Articles | Volume 25, issue 12
https://doi.org/10.5194/hess-25-6407-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-6407-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Dec 2021
Research article |
| 17 Dec 2021
| 17 Dec 2021
An inverse dielectric mixing model at 50 MHz that considers soil organic carbon
Chang-Hwan Park
CORRESPONDING AUTHOR
National Institute of Meteorological Sciences, Earth System Research Division, Korea Meteorological Administration, Jeju, Republic of Korea
Department of Civil Systems Engineering, Ajou University, Tashkent,
Uzbekistan
Aaron Berg
Department of Geography, Environment and Geomatics, University of
Guelph, Guelph, ON N1G 2W1, Canada
Michael H. Cosh
United States Department of Agriculture, Agricultural Research
Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA
Andreas Colliander
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
Andreas Behrendt
Institute of Physics and Meteorology, University of Hohenheim,
Stuttgart 70599, Germany
Hida Manns
Department of Geography, Environment and Geomatics, University of
Guelph, Guelph, ON N1G 2W1, Canada
Jinkyu Hong
Ecosystem-Atmosphere Process Lab., Department of Atmospheric Science, Yonsei University, Seoul, 03722 Republic of Korea
Johan Lee
National Institute of Meteorological Sciences, Earth System Research Division, Korea Meteorological Administration, Jeju, Republic of Korea
Runze Zhang
Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA 22904, USA
Volker Wulfmeyer
Institute of Physics and Meteorology, University of Hohenheim,
Stuttgart 70599, Germany
Related authors
No articles found.
Alamgir Hossan, Andreas Colliander, Baptiste Vandecrux, Nicole-Jeanne Schlegel, Joel Harper, Shawn Marshall, and Julie Z. Miller
The Cryosphere, 19, 4237–4258, https://doi.org/10.5194/tc-19-4237-2025, https://doi.org/10.5194/tc-19-4237-2025, 2025
Short summary
Short summary
We used L-band observations from the Soil Moisture Active Passive (SMAP) mission to quantify the surface and subsurface liquid water amounts (LWAs) in the percolation zone of the Greenland ice sheet. The algorithm is described, and the validation results are provided. The results demonstrate the potential for creating an LWA data product across the Greenland ice sheet (GrIS), which will advance our understanding of ice sheet physical processes for better projection of Greenland’s contribution to global sea level rise.
Johannes Speidel, Hannes Vogelmann, Andreas Behrendt, Diego Lange, Matthias Mauder, Jens Reichardt, and Kevin Wolz
Atmos. Meas. Tech., 18, 4923–4948, https://doi.org/10.5194/amt-18-4923-2025, https://doi.org/10.5194/amt-18-4923-2025, 2025
Short summary
Short summary
Humidity transport from the Earth's surface into the atmosphere is relevant for many processes. However, knowledge of the actual distribution of humidity concentrations is sparse – mainly due to technological limitations. With the lidar presented herein, it is possible to measure humidity concentrations and their vertical fluxes up to altitudes of > 3 km with high spatiotemporal resolution, opening new possibilities for detailed process understanding and, ultimately, better model representation.
Kerttu Kouki and Andreas Colliander
Hydrol. Earth Syst. Sci., 29, 4791–4810, https://doi.org/10.5194/hess-29-4791-2025, https://doi.org/10.5194/hess-29-4791-2025, 2025
Short summary
Short summary
Precipitation (P) and soil moisture (SM) are critical components of the climate system but poorly understood in the Arctic. Using NASA's SMAP satellite, we analyzed SM and P patterns in Finland. SM and P correlate strongly in summer and fall but weakly in spring due to snowmelt. While the area of P can be estimated from SM, estimating its intensity is more challenging. Water bodies complicate SM retrieval. The promising results suggest this method could be applied across the Arctic.
Linus von Klitzing, David D. Turner, Diego Lange, and Volker Wulfmeyer
EGUsphere, https://doi.org/10.5194/egusphere-2025-2101, https://doi.org/10.5194/egusphere-2025-2101, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
Many atmospheric science endeavors require temporally resolved profiles of temperature, humidity, and winds. Radiosondes are considered the gold standard for measuring these profiles, but the temporal resolution is frequently too coarse for many applications within the atmospheric boundary layer. This study proposes a new method using a normalized height grid in the temporal interpolation process that yields more accurate profiles in the convective boundary layer.
Zhimeng Zhang, Shannon Brown, and Andreas Colliander
The Cryosphere, 19, 4011–4026, https://doi.org/10.5194/tc-19-4011-2025, https://doi.org/10.5194/tc-19-4011-2025, 2025
Short summary
Short summary
Retrieving accurate water vapor and temperature profiles over land is challenging due to uncertainties in estimating surface emissions. To address this, we have developed an iterative method that combines atmospheric retrieval with surface emissions estimation. Using Advanced Technology Microwave Sounder (ATMS) data across various microwave frequencies, we successfully tracked atmospheric temperature and humidity changes. Testing against Radiosonde data showed our method is efficient and accurate, especially in detecting melting events.
Juliette Ortet, Arnaud Mialon, Alain Royer, Mike Schwank, Manu Holmberg, Kimmo Rautiainen, Simone Bircher-Adrot, Andreas Colliander, Yann Kerr, and Alexandre Roy
The Cryosphere, 19, 3571–3598, https://doi.org/10.5194/tc-19-3571-2025, https://doi.org/10.5194/tc-19-3571-2025, 2025
Short summary
Short summary
We propose a new method to determine the ground surface temperature under the snowpack in the Arctic area from satellite observations. The obtained ground temperature time series were evaluated over 21 reference sites in Northern Alaska and compared with ground temperatures obtained with global models. The method is extremely promising for monitoring ground temperature below the snowpack and studying the spatio-temporal variability thanks to 15 years of observations over the whole Arctic area.
Alamgir Hossan, Andreas Colliander, Nicole-Jeanne Schlegel, Joel Harper, Lauren Andrews, Jana Kolassa, Julie Z. Miller, and Richard Cullather
EGUsphere, https://doi.org/10.5194/egusphere-2025-2681, https://doi.org/10.5194/egusphere-2025-2681, 2025
Short summary
Short summary
Microwave L-band radiometry offers a promising tool for estimating the total surface-to-subsurface liquid water amount (LWA) in the snow and firn in polar ice sheets. An accurate modelling of wet snow effective permittivity is a key to this. Here, we evaluated the performance of ten commonly used microwave dielectric mixing models for estimating LWA in the percolation zone of the Greenland Ice Sheet to help an appropriate choice of dielectric mixing model for LWA retrieval algorithms.
Hesam Salmabadi, Renato Pardo Lara, Aaron Berg, Alex Mavrovic, Chelene Hanes, Benoit Montpetit, and Alexandre Roy
EGUsphere, https://doi.org/10.5194/egusphere-2025-620, https://doi.org/10.5194/egusphere-2025-620, 2025
Short summary
Short summary
Our research introduces a framework for monitoring seasonally frozen ground that goes beyond simply checking whether soil temperature is above or below freezing. We found that soil often remains in a transitional state between frozen and unfrozen for as long as fully frozen periods – something traditional monitoring methods fail to capture. These findings enhance our understanding of seasonally frozen ground, its climate change impacts, and carbon release in cold regions.
Thomas Schwitalla, Lisa Jach, Volker Wulfmeyer, and Kirsten Warrach-Sagi
Nat. Hazards Earth Syst. Sci., 25, 1405–1424, https://doi.org/10.5194/nhess-25-1405-2025, https://doi.org/10.5194/nhess-25-1405-2025, 2025
Short summary
Short summary
During recent decades, Europe has experienced increasing periods of severe drought and heatwave. To provide an overview of how land-surface conditions shape land–atmosphere (LA) coupling, the interannual LA coupling strength variability for the summer seasons of 1991–2022 is investigated by means of ERA5 data. The results clearly reflect ongoing climate change by a shift in the coupling relationships towards reinforced heating and drying by the land surface.
Min Huang, Gregory R. Carmichael, Kevin W. Bowman, Isabelle De Smedt, Andreas Colliander, Michael H. Cosh, Sujay V. Kumar, Alex B. Guenther, Scott J. Janz, Ryan M. Stauffer, Anne M. Thompson, Niko M. Fedkin, Robert J. Swap, John D. Bolten, and Alicia T. Joseph
Atmos. Chem. Phys., 25, 1449–1476, https://doi.org/10.5194/acp-25-1449-2025, https://doi.org/10.5194/acp-25-1449-2025, 2025
Short summary
Short summary
We use model simulations along with multiplatform, multidisciplinary observations and a range of analysis methods to estimate and understand the distributions, temporal changes, and impacts of reactive nitrogen and ozone over the most populous US region that has undergone significant environmental changes. Deposition, biogenic emissions, and extra-regional sources have been playing increasingly important roles in controlling pollutant budgets in this area as local anthropogenic emissions drop.
Syed Saqlain Abbas, Andreas Behrendt, Oliver Branch, and Volker Wulfmeyer
EGUsphere, https://doi.org/10.5194/egusphere-2024-3878, https://doi.org/10.5194/egusphere-2024-3878, 2024
Preprint archived
Short summary
Short summary
This study investigates turbulence statistics convective boundary layer. For this, we used data of two Doppler lidars, and an eddy covariance station between May to July 2021. We believe that these statistics are important to improve the land-atmosphere characterization in numerical weather prediction models.
José Alex Zenteno-Hernández, Adolfo Comerón, Federico Dios, Alejandro Rodríguez-Gómez, Constantino Muñoz-Porcar, Michaël Sicard, Noemi Franco, Andreas Behrendt, and Paolo Di Girolamo
Atmos. Meas. Tech., 17, 4687–4694, https://doi.org/10.5194/amt-17-4687-2024, https://doi.org/10.5194/amt-17-4687-2024, 2024
Short summary
Short summary
We study how the spectral characteristics of a solid-state laser in an atmospheric temperature profiling lidar using the Raman technique impact the temperature retrieval accuracy. We find that the spectral widening, with respect to a seeded laser, has virtually no impact, while crystal-rod temperature variations in the laser must be kept within a range of 1 K for the uncertainty in the atmospheric temperature below 1 K. The study is carried out through spectroscopy simulations.
Kyoung-Min Kim, Si-Wan Kim, Seunghwan Seo, Donald R. Blake, Seogju Cho, James H. Crawford, Louisa K. Emmons, Alan Fried, Jay R. Herman, Jinkyu Hong, Jinsang Jung, Gabriele G. Pfister, Andrew J. Weinheimer, Jung-Hun Woo, and Qiang Zhang
Geosci. Model Dev., 17, 1931–1955, https://doi.org/10.5194/gmd-17-1931-2024, https://doi.org/10.5194/gmd-17-1931-2024, 2024
Short summary
Short summary
Three emission inventories were evaluated for East Asia using data acquired during a field campaign in 2016. The inventories successfully reproduced the daily variations of ozone and nitrogen dioxide. However, the spatial distributions of model ozone did not fully agree with the observations. Additionally, all simulations underestimated carbon monoxide and volatile organic compound (VOC) levels. Increasing VOC emissions over South Korea resulted in improved ozone simulations.
Volker Wulfmeyer, Christoph Senff, Florian Späth, Andreas Behrendt, Diego Lange, Robert M. Banta, W. Alan Brewer, Andreas Wieser, and David D. Turner
Atmos. Meas. Tech., 17, 1175–1196, https://doi.org/10.5194/amt-17-1175-2024, https://doi.org/10.5194/amt-17-1175-2024, 2024
Short summary
Short summary
A simultaneous deployment of Doppler, temperature, and water-vapor lidar systems is used to provide profiles of molecular destruction rates and turbulent kinetic energy (TKE) dissipation in the convective boundary layer (CBL). The results can be used for the parameterization of turbulent variables, TKE budget analyses, and the verification of weather forecast and climate models.
Oliver Branch, Lisa Jach, Thomas Schwitalla, Kirsten Warrach-Sagi, and Volker Wulfmeyer
Earth Syst. Dynam., 15, 109–129, https://doi.org/10.5194/esd-15-109-2024, https://doi.org/10.5194/esd-15-109-2024, 2024
Short summary
Short summary
In the United Arab Emirates, water scarcity is reaching a crisis point, and new methods for obtaining freshwater are urgently needed. Regional climate engineering with large artificial heat islands can enhance desert precipitation by increasing cloud development. Through model simulation, we show that heat islands of 20 × 20 km or larger can potentially produce enough annual rainfall to supply thousands of people. Thus, artificial heat islands should be made a high priority for further research.
Florian Späth, Verena Rajtschan, Tobias K. D. Weber, Shehan Morandage, Diego Lange, Syed Saqlain Abbas, Andreas Behrendt, Joachim Ingwersen, Thilo Streck, and Volker Wulfmeyer
Geosci. Instrum. Method. Data Syst., 12, 25–44, https://doi.org/10.5194/gi-12-25-2023, https://doi.org/10.5194/gi-12-25-2023, 2023
Short summary
Short summary
Important topics in land–atmosphere feedback research are water and energy balances and heterogeneities of fluxes at the land surface and in the atmosphere. To target these questions, the Land–Atmosphere Feedback Observatory (LAFO) has been installed in Germany. The instrumentation allows for comprehensive measurements from the bedrock to the troposphere. The LAFO observation strategy aims for simultaneous measurements in all three compartments: atmosphere, soil and land surface, and vegetation.
Mathew Lipson, Sue Grimmond, Martin Best, Winston T. L. Chow, Andreas Christen, Nektarios Chrysoulakis, Andrew Coutts, Ben Crawford, Stevan Earl, Jonathan Evans, Krzysztof Fortuniak, Bert G. Heusinkveld, Je-Woo Hong, Jinkyu Hong, Leena Järvi, Sungsoo Jo, Yeon-Hee Kim, Simone Kotthaus, Keunmin Lee, Valéry Masson, Joseph P. McFadden, Oliver Michels, Wlodzimierz Pawlak, Matthias Roth, Hirofumi Sugawara, Nigel Tapper, Erik Velasco, and Helen Claire Ward
Earth Syst. Sci. Data, 14, 5157–5178, https://doi.org/10.5194/essd-14-5157-2022, https://doi.org/10.5194/essd-14-5157-2022, 2022
Short summary
Short summary
We describe a new openly accessible collection of atmospheric observations from 20 cities around the world, capturing 50 site years. The observations capture local meteorology (temperature, humidity, wind, etc.) and the energy fluxes between the land and atmosphere (e.g. radiation and sensible and latent heat fluxes). These observations can be used to improve our understanding of urban climate processes and to test the accuracy of urban climate models.
Alberto Caldas-Alvarez, Markus Augenstein, Georgy Ayzel, Klemens Barfus, Ribu Cherian, Lisa Dillenardt, Felix Fauer, Hendrik Feldmann, Maik Heistermann, Alexia Karwat, Frank Kaspar, Heidi Kreibich, Etor Emanuel Lucio-Eceiza, Edmund P. Meredith, Susanna Mohr, Deborah Niermann, Stephan Pfahl, Florian Ruff, Henning W. Rust, Lukas Schoppa, Thomas Schwitalla, Stella Steidl, Annegret H. Thieken, Jordis S. Tradowsky, Volker Wulfmeyer, and Johannes Quaas
Nat. Hazards Earth Syst. Sci., 22, 3701–3724, https://doi.org/10.5194/nhess-22-3701-2022, https://doi.org/10.5194/nhess-22-3701-2022, 2022
Short summary
Short summary
In a warming climate, extreme precipitation events are becoming more frequent. To advance our knowledge on such phenomena, we present a multidisciplinary analysis of a selected case study that took place on 29 June 2017 in the Berlin metropolitan area. Our analysis provides evidence of the extremeness of the case from the atmospheric and the impacts perspectives as well as new insights on the physical mechanisms of the event at the meteorological and climate scales.
Sara Sadri, James S. Famiglietti, Ming Pan, Hylke E. Beck, Aaron Berg, and Eric F. Wood
Hydrol. Earth Syst. Sci., 26, 5373–5390, https://doi.org/10.5194/hess-26-5373-2022, https://doi.org/10.5194/hess-26-5373-2022, 2022
Short summary
Short summary
A farm-scale hydroclimatic machine learning framework to advise farmers was developed. FarmCan uses remote sensing data and farmers' input to forecast crop water deficits. The 8 d composite variables are better than daily ones for forecasting water deficit. Evapotranspiration (ET) and potential ET are more effective than soil moisture at predicting crop water deficit. FarmCan uses a crop-specific schedule to use surface or root zone soil moisture.
Lim-Seok Chang, Donghee Kim, Hyunkee Hong, Deok-Rae Kim, Jeong-Ah Yu, Kwangyul Lee, Hanlim Lee, Daewon Kim, Jinkyu Hong, Hyun-Young Jo, and Cheol-Hee Kim
Atmos. Chem. Phys., 22, 10703–10720, https://doi.org/10.5194/acp-22-10703-2022, https://doi.org/10.5194/acp-22-10703-2022, 2022
Short summary
Short summary
Our study explored the synergy of combined column and surface measurements during GMAP (GEMS Map of Air Pollution) campaign. It has several points to note for vertical distribution analysis. Particularly under prevailing local wind meteorological conditions, Pandora-based vertical structures sometimes showed negative correlations between column and surface measurements. Vertical analysis should be done carefully in some local meteorological conditions when employing either surface or columns.
Tobias K. D. Weber, Joachim Ingwersen, Petra Högy, Arne Poyda, Hans-Dieter Wizemann, Michael Scott Demyan, Kristina Bohm, Ravshan Eshonkulov, Sebastian Gayler, Pascal Kremer, Moritz Laub, Yvonne Funkiun Nkwain, Christian Troost, Irene Witte, Tim Reichenau, Thomas Berger, Georg Cadisch, Torsten Müller, Andreas Fangmeier, Volker Wulfmeyer, and Thilo Streck
Earth Syst. Sci. Data, 14, 1153–1181, https://doi.org/10.5194/essd-14-1153-2022, https://doi.org/10.5194/essd-14-1153-2022, 2022
Short summary
Short summary
Presented are measurement results from six agricultural fields operated by local farmers in southwestern Germany over 9 years. Six eddy-covariance stations measuring water, energy, and carbon fluxes between the vegetated soil surface and the atmosphere provided the backbone of the measurement sites and were supplemented by extensive soil and vegetation state monitoring. The dataset is ideal for testing process models characterizing fluxes at the vegetated soil surface and in the atmosphere.
Jooyeop Lee, Martin Claussen, Jeongwon Kim, Je-Woo Hong, In-Sun Song, and Jinkyu Hong
Clim. Past, 18, 313–326, https://doi.org/10.5194/cp-18-313-2022, https://doi.org/10.5194/cp-18-313-2022, 2022
Short summary
Short summary
It is still a challenge to simulate the so–called Green Sahara (GS), which was a wet and vegetative Sahara region in the mid–Holocene, using current climate models. Our analysis shows that Holocene greening is simulated better if the amount of soil nitrogen and soil texture is properly modified for the humid and vegetative GS period. Future climate simulation needs to consider consequent changes in soil nitrogen and texture with changes in vegetation cover for proper climate simulations.
Lisa Jach, Thomas Schwitalla, Oliver Branch, Kirsten Warrach-Sagi, and Volker Wulfmeyer
Earth Syst. Dynam., 13, 109–132, https://doi.org/10.5194/esd-13-109-2022, https://doi.org/10.5194/esd-13-109-2022, 2022
Short summary
Short summary
The land surface can influence the occurrence of local rainfall through different feedback mechanisms. In Europe, this happens most frequently in summer. Here, we examine how differences in atmospheric temperature and moisture change where and how often the land surface can influence rainfall. The results show that the differences barely move the region of strong surface influence over Scandinavia and eastern Europe, but they can change the frequency of coupling events.
Keunmin Lee, Je-Woo Hong, Jeongwon Kim, Sungsoo Jo, and Jinkyu Hong
Atmos. Chem. Phys., 21, 17833–17853, https://doi.org/10.5194/acp-21-17833-2021, https://doi.org/10.5194/acp-21-17833-2021, 2021
Short summary
Short summary
This study examine two benefits of urban forest, thermal mitigation and carbon uptake. Our analysis indicates that the urban forest reduces both the warming trend and urban heat island intensity. Urban forest is a net CO2 source despite larger photosynthetic carbon uptake because of strong contribution of ecosystem respiration, which can be attributed to the substantial amount of soil organic carbon by intensive historical soil use and warm temperature in a city.
Wouter Dorigo, Irene Himmelbauer, Daniel Aberer, Lukas Schremmer, Ivana Petrakovic, Luca Zappa, Wolfgang Preimesberger, Angelika Xaver, Frank Annor, Jonas Ardö, Dennis Baldocchi, Marco Bitelli, Günter Blöschl, Heye Bogena, Luca Brocca, Jean-Christophe Calvet, J. Julio Camarero, Giorgio Capello, Minha Choi, Michael C. Cosh, Nick van de Giesen, Istvan Hajdu, Jaakko Ikonen, Karsten H. Jensen, Kasturi Devi Kanniah, Ileen de Kat, Gottfried Kirchengast, Pankaj Kumar Rai, Jenni Kyrouac, Kristine Larson, Suxia Liu, Alexander Loew, Mahta Moghaddam, José Martínez Fernández, Cristian Mattar Bader, Renato Morbidelli, Jan P. Musial, Elise Osenga, Michael A. Palecki, Thierry Pellarin, George P. Petropoulos, Isabella Pfeil, Jarrett Powers, Alan Robock, Christoph Rüdiger, Udo Rummel, Michael Strobel, Zhongbo Su, Ryan Sullivan, Torbern Tagesson, Andrej Varlagin, Mariette Vreugdenhil, Jeffrey Walker, Jun Wen, Fred Wenger, Jean Pierre Wigneron, Mel Woods, Kun Yang, Yijian Zeng, Xiang Zhang, Marek Zreda, Stephan Dietrich, Alexander Gruber, Peter van Oevelen, Wolfgang Wagner, Klaus Scipal, Matthias Drusch, and Roberto Sabia
Hydrol. Earth Syst. Sci., 25, 5749–5804, https://doi.org/10.5194/hess-25-5749-2021, https://doi.org/10.5194/hess-25-5749-2021, 2021
Short summary
Short summary
The International Soil Moisture Network (ISMN) is a community-based open-access data portal for soil water measurements taken at the ground and is accessible at https://ismn.earth. Over 1000 scientific publications and thousands of users have made use of the ISMN. The scope of this paper is to inform readers about the data and functionality of the ISMN and to provide a review of the scientific progress facilitated through the ISMN with the scope to shape future research and operations.
Bjorn Stevens, Sandrine Bony, David Farrell, Felix Ament, Alan Blyth, Christopher Fairall, Johannes Karstensen, Patricia K. Quinn, Sabrina Speich, Claudia Acquistapace, Franziska Aemisegger, Anna Lea Albright, Hugo Bellenger, Eberhard Bodenschatz, Kathy-Ann Caesar, Rebecca Chewitt-Lucas, Gijs de Boer, Julien Delanoë, Leif Denby, Florian Ewald, Benjamin Fildier, Marvin Forde, Geet George, Silke Gross, Martin Hagen, Andrea Hausold, Karen J. Heywood, Lutz Hirsch, Marek Jacob, Friedhelm Jansen, Stefan Kinne, Daniel Klocke, Tobias Kölling, Heike Konow, Marie Lothon, Wiebke Mohr, Ann Kristin Naumann, Louise Nuijens, Léa Olivier, Robert Pincus, Mira Pöhlker, Gilles Reverdin, Gregory Roberts, Sabrina Schnitt, Hauke Schulz, A. Pier Siebesma, Claudia Christine Stephan, Peter Sullivan, Ludovic Touzé-Peiffer, Jessica Vial, Raphaela Vogel, Paquita Zuidema, Nicola Alexander, Lyndon Alves, Sophian Arixi, Hamish Asmath, Gholamhossein Bagheri, Katharina Baier, Adriana Bailey, Dariusz Baranowski, Alexandre Baron, Sébastien Barrau, Paul A. Barrett, Frédéric Batier, Andreas Behrendt, Arne Bendinger, Florent Beucher, Sebastien Bigorre, Edmund Blades, Peter Blossey, Olivier Bock, Steven Böing, Pierre Bosser, Denis Bourras, Pascale Bouruet-Aubertot, Keith Bower, Pierre Branellec, Hubert Branger, Michal Brennek, Alan Brewer, Pierre-Etienne Brilouet, Björn Brügmann, Stefan A. Buehler, Elmo Burke, Ralph Burton, Radiance Calmer, Jean-Christophe Canonici, Xavier Carton, Gregory Cato Jr., Jude Andre Charles, Patrick Chazette, Yanxu Chen, Michal T. Chilinski, Thomas Choularton, Patrick Chuang, Shamal Clarke, Hugh Coe, Céline Cornet, Pierre Coutris, Fleur Couvreux, Susanne Crewell, Timothy Cronin, Zhiqiang Cui, Yannis Cuypers, Alton Daley, Gillian M. Damerell, Thibaut Dauhut, Hartwig Deneke, Jean-Philippe Desbios, Steffen Dörner, Sebastian Donner, Vincent Douet, Kyla Drushka, Marina Dütsch, André Ehrlich, Kerry Emanuel, Alexandros Emmanouilidis, Jean-Claude Etienne, Sheryl Etienne-Leblanc, Ghislain Faure, Graham Feingold, Luca Ferrero, Andreas Fix, Cyrille Flamant, Piotr Jacek Flatau, Gregory R. Foltz, Linda Forster, Iulian Furtuna, Alan Gadian, Joseph Galewsky, Martin Gallagher, Peter Gallimore, Cassandra Gaston, Chelle Gentemann, Nicolas Geyskens, Andreas Giez, John Gollop, Isabelle Gouirand, Christophe Gourbeyre, Dörte de Graaf, Geiske E. de Groot, Robert Grosz, Johannes Güttler, Manuel Gutleben, Kashawn Hall, George Harris, Kevin C. Helfer, Dean Henze, Calvert Herbert, Bruna Holanda, Antonio Ibanez-Landeta, Janet Intrieri, Suneil Iyer, Fabrice Julien, Heike Kalesse, Jan Kazil, Alexander Kellman, Abiel T. Kidane, Ulrike Kirchner, Marcus Klingebiel, Mareike Körner, Leslie Ann Kremper, Jan Kretzschmar, Ovid Krüger, Wojciech Kumala, Armin Kurz, Pierre L'Hégaret, Matthieu Labaste, Tom Lachlan-Cope, Arlene Laing, Peter Landschützer, Theresa Lang, Diego Lange, Ingo Lange, Clément Laplace, Gauke Lavik, Rémi Laxenaire, Caroline Le Bihan, Mason Leandro, Nathalie Lefevre, Marius Lena, Donald Lenschow, Qiang Li, Gary Lloyd, Sebastian Los, Niccolò Losi, Oscar Lovell, Christopher Luneau, Przemyslaw Makuch, Szymon Malinowski, Gaston Manta, Eleni Marinou, Nicholas Marsden, Sebastien Masson, Nicolas Maury, Bernhard Mayer, Margarette Mayers-Als, Christophe Mazel, Wayne McGeary, James C. McWilliams, Mario Mech, Melina Mehlmann, Agostino Niyonkuru Meroni, Theresa Mieslinger, Andreas Minikin, Peter Minnett, Gregor Möller, Yanmichel Morfa Avalos, Caroline Muller, Ionela Musat, Anna Napoli, Almuth Neuberger, Christophe Noisel, David Noone, Freja Nordsiek, Jakub L. Nowak, Lothar Oswald, Douglas J. Parker, Carolyn Peck, Renaud Person, Miriam Philippi, Albert Plueddemann, Christopher Pöhlker, Veronika Pörtge, Ulrich Pöschl, Lawrence Pologne, Michał Posyniak, Marc Prange, Estefanía Quiñones Meléndez, Jule Radtke, Karim Ramage, Jens Reimann, Lionel Renault, Klaus Reus, Ashford Reyes, Joachim Ribbe, Maximilian Ringel, Markus Ritschel, Cesar B. Rocha, Nicolas Rochetin, Johannes Röttenbacher, Callum Rollo, Haley Royer, Pauline Sadoulet, Leo Saffin, Sanola Sandiford, Irina Sandu, Michael Schäfer, Vera Schemann, Imke Schirmacher, Oliver Schlenczek, Jerome Schmidt, Marcel Schröder, Alfons Schwarzenboeck, Andrea Sealy, Christoph J. Senff, Ilya Serikov, Samkeyat Shohan, Elizabeth Siddle, Alexander Smirnov, Florian Späth, Branden Spooner, M. Katharina Stolla, Wojciech Szkółka, Simon P. de Szoeke, Stéphane Tarot, Eleni Tetoni, Elizabeth Thompson, Jim Thomson, Lorenzo Tomassini, Julien Totems, Alma Anna Ubele, Leonie Villiger, Jan von Arx, Thomas Wagner, Andi Walther, Ben Webber, Manfred Wendisch, Shanice Whitehall, Anton Wiltshire, Allison A. Wing, Martin Wirth, Jonathan Wiskandt, Kevin Wolf, Ludwig Worbes, Ethan Wright, Volker Wulfmeyer, Shanea Young, Chidong Zhang, Dongxiao Zhang, Florian Ziemen, Tobias Zinner, and Martin Zöger
Earth Syst. Sci. Data, 13, 4067–4119, https://doi.org/10.5194/essd-13-4067-2021, https://doi.org/10.5194/essd-13-4067-2021, 2021
Short summary
Short summary
The EUREC4A field campaign, designed to test hypothesized mechanisms by which clouds respond to warming and benchmark next-generation Earth-system models, is presented. EUREC4A comprised roughly 5 weeks of measurements in the downstream winter trades of the North Atlantic – eastward and southeastward of Barbados. It was the first campaign that attempted to characterize the full range of processes and scales influencing trade wind clouds.
Yongkang Xue, Tandong Yao, Aaron A. Boone, Ismaila Diallo, Ye Liu, Xubin Zeng, William K. M. Lau, Shiori Sugimoto, Qi Tang, Xiaoduo Pan, Peter J. van Oevelen, Daniel Klocke, Myung-Seo Koo, Tomonori Sato, Zhaohui Lin, Yuhei Takaya, Constantin Ardilouze, Stefano Materia, Subodh K. Saha, Retish Senan, Tetsu Nakamura, Hailan Wang, Jing Yang, Hongliang Zhang, Mei Zhao, Xin-Zhong Liang, J. David Neelin, Frederic Vitart, Xin Li, Ping Zhao, Chunxiang Shi, Weidong Guo, Jianping Tang, Miao Yu, Yun Qian, Samuel S. P. Shen, Yang Zhang, Kun Yang, Ruby Leung, Yuan Qiu, Daniele Peano, Xin Qi, Yanling Zhan, Michael A. Brunke, Sin Chan Chou, Michael Ek, Tianyi Fan, Hong Guan, Hai Lin, Shunlin Liang, Helin Wei, Shaocheng Xie, Haoran Xu, Weiping Li, Xueli Shi, Paulo Nobre, Yan Pan, Yi Qin, Jeff Dozier, Craig R. Ferguson, Gianpaolo Balsamo, Qing Bao, Jinming Feng, Jinkyu Hong, Songyou Hong, Huilin Huang, Duoying Ji, Zhenming Ji, Shichang Kang, Yanluan Lin, Weiguang Liu, Ryan Muncaster, Patricia de Rosnay, Hiroshi G. Takahashi, Guiling Wang, Shuyu Wang, Weicai Wang, Xu Zhou, and Yuejian Zhu
Geosci. Model Dev., 14, 4465–4494, https://doi.org/10.5194/gmd-14-4465-2021, https://doi.org/10.5194/gmd-14-4465-2021, 2021
Short summary
Short summary
The subseasonal prediction of extreme hydroclimate events such as droughts/floods has remained stubbornly low for years. This paper presents a new international initiative which, for the first time, introduces spring land surface temperature anomalies over high mountains to improve precipitation prediction through remote effects of land–atmosphere interactions. More than 40 institutions worldwide are participating in this effort. The experimental protocol and preliminary results are presented.
Chris M. DeBeer, Howard S. Wheater, John W. Pomeroy, Alan G. Barr, Jennifer L. Baltzer, Jill F. Johnstone, Merritt R. Turetsky, Ronald E. Stewart, Masaki Hayashi, Garth van der Kamp, Shawn Marshall, Elizabeth Campbell, Philip Marsh, Sean K. Carey, William L. Quinton, Yanping Li, Saman Razavi, Aaron Berg, Jeffrey J. McDonnell, Christopher Spence, Warren D. Helgason, Andrew M. Ireson, T. Andrew Black, Mohamed Elshamy, Fuad Yassin, Bruce Davison, Allan Howard, Julie M. Thériault, Kevin Shook, Michael N. Demuth, and Alain Pietroniro
Hydrol. Earth Syst. Sci., 25, 1849–1882, https://doi.org/10.5194/hess-25-1849-2021, https://doi.org/10.5194/hess-25-1849-2021, 2021
Short summary
Short summary
This article examines future changes in land cover and hydrological cycling across the interior of western Canada under climate conditions projected for the 21st century. Key insights into the mechanisms and interactions of Earth system and hydrological process responses are presented, and this understanding is used together with model application to provide a synthesis of future change. This has allowed more scientifically informed projections than have hitherto been available.
Thomas Schwitalla, Hans-Stefan Bauer, Kirsten Warrach-Sagi, Thomas Bönisch, and Volker Wulfmeyer
Atmos. Chem. Phys., 21, 4575–4597, https://doi.org/10.5194/acp-21-4575-2021, https://doi.org/10.5194/acp-21-4575-2021, 2021
Short summary
Short summary
A prototype of an air quality forecasting system (AQFS) on a turbulence-permitting (TP) horizontal resolution of 50 m is developed. AQFS is based on the WRF-Chem model and uses high-resolution emission data from different pollution sources. A simulation case study of a typical winter day in south Germany serves as a test bed. Results indicate that the complex topography plays an important role for the horizontal and vertical pollution distribution over the Stuttgart metropolitan area.
Oliver Branch, Thomas Schwitalla, Marouane Temimi, Ricardo Fonseca, Narendra Nelli, Michael Weston, Josipa Milovac, and Volker Wulfmeyer
Geosci. Model Dev., 14, 1615–1637, https://doi.org/10.5194/gmd-14-1615-2021, https://doi.org/10.5194/gmd-14-1615-2021, 2021
Short summary
Short summary
Effective numerical weather forecasting is vital in arid regions like the United Arab Emirates where extreme events like heat waves, flash floods, and dust storms are becoming more severe. This study employs a high-resolution simulation with the WRF-NOAHMP model, and the output is compared with seasonal observation data from 50 weather stations. This type of verification is vital to identify model deficiencies and improve forecasting systems for arid regions.
Alex Mavrovic, Renato Pardo Lara, Aaron Berg, François Demontoux, Alain Royer, and Alexandre Roy
Hydrol. Earth Syst. Sci., 25, 1117–1131, https://doi.org/10.5194/hess-25-1117-2021, https://doi.org/10.5194/hess-25-1117-2021, 2021
Short summary
Short summary
This paper presents a new probe that measures soil microwave permittivity in the frequency range of satellite L-band sensors. The probe capacities will allow for validation and calibration of the models used to estimate landscape physical properties from raw microwave satellite datasets. Our results show important discrepancies between model estimates and instrument measurements that will need to be addressed.
Nataniel M. Holtzman, Leander D. L. Anderegg, Simon Kraatz, Alex Mavrovic, Oliver Sonnentag, Christoforos Pappas, Michael H. Cosh, Alexandre Langlois, Tarendra Lakhankar, Derek Tesser, Nicholas Steiner, Andreas Colliander, Alexandre Roy, and Alexandra G. Konings
Biogeosciences, 18, 739–753, https://doi.org/10.5194/bg-18-739-2021, https://doi.org/10.5194/bg-18-739-2021, 2021
Short summary
Short summary
Microwave radiation coming from Earth's land surface is affected by both soil moisture and the water in plants that cover the soil. We measured such radiation with a sensor elevated above a forest canopy while repeatedly measuring the amount of water stored in trees at the same location. Changes in the microwave signal over time were closely related to tree water storage changes. Satellites with similar sensors could thus be used to monitor how trees in an entire region respond to drought.
Rogier van der Velde, Andreas Colliander, Michiel Pezij, Harm-Jan F. Benninga, Rajat Bindlish, Steven K. Chan, Thomas J. Jackson, Dimmie M. D. Hendriks, Denie C. M. Augustijn, and Zhongbo Su
Hydrol. Earth Syst. Sci., 25, 473–495, https://doi.org/10.5194/hess-25-473-2021, https://doi.org/10.5194/hess-25-473-2021, 2021
Short summary
Short summary
NASA’s SMAP satellite provides estimates of the amount of water in the soil. With measurements from a network of 20 monitoring stations, the accuracy of these estimates has been studied for a 4-year period. We found an agreement between satellite and in situ estimates in line with the mission requirements once the large mismatches associated with rapidly changing water contents, e.g. soil freezing and rainfall, are excluded.
Seyedmohammad Mousavi, Andreas Colliander, Julie Z. Miller, and John S. Kimball
The Cryosphere Discuss., https://doi.org/10.5194/tc-2020-297, https://doi.org/10.5194/tc-2020-297, 2020
Manuscript not accepted for further review
Cited articles
Ball, D. F.: Loss-on-Ignition as an Estimate of Organic Matter and Organic
Carbon in Non-Calcareous Soils, J. Soil Sci., 15, 84–92,
https://doi.org/10.1111/j.1365-2389.1964.tb00247.x, 1964.
Batjes, N. H.: Harmonized soil property values for broad-scale modelling
(WISE30sec) with estimates of global soil carbon stocks, Geoderma, 269,
61–68, https://doi.org/10.1016/j.geoderma.2016.01.034, 2016.
Bell, J. E., Palecki, M. A., Baker, C. B., Collins, W. G., Lawrimore, J. H.,
Leeper, R. D., Hall, M. E., Kochendorfer, J., Meyers, T. P., Wilson, T.,
and Diamond, H. J.: U.S. Climate Reference Network Soil Moisture and Temperature
Observations, J. Hydrometeor., 14, 977–988,
https://doi.org/10.1175/JHM-D-12-0146.1, 2013.
Bircher, S., Andreasen, M., Vuollet, J., Vehviläinen, J., Rautiainen, K., Jonard, F., Weihermüller, L., Zakharova, E., Wigneron, J.-P., and Kerr, Y. H.: Soil moisture sensor calibration for organic soil surface layers, Geosci. Instrum. Method. Data Syst., 5, 109–125, https://doi.org/10.5194/gi-5-109-2016, 2016.
Bircher, S., Skou, N., Jensen, K. H., Walker, J. P., and Rasmussen, L.: A soil moisture and temperature network for SMOS validation in Western Denmark, Hydrol. Earth Syst. Sci., 16, 1445–1463, https://doi.org/10.5194/hess-16-1445-2012, 2012.
Broadbent, F. E.: The Soil Organic Fraction, in: Norman, A.G. (Ed.), Advances
in Agronomy, Academic Press, 153–183,
https://doi.org/10.1016/S0065-2113(08)60229-1, 1953.
Bullock, P., Berg, A., and Wiseman, G.: SMAPVEX12 Core-Based Soil
Texture Data, Version 1, https://doi.org/10.5067/376D19WSS9VT, 2014.
Chadburn, S., Burke, E., Essery, R., Boike, J., Langer, M., Heikenfeld, M., Cox, P., and Friedlingstein, P.: An improved representation of physical permafrost dynamics in the JULES land-surface model, Geosci. Model Dev., 8, 1493–1508, https://doi.org/10.5194/gmd-8-1493-2015, 2015.
Cosh, M. H., Caldwell, T. G., Baker, C. B., Bolten, J. D., Edwards, N., Goble,
P., Hofman, H., Ochsner, T. E., Quiring, S., Schalk, C., Skumanich, M.,
Svoboda, M., and Woloszyn, M. E.: Developing a strategy for the national
coordinated soil moisture monitoring network, Vadose Zone J., 20,
e20139, https://doi.org/10.1002/vzj2.20139, 2021.
Curtis, J. O., Weiss Jr., C. A., and Everett, J. B.: Effect of Soil Composition on Complex Dielectric Properties, Army Engineer Waterways Experiment Station Vicksburg Ms Environmental Lab, 1995.
Entekhabi, D., Njoku, E., O'Neill, P., Kellogg, K., Crow, W., Edelstein, W.,
Entin, J., Goodman, S., Jackson, T., Johnson, J., Kimball, J., Piepmeier,
J., Koster, R., Martin, N., McDonald, K., Moghaddam, M., Moran, S., Reichle,
R., Shi, J., Spencer, M., Thurman, S., Tsang, L., and van Zyl, J.: The Soil
Moisture Active Passive (SMAP) Mission, Proc. IEEE, 98,
704–716, https://doi.org/10.1109/JPROC.2010.2043918, 2010.
Fal, J., Barylyak, A., Besaha, K., Bobitski, Y. V., Cholewa, M., Zawlik, I.,
Szmuc, K., Cebulski, J., and Żyła, G.: Experimental Investigation of
Electrical Conductivity and Permittivity of SC-TiO 2-EG Nanofluids,
Nanoscale Res. Lett., 11, 375, https://doi.org/10.1186/s11671-016-1590-7, 2016.
Federer, C., Turcotte, D., and Smith, C.: The organic fraction–bulk density
relationship and the expression of nutrient content in forest soils, Can. J.
For. Res., 23, 1026–1032, https://doi.org/10.1139/x93-131, 1993.
Fischer, E. M., Seneviratne, S. I., Vidale, P. L., Lüthi, D., and Schär,
C.: Soil Moisture–Atmosphere Interactions during the 2003 European Summer
Heat Wave, J. Climate, 20, 5081–5099,
https://doi.org/10.1175/JCLI4288.1, 2007.
Government of Canada: Soil Landscapes of Canada, Ottawa, available at: http://sis.agr.gc.ca/cansis/nsdb/slc/index.html (last access: 1 February 2013), 2011.
Guillod, B. P., Orlowsky, B., Miralles, D. G., Teuling, A. J., and Seneviratne,
S. I.: Reconciling spatial and temporal soil moisture effects on afternoon
rainfall, Nat. Commun., 6, 6443, https://doi.org/10.1038/ncomms7443,
2015.
Harmonized world soil database: v1.2, FAO SOILS PORTAL, available at: https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soil-database-v12/en/ (last access: 23 January 2020), 2020.
Hauser, M., Orth, R., and Seneviratne, S. I.: Role of soil moisture versus recent
climate change for the 2010 heat wave in western Russia, Geophys.
Res. Lett., 43, 2819–2826, https://doi.org/10.1002/2016GL068036, 2016.
Hengl, T., de Jesus, J. M., MacMillan, R. A., Batjes, N. H., Heuvelink, G. B. M.,
Ribeiro, E., Samuel-Rosa, A., Kempen, B., Leenaars, J. G. B., Walsh, M. G.,
and Gonzalez, M. R.: SoilGrids1km – Global Soil Information Based on Automated
Mapping, PLOS ONE, 9, e105992, https://doi.org/10.1371/journal.pone.0105992,
2014.
Hengl, T., de Jesus, J. M., Heuvelink, G. B. M., Gonzalez, M. R., Kilibarda, M.,
Blagotić, A., Shangguan, W., Wright, M. N., Geng, X.,
Bauer-Marschallinger, B., Guevara, M. A., Vargas, R., MacMillan, R. A.,
Batjes, N. H., Leenaars, J. G. B., Ribeiro, E., Wheeler, I., Mantel, S.,
and Kempen, B.: SoilGrids250m: Global gridded soil information based on machine
learning, PLOS ONE, 12, e0169748,
https://doi.org/10.1371/journal.pone.0169748, 2017.
Hirschi, M., Seneviratne, S. I., Alexandrov, V., Boberg, F., Boroneant, C.,
Christensen, O. B., Formayer, H., Orlowsky, B., and Stepanek, P.: Observational
evidence for soil-moisture impact on hot extremes in southeastern Europe,
Nat. Geosci., 4, 17–21, https://doi.org/10.1038/ngeo1032, 2011.
Hirschi, M., Mueller, B., Dorigo, W., and Seneviratne, S. I.: Using remotely
sensed soil moisture for land–atmosphere coupling diagnostics: The role of
surface vs. root-zone soil moisture variability, Remote Sens.
Environ., 154, 246–252, https://doi.org/10.1016/j.rse.2014.08.030, 2014.
Hossain, M. F., Chen, W., and Zhang, Y.: Bulk density of mineral and organic
soils in the Canada's arctic and sub-arctic, Inform. Process.
Agric., 2, 183–190, https://doi.org/10.1016/j.inpa.2015.09.001, 2015.
Hugelius, G., Bockheim, J. G., Camill, P., Elberling, B., Grosse, G., Harden, J. W., Johnson, K., Jorgenson, T., Koven, C. D., Kuhry, P., Michaelson, G., Mishra, U., Palmtag, J., Ping, C.-L., O'Donnell, J., Schirrmeister, L., Schuur, E. A. G., Sheng, Y., Smith, L. C., Strauss, J., and Yu, Z.: A new data set for estimating organic carbon storage to 3 m depth in soils of the northern circumpolar permafrost region, Earth Syst. Sci. Data, 5, 393–402, https://doi.org/10.5194/essd-5-393-2013, 2013.
Ishida, T.: Dielectric-Relaxation Spectroscopy of Kaolinite,
Montmorillonite, Allophane, and Imogolite under Moist Conditions, Clay.
Clay Miner., 48, 75–84, https://doi.org/10.1346/CCMN.2000.0480110, 2000.
Jackson, T. J., Schmugge, T. J., and Wang, J. R.: Passive microwave sensing of soil
moisture under vegetation canopies, Water Resour. Res., 18, 1137–1142,
https://doi.org/10.1029/WR018i004p01137, 1982.
Jackson, T. J., O'Neill, P. E., and Swift, C. T.: Passive microwave observation of
diurnal surface soil moisture, IEEE T. Geosci. Remote, 35, 1210–1222, https://doi.org/10.1109/36.628788, 1997.
Jaeger, E. B. and Seneviratne, S. I.: Impact of soil moisture–atmosphere
coupling on European climate extremes and trends in a regional climate
model, Clim. Dynam., 36, 1919–1939, https://doi.org/10.1007/s00382-010-0780-8,
2011.
Jin, M., Zheng, X., Jiang, T., Li, X., Li, X.-J., and Zhao, K.: Evaluation and
Improvement of SMOS and SMAP Soil Moisture Products for Soils with High
Organic Matter over a Forested Area in Northeast China, Remote Sens., 9,
387, https://doi.org/10.3390/rs9040387, 2017.
Kelleners, T. J., Robinson, D. A., Shouse, P. J., Ayars, J. E., and Skaggs, T. H.:
Frequency Dependence of the Complex Permittivity and Its Impact on
Dielectric Sensor Calibration in Soils, Soil Sci. Soc. Am. J., 69, 67–76,
https://doi.org/10.2136/sssaj2005.0067a, 2005.
Kerr, Y. H., Waldteufel, P., Wigneron, J.-P., Delwart, S., Cabot, F., Boutin,
J., Escorihuela, M.-J., Font, J., Reul, N., Gruhier, C., Juglea, S. E.,
Drinkwater, M. R., Hahne, A., Martin-Neira, M., and Mecklenburg, S.: The SMOS
Mission: New Tool for Monitoring Key Elements of the Global Water Cycle,
Proc. IEEE, 98, 666–687,
https://doi.org/10.1109/JPROC.2010.2043032, 2010.
Kim, S.-B., Moghaddam, M., Tsang, L., Burgin, M., Xu, X., and Njoku, E. G.:
Models of L-Band Radar Backscattering Coefficients Over Global Terrain for
Soil Moisture Retrieval, IEEE T. Geosci. Remote,
52, 1381–1396, https://doi.org/10.1109/TGRS.2013.2250980, 2014.
Koster, R. D., Dirmeyer, P. A., Guo, Z., Bonan, G., Chan, E., Cox, P., Gordon,
C. T., Kanae, S., Kowalczyk, E., Lawrence, D., Liu, P., Lu, C.-H., Malyshev,
S., McAvaney, B., Mitchell, K., Mocko, D., Oki, T., Oleson, K., Pitman, A.,
Sud, Y. C., Taylor, C. M., Verseghy, D., Vasic, R., Xue, Y., and Yamada, T.:
Regions of Strong Coupling Between Soil Moisture and Precipitation, Science,
305, 1138–1140, https://doi.org/10.1126/science.1100217, 2004.
Koster, R. D., Guo, Z., Yang, R., Dirmeyer, P. A., Mitchell, K., and Puma, M. J.:
On the Nature of Soil Moisture in Land Surface Models, J. Climate,
22, 4322–4335, https://doi.org/10.1175/2009JCLI2832.1, 2009.
Liu, J., Zhao, S., Jiang, L., Chai, L., and Wu, F.: The influence of organic matter on soil dielectric constant at microwave frequencies (0.5–40 GHZ), IEEE International Geoscience and Remote Sensing Symposium – IGARSS, 21 July 2013, Melbourne, Australia, 1316, https://doi.org/10.1109/IGARSS.2013.6721080, 2013.
Manns, H. R. and Berg, A. A.: Importance of soil organic carbon on surface soil
water content variability among agricultural fields, J. Hydrol., 516,
297–303, https://doi.org/10.1016/j.jhydrol.2013.11.018, 2014.
McNairn, H., Jackson, T. J., Wiseman, G., Belair, S., Berg, A., Bullock, P.,
Colliander, A., Cosh, M. H., Kim, S.-B., Magagi, R., Moghaddam, M., Njoku,
E. G., Adams, J. R., Homayouni, S., Ojo, E., Rowlandson, T., Shang, J., Goita,
K., and Hosseini, M.: The Soil Moisture Active Passive Validation Experiment
2012 (SMAPVEX12): Prelaunch Calibration and Validation of the SMAP Soil
Moisture Algorithms, IEEE T. Geosci. Remote, 5,
2784–2801, https://doi.org/10.1109/TGRS.2014.2364913, 2015.
Miralles, D. G., Holmes, T. R. H., De Jeu, R. A. M., Gash, J. H., Meesters, A. G. C. A., and Dolman, A. J.: Global land-surface evaporation estimated from satellite-based observations, Hydrol. Earth Syst. Sci., 15, 453–469, https://doi.org/10.5194/hess-15-453-2011, 2011.
Mironov, V. L., Bobrov, P. P., and Fomin, S. V.: Multirelaxation Generalized
Refractive Mixing Dielectric Model of Moist Soils, IEEE Geosci. Remote
Sensing Lett., 10, 603–606, https://doi.org/10.1109/LGRS.2012.2215574, 2013.
Mueller, B. and Seneviratne, S. I.: Hot days induced by precipitation deficits
at the global scale, P. Natl. Acad. Sci., 109, 12398–12403,
https://doi.org/10.1073/pnas.1204330109, 2012.
Park, C.-H., Behrendt, A., LeDrew, E., and Wulfmeyer, V.: New Approach for
Calculating the Effective Dielectric Constant of the Moist Soil for
Microwaves, Remote Sens., 9, 732, https://doi.org/10.3390/rs9070732, 2017.
Park, C.-H., Montzka, C., Jagdhuber, T., Jonard, F., De Lannoy, G., Hong,
J., Jackson, T. J., and Wulfmeyer, V.: A Dielectric Mixing Model Accounting for
Soil Organic Matter, Vadose Zone J., 18, 190036,
https://doi.org/10.2136/vzj2019.04.0036, 2019.
Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., and Rossiter, D.: SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty, SOIL, 7, 217–240, https://doi.org/10.5194/soil-7-217-2021, 2021.
Pribyl, D. W.: A critical review of the conventional SOC to SOM conversion
factor, Geoderma, 156, 75–83,
https://doi.org/10.1016/j.geoderma.2010.02.003, 2010.
Ranney, R. W.: An Organic Carbon-Organic Matter Conversion Equation for
Pennsylvania Surface Soils, Soil Sci. Soc. Am. J., 33,
809–811, https://doi.org/10.2136/sssaj1969.03615995003300050049x, 1969.
Roth, C. H., Malicki, M. A., and Plagge, R.: Empirical evaluation of the
relationship between soil dielectric constant and volumetric water content
as the basis for calibrating soil moisture measurements by TDR, J.
Soil Sci., 43, 1–13, https://doi.org/10.1111/j.1365-2389.1992.tb00115.x,
1992.
Rowlandson, T. L., Berg, A. A., Bullock, P. R., Ojo, E. R., McNairn, H.,
Wiseman, G., and Cosh, M. H.: Evaluation of several calibration procedures for a
portable soil moisture sensor, J. Hydrol., 498, 335–344,
https://doi.org/10.1016/j.jhydrol.2013.05.021, 2013.
Santanello, J. A., Dirmeyer, P. A., Ferguson, C. R., Findell, K. L., Tawfik,
A. B., Berg, A., Ek, M., Gentine, P., Guillod, B. P., van Heerwaarden, C.,
Roundy, J., and Wulfmeyer, V.: Land–Atmosphere Interactions: The LoCo
Perspective, B. Am. Meteorol. Soc., 99, 1253–1272,
https://doi.org/10.1175/BAMS-D-17-0001.1, 2018.
Santanello Jr., J. A., Lawston, P., Kumar, S., and Dennis, E.: Understanding the
Impacts of Soil Moisture Initial Conditions on NWP in the Context of
Land–Atmosphere Coupling, J. Hydrometeorol., 20, 793–819,
https://doi.org/10.1175/JHM-D-18-0186.1, 2019.
Savin, I., Mironov, V., Muzalevskiy, K., Fomin, S., Karavayskiy, A., Ruzicka, Z., and Lukin, Y.: Dielectric database of organic Arctic soils (DDOAS), Earth Syst. Sci. Data, 12, 3481–3487, https://doi.org/10.5194/essd-12-3481-2020, 2020.
Saxton, K. E., Rawls, W. J., Romberger, J. S., and Papendick, R. I.: Estimating
Generalized Soil-water Characteristics from Texture, Soil Sci. Soc.
Am. J., 50, 1031–1036,
https://doi.org/10.2136/sssaj1986.03615995005000040039x, 1986.
Schaap, M. G., Leij, F. J., and van Genuchten, M. Th.: Neural Network Analysis for
Hierarchical Prediction of Soil Hydraulic Properties, Soil Sci. Soc.
Am. J., 62, 847–855,
https://doi.org/10.2136/sssaj1998.03615995006200040001x, 1998.
Schaap, M. G., Leij, F. J., and van Genuchten, M. Th.: rosetta: a computer program
for estimating soil hydraulic parameters with hierarchical pedotransfer
functions, J. Hydrol., 251, 163–176,
https://doi.org/10.1016/S0022-1694(01)00466-8, 2001.
Schmugge, T.: Remote Sensing of Soil Moisture with Microwave Radiometers,
Transactions of the ASAE, 26, 0748–0753,
https://doi.org/10.13031/2013.34017, 1983.
Seneviratne, S. I., Lüthi, D., Litschi, M., and Schär, C.:
Land–atmosphere coupling and climate change in Europe, Nature, 443,
205–209, https://doi.org/10.1038/nature05095, 2006.
Seneviratne, S. I., Corti, T., Davin, E. L., Hirschi, M., Jaeger, E. B.,
Lehner, I., Orlowsky, B., and Teuling, A. J.: Investigating soil
moisture–climate interactions in a changing climate: A review,
Earth-Sci. Rev., 99, 125–161,
https://doi.org/10.1016/j.earscirev.2010.02.004, 2010.
Seneviratne, S. I., Wilhelm, M., Stanelle, T., van den Hurk, B., Hagemann,
S., Berg, A., Cheruy, F., Higgins, M. E., Meier, A., Brovkin, V., Claussen,
M., Ducharne, A., Dufresne, J.-L., Findell, K. L., Ghattas, J., Lawrence,
D. M., Malyshev, S., Rummukainen, M., and Smith, B.: Impact of soil
moisture-climate feedbacks on CMIP5 projections: First results from the
GLACE-CMIP5 experiment, Geophys. Res. Lett., 40, 5212–5217,
https://doi.org/10.1002/grl.50956, 2013.
Seo, E., Lee, M.-I., Jeong, J.-H., Koster, R. D., Schubert, S. D., Kim, H.-M.,
Kim, D., Kang, H.-S., Kim, H.-K., MacLachlan, C., and Scaife, A. A.: Impact of
soil moisture initialization on boreal summer subseasonal forecasts:
mid-latitude surface air temperature and heat wave events, Clim. Dynam., 52,
1695–1709, https://doi.org/10.1007/s00382-018-4221-4, 2019.
Seyfried, M. S. and Murdock, M. D.: Measurement of Soil Water Content with a
50-MHz Soil Dielectric Sensor, Soil Sci. Soc. Am. J., 68,
394–403, https://doi.org/10.2136/sssaj2004.3940, 2004.
Stafford, J. V.: Remote, non-contact and in-situ measurement of soil moisture
content: a review, J. Agric. Eng. Res., 41,
151–172, https://doi.org/10.1016/0021-8634(88)90175-8, 1988.
Stenberg, B., Viscarra Rossel, R. A., Mouazen, A. M., and Wetterlind, J.: Chapter
Five – Visible and Near Infrared Spectroscopy in Soil Science, in: Advances in Agronomy, edited by: Sparks,
D. L., Academic Press, 163–215,
https://doi.org/10.1016/S0065-2113(10)07005-7, 2010.
Taylor, C. M., de Jeu, R. A. M., Guichard, F., Harris, P. P., and Dorigo, W. A.:
Afternoon rain more likely over drier soils, Nature, 489, 423–426,
https://doi.org/10.1038/nature11377, 2012.
Topp, G. C., Davis, J. L., and Annan, A. P.: Electromagnetic determination of soil
water content: Measurements in coaxial transmission lines, Water Resour.
Res., 16, 574–582, https://doi.org/10.1029/WR016i003p00574, 1980.
Tsang, L. and Li, Q.: Microwave Remote Sensing Theory, in: Wiley Encyclopedia
of Electrical and Electronics Engineering, American Cancer Society,
https://doi.org/10.1002/047134608X.W3615, 1999.
Vereecken, H., Maes, J., Feyen, J., and Darius, P.: Estimating the soil moisture retention characteristic from texture, bulk density, and carbon content, Soil Sci., 148, 389–403, https://doi.org/10.1097/00010694198912000-00001, 1989.
Verry, E. S., Boelter, D. H., Päivänen, J., Nichols, D. S., Malterer, T., and Gafni, A.: Physical Properties of Organic Soils, in: Peatland Biogeochemistry and Watershed Hydrology at the Marcell Experimental Forest, CRC Press, Boca Raton, FL, 2011.
Waksman, S. A. and Stevens, K. R.: A critical study of the methods for determining the nature and abundance of soil organic matter, Soil Sci., 30, 97–116, 1930.
Wang, Q., Li, Y., and Wang, Y.: Optimizing the weight loss-on-ignition
methodology to quantify organic and carbonate carbon of sediments from
diverse sources, Environ. Monit. Assess., 174, 241–257,
https://doi.org/10.1007/s10661-010-1454-z, 2011.
Whan, K., Zscheischler, J., Orth, R., Shongwe, M., Rahimi, M., Asare, E. O.,
and Seneviratne, S. I.: Impact of soil moisture on extreme maximum temperatures
in Europe. Weather and Climate Extremes, The World Climate Research Program
Grand Challenge on Extremes – WCRP-ICTP Summer School on Attribution and
Prediction of Extreme Events, 9, 57–67,
https://doi.org/10.1016/j.wace.2015.05.001, 2015.
Wigneron, J.-P., Kerr, Y., Waldteufel, P., Saleh, K., Escorihuela, M.-J.,
Richaume, P., Ferrazzoli, P., de Rosnay, P., Gurney, R., Calvet, J.-C.,
Grant, J.P., Guglielmetti, M., Hornbuckle, B., Mätzler, C., Pellarin,
T., and Schwank, M.: L-band Microwave Emission of the Biosphere (L-MEB) Model:
Description and calibration against experimental data sets over crop fields,
Remote Sens. Environ., 107, 639–655,
https://doi.org/10.1016/j.rse.2006.10.014, 2007.
Wilheit, T. T.: Radiative Transfer in a Plane Stratified Dielectric, IEEE
T. Geosci. Electronics, 16, 138–143,
https://doi.org/10.1109/TGE.1978.294577, 1978.
Yuan, S. and Quiring, S. M.: Evaluation of soil moisture in CMIP5 simulations over the contiguous United States using in situ and satellite observations, Hydrol. Earth Syst. Sci., 21, 2203–2218, https://doi.org/10.5194/hess-21-2203-2017, 2017.
Zampieri, M., D'Andrea, F., Vautard, R., Ciais, P., Noblet-Ducoudré,
de N., and Yiou, P.: Hot European Summers and the Role of Soil Moisture in the
Propagation of Mediterranean Drought, J. Climate, 22, 4747–4758,
https://doi.org/10.1175/2009JCLI2568.1, 2009.
Zhang, F., Pu, Z., and Wang, C.: Impacts of Soil Moisture on the Numerical
Simulation of a Post-Landfall Storm, J. Meteorol. Res., 33, 206–218,
https://doi.org/10.1007/s13351-019-8002-8, 2019.
Zhu, D., Ciais, P., Krinner, G., Maignan, F., Jornet Puig, A., and Hugelius, G.:
Controls of soil organic matter on soil thermal dynamics in the northern
high latitudes, Nat. Commun., 10, 3172,
https://doi.org/10.1038/s41467-019-11103-1, 2019.
Zhuo, L., Dai, Q., Han, D., Chen, N., and Zhao, B.: Assessment of simulated soil moisture from WRF Noah, Noah-MP, and CLM land surface schemes for landslide hazard application, Hydrol. Earth Syst. Sci., 23, 4199–4218, https://doi.org/10.5194/hess-23-4199-2019, 2019.
Short summary
In this study, we proposed an inversion of the dielectric mixing model for a 50 Hz soil sensor for agricultural organic soil. This model can reflect the variability of soil organic matter (SOM) in wilting point and porosity, which play a critical role in improving the accuracy of SM estimation, using a dielectric-based soil sensor. The results of statistical analyses demonstrated a higher performance of the new model than the factory setting probe algorithm.
In this study, we proposed an inversion of the dielectric mixing model for a 50 Hz soil sensor...
