Articles | Volume 17, issue 20
https://doi.org/10.5194/amt-17-6025-2024
© Author(s) 2024. 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-17-6025-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Oct 2024
Research article |
| 17 Oct 2024
| 17 Oct 2024
HAMSTER: Hyperspectral Albedo Maps dataset with high Spatial and TEmporal Resolution
Meteorologisches Institut, Ludwig-Maximilians-Universität München, Munich, Germany
European Southern Observatory, Karl-Schwarzschild-Straße 2, 85748 Garching bei München, Germany
Luca Bugliaro
Institut für Physik der Atmosphäre, Deutsches Zentrum für Luft- und Raumfahrt, Oberpfaffenhofen, Germany
Felix Gödde
Meteorologisches Institut, Ludwig-Maximilians-Universität München, Munich, Germany
Claudia Emde
Institut für Physik der Atmosphäre, Deutsches Zentrum für Luft- und Raumfahrt, Oberpfaffenhofen, Germany
Meteorologisches Institut, Ludwig-Maximilians-Universität München, Munich, Germany
Ulrich Hamann
Federal Office of Meteorology and Climatology (MeteoSwiss), SEP, Locarno-Monti, Switzerland
Mihail Manev
Meteorologisches Institut, Ludwig-Maximilians-Universität München, Munich, Germany
Michael Fritz Sterzik
European Southern Observatory, Karl-Schwarzschild-Straße 2, 85748 Garching bei München, Germany
Cedric Wehrum
Meteorologisches Institut, Ludwig-Maximilians-Universität München, Munich, Germany
Related authors
No articles found.
Matteo Aricò, Dennis Piontek, Luca Bugliaro, Johanna Mayer, Richard Müller, Frank Kalinka, and Max Butter
EGUsphere, https://doi.org/10.5194/egusphere-2025-2985, https://doi.org/10.5194/egusphere-2025-2985, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
The goal is to assess the feasibility of an ice crystal icing detection algorithm based exclusively on remote sensing data. Active measurements are used to train and validate a newly developed random forest algorithm that is applied to passive satellite imagery to estimate the ice crystal icing conditions probability. 83 % of ice crystal icing conditions are correctly detected, showing potential for an operational implementation to mitigate its negative effects on the fleet.
Patrick Peter, Sigrun Matthes, Christine Frömming, Patrick Jöckel, Luca Bugliaro, Andreas Giez, Martina Krämer, and Volker Grewe
Atmos. Chem. Phys., 25, 5911–5934, https://doi.org/10.5194/acp-25-5911-2025, https://doi.org/10.5194/acp-25-5911-2025, 2025
Short summary
Short summary
Our study examines how well the global climate model EMAC (ECHAM/MESSy Atmospheric Chemistry) predicts contrail formation by analysing temperature and humidity – two key factors for contrail development and persistence. The model underestimates temperature, leading to an overprediction of contrail formation and larger ice-supersaturated regions. Adjusting the model improves temperature accuracy but adds uncertainties. Better predictions of contrail formation areas can help optimise flight tracks to reduce aviation's climate effect.
Ziming Wang, Luca Bugliaro, Klaus Gierens, Michaela I. Hegglin, Susanne Rohs, Andreas Petzold, Stefan Kaufmann, and Christiane Voigt
Atmos. Chem. Phys., 25, 2845–2861, https://doi.org/10.5194/acp-25-2845-2025, https://doi.org/10.5194/acp-25-2845-2025, 2025
Short summary
Short summary
Upper-tropospheric relative humidity bias in the ERA5 weather model is corrected by 10 % by an artificial neural network using aircraft in-service humidity data and thermodynamic and dynamical variables. The improved skills of the weather model will advance cirrus research, weather forecasts, and measures for contrail reduction.
Claudia Emde, Veronika Pörtge, Mihail Manev, and Bernhard Mayer
Atmos. Meas. Tech., 17, 6769–6789, https://doi.org/10.5194/amt-17-6769-2024, https://doi.org/10.5194/amt-17-6769-2024, 2024
Short summary
Short summary
We introduce an innovative method to retrieve the cloud fraction and optical thickness of liquid water clouds over the ocean based on polarimetry. This is well suited for satellite observations providing multi-angle polarization measurements. Cloud fraction and cloud optical thickness can be derived from measurements at two viewing angles: one within the cloudbow and one in the sun glint region.
Johanna Mayer, Bernhard Mayer, Luca Bugliaro, Ralf Meerkötter, and Christiane Voigt
Atmos. Meas. Tech., 17, 5161–5185, https://doi.org/10.5194/amt-17-5161-2024, https://doi.org/10.5194/amt-17-5161-2024, 2024
Short summary
Short summary
This study uses radiative transfer calculations to characterize the relation of two satellite channel combinations (namely infrared window brightness temperature differences – BTDs – of SEVIRI) to the thermodynamic cloud phase. A sensitivity analysis reveals the complex interplay of cloud parameters and their contribution to the observed phase dependence of BTDs. This knowledge helps to design optimal cloud-phase retrievals and to understand their potential and limitations.
Johanna Mayer, Luca Bugliaro, Bernhard Mayer, Dennis Piontek, and Christiane Voigt
Atmos. Meas. Tech., 17, 4015–4039, https://doi.org/10.5194/amt-17-4015-2024, https://doi.org/10.5194/amt-17-4015-2024, 2024
Short summary
Short summary
ProPS (PRObabilistic cloud top Phase retrieval for SEVIRI) is a method to detect clouds and their thermodynamic phase with a geostationary satellite, distinguishing between clear sky and ice, mixed-phase, supercooled and warm liquid clouds. It uses a Bayesian approach based on the lidar–radar product DARDAR. The method allows studying cloud phases, especially mixed-phase and supercooled clouds, rarely observed from geostationary satellites. This can be used for comparison with climate models.
Ziming Wang, Husi Letu, Huazhe Shang, and Luca Bugliaro
Atmos. Chem. Phys., 24, 7559–7574, https://doi.org/10.5194/acp-24-7559-2024, https://doi.org/10.5194/acp-24-7559-2024, 2024
Short summary
Short summary
The supercooled liquid fraction (SLF) in mixed-phase clouds is retrieved for the first time using passive geostationary satellite observations based on differences in liquid droplet and ice particle radiative properties. The retrieved results are comparable to global distributions observed by active instruments, and the feasibility of the retrieval method to analyze the observed trends of the SLF has been validated.
Richard Maier, Fabian Jakub, Claudia Emde, Mihail Manev, Aiko Voigt, and Bernhard Mayer
Geosci. Model Dev., 17, 3357–3383, https://doi.org/10.5194/gmd-17-3357-2024, https://doi.org/10.5194/gmd-17-3357-2024, 2024
Short summary
Short summary
Based on the TenStream solver, we present a new method to accelerate 3D radiative transfer towards the speed of currently used 1D solvers. Using a shallow-cumulus-cloud time series, we evaluate the performance of this new solver in terms of both speed and accuracy. Compared to a 3D benchmark simulation, we show that our new solver is able to determine much more accurate irradiances and heating rates than a 1D δ-Eddington solver, even when operated with a similar computational demand.
Ilias Fountoulakis, Alexandra Tsekeri, Stelios Kazadzis, Vassilis Amiridis, Angelos Nersesian, Maria Tsichla, Emmanouil Proestakis, Antonis Gkikas, Kyriakoula Papachristopoulou, Vasileios Barlakas, Claudia Emde, and Bernhard Mayer
Atmos. Chem. Phys., 24, 4915–4948, https://doi.org/10.5194/acp-24-4915-2024, https://doi.org/10.5194/acp-24-4915-2024, 2024
Short summary
Short summary
In our study we provide an assessment, through a sensitivity study, of the limitations of models to calculate the dust direct radiative effect (DRE) due to the underrepresentation of its size, refractive index (RI), and shape. Our results indicate the necessity of including more realistic sizes and RIs for dust particles in dust models, in order to derive better estimations of the dust direct radiative effects.
James Barry, Stefanie Meilinger, Klaus Pfeilsticker, Anna Herman-Czezuch, Nicola Kimiaie, Christopher Schirrmeister, Rone Yousif, Tina Buchmann, Johannes Grabenstein, Hartwig Deneke, Jonas Witthuhn, Claudia Emde, Felix Gödde, Bernhard Mayer, Leonhard Scheck, Marion Schroedter-Homscheidt, Philipp Hofbauer, and Matthias Struck
Atmos. Meas. Tech., 16, 4975–5007, https://doi.org/10.5194/amt-16-4975-2023, https://doi.org/10.5194/amt-16-4975-2023, 2023
Short summary
Short summary
Measured power data from solar photovoltaic (PV) systems contain information about the state of the atmosphere. In this work, power data from PV systems in the Allgäu region in Germany were used to determine the solar irradiance at each location, using state-of-the-art simulation and modelling. The results were validated using concurrent measurements of the incoming solar radiation in each case. If applied on a wider scale, this algorithm could help improve weather and climate models.
Ziming Wang, Luca Bugliaro, Tina Jurkat-Witschas, Romy Heller, Ulrike Burkhardt, Helmut Ziereis, Georgios Dekoutsidis, Martin Wirth, Silke Groß, Simon Kirschler, Stefan Kaufmann, and Christiane Voigt
Atmos. Chem. Phys., 23, 1941–1961, https://doi.org/10.5194/acp-23-1941-2023, https://doi.org/10.5194/acp-23-1941-2023, 2023
Short summary
Short summary
Differences in the microphysical properties of contrail cirrus and natural cirrus in a contrail outbreak situation during the ML-CIRRUS campaign over the North Atlantic flight corridor can be observed from in situ measurements. The cirrus radiative effect in the area of the outbreak, derived from satellite observation-based radiative transfer modeling, is warming in the early morning and cooling during the day.
Veronika Pörtge, Tobias Kölling, Anna Weber, Lea Volkmer, Claudia Emde, Tobias Zinner, Linda Forster, and Bernhard Mayer
Atmos. Meas. Tech., 16, 645–667, https://doi.org/10.5194/amt-16-645-2023, https://doi.org/10.5194/amt-16-645-2023, 2023
Short summary
Short summary
In this work, we analyze polarized cloudbow observations by the airborne camera system specMACS to retrieve the cloud droplet size distribution defined by the effective radius (reff) and the effective variance (veff). Two case studies of trade-wind cumulus clouds observed during the EUREC4A field campaign are presented. The results are combined into maps of reff and veff with a very high spatial resolution (100 m × 100 m) that allow new insights into cloud microphysics.
Huan Yu, Claudia Emde, Arve Kylling, Ben Veihelmann, Bernhard Mayer, Kerstin Stebel, and Michel Van Roozendael
Atmos. Meas. Tech., 15, 5743–5768, https://doi.org/10.5194/amt-15-5743-2022, https://doi.org/10.5194/amt-15-5743-2022, 2022
Short summary
Short summary
In this study, we have investigated the impact of 3D clouds on the tropospheric NO2 retrieval from UV–visible sensors. We applied standard NO2 retrieval methods including cloud corrections to synthetic data generated by the 3D radiative transfer model. A sensitivity study was done for synthetic data, and dependencies on various parameters were investigated. Possible mitigation strategies were investigated and compared based on 3D simulations and observed data.
Arve Kylling, Claudia Emde, Huan Yu, Michel van Roozendael, Kerstin Stebel, Ben Veihelmann, and Bernhard Mayer
Atmos. Meas. Tech., 15, 3481–3495, https://doi.org/10.5194/amt-15-3481-2022, https://doi.org/10.5194/amt-15-3481-2022, 2022
Short summary
Short summary
Atmospheric trace gases such as nitrogen dioxide (NO2) may be measured by satellite instruments sensitive to solar ultraviolet–visible radiation reflected from Earth and its atmosphere. For a single pixel, clouds in neighbouring pixels may affect the radiation and hence the retrieved trace gas amount. We found that for a solar zenith angle less than about 40° this cloud-related NO2 bias is typically below 10 %, while for larger solar zenith angles the NO2 bias is on the order of tens of percent.
Mireia Papke Chica, Valerian Hahn, Tiziana Braeuer, Elena de la Torre Castro, Florian Ewald, Mathias Gergely, Simon Kirschler, Luca Bugliaro Goggia, Stefanie Knobloch, Martina Kraemer, Johannes Lucke, Johanna Mayer, Raphael Maerkl, Manuel Moser, Laura Tomsche, Tina Jurkat-Witschas, Martin Zoeger, Christian von Savigny, and Christiane Voigt
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-255, https://doi.org/10.5194/acp-2022-255, 2022
Preprint withdrawn
Short summary
Short summary
The mixed-phase temperature regime in convective clouds challenges our understanding of microphysical and radiative cloud properties. We provide a rare and unique dataset of aircraft in situ measurements in a strong mid-latitude convective system. We find that mechanisms initiating ice nucleation and growth strongly depend on temperature, relative humidity, and vertical velocity and variate within the measured system, resulting in altitude dependent changes of the cloud liquid and ice fraction.
Luca Bugliaro, Dennis Piontek, Stephan Kox, Marius Schmidl, Bernhard Mayer, Richard Müller, Margarita Vázquez-Navarro, Daniel M. Peters, Roy G. Grainger, Josef Gasteiger, and Jayanta Kar
Nat. Hazards Earth Syst. Sci., 22, 1029–1054, https://doi.org/10.5194/nhess-22-1029-2022, https://doi.org/10.5194/nhess-22-1029-2022, 2022
Short summary
Short summary
The monitoring of ash dispersion in the atmosphere is an important task for satellite remote sensing since ash represents a threat to air traffic. We present an AI-based method that retrieves the spatial extension and properties of volcanic ash clouds with high temporal resolution during day and night by means of geostationary satellite measurements. This algorithm, trained on realistic observations simulated with a radiative transfer model, runs operationally at the German Weather Service.
Claudia Emde, Huan Yu, Arve Kylling, Michel van Roozendael, Kerstin Stebel, Ben Veihelmann, and Bernhard Mayer
Atmos. Meas. Tech., 15, 1587–1608, https://doi.org/10.5194/amt-15-1587-2022, https://doi.org/10.5194/amt-15-1587-2022, 2022
Short summary
Short summary
Retrievals of trace gas concentrations from satellite observations can be affected by clouds in the vicinity, either by shadowing or by scattering of radiation from clouds in the clear region. We used a Monte Carlo radiative transfer model to generate synthetic satellite observations, which we used to test retrieval algorithms and to quantify the error of retrieved NO2 vertical column density due to cloud scattering.
Matthieu Plu, Guillaume Bigeard, Bojan Sič, Emanuele Emili, Luca Bugliaro, Laaziz El Amraoui, Jonathan Guth, Beatrice Josse, Lucia Mona, and Dennis Piontek
Nat. Hazards Earth Syst. Sci., 21, 3731–3747, https://doi.org/10.5194/nhess-21-3731-2021, https://doi.org/10.5194/nhess-21-3731-2021, 2021
Short summary
Short summary
Volcanic eruptions that spread out ash over large areas, like Eyjafjallajökull in 2010, may have huge economic consequences due to flight cancellations. In this article, we demonstrate the benefits of source term improvement and of data assimilation for quantifying volcanic ash concentrations. The work, which was supported by the EUNADICS-AV project, is the first one, to our knowledge, that demonstrates the benefit of the assimilation of ground-based lidar data over Europe during an eruption.
Heike Konow, Florian Ewald, Geet George, Marek Jacob, Marcus Klingebiel, Tobias Kölling, Anna E. Luebke, Theresa Mieslinger, Veronika Pörtge, Jule Radtke, Michael Schäfer, Hauke Schulz, Raphaela Vogel, Martin Wirth, Sandrine Bony, Susanne Crewell, André Ehrlich, Linda Forster, Andreas Giez, Felix Gödde, Silke Groß, Manuel Gutleben, Martin Hagen, Lutz Hirsch, Friedhelm Jansen, Theresa Lang, Bernhard Mayer, Mario Mech, Marc Prange, Sabrina Schnitt, Jessica Vial, Andreas Walbröl, Manfred Wendisch, Kevin Wolf, Tobias Zinner, Martin Zöger, Felix Ament, and Bjorn Stevens
Earth Syst. Sci. Data, 13, 5545–5563, https://doi.org/10.5194/essd-13-5545-2021, https://doi.org/10.5194/essd-13-5545-2021, 2021
Short summary
Short summary
The German research aircraft HALO took part in the research campaign EUREC4A in January and February 2020. The focus area was the tropical Atlantic east of the island of Barbados. We describe the characteristics of the 15 research flights, provide auxiliary information, derive combined cloud mask products from all instruments that observe clouds on board the aircraft, and provide code examples that help new users of the data to get started.
Hugues Brenot, Nicolas Theys, Lieven Clarisse, Jeroen van Gent, Daniel R. Hurtmans, Sophie Vandenbussche, Nikolaos Papagiannopoulos, Lucia Mona, Timo Virtanen, Andreas Uppstu, Mikhail Sofiev, Luca Bugliaro, Margarita Vázquez-Navarro, Pascal Hedelt, Michelle Maree Parks, Sara Barsotti, Mauro Coltelli, William Moreland, Simona Scollo, Giuseppe Salerno, Delia Arnold-Arias, Marcus Hirtl, Tuomas Peltonen, Juhani Lahtinen, Klaus Sievers, Florian Lipok, Rolf Rüfenacht, Alexander Haefele, Maxime Hervo, Saskia Wagenaar, Wim Som de Cerff, Jos de Laat, Arnoud Apituley, Piet Stammes, Quentin Laffineur, Andy Delcloo, Robertson Lennart, Carl-Herbert Rokitansky, Arturo Vargas, Markus Kerschbaum, Christian Resch, Raimund Zopp, Matthieu Plu, Vincent-Henri Peuch, Michel Van Roozendael, and Gerhard Wotawa
Nat. Hazards Earth Syst. Sci., 21, 3367–3405, https://doi.org/10.5194/nhess-21-3367-2021, https://doi.org/10.5194/nhess-21-3367-2021, 2021
Short summary
Short summary
The purpose of the EUNADICS-AV (European Natural Airborne Disaster Information and Coordination System for Aviation) prototype early warning system (EWS) is to develop the combined use of harmonised data products from satellite, ground-based and in situ instruments to produce alerts of airborne hazards (volcanic, dust, smoke and radionuclide clouds), satisfying the requirement of aviation air traffic management (ATM) stakeholders (https://cordis.europa.eu/project/id/723986).
Marc Schwaerzel, Dominik Brunner, Fabian Jakub, Claudia Emde, Brigitte Buchmann, Alexis Berne, and Gerrit Kuhlmann
Atmos. Meas. Tech., 14, 6469–6482, https://doi.org/10.5194/amt-14-6469-2021, https://doi.org/10.5194/amt-14-6469-2021, 2021
Short summary
Short summary
NO2 maps from airborne imaging remote sensing often appear much smoother than one would expect from high-resolution model simulations of NO2 over cities, despite the small ground-pixel size of the sensors. Our case study over Zurich, using the newly implemented building module of the MYSTIC radiative transfer solver, shows that the 3D effect can explain part of the smearing and that building shadows cause a noticeable underestimation and noise in the measured NO2 columns.
Matthieu Plu, Barbara Scherllin-Pirscher, Delia Arnold Arias, Rocio Baro, Guillaume Bigeard, Luca Bugliaro, Ana Carvalho, Laaziz El Amraoui, Kurt Eschbacher, Marcus Hirtl, Christian Maurer, Marie D. Mulder, Dennis Piontek, Lennart Robertson, Carl-Herbert Rokitansky, Fritz Zobl, and Raimund Zopp
Nat. Hazards Earth Syst. Sci., 21, 2973–2992, https://doi.org/10.5194/nhess-21-2973-2021, https://doi.org/10.5194/nhess-21-2973-2021, 2021
Short summary
Short summary
Past volcanic eruptions that spread out ash over large areas, like Eyjafjallajökull in 2010, forced the cancellation of thousands of flights and had huge economic consequences.
In this article, an international team in the H2020 EU-funded EUNADICS-AV project has designed a probabilistic model approach to quantify ash concentrations. This approach is evaluated against measurements, and its potential use to mitigate the impact of future large-scale eruptions is discussed.
Daniel Zawada, Ghislain Franssens, Robert Loughman, Antti Mikkonen, Alexei Rozanov, Claudia Emde, Adam Bourassa, Seth Dueck, Hannakaisa Lindqvist, Didier Ramon, Vladimir Rozanov, Emmanuel Dekemper, Erkki Kyrölä, John P. Burrows, Didier Fussen, and Doug Degenstein
Atmos. Meas. Tech., 14, 3953–3972, https://doi.org/10.5194/amt-14-3953-2021, https://doi.org/10.5194/amt-14-3953-2021, 2021
Short summary
Short summary
Satellite measurements of atmospheric composition often rely on computer tools known as radiative transfer models to model the propagation of sunlight within the atmosphere. Here we have performed a detailed inter-comparison of seven different radiative transfer models in a variety of conditions. We have found that the models agree remarkably well, at a level better than previously reported. This result provides confidence in our understanding of atmospheric radiative transfer.
Ulrich Schumann, Ian Poll, Roger Teoh, Rainer Koelle, Enrico Spinielli, Jarlath Molloy, George S. Koudis, Robert Baumann, Luca Bugliaro, Marc Stettler, and Christiane Voigt
Atmos. Chem. Phys., 21, 7429–7450, https://doi.org/10.5194/acp-21-7429-2021, https://doi.org/10.5194/acp-21-7429-2021, 2021
Short summary
Short summary
The roughly 70 % reduction of air traffic during the COVID-19 pandemic from March–August 2020 compared to 2019 provides a test case for the relationship between air traffic density, contrails, and their radiative forcing of climate change. This paper investigates the induced traffic and contrail changes in a model study. Besides strong weather changes, the model results indicate aviation-induced cirrus and top-of-the-atmosphere irradiance changes, which can be tested with observations.
Harald Rybka, Ulrike Burkhardt, Martin Köhler, Ioanna Arka, Luca Bugliaro, Ulrich Görsdorf, Ákos Horváth, Catrin I. Meyer, Jens Reichardt, Axel Seifert, and Johan Strandgren
Atmos. Chem. Phys., 21, 4285–4318, https://doi.org/10.5194/acp-21-4285-2021, https://doi.org/10.5194/acp-21-4285-2021, 2021
Short summary
Short summary
Estimating the impact of convection on the upper-tropospheric water budget remains a problem for models employing resolutions of several kilometers or more. A sub-kilometer high-resolution model is used to study summertime convection. The results suggest mostly close agreement with ground- and satellite-based observational data while slightly overestimating total frozen water path and anvil lifetime. The simulations are well suited to supplying information for parameterization development.
Cited articles
Baldridge, A., Hook, S., Grove, C., and Rivera, G.: The ASTER spectral library version 2.0, Remote Sens. Environ., 113, 711–715, https://doi.org/10.1016/j.rse.2008.11.007, 2009. a, b
Braghiere, R. K., Wang, Y., Gagné-Landmann, A., Brodrick, P. G., Bloom, A. A., Norton, A. J., Ma, S., Levine, P., Longo, M., Deck, K., Gentine, P., Worden, J. R., Frankenberg, C., and Schneider, T.: The Importance of Hyperspectral Soil Albedo Information for Improving Earth System Model Projections, AGU Advances, 4, e2023AV000910, https://doi.org/10.1029/2023AV000910, 2023. a, b, c, d, e, f
Buchhorn, M., Lesiv, M., Tsendbazar, N.-E., Herold, M., Bertels, L., and Smets, B.: Copernicus Global Land Cover Layers – Collection 2, Remote Sens., 12, 1044, https://doi.org/10.3390/rs12061044, 2020. a
Carrer, D., Roujean, J.-L., and Meurey, C.: Comparing Operational MSG/SEVIRI Land Surface Albedo Products From Land SAF With Ground Measurements and MODIS, IEEE T. Geosci. Remote, 48, 1714–1728, https://doi.org/10.1109/TGRS.2009.2034530, 2010. a
Coddington, O., Schmidt, K. S., Pilewskie, P., Gore, W. J., Bergstrom, R. W., Román, M., Redemann, J., Russell, P. B., Liu, J., and Schaaf, C. C.: Aircraft measurements of spectral surface albedo and its consistency with ground-based and space-borne observations, J. Geophys. Res.-Atmos., 113, D17209, https://doi.org/10.1029/2008JD010089, 2008. a
Cox, C. and Munk, W.: Measurement of the roughness of the sea surface from photographs of the sun's glitter, J. Opt. Soc. Am., 44, 838–850, 1954a. a
Cox, C. and Munk, W.: Statistics of the sea surface derived from sun glitter, J. Mar. Res., 13, 198–227, 1954b. a
Emde, C., Buras-Schnell, R., Kylling, A., Mayer, B., Gasteiger, J., Hamann, U., Kylling, J., Richter, B., Pause, C., Dowling, T., and Bugliaro, L.: The libRadtran software package for radiative transfer calculations (version 2.0.1), Geosci. Model Dev., 9, 1647–1672, https://doi.org/10.5194/gmd-9-1647-2016, 2016. a
Emde, C., Buras-Schnell, R., Sterzik, M., and Bagnulo, S.: Influence of aerosols, clouds, and sunglint on polarization spectra of Earthshine, Astron. Astrophys., 605, A2, https://doi.org/10.1051/0004-6361/201629948, 2017. a
Geiger, B., Carrer, D., Franchisteguy, L., Roujean, J.-L., and Meurey, C.: Land Surface Albedo Derived on a Daily Basis From Meteosat Second Generation Observations, IEEE T. Geosci. Remote, 46, 3841–3856, https://doi.org/10.1109/TGRS.2008.2001798, 2008. a
Govaerts, Y. and Lattanzio, A.: Estimation of surface albedo increase during the eighties Sahel drought from Meteosat observations, Global Planet. Change, 64, 139–145, https://doi.org/10.1016/j.gloplacha.2008.04.004, 2008. a
Halko, N., Martinsson, P.-G., and Tropp, J. A.: Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions, arXiv [preprint], https://doi.org/10.48550/arXiv.0909.4061, 2009. a
He, T., Liang, S., Yu, Y., Wang, D., Gao, F., and Liu, Q.: Greenland surface albedo changes in July 1981–2012 from satellite observations, Environ. Res. Lett., 8, 044043, https://doi.org/10.1088/1748-9326/8/4/044043, 2013. a
He, T., Liang, S., and Song, D.-X.: Analysis of global land surface albedo climatology and spatial-temporal variation during 1981–2010 from multiple satellite products, J. Geophys. Res.-Atmos., 119, 10281–10298, https://doi.org/10.1002/2014JD021667, 2014. a, b
ICRAF-ISRIC: ICRAF-ISRIC Soil VNIR Spectral Library, World Agroforestry Centre [data set], https://doi.org/10.34725/DVN/MFHA9C, 2021. a
Juncu, D., Ceamanos, X., Trigo, I. F., Gomes, S., and Freitas, S. C.: Upgrade of LSA-SAF Meteosat Second Generation daily surface albedo (MDAL) retrieval algorithm incorporating aerosol correction and other improvements, Geosci. Instrum. Method. Data Syst., 11, 389–412, https://doi.org/10.5194/gi-11-389-2022, 2022. a
Kurucz, R. L.: Synthetic Infrared Spectra, in: Infrared Solar Physics: Proceedings of the 154th Symposium of the International Astronomical Union, Tucson, Arizona, USA, 2–6 March 1992, edited by: Rabin, D. M., Jefferies, J. T., and Lindsey, C., vol. 154, p. 523, Kluwer Academic Publishers, Dordrecht, https://doi.org/10.1007/978-94-011-1926-9_62, 1992. a
Lawrence, D. M., Fisher, R. A., Koven, C. D., Oleson, K. W., Swenson, S. C., Bonan, G., Collier, N., Ghimire, B., van Kampenhout, L., Kennedy, D., Kluzek, E., Lawrence, P. J., Li, F., Li, H., Lombardozzi, D., Riley, W. J., Sacks, W. J., Shi, M., Vertenstein, M., Wieder, W. R., Xu, C., Ali, A. A., Badger, A. M., Bisht, G., van den Broeke, M., Brunke, M. A., Burns, S. P., Buzan, J., Clark, M., Craig, A., Dahlin, K., Drewniak, B., Fisher, J. B., Flanner, M., Fox, A. M., Gentine, P., Hoffman, F., Keppel-Aleks, G., Knox, R., Kumar, S., Lenaerts, J., Leung, L. R., Lipscomb, W. H., Lu, Y., Pandey, A., Pelletier, J. D., Perket, J., Randerson, J. T., Ricciuto, D. M., Sanderson, B. M., Slater, A., Subin, Z. M., Tang, J., Thomas, R. Q., Val Martin, M., and Zeng, X.: The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty, J. Adv. Model. Earth Sy., 11, 4245–4287, https://doi.org/10.1029/2018MS001583, 2019. a
Li, S. and Yang, J.: Retrieving global single-layer liquid cloud thickness from OCO-2 hyperspectral oxygen A-band, Remote Sens. Environ., 311, 114272, https://doi.org/10.1016/j.rse.2024.114272, 2024. a
Liang, S., Wang, K., Zhang, X., and Wild, M.: Review on Estimation of Land Surface Radiation and Energy Budgets From Ground Measurement, Remote Sensing and Model Simulations, IEEE J. Sel. Top. Appl., 3, 225–240, https://doi.org/10.1109/JSTARS.2010.2048556, 2010. a
Liu, B., Guo, B., Zhuo, R., Dai, F., and Chi, H.: Prediction of the soil organic carbon in the LUCAS soil database based on spectral clustering, Soil Water Res., 18, 43–54, https://doi.org/10.17221/97/2022-SWR, 2023. a
Liu, N. F., Liu, Q., Wang, L. Z., Liang, S. L., Wen, J. G., Qu, Y., and Liu, S. H.: A statistics-based temporal filter algorithm to map spatiotemporally continuous shortwave albedo from MODIS data, Hydrol. Earth Syst. Sci., 17, 2121–2129, https://doi.org/10.5194/hess-17-2121-2013, 2013. a
Loarie, S. R., Lobell, D. B., Asner, G. P., Mu, Q., and Field, C. B.: Direct impacts on local climate of sugar-cane expansion in Brazil, Nat. Clim. Change, 1, 105–109, https://doi.org/10.1038/nclimate1067, 2011. a
Lyons, E. A., Jin, Y., and Randerson, J. T.: Changes in surface albedo after fire in boreal forest ecosystems of interior Alaska assessed using MODIS satellite observations, J. Geophys. Res.-Biogeo., 113, G02012, https://doi.org/10.1029/2007JG000606, 2008. a
Mayer, B. and Kylling, A.: Technical note: The libRadtran software package for radiative transfer calculations – description and examples of use, Atmos. Chem. Phys., 5, 1855–1877, https://doi.org/10.5194/acp-5-1855-2005, 2005. a
Meerdink, S. K., Hook, S. J., Roberts, D. A., and Abbott, E. A.: The ECOSTRESS spectral library version 1.0, Remote Sens. Environ., 230, 111196, https://doi.org/10.1016/j.rse.2019.05.015, 2019. a, b
Nakajima, T.: Effect of wind-generated waves on the transfer of solar radiation in the atmosphere-ocean system, J. Quant. Spectrosc. Ra., 29, 521–537, https://doi.org/10.1016/0022-4073(83)90129-2, 1983. a
Offerle, B., Jonsson, P., Eliasson, I., and Grimmond, C. S. B.: Urban Modification of the Surface Energy Balance in the West African Sahel: Ouagadougou, Burkina Faso, J. Climate, 18, 3983–3995, https://doi.org/10.1175/JCLI3520.1, 2005. a
Orgiazzi, A., Ballabio, C., Panagos, P., Jones, A., and Fernández-Ugalde, O.: LUCAS Soil, the largest expandable soil dataset for Europe: a review, Eur. J. Soil Sci., 69, 140–153, https://doi.org/10.1111/ejss.12499, 2018. a
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.: Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011. a, b
Qu, Y., Liu, Q., Liang, S., Wang, L., Liu, N., and Liu, S.: Direct-Estimation Algorithm for Mapping Daily Land-Surface Broadband Albedo From MODIS Data, IEEE T. Geosci. Remote, 52, 907–919, https://doi.org/10.1109/TGRS.2013.2245670, 2014. a
Roccetti: HAMSTER dataset, TIB [video], https://doi.org/10.5446/66248, 2024.
Roccetti, G., Bugliaro, L., Goedde, F., Emde, C., Hamann, U., Manev, M. G., Sterzik M. F., and Wehrum, C. P.: MODIS Black-Sky Albedo Climatology (2013–2022), LMU [data set], https://doi.org/10.57970/pt52a-nhm92, 2024a.
Roccetti, G., Bugliaro, L., Goedde, F., Emde, C., Hamann, U., Manev, M. G., Sterzik M. F., and Wehrum, C. P.: HAMSTER: Hyperspectral Albedo Maps dataset with high Spatial and TEmporal Resolution, LMU [data set], https://doi.org/10.57970/04zd8-7et52, 2024b.
Roccetti, G., Bugliaro, L., Goedde, F., Emde, C., Hamann, U., Manev, M. G., Sterzik M. F., and Wehrum, C. P.: HAMSTER: Hyperspectral Albedo Maps dataset with high Spatial and TEmporal Resolution, Zenodo [data set], https://doi.org/10.5281/zenodo.11459410, 2024c.
Salomonson, V., Barnes, W., Maymon, P., Montgomery, H., and Ostrow, H.: MODIS: advanced facility instrument for studies of the Earth as a system, IEEE T. Geosci. Remote, 27, 145–153, https://doi.org/10.1109/36.20292, 1989. a
Sánchez-Zapero, J., Martínez-Sánchez, E., Camacho, F., Wang, Z., Carrer, D., Schaaf, C., García-Haro, F. J., Nickeson, J., and Cosh, M.: Surface ALbedo VALidation (SALVAL) Platform: Towards CEOS LPV Validation Stage 4 – Application to Three Global Albedo Climate Data Records, Remote Sensing, 15, 1081, https://doi.org/10.3390/rs15041081, 2023. a
Schaaf, C. and Wang, Z.: MODIS/Terra+Aqua BRDF/Albedo Albedo Daily L3 Global 0.05Deg CMG V061, USGS [data set], https://doi.org/10.5067/MODIS/MCD43C3.061, 2021. a, b
Schaaf, C. B., Gao, F., Strahler, A. H., Lucht, W., Li, X., Tsang, T., Strugnell, N. C., Zhang, X., Jin, Y., Muller, J.-P., Lewis, P., Barnsley, M., Hobson, P., Disney, M., Roberts, G., Dunderdale, M., Doll, C., d'Entremont, R. P., Hu, B., Liang, S., Privette, J. L., and Roy, D.: First operational BRDF, albedo nadir reflectance products from MODIS, Remote Sens. Environ., 83, 135–148, https://doi.org/10.1016/S0034-4257(02)00091-3, 2002. a
Schmetz, J., Pili, P., Tjemkes, S., Just, D., Kerkmann, J., Rota, S., and Ratier, A.: AN INTRODUCTION TO METEOSAT SECOND GENERATION (MSG), B. Am. Meteorol. Soc., 83, 977–992, https://doi.org/10.1175/1520-0477(2002)083<0977:AITMSG>2.3.CO;2, 2002. a
Scott, D.: Multivariate Density Estimation: Theory, Practice, and Visualization, A Wiley-interscience publication, Wiley, ISBN 9780471547709, https://books.google.de/books?id=7crCUS_F2ocC (last access: 1 December 2023), 1992. a
Sellers, P. J., Meeson, B. W., Hall, F. G., Asrar, G., Murphy, R. E., Schiffer, R. A., Bretherton, F. P., Dickinson, R. E., Ellingson, R. G., Field, C. B., Huemmrich, K. F., Justice, C. O., Melack, J. M., Roulet, N. T., Schimel, D. S., and Try, P. D.: Remote sensing of the land surface for studies of global change: Models – algorithms – experiments, Remote Sens. Environ., 51, 3–26, https://doi.org/10.1016/0034-4257(94)00061-Q, 1995. a
Shao, C., Shuai, Y., Tuerhanjiang, L., Ma, X., Hu, W., Zhang, Q., Xu, A., Liu, T., Tian, Y., Wang, C., and Ma, Y.: Cross-Comparison of Global Surface Albedo Operational Products-MODIS, GLASS, and CGLS, Remote Sens., 13, 4869, https://doi.org/10.3390/rs13234869, 2021. a, b, c
Shepherd, K. D., Palm, C. A., Gachengo, C. N., and Vanlauwe, B.: Rapid Characterization of Organic Resource Quality for Soil and Livestock Management in Tropical Agroecosystems Using Near-Infrared Spectroscopy, Agron. J., 95, 1314–1322, https://doi.org/10.2134/agronj2003.1314, 2003. a
Sterzik, M. F., Bagnulo, S., and Palle, E.: Biosignatures as revealed by spectropolarimetry of Earthshine, Nature, 483, 64–66, https://doi.org/10.1038/nature10778, 2012. a
Sterzik, M. F., Bagnulo, S., Stam, D. M., Emde, C., and Manev, M.: Spectral and temporal variability of Earth observed in polarization, Astron. Astrophys., 622, A41, https://doi.org/10.1051/0004-6361/201834213, 2019. a
Tilstra, L. G., Tuinder, O. N. E., Wang, P., and Stammes, P.: Directionally dependent Lambertian-equivalent reflectivity (DLER) of the Earth's surface measured by the GOME-2 satellite instruments, Atmos. Meas. Tech., 14, 4219–4238, https://doi.org/10.5194/amt-14-4219-2021, 2021. a
Tilstra, L. G., de Graaf, M., Trees, V. J. H., Litvinov, P., Dubovik, O., and Stammes, P.: A directional surface reflectance climatology determined from TROPOMI observations, Atmos. Meas. Tech., 17, 2235–2256, https://doi.org/10.5194/amt-17-2235-2024, 2024. a
Zhang, X., Liang, S., Wang, K., Li, L., and Gui, S.: Analysis of Global Land Surface Shortwave Broadband Albedo From Multiple Data Sources, IEEE J. Sel. Top. Appl., 3, 296–305, https://doi.org/10.1109/JSTARS.2010.2049342, 2010. a, b
Zhu, X., Liang, S., Pan, Y., and Zhang, X.: Agricultural Irrigation Impacts on Land Surface Characteristics Detected From Satellite Data Products in Jilin Province, China, IEEE J. Sel. Top. Appl., 4, 721–729, https://doi.org/10.1109/JSTARS.2011.2106152, 2011. a
Short summary
The amount of sunlight reflected by the Earth’s surface (albedo) is vital for the Earth's radiative system. While satellite instruments offer detailed spatial and temporal albedo maps, they only cover seven wavelength bands. We generate albedo maps that fully span the visible and near-infrared range using a machine learning algorithm. These maps reveal how the reflectivity of different land surfaces varies throughout the year. Our dataset enhances the understanding of the Earth's energy balance.
The amount of sunlight reflected by the Earth’s surface (albedo) is vital for the Earth's...
