3390/rs12030383, 2020. a
Gill, D., Dudhia, J. Wang, W.: WRF-ARW Modeling System, GitHub, https://github.com/wrf-model/WRF/releases/tag/V3.9.1.1 (last access: 29 November 2022), 2017. a
Giorgi, F.: Thirty Years of Regional Climate Modeling: Where Are We and Where
Are We Going next?, J. Geophys. Res.-Atmos., 124, 5696–5723,
https://doi.org/10.1029/2018JD030094, 2019. a
González, A., Expósito, F. J., Pérez, J. C., Díaz,
J. P., and Taima, D.: Verification of precipitable water vapour in
high-resolution WRF simulations over a mountainous archipelago, Q.
J. Roy. Meteor. Soc., 139, 2119–2133,
https://doi.org/10.1002/qj.2092, 2013. a
Gupta, H., Kling, H., Yilmaz, K., and Martinez, G.: Decomposition of the Mean
Squared Error and NSE Performance Criteria: Implications for Improving
Hydrological Modelling, J. Hydrol., 377, 80–91,
https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009. a
Hanssen, R. F.: Radar interferometry – Data Interpretation and Error
Analysis, vol. 2 of Remote Sensing and Digital Image Processing,
Springer Netherlands, Dordrecht, https://doi.org/10.1007/0-306-47633-9, 2001. a, b
Herring, T. A., King, R. W., Floyd, M. A., and McClusky, S. C.: Introduction to
GAMIT/GLOBK, Release 10.7, Massachusetts Institute of Technology, Cambridge,
Massachusetts, http://geoweb.mit.edu/gg/Intro_GG.pdf (last access: 28 November 2022), 2018. a
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 single levels from 1959 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2018a. a
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 1959 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2018b. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons,
A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati,
G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M.,
Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P.,
Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 Global
Reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Heublein, M. E. A.: GNSS and InSAR based water vapor tomography: A Compressive
Sensing solution, PhD thesis, Karlsruhe Institute of Technology,
https://doi.org/10.5445/IR/1000093403, 2019. a
Hong, S. and Lim, J. J.: The WRF single-moment 6–-lass microphysics
scheme (WSM6), J. Korean Meteor. Soc., 42, 129–151, 2006. a
Hong, S., Noh, Y., and Dudhia, J.: A new vertical diffusion package with an
explicit treatment of entrainment processes, Mon. Weather Rev., 134, 2318–2341, https://doi.org/10.1175/MWR3199.1,
2006. a
Hooper, A., Segall, P., and Zebker, H.: Persistent scatterer interferometric
synthetic aperture radar for crustal deformation analysis, with application
to Volcán Alcedo, Galápagos, J. Geophys. Res.,
112, B07407, https://doi.org/10.1029/2006JB004763, 2007. a
Hooper, A., Bekaert, D., Spaans, K., and Arikan, M.: Recent advances in SAR
interferometry time series analysis for measuring crustal deformation,
Tectonophysics, 514–517, 1–13, https://doi.org/10.1016/j.tecto.2011.10.013, 2012. a
Hurter, F.: GNSS meteorology in spatially dense networks, PhD thesis, ETH Zurich, https://doi.org/10.3929/ethz-a-010276927, 2014. a, b, c, d
Ide, K., Ghil, M., and Lorenc, A.: Unified Notation for Data Assimilation:
Operational, Sequential and Variational, J. Meteorol.
Soc. Jpn., 75, 181–189, https://doi.org/10.2151/jmsj1965.75.1B_181, 1999. a
Jade, S. and Vijayan, M.: GPS-based atmospheric precipitable water vapor
estimation using meteorological parameters interpolated from NCEP global
reanalysis data, J. Geophys. Res.-Atmos., 113, D03106, https://doi.org/10.1029/2007JD008758, 2008. a
Jiang, X., Li, J., Li, Z., Xue, Y., Di, D., Wang, P., and Li, J.: Evaluation
of Environmental Moisture from NWP Models with Measurements from Advanced
Geostationary Satellite Imager – A Case Study, Remote Sensing, 12,
670, https://doi.org/10.3390/rs12040670, 2020. a
Jin, S., Feng, G., and Gleason, S.: Remote sensing using GNSS signals:
Current status and future directions, Adv. Space Res., 47,
1645–1653, https://doi.org/10.1016/j.asr.2011.01.036, 2011. a
Jochum, A. M., Camino, E. R., de Bruin, H. A. R., and Holtslag, A. A. M.:
Performance of HIRLAM in a Semiarid Heterogeneous Region: Evaluation of
the Land Surface and Boundary Layer Description Using EFEDA Observations,
Mon. Weather Rev., 132, 2745–2760, https://doi.org/10.1175/mwr2820.1, 2004. a
Jones, J., Guerova, G., Douša, J., Dick, G., de Haan, S., Pottiaux, E.,
and van Malderen, R.: Advanced GNSS Tropospheric Products for Monitoring
Severe Weather Events and Climate, COST action ES1206 final action
dissemination report, 563 pp., https://doi.org/10.1007/978-3-030-13901-8, 2019. a
King, R. W.: GAMIT/GLOBK, http://geoweb.mit.edu/gg/, last access: 4 January 2022. a
Klanner, L., Höveler, K., Khordakova, D., Perfahl, M., Rolf, C., Trickl, T., and Vogelmann, H.: A powerful lidar system capable of 1 h measurements of water vapour in the troposphere and the lower stratosphere as well as the temperature in the upper stratosphere and mesosphere, Atmos. Meas. Tech., 14, 531–555, https://doi.org/10.5194/amt-14-531-2021, 2021. a
Kunz, A., Spelten, N., Konopka, P., Müller, R., Forbes, R. M., and Wernli, H.: Comparison of Fast In situ Stratospheric Hygrometer (FISH) measurements of water vapor in the upper troposphere and lower stratosphere (UTLS) with ECMWF (re)analysis data, Atmos. Chem. Phys., 14, 10803–10822, https://doi.org/10.5194/acp-14-10803-2014, 2014. a
Leontiev, A. and Reuveni, Y.: Augmenting GPS IWV estimations using
spatio-temporal cloud distribution extracted from satellite data, Sci.
Rep., 8, 14785, https://doi.org/10.1038/s41598-018-33163-x, 2018. a
Lindenbergh, R., Keshin, M., van der Marel, H., and Hanssen, R.: High
resolution spatio‐temporal water vapour mapping using GPS and MERIS
observations, Int. J. Remote Sens., 29, 2393–2409,
https://doi.org/10.1080/01431160701436825, 2008. a
Lindskog, M., Ridal, M., Thorsteinsson, S., and Ning, T.: Data assimilation of GNSS zenith total delays from a Nordic processing centre, Atmos. Chem. Phys., 17, 13983–13998, https://doi.org/10.5194/acp-17-13983-2017, 2017. a
Löhnert, U., Turner, D. D., and Crewell, S.: Ground-Based Temperature and
Humidity Profiling Using Spectral Infrared and Microwave Observations. Part
I: Simulated Retrieval Performance in Clear-Sky Conditions, J.
Appl. Meteorol. Clim., 48, 1017–1032,
https://doi.org/10.1175/2008jamc2060.1, 2009. a
Massaro, G., Stiperski, I., Pospichal, B., and Rotach, M. W.: Accuracy of retrieving temperature and humidity profiles by ground-based microwave radiometry in truly complex terrain, Atmos. Meas. Tech., 8, 3355–3367, https://doi.org/10.5194/amt-8-3355-2015, 2015. a
Mateus, P., Tomé, R., Nico, G., and Catalao, J.: Three-Dimensional Variational
Assimilation of InSAR PWV Using the WRFDA Model, IEEE T.
Geosci. Remote, 12, 7323–7330, https://doi.org/10.1109/TGRS.2016.2599219,
2016. a
Mateus, P., Miranda, P. M. A., Nico, G., and Catalao, J.: Continuous Multitrack
Assimilation of Sentinel-1 Precipitable Water Vapor Maps for Numerical
Weather Prediction: How Far Can We Go With Current InSAR Data?, J.
Geophys. Res.-Atmos., 126, e2020JD034171,
https://doi.org/10.1029/2020JD034171, 2021. a, b
Mayer, M., Knöpfler, A., Heck, B., Masson, F., Ulrich, P., and Ferhat, G.:
GURN (GNSS Upper Rhine Graben Network): Research Goals and First Results of a
Transnational Geo-scientific Network, in: Geodesy for Planet Earth, edited by:
Kenyon, S., Pacino, M. C., and Marti, U., Springer Berlin
Heidelberg, Berlin, Heidelberg, 673–681, https://doi.org/10.1007/978-3-642-20338-1_83, 2012. a
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S. A.:
Radiative transfer for inhomogeneous atmospheres: RRTM, a validated
correlated-k model for the longwave, J. Geophys. Res., 102,
16663–16682, https://doi.org/10.1029/97JD00237, 1997. a
Moeller, G.: Reconstruction of 3D wet refractivity fields in the lower
atmosphere along bended GNSS signal paths, Phd thesis, TU Wien, Department
of Geodesy and Geoinformation, 196 pp.,
http://repositum.tuwien.ac.at/obvutwoa/download/pdf/2284850 (last access: 28 November 2022),
2017. a
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. a
Pacione, R. and Douša, J.: SINEX-TRO V2. 00 format description, COST Action ES1206 Final Action Dissemination Report, edited by: Jones, J., Guerova, G., Douša, J., Dick, G., de Haan, S., Pottiaux, E., and van Malderen, R., 537–563,
https://doi.org/10.1007/978-3-030-13901-8, 2017. a
Parrish, D. F. and Derber, J. C.: The National Meteorological Center's Spectral
Statistical-Interpolation Analysis System, Mon. Weather Rev., 120,
1747–1763, https://doi.org/10.1175/1520-0493(1992)120<1747:TNMCSS>2.0.CO;2, 1992. a
Petit, G. and Luzum, B. (Eds.): IERS Conventions (2010), IERS Technical Note; 36, Frankfurt am Main: Verlag des Bundesamts für Kartographie und Geodäsie, 179 pp., ISBN 3-89888-989-6,
https://www.iers.org/IERS/EN/Publications/TechnicalNotes/tn36.html (last access: 28 November 2022), 2010. a
Pichelli, E., Ferretti, R., Cimini, D., Panegrossi, G., Perissin, D.,
Pierdicca, N., Member, S., Rocca, F., and Rommen, B.: InSAR Water Vapor Data Assimilation into Mesoscale Model MM5: Technique and Pilot Study, IEEE J. Sel. Top. Appl., 8, 3859–3875, https://doi.org/10.1109/JSTARS.2014.2357685, 2015. a
Poli, P., Healy, S., Rabier, F., and Pailleux, J.: Preliminary assessment of
the scalability of GPS radio occultations impact in numerical weather
prediction, Geophys. Res. Lett., 35, L23811,
https://doi.org/10.1029/2008GL035873, 2008. a
Pondeca, M. and Zou, X.: A Case Study of the Variational Assimilation of GPS
Zenith Delay Observations into a Mesoscale Model, J. Appl.
Meteorol., 40, 1559–1576,
https://doi.org/10.1175/1520-0450(2001)040<1559:ACSOTV>2.0.CO;2, 2001. a
Pospichal, B. and Crewell, S.: Boundary layer observations in West Africa
using a novel microwave radiometer, Meteorol. Z., 16,
513–523, https://doi.org/10.1127/0941-2948/2007/0228, 2007. a
Prein, A. F., Langhans, W., Fosser, G., Ferrone, A., Ban, N., Goergen, K.,
Keller, M., Tölle, M., Gutjahr, O., Feser, F., Brisson, E., Kollet, S.,
Schmidli, J., Lipzig, N. P. M., and Leung, R.: A review on regional
convection-permitting climate modeling: Demonstrations, prospects, and
challenges, Rev. Geophys., 53, 323–361, https://doi.org/10.1002/2014RG000475, 2015. a
Randel, D. L., Greenwald, T. J., Haar, T. H. V., Stephens, G. L., Ringerud,
M. A., and Combs, C. L.: A New Global Water Vapor Dataset, B. Am. Meteorol. Soc., 77, 1233–1246,
https://doi.org/10.1175/1520-0477(1996)077<1233:angwvd>2.0.co;2, 1996. a
Reale, A., Tilley, F., Ferguson, M., and Allegrino, A.: NOAA operational
sounding products for advanced TOVS, Int. J. Remote
Sens., 29, 4615–4651, https://doi.org/10.1080/01431160802020502, 2008. a
Rocken, C., Kuo, Y.-H., Sokolovskiy, S. V., and Anthes, R. A.: The
ROCSAT-3/COSMIC mission and applications of GPS radio occultation data to
weather and climate, in: Remote Sensing Applications of the Global
Positioning System, edited by: Bevis, M., Shoji, Y., and Businger, S., SPIE,
https://doi.org/10.1117/12.566544, 2004. a
Saastamoinen, J.: Atmospheric correction for the troposphere and
stratosphere in radio ranging satellites, in: The Use of Artificial Satellites for Geodesy, edited by: Henriksen, S. W., Mancini, A., and Chovitz, B. H., 15, 247–251, https://doi.org/10.1029/GM015p0247, 1972. a, b
Schaer, S.: Mapping and predicting the Earth's ionosphere using the Global
Positioning System, Geodätisch-geophysikalische Arbeiten in der Schweiz, 59, 205 pp., 1999. a
Schmid, R., Dach, R., Collilieux, X., Jäggi, A., Schmitz, M., and Dilssner,
F.: Absolute IGS antenna phase center model igs08. atx: status and potential
improvements, J. Geodesy, 90, 343–364, https://doi.org/10.1007/s00190-015-0876-3, 2016. a
Shehaj, E., Wilgan, K., Frey, O., and Geiger, A.: A collocation framework to
retrieve tropospheric delays from a combination of GNSS and InSAR,
Navigation, 67, 823–842, https://doi.org/10.1002/navi.398, 2020. a, b, c
Shi, L., Matthews, J., peng Ho, S., Yang, Q., and Bates, J.: Algorithm
Development of Temperature and Humidity Profile Retrievals for Long-Term
HIRS Observations, Remote Sensing, 8, 280, https://doi.org/10.3390/rs8040280, 2016. a
Simmons, A. and Gibson, J.: The era-40 project plan, ERA-40 project report
series 1, ECMWF, Reading, United Kingdom, https://www.ecmwf.int/node/12272 (last access: 28 November 2022), 2000. a
Skamarock, W. C. and Klemp, J. B.: A time-split nonhydrostatic atmospheric
model for weather research and forecasting applications, J.
Comput. Phys., 227, 3465–3485, https://doi.org/10.1016/j.jcp.2007.01.037,
2008. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda, M. G., Huang, X.-Y., Wang, W., and Powers, J. G.: A Description of the Advanced Research WRF Version 3, NCAR Tech. Note NCAR/TN-475+STR, 113 pp., https://doi.org/10.5065/D68S4MVH, 2008. a
SNAP: S1TBX – ESA Sentinel-1 Toolbox v8.0.1,
http://step.esa.int/, last access: 1 February 2021. a
Steinke, S., Eikenberg, S., Löhnert, U., Dick, G., Klocke, D., Di Girolamo, P., and Crewell, S.: Assessment of small-scale integrated water vapour variability during HOPE, Atmos. Chem. Phys., 15, 2675–2692, https://doi.org/10.5194/acp-15-2675-2015, 2015. a
Steinke, S., Wahl, S., and Crewell, S.: Benefit of high resolution COSMO
reanalysis: The diurnal cycle of column-integrated water vapor over Germany,
Meteorol. Z., 28, 165–177, https://doi.org/10.1127/metz/2019/0936,
2019. a
Stevens, B. and Bony, S.: Water in the atmosphere, Physics Today, 66, 29–34,
https://doi.org/10.1063/PT.3.2009, 2013. a
Teunissen, P. and Montenbruck, O.: Springer Handbook of Global Navigation
Satellite Systems, Springer, https://doi.org/10.1007/978-3-319-42928-1, 2017. a
Thébault, E., Finlay, C. C., Beggan, C. D., Alken, P., Aubert, J., Barrois, O., Bertrand, F., Bondar, T., Boness, A., Brocco, L., Canet, E., Chambodut, A., Chulliat, A., Coïsson, P., Civet, F., Du, A., Fournier, A., Fratter, I., Gillet, N., Hamilton, B., Hamoudi, M., Hulot, G., Jager, T., Korte, M., Kuang, W., Lalanne, X., Langlais, B., Léger, J.-M., Lesur, V., Lowes, F. J., Macmillan, S., Mandea, M., Manoj, C., Maus, S., Olsen, N., Petrov, V., Ridley, V., Rother, M., Sabaka, T. J., Saturnino, D., Schachtschneider, R., Sirol, O., Tangborn, A., Thomson, A., Tøffner-Clausen, L., Vigneron, P., Wardinski, I., and Zvereva, T.: International
geomagnetic reference field: the 12th generation, Earth Planet. Space,
67, 1–19, 2015. a
Troller, M.: GPS based determination of the integrated and spatially distributed water vapor in the troposphere, PhD thesis, ETH Zurich, https://doi.org/10.3929/ethz-a-004796376, 2004. a
Vogelmann, H., Sussmann, R., Trickl, T., and Reichert, A.: Spatiotemporal variability of water vapor investigated using lidar and FTIR vertical soundings above the Zugspitze, Atmos. Chem. Phys., 15, 3135–3148, https://doi.org/10.5194/acp-15-3135-2015, 2015. a
Wagner, A., Heinzeller, D., Wagner, S., Rummler, T., and Kunstmann, H.:
Explicit Convection and Scale-Aware Cumulus Parameterizations:
High-Resolution Simulations over Areas of Different Topography in Germany,
Mon. Weather Rev., 146, 1925–1944, https://doi.org/10.1175/MWR-D-17-0238.1, 2018. a
Wagner, A., Fersch, B., Yuan, P., Rummler, T., and Kunstmann, H.: Assimilation
of GNSS and Synoptic Data in a Convection Permitting Limited Area Model:
Improvement of Simulated Tropospheric Water Vapor Content, Front. Earth
Sci., 10, 869504, https://doi.org/10.3389/feart.2022.869504, 2022.
a, b
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, D21101, https://doi.org/10.1029/2005JD006215, 2005. a
Yuan, P., Hunegnaw, A., Alshawaf, F., Awange, J., Klos, A., Teferle, F. N., and
Kutterer, H.: 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 Sens. Environ., 260,
112416, https://doi.org/10.1016/j.rse.2021.112416, 2021. a
Zhou, L., Lin, S.-J., Chen, J.-H., Harris, L. M., Chen, X., and Rees, S. L.:
Toward Convective-Scale Prediction within the Next Generation Global
Prediction System, B. Am. Meteorol. Soc., 100,
1225–1243, https://doi.org/10.1175/bams-d-17-0246.1, 2019. a
Zhu, M., Liu, Z., and Hu, W.: 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, https://doi.org/10.1029/2019JD032318, 2020. a
Short summary
In this study, a comprehensive multi-disciplinary dataset for tropospheric water vapor was developed. Geodetic, photogrammetric, and atmospheric modeling and data fusion techniques were used to obtain maps of water vapor in a high spatial and temporal resolution. It could be shown that regional weather simulations for different seasons benefit from assimilating these maps and that the combination of the different observation techniques led to positive synergies.
In this study, a comprehensive multi-disciplinary dataset for tropospheric water vapor was...
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