Articles | Volume 15, issue 5
https://doi.org/10.5194/gmd-15-1855-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-1855-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
07 Mar 2022
Model description paper |
| 07 Mar 2022
| 07 Mar 2022
Fast infrared radiative transfer calculations using graphics processing units: JURASSIC-GPU v2.0
Paul F. Baumeister
CORRESPONDING AUTHOR
Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany
Lars Hoffmann
Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany
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Geosci. Model Dev., 15, 2731–2762, https://doi.org/10.5194/gmd-15-2731-2022, https://doi.org/10.5194/gmd-15-2731-2022, 2022
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We describe the new version (2.2) of the Lagrangian transport model MPTRAC, which has been ported for application on GPUs. The model was verified by comparing kinematic trajectories and synthetic tracer simulations for the free troposphere and stratosphere from GPUs and CPUs. Benchmarking showed a speed-up of a factor of 16 of GPU-enabled simulations compared to CPU-only runs, indicating the great potential of applying GPUs for Lagrangian transport simulations on upcoming HPC systems.
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In this study, we performed an assessment of truncation errors and computational efficiency of trajectory calculations using six popular numerical integration schemes of the Runge–Kutta family. More than 5000 transport simulations for different seasons and regions of the free troposphere and stratosphere were conducted, driven by the latest version of ECMWF operational analyses and forecasts. The study provides guidelines to achieve the most accurate and efficient trajectory calculations.
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Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-214, https://doi.org/10.5194/gmd-2023-214, 2023
Preprint under review for GMD
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Lagrangian transport models simulate the transport of air masses in the atmosphere. For example, one model (CLaMS), is well suited for calculating transport, as it uses a special coordinate system and special vertical wind. However, it only runs inefficiently on modern supercomputers. Hence, we have implemented the benefits of CLaMS into a new model (MPTRAC), which is already highly efficient on modern supercomputers. Finally, in extensive tests, we showed that CLaMS and MPTRAC agree very well.
Mingzhao Liu, Lars Hoffmann, Sabine Griessbach, Zhongyin Cai, Yi Heng, and Xue Wu
Geosci. Model Dev., 16, 5197–5217, https://doi.org/10.5194/gmd-16-5197-2023, https://doi.org/10.5194/gmd-16-5197-2023, 2023
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We introduce new and revised chemistry and physics modules in the Massive-Parallel Trajectory Calculations (MPTRAC) Lagrangian transport model aiming to improve the representation of volcanic SO2 transport and depletion. We test these modules in a case study of the Ambae eruption in July 2018 in which the SO2 plume underwent wet removal and convection. The lifetime of SO2 shows highly variable and complex dependencies on the atmospheric conditions at different release heights.
Abhiraj Bishnoi, Olaf Stein, Catrin I. Meyer, René Redler, Norbert Eicker, Helmuth Haak, Lars Hoffmann, Daniel Klocke, Luis Kornblueh, and Estela Suarez
EGUsphere, https://doi.org/10.5194/egusphere-2023-1476, https://doi.org/10.5194/egusphere-2023-1476, 2023
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We enabled the weather and climate model ICON to run in a high-resolution coupled atmosphere-ocean setup on the JUWELS supercomputer, where the ocean and the model I/O runs on the CPU Cluster, while the atmosphere is running simultaneously on GPUs. Compared to a simulation performed on CPUs only, our approach reduces energy consumption by 59 % with comparable runtimes. The experiments serve as preparation for efficient computing of kilometer-scale climate models on future supercomputing systems.
Lars Hoffmann, Paul Konopka, Jan Clemens, and Bärbel Vogel
Atmos. Chem. Phys., 23, 7589–7609, https://doi.org/10.5194/acp-23-7589-2023, https://doi.org/10.5194/acp-23-7589-2023, 2023
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Atmospheric convection plays a key role in tracer transport in the troposphere. Global meteorological forecasts and reanalyses typically have a coarse spatiotemporal resolution that does not adequately resolve the dynamics, transport, and mixing of air associated with storm systems or deep convection. We discuss the application of the extreme convection parameterization in a Lagrangian transport model to improve simulations of tracer transport from the boundary layer into the free troposphere.
Bärbel Vogel, Michael Volk, Johannes Wintel, Valentin Lauther, Jan Clemens, Jens-Uwe Grooß, Gebhard Günther, Lars Hoffmann, Johannes C. Laube, Rolf Müller, Felix Ploeger, and Fred Stroh
EGUsphere, https://doi.org/10.5194/egusphere-2023-1026, https://doi.org/10.5194/egusphere-2023-1026, 2023
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Atmospheric concentrations of the greenhouse gas carbon dioxide have increased substantially because of human activities. However, their sources in South Asia are poorly quantified. Here, we present high temporal and vertical resolution aircraft measurements up to 20 km during the Asian summer monsoon where rapid upward transport of surface pollutants to greater altitudes occurs. Using model simulations, we successfully reconstruct observed CO2 profiles and evaluate meteorological reanalysis.
Jan Clemens, Bärbel Vogel, Lars Hoffmann, Sabine Griessbach, Nicole Thomas, Survana Fadnavis, Rolf Müller, Thomas Peter, and Felix Ploeger
EGUsphere, https://doi.org/10.5194/egusphere-2022-1462, https://doi.org/10.5194/egusphere-2022-1462, 2023
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The source regions of the Asian tropopause aerosol layer are under debate. We use balloon-borne measurements of the layer above Nainital (India) in August 2016 and atmospheric transport models, to find the ATALs source regions. Most air originate from the Tibetan plateau. However, the measured ATAL was stronger when more air originated from the Indo-Gangetic plain and weaker when more air originated from the Pacific. Hence, the results indicate important anthropogenic contributions to the ATAL.
Reimar Bauer, Jens-Uwe Grooß, Jörn Ungermann, May Bär, Markus Geldenhuys, and Lars Hoffmann
Geosci. Model Dev., 15, 8983–8997, https://doi.org/10.5194/gmd-15-8983-2022, https://doi.org/10.5194/gmd-15-8983-2022, 2022
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The Mission Support System (MSS) is an open source software package that has been used for planning flight tracks of scientific aircraft in multiple measurement campaigns during the last decade. Here, we describe the MSS software and its use during the SouthTRAC measurement campaign in 2019. As an example for how the MSS software is used in conjunction with many datasets, we describe the planning of a single flight probing orographic gravity waves propagating up into the lower mesosphere.
Paul Konopka, Mengchu Tao, Marc von Hobe, Lars Hoffmann, Corinna Kloss, Fabrizio Ravegnani, C. Michael Volk, Valentin Lauther, Andreas Zahn, Peter Hoor, and Felix Ploeger
Geosci. Model Dev., 15, 7471–7487, https://doi.org/10.5194/gmd-15-7471-2022, https://doi.org/10.5194/gmd-15-7471-2022, 2022
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Pure trajectory-based transport models driven by meteorology derived from reanalysis products (ERA5) take into account only the resolved, advective part of transport. That means neither mixing processes nor unresolved subgrid-scale advective processes like convection are included. The Chemical Lagrangian Model of the Stratosphere (CLaMS) includes these processes. We show that isentropic mixing dominates unresolved transport. The second most important transport process is unresolved convection.
Zhongyin Cai, Sabine Griessbach, and Lars Hoffmann
Atmos. Chem. Phys., 22, 6787–6809, https://doi.org/10.5194/acp-22-6787-2022, https://doi.org/10.5194/acp-22-6787-2022, 2022
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Using AIRS and TROPOMI sulfur dioxide retrievals and the Lagrangian transport model MPTRAC, we present an improved reconstruction of injection parameters of the 2019 Raikoke eruption. Reconstructions agree well between using AIRS nighttime and TROPOMI daytime retrievals, showing the potential of our approach to create a long-term volcanic sulfur dioxide inventory from nearly 20 years of AIRS retrievals.
Ling Zou, Sabine Griessbach, Lars Hoffmann, and Reinhold Spang
Atmos. Chem. Phys., 22, 6677–6702, https://doi.org/10.5194/acp-22-6677-2022, https://doi.org/10.5194/acp-22-6677-2022, 2022
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Ice clouds in the stratosphere (SICs) greatly affect the water vapor balance and radiation budget in the upper troposphere and lower stratosphere (UTLS). We quantified the global SICs and analyzed their relationships with tropopause temperature, double tropopauses, UTLS clouds, gravity waves, and stratospheric aerosols. The correlations between SICs and all abovementioned processes indicate that the occurrence of and variability in SICs are spatiotemporally dependent on different processes.
Lars Hoffmann, Paul F. Baumeister, Zhongyin Cai, Jan Clemens, Sabine Griessbach, Gebhard Günther, Yi Heng, Mingzhao Liu, Kaveh Haghighi Mood, Olaf Stein, Nicole Thomas, Bärbel Vogel, Xue Wu, and Ling Zou
Geosci. Model Dev., 15, 2731–2762, https://doi.org/10.5194/gmd-15-2731-2022, https://doi.org/10.5194/gmd-15-2731-2022, 2022
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We describe the new version (2.2) of the Lagrangian transport model MPTRAC, which has been ported for application on GPUs. The model was verified by comparing kinematic trajectories and synthetic tracer simulations for the free troposphere and stratosphere from GPUs and CPUs. Benchmarking showed a speed-up of a factor of 16 of GPU-enabled simulations compared to CPU-only runs, indicating the great potential of applying GPUs for Lagrangian transport simulations on upcoming HPC systems.
Lars Hoffmann and Reinhold Spang
Atmos. Chem. Phys., 22, 4019–4046, https://doi.org/10.5194/acp-22-4019-2022, https://doi.org/10.5194/acp-22-4019-2022, 2022
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We present an intercomparison of 2009–2018 lapse rate tropopause characteristics as derived from ECMWF's ERA5 and ERA-Interim reanalyses. Large-scale features are similar, but ERA5 shows notably larger variability, which we mainly attribute to UTLS temperature fluctuations due to gravity waves being better resolved by ECMWF's IFS forecast model. Following evaluation with radiosondes and GPS data, we conclude ERA5 will be a more suitable asset for tropopause-related studies in future work.
Corwin J. Wright, Neil P. Hindley, M. Joan Alexander, Laura A. Holt, and Lars Hoffmann
Atmos. Meas. Tech., 14, 5873–5886, https://doi.org/10.5194/amt-14-5873-2021, https://doi.org/10.5194/amt-14-5873-2021, 2021
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Measuring atmospheric gravity waves in low vertical-resolution data is technically challenging, especially when the waves are significantly longer in the vertical than in the length of the measurement domain. We introduce and demonstrate a modification to the existing Stockwell transform methods of characterising these waves that address these problems, with no apparent reduction in the other capabilities of the technique.
Ling Zou, Lars Hoffmann, Sabine Griessbach, Reinhold Spang, and Lunche Wang
Atmos. Chem. Phys., 21, 10457–10475, https://doi.org/10.5194/acp-21-10457-2021, https://doi.org/10.5194/acp-21-10457-2021, 2021
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Ice clouds in the lowermost stratosphere (SICs) have important impacts on the radiation budget and climate change. We quantified the occurrence of SICs over North America and analysed its relations with convective systems and gravity waves to investigate potential formation mechanisms of SICs. Deep convection is proved to be the primary factor linked to the occurrence of SICs over North America.
Michael Weimer, Jennifer Buchmüller, Lars Hoffmann, Ole Kirner, Beiping Luo, Roland Ruhnke, Michael Steiner, Ines Tritscher, and Peter Braesicke
Atmos. Chem. Phys., 21, 9515–9543, https://doi.org/10.5194/acp-21-9515-2021, https://doi.org/10.5194/acp-21-9515-2021, 2021
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We show that we are able to directly simulate polar stratospheric clouds formed locally in a mountain wave and represent their effect on the ozone chemistry with the global atmospheric chemistry model ICON-ART. Thus, we show the first simulations that close the gap between directly resolved mountain-wave-induced polar stratospheric clouds and their representation at coarse global resolutions.
Neil P. Hindley, Corwin J. Wright, Alan M. Gadian, Lars Hoffmann, John K. Hughes, David R. Jackson, John C. King, Nicholas J. Mitchell, Tracy Moffat-Griffin, Andrew C. Moss, Simon B. Vosper, and Andrew N. Ross
Atmos. Chem. Phys., 21, 7695–7722, https://doi.org/10.5194/acp-21-7695-2021, https://doi.org/10.5194/acp-21-7695-2021, 2021
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One limitation of numerical atmospheric models is spatial resolution. For atmospheric gravity waves (GWs) generated over small mountainous islands, the driving effect of these waves on atmospheric circulations can be underestimated. Here we use a specialised high-resolution model over South Georgia island to compare simulated stratospheric GWs to colocated 3-D satellite observations. We find reasonable model agreement with observations, with some GW amplitudes much larger than expected.
Andrew Orr, J. Scott Hosking, Aymeric Delon, Lars Hoffmann, Reinhold Spang, Tracy Moffat-Griffin, James Keeble, Nathan Luke Abraham, and Peter Braesicke
Atmos. Chem. Phys., 20, 12483–12497, https://doi.org/10.5194/acp-20-12483-2020, https://doi.org/10.5194/acp-20-12483-2020, 2020
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Polar stratospheric clouds (PSCs) are clouds found in the Antarctic winter stratosphere and are implicated in the formation of the ozone hole. These clouds can sometimes be formed or enhanced by mountain waves, formed as air passes over hills or mountains. However, this important mechanism is missing in coarse-resolution climate models, limiting our ability to simulate ozone. This study examines an attempt to include the effects of mountain waves and their impact on PSCs and ozone.
Isabell Krisch, Manfred Ern, Lars Hoffmann, Peter Preusse, Cornelia Strube, Jörn Ungermann, Wolfgang Woiwode, and Martin Riese
Atmos. Chem. Phys., 20, 11469–11490, https://doi.org/10.5194/acp-20-11469-2020, https://doi.org/10.5194/acp-20-11469-2020, 2020
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In 2016, a scientific research flight above Scandinavia acquired various atmospheric data (temperature, gas composition, etc.). Through advanced 3-D reconstruction methods, a superposition of multiple gravity waves was identified. An in-depth analysis enabled the characterisation of these waves as well as the identification of their sources. This work will enable a better understanding of atmosphere dynamics and could lead to improved climate projections.
Ling Zou, Sabine Griessbach, Lars Hoffmann, Bing Gong, and Lunche Wang
Atmos. Chem. Phys., 20, 9939–9959, https://doi.org/10.5194/acp-20-9939-2020, https://doi.org/10.5194/acp-20-9939-2020, 2020
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Cirrus clouds appearing in the upper troposphere and lower stratosphere have important impacts on the radiation budget and climate change. We revisited global stratospheric cirrus clouds with CALIPSO and for the first time with MIPAS satellite observations. Stratospheric cirrus clouds related to deep convection are frequently detected in the tropics. At middle latitudes, MIPAS detects more than twice as many stratospheric cirrus clouds due to higher detection sensitivity.
Rocco Sedona, Lars Hoffmann, Reinhold Spang, Gabriele Cavallaro, Sabine Griessbach, Michael Höpfner, Matthias Book, and Morris Riedel
Atmos. Meas. Tech., 13, 3661–3682, https://doi.org/10.5194/amt-13-3661-2020, https://doi.org/10.5194/amt-13-3661-2020, 2020
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Polar stratospheric clouds (PSCs) play a key role in polar ozone depletion in the stratosphere. In this paper, we explore the potential of applying machine learning (ML) methods to classify PSC observations of infrared spectra to classify PSC types. ML methods have proved to reach results in line with those obtained using well-established approaches. Among the considered ML methods, random forest (RF) seems to be the most promising one, being able to produce explainable classification results.
Sabine Griessbach, Lars Hoffmann, Reinhold Spang, Peggy Achtert, Marc von Hobe, Nina Mateshvili, Rolf Müller, Martin Riese, Christian Rolf, Patric Seifert, and Jean-Paul Vernier
Atmos. Meas. Tech., 13, 1243–1271, https://doi.org/10.5194/amt-13-1243-2020, https://doi.org/10.5194/amt-13-1243-2020, 2020
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In this paper we study the cloud top height derived from MIPAS measurements. Previous studies showed contradictory results with respect to MIPAS, both underestimating and overestimating cloud top height. We used simulations and found that overestimation and/or underestimation depend on cloud extinction. To support our findings we compared MIPAS cloud top heights of volcanic sulfate aerosol with measurements from CALIOP, ground-based lidar, and ground-based twilight measurements.
Neil P. Hindley, Corwin J. Wright, Nathan D. Smith, Lars Hoffmann, Laura A. Holt, M. Joan Alexander, Tracy Moffat-Griffin, and Nicholas J. Mitchell
Atmos. Chem. Phys., 19, 15377–15414, https://doi.org/10.5194/acp-19-15377-2019, https://doi.org/10.5194/acp-19-15377-2019, 2019
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In this study, a 3–D Stockwell transform is applied to AIRS–Aqua satellite observations in the first extended 3–D study of stratospheric gravity waves over the Southern Ocean during winter. A dynamic environment is revealed that contains some of the most intense gravity wave sources on Earth. A particularly striking result is a large–scale meridional convergence of gravity wave momentum flux towards latitudes near 60 °S, something which is not normally considered in model parameterisations.
Lars Hoffmann, Gebhard Günther, Dan Li, Olaf Stein, Xue Wu, Sabine Griessbach, Yi Heng, Paul Konopka, Rolf Müller, Bärbel Vogel, and Jonathon S. Wright
Atmos. Chem. Phys., 19, 3097–3124, https://doi.org/10.5194/acp-19-3097-2019, https://doi.org/10.5194/acp-19-3097-2019, 2019
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ECMWF's new ERA5 reanalysis provides higher spatiotemporal resolution, yielding an improved representation of meso- and synoptic-scale features of the atmosphere. We assessed the impact of this challenging new data set on Lagrangian trajectory calculations for the free troposphere and stratosphere. Key findings are considerable transport deviations between the ERA5 and ERA-Interim simulations as well as significantly improved conservation of potential temperature in the stratosphere for ERA5.
Xue Wu, Sabine Griessbach, and Lars Hoffmann
Atmos. Chem. Phys., 18, 15859–15877, https://doi.org/10.5194/acp-18-15859-2018, https://doi.org/10.5194/acp-18-15859-2018, 2018
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Volcanic aerosol is an important source of sulfur for Antarctica, where local sources of sulfur are rare. Midlatitude and high-latitude volcanism can directly influence the aerosol budget of the polar stratosphere, but tropical volcanic eruptions can also enhance polar aerosols by transport. Our study investigates pathway and transport processes of volcanic aerosol from the tropics to the lower stratosphere over Antarctica by combining Lagrangian transport simulation and satellite observations.
Reinhold Spang, Lars Hoffmann, Rolf Müller, Jens-Uwe Grooß, Ines Tritscher, Michael Höpfner, Michael Pitts, Andrew Orr, and Martin Riese
Atmos. Chem. Phys., 18, 5089–5113, https://doi.org/10.5194/acp-18-5089-2018, https://doi.org/10.5194/acp-18-5089-2018, 2018
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This paper represents an unprecedented pole-covering day- and nighttime climatology of the polar stratospheric clouds (PSCs) based on satellite measurements, their spatial distribution, and composition of different particle types. The climatology has a high potential for the validation and improvement of PSC schemes in chemical transport and chemistry–climate models, which is important for a better prediction of future polar ozone loss in a changing climate.
Thomas Rößler, Olaf Stein, Yi Heng, Paul Baumeister, and Lars Hoffmann
Geosci. Model Dev., 11, 575–592, https://doi.org/10.5194/gmd-11-575-2018, https://doi.org/10.5194/gmd-11-575-2018, 2018
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In this study, we performed an assessment of truncation errors and computational efficiency of trajectory calculations using six popular numerical integration schemes of the Runge–Kutta family. More than 5000 transport simulations for different seasons and regions of the free troposphere and stratosphere were conducted, driven by the latest version of ECMWF operational analyses and forecasts. The study provides guidelines to achieve the most accurate and efficient trajectory calculations.
Catrin I. Meyer, Manfred Ern, Lars Hoffmann, Quang Thai Trinh, and M. Joan Alexander
Atmos. Meas. Tech., 11, 215–232, https://doi.org/10.5194/amt-11-215-2018, https://doi.org/10.5194/amt-11-215-2018, 2018
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We investigate stratospheric gravity wave observations by the Atmospheric InfraRed Sounder (AIRS) and the High Resolution Dynamics Limb Sounder (HIRDLS). Waves seen by AIRS contribute significantly to momentum flux, which indicates a calculated momentum flux factor. AIRS and HIRDLS agree well in the phase structure of the wave events and also in the seasonal and latitudinal patterns of gravity wave activity and can be used complementary to each other.
Xue Wu, Sabine Griessbach, and Lars Hoffmann
Atmos. Chem. Phys., 17, 13439–13455, https://doi.org/10.5194/acp-17-13439-2017, https://doi.org/10.5194/acp-17-13439-2017, 2017
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This study is focused on the Sarychev eruption in 2009. Based on Lagrangian model simulations and satellite data, the equatorward transport of the plume and aerosol from the Sarychev eruption is confirmed, and the transport is facilitated by the Asian summer monsoon anticyclonic circulations. The aerosol transported to the tropics remained for months and dispersed upward, which could make the Sarychev eruption have a similar global climate impact as a tropical volcanic eruption.
Corwin J. Wright, Neil P. Hindley, Lars Hoffmann, M. Joan Alexander, and Nicholas J. Mitchell
Atmos. Chem. Phys., 17, 8553–8575, https://doi.org/10.5194/acp-17-8553-2017, https://doi.org/10.5194/acp-17-8553-2017, 2017
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We introduce a novel 3-D method of measuring atmospheric gravity waves, based around a 3-D Stockwell transform. Our method lets us measure new properties, including wave intrinsic frequencies and phase and group velocities. We apply it to data from the AIRS satellite instrument over the Southern Andes for two consecutive winters. Our results show clear evidence that the waves measured are primarily orographic in origin, and that their group velocity vectors are focused into the polar night jet.
Lars Hoffmann, Albert Hertzog, Thomas Rößler, Olaf Stein, and Xue Wu
Atmos. Chem. Phys., 17, 8045–8061, https://doi.org/10.5194/acp-17-8045-2017, https://doi.org/10.5194/acp-17-8045-2017, 2017
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We present an intercomparison of temperatures and horizontal winds of five meteorological data sets (ECMWF operational analysis, ERA-Interim, MERRA, MERRA-2, and NCEP/NCAR) in the Antarctic lower stratosphere. The assessment is based on 19 superpressure balloon flights during the Concordiasi field campaign in September 2010 to January 2011. The balloon data are used to successfully validate trajectory calculations with the new Lagrangian particle dispersion model MPTRAC.
Lars Hoffmann, Reinhold Spang, Andrew Orr, M. Joan Alexander, Laura A. Holt, and Olaf Stein
Atmos. Chem. Phys., 17, 2901–2920, https://doi.org/10.5194/acp-17-2901-2017, https://doi.org/10.5194/acp-17-2901-2017, 2017
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We introduce a 10-year record (2003–2012) of AIRS/Aqua observations of gravity waves in the polar lower stratosphere. The data set was optimized to study the impact of gravity waves on the formation of polar stratospheric clouds (PSCs). We discuss the temporal and spatial patterns of gravity wave activity, validate explicitly resolved small-scale temperature fluctuations in the ECMWF data, and present a survey of gravity-wave-induced PSC formation events using joint AIRS and MIPAS observations.
Sabine Griessbach, Lars Hoffmann, Reinhold Spang, Marc von Hobe, Rolf Müller, and Martin Riese
Atmos. Meas. Tech., 9, 4399–4423, https://doi.org/10.5194/amt-9-4399-2016, https://doi.org/10.5194/amt-9-4399-2016, 2016
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A new method for detecting aerosol in the UTLS based on infrared limb emission measurements is presented. The method was developed using radiative transfer simulations (including scattering) and Envisat MIPAS measurements. Results are presented for volcanic ash and sulfate aerosol originating from the Grimsvötn (Iceland), Puyehue–Cordon Caulle (Chile), and Nabro (Eritrea) eruptions in 2011 and compared with AIRS volcanic ash and SO2 measurements.
Reinhold Spang, Lars Hoffmann, Michael Höpfner, Sabine Griessbach, Rolf Müller, Michael C. Pitts, Andrew M. W. Orr, and Martin Riese
Atmos. Meas. Tech., 9, 3619–3639, https://doi.org/10.5194/amt-9-3619-2016, https://doi.org/10.5194/amt-9-3619-2016, 2016
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We present a new classification approach for different polar stratospheric cloud types. The so-called Bayesian classifier estimates the most likely probability that one of the three PSC types (ice, NAT, or STS) dominates the characteristics of a measured infrared spectrum. The entire measurement period of the satellite instrument MIPAS from July 2002 to April 2013 is processed using the new classifier.
Lars Hoffmann, Alison W. Grimsdell, and M. Joan Alexander
Atmos. Chem. Phys., 16, 9381–9397, https://doi.org/10.5194/acp-16-9381-2016, https://doi.org/10.5194/acp-16-9381-2016, 2016
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We present a 12-year record (2003-2014) of stratospheric gravity wave activity at Southern Hemisphere orographic hotspots as observed by the AIRS/Aqua satellite instrument. We introduce a method to discriminate between gravity waves from orographic or other sources and propose a simple model to predict the occurrence of mountain waves using zonal wind thresholds. The prediction model can help to disentangle upper level wind effects from low level source and other influences.
Yi Heng, Lars Hoffmann, Sabine Griessbach, Thomas Rößler, and Olaf Stein
Geosci. Model Dev., 9, 1627–1645, https://doi.org/10.5194/gmd-9-1627-2016, https://doi.org/10.5194/gmd-9-1627-2016, 2016
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A new inverse modeling and simulation system is developed to enable efficient and reliable transport simulations of volcanic SO2 at large scale. The complex time- and altitude-dependent volcanic emission pattern of the Nabro eruption is identified by our inversion algorithm. The simulation results show good agreements with different satellite observations in terms of SO2 horizontal distributions, and help to further reveal the complex transport processes such as the Asian monsoon circulation.
A. Orr, J. S. Hosking, L. Hoffmann, J. Keeble, S. M. Dean, H. K. Roscoe, N. L. Abraham, S. Vosper, and P. Braesicke
Atmos. Chem. Phys., 15, 1071–1086, https://doi.org/10.5194/acp-15-1071-2015, https://doi.org/10.5194/acp-15-1071-2015, 2015
R. Spang, G. Günther, M. Riese, L. Hoffmann, R. Müller, and S. Griessbach
Atmos. Chem. Phys., 15, 927–950, https://doi.org/10.5194/acp-15-927-2015, https://doi.org/10.5194/acp-15-927-2015, 2015
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Here we present observations of the Cryogenic Infrared Spectrometers and Telescopes for the Atmosphere (CRISTA) of cirrus cloud and water vapour in August 1997 in the upper troposphere and lower stratosphere (UTLS) region. The observations indicate a considerable flux of moisture from the upper tropical troposphere into the extra-tropical lowermost stratosphere (LMS), resulting in the occurrence of high-altitude optically thin cirrus clouds in the LMS.
L. Hoffmann, M. J. Alexander, C. Clerbaux, A. W. Grimsdell, C. I. Meyer, T. Rößler, and B. Tournier
Atmos. Meas. Tech., 7, 4517–4537, https://doi.org/10.5194/amt-7-4517-2014, https://doi.org/10.5194/amt-7-4517-2014, 2014
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We present stratospheric gravity wave observations from 4.3 micron radiance measurements by the nadir sounders AIRS and IASI. Three case studies demonstrate that AIRS and IASI provide a consistent picture of the temporal development of individual gravity wave events. Statistical comparisons based on five years of data (2008-2012) also showed similar patterns of gravity wave activity. Long-term records from combined satellite data are an exciting prospect for future gravity wave research.
R. Pommrich, R. Müller, J.-U. Grooß, P. Konopka, F. Ploeger, B. Vogel, M. Tao, C. M. Hoppe, G. Günther, N. Spelten, L. Hoffmann, H.-C. Pumphrey, S. Viciani, F. D'Amato, C. M. Volk, P. Hoor, H. Schlager, and M. Riese
Geosci. Model Dev., 7, 2895–2916, https://doi.org/10.5194/gmd-7-2895-2014, https://doi.org/10.5194/gmd-7-2895-2014, 2014
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A version of the chemical transport model CLaMS is presented, which features a simplified (numerically inexpensive) chemistry scheme. The model results using this version of CLaMS show a good representation of anomaly fields of CO, CH4, N2O, and CFC-11 in the lower stratosphere. CO measurements of three instruments (COLD, HAGAR, and Falcon-CO) in the lower tropical stratosphere (during the campaign TROCCINOX in 2005) have been compared and show a good agreement within the error bars.
L. Hoffmann, C. M. Hoppe, R. Müller, G. S. Dutton, J. C. Gille, S. Griessbach, A. Jones, C. I. Meyer, R. Spang, C. M. Volk, and K. A. Walker
Atmos. Chem. Phys., 14, 12479–12497, https://doi.org/10.5194/acp-14-12479-2014, https://doi.org/10.5194/acp-14-12479-2014, 2014
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Stratospheric lifetimes determine the global warming and ozone depletion potentials of chlorofluorocarbons. We present new estimates of the CFC-11/CFC-12 lifetime ratio from satellite and model data (ACE-FTS, HIRDLS, MIPAS, and EMAC/CLaMS). Our estimates of 0.46+/-0.04 (satellites) and 0.48+/-0.07 (model) are in excellent agreement with the recent SPARC reassessment. Having smaller uncertainties than other studies, our results can help to better constrain future CFC lifetime recommendations.
C. M. Hoppe, L. Hoffmann, P. Konopka, J.-U. Grooß, F. Ploeger, G. Günther, P. Jöckel, and R. Müller
Geosci. Model Dev., 7, 2639–2651, https://doi.org/10.5194/gmd-7-2639-2014, https://doi.org/10.5194/gmd-7-2639-2014, 2014
S. Griessbach, L. Hoffmann, R. Spang, and M. Riese
Atmos. Meas. Tech., 7, 1487–1507, https://doi.org/10.5194/amt-7-1487-2014, https://doi.org/10.5194/amt-7-1487-2014, 2014
C. Kalicinsky, J.-U. Grooß, G. Günther, J. Ungermann, J. Blank, S. Höfer, L. Hoffmann, P. Knieling, F. Olschewski, R. Spang, F. Stroh, and M. Riese
Atmos. Chem. Phys., 13, 10859–10871, https://doi.org/10.5194/acp-13-10859-2013, https://doi.org/10.5194/acp-13-10859-2013, 2013
J. Ungermann, L. L. Pan, C. Kalicinsky, F. Olschewski, P. Knieling, J. Blank, K. Weigel, T. Guggenmoser, F. Stroh, L. Hoffmann, and M. Riese
Atmos. Chem. Phys., 13, 10517–10534, https://doi.org/10.5194/acp-13-10517-2013, https://doi.org/10.5194/acp-13-10517-2013, 2013
M. von Hobe, S. Bekki, S. Borrmann, F. Cairo, F. D'Amato, G. Di Donfrancesco, A. Dörnbrack, A. Ebersoldt, M. Ebert, C. Emde, I. Engel, M. Ern, W. Frey, S. Genco, S. Griessbach, J.-U. Grooß, T. Gulde, G. Günther, E. Hösen, L. Hoffmann, V. Homonnai, C. R. Hoyle, I. S. A. Isaksen, D. R. Jackson, I. M. Jánosi, R. L. Jones, K. Kandler, C. Kalicinsky, A. Keil, S. M. Khaykin, F. Khosrawi, R. Kivi, J. Kuttippurath, J. C. Laube, F. Lefèvre, R. Lehmann, S. Ludmann, B. P. Luo, M. Marchand, J. Meyer, V. Mitev, S. Molleker, R. Müller, H. Oelhaf, F. Olschewski, Y. Orsolini, T. Peter, K. Pfeilsticker, C. Piesch, M. C. Pitts, L. R. Poole, F. D. Pope, F. Ravegnani, M. Rex, M. Riese, T. Röckmann, B. Rognerud, A. Roiger, C. Rolf, M. L. Santee, M. Scheibe, C. Schiller, H. Schlager, M. Siciliani de Cumis, N. Sitnikov, O. A. Søvde, R. Spang, N. Spelten, F. Stordal, O. Sumińska-Ebersoldt, A. Ulanovski, J. Ungermann, S. Viciani, C. M. Volk, M. vom Scheidt, P. von der Gathen, K. Walker, T. Wegner, R. Weigel, S. Weinbruch, G. Wetzel, F. G. Wienhold, I. Wohltmann, W. Woiwode, I. A. K. Young, V. Yushkov, B. Zobrist, and F. Stroh
Atmos. Chem. Phys., 13, 9233–9268, https://doi.org/10.5194/acp-13-9233-2013, https://doi.org/10.5194/acp-13-9233-2013, 2013
K. Minschwaner, L. Hoffmann, A. Brown, M. Riese, R. Müller, and P. F. Bernath
Atmos. Chem. Phys., 13, 4253–4263, https://doi.org/10.5194/acp-13-4253-2013, https://doi.org/10.5194/acp-13-4253-2013, 2013
Related subject area
Earth and space science informatics
Machine learning for numerical weather and climate modelling: a review
Overcoming barriers to enable convergence research by integrating ecological and climate sciences: the NCAR–NEON system Version 1
Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale
Hazard assessment modeling and software development of earthquake-triggered landslides in the Sichuan–Yunnan area, China
A generalized spatial autoregressive neural network method for three-dimensional spatial interpolation
The Common Community Physics Package (CCPP) Framework v6
Causal deep learning models for studying the Earth system
A methodological framework for improving the performance of data-driven models: a case study for daily runoff prediction in the Maumee domain, USA
SHAFTS (v2022.3): a deep-learning-based Python package for simultaneous extraction of building height and footprint from sentinel imagery
Bayesian atmospheric correction over land: Sentinel-2/MSI and Landsat 8/OLI
Twenty-five years of the IPCC Data Distribution Centre at the DKRZ and the Reference Data Archive for CMIP data
Effectiveness and computational efficiency of absorbing boundary conditions for full-waveform inversion
LAND-SUITE V1.0: a suite of tools for statistically based landslide susceptibility zonation
Towards physics-inspired data-driven weather forecasting: integrating data assimilation with a deep spatial-transformer-based U-NET in a case study with ERA5
CSDMS: a community platform for numerical modeling of Earth surface processes
A new methodological framework for geophysical sensor combinations associated with machine learning algorithms to understand soil attributes
Model calibration using ESEm v1.1.0 – an open, scalable Earth system emulator
Turbidity maximum zone index: a novel model for remote extraction of the turbidity maximum zone in different estuaries
dh2loop 1.0: an open-source Python library for automated processing and classification of geological logs
Copula-based synthetic data augmentation for machine-learning emulators
Automated geological map deconstruction for 3D model construction using map2loop 1.0 and map2model 1.0
A spatially explicit approach to simulate urban heat mitigation with InVEST (v3.8.0)
S-SOM v1.0: a structural self-organizing map algorithm for weather typing
Using Shapley additive explanations to interpret extreme gradient boosting predictions of grassland degradation in Xilingol, China
Current status on the need for improved accessibility to climate models code
ClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather
A spatiotemporal weighted regression model (STWR v1.0) for analyzing local nonstationarity in space and time
A new end-to-end workflow for the Community Earth System Model (version 2.0) for the Coupled Model Intercomparison Project Phase 6 (CMIP6)
HyLands 1.0: a hybrid landscape evolution model to simulate the impact of landslides and landslide-derived sediment on landscape evolution
Comparative analysis of atmospheric radiative transfer models using the Atmospheric Look-up table Generator (ALG) toolbox (version 2.0)
Fast domain-aware neural network emulation of a planetary boundary layer parameterization in a numerical weather forecast model
VISIR-1.b: ocean surface gravity waves and currents for energy-efficient navigation
Topological data analysis and machine learning for recognizing atmospheric river patterns in large climate datasets
Global hydro-climatic biomes identified via multitask learning
A run control framework to streamline profiling, porting, and tuning simulation runs and provenance tracking of geoscientific applications
An improved logistic regression model based on a spatially weighted technique (ILRBSWT v1.0) and its application to mineral prospectivity mapping
High-performance software framework for the calculation of satellite-to-satellite data matchups (MMS version 1.2)
A data model of the Climate and Forecast metadata conventions (CF-1.6) with a software implementation (cf-python v2.1)
Reverse engineering model structures for soil and ecosystem respiration: the potential of gene expression programming
A high-fidelity multiresolution digital elevation model for Earth systems
CPMIP: measurements of real computational performance of Earth system models in CMIP6
Automatic delineation of geomorphological slope units with r.slopeunits v1.0 and their optimization for landslide susceptibility modeling
Community Intercomparison Suite (CIS) v1.4.0: a tool for intercomparing models and observations
Asynchronous communication in spectral-element and discontinuous Galerkin methods for atmospheric dynamics – a case study using the High-Order Methods Modeling Environment (HOMME-homme_dg_branch)
GO2OGS 1.0: a versatile workflow to integrate complex geological information with fault data into numerical simulation models
An open and extensible framework for spatially explicit land use change modelling: the lulcc R package
Plant functional type classification for earth system models: results from the European Space Agency's Land Cover Climate Change Initiative
Non-singular spherical harmonic expressions of geomagnetic vector and gradient tensor fields in the local north-oriented reference frame
An approach to enhance pnetCDF performance in environmental modeling applications
A strategy for GIS-based 3-D slope stability modelling over large areas
Catherine O. de Burgh-Day and Tennessee Leeuwenburg
Geosci. Model Dev., 16, 6433–6477, https://doi.org/10.5194/gmd-16-6433-2023, https://doi.org/10.5194/gmd-16-6433-2023, 2023
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Machine learning (ML) is an increasingly popular tool in the field of weather and climate modelling. While ML has been used in this space for a long time, it is only recently that ML approaches have become competitive with more traditional methods. In this review, we have summarized the use of ML in weather and climate modelling over time; provided an overview of key ML concepts, methodologies, and terms; and suggested promising avenues for further research.
Danica L. Lombardozzi, William R. Wieder, Negin Sobhani, Gordon B. Bonan, David Durden, Dawn Lenz, Michael SanClements, Samantha Weintraub-Leff, Edward Ayres, Christopher R. Florian, Kyla Dahlin, Sanjiv Kumar, Abigail L. S. Swann, Claire M. Zarakas, Charles Vardeman, and Valerio Pascucci
Geosci. Model Dev., 16, 5979–6000, https://doi.org/10.5194/gmd-16-5979-2023, https://doi.org/10.5194/gmd-16-5979-2023, 2023
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We present a novel cyberinfrastructure system that uses National Ecological Observatory Network measurements to run Community Terrestrial System Model point simulations in a containerized system. The simple interface and tutorials expand access to data and models used in Earth system research by removing technical barriers and facilitating research, educational opportunities, and community engagement. The NCAR–NEON system enables convergence of climate and ecological sciences.
Qianqian Han, Yijian Zeng, Lijie Zhang, Calimanut-Ionut Cira, Egor Prikaziuk, Ting Duan, Chao Wang, Brigitta Szabó, Salvatore Manfreda, Ruodan Zhuang, and Bob Su
Geosci. Model Dev., 16, 5825–5845, https://doi.org/10.5194/gmd-16-5825-2023, https://doi.org/10.5194/gmd-16-5825-2023, 2023
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Using machine learning, we estimated global surface soil moisture (SSM) to aid in understanding water, energy, and carbon exchange. Ensemble models outperformed individual algorithms in predicting SSM under different climates. The best-performing ensemble included K-neighbours Regressor, Random Forest Regressor, and Extreme Gradient Boosting. This is important for hydrological and climatological applications such as water cycle monitoring, irrigation management, and crop yield prediction.
Xiaoyi Shao, Siyuan Ma, and Chong Xu
Geosci. Model Dev., 16, 5113–5129, https://doi.org/10.5194/gmd-16-5113-2023, https://doi.org/10.5194/gmd-16-5113-2023, 2023
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Scientific understandings of the distribution of coseismic landslides, followed by emergency and medium- and long-term risk assessment, can reduce landslide risk. The aim of this study is to propose an improved three-stage spatial prediction strategy and develop corresponding hazard assessment software called Mat.LShazard V1.0, which provides a new application tool for coseismic landslide disaster prevention and mitigation in different stages.
Junda Zhan, Sensen Wu, Jin Qi, Jindi Zeng, Mengjiao Qin, Yuanyuan Wang, and Zhenhong Du
Geosci. Model Dev., 16, 2777–2794, https://doi.org/10.5194/gmd-16-2777-2023, https://doi.org/10.5194/gmd-16-2777-2023, 2023
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We develop a generalized spatial autoregressive neural network model used for three-dimensional spatial interpolation. Taking the different changing trend of geographic elements along various directions into consideration, the model defines spatial distance in a generalized way and integrates it into the process of spatial interpolation with the theories of spatial autoregression and neural network. Compared with traditional methods, the model achieves better performance and is more adaptable.
Dominikus Heinzeller, Ligia Bernardet, Grant Firl, Man Zhang, Xia Sun, and Michael Ek
Geosci. Model Dev., 16, 2235–2259, https://doi.org/10.5194/gmd-16-2235-2023, https://doi.org/10.5194/gmd-16-2235-2023, 2023
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The Common Community Physics Package is a collection of physical atmospheric parameterizations for use in Earth system models and a framework that couples the physics to a host model’s dynamical core. A primary goal for this effort is to facilitate research and development of physical parameterizations and physics–dynamics coupling methods while offering capabilities for numerical weather prediction operations, for example in the upcoming implementation of the Global Forecast System (GFS) v17.
Tobias Tesch, Stefan Kollet, and Jochen Garcke
Geosci. Model Dev., 16, 2149–2166, https://doi.org/10.5194/gmd-16-2149-2023, https://doi.org/10.5194/gmd-16-2149-2023, 2023
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A recent statistical approach for studying relations in the Earth system is to train deep learning (DL) models to predict Earth system variables given one or several others and use interpretable DL to analyze the relations learned by the models. Here, we propose to combine the approach with a theorem from causality research to ensure that the deep learning model learns causal rather than spurious relations. As an example, we apply the method to study soil-moisture–precipitation coupling.
Yao Hu, Chirantan Ghosh, and Siamak Malakpour-Estalaki
Geosci. Model Dev., 16, 1925–1936, https://doi.org/10.5194/gmd-16-1925-2023, https://doi.org/10.5194/gmd-16-1925-2023, 2023
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Data-driven models (DDMs) gain popularity in earth and environmental systems, thanks in large part to advancements in data collection techniques and artificial intelligence (AI). The performance of these models is determined by the underlying machine learning (ML) algorithms. In this study, we develop a framework to improve the model performance by optimizing ML algorithms and demonstrate the effectiveness of the framework using a DDM to predict edge-of-field runoff in the Maumee domain, USA.
Ruidong Li, Ting Sun, Fuqiang Tian, and Guang-Heng Ni
Geosci. Model Dev., 16, 751–778, https://doi.org/10.5194/gmd-16-751-2023, https://doi.org/10.5194/gmd-16-751-2023, 2023
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We developed SHAFTS (Simultaneous building Height And FootprinT extraction from Sentinel imagery), a multi-task deep-learning-based Python package, to estimate average building height and footprint from Sentinel imagery. Evaluation in 46 cities worldwide shows that SHAFTS achieves significant improvement over existing machine-learning-based methods.
Feng Yin, Philip E. Lewis, and Jose L. Gómez-Dans
Geosci. Model Dev., 15, 7933–7976, https://doi.org/10.5194/gmd-15-7933-2022, https://doi.org/10.5194/gmd-15-7933-2022, 2022
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The proposed SIAC atmospheric correction method provides consistent surface reflectance estimations from medium spatial-resolution satellites (Sentinel 2 and Landsat 8) with per-pixel uncertainty information. The outputs from SIAC have been validated against a wide range of ground measurements, and it shows that SIAC can provide accurate estimations of both surface reflectance and atmospheric parameters, with meaningful uncertainty information.
Martina Stockhause and Michael Lautenschlager
Geosci. Model Dev., 15, 6047–6058, https://doi.org/10.5194/gmd-15-6047-2022, https://doi.org/10.5194/gmd-15-6047-2022, 2022
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The Data Distribution Centre (DDC) of the Intergovernmental Panel on Climate Change (IPCC) celebrates its 25th anniversary in 2022. DDC Partner DKRZ has supported the IPCC Assessments and preserved the quality-assured, citable climate model data underpinning the Assessment Reports over these years over the long term. With the introduction of the IPCC FAIR Guidelines into the current AR6, the value of DDC services has been recognized. However, DDC sustainability remains unresolved.
Daiane Iglesia Dolci, Felipe A. G. Silva, Pedro S. Peixoto, and Ernani V. Volpe
Geosci. Model Dev., 15, 5857–5881, https://doi.org/10.5194/gmd-15-5857-2022, https://doi.org/10.5194/gmd-15-5857-2022, 2022
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We investigate and compare the theoretical and computational characteristics of several absorbing boundary conditions (ABCs) for the full-waveform inversion (FWI) problem. The different ABCs are implemented in an optimized computational framework called Devito. The computational efficiency and memory requirements of the ABC methods are evaluated in the forward and adjoint wave propagators, from simple to realistic velocity models.
Mauro Rossi, Txomin Bornaetxea, and Paola Reichenbach
Geosci. Model Dev., 15, 5651–5666, https://doi.org/10.5194/gmd-15-5651-2022, https://doi.org/10.5194/gmd-15-5651-2022, 2022
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LAND-SUITE is a software package designed to support landslide susceptibility zonation. The software integrates, extends, and completes LAND-SE (Rossi et al., 2010; Rossi and Reichenbach, 2016). The software is implemented in R, a free software environment for statistical computing and graphics, and gives expert users the possibility to perform easier, more flexible, and more informed statistically based landslide susceptibility applications and zonations.
Ashesh Chattopadhyay, Mustafa Mustafa, Pedram Hassanzadeh, Eviatar Bach, and Karthik Kashinath
Geosci. Model Dev., 15, 2221–2237, https://doi.org/10.5194/gmd-15-2221-2022, https://doi.org/10.5194/gmd-15-2221-2022, 2022
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There is growing interest in data-driven weather forecasting, i.e., to predict the weather by using a deep neural network that learns from the evolution of past atmospheric patterns. Here, we propose three components to add to the current data-driven weather forecast models to improve their performance. These components involve a feature that incorporates physics into the neural network, a method to add data assimilation, and an algorithm to use several different time intervals in the forecast.
Gregory E. Tucker, Eric W. H. Hutton, Mark D. Piper, Benjamin Campforts, Tian Gan, Katherine R. Barnhart, Albert J. Kettner, Irina Overeem, Scott D. Peckham, Lynn McCready, and Jaia Syvitski
Geosci. Model Dev., 15, 1413–1439, https://doi.org/10.5194/gmd-15-1413-2022, https://doi.org/10.5194/gmd-15-1413-2022, 2022
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Scientists use computer simulation models to understand how Earth surface processes work, including floods, landslides, soil erosion, river channel migration, ocean sedimentation, and coastal change. Research benefits when the software for simulation modeling is open, shared, and coordinated. The Community Surface Dynamics Modeling System (CSDMS) is a US-based facility that supports research by providing community support, computing tools and guidelines, and educational resources.
Danilo César de Mello, Gustavo Vieira Veloso, Marcos Guedes de Lana, Fellipe Alcantara de Oliveira Mello, Raul Roberto Poppiel, Diego Ribeiro Oquendo Cabrero, Luis Augusto Di Loreto Di Raimo, Carlos Ernesto Gonçalves Reynaud Schaefer, Elpídio Inácio Fernandes Filho, Emilson Pereira Leite, and José Alexandre Melo Demattê
Geosci. Model Dev., 15, 1219–1246, https://doi.org/10.5194/gmd-15-1219-2022, https://doi.org/10.5194/gmd-15-1219-2022, 2022
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We used soil parent material, terrain attributes, and geophysical data from the soil surface to test and compare different and unprecedented geophysical sensor combination, as well as different machine learning algorithms to model and predict several soil attributes. Also, we analyzed the importance of pedoenvironmental variables. The soil attributes were modeled throughout different machine learning algorithms and related to different geophysical sensor combinations.
Duncan Watson-Parris, Andrew Williams, Lucia Deaconu, and Philip Stier
Geosci. Model Dev., 14, 7659–7672, https://doi.org/10.5194/gmd-14-7659-2021, https://doi.org/10.5194/gmd-14-7659-2021, 2021
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The Earth System Emulator (ESEm) provides a fast and flexible framework for emulating a wide variety of Earth science datasets and tools for constraining (or tuning) models of any complexity. Three distinct use cases are presented that demonstrate the utility of ESEm and provide some insight into the use of machine learning for emulation in these different settings. The open-source Python package is freely available so that it might become a valuable tool for the community.
Chongyang Wang, Li Wang, Danni Wang, Dan Li, Chenghu Zhou, Hao Jiang, Qiong Zheng, Shuisen Chen, Kai Jia, Yangxiaoyue Liu, Ji Yang, Xia Zhou, and Yong Li
Geosci. Model Dev., 14, 6833–6846, https://doi.org/10.5194/gmd-14-6833-2021, https://doi.org/10.5194/gmd-14-6833-2021, 2021
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The turbidity maximum zone (TMZ) is a special phenomenon in estuaries worldwide. However, the extraction methods and criteria used to describe the TMZ vary significantly both spatially and temporally. This study proposes an new index, the turbidity maximum zone index, based on the corresponding relationship of total suspended solid concentration and Chl a concentration, which could better extract TMZs in different estuaries and on different dates.
Ranee Joshi, Kavitha Madaiah, Mark Jessell, Mark Lindsay, and Guillaume Pirot
Geosci. Model Dev., 14, 6711–6740, https://doi.org/10.5194/gmd-14-6711-2021, https://doi.org/10.5194/gmd-14-6711-2021, 2021
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We have developed a software that allows the user to extract and standardize drill hole information from legacy datasets and/or different drilling campaigns. It also provides functionality to upscale the lithological information. These functionalities were possible by developing thesauri to identify and group geological terminologies together.
David Meyer, Thomas Nagler, and Robin J. Hogan
Geosci. Model Dev., 14, 5205–5215, https://doi.org/10.5194/gmd-14-5205-2021, https://doi.org/10.5194/gmd-14-5205-2021, 2021
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A major limitation in training machine-learning emulators is often caused by the lack of data. This paper presents a cheap way to increase the size of training datasets using statistical techniques and thereby improve the performance of machine-learning emulators.
Mark Jessell, Vitaliy Ogarko, Yohan de Rose, Mark Lindsay, Ranee Joshi, Agnieszka Piechocka, Lachlan Grose, Miguel de la Varga, Laurent Ailleres, and Guillaume Pirot
Geosci. Model Dev., 14, 5063–5092, https://doi.org/10.5194/gmd-14-5063-2021, https://doi.org/10.5194/gmd-14-5063-2021, 2021
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We have developed software that allows the user to extract sufficient information from unmodified digital maps and associated datasets that we are able to use to automatically build 3D geological models. By automating the process we are able to remove human bias from the procedure, which makes the workflow reproducible.
Martí Bosch, Maxence Locatelli, Perrine Hamel, Roy P. Remme, Jérôme Chenal, and Stéphane Joost
Geosci. Model Dev., 14, 3521–3537, https://doi.org/10.5194/gmd-14-3521-2021, https://doi.org/10.5194/gmd-14-3521-2021, 2021
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The article presents a novel approach to simulate urban heat mitigation from land use/land cover data based on three biophysical mechanisms: tree shade, evapotranspiration and albedo. An automated procedure is proposed to calibrate the model parameters to best fit temperature observations from monitoring stations. A case study in Lausanne, Switzerland, shows that the approach outperforms regressions based on satellite data and provides valuable insights into design heat mitigation policies.
Quang-Van Doan, Hiroyuki Kusaka, Takuto Sato, and Fei Chen
Geosci. Model Dev., 14, 2097–2111, https://doi.org/10.5194/gmd-14-2097-2021, https://doi.org/10.5194/gmd-14-2097-2021, 2021
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This study proposes a novel structural self-organizing map (S-SOM) algorithm. The superiority of S-SOM is that it can better recognize the difference (or similarity) among spatial (or temporal) data used for training and thus improve the clustering quality compared to traditional SOM algorithms.
Batunacun, Ralf Wieland, Tobia Lakes, and Claas Nendel
Geosci. Model Dev., 14, 1493–1510, https://doi.org/10.5194/gmd-14-1493-2021, https://doi.org/10.5194/gmd-14-1493-2021, 2021
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Extreme gradient boosting (XGBoost) can provide alternative insights that conventional land-use models are unable to generate. Shapley additive explanations (SHAP) can interpret the results of the purely data-driven approach. XGBoost achieved similar and robust simulation results. SHAP values were useful for analysing the complex relationship between the different drivers of grassland degradation.
Juan A. Añel, Michael García-Rodríguez, and Javier Rodeiro
Geosci. Model Dev., 14, 923–934, https://doi.org/10.5194/gmd-14-923-2021, https://doi.org/10.5194/gmd-14-923-2021, 2021
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This work shows that it continues to be hard, if not impossible, to obtain some of the most used climate models worldwide. We reach this conclusion through a systematic study and encourage all development teams and research centres to make public the models they use to produce scientific results.
Prabhat, Karthik Kashinath, Mayur Mudigonda, Sol Kim, Lukas Kapp-Schwoerer, Andre Graubner, Ege Karaismailoglu, Leo von Kleist, Thorsten Kurth, Annette Greiner, Ankur Mahesh, Kevin Yang, Colby Lewis, Jiayi Chen, Andrew Lou, Sathyavat Chandran, Ben Toms, Will Chapman, Katherine Dagon, Christine A. Shields, Travis O'Brien, Michael Wehner, and William Collins
Geosci. Model Dev., 14, 107–124, https://doi.org/10.5194/gmd-14-107-2021, https://doi.org/10.5194/gmd-14-107-2021, 2021
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Detecting extreme weather events is a crucial step in understanding how they change due to climate change. Deep learning (DL) is remarkable at pattern recognition; however, it works best only when labeled datasets are available. We create
ClimateNet– an expert-labeled curated dataset – to train a DL model for detecting weather events and predicting changes in extreme precipitation. This work paves the way for DL-based automated, high-fidelity, and highly precise analytics of climate data.
Xiang Que, Xiaogang Ma, Chao Ma, and Qiyu Chen
Geosci. Model Dev., 13, 6149–6164, https://doi.org/10.5194/gmd-13-6149-2020, https://doi.org/10.5194/gmd-13-6149-2020, 2020
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This paper presents a spatiotemporal weighted regression (STWR) model for exploring nonstationary spatiotemporal processes in nature and socioeconomics. A value change rate is introduced in the temporal kernel, which presents significant model fitting and accuracy in both simulated and real-world data. STWR fully incorporates observed data in the past and outperforms geographic temporal weighted regression (GTWR) and geographic weighted regression (GWR) models in several experiments.
Sheri Mickelson, Alice Bertini, Gary Strand, Kevin Paul, Eric Nienhouse, John Dennis, and Mariana Vertenstein
Geosci. Model Dev., 13, 5567–5581, https://doi.org/10.5194/gmd-13-5567-2020, https://doi.org/10.5194/gmd-13-5567-2020, 2020
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Every generation of MIP exercises introduces new layers of complexity and an exponential growth in the amount of data requested. CMIP6 required us to develop a new tool chain and forced us to change our methodologies. The new methods discussed in this paper provided us with an 18 times faster speedup over our existing methods. This allowed us to meet our deadlines and we were able to publish more than half a million data sets on the Earth System Grid Federation (ESGF) for the CMIP6 project.
Benjamin Campforts, Charles M. Shobe, Philippe Steer, Matthias Vanmaercke, Dimitri Lague, and Jean Braun
Geosci. Model Dev., 13, 3863–3886, https://doi.org/10.5194/gmd-13-3863-2020, https://doi.org/10.5194/gmd-13-3863-2020, 2020
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Landslides shape the Earth’s surface and are a dominant source of terrestrial sediment. Rivers, then, act as conveyor belts evacuating landslide-produced sediment. Understanding the interaction among rivers and landslides is important to predict the Earth’s surface response to past and future environmental changes and for mitigating natural hazards. We develop HyLands, a new numerical model that provides a toolbox to explore how landslides and rivers interact over several timescales.
Jorge Vicent, Jochem Verrelst, Neus Sabater, Luis Alonso, Juan Pablo Rivera-Caicedo, Luca Martino, Jordi Muñoz-Marí, and José Moreno
Geosci. Model Dev., 13, 1945–1957, https://doi.org/10.5194/gmd-13-1945-2020, https://doi.org/10.5194/gmd-13-1945-2020, 2020
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The modeling of light propagation through the atmosphere is key to process satellite images and to understand atmospheric processes. However, existing atmospheric models can be complex to use in practical applications. Here we aim at providing a new software tool to facilitate using advanced models and to generate large databases of simulated data. As a test case, we use this tool to analyze differences between several atmospheric models, showing the capabilities of this open-source tool.
Jiali Wang, Prasanna Balaprakash, and Rao Kotamarthi
Geosci. Model Dev., 12, 4261–4274, https://doi.org/10.5194/gmd-12-4261-2019, https://doi.org/10.5194/gmd-12-4261-2019, 2019
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Parameterizations are frequently used in models representing physical phenomena and are often the computationally expensive portions of the code. Using model output from simulations performed using a weather model, we train deep neural networks to provide an accurate alternative to a physics-based parameterization. We demonstrate that a domain-aware deep neural network can successfully simulate the entire diurnal cycle of the boundary layer physics and the results are transferable.
Gianandrea Mannarini and Lorenzo Carelli
Geosci. Model Dev., 12, 3449–3480, https://doi.org/10.5194/gmd-12-3449-2019, https://doi.org/10.5194/gmd-12-3449-2019, 2019
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The VISIR ship-routing model is updated in order to deal with ocean currents.
The optimal tracks we computed through VISIR in the Atlantic ocean show great seasonal and regional variability, following a variable influence of surface gravity waves and currents. We assess how these tracks contribute to voyage energy-efficiency gains through a standard indicator (EEOI) of the International Maritime Organization. Also, the new model features are validated against an exact analytical benchmark.
Grzegorz Muszynski, Karthik Kashinath, Vitaliy Kurlin, Michael Wehner, and Prabhat
Geosci. Model Dev., 12, 613–628, https://doi.org/10.5194/gmd-12-613-2019, https://doi.org/10.5194/gmd-12-613-2019, 2019
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We present the automated method for recognizing atmospheric rivers in climate data, i.e., climate model output and reanalysis product. The method is based on topological data analysis and machine learning, both of which are powerful tools that the climate science community often does not use. An advantage of the proposed method is that it is free of selection of subjective threshold conditions on a physical variable. This method is also suitable for rapidly analyzing large amounts of data.
Christina Papagiannopoulou, Diego G. Miralles, Matthias Demuzere, Niko E. C. Verhoest, and Willem Waegeman
Geosci. Model Dev., 11, 4139–4153, https://doi.org/10.5194/gmd-11-4139-2018, https://doi.org/10.5194/gmd-11-4139-2018, 2018
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Common global land cover and climate classifications are based on vegetation–climatic characteristics derived from observational data, ignoring the interaction between the local climate and biome. Here, we model the interplay between vegetation and local climate by discovering spatial relationships among different locations. The resulting global
hydro-climatic biomescorrespond to regions of coherent climate–vegetation interactions that agree well with traditional global land cover maps.
Wendy Sharples, Ilya Zhukov, Markus Geimer, Klaus Goergen, Sebastian Luehrs, Thomas Breuer, Bibi Naz, Ketan Kulkarni, Slavko Brdar, and Stefan Kollet
Geosci. Model Dev., 11, 2875–2895, https://doi.org/10.5194/gmd-11-2875-2018, https://doi.org/10.5194/gmd-11-2875-2018, 2018
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Next-generation geoscientific models are based on complex model implementations and workflows. Next-generation HPC systems require new programming paradigms and code optimization. In order to meet the challenge of running complex simulations on new massively parallel HPC systems, we developed a run control framework that facilitates code portability, code profiling, and provenance tracking to reduce both the duration and the cost of code migration and development, while ensuring reproducibility.
Daojun Zhang, Na Ren, and Xianhui Hou
Geosci. Model Dev., 11, 2525–2539, https://doi.org/10.5194/gmd-11-2525-2018, https://doi.org/10.5194/gmd-11-2525-2018, 2018
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Geographically weighted regression is a widely used method to deal with spatial heterogeneity, which is common in geostatistics. However, most existing software does not support logistic regression and cannot deal with missing data, which exist extensively in mineral prospectivity mapping. This work generalized logistic regression to spatial statistics based on a spatially weighted technique. The new model also supports an anisotropic local window, which is another innovative point.
Thomas Block, Sabine Embacher, Christopher J. Merchant, and Craig Donlon
Geosci. Model Dev., 11, 2419–2427, https://doi.org/10.5194/gmd-11-2419-2018, https://doi.org/10.5194/gmd-11-2419-2018, 2018
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For calibration and validation purposes it is necessary to detect simultaneous data acquisitions from different spaceborne platforms. We present an algorithm and a software system which implements a general approach to resolve this problem. The multisensor matchup system (MMS) can detect simultaneous acquisitions in a large dataset (> 100 TB) and extract data for matching locations for further analysis. The MMS implements a flexible software infrastructure and allows for high parallelization.
David Hassell, Jonathan Gregory, Jon Blower, Bryan N. Lawrence, and Karl E. Taylor
Geosci. Model Dev., 10, 4619–4646, https://doi.org/10.5194/gmd-10-4619-2017, https://doi.org/10.5194/gmd-10-4619-2017, 2017
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We present a formal data model for version 1.6 of the CF (Climate and Forecast) metadata conventions that provide a description of the physical meaning of geoscientific data and their spatial and temporal properties. We describe the CF conventions and how they lead to our CF data model, and compare it other data models for storing data and metadata. We present cf-python version 2.1: a software implementation of the CF data model capable of manipulating any CF-compliant dataset.
Iulia Ilie, Peter Dittrich, Nuno Carvalhais, Martin Jung, Andreas Heinemeyer, Mirco Migliavacca, James I. L. Morison, Sebastian Sippel, Jens-Arne Subke, Matthew Wilkinson, and Miguel D. Mahecha
Geosci. Model Dev., 10, 3519–3545, https://doi.org/10.5194/gmd-10-3519-2017, https://doi.org/10.5194/gmd-10-3519-2017, 2017
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Accurate representation of land-atmosphere carbon fluxes is essential for future climate projections, although some of the responses of CO2 fluxes to climate often remain uncertain. The increase in available data allows for new approaches in their modelling. We automatically developed models for ecosystem and soil carbon respiration using a machine learning approach. When compared with established respiration models, we found that they are better in prediction as well as offering new insights.
Xinqiao Duan, Lin Li, Haihong Zhu, and Shen Ying
Geosci. Model Dev., 10, 239–253, https://doi.org/10.5194/gmd-10-239-2017, https://doi.org/10.5194/gmd-10-239-2017, 2017
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This article proposes an optimized transformation for topographic datasets. The resulting topographic grid exhibits good surface approximation and quasi-uniform high-quality. Both features of the processed topography build a concrete base from which improved endogenous or exogenous parameters can be derived, and makes it suitable for Earth and environmental simulations.
Venkatramani Balaji, Eric Maisonnave, Niki Zadeh, Bryan N. Lawrence, Joachim Biercamp, Uwe Fladrich, Giovanni Aloisio, Rusty Benson, Arnaud Caubel, Jeffrey Durachta, Marie-Alice Foujols, Grenville Lister, Silvia Mocavero, Seth Underwood, and Garrett Wright
Geosci. Model Dev., 10, 19–34, https://doi.org/10.5194/gmd-10-19-2017, https://doi.org/10.5194/gmd-10-19-2017, 2017
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Climate models are among the most computationally expensive scientific applications in the world. We present a set of measures of computational performance that can be used to compare models that are independent of underlying hardware and the model formulation. They are easy to collect and reflect performance actually achieved in practice. We are preparing a systematic effort to collect these metrics for the world's climate models during CMIP6, the next Climate Model Intercomparison Project.
Massimiliano Alvioli, Ivan Marchesini, Paola Reichenbach, Mauro Rossi, Francesca Ardizzone, Federica Fiorucci, and Fausto Guzzetti
Geosci. Model Dev., 9, 3975–3991, https://doi.org/10.5194/gmd-9-3975-2016, https://doi.org/10.5194/gmd-9-3975-2016, 2016
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Slope units are morphological mapping units bounded by drainage and divide lines that maximize within-unit homogeneity and between-unit heterogeneity. We use r.slopeunits, a software for the automatic delination of slope units. We outline an objective procedure to optimize the software input parameters for landslide susceptibility (LS) zonation. Optimization is achieved by maximizing an objective function that simultaneously evaluates terrain aspect segmentation quality and LS model performance.
Duncan Watson-Parris, Nick Schutgens, Nicholas Cook, Zak Kipling, Philip Kershaw, Edward Gryspeerdt, Bryan Lawrence, and Philip Stier
Geosci. Model Dev., 9, 3093–3110, https://doi.org/10.5194/gmd-9-3093-2016, https://doi.org/10.5194/gmd-9-3093-2016, 2016
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In this paper we describe CIS, a new command line tool for the easy visualization, analysis and comparison of a wide variety of gridded and ungridded data sets used in Earth sciences. Users can now use a single tool to not only view plots of satellite, aircraft, station or model data, but also bring them onto the same spatio-temporal sampling. This allows robust, quantitative comparisons to be made easily. CIS is an open-source project and welcomes input from the community.
Benjamin F. Jamroz and Robert Klöfkorn
Geosci. Model Dev., 9, 2881–2892, https://doi.org/10.5194/gmd-9-2881-2016, https://doi.org/10.5194/gmd-9-2881-2016, 2016
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The scalability of computational applications on current and next-generation supercomputers is increasingly limited by the cost of inter-process communication. We implement communication hiding data exchange in the High-Order Methods Modeling Environment (HOMME) for the time integration of the hydrostatic fluid equations using both the spectral-element and discontinuous Galerkin methods. The presented approach produces significant performance and scalability gains in large-scale simulations.
T. Fischer, D. Naumov, S. Sattler, O. Kolditz, and M. Walther
Geosci. Model Dev., 8, 3681–3694, https://doi.org/10.5194/gmd-8-3681-2015, https://doi.org/10.5194/gmd-8-3681-2015, 2015
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We present a workflow to convert geological models into the open-source VTU format for usage in numerical simulation models. Tackling relevant scientific questions or engineering tasks often involves multidisciplinary approaches. Conversion workflows are needed between the diverse tools of the various disciplines. Our approach offers an open-source, platform-independent, robust, and comprehensible method that is potentially useful for a multitude of similar environmental studies.
S. Moulds, W. Buytaert, and A. Mijic
Geosci. Model Dev., 8, 3215–3229, https://doi.org/10.5194/gmd-8-3215-2015, https://doi.org/10.5194/gmd-8-3215-2015, 2015
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The contribution of lulcc is to provide a free and open-source framework for land use change modelling. The software, which is provided as an R package, addresses problems associated with the current paradigm of closed-source, specialised land use change modelling software which disrupt the scientific process. It is an attempt to move the discipline towards open and transparent science and to ensure land use change models are accessible to scientists working across the geosciences.
B. Poulter, N. MacBean, A. Hartley, I. Khlystova, O. Arino, R. Betts, S. Bontemps, M. Boettcher, C. Brockmann, P. Defourny, S. Hagemann, M. Herold, G. Kirches, C. Lamarche, D. Lederer, C. Ottlé, M. Peters, and P. Peylin
Geosci. Model Dev., 8, 2315–2328, https://doi.org/10.5194/gmd-8-2315-2015, https://doi.org/10.5194/gmd-8-2315-2015, 2015
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Land cover is an essential variable in earth system models and determines conditions driving biogeochemical, energy and water exchange between ecosystems and the atmosphere. A methodology is presented for mapping plant functional types used in global vegetation models from a updated land cover classification system and open-source conversion tool, resulting from a consultative process among map producers and modelers engaged in the European Space Agency’s Land Cover Climate Change Initiative.
J. Du, C. Chen, V. Lesur, and L. Wang
Geosci. Model Dev., 8, 1979–1990, https://doi.org/10.5194/gmd-8-1979-2015, https://doi.org/10.5194/gmd-8-1979-2015, 2015
D. C. Wong, C. E. Yang, J. S. Fu, K. Wong, and Y. Gao
Geosci. Model Dev., 8, 1033–1046, https://doi.org/10.5194/gmd-8-1033-2015, https://doi.org/10.5194/gmd-8-1033-2015, 2015
M. Mergili, I. Marchesini, M. Alvioli, M. Metz, B. Schneider-Muntau, M. Rossi, and F. Guzzetti
Geosci. Model Dev., 7, 2969–2982, https://doi.org/10.5194/gmd-7-2969-2014, https://doi.org/10.5194/gmd-7-2969-2014, 2014
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The article deals with strategies to (i) reduce computation time and to (ii) appropriately account for uncertain input parameters when applying an open source GIS sliding surface model to estimate landslide susceptibility for a 90km² study area in central Italy. For (i), the area is split into a large number of tiles, enabling the exploitation of multi-processor computing environments. For (ii), the model is run with various parameter combinations to compute the slope failure probability.
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Short summary
The efficiency of the numerical simulation of radiative transport is shown on modern server-class graphics cards (GPUs). The low-cost prefactor on GPUs compared to general-purpose processors (CPUs) enables future large retrieval campaigns for multi-channel data from infrared sounders aboard low-orbit satellites. The validated research software JURASSIC is available in the public domain.
The efficiency of the numerical simulation of radiative transport is shown on modern...
