Articles | Volume 13, issue 10
https://doi.org/10.5194/amt-13-5481-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-13-5481-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Inter-calibrating SMMR brightness temperatures over continental surfaces
Samuel Favrichon
CORRESPONDING AUTHOR
Sorbonne Université, Observatoire de Paris, Université PSL, CNRS, LERMA, Paris, France
Estellus, Paris, France
Carlos Jimenez
Estellus, Paris, France
Sorbonne Université, Observatoire de Paris, Université PSL, CNRS, LERMA, Paris, France
Catherine Prigent
Sorbonne Université, Observatoire de Paris, Université PSL, CNRS, LERMA, Paris, France
Estellus, Paris, France
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Juliette Bernard, Catherine Prigent, Carlos Jimenez, Etienne Fluet-Chouinard, Bernhard Lehner, Elodie Salmon, Philippe Ciais, Zhen Zhang, Shushi Peng, and Marielle Saunois
Earth Syst. Sci. Data, 17, 2985–3008, https://doi.org/10.5194/essd-17-2985-2025, https://doi.org/10.5194/essd-17-2985-2025, 2025
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Wetlands are responsible for about a third of global emissions of methane, a potent greenhouse gas. We have developed the Global Inundation Extent from Multi-Satellites-MethaneCentric (GIEMS-MC) dataset to represent the dynamics of wetland extent on a global scale (0.25° × 0.25° resolution, monthly time step). This updated resource combines satellite data and existing wetland databases, covering 1992 to 2020. Consistent maps of other methane-emitting surface waters (lakes, rivers, reservoirs, rice paddies) are also provided.
Bernhard Lehner, Mira Anand, Etienne Fluet-Chouinard, Florence Tan, Filipe Aires, George H. Allen, Philippe Bousquet, Josep G. Canadell, Nick Davidson, Meng Ding, C. Max Finlayson, Thomas Gumbricht, Lammert Hilarides, Gustaf Hugelius, Robert B. Jackson, Maartje C. Korver, Liangyun Liu, Peter B. McIntyre, Szabolcs Nagy, David Olefeldt, Tamlin M. Pavelsky, Jean-Francois Pekel, Benjamin Poulter, Catherine Prigent, Jida Wang, Thomas A. Worthington, Dai Yamazaki, Xiao Zhang, and Michele Thieme
Earth Syst. Sci. Data, 17, 2277–2329, https://doi.org/10.5194/essd-17-2277-2025, https://doi.org/10.5194/essd-17-2277-2025, 2025
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The Global Lakes and Wetlands Database (GLWD) version 2 distinguishes a total of 33 non-overlapping wetland classes, providing a static map of the world’s inland surface waters. It contains cell fractions of wetland extents per class at a grid cell resolution of ~500 m. The total combined extent of all classes including all inland and coastal waterbodies and wetlands of all inundation frequencies – that is, the maximum extent – covers 18.2 × 106 km2, equivalent to 13.4 % of total global land area.
Léa Elise Bonnefoy, Catherine Prigent, Ghislain Picard, Clément Soriot, Alice Le Gall, Lise Kilic, and Carlos Jimenez
EGUsphere, https://doi.org/10.5194/egusphere-2024-3972, https://doi.org/10.5194/egusphere-2024-3972, 2025
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Microwave radiometry senses the thermal emission from a target, whereas its active counterpart, radar, sends a signal to the target and measures the signal reflected back. We simultaneously model radar and radiometry over the East Antarctic ice sheet, which we propose as an analog for icy moons: we can reproduce most data with a unique model. Saturn's moons' radar brightness cannot be reproduced and must be caused by processes unaccounted for in the model and less active in the Antarctic.
Nils Risse, Mario Mech, Catherine Prigent, Gunnar Spreen, and Susanne Crewell
The Cryosphere, 18, 4137–4163, https://doi.org/10.5194/tc-18-4137-2024, https://doi.org/10.5194/tc-18-4137-2024, 2024
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Passive microwave observations from satellites are crucial for monitoring Arctic sea ice and atmosphere. To do this effectively, it is important to understand how sea ice emits microwaves. Through unique Arctic sea ice observations, we improved our understanding, identified four distinct emission types, and expanded current knowledge to include higher frequencies. These findings will enhance our ability to monitor the Arctic climate and provide valuable information for new satellite missions.
Danny M. Leung, Jasper F. Kok, Longlei Li, Gregory S. Okin, Catherine Prigent, Martina Klose, Carlos Pérez García-Pando, Laurent Menut, Natalie M. Mahowald, David M. Lawrence, and Marcelo Chamecki
Atmos. Chem. Phys., 23, 6487–6523, https://doi.org/10.5194/acp-23-6487-2023, https://doi.org/10.5194/acp-23-6487-2023, 2023
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Desert dust modeling is important for understanding climate change, as dust regulates the atmosphere's greenhouse effect and radiation. This study formulates and proposes a more physical and realistic desert dust emission scheme for global and regional climate models. By considering more aeolian processes in our emission scheme, our simulations match better against dust observations than existing schemes. We believe this work is vital in improving dust representation in climate models.
Toby R. Marthews, Simon J. Dadson, Douglas B. Clark, Eleanor M. Blyth, Garry D. Hayman, Dai Yamazaki, Olivia R. E. Becher, Alberto Martínez-de la Torre, Catherine Prigent, and Carlos Jiménez
Hydrol. Earth Syst. Sci., 26, 3151–3175, https://doi.org/10.5194/hess-26-3151-2022, https://doi.org/10.5194/hess-26-3151-2022, 2022
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Reliable data on global inundated areas remain uncertain. By matching a leading global data product on inundation extents (GIEMS) against predictions from a global hydrodynamic model (CaMa-Flood), we found small but consistent and non-random biases in well-known tropical wetlands (Sudd, Pantanal, Amazon and Congo). These result from known limitations in the data and the models used, which shows us how to improve our ability to make critical predictions of inundation events in the future.
Benjamin Kitambo, Fabrice Papa, Adrien Paris, Raphael M. Tshimanga, Stephane Calmant, Ayan Santos Fleischmann, Frederic Frappart, Melanie Becker, Mohammad J. Tourian, Catherine Prigent, and Johary Andriambeloson
Hydrol. Earth Syst. Sci., 26, 1857–1882, https://doi.org/10.5194/hess-26-1857-2022, https://doi.org/10.5194/hess-26-1857-2022, 2022
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This study presents a better characterization of surface hydrology variability in the Congo River basin, the second largest river system in the world. We jointly use a large record of in situ and satellite-derived observations to monitor the spatial distribution and different timings of the Congo River basin's annual flood dynamic, including its peculiar bimodal pattern.
Ronny Meier, Edouard L. Davin, Gordon B. Bonan, David M. Lawrence, Xiaolong Hu, Gregory Duveiller, Catherine Prigent, and Sonia I. Seneviratne
Geosci. Model Dev., 15, 2365–2393, https://doi.org/10.5194/gmd-15-2365-2022, https://doi.org/10.5194/gmd-15-2365-2022, 2022
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We revise the roughness of the land surface in the CESM climate model. Guided by observational data, we increase the surface roughness of forests and decrease that of bare soil, snow, ice, and crops. These modifications alter simulated temperatures and wind speeds at and above the land surface considerably, in particular over desert regions. The revised model represents the diurnal variability of the land surface temperature better compared to satellite observations over most regions.
Martina Klose, Oriol Jorba, María Gonçalves Ageitos, Jeronimo Escribano, Matthew L. Dawson, Vincenzo Obiso, Enza Di Tomaso, Sara Basart, Gilbert Montané Pinto, Francesca Macchia, Paul Ginoux, Juan Guerschman, Catherine Prigent, Yue Huang, Jasper F. Kok, Ron L. Miller, and Carlos Pérez García-Pando
Geosci. Model Dev., 14, 6403–6444, https://doi.org/10.5194/gmd-14-6403-2021, https://doi.org/10.5194/gmd-14-6403-2021, 2021
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Mineral soil dust is a major atmospheric airborne particle type. We present and evaluate MONARCH, a model used for regional and global dust-weather prediction. An important feature of the model is that it allows different approximations to represent dust, ranging from more simplified to more complex treatments. Using these different treatments, MONARCH can help us better understand impacts of dust in the Earth system, such as its interactions with radiation.
Zhen Zhang, Etienne Fluet-Chouinard, Katherine Jensen, Kyle McDonald, Gustaf Hugelius, Thomas Gumbricht, Mark Carroll, Catherine Prigent, Annett Bartsch, and Benjamin Poulter
Earth Syst. Sci. Data, 13, 2001–2023, https://doi.org/10.5194/essd-13-2001-2021, https://doi.org/10.5194/essd-13-2001-2021, 2021
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The spatiotemporal distribution of wetlands is one of the important and yet uncertain factors determining the time and locations of methane fluxes. The Wetland Area and Dynamics for Methane Modeling (WAD2M) dataset describes the global data product used to quantify the areal dynamics of natural wetlands and how global wetlands are changing in response to climate.
Lise Kilic, Catherine Prigent, Carlos Jimenez, and Craig Donlon
Ocean Sci., 17, 455–461, https://doi.org/10.5194/os-17-455-2021, https://doi.org/10.5194/os-17-455-2021, 2021
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The Copernicus Imaging Microwave Radiometer (CIMR) is one of the high-priority satellite missions of the Copernicus program within the European Space Agency. It is designed to respond to the European Union Arctic policy. Its channels, incidence angle, precisions, and spatial resolutions have been selected to observe the Arctic Ocean with the recommendations expressed by the user communities.
In this note, we present the sensitivity analysis that has led to the choice of the CIMR channels.
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Short summary
Long-term monitoring of satellite-derived variables is necessary for a better understanding of the evolution of Earth parameters at global scale. However different instruments' observations used over the years need to be inter-calibrated with each other to provide meaningful information. This paper describes how a linear correction can improve the observations from the Scanning Multichannel Microwave Radiometer over continental surfaces to be more consistent with more recent radiometers.
Long-term monitoring of satellite-derived variables is necessary for a better understanding of...