Articles | Volume 17, issue 12
https://doi.org/10.5194/gmd-17-4755-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-4755-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
18 Jun 2024
Model evaluation paper |
| 18 Jun 2024
| 18 Jun 2024
Calibrating and validating the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) urban cooling model: case studies in France and the United States
Asian School of the Environment, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore
Earth Observatory of Singapore, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore
Martí Bosch
Urban and Regional Planning Community, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
Léa Tardieu
TETIS, INRAE, AgroParisTech, CIRAD, CNRS, Université de Montpellier, Montpellier, France
CIRED, Ecole des Ponts ParisTech, AgroParisTech, Cirad, CNRS, EHESS, Université Paris-Saclay, 94736, Nogent-sur-Marne, France
Aude Lemonsu
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Cécile de Munck
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Chris Nootenboom
Ecognosis, Warwick, NY 10990, USA
Vincent Viguié
CIRED, Ecole des Ponts ParisTech, AgroParisTech, Cirad, CNRS, EHESS, Université Paris-Saclay, 94736, Nogent-sur-Marne, France
Eric Lonsdorf
Department of Environmental Sciences, Emory College, Atlanta, GA 30322, USA
James A. Douglass
Natural Capital Project, Dept. of Biology and Woods Institute for the Environment, Stanford University, Stanford, CA 94305, USA
Richard P. Sharp
Spring, 5455 Shafter Avenue, Oakland, CA 94618, USA
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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.
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
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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.
Martial Haeffelin, Jean-François Ribaud, Jonnathan Céspedes, Jean-Charles Dupont, Aude Lemonsu, Valéry Masson, Tim Nagel, and Simone Kotthaus
Atmos. Chem. Phys., 24, 14101–14122, https://doi.org/10.5194/acp-24-14101-2024, https://doi.org/10.5194/acp-24-14101-2024, 2024
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This study highlights how the state of the urban atmospheric boundary layer impacts urban park cooling effect intensity at night. Under summertime heat wave conditions, the urban atmosphere becomes stable at night, which inhibits turbulent motions. Under those specific conditions, urban parks and woods cool much more efficiently than the surrounding built-up neighbourhoods in the evening and through the night, providing cooler air temperatures by 4 to 6° C depending on park size.
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.
Robert Schoetter, Yu Ting Kwok, Cécile de Munck, Kevin Ka Lun Lau, Wai Kin Wong, and Valéry Masson
Geosci. Model Dev., 13, 5609–5643, https://doi.org/10.5194/gmd-13-5609-2020, https://doi.org/10.5194/gmd-13-5609-2020, 2020
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Cities change the local meteorological conditions, e.g. by increasing air temperature, which can negatively impact humans and infrastructure. The urban climate model TEB is able to calculate the meteorological conditions in low- and mid-rise cities since it interacts with the lowest level of an atmospheric model. Here, a multi-layer coupling of TEB is introduced to enable modelling the urban climate of cities with many skyscrapers; the new version is tested for the high-rise city of Hong Kong.
Emilie Redon, Aude Lemonsu, and Valéry Masson
Geosci. Model Dev., 13, 385–399, https://doi.org/10.5194/gmd-13-385-2020, https://doi.org/10.5194/gmd-13-385-2020, 2020
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The TEB urban climate model simulates micrometeorological conditions from the neighborhood scale to the entire city. It has recently been improved to more realistically address the radiative effects of trees within the urban canopy. This article presents additional developments that have been made to better represent the effect of trees on heat and moisture exchange, as well as on air flow in the streets, and on thermal comfort.
Xenia Stavropulos-Laffaille, Katia Chancibault, Jean-Marc Brun, Aude Lemonsu, Valéry Masson, Aaron Boone, and Hervé Andrieu
Geosci. Model Dev., 11, 4175–4194, https://doi.org/10.5194/gmd-11-4175-2018, https://doi.org/10.5194/gmd-11-4175-2018, 2018
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Integrating vegetation in urban planning is promoted to counter steer potential impacts of climate and demographic changes. Assessing the multiple benefits of such strategies on the urban microclimate requires a detailed coupling of both the water and energy transfers in numerical tools. In this respect, the representation of water-related processes in the urban subsoil of the existing model TEB-Veg has been improved. The new model thus allows a better evaluation of urban adaptation strategies.
Emilie C. Redon, Aude Lemonsu, Valéry Masson, Benjamin Morille, and Marjorie Musy
Geosci. Model Dev., 10, 385–411, https://doi.org/10.5194/gmd-10-385-2017, https://doi.org/10.5194/gmd-10-385-2017, 2017
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In order to assess the potential of cooling of urban vegetation in cities, we need to refine some processes in the microclimate models running on cities as the TEB model. The shading effects of trees on roads, low vegetation (grass), or walls are key processes impacting both air and surface temperatures in the streets by reducing them and improving the thermal comfort of inhabitants. They have been implemented into the TEB model and simulations have been evaluated by a fine-scale model, SOLENE.
C. S. de Munck, A. Lemonsu, R. Bouzouidja, V. Masson, and R. Claverie
Geosci. Model Dev., 6, 1941–1960, https://doi.org/10.5194/gmd-6-1941-2013, https://doi.org/10.5194/gmd-6-1941-2013, 2013
V. Masson, P. Le Moigne, E. Martin, S. Faroux, A. Alias, R. Alkama, S. Belamari, A. Barbu, A. Boone, F. Bouyssel, P. Brousseau, E. Brun, J.-C. Calvet, D. Carrer, B. Decharme, C. Delire, S. Donier, K. Essaouini, A.-L. Gibelin, H. Giordani, F. Habets, M. Jidane, G. Kerdraon, E. Kourzeneva, M. Lafaysse, S. Lafont, C. Lebeaupin Brossier, A. Lemonsu, J.-F. Mahfouf, P. Marguinaud, M. Mokhtari, S. Morin, G. Pigeon, R. Salgado, Y. Seity, F. Taillefer, G. Tanguy, P. Tulet, B. Vincendon, V. Vionnet, and A. Voldoire
Geosci. Model Dev., 6, 929–960, https://doi.org/10.5194/gmd-6-929-2013, https://doi.org/10.5194/gmd-6-929-2013, 2013
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Integrated Methane Inversion (IMI) 2.0: an improved research and stakeholder tool for monitoring total methane emissions with high resolution worldwide using TROPOMI satellite observations
HTAP3 Fires: towards a multi-model, multi-pollutant study of fire impacts
Using a data-driven statistical model to better evaluate surface turbulent heat fluxes in weather and climate numerical models: a demonstration study
Pochva: a new hydro-thermal process model in soil, snow, and vegetation for application in atmosphere numerical models
ClimKern v1.2: a new Python package and kernel repository for calculating radiative feedbacks
Accounting for effects of coagulation and model uncertainties in particle number concentration estimates based on measurements from sampling lines – a Bayesian inversion approach with SLIC v1.0
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The Multi-Compartment Hg Modeling and Analysis Project (MCHgMAP): mercury modeling to support international environmental policy
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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
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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
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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.
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.
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
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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.
Zichen Wu, Xueshun Chen, Zifa Wang, Huansheng Chen, Zhe Wang, Qing Mu, Lin Wu, Wending Wang, Xiao Tang, Jie Li, Ying Li, Qizhong Wu, Yang Wang, Zhiyin Zou, and Zijian Jiang
Geosci. Model Dev., 17, 8885–8907, https://doi.org/10.5194/gmd-17-8885-2024, https://doi.org/10.5194/gmd-17-8885-2024, 2024
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We developed a model to simulate polycyclic aromatic hydrocarbons (PAHs) from global to regional scales. The model can reproduce PAH distribution well. The concentration of BaP (indicator species for PAHs) could exceed the target values of 1 ng m-3 over some areas (e.g., in central Europe, India, and eastern China). The change in BaP is lower than that in PM2.5 from 2013 to 2018. China still faces significant potential health risks posed by BaP although the Action Plan has been implemented.
Marie Taufour, Jean-Pierre Pinty, Christelle Barthe, Benoît Vié, and Chien Wang
Geosci. Model Dev., 17, 8773–8798, https://doi.org/10.5194/gmd-17-8773-2024, https://doi.org/10.5194/gmd-17-8773-2024, 2024
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We have developed a complete two-moment version of the LIMA (Liquid Ice Multiple Aerosols) microphysics scheme. We have focused on collection processes, where the hydrometeor number transfer is often estimated in proportion to the mass transfer. The impact of these parameterizations on a convective system and the prospects for more realistic estimates of secondary parameters (reflectivity, hydrometeor size) are shown in a first test on an idealized case.
Jize Jiang, David S. Stevenson, Aimable Uwizeye, Giuseppe Tempio, Alessandra Falcucci, Flavia Casu, and Mark A. Sutton
EGUsphere, https://doi.org/10.5194/egusphere-2024-3803, https://doi.org/10.5194/egusphere-2024-3803, 2024
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A special model called AMmonia–CLIMate (AMCLIM) has been developed to understand and calculate NH3 emissions from livestock farming. It is estimated that about 30 % of excreted N from livestock was lost due to NH3 emissions from housing, manure management and land application of manure. High NH3 volatilization often occurred in hot regions, while poor management practices also result in significant N losses through NH3 emissions.
Maksym Vasiuta, Angel Navarro Trastoy, Sanam Motlaghzadeh, Lauri Tuppi, Torsten Mayer-Gürr, and Heikki Järvinen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-136, https://doi.org/10.5194/gmd-2024-136, 2024
Revised manuscript accepted for GMD
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Propagation of electromagnetic signals in the Earth's neutral atmosphere inflicts errors in space geodetic observations. To model these errors as accurately as possible, it is necessary to use a signal ray tracing algorithm which is informed of the state of the atmosphere. We developed such algorithm and tested it by modelling errors in GNSS network observations. Our algorithm's main advantage is loss-less utilization of atmospheric information provided by numerical weather prediction models.
Yuya Takane, Yukihiro Kikegawa, Ko Nakajima, and Hiroyuki Kusaka
Geosci. Model Dev., 17, 8639–8664, https://doi.org/10.5194/gmd-17-8639-2024, https://doi.org/10.5194/gmd-17-8639-2024, 2024
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A new parameterisation for dynamic anthropogenic heat and electricity consumption is described. The model reproduced the temporal variation in and spatial distributions of electricity consumption and temperature well in summer and winter. The partial air conditioning was the most critical factor, significantly affecting the value of anthropogenic heat emission.
Arseniy Karagodin-Doyennel, Fredrik Jansson, Bart van Stratum, Hugo Denier van der Gon, Jordi Vilà-Guerau de Arellano, and Sander Houweling
EGUsphere, https://doi.org/10.5194/egusphere-2024-3721, https://doi.org/10.5194/egusphere-2024-3721, 2024
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We introduce a new simulation platform based on the Dutch Large-Eddy Simulation (DALES) to simulate carbon dioxide (CO2) emissions and their dispersion in the turbulent environments with hectometer resolution. This model incorporates both anthropogenic emission inventory and ecosystem exchanges. Simulation results for the main urban area in the Netherlands demonstrate a strong potential of DALES to enhance CO2 emission modeling, which is important for refining their reduction strategies.
Cited articles
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: FAO Irrigation and Drainage Paper No. 56. Irrigation and Drainage, 56, No. 97, Food and Agriculture Organization of the United Nations, Rome, ISBN 978-9251042199, 1998.
APUR: Atténuer les îlots de chaleur urbains – Cahier n° 5: méthodes et outils de conception des projets, https://www.apur.org/fr/nos-travaux/attenuer-ilots-chaleur-urbains-cahier-5-methodes-outils-conception-projets (last access: 30 April 2023), 2020.
Aslam, A. and Rana, I. A.: The use of local climate zones in the urban environment: A systematic review of data sources, methods, and themes, Urban Clim., 42, 101120, https://doi.org/10.1016/J.UCLIM.2022.101120, 2022.
Bartesaghi Koc, C., Osmond, P., and Peters, A.: Evaluating the cooling effects of green infrastructure: A systematic review of methods, indicators and data sources, Sol. Energy, 166, 486–508, https://doi.org/10.1016/J.SOLENER.2018.03.008, 2018.
Bolund, P. and Hunhammar, S.: Ecosystem services in urban areas, Ecol. Econ., 29, 293–301, https://doi.org/10.1016/S0921-8009(99)00013-0, 1999.
Bosch, M., Locatelli, M., Hamel, P., Remme, R. P., Chenal, J., and Joost, S.: A spatially explicit approach to simulate urban heat mitigation with InVEST (v3.8.0), Geosci. Model Dev., 14, 3521–3537, https://doi.org/10.5194/gmd-14-3521-2021, 2021.
Bosch, M., Locatelli, M., Hamel, P., Remme, R. P., Jaligot, R., Chenal, J., and Joost, S.: Evaluating urban greening scenarios for urban heat mitigation: a spatially explicit approach, R. Soc. Open Sci., 8, 202174, https://doi.org/10.1098/rsos.202174, 2022.
Bowler, D. E., Buyung-Ali, L. M., Knight, T. M., and Pullin, A. S.: A systematic review of evidence for the added benefits to health of exposure to natural environments, BMC Public Health, 10, 456, https://doi.org/10.1186/1471-2458-10-456, 2010.
Cao, X., Onishi, A., Chen, J., and Imura, H.: Quantifying the cool island intensity of urban parks using ASTER and IKONOS data, Landsc. Urban Plan., 96, 224–231, https://doi.org/10.1016/J.LANDURBPLAN.2010.03.008, 2010.
Chakraborty, T. and Lee, X.: A simplified urban-extent algorithm to characterize surface urban heat islands on a global scale and examine vegetation control on their spatiotemporal variability, Int. J. Appl. Earth Obs., 74, 269–280, https://doi.org/10.1016/J.JAG.2018.09.015, 2019.
Cheung, P. K., Livesley, S. J., and Nice, K. A.: Estimating the cooling potential of irrigating green spaces in 100 global cities with arid, temperate or continental climates, Sustain. Cities Soc., 71, 102974, https://doi.org/10.1016/J.SCS.2021.102974, 2021.
Corburn, J.: Cities, Climate Change and Urban Heat Island Mitigation: Localising Global Environmental Science, Urban Stud., 46, 413–427, https://doi.org/10.1177/0042098008099361, 2009.
Cortinovis, C. and Geneletti, D.: A framework to explore the effects of urban planning decisions on regulating ecosystem services in cities, Ecosyst. Serv., 38, 100946, https://doi.org/10.1016/J.ECOSER.2019.100946, 2019.
Cortinovis, C., Olsson, P., Boke-Olén, N., and Hedlund, K.: Scaling up nature-based solutions for climate-change adaptation: Potential and benefits in three European cities, Urban For Urban Green, 67, 127450, https://doi.org/10.1016/J.UFUG.2021.127450, 2022.
de Munck, C., Lemonsu, A., Masson, V., Le Bras, J., and Bonhomme, M.: Evaluating the impacts of greening scenarios on thermal comfort and energy and water consumptions for adapting Paris city to climate change, Urban Clim., 23, 260–286, https://doi.org/10.1016/J.UCLIM.2017.01.003, 2018.
Derkzen, M. L., van Teeffelen, A. J. A., and Verburg, P. H.: Review: Quantifying urban ecosystem services based on high-resolution data of urban green space: an assessment for Rotterdam, the Netherlands, J. Appl. Ecol., 52, 1020–1032, https://doi.org/10.1111/1365-2664.12469, 2015.
Eliasson, I.: Urban nocturnal temperatures, street geometry and land use, Atmos. Environ., 30, 379–392, https://doi.org/10.1016/1352-2310(95)00033-X, 1996.
Eumorfopoulou, E. A. and Kontoleon, K. J.: Experimental approach to the contribution of plant-covered walls to the thermal behaviour of building envelopes, Build. Environ., 44, 1024–1038, https://doi.org/10.1016/J.BUILDENV.2008.07.004, 2009.
Farrugia, S., Hudson, M. D., and McCulloch, L.: An evaluation of flood control and urban cooling ecosystem services delivered by urban green infrastructure, International Journal of Biodiversity Science, Ecosystem Services & Management, 9, 136–145, https://doi.org/10.1080/21513732.2013.782342, 2013.
github-actions[bot]: martibosch/invest-ucm-calibration: Release v0.6.0 (v0.6.0), Zenodo [code and data set], https://doi.org/10.5281/zenodo.8081822, 2023.
Gosling, S. N., Lowe, J. A., McGregor, G. R., Pelling, M., and Malamud, B. D.: Associations between elevated atmospheric temperature and human mortality: A critical review of the literature, Climatic Change, 92, 299–341, https://doi.org/10.1007/S10584-008-9441-X, 2009.
Hamel, P., Guerry, A. D., Polasky, S., Han, B., Douglass, J. A., Hamann, M., Janke, B., Kuiper, J. J., Levrel, H., Liu, H., Lonsdorf, E., McDonald, R. I., Nootenboom, C., Ouyang, Z., Remme, R. P., Sharp, R. P., Tardieu, L., Viguié, V., Xu, D., Zheng, H., and Daily, G. C.: Mapping the benefits of nature in cities with the InVEST software, npj Urban Sustainability, 1, 25, https://doi.org/10.1038/s42949-021-00027-9, 2021.
Hémon, D. and Jougla, E.: Surmortalité liée à la canicule d'août 2003, Rapport remis au Ministre de la Santé et de la protection sociale, https://www.inserm.fr/wp-content/uploads/2017-11/inserm-rapportthematique-surmortalitecaniculeaout2003-rapportfinal.pdf (last access: 20 March 2024), 2004.
Homer, C., Dewitz, J., Jin, S., Xian, G., Costello, C., Danielson, P., Gass, L., Funk, M., Wickham, J., Stehman, S., Auch, R., and Riitters, K.: Conterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover Database, ISPRS J. Photogramm., 162, 184–199, https://doi.org/10.1016/j.isprsjprs.2020.02.019, 2020.
Jauregui, E.: Influence of a large urban park on temperature and convective precipitation in a tropical city, Energy Build., 15, 457–463, https://doi.org/10.1016/0378-7788(90)90021-A, 1990.
Kadaverugu, R., Gurav, C., Rai, A., Sharma, A., Matli, C., and Biniwale, R.: Quantification of heat mitigation by urban green spaces using InVEST model – a scenario analysis of Nagpur City, India, Arab. J. Geosci., 14, 82, https://doi.org/10.1007/S12517-020-06380-W, 2021.
Kroeger, T., McDonald, R. I., Boucher, T., Zhang, P., and Wang, L.: Where the people are: Current trends and future potential targeted investments in urban trees for PM10 and temperature mitigation in 27 U.S. Cities, Landsc. Urban Plan., 177, 227–240, https://doi.org/10.1016/j.landurbplan.2018.05.014, 2018.
Kunapo, J., Fletcher, T. D., Ladson, A. R., Cunningham, L., and Burns, M. J.: A spatially explicit framework for climate adaptation, 15, 159–166, https://doi.org/10.1080/1573062X.2018.1424216, 2018.
Lai, L. W. and Cheng, W. L.: Air quality influenced by urban heat island coupled with synoptic weather patterns, Sci. Total Environ., 407, 2724–2733, https://doi.org/10.1016/j.scitotenv.2008.12.002, 2009.
Larondelle, N. and Haase, D.: Urban ecosystem services assessment along a rural–urban gradient: A cross-analysis of European cities, Ecol. Indic., 29, 179–190, https://doi.org/10.1016/J.ECOLIND.2012.12.022, 2013.
Lavigne, P., Brejon, P., and Fernandez, P.: Architecture Climatique: Une Contribution Au Développement Durable, Edisud, ISBN 978-2857447504, 1994.
Lehmann, S.: Low carbon districts: Mitigating the urban heat island with green roof infrastructure, City, Culture and Society, 5, 1–18, https://doi.org/10.1016/j.ccs.2014.02.002, 2014.
Lemonsu, A., Masson, V., Shashua-Bar, L., Erell, E., and Pearlmutter, D.: Inclusion of vegetation in the Town Energy Balance model for modelling urban green areas, Geosci. Model Dev., 5, 1377–1393, https://doi.org/10.5194/gmd-5-1377-2012, 2012.
Le Roy, B., Lemonsu, A., Kounkou-Arnaud, R., Brion, D., and Masson, V.: Long time series spatialized data for urban climatological studies: A case study of Paris, France, Int. J. Climatol., 40, 3567–3584, https://doi.org/10.1002/JOC.6414, 2020.
Li, X., Zhou, Y., Asrar, G. R., Imhoff, M., and Li, X.: The surface urban heat island response to urban expansion: A panel analysis for the conterminous United States, Sci. Total Environ., 605–606, 426–435, https://doi.org/10.1016/J.SCITOTENV.2017.06.229, 2017.
Lonsdorf, E. V., Nootenboom, C., Janke, B., and Horgan, B. P.: Assessing urban ecosystem services provided by green infrastructure: Golf courses in the Minneapolis-St. Paul metro area, Landsc. Urban Plan., 208, 104022, https://doi.org/10.1016/J.LANDURBPLAN.2020.104022, 2021.
Manoli, G., Fatichi, S., Schläpfer, M., Yu, K., Crowther, T. W., Meili, N., Burlando, P., Katul, G. G., and Bou-Zeid, E.: Magnitude of urban heat islands largely explained by climate and population, Nature, 573, 573, 55–60, https://doi.org/10.1038/s41586-019-1512-9, 2019.
Martilli, A., Roth, M., Chow, W. T. L., Demuzere, M., Lipson, M., Krayenhoff, E. S., Sailor, D., Nazarian, N., Voogt, J., Wouters, H., Middel, A., Stewart, I. D., Bechtel, B., Christen, A., and Hart, M. A.: Summer average urban-rural surface temperature differences do not indicate the need for urban heat reduction, Research Collection School of Social Sciences, Paper 3391, https://ink.library.smu.edu.sg/soss_research/3391 (last access: 20 March 2024), 2020.
Masson, V.: A physically-based scheme for the urban energy budget in atmospheric models, Bound.-Lay. Meteorol., 94, 357–397, https://doi.org/10.1023/A:1002463829265, 2000.
Masson, V., Le Moigne, P., Martin, E., Faroux, S., Alias, A., Alkama, R., Belamari, S., Barbu, A., Boone, A., Bouyssel, F., Brousseau, P., Brun, E., Calvet, J.-C., Carrer, D., Decharme, B., Delire, C., Donier, S., Essaouini, K., Gibelin, A.-L., Giordani, H., Habets, F., Jidane, M., Kerdraon, G., Kourzeneva, E., Lafaysse, M., Lafont, S., Lebeaupin Brossier, C., Lemonsu, A., Mahfouf, J.-F., Marguinaud, P., Mokhtari, M., Morin, S., Pigeon, G., Salgado, R., Seity, Y., Taillefer, F., Tanguy, G., Tulet, P., Vincendon, B., Vionnet, V., and Voldoire, A.: The SURFEXv7.2 land and ocean surface platform for coupled or offline simulation of earth surface variables and fluxes, Geosci. Model Dev., 6, 929–960, https://doi.org/10.5194/gmd-6-929-2013, 2013.
McDonald, R., Kroeger, T., Boucher, T., LongZhu, W., and Salem, R.: Planting healthy air: a global analysis of the role of urban trees in addressing particulate matter pollution and extreme heat. The Nature Conservancy, https://www.nature.org/content/dam/tnc/nature/en/documents/20160825_PHA_Report_Final.pdf (last access: 20 March 2024), 2016.
Meili, N., Manoli, G., Burlando, P., Bou-Zeid, E., Chow, W. T. L., Coutts, A. M., Daly, E., Nice, K. A., Roth, M., Tapper, N. J., Velasco, E., Vivoni, E. R., and Fatichi, S.: An urban ecohydrological model to quantify the effect of vegetation on urban climate and hydrology (UT&C v1.0), Geosci. Model Dev., 13, 335–362, https://doi.org/10.5194/gmd-13-335-2020, 2020.
Natural Capital Project: InVEST 3.13.0 User's Guide, Stanford University, University of Minnesota, Chinese Academy of Sciences, The Nature Conservancy, World Wildlife Fund, and Stockholm Resilience Centre, https://naturalcapitalproject.stanford.edu/software/invest (last access: 20 March 2024), 2022.
Nedkov, S., Zhiyanski, M., Dimitrov, S., Borisova, B., Popov, A., Ihtimanski, I., Yaneva, R., Nikolov, P., and Bratanova-Doncheva, S.: Mapping and assessment of urban ecosystem condition and services using integrated index of spatial structure, One Ecosystem, 2, e14499, https://doi.org/10.3897/oneeco.2.e14499, 2017.
NOAA: National Oceanic and Atmospheric Administration’s Online Weather Data (NOWData), Minneapolis-St Paul Area monthly summarized data, mean of monthly average temperatures 1981–2010, https://www.weather.gov/climateservices/nowdatafaq (last access: 20 March 2024), 2020.
Oke, T. R.: The energetic basis of the urban heat island, Q. J. Roy. Meteor. Soc., 108, 1–24, https://doi.org/10.1002/qj.49710845502, 1982.
Phelan, P. E., Kaloush, K., Miner, M., Golden, J., Phelan, B., Silva, H., and Taylor, R. A.: Urban Heat Island: Mechanisms, Implications, and Possible Remedies, Annu. Rev. Environ. Resour., 40, 285–307, https://doi.org/10.1146/annurev-environ-102014-021155, 2015.
Potchter, O., Cohen, P., and Bitan, A.: Climatic behavior of various urban parks during hot and humid summer in the mediterranean city of Tel Aviv, Israel, Int. J. Climatol., 26, 1695–1711, https://doi.org/10.1002/joc.1330, 2006.
Reid, C. E., Snowden, J. M., Kontgis, C., and Tager, I. B.: The Role of Ambient Ozone in Epidemiologic Studies of Heat-Related Mortality, Environ. Health Perspect., 120, 1627–1630, https://doi.org/10.1289/EHP.1205251, 2012.
Rosenzweig, C., Solecki, W., and Slosberg, R.: Mitigating New York City’s heat island with urban forestry, living roofs, and light surfaces, A report to the New York State Energy Research and Development Authority, http://usclimateandhealthalliance.org/post_resource/mitigating-new-york-citys-heat-island-with-urban-forestry-living-roofs-and-light-surfaces/ (last access: 20 March 2024), 2006.
Santamouris, M.: Regulating the damaged thermostat of the cities – Status, impacts and mitigation challenges, Energy Build., 91, 43–56, https://doi.org/10.1016/J.ENBUILD.2015.01.027, 2015.
Santamouris, M.: Innovating to zero the building sector in Europe: Minimising the energy consumption, eradication of the energy poverty and mitigating the local climate change, Sol. Energy, 128, 61–94, https://doi.org/10.1016/J.SOLENER.2016.01.021, 2016.
Santamouris, M.: Recent progress on urban overheating and heat island research. Integrated assessment of the energy, environmental, vulnerability and health impact. Synergies with the global climate change, Energy Build., 207, 109482, https://doi.org/10.1016/J.ENBUILD.2019.109482, 2020.
Schatz, J. and Kucharik, C. J.: Seasonality of the Urban Heat Island Effect in Madison, Wisconsin, J. Appl. Meteorol. Climatol., 53, 2371–2386, https://doi.org/10.1175/JAMC-D-14-0107.1, 2014.
Shashua-Bar, L. and Hoffman, M. E.: Vegetation as a climatic component in the design of an urban street: An empirical model for predicting the cooling effect of urban green areas with trees, Energy Build., 31, 221–235, https://doi.org/10.1016/S0378-7788(99)00018-3, 2000.
Smoliak, B. V., Snyder, P. K., Twine, T. E., Mykleby, P. M., and Hertel, W. F.: Dense network observations of the Twin Cities Canopy-Layer urban heat island, J. Appl. Meteorol. Climatol., 54, 1899–1917, https://doi.org/10.1175/JAMC-D-14-0239.1, 2015.
Spronken-Smith, R. A. and Oke, T. R.: Scale Modelling of Nocturnal Cooling in Urban Parks, Bound.-Lay. Meteorol., 93, 287–312, https://doi.org/10.1023/A:1002001408973, 1999.
Stéfanon, M., Martin-StPaul, N. K., Leadley, P., Bastin, S., Dell'Aquila, A., Drobinski, P., and Gallardo, C.: Testing climate models using an impact model: what are the advantages?, Climatic Change, 131, 649–661, https://doi.org/10.1007/s10584-015-1412-4, 2015.
Stewart, I. D.: A systematic review and scientific critique of methodology in modern urban heat island literature, Int. J. Climatol., 31, 200–217, https://doi.org/10.1002/JOC.2141, 2011.
Stewart, I. D. and Oke, T. R.: Local Climate Zones for Urban Temperature Studies, B. Am. Meteorol. Soc., 93, 1879–1900, 2012.
Tan, P. Y., Wong, N. H., Tan, C. L., Jusuf, S. K., Chang, M. F., and Chiam, Z. Q.: A method to partition the relative effects of evaporative cooling and shading on air temperature within vegetation canopy, Journal of Urban Ecology, 4, juy012, https://doi.org/10.1093/JUE/JUY012, 2018.
Tardieu, L., Hamel, P., Viguié, V., Coste, L., and Levrel, H.: Are soil sealing indicators sufficient to guide urban planning? Insights from an ecosystem services assessment in the Paris metropolitan area, Environ. Res. Lett., 16, 104019, https://doi.org/10.1088/1748-9326/ac24d0, 2021.
Velasco, E.: Go to field, look around, measure and then run models, Urban Clim., 24, 231–236, https://doi.org/10.1016/J.UCLIM.2018.04.001, 2018.
Venter, Z. S., Chakraborty, T., and Lee, X.: Crowdsourced air temperatures contrast satellite measures of the urban heat island and its mechanisms, Sci. Adv., 7, eabb9569, https://doi.org/10.1126/SCIADV.ABB9569, 2021.
Viguié, V., Lemonsu, A., Hallegatte, S., Beaulant, A. L., Marchadier, C., Masson, V., Pigeon, G., and Salagnac, J. L.: Early adaptation to heat waves and future reduction of air-conditioning energy use in Paris, Environ. Res. Lett., 15, 075006, https://doi.org/10.1088/1748-9326/ab6a24, 2020.
Villanueva-Solis, J.: Urban Heat Island Mitigation and Urban Planning: The Case of the Mexicali, B. C. Mexico, Am. J. Clim. Change, 6, 22–39, https://doi.org/10.4236/ajcc.2017.61002, 2017.
Wan, Z.: Collection-6 MODIS Land Surface Temperature Products Users' Guide, https://lpdaac.usgs.gov/documents/118/MOD11_User_Guide_V6.pdf (last access: 20 March 2024), 2013.
Wang, T., Xue, L., Brimblecombe, P., Lam, Y. F., Li, L., and Zhang, L.: Ozone pollution in China: A review of concentrations, meteorological influences, chemical precursors, and effects, Sci. Total Environ., 575, 1582–1596, https://doi.org/10.1016/j.scitotenv.2016.10.081, 2017.
Wong, N. H., Tan, C. L., Kolokotsa, D. D., and Takebayashi, H.: Greenery as a mitigation and adaptation strategy to urban heat, Nature Reviews Earth & Environment, 2, 166–181, https://doi.org/10.1038/s43017-020-00129-5, 2021.
Yu, Z., Yang, G., Zuo, S., Jørgensen, G., Koga, M., and Vejre, H.: Critical review on the cooling effect of urban blue-green space: A threshold-size perspective, Urban For Urban Green, 49, 126630, https://doi.org/10.1016/J.UFUG.2020.126630, 2020.
Zardo, L., Geneletti, D., Pérez-Soba, M., and Van Eupen, M.: Estimating the cooling capacity of green infrastructures to support urban planning, Ecosyst. Serv., 26, 225–235, https://doi.org/10.1016/J.ECOSER.2017.06.016, 2017.
Zawadzka, J. E., Harris, J. A., and Corstanje, R.: Assessment of heat mitigation capacity of urban greenspaces with the use of InVEST urban cooling model, verified with day-time land surface temperature data, Landsc. Urban Plan., 214, 104163, https://doi.org/10.1016/J.LANDURBPLAN.2021.104163, 2021.
Zhao, L., Lee, X., Smith, R. B., and Oleson, K.: Strong contributions of local background climate to urban heat islands, Nature, 511, 216–219, https://doi.org/10.1038/nature13462, 2014.
Ziter, C. D., Pedersen, E. J., Kucharik, C. J., and Turner, M. G.: Scale-dependent interactions between tree canopy cover and impervious surfaces reduce daytime urban heat during summer, P. Natl. Acad. Sci. USA, 116, 7575–7580, https://doi.org/10.1073/pnas.1817561116, 2019.
Short summary
The InVEST Urban Cooling model estimates the cooling effect of vegetation in cities. We further developed an algorithm to facilitate model calibration and evaluation. Applying the algorithm to case studies in France and in the United States, we found that nighttime air temperature estimates compare well with reference datasets. Estimated change in temperature from a land cover scenario compares well with an alternative model estimate, supporting the use of the model for urban planning decisions.
The InVEST Urban Cooling model estimates the cooling effect of vegetation in cities. We further...
