Articles | Volume 15, issue 1
https://doi.org/10.5194/gmd-15-291-2022
© 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-15-291-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
17 Jan 2022
Model evaluation paper |
| 17 Jan 2022
| 17 Jan 2022
Evaluation of the COSMO model (v5.1) in polarimetric radar space – impact of uncertainties in model microphysics, retrievals and forward operators
Institute for Geosciences, Department of Meteorology, University of Bonn, Bonn, Germany
Jana Mendrok
Deutscher Wetterdienst, Offenbach, Germany
Velibor Pejcic
Institute for Geosciences, Department of Meteorology, University of Bonn, Bonn, Germany
Silke Trömel
Institute for Geosciences, Department of Meteorology, University of Bonn, Bonn, Germany
Laboratory for Clouds and Precipitation
Exploration, Geoverbund ABC/J, Bonn, Germany
Ulrich Blahak
Deutscher Wetterdienst, Offenbach, Germany
Jacob T. Carlin
Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma, USA
NOAA/OAR National Severe Storms Laboratory, Norman, Oklahoma, USA
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Prabhakar Shrestha, Jana Mendrok, and Dominik Brunner
Atmos. Chem. Phys., 22, 14095–14117, https://doi.org/10.5194/acp-22-14095-2022, https://doi.org/10.5194/acp-22-14095-2022, 2022
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The study extends the Terrestrial Systems Modeling Platform with gas-phase chemistry aerosol dynamics and a radar forward operator to enable detailed studies of aerosol–cloud–precipitation interactions. This is demonstrated using a case study of a deep convective storm, which showed that the strong updraft in the convective core of the storm produced aerosol-tower-like features, which affected the size of the hydrometeors and the simulated polarimetric features (e.g., ZDR and KDP columns).
Prabhakar Shrestha, Silke Trömel, Raquel Evaristo, and Clemens Simmer
Atmos. Chem. Phys., 22, 7593–7618, https://doi.org/10.5194/acp-22-7593-2022, https://doi.org/10.5194/acp-22-7593-2022, 2022
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The study makes use of ensemble numerical simulations with forward operator to evaluate the simulated cloud and precipitation processes with radar observations. While comparing model data with radar has its own challenges due to errors in the forward operator and processed radar measurements, the model was generally found to underestimate the high reflectivity, width/magnitude (value) of ZDR columns and high precipitation.
Silke Trömel, Clemens Simmer, Ulrich Blahak, Armin Blanke, Sabine Doktorowski, Florian Ewald, Michael Frech, Mathias Gergely, Martin Hagen, Tijana Janjic, Heike Kalesse-Los, Stefan Kneifel, Christoph Knote, Jana Mendrok, Manuel Moser, Gregor Köcher, Kai Mühlbauer, Alexander Myagkov, Velibor Pejcic, Patric Seifert, Prabhakar Shrestha, Audrey Teisseire, Leonie von Terzi, Eleni Tetoni, Teresa Vogl, Christiane Voigt, Yuefei Zeng, Tobias Zinner, and Johannes Quaas
Atmos. Chem. Phys., 21, 17291–17314, https://doi.org/10.5194/acp-21-17291-2021, https://doi.org/10.5194/acp-21-17291-2021, 2021
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The article introduces the ACP readership to ongoing research in Germany on cloud- and precipitation-related process information inherent in polarimetric radar measurements, outlines pathways to inform atmospheric models with radar-based information, and points to remaining challenges towards an improved fusion of radar polarimetry and atmospheric modelling.
P. Shrestha, M. Sulis, C. Simmer, and S. Kollet
Hydrol. Earth Syst. Sci., 19, 4317–4326, https://doi.org/10.5194/hess-19-4317-2015, https://doi.org/10.5194/hess-19-4317-2015, 2015
Short summary
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This study highlights the grid resolution dependence of energy and water balance of the 3-D physically based integrated surface-groundwater model. The non-local controls of soil moisture were found to be highly grid resolution dependent, but the local vegetation control strongly modulates the scaling behavior of surface energy fluxes. For coupled runs, variability in patterns of surface fluxes due to this scale dependence can affect the simulated atmospheric boundary layer and local circulation.
F. Gasper, K. Goergen, P. Shrestha, M. Sulis, J. Rihani, M. Geimer, and S. Kollet
Geosci. Model Dev., 7, 2531–2543, https://doi.org/10.5194/gmd-7-2531-2014, https://doi.org/10.5194/gmd-7-2531-2014, 2014
Wonbae Bang, Jacob T. Carlin, Kwonil Kim, Alexander V. Ryzhkov, Guosheng Liu, and GyuWon Lee
Geosci. Model Dev., 18, 3559–3581, https://doi.org/10.5194/gmd-18-3559-2025, https://doi.org/10.5194/gmd-18-3559-2025, 2025
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Microphysics model-based diagnosis, such as the spectral bin model (SBM), has recently been attempted to diagnose winter precipitation types. In this study, the accuracy of SBM-based precipitation type diagnosis is compared with other traditional methods. SBM has a relatively higher accuracy for dry-snow and wet-snow events, whereas it has lower accuracy for rain events. When the microphysics scheme in the SBM was optimized for the corresponding region, the accuracy for rain events improved.
Velibor Pejcic, Kamil Mroz, Kai Mühlbauer, and Silke Trömel
EGUsphere, https://doi.org/10.5194/egusphere-2025-1414, https://doi.org/10.5194/egusphere-2025-1414, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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Estimating the proportions of individual hydrometeor types (hydrometeor partitioning ratios, HPRs) in a mixture of a resolved radar volume and their evaluation is challenging. This study has three objectives, (1) to evaluate HPR retrievals, (2) to exploit the combination of dual-frequency (DF) space-borne radar (SR) and dual-polarisation (DP) ground-based radar (GR) observations for estimating HPRs based on SR DF observations and (3) to further improve HPR estimates based on DP GR observations.
Armin Blanke, Mathias Gergely, and Silke Trömel
Atmos. Chem. Phys., 25, 4167–4184, https://doi.org/10.5194/acp-25-4167-2025, https://doi.org/10.5194/acp-25-4167-2025, 2025
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The area-wide radar-based distinction between riming and aggregation is crucial for model microphysics and data assimilation. This study introduces a discrimination algorithm based on polarimetric radar networks only. Exploiting the unique opportunity to link fall velocities from Doppler spectra to polarimetric variables in an operational setting enables us to set up and evaluate a well-performing machine learning algorithm.
Elizabeth N. Smith and Jacob T. Carlin
Atmos. Meas. Tech., 17, 4087–4107, https://doi.org/10.5194/amt-17-4087-2024, https://doi.org/10.5194/amt-17-4087-2024, 2024
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Boundary-layer height observations remain sparse in time and space. In this study we create a new fuzzy logic method for synergistically combining boundary-layer height estimates from a suite of instruments. These estimates generally compare well to those from radiosondes; plus, the approach offers near-continuous estimates through the entire diurnal cycle. Suspected reasons for discrepancies are discussed. The code for the newly presented fuzzy logic method is provided for the community to use.
Lucas Reimann, Clemens Simmer, and Silke Trömel
Atmos. Chem. Phys., 23, 14219–14237, https://doi.org/10.5194/acp-23-14219-2023, https://doi.org/10.5194/acp-23-14219-2023, 2023
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Polarimetric radar observations were assimilated for the first time in a convective-scale numerical weather prediction system in Germany and their impact on short-term precipitation forecasts was evaluated. The assimilation was performed using microphysical retrievals of liquid and ice water content and yielded slightly improved deterministic 9 h precipitation forecasts for three intense summer precipitation cases with respect to the assimilation of radar reflectivity alone.
Armin Blanke, Andrew J. Heymsfield, Manuel Moser, and Silke Trömel
Atmos. Meas. Tech., 16, 2089–2106, https://doi.org/10.5194/amt-16-2089-2023, https://doi.org/10.5194/amt-16-2089-2023, 2023
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We present an evaluation of current retrieval techniques in the ice phase applied to polarimetric radar measurements with collocated in situ observations of aircraft conducted over the Olympic Mountains, Washington State, during winter 2015. Radar estimates of ice properties agreed most with aircraft observations in regions with pronounced radar signatures, but uncertainties were identified that indicate issues of some retrievals, particularly in warmer temperature regimes.
Mohamed Saadi, Carina Furusho-Percot, Alexandre Belleflamme, Ju-Yu Chen, Silke Trömel, and Stefan Kollet
Nat. Hazards Earth Syst. Sci., 23, 159–177, https://doi.org/10.5194/nhess-23-159-2023, https://doi.org/10.5194/nhess-23-159-2023, 2023
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On 14 July 2021, heavy rainfall fell over central Europe, causing considerable damage and human fatalities. We analyzed how accurate our estimates of rainfall and peak flow were for these flooding events in western Germany. We found that the rainfall estimates from radar measurements were improved by including polarimetric variables and their vertical gradients. Peak flow estimates were highly uncertain due to uncertainties in hydrological model parameters and rainfall measurements.
Prabhakar Shrestha, Jana Mendrok, and Dominik Brunner
Atmos. Chem. Phys., 22, 14095–14117, https://doi.org/10.5194/acp-22-14095-2022, https://doi.org/10.5194/acp-22-14095-2022, 2022
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The study extends the Terrestrial Systems Modeling Platform with gas-phase chemistry aerosol dynamics and a radar forward operator to enable detailed studies of aerosol–cloud–precipitation interactions. This is demonstrated using a case study of a deep convective storm, which showed that the strong updraft in the convective core of the storm produced aerosol-tower-like features, which affected the size of the hydrometeors and the simulated polarimetric features (e.g., ZDR and KDP columns).
Sachin Patade, Deepak Waman, Akash Deshmukh, Ashok Kumar Gupta, Arti Jadav, Vaughan T. J. Phillips, Aaron Bansemer, Jacob Carlin, and Alexander Ryzhkov
Atmos. Chem. Phys., 22, 12055–12075, https://doi.org/10.5194/acp-22-12055-2022, https://doi.org/10.5194/acp-22-12055-2022, 2022
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This modeling study focuses on the role of multiple groups of primary biological aerosol particles as ice nuclei on cloud properties and precipitation. This was done by implementing a more realistic scheme for biological ice nucleating particles in the aerosol–cloud model. Results show that biological ice nucleating particles have a limited role in altering the ice phase and precipitation in deep convective clouds.
Prabhakar Shrestha, Silke Trömel, Raquel Evaristo, and Clemens Simmer
Atmos. Chem. Phys., 22, 7593–7618, https://doi.org/10.5194/acp-22-7593-2022, https://doi.org/10.5194/acp-22-7593-2022, 2022
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The study makes use of ensemble numerical simulations with forward operator to evaluate the simulated cloud and precipitation processes with radar observations. While comparing model data with radar has its own challenges due to errors in the forward operator and processed radar measurements, the model was generally found to underestimate the high reflectivity, width/magnitude (value) of ZDR columns and high precipitation.
Alan J. Geer, Peter Bauer, Katrin Lonitz, Vasileios Barlakas, Patrick Eriksson, Jana Mendrok, Amy Doherty, James Hocking, and Philippe Chambon
Geosci. Model Dev., 14, 7497–7526, https://doi.org/10.5194/gmd-14-7497-2021, https://doi.org/10.5194/gmd-14-7497-2021, 2021
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Satellite observations of radiation from the earth can have strong sensitivity to cloud and precipitation in the atmosphere, with applications in weather forecasting and the development of models. Computing the radiation received at the satellite sensor using radiative transfer theory requires a simulation of the optical properties of a volume containing a large number of cloud and precipitation particles. This article describes the physics used to generate these
bulkoptical properties.
Silke Trömel, Clemens Simmer, Ulrich Blahak, Armin Blanke, Sabine Doktorowski, Florian Ewald, Michael Frech, Mathias Gergely, Martin Hagen, Tijana Janjic, Heike Kalesse-Los, Stefan Kneifel, Christoph Knote, Jana Mendrok, Manuel Moser, Gregor Köcher, Kai Mühlbauer, Alexander Myagkov, Velibor Pejcic, Patric Seifert, Prabhakar Shrestha, Audrey Teisseire, Leonie von Terzi, Eleni Tetoni, Teresa Vogl, Christiane Voigt, Yuefei Zeng, Tobias Zinner, and Johannes Quaas
Atmos. Chem. Phys., 21, 17291–17314, https://doi.org/10.5194/acp-21-17291-2021, https://doi.org/10.5194/acp-21-17291-2021, 2021
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The article introduces the ACP readership to ongoing research in Germany on cloud- and precipitation-related process information inherent in polarimetric radar measurements, outlines pathways to inform atmospheric models with radar-based information, and points to remaining challenges towards an improved fusion of radar polarimetry and atmospheric modelling.
Yuefei Zeng, Tijana Janjic, Yuxuan Feng, Ulrich Blahak, Alberto de Lozar, Elisabeth Bauernschubert, Klaus Stephan, and Jinzhong Min
Atmos. Meas. Tech., 14, 5735–5756, https://doi.org/10.5194/amt-14-5735-2021, https://doi.org/10.5194/amt-14-5735-2021, 2021
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Observation errors (OEs) of radar measurements are correlated. The Desroziers method has been often used to estimate statistics of OE in data assimilation. However, the resulting statistics consist of contributions from different sources and are difficult to interpret. Here, we use an approach based on samples for truncation error to approximate the representation error due to unresolved scales and processes (RE) and compare its statistics with OE statistics estimated by the Desroziers method.
José Dias Neto, Stefan Kneifel, Davide Ori, Silke Trömel, Jan Handwerker, Birger Bohn, Normen Hermes, Kai Mühlbauer, Martin Lenefer, and Clemens Simmer
Earth Syst. Sci. Data, 11, 845–863, https://doi.org/10.5194/essd-11-845-2019, https://doi.org/10.5194/essd-11-845-2019, 2019
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This study describes a 2-month dataset of ground-based, vertically pointing triple-frequency cloud radar observations recorded during the winter season 2015/2016 in Jülich, Germany. Intensive quality control has been applied to the unique long-term dataset, which allows the multifrequency signatures of ice and snow particles to be statistically analyzed for the first time. The analysis includes, for example, aggregation and its dependence on cloud temperature, riming, and onset of melting.
Michael Weger, Bernd Heinold, Christa Engler, Ulrich Schumann, Axel Seifert, Romy Fößig, Christiane Voigt, Holger Baars, Ulrich Blahak, Stephan Borrmann, Corinna Hoose, Stefan Kaufmann, Martina Krämer, Patric Seifert, Fabian Senf, Johannes Schneider, and Ina Tegen
Atmos. Chem. Phys., 18, 17545–17572, https://doi.org/10.5194/acp-18-17545-2018, https://doi.org/10.5194/acp-18-17545-2018, 2018
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The impact of desert dust on cloud formation is investigated for a major Saharan dust event over Europe by interactive regional dust modeling. Dust particles are very efficient ice-nucleating particles promoting the formation of ice crystals in clouds. The simulations show that the observed extensive cirrus development was likely related to the above-average dust load. The interactive dust–cloud feedback in the model significantly improves the agreement with aircraft and satellite observations.
Philippe Baron, Donal Murtagh, Patrick Eriksson, Jana Mendrok, Satoshi Ochiai, Kristell Pérot, Hideo Sagawa, and Makoto Suzuki
Atmos. Meas. Tech., 11, 4545–4566, https://doi.org/10.5194/amt-11-4545-2018, https://doi.org/10.5194/amt-11-4545-2018, 2018
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This paper investigates with computer simulations the measurement performances of the satellite Stratospheric Inferred Winds (SIW) in the altitude range 10–90 km. SIW is a Swedish mission that will be launched close to 2022. It is intended to fill the current altitude gap between 30 and 70 km in wind measurements and to pursue the monitoring of temperature and key stratospheric constituents for better understanding climate change effects.
Patrick Eriksson, Robin Ekelund, Jana Mendrok, Manfred Brath, Oliver Lemke, and Stefan A. Buehler
Earth Syst. Sci. Data, 10, 1301–1326, https://doi.org/10.5194/essd-10-1301-2018, https://doi.org/10.5194/essd-10-1301-2018, 2018
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A main application of microwave remote sensing is to observe atmospheric particles consisting of ice. This application requires data on how particles with different shapes and sizes affect the observations. A database of such properties has been developed. The database is the most comprehensive of its type. Main strengths are a good representation of particles of aggregate type and broad frequency coverage.
Stefan A. Buehler, Jana Mendrok, Patrick Eriksson, Agnès Perrin, Richard Larsson, and Oliver Lemke
Geosci. Model Dev., 11, 1537–1556, https://doi.org/10.5194/gmd-11-1537-2018, https://doi.org/10.5194/gmd-11-1537-2018, 2018
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The Atmospheric Radiative Transfer Simulator (ARTS) is a public domain
software for simulating how radiation in the microwave to infrared
spectral range travels through an atmosphere. The program can simulate
satellite observations, in cloudy and clear atmospheres, and can also
be used to calculate radiative energy fluxes. The main feature of this
release is a planetary toolbox that allows simulations for the
planets Venus, Mars, and Jupiter, in addition to Earth.
Andreas Macke, Patric Seifert, Holger Baars, Christian Barthlott, Christoph Beekmans, Andreas Behrendt, Birger Bohn, Matthias Brueck, Johannes Bühl, Susanne Crewell, Thomas Damian, Hartwig Deneke, Sebastian Düsing, Andreas Foth, Paolo Di Girolamo, Eva Hammann, Rieke Heinze, Anne Hirsikko, John Kalisch, Norbert Kalthoff, Stefan Kinne, Martin Kohler, Ulrich Löhnert, Bomidi Lakshmi Madhavan, Vera Maurer, Shravan Kumar Muppa, Jan Schween, Ilya Serikov, Holger Siebert, Clemens Simmer, Florian Späth, Sandra Steinke, Katja Träumner, Silke Trömel, Birgit Wehner, Andreas Wieser, Volker Wulfmeyer, and Xinxin Xie
Atmos. Chem. Phys., 17, 4887–4914, https://doi.org/10.5194/acp-17-4887-2017, https://doi.org/10.5194/acp-17-4887-2017, 2017
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This article provides an overview of the instrumental setup and the main results obtained during the two HD(CP)2 Observational Prototype Experiments HOPE-Jülich and HOPE-Melpitz conducted in Germany in April–May and Sept 2013, respectively. Goal of the field experiments was to provide high-resolution observational datasets for both, improving the understaning of boundary layer and cloud processes, as well as for the evaluation of the new ICON model that is run at 156 m horizontal resolution.
Hendrik Wouters, Matthias Demuzere, Ulrich Blahak, Krzysztof Fortuniak, Bino Maiheu, Johan Camps, Daniël Tielemans, and Nicole P. M. van Lipzig
Geosci. Model Dev., 9, 3027–3054, https://doi.org/10.5194/gmd-9-3027-2016, https://doi.org/10.5194/gmd-9-3027-2016, 2016
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A methodology is presented for translating three-dimensional information of urban areas into land-surface parameters that can be easily employed in atmospheric modelling. As demonstrated with the COSMO-CLM model for a Belgian summer, it enables them to represent urban heat islands and their dependency on urban design with a low computational cost. It allows for efficiently incorporating urban information systems (e.g., WUDAPT) into climate change assessment and numerical weather prediction.
Xinxin Xie, Raquel Evaristo, Clemens Simmer, Jan Handwerker, and Silke Trömel
Atmos. Chem. Phys., 16, 7105–7116, https://doi.org/10.5194/acp-16-7105-2016, https://doi.org/10.5194/acp-16-7105-2016, 2016
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This study provides a first analysis of rainfall observations and related microphysical processes during the HOPE campaign, which will benefit future studies on the evaluation and improvement of climate models within the HD(CP)2 framework. The results conveyed in this study confirm that polarimetric radars have the capability to validate weather and climate models with respect to rainfall estimation and the ongoing microphysical processes.
P. Shrestha, M. Sulis, C. Simmer, and S. Kollet
Hydrol. Earth Syst. Sci., 19, 4317–4326, https://doi.org/10.5194/hess-19-4317-2015, https://doi.org/10.5194/hess-19-4317-2015, 2015
Short summary
Short summary
This study highlights the grid resolution dependence of energy and water balance of the 3-D physically based integrated surface-groundwater model. The non-local controls of soil moisture were found to be highly grid resolution dependent, but the local vegetation control strongly modulates the scaling behavior of surface energy fluxes. For coupled runs, variability in patterns of surface fluxes due to this scale dependence can affect the simulated atmospheric boundary layer and local circulation.
P. Eriksson, M. Jamali, J. Mendrok, and S. A. Buehler
Atmos. Meas. Tech., 8, 1913–1933, https://doi.org/10.5194/amt-8-1913-2015, https://doi.org/10.5194/amt-8-1913-2015, 2015
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The optical properties of randomly oriented ice hydrometeors are reviewed from a perspective of microwave mass retrievals. The soft particle approximation is found to be highly problematic, and the alternative approach presented by Geer and Baordo (2014) should instead be used. We present a simplified version of this approach, and point out several critical limitations of existing DDA data.
F. Gasper, K. Goergen, P. Shrestha, M. Sulis, J. Rihani, M. Geimer, and S. Kollet
Geosci. Model Dev., 7, 2531–2543, https://doi.org/10.5194/gmd-7-2531-2014, https://doi.org/10.5194/gmd-7-2531-2014, 2014
A. Seifert, U. Blahak, and R. Buhr
Geosci. Model Dev., 7, 463–478, https://doi.org/10.5194/gmd-7-463-2014, https://doi.org/10.5194/gmd-7-463-2014, 2014
Y. Kasai, H. Sagawa, D. Kreyling, E. Dupuy, P. Baron, J. Mendrok, K. Suzuki, T. O. Sato, T. Nishibori, S. Mizobuchi, K. Kikuchi, T. Manabe, H. Ozeki, T. Sugita, M. Fujiwara, Y. Irimajiri, K. A. Walker, P. F. Bernath, C. Boone, G. Stiller, T. von Clarmann, J. Orphal, J. Urban, D. Murtagh, E. J. Llewellyn, D. Degenstein, A. E. Bourassa, N. D. Lloyd, L. Froidevaux, M. Birk, G. Wagner, F. Schreier, J. Xu, P. Vogt, T. Trautmann, and M. Yasui
Atmos. Meas. Tech., 6, 2311–2338, https://doi.org/10.5194/amt-6-2311-2013, https://doi.org/10.5194/amt-6-2311-2013, 2013
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Microphysics model-based diagnosis, such as the spectral bin model (SBM), has recently been attempted to diagnose winter precipitation types. In this study, the accuracy of SBM-based precipitation type diagnosis is compared with other traditional methods. SBM has a relatively higher accuracy for dry-snow and wet-snow events, whereas it has lower accuracy for rain events. When the microphysics scheme in the SBM was optimized for the corresponding region, the accuracy for rain events improved.
Gabriel Colas, Valéry Masson, François Bouttier, Ludovic Bouilloud, Laura Pavan, and Virve Karsisto
Geosci. Model Dev., 18, 3453–3472, https://doi.org/10.5194/gmd-18-3453-2025, https://doi.org/10.5194/gmd-18-3453-2025, 2025
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In winter, snow- and ice-covered artificial surfaces are important aspects of the urban climate. They may influence the magnitude of the urban heat island effect, but this is still unclear. In this study, we improved the representation of the snow and ice cover in the Town Energy Balance (TEB) urban climate model. Evaluations have shown that the results are promising for using TEB to study the climate of cold cities.
Markus Kunze, Christoph Zülicke, Tarique A. Siddiqui, Claudia C. Stephan, Yosuke Yamazaki, Claudia Stolle, Sebastian Borchert, and Hauke Schmidt
Geosci. Model Dev., 18, 3359–3385, https://doi.org/10.5194/gmd-18-3359-2025, https://doi.org/10.5194/gmd-18-3359-2025, 2025
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We present the Icosahedral Nonhydrostatic (ICON) general circulation model with an upper-atmospheric extension with the physics package for numerical weather prediction (UA-ICON(NWP)). We optimized the parameters for the gravity wave parameterizations and achieved realistic modeling of the thermal and dynamic states of the mesopause regions. UA-ICON(NWP) now shows a realistic frequency of major sudden stratospheric warmings and well-represented solar tides in temperature.
Lucas A. Estrada, Daniel J. Varon, Melissa Sulprizio, Hannah Nesser, Zichong Chen, Nicholas Balasus, Sarah E. Hancock, Megan He, James D. East, Todd A. Mooring, Alexander Oort Alonso, Joannes D. Maasakkers, Ilse Aben, Sabour Baray, Kevin W. Bowman, John R. Worden, Felipe J. Cardoso-Saldaña, Emily Reidy, and Daniel J. Jacob
Geosci. Model Dev., 18, 3311–3330, https://doi.org/10.5194/gmd-18-3311-2025, https://doi.org/10.5194/gmd-18-3311-2025, 2025
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Reducing emissions of methane, a powerful greenhouse gas, is a top policy concern for mitigating anthropogenic climate change. The Integrated Methane Inversion (IMI) is an advanced, cloud-based software that translates satellite observations into actionable emissions data. Here we present IMI version 2.0 with vastly expanded capabilities. These updates enable a wider range of scientific and stakeholder applications from individual basin to global scales with continuous emissions monitoring.
Cynthia H. Whaley, Tim Butler, Jose A. Adame, Rupal Ambulkar, Steve R. Arnold, Rebecca R. Buchholz, Benjamin Gaubert, Douglas S. Hamilton, Min Huang, Hayley Hung, Johannes W. Kaiser, Jacek W. Kaminski, Christoph Knote, Gerbrand Koren, Jean-Luc Kouassi, Meiyun Lin, Tianjia Liu, Jianmin Ma, Kasemsan Manomaiphiboon, Elisa Bergas Masso, Jessica L. McCarty, Mariano Mertens, Mark Parrington, Helene Peiro, Pallavi Saxena, Saurabh Sonwani, Vanisa Surapipith, Damaris Y. T. Tan, Wenfu Tang, Veerachai Tanpipat, Kostas Tsigaridis, Christine Wiedinmyer, Oliver Wild, Yuanyu Xie, and Paquita Zuidema
Geosci. Model Dev., 18, 3265–3309, https://doi.org/10.5194/gmd-18-3265-2025, https://doi.org/10.5194/gmd-18-3265-2025, 2025
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The multi-model experiment design of the HTAP3 Fires project takes a multi-pollutant approach to improving our understanding of transboundary transport of wildland fire and agricultural burning emissions and their impacts. The experiments are designed with the goal of answering science policy questions related to fires. The options for the multi-model approach, including inputs, outputs, and model setup, are discussed, and the official recommendations for the project are presented.
Maurin Zouzoua, Sophie Bastin, Fabienne Lohou, Marie Lothon, Marjolaine Chiriaco, Mathilde Jome, Cécile Mallet, Laurent Barthes, and Guylaine Canut
Geosci. Model Dev., 18, 3211–3239, https://doi.org/10.5194/gmd-18-3211-2025, https://doi.org/10.5194/gmd-18-3211-2025, 2025
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This study proposes using a statistical model to freeze errors due to differences in environmental forcing when evaluating the surface turbulent heat fluxes from numerical simulations with observations. The statistical model is first built with observations and then applied to the simulated environment to generate possibly observed fluxes. This novel method provides insight into differently evaluating the numerical formulation of turbulent heat fluxes with a long period of observational data.
Oxana Drofa
Geosci. Model Dev., 18, 3175–3209, https://doi.org/10.5194/gmd-18-3175-2025, https://doi.org/10.5194/gmd-18-3175-2025, 2025
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This paper presents the result of many years of effort of the author, who developed an original mathematical numerical model of heat and moisture exchange processes in soil, vegetation, and snow. The author relied on her 30 years of research experience in atmospheric numerical modelling. The presented model is the fruit of the author's research on physical processes at the surface–atmosphere interface and their numerical approximation and aims at improving numerical weather forecasting and climate simulations.
Tyler P. Janoski, Ivan Mitevski, Ryan J. Kramer, Michael Previdi, and Lorenzo M. Polvani
Geosci. Model Dev., 18, 3065–3079, https://doi.org/10.5194/gmd-18-3065-2025, https://doi.org/10.5194/gmd-18-3065-2025, 2025
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We developed ClimKern, a Python package and radiative kernel repository, to simplify calculating radiative feedbacks and make climate sensitivity studies more reproducible. Testing of ClimKern with sample climate model data reveals that radiative kernel choice may be more important than previously thought, especially in polar regions. Our work highlights the need for kernel sensitivity analyses to be included in future studies.
Matti Niskanen, Aku Seppänen, Henri Oikarinen, Miska Olin, Panu Karjalainen, Santtu Mikkonen, and Kari Lehtinen
Geosci. Model Dev., 18, 2983–3001, https://doi.org/10.5194/gmd-18-2983-2025, https://doi.org/10.5194/gmd-18-2983-2025, 2025
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Particle size is a key factor determining the properties of aerosol particles which have a major influence on the climate and on human health. When measuring the particle sizes, however, sometimes the sampling lines that transfer the aerosol to the measurement device distort the size distribution, making the measurement unreliable. We propose a method to correct for the distortions and estimate the true particle sizes, improving measurement accuracy.
Johann Rasmus Nüß, Nikos Daskalakis, Fabian Günther Piwowarczyk, Angelos Gkouvousis, Oliver Schneising, Michael Buchwitz, Maria Kanakidou, Maarten C. Krol, and Mihalis Vrekoussis
Geosci. Model Dev., 18, 2861–2890, https://doi.org/10.5194/gmd-18-2861-2025, https://doi.org/10.5194/gmd-18-2861-2025, 2025
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We estimate carbon monoxide emissions through inverse modeling, an approach where measurements of tracers in the atmosphere are fed to a model to calculate backwards in time (inverse) where the tracers came from. We introduce measurements from a new satellite instrument and show that, in most places globally, these on their own sufficiently constrain the emissions. This alleviates the need for additional datasets, which could shorten the delay for future carbon monoxide source estimates.
Ashu Dastoor, Hélène Angot, Johannes Bieser, Flora Brocza, Brock Edwards, Aryeh Feinberg, Xinbin Feng, Benjamin Geyman, Charikleia Gournia, Yipeng He, Ian M. Hedgecock, Ilia Ilyin, Jane Kirk, Che-Jen Lin, Igor Lehnherr, Robert Mason, David McLagan, Marilena Muntean, Peter Rafaj, Eric M. Roy, Andrei Ryjkov, Noelle E. Selin, Francesco De Simone, Anne L. Soerensen, Frits Steenhuisen, Oleg Travnikov, Shuxiao Wang, Xun Wang, Simon Wilson, Rosa Wu, Qingru Wu, Yanxu Zhang, Jun Zhou, Wei Zhu, and Scott Zolkos
Geosci. Model Dev., 18, 2747–2860, https://doi.org/10.5194/gmd-18-2747-2025, https://doi.org/10.5194/gmd-18-2747-2025, 2025
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This paper introduces the Multi-Compartment Mercury (Hg) Modeling and Analysis Project (MCHgMAP) aimed at informing the effectiveness evaluations of two multilateral environmental agreements: the Minamata Convention on Mercury and the Convention on Long-Range Transboundary Air Pollution. The experimental design exploits a variety of models (atmospheric, land, oceanic ,and multimedia mass balance models) to assess the short- and long-term influences of anthropogenic Hg releases into the environment.
Hilda Sandström and Patrick Rinke
Geosci. Model Dev., 18, 2701–2724, https://doi.org/10.5194/gmd-18-2701-2025, https://doi.org/10.5194/gmd-18-2701-2025, 2025
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Machine learning has the potential to aid the identification of organic molecules involved in aerosol formation. Yet, progress is stalled by a lack of curated atmospheric molecular datasets. Here, we compared atmospheric compounds with large molecular datasets used in machine learning and found minimal overlap with similarity algorithms. Our result underlines the need for collaborative efforts to curate atmospheric molecular data to facilitate machine learning models in atmospheric sciences.
Juan Escobar, Philippe Wautelet, Joris Pianezze, Florian Pantillon, Thibaut Dauhut, Christelle Barthe, and Jean-Pierre Chaboureau
Geosci. Model Dev., 18, 2679–2700, https://doi.org/10.5194/gmd-18-2679-2025, https://doi.org/10.5194/gmd-18-2679-2025, 2025
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The Meso-NH weather research code is adapted for GPUs using OpenACC, leading to significant performance and energy efficiency improvements. Called MESONH-v55-OpenACC, it includes enhanced memory management, communication optimizations and a new solver. On the AMD MI250X Adastra platform, it achieved up to 6× speedup and 2.3× energy efficiency gain compared to CPUs. Storm simulations at 100 m resolution show positive results, positioning the code for future use on exascale supercomputers.
Jie Gao, Yi Huang, Jonathon S. Wright, Ke Li, Tao Geng, and Qiurun Yu
Geosci. Model Dev., 18, 2569–2586, https://doi.org/10.5194/gmd-18-2569-2025, https://doi.org/10.5194/gmd-18-2569-2025, 2025
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The aerosol in the upper troposphere and stratosphere is highly variable, and its radiative effect is poorly understood. To estimate this effect, the radiative kernel is constructed and applied. The results show that the kernels can reproduce aerosol radiative effects and are expected to simulate stratospheric aerosol radiative effects. This approach reduces computational expense, is consistent with radiative model calculations, and can be applied to atmospheric models with speed requirements.
Ji Won Yoon, Seungyeon Lee, Ebony Lee, and Seon Ki Park
Geosci. Model Dev., 18, 2303–2328, https://doi.org/10.5194/gmd-18-2303-2025, https://doi.org/10.5194/gmd-18-2303-2025, 2025
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This study evaluates the Weather Research and Forecasting Model (WRF) coupled with Chemistry (WRF-Chem) to predict a mega Asian dust storm (ADS) over South Korea on 28–29 March 2021. We assessed combinations of five dust emission and four land surface schemes by analyzing meteorological and air quality variables. The best scheme combination reduced the root mean square error (RMSE) for particulate matter 10 (PM10) by up to 29.6 %, demonstrating the highest performance.
Jianyu Lin, Tie Dai, Lifang Sheng, Weihang Zhang, Shangfei Hai, and Yawen Kong
Geosci. Model Dev., 18, 2231–2248, https://doi.org/10.5194/gmd-18-2231-2025, https://doi.org/10.5194/gmd-18-2231-2025, 2025
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The effectiveness of this assimilation system and its sensitivity to the ensemble member size and length of the assimilation window are investigated. This study advances our understanding of the selection of basic parameters in the four-dimensional local ensemble transform Kalman filter assimilation system and the performance of ensemble simulation in a particulate-matter-polluted environment.
Jens Peter Karolus Wenceslaus Frankemölle, Johan Camps, Pieter De Meutter, and Johan Meyers
Geosci. Model Dev., 18, 1989–2003, https://doi.org/10.5194/gmd-18-1989-2025, https://doi.org/10.5194/gmd-18-1989-2025, 2025
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To detect anomalous radioactivity in the environment, it is paramount that we understand the natural background level. In this work, we propose a statistical model to describe the most likely background level and the associated uncertainty in a network of dose rate detectors. We train, verify, and validate the model using real environmental data. Using the model, we show that we can correctly predict the background level in a subset of the detector network during a known
anomalous event.
Jean-François Grailet, Robin J. Hogan, Nicolas Ghilain, David Bolsée, Xavier Fettweis, and Marilaure Grégoire
Geosci. Model Dev., 18, 1965–1988, https://doi.org/10.5194/gmd-18-1965-2025, https://doi.org/10.5194/gmd-18-1965-2025, 2025
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The MAR (Modèle Régional Atmosphérique) is a regional climate model used for weather forecasting and studying the climate over various regions. This paper presents an update of MAR thanks to which it can precisely decompose solar radiation, in particular in the UV (ultraviolet) and photosynthesis ranges, both being critical to human health and ecosystems. As a first application of this new capability, this paper presents a method for predicting UV indices with MAR.
Yi-Ning Shi, Jun Yang, Wei Han, Lujie Han, Jiajia Mao, Wanlin Kan, and Fuzhong Weng
Geosci. Model Dev., 18, 1947–1964, https://doi.org/10.5194/gmd-18-1947-2025, https://doi.org/10.5194/gmd-18-1947-2025, 2025
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Direct assimilation of observations from ground-based microwave radiometers (GMRs) holds significant potential for improving forecast accuracy. Radiative transfer models (RTMs) play a crucial role in direct data assimilation. In this study, we introduce a new RTM, the Advanced Radiative Transfer Modeling System – Ground-Based (ARMS-gb), designed to simulate brightness temperatures observed by GMRs along with their Jacobians. Several enhancements have been incorporated to achieve higher accuracy.
R. Phani Murali Krishna, Siddharth Kumar, A. Gopinathan Prajeesh, Peter Bechtold, Nils Wedi, Kumar Roy, Malay Ganai, B. Revanth Reddy, Snehlata Tirkey, Tanmoy Goswami, Radhika Kanase, Sahadat Sarkar, Medha Deshpande, and Parthasarathi Mukhopadhyay
Geosci. Model Dev., 18, 1879–1894, https://doi.org/10.5194/gmd-18-1879-2025, https://doi.org/10.5194/gmd-18-1879-2025, 2025
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The High-Resolution Global Forecast Model (HGFM) is an advanced iteration of the operational Global Forecast System (GFS) model. HGFM can produce forecasts at a spatial scale of ~6 km in tropics. It demonstrates improved accuracy in short- to medium-range weather prediction over the Indian region, with notable success in predicting extreme events. Further, the model will be entrusted to operational forecasting agencies after validation and testing.
Jenna Ritvanen, Seppo Pulkkinen, Dmitri Moisseev, and Daniele Nerini
Geosci. Model Dev., 18, 1851–1878, https://doi.org/10.5194/gmd-18-1851-2025, https://doi.org/10.5194/gmd-18-1851-2025, 2025
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Nowcasting models struggle with the rapid evolution of heavy rain, and common verification methods are unable to describe how accurately the models predict the growth and decay of heavy rain. We propose a framework to assess model performance. In the framework, convective cells are identified and tracked in the forecasts and observations, and the model skill is then evaluated by comparing differences between forecast and observed cells. We demonstrate the framework with four open-source models.
Andrew Geiss and Po-Lun Ma
Geosci. Model Dev., 18, 1809–1827, https://doi.org/10.5194/gmd-18-1809-2025, https://doi.org/10.5194/gmd-18-1809-2025, 2025
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Particles in the Earth's atmosphere strongly impact the planet's energy budget, and atmosphere simulations require accurate representation of their interaction with light. This work introduces two approaches to represent light scattering by small particles. The first is a scattering simulator based on Mie theory implemented in Python. The second is a neural network emulator that is more accurate than existing methods and is fast enough to be used in climate and weather simulations.
Qin Wang, Bo Zeng, Gong Chen, and Yaoting Li
Geosci. Model Dev., 18, 1769–1784, https://doi.org/10.5194/gmd-18-1769-2025, https://doi.org/10.5194/gmd-18-1769-2025, 2025
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This study evaluates the performance of four planetary boundary layer (PBL) schemes in near-surface wind fields over the Sichuan Basin, China. Using 112 sensitivity experiments with the Weather Research and Forecasting (WRF) model and focusing on 28 wind events, it is found that wind direction was less sensitive to the PBL schemes. The quasi-normal scale elimination (QNSE) scheme captured temporal variations best, while the Mellor–Yamada–Janjić (MYJ) scheme had the least error in wind speed.
Tai-Long He, Nikhil Dadheech, Tammy M. Thompson, and Alexander J. Turner
Geosci. Model Dev., 18, 1661–1671, https://doi.org/10.5194/gmd-18-1661-2025, https://doi.org/10.5194/gmd-18-1661-2025, 2025
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It is computationally expensive to infer greenhouse gas (GHG) emissions using atmospheric observations. This is partly due to the detailed model used to represent atmospheric transport. We demonstrate how a machine learning (ML) model can be used to simulate high-resolution atmospheric transport. This type of ML model will help estimate GHG emissions using dense observations, which are becoming increasingly common with the proliferation of urban monitoring networks and geostationary satellites.
Wei Li, Beiming Tang, Patrick C. Campbell, Youhua Tang, Barry Baker, Zachary Moon, Daniel Tong, Jianping Huang, Kai Wang, Ivanka Stajner, and Raffaele Montuoro
Geosci. Model Dev., 18, 1635–1660, https://doi.org/10.5194/gmd-18-1635-2025, https://doi.org/10.5194/gmd-18-1635-2025, 2025
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The study describes the updates of NOAA's current UFS-AQMv7 air quality forecast model by incorporating the latest scientific and structural changes in CMAQv5.4. An evaluation during the summer of 2023 shows that the updated model overall improves the simulation of MDA8 O3 by reducing the bias by 8%–12% in the contiguous US. PM2.5 predictions have mixed results due to wildfire, highlighting the need for future refinements.
Yanwei Zhu, Aitor Atencia, Markus Dabernig, and Yong Wang
Geosci. Model Dev., 18, 1545–1559, https://doi.org/10.5194/gmd-18-1545-2025, https://doi.org/10.5194/gmd-18-1545-2025, 2025
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Most works have delved into convective weather nowcasting, and only a few works have discussed the nowcasting uncertainty for variables at the surface level. Hence, we proposed a method to estimate uncertainty. Generating appropriate noises associated with the characteristic of the error in analysis can simulate the uncertainty of nowcasting. This method can contribute to the estimation of near–surface analysis uncertainty in both nowcasting applications and ensemble nowcasting development.
Joël Thanwerdas, Antoine Berchet, Lionel Constantin, Aki Tsuruta, Michael Steiner, Friedemann Reum, Stephan Henne, and Dominik Brunner
Geosci. Model Dev., 18, 1505–1544, https://doi.org/10.5194/gmd-18-1505-2025, https://doi.org/10.5194/gmd-18-1505-2025, 2025
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The Community Inversion Framework (CIF) brings together methods for estimating greenhouse gas fluxes from atmospheric observations. The initial ensemble method implemented in CIF was found to be incomplete and could hardly be compared to other ensemble methods employed in the inversion community. In this paper, we present and evaluate a new implementation of the ensemble mode, building upon the initial developments.
Astrid Kerkweg, Timo Kirfel, Duong H. Do, Sabine Griessbach, Patrick Jöckel, and Domenico Taraborrelli
Geosci. Model Dev., 18, 1265–1286, https://doi.org/10.5194/gmd-18-1265-2025, https://doi.org/10.5194/gmd-18-1265-2025, 2025
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Normally, the Modular Earth Submodel System (MESSy) is linked to complete dynamic models to create chemical climate models. However, the modular concept of MESSy and the newly developed DWARF component presented here make it possible to create simplified models that contain only one or a few process descriptions. This is very useful for technical optimisation, such as porting to GPUs, and can be used to create less complex models, such as a chemical box model.
Peter Wind and Willem van Caspel
EGUsphere, https://doi.org/10.5194/egusphere-2024-3571, https://doi.org/10.5194/egusphere-2024-3571, 2025
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This paper presents a numerical method to assess the origin of air pollution. Combined with a numerical air pollution transport and chemistry model, it can follow the contributions from a large number of emission sources. The result is a series of maps that give the relative contributions from for example all European countries at each point.
Julian Vogel, Sebastian Stadler, Ganesh Chockalingam, Afshin Afshari, Johanna Henning, and Matthias Winkler
EGUsphere, https://doi.org/10.5194/egusphere-2025-144, https://doi.org/10.5194/egusphere-2025-144, 2025
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This study presents a toolkit to simplify input data creation for the urban microclimate model PALM-4U. It introduces novel methods to automate the use of open data sources. Our analysis of four test cases created from different geographic data sources shows variations in temperature, humidity, and wind speed, influenced by data quality. Validation indicates that the automated methods yield results comparable to expert-driven approaches, facilitating user-friendly urban climate modeling.
Edward C. Chan, Ilona J. Jäkel, Basit Khan, Martijn Schaap, Timothy M. Butler, Renate Forkel, and Sabine Banzhaf
Geosci. Model Dev., 18, 1119–1139, https://doi.org/10.5194/gmd-18-1119-2025, https://doi.org/10.5194/gmd-18-1119-2025, 2025
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An enhanced emission module has been developed for the PALM model system, improving flexibility and scalability of emission source representation across different sectors. A model for parametrized domestic emissions has also been included, for which an idealized model run is conducted for particulate matter (PM10). The results show that, in addition to individual sources and diurnal variations in energy consumption, vertical transport and urban topology play a role in concentration distribution.
Gregor Ehrensperger, Thorsten Simon, Georg J. Mayr, and Tobias Hell
Geosci. Model Dev., 18, 1141–1153, https://doi.org/10.5194/gmd-18-1141-2025, https://doi.org/10.5194/gmd-18-1141-2025, 2025
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As lightning is a brief and localized event, it is not explicitly resolved in atmospheric models. Instead, expert-based auxiliary descriptions are used to assess it. This study explores how AI can improve our understanding of lightning without relying on traditional expert knowledge. We reveal that AI independently identified the key factors known to experts as essential for lightning in the Alps region. This shows how knowledge discovery could be sped up in areas with limited expert knowledge.
David Patoulias, Kalliopi Florou, and Spyros N. Pandis
Geosci. Model Dev., 18, 1103–1118, https://doi.org/10.5194/gmd-18-1103-2025, https://doi.org/10.5194/gmd-18-1103-2025, 2025
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The effect of the assumed atmospheric nucleation mechanism on particle number concentrations and size distribution was investigated. Two quite different mechanisms involving sulfuric acid and ammonia or a biogenic organic vapor gave quite similar results which were consistent with measurements at 26 measurement stations across Europe. The number of larger particles that serve as cloud condensation nuclei showed little sensitivity to the assumed nucleation mechanism.
Tim Radke, Susanne Fuchs, Christian Wilms, Iuliia Polkova, and Marc Rautenhaus
Geosci. Model Dev., 18, 1017–1039, https://doi.org/10.5194/gmd-18-1017-2025, https://doi.org/10.5194/gmd-18-1017-2025, 2025
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In our study, we built upon previous work to investigate the patterns artificial intelligence (AI) learns to detect atmospheric features like tropical cyclones (TCs) and atmospheric rivers (ARs). As primary objective, we adopt a method to explain the AI used and investigate the plausibility of learned patterns. We find that plausible patterns are learned for both TCs and ARs. Hence, the chosen method is very useful for gaining confidence in the AI-based detection of atmospheric features.
Raphaël Périllat, Sylvain Girard, and Irène Korsakissok
EGUsphere, https://doi.org/10.5194/egusphere-2024-3838, https://doi.org/10.5194/egusphere-2024-3838, 2025
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We developed a method to improve decision-making during nuclear crises by predicting the spread of radiation more efficiently. Existing approaches are often too slow, especially when analyzing complex data like radiation maps. Our method combines techniques to simplify these maps and predict them quickly using statistical tools. This approach could help authorities respond faster and more accurately in emergencies, reducing risks to the population and the environment.
Shaofeng Hua, Gang Chen, Baojun Chen, Mingshan Li, and Xin Xu
EGUsphere, https://doi.org/10.5194/egusphere-2024-3834, https://doi.org/10.5194/egusphere-2024-3834, 2025
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Hail forecasting using numerical models remains a challenge. In this study, we found that the commonly used graupel-to-hail conversion parameterization method led to hail overforecasting in heavy rainfall cases where no hail was observed. By incorporating the spongy wet growth process, we successfully mitigated hail overforecasting. The modified scheme also produced hail in real hail events. This research contributes to a better understanding of hail formation.
Stefan Noll, Carsten Schmidt, Patrick Hannawald, Wolfgang Kausch, and Stefan Kimeswenger
EGUsphere, https://doi.org/10.5194/egusphere-2024-3512, https://doi.org/10.5194/egusphere-2024-3512, 2025
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Non-thermal emission from chemical reactions in the Earth's middle und upper atmosphere strongly contributes to the brightness of the night sky below about 2.3 µm. The new Paranal Airglow Line and Continuum Emission model calculates the emission spectrum and its variability with an unprecedented accuracy. Relying on a large spectroscopic data set from astronomical spectrographs and theoretical molecular/atomic data, it is valuable for airglow research and astronomical observatories.
Felipe Cifuentes, Henk Eskes, Enrico Dammers, Charlotte Bryan, and Folkert Boersma
Geosci. Model Dev., 18, 621–649, https://doi.org/10.5194/gmd-18-621-2025, https://doi.org/10.5194/gmd-18-621-2025, 2025
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We tested the capability of the flux divergence approach (FDA) to reproduce known NOx emissions using synthetic NO2 satellite column retrievals from high-resolution model simulations. The FDA accurately reproduced NOx emissions when column observations were limited to the boundary layer and when the variability of the NO2 lifetime, the NOx : NO2 ratio, and NO2 profile shapes were correctly modeled. This introduces strong model dependency, reducing the simplicity of the original FDA formulation.
Stefano Ubbiali, Christian Kühnlein, Christoph Schär, Linda Schlemmer, Thomas C. Schulthess, Michael Staneker, and Heini Wernli
Geosci. Model Dev., 18, 529–546, https://doi.org/10.5194/gmd-18-529-2025, https://doi.org/10.5194/gmd-18-529-2025, 2025
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We explore a high-level programming model for porting numerical weather prediction (NWP) model codes to graphics processing units (GPUs). We present a Python rewrite with the domain-specific library GT4Py (GridTools for Python) of two renowned cloud microphysics schemes and the associated tangent-linear and adjoint algorithms. We find excellent portability, competitive GPU performance, robust execution on diverse computing architectures, and enhanced code maintainability and user productivity.
Pieter Rijsdijk, Henk Eskes, Arlene Dingemans, K. Folkert Boersma, Takashi Sekiya, Kazuyuki Miyazaki, and Sander Houweling
Geosci. Model Dev., 18, 483–509, https://doi.org/10.5194/gmd-18-483-2025, https://doi.org/10.5194/gmd-18-483-2025, 2025
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Clustering high-resolution satellite observations into superobservations improves model validation and data assimilation applications. In our paper, we derive quantitative uncertainties for satellite NO2 column observations based on knowledge of the retrievals, including a detailed analysis of spatial error correlations and representativity errors. The superobservations and uncertainty estimates are tested in a global chemical data assimilation system and are found to improve the forecasts.
Dario Di Santo, Cenlin He, Fei Chen, and Lorenzo Giovannini
Geosci. Model Dev., 18, 433–459, https://doi.org/10.5194/gmd-18-433-2025, https://doi.org/10.5194/gmd-18-433-2025, 2025
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This paper presents the Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool (ML-AMPSIT), a computationally efficient tool that uses machine learning algorithms for sensitivity analysis in atmospheric models. It is tested with the Weather Research and Forecasting (WRF) model coupled with the Noah-Multiparameterization (Noah-MP) land surface model to investigate sea breeze circulation sensitivity to vegetation-related parameters.
Robert Schoetter, Robin James Hogan, Cyril Caliot, and Valéry Masson
Geosci. Model Dev., 18, 405–431, https://doi.org/10.5194/gmd-18-405-2025, https://doi.org/10.5194/gmd-18-405-2025, 2025
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Radiation is relevant to the atmospheric impact on people and infrastructure in cities as it can influence the urban heat island, building energy consumption, and human thermal comfort. A new urban radiation model, assuming a more realistic form of urban morphology, is coupled to the urban climate model Town Energy Balance (TEB). The new TEB is evaluated with a reference radiation model for a variety of urban morphologies, and an improvement in the simulated radiative observables is found.
Qike Yang, Chun Zhao, Jiawang Feng, Gudongze Li, Jun Gu, Zihan Xia, Mingyue Xu, and Zining Yang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-229, https://doi.org/10.5194/gmd-2024-229, 2025
Revised manuscript accepted for GMD
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This study presents the first comprehensive evaluation of unstructured meshes using the iAMAS model over Antarctica, encompassing both surface and upper-level meteorological fields. Comparison with ERA5 and observational data reveals that the iAMAS model performs well in simulating the Antarctic atmosphere; iAMAS demonstrates comparable, and in some cases superior, performance in simulating temperature and wind speed in East Antarctica when compared to ERA5.
Zebediah Engberg, Roger Teoh, Tristan Abbott, Thomas Dean, Marc E. J. Stettler, and Marc L. Shapiro
Geosci. Model Dev., 18, 253–286, https://doi.org/10.5194/gmd-18-253-2025, https://doi.org/10.5194/gmd-18-253-2025, 2025
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Contrails forming in some atmospheric conditions may persist and become strongly warming cirrus, while in other conditions may be neutral or cooling. We develop a contrail forecast model to predict contrail climate forcing for any arbitrary point in space and time and explore integration into flight planning and air traffic management. This approach enables contrail interventions to target high-probability high-climate-impact regions and reduce unintended consequences of contrail management.
Nils Eingrüber, Alina Domm, Wolfgang Korres, and Karl Schneider
Geosci. Model Dev., 18, 141–160, https://doi.org/10.5194/gmd-18-141-2025, https://doi.org/10.5194/gmd-18-141-2025, 2025
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Climate change adaptation measures like unsealings can reduce urban heat stress. As grass grid pavers have never been parameterized for microclimate model simulations with ENVI-met, a new parameterization was developed based on field measurements. To analyse the cooling potential, scenario analyses were performed for a densely developed area in Cologne. Statistically significant average cooling effects of up to −11.1 K were found for surface temperature and up to −2.9 K for 1 m air temperature.
Xuan Wang, Lei Bi, Hong Wang, Yaqiang Wang, Wei Han, Xueshun Shen, and Xiaoye Zhang
Geosci. Model Dev., 18, 117–139, https://doi.org/10.5194/gmd-18-117-2025, https://doi.org/10.5194/gmd-18-117-2025, 2025
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The Artificial-Intelligence-based Nonspherical Aerosol Optical Scheme (AI-NAOS) was developed to improve the estimation of the aerosol direct radiation effect and was coupled online with a chemical weather model. The AI-NAOS scheme considers black carbon as fractal aggregates and soil dust as super-spheroids, encapsulated with hygroscopic aerosols. Real-case simulations emphasize the necessity of accurately representing nonspherical and inhomogeneous aerosols in chemical weather models.
Lukas Pfitzenmaier, Pavlos Kollias, Nils Risse, Imke Schirmacher, Bernat Puigdomenech Treserras, and Katia Lamer
Geosci. Model Dev., 18, 101–115, https://doi.org/10.5194/gmd-18-101-2025, https://doi.org/10.5194/gmd-18-101-2025, 2025
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The Python tool Orbital-Radar transfers suborbital radar data (ground-based, airborne, and forward-simulated numerical weather prediction model) into synthetic spaceborne cloud profiling radar data, mimicking platform-specific instrument characteristics, e.g. EarthCARE or CloudSat. The tool's novelty lies in simulating characteristic errors and instrument noise. Thus, existing data sets are transferred into synthetic observations and can be used for satellite calibration–validation studies.
Aurélien Mirebeau, Cécile de Munck, Bertrand Bonan, Christine Delire, Aude Lemonsu, Valéry Masson, and Stephan Weber
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-233, https://doi.org/10.5194/gmd-2024-233, 2025
Revised manuscript accepted for GMD
Short summary
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The greening of cities is recommended to limit the effects of climate change. In particular, green roofs can provide numerous environmental benefits, such as urban cooling, water retention and carbon sequestration. The aim of this research is to develop a new module for calculating green roof CO2 fluxes within a model that can already simulate hydrological and thermal processes of such roofs. The calibration and evaluation of this module take advantage of long term experimental data.
Mark Buehner, Jean-Francois Caron, Ervig Lapalme, Alain Caya, Ping Du, Yves Rochon, Sergey Skachko, Maziar Bani Shahabadi, Sylvain Heilliette, Martin Deshaies-Jacques, Weiguang Chang, and Michael Sitwell
Geosci. Model Dev., 18, 1–18, https://doi.org/10.5194/gmd-18-1-2025, https://doi.org/10.5194/gmd-18-1-2025, 2025
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The Modular and Integrated Data Assimilation System (MIDAS) software is described. The flexible design of MIDAS enables both deterministic and ensemble prediction applications for the atmosphere and several other Earth system components. It is currently used for all main operational weather prediction systems in Canada and also for sea ice and sea surface temperature analysis. The use of MIDAS for multiple Earth system components will facilitate future research on coupled data assimilation.
Alexander de Meij, Cornelis Cuvelier, Philippe Thunis, and Enrico Pisoni
EGUsphere, https://doi.org/10.5194/egusphere-2024-3690, https://doi.org/10.5194/egusphere-2024-3690, 2025
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We assess the relevance and utility indicators developed within FAIRMODE by evaluating 9 CAMS models in calculated air pollutant values. For NO2, the results highlight difficulties at traffic stations. For PM2.5 and PM10 the bias and Winter-Summer gradients reveal issues. O3 evaluation shows that e.g. seasonal gradients are useful. Overall, the indicators provide valuable insights into model limitations, yet there is a need to reconsider the strictness of some indicators for certain pollutants.
Cited articles
Barrett, A. I., Wellmann, C., Seifert, A., Hoose, C., Vogel, B., and Kunz, M.:
One step at a time: How model time step significantly affects
convection-permitting simulations, J. Adv. Model. Earth
Sy., 11, 641–658, 2019. a
Battaglia, A., Kummerow, C. D., Shin, D.-B., and Williams, C.: Constraining
microwave brightness temperatures by radar bright band observations., J. Ocean. Atmos. Tech., 20, 856–871, 2003. a
Besic, N., Figueras i Ventura, J., Grazioli, J., Gabella, M., Germann, U., and Berne, A.: Hydrometeor classification through statistical clustering of polarimetric radar measurements: a semi-supervised approach, Atmos. Meas. Tech., 9, 4425–4445, https://doi.org/10.5194/amt-9-4425-2016, 2016. a, b
Besic, N., Gehring, J., Praz, C., Figueras i Ventura, J., Grazioli, J., Gabella, M., Germann, U., and Berne, A.: Unraveling hydrometeor mixtures in polarimetric radar measurements, Atmos. Meas. Tech., 11, 4847–4866, https://doi.org/10.5194/amt-11-4847-2018, 2018. a, b
Blahak, U.: Towards a better representation of high density ice particles in a
state-of-the-art two-moment bulk microphysical scheme, in: Proc. 15th Int.
Conf. Clouds and Precip., Cancun, Mexico, vol. 20208, 2008. a
Blahak, U.: RADAR_MIE_LM and RADAR_MIELIB – Calculation of Radar
Reflectivity from Model Output, COSMO Technical Report 28, Consortium for
Small Scale Modeling (COSMO),
available at: http://www.cosmo-model.org/content/model/documentation/techReports/cosmo/docs/techReport28.pdf (last access: 10 January 2022),
2016. a, b, c, d, e
Bohren, C. F. and Huffman, D. R.: Absorption and Scattering of Light by Small
Particles, John Wiley and Sons, Inc., https://doi.org/10.1002/9783527618156, 1983. a, b
Brandes, E. A., Zhang, G., and Vivekanandan, J.: Experiments in Rainfall
Estimation with a Polarimetric Radar in a Subtropical Environment, J.
Appl. Meteorol., 41, 674–685,
https://doi.org/10.1175/1520-0450(2002)041<0674:EIREWA>2.0.CO;2, 2002. a
Carlin, J. T.: The use of polarimetric radar data for informing numerical
weather prediction models, PhD thesis, University of Oklahoma,
available at: https://shareok.org/handle/11244/299801 (last access: 10 January 2022), 2018. a
Dolan, B. and Rutledge, S. A.: A theory-based hydrometeor identification
algorithm for X-band polarimetric radars, J. Atmos. Ocean.
Tech., 26, 2071–2088, https://doi.org/10.1175/2009JTECHA1208.1, 2009. a, b
Dolan, B., Rutledge, S. A., Lim, S., Chandrasekar, V., and Thurai, M.: A robust
C-band hydrometeor identification algorithm and application to a long-term
polarimetric radar dataset, J. Appl. Meteorol. Clim.,
52, 2162–2186, https://doi.org/10.1175/JAMC-D-12-0275.1, 2013. a, b
Dunnavan, E. L., Jiang, Z., Harrington, J. Y., Verlinde, J., Fitch, K., and
Garrett, T. J.: The Shape and Density Evolution of Snow Aggregates, J. Atmos. Sci., 76, 3919–3940, https://doi.org/10.1175/JAS-D-19-0066.1,
2019. a
Fabry, F. and Szyrmer, W.: Modeling of the melting layer. Part I:
Electromagnetics, J. Atmos. Sci., 56, 3593–3600, 1999. a
Fridlind, A. M., Ackerman, A., McFarquhar, G., Zhang, G., Poellot, M., DeMott,
P., Prenni, A., and Heymsfield, A.: Ice properties of single-layer
stratocumulus during the Mixed-Phase Arctic Cloud Experiment: 2. Model
results, J. Geophys. Res.-Atmos., 112, D24202, https://doi.org/10.1029/2007JD008646, 2007. a
Fridlind, A. M., Li, X., Wu, D., van Lier-Walqui, M., Ackerman, A. S., Tao, W.-K., McFarquhar, G. M., Wu, W., Dong, X., Wang, J., Ryzhkov, A., Zhang, P., Poellot, M. R., Neumann, A., and Tomlinson, J. M.: Derivation of aerosol profiles for MC3E convection studies and use in simulations of the 20 May squall line case, Atmos. Chem. Phys., 17, 5947–5972, https://doi.org/10.5194/acp-17-5947-2017, 2017. a, b, c
Garrett, T. J., Fallgatter, C., Shkurko, K., and Howlett, D.: Fall speed measurement and high-resolution multi-angle photography of hydrometeors in free fall, Atmos. Meas. Tech., 5, 2625–2633, https://doi.org/10.5194/amt-5-2625-2012, 2012. a
Grazioli, J., Tuia, D., and Berne, A.: Hydrometeor classification from polarimetric radar measurements: a clustering approach, Atmos. Meas. Tech., 8, 149–170, https://doi.org/10.5194/amt-8-149-2015, 2015. a
Griffin, E. M., Schuur, T. J., and Ryzhkov, A. V.: A polarimetric analysis of
ice microphysical processes in snow, using quasi-vertical profiles, J. Appl. Meteorol. Clim., 57, 31–50,
https://doi.org/10.1175/JAMC-D-17-0033.1, 2018. a
Han, B., Fan, J., Varble, A., Morrison, H.,Williams, C. R., Chen, B., Dong, X., Giangrande, S. E., Khain, A., Mansell, E., Milbrandt J. A., Sphund J., and Thompson G.: Cloud-resolving model
intercomparison of an MC3E squall line case: Part II. Stratiform
precipitation properties, J. Geophys. Res.-Atmos., 124,
1090–1117, 2019. a, b
Janjić, T., Bormann, N., Bocquet, M., Carton, J. A., Cohn, S. E., Dance,
S. L., Losa, S. N., Nichols, N. K., Potthast, R., Waller, J. A., and Weston,
P.: On the representation error in data assimilation, Q. J. Roy. Meteorol. Soc., 144, 1257–1278, https://doi.org/10.1002/qj.3130,
2018. a
Jung, Y., Zhang, G., and Xue, M.: Assimilation of Simulated Polarimetric Radar
Data for a Convective Storm Using the Ensemble Kalman Filter. Part I:
Observation Operators for Reflectivity and Polarimetric Variables, Mon.
Weather Rev., 136, 2228–2245, 2008. a
Kärcher, B. and Lohmann, U.: A parameterization of cirrus cloud formation:
Homogeneous freezing of supercooled aerosols, J. Geophys. Res.-Atmos., 107, D24010,
https://doi.org/10.1029/2002JD003220, 2002. a
Kärcher, B., Hendricks, J., and Lohmann, U.: Physically based
parameterization of cirrus cloud formation for use in global atmospheric
models, J. Geophys. Res.-Atmos., 111, D01205,
https://doi.org/10.1029/2005JD006219, 2006. a
Korolev, A. and Leisner, T.: Review of experimental studies of secondary ice production, Atmos. Chem. Phys., 20, 11767–11797, https://doi.org/10.5194/acp-20-11767-2020, 2020. a
Korolev, A., Heckman, I., Wolde, M., Ackerman, A. S., Fridlind, A. M., Ladino, L. A., Lawson, R. P., Milbrandt, J., and Williams, E.: A new look at the environmental conditions favorable to secondary ice production, Atmos. Chem. Phys., 20, 1391–1429, https://doi.org/10.5194/acp-20-1391-2020, 2020. a
Kumjian, M. R.: Weather Radars, in: Remote Sensing of Clouds and Precipitation,
edited by: Andronache, C., Springer Remote Sensing/Photogrammetry, 15–63,
2018. a
Matrosov, S., Reinking, R., and Rjalalova, I.: Inferring fall attitudes of
pristine dendritic crystals from polarimetric radar data, J.
Atmos. Sci., 62, 241–250, 2005. a
Matsui, T., Dolan, B., Rutledge, S. A., Tao, W., Iguchi, T., Barnum, J., and
Lang, S. E.: POLARRIS: A POLArimetric Radar Retrieval and Instrument
Simulator, J. Geophys. Res.-Atmos., 124, 4634–4657,
https://doi.org/10.1029/2018JD028317, 2019. a, b
Maxwell Garnett, J. C.: Colours in metal glasses, in metallic films and in
metallic solutions – II, Proc. Roy. Soc. A, 76, 370–373,
https://doi.org/10.1098/rspa.1905.0039, 1905. a
Mishchenko, M. I.: Calculation of the amplitude matrix for a nonspherical
particle in a fixed orientation, Appl. Optics, 39, 1026–1031,
https://doi.org/10.1364/AO.39.001026, 2000. a
Morrison, H. and Milbrandt, J. A.: Parameterization of Cloud Microphysics Based
on the Prediction of Bulk Ice Particle Properties. Part I: Scheme Description
and Idealized Tests, J. Atmos. Sci., 72, 287–311,
https://doi.org/10.1175/JAS-D-14-0065.1, 2015. a
Morrison, H., van Lier-Walqui, M., Fridlind, A. M., Grabowski, W. W., Harrington, J.
Y., Hoose, C., Korolev, A., Kumjian, M. R., Milbrandt, J. A., Pawlowska, H., Posselt, D. J., Prat,
O. P., Remel, K. J., Shima, S.-I., Diedenhoven, B., and Xue, L.: Confronting the challenge of modeling cloud and
precipitation microphysics, J. Adv. Model. Earth Sy.,
12, e2019MS001689, https://doi.org/10.1029/2019MS001689, 2020. a
Noppel, H., Blahak, U., Seifert, A., and Beheng, K. D.: Simulations of a
hailstorm and the impact of CCN using an advanced two-moment cloud
microphysical scheme, Atmos. Res., 96, 286–301, 2010. a
Ori, D., Schemann, V., Karrer, M., Dias Neto, J., von Terzi, L., Seifert, A.,
and Kneifel, S.: Evaluation of ice particle growth in ICON using statistics
of multi-frequency Doppler cloud radar observations, Q. J.
Roy. Meteor. Soc., 146, 3830–3849, 2020. a
Pejcic, V., Simmer, C., and Trömel, S.: Polarimetric radar-based methods
for evaluation of hydrometeor mixtures in numerical weather prediction
models, in: 2021 21st International Radar Symposium (IRS), 1–10,
https://doi.org/10.23919/IRS51887.2021.9466201, 2021. a
Pejcic, V., Soderholm, J., Mühlbauer, K., Louf, V., and Trömel, S.: Five
Years Calibrated Observations from the University of Bonn X-band Weather
Radar (BoXPol), Sci. Data, in review, 2022. a
Peters-Lidard, C. D., Kemp, E. M., Matsui, T., Santanello Jr., J. A., Kumar, S. V., Jacob, J.
P., Clune, T., Tao, W.-K., Chin, M., Hou, A., Case, J. L., Kim, D., Kim K.-M., Lau, W., Liu, Y.,
Shi, J., Starr, D., Tan, Q., Tao, Z., Zaitchik, B. F., Zavodsky, B., Zhang, S. Q., and Zupanski, M.:
Integrated modeling of aerosol, cloud, precipitation and land processes at
satellite-resolved scales, Environ. Modell. Softw., 67, 149–159,
2015. a
Petty, G. W. and Huang, W.: The Modified Gamma Size Distribution Applied to
Inhomogeneous and Nonspherical Particles: Key Relationships and Conversions,
J. Atmos. Sci., 68, 1460–1473,
https://doi.org/10.1175/2011JAS3645.1, 2011. a
Ribaud, J.-F., Machado, L. A. T., and Biscaro, T.: X-band dual-polarization radar-based hydrometeor classification for Brazilian tropical precipitation systems, Atmos. Meas. Tech., 12, 811–837, https://doi.org/10.5194/amt-12-811-2019, 2019. a
Ryzhkov, A., Zhang, P., Reeves, H., Kumjian, M., Tschallener, T., Trömel,
S., and Simmer, C.: Quasi-vertical profiles – A new way to look at
polarimetric radar data, J. Atmos. Ocean. Tech., 33,
551–562, 2016. a
Ryzhkov, A. V., Kumjian, M. R., Ganson, S. M., and Khain, A. P.: Polarimetric
Radar Characteristics of Melting Hail. Part I: Theoretical Simulations Using
Spectral Microphysical Modeling, J. Appl. Meteorol.
Clim., 52, 2849–2870, https://doi.org/10.1175/JAMC-D-13-073.1,
2013a. a
Ryzhkov, A. V., Kumjian, M. R., Ganson, S. M., and Zhang, P.: Polarimetric
Radar Characteristics of Melting Hail. Part II: Practical Implications,
J. Appl. Meteorol. Clim., 52, 2871–2886,
https://doi.org/10.1175/JAMC-D-13-074.1, 2013b. a
Ryzhkov, A. V., Snyder, J., Carlin, J. T., Khain, A., and Pinsky, M.: What
polarimetric weather radars offer to cloud modelers: forward radar operators
and microphysical/thermodynamic retrievals, Atmosphere, 11, 362,
https://doi.org/10.3390/atmos11040362, 2020. a
Schrom, R. S. and Kumjian, M. R.: Bulk-Density Representations of Branched
Planar Ice Crystals: Errors in the Polarimetric Radar Variables, J.
Appl. Meteorol. Clim., 57, 333–346,
https://doi.org/10.1175/JAMC-D-17-0114.1, 2018. a
Segal, Y. and Khain, A.: Dependence of droplet concentration on aerosol
conditions in different cloud types: Application to droplet concentration
parameterization of aerosol conditions, J. Geophys. Res.-Atmos., 111, D15204, https://doi.org/10.1029/2005JD006561, 2006. a
Seifert, A.: On the parameterization of evaporation of raindrops as simulated
by a one-dimensional rainshaft model, J. Atmos. Sci.,
65, 3608–3619, 2008. a
Shrestha, P.: Clouds and Vegetation Modulate Shallow Groundwater Table Depth,
J. Hydrometeorol., 22, 753–763, https://doi.org/10.1175/JHM-D-20-0171.1,
2021. a
Shrestha, P., Mendrok, J., Pejcic, V., Trömel, S., Blahak, U., and Carlin,
J. T.: Software documentation for COSMO model (v5.1) evaluation with X-band
polarimetric radar data using B-PRO (v2.0), Zenodo [data set], https://doi.org/10.5281/zenodo.5218717,
2021. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Wang,
W., and Powers, J. G.: A description of the Advanced Research WRF version 3.
NCAR Technical note-475+ STR, Tech. rep., University Corporation for
Atmospheric Research, https://doi.org/10.5065/D68S4MVH, 2008. a
Snyder, J. C., Bluestein, H. B., Dawson II, D. T., and Jung, Y.: Simulations of
polarimetric, X-band radar signatures in supercells. Part I: Description of
experiment and simulated ρ hv rings, J. Appl. Meteorol.
Climatol., 56, 1977–1999, 2017. a
Steppeler, J., Doms, G., Schättler, U., Bitzer, H., Gassmann, A., Damrath,
U., and Gregoric, G.: Meso-gamma scale forecasts using the nonhydrostatic
model LM, Meteorol. Atmos. Phys., 82, 75–96, 2003. a
Stewart, R. E., Marwitz, J. D., Pace, J. C., and Carbone, R. E.:
Characteristics through the melting layer of stratiform clouds, J. Atmos. Sci., 41, 3227–3237, 1984. a
Straka, J. M., Zrnić, D. S., and Ryzhkov, A. V.: Bulk hydrometeor
classification and quantification using polarimetric radar data: Synthesis of
relations, J. Appl. Meteorol., 39, 1341–1372,
https://doi.org/10.1175/1520-0450(2000)039<1341:BHCAQU>2.0.CO;2, 2000. a, b, c
Takahashi, T., Nagao, Y., and Kushiyama, Y.: Possible High Ice Particle
Production during Graupel–Graupel Collisions, J. Atmos.
Sci., 52, 4523–4527,
https://doi.org/10.1175/1520-0469(1995)052<4523:PHIPPD>2.0.CO;2, 1995. a
Tao, W.-K. and Simpson, J.: The Goddard cumulus ensemble model. Part I: Model
description, Terr.-Atmos. Ocean. Sci, 4, 35–72, 1993. a
Thompson, G., Berner, J., Frediani, M., Otkin, J. A., and Griffin, S. M.: A
Stochastic Parameter Perturbation Method to Represent Uncertainty in a
Microphysics Scheme, Mon. Weather Rev., 149, 1481–1497, https://doi.org/10.1175/MWR-D-20-0077.1,
2021. a
Trömel, S., Ryzhkov, A. V., Zhang, P., and Simmer, C.: Investigations of
backscatter differential phase in the melting layer, J. Appl.
Meteorol. Clim., 53, 2344–2359, 2014. a
Trömel, S., Ryzhkov, A. V., Hickman, B., Mühlbauer, K., and Simmer, C.:
Polarimetric Radar Variables in the Layers of Melting and Dendritic Growth at
X Band–Implications for a Nowcasting Strategy in Stratiform Rain, J.
Appl. Meteorol. Clim., 58, 2497–2522, 2019. a
Trömel, S., Simmer, C., Blahak, U., Blanke, A., Doktorowski, S., Ewald, F., Frech, M., Gergely, M., Hagen, M., Janjic, T., Kalesse-Los, H., Kneifel, S., Knote, C., Mendrok, J., Moser, M., Köcher, G., Mühlbauer, K., Myagkov, A., Pejcic, V., Seifert, P., Shrestha, P., Teisseire, A., von Terzi, L., Tetoni, E., Vogl, T., Voigt, C., Zeng, Y., Zinner, T., and Quaas, J.: Overview: Fusion of radar polarimetry and numerical atmospheric modelling towards an improved understanding of cloud and precipitation processes, Atmos. Chem. Phys., 21, 17291–17314, https://doi.org/10.5194/acp-21-17291-2021, 2021.
a, b
van Lier-Walqui, M., Vukicevic, T., and Posselt, D. J.: Quantification of Cloud
Microphysical Parameterization Uncertainty Using Radar Reflectivity, Mon.
Weather Rev., 140, 3442–3466, https://doi.org/10.1175/MWR-D-11-00216.1, 2012. a
van Weverberg, K., Goudenhoofdt, E., Blahak, U., Marbaix, P., and van Ypersele,
J.-P.: Comparison of One-Moment and Two-Moment Bulk Microphysics for
High-Resolution Climate Simulations of Intense Precipitation, Atmos.
Res., 147–148, 145–161, https://doi.org/10.1016/j.atmosres.2014.05.012, 2014. a, b
Ward, J. H. J.: Hierarchical Grouping to Optimize an Objective Function,
J. Am. Stat. Assoc., 58, 236–244,
https://doi.org/10.1080/01621459.1963.10500845, 1963. a
Wolfensberger, D. and Berne, A.: From model to radar variables: a new forward polarimetric radar operator for COSMO, Atmos. Meas. Tech., 11, 3883–3916, https://doi.org/10.5194/amt-11-3883-2018, 2018. a, b, c
Wolfensberger, D., Scipion, D., and Berne, A.: Detection and characterization
of the melting layer based on polarimetric radar scans, Q. J. Roy. Meteor.
Soc., 142, 108–124, 2016. a
Xie, X., Shrestha, P., Mendrok, J., Carlin, J., Trömel, S., and Blahak, U.:
Bonn Polarimetric Radar forward Operator (B-PRO), Collaborative Research Centre / Transregio 32 Database [code], https://doi.org/10.5880/TR32DB.41,
2021. a, b
Zeng, Y., Blahak, U., and Jerger, D.: An efficient modular volume-scanning
radar forward operator for NWP models: description and coupling to the COSMO
model, Q. J. Roy. Meteor. Soc., 142,
3234–3256, https://doi.org/10.1002/qj.2904, 2016. a, b
Zrnic, D. S., Ryzhkov, A., Straka, J., Liu, Y., and Vivekanandan, J.: Testing
a Procedure for Automatic Classification of Hydrometeor Types, J.
Atmos. Ocean. Tech., 18, 892–913,
https://doi.org/10.1175/1520-0426(2001)018<0892:TAPFAC>2.0.CO;2, 2001. a, b, c
Short summary
The article focuses on the exploitation of radar polarimetry for model evaluation of stratiform precipitation. The model exhibited a low bias in simulated polarimetric moments at lower levels above the melting layer where snow was found to dominate. This necessitates further research into the missing microphysical processes in these lower levels (e.g. fragmentation due to ice–ice collisions) and use of more reliable snow-scattering models in the forward operator to draw valid conclusions.
The article focuses on the exploitation of radar polarimetry for model evaluation of stratiform...
