Articles | Volume 18, issue 2
https://doi.org/10.5194/gmd-18-433-2025
© Author(s) 2025. 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-18-433-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
27 Jan 2025
Methods for assessment of models |
| 27 Jan 2025
| 27 Jan 2025
ML-AMPSIT: Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool
Department of Civil, Environmental and Mechanical Engineering, University of Trento, Trento, Italy
Cenlin He
NSF National Center for Atmospheric Research (NCAR), Boulder, CO, USA
Fei Chen
Division of Environment and Sustainability, Hong Kong University of Science and Technology, Hong Kong SAR, China
Lorenzo Giovannini
Department of Civil, Environmental and Mechanical Engineering, University of Trento, Trento, Italy
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Mohamed Hamitouche, Giorgia Fosser, Alessandro Anav, Cenlin He, and Tzu-Shun Lin
Hydrol. Earth Syst. Sci., 29, 1221–1240, https://doi.org/10.5194/hess-29-1221-2025, https://doi.org/10.5194/hess-29-1221-2025, 2025
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This study evaluates how different methods of simulating runoff impact river flow predictions globally. By comparing seven approaches within the Noah-Multi-parameterisation (Noah-MP) land surface model, we found significant differences in accuracy, with some methods underestimating or overestimating runoff. The results are crucial for improving water resource management and flood prediction. Our work highlights the need for precise modelling to better prepare for climate-related challenges.
Cenlin He, Tzu-Shun Lin, David M. Mocko, Ronnie Abolafia-Rosenzweig, Jerry W. Wegiel, and Sujay V. Kumar
EGUsphere, https://doi.org/10.5194/egusphere-2024-4176, https://doi.org/10.5194/egusphere-2024-4176, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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This study integrates the refactored community Noah-MP version 5.0 model with the NASA Land Information System (LIS) version 7.5.2 to streamline the synchronization, development, and maintenance of Noah-MP within LIS and to enhance their interoperability and applicability. The model benchmarking and evaluation results reveal key model strengths and weaknesses in simulating land surface quantities and show implications for future model improvements.
Chayan Roychoudhury, Cenlin He, Rajesh Kumar, and Avelino F. Arellano Jr.
EGUsphere, https://doi.org/10.5194/egusphere-2024-2298, https://doi.org/10.5194/egusphere-2024-2298, 2024
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We aim to understand the complexity of Earth's climate by proposing a novel, cost-effective approach to understand the web of interactions driving climate change. We focus on how pollution and weather processes interact and drive snowmelt in Asian glaciers. Our findings reveal significant yet overlooked processes across different climate models. Our approach can help in refining the development of these models for more reliable predictions in climate-vulnerable regions.
Rajesh Kumar, Piyush Bhardwaj, Cenlin He, Jennifer Boehnert, Forrest Lacey, Stefano Alessandrini, Kevin Sampson, Matthew Casali, Scott Swerdlin, Olga Wilhelmi, Gabriele G. Pfister, Benjamin Gaubert, and Helen Worden
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-180, https://doi.org/10.5194/essd-2024-180, 2024
Revised manuscript accepted for ESSD
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We have created a 14-year hourly air quality dataset at 12 km resolution by combining satellite observations of atmospheric composition with air quality models over the contiguous United States (CONUS) . The dataset has been found to reproduce key observed features of air quality over the CONUS. To enable easy visualization and interpretation of county level air quality measures and trends by stakeholders, an ArcGIS air quality dashboard has also been developed.
Wenfu Tang, Louisa K. Emmons, Helen M. Worden, Rajesh Kumar, Cenlin He, Benjamin Gaubert, Zhonghua Zheng, Simone Tilmes, Rebecca R. Buchholz, Sara-Eva Martinez-Alonso, Claire Granier, Antonin Soulie, Kathryn McKain, Bruce C. Daube, Jeff Peischl, Chelsea Thompson, and Pieternel Levelt
Geosci. Model Dev., 16, 6001–6028, https://doi.org/10.5194/gmd-16-6001-2023, https://doi.org/10.5194/gmd-16-6001-2023, 2023
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The new MUSICAv0 model enables the study of atmospheric chemistry across all relevant scales. We develop a MUSICAv0 grid for Africa. We evaluate MUSICAv0 with observations and compare it with a previously used model – WRF-Chem. Overall, the performance of MUSICAv0 is comparable to WRF-Chem. Based on model–satellite discrepancies, we find that future field campaigns in an eastern African region (30°E–45°E, 5°S–5°N) could substantially improve the predictive skill of air quality models.
Cenlin He, Prasanth Valayamkunnath, Michael Barlage, Fei Chen, David Gochis, Ryan Cabell, Tim Schneider, Roy Rasmussen, Guo-Yue Niu, Zong-Liang Yang, Dev Niyogi, and Michael Ek
Geosci. Model Dev., 16, 5131–5151, https://doi.org/10.5194/gmd-16-5131-2023, https://doi.org/10.5194/gmd-16-5131-2023, 2023
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Noah-MP is one of the most widely used open-source community land surface models in the world, designed for applications ranging from uncoupled land surface and ecohydrological process studies to coupled numerical weather prediction and decadal climate simulations. To facilitate model developments and applications, we modernize Noah-MP by adopting modern Fortran code and data structures and standards, which substantially enhance model modularity, interoperability, and applicability.
Zhe Zhang, Yanping Li, Fei Chen, Phillip Harder, Warren Helgason, James Famiglietti, Prasanth Valayamkunnath, Cenlin He, and Zhenhua Li
Geosci. Model Dev., 16, 3809–3825, https://doi.org/10.5194/gmd-16-3809-2023, https://doi.org/10.5194/gmd-16-3809-2023, 2023
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Crop models incorporated in Earth system models are essential to accurately simulate crop growth processes on Earth's surface and agricultural production. In this study, we aim to model the spring wheat in the Northern Great Plains, focusing on three aspects: (1) develop the wheat model at a point scale, (2) apply dynamic planting and harvest schedules, and (3) adopt a revised heat stress function. The results show substantial improvements and have great importance for agricultural production.
Wenfu Tang, Simone Tilmes, David M. Lawrence, Fang Li, Cenlin He, Louisa K. Emmons, Rebecca R. Buchholz, and Lili Xia
Atmos. Chem. Phys., 23, 5467–5486, https://doi.org/10.5194/acp-23-5467-2023, https://doi.org/10.5194/acp-23-5467-2023, 2023
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Globally, total wildfire burned area is projected to increase over the 21st century under scenarios without geoengineering and decrease under the two geoengineering scenarios. Geoengineering reduces fire by decreasing surface temperature and wind speed and increasing relative humidity and soil water. However, geoengineering also yields reductions in precipitation, which offset some of the fire reduction.
Quang-Van Doan, Toshiyuki Amagasa, Thanh-Ha Pham, Takuto Sato, Fei Chen, and Hiroyuki Kusaka
Geosci. Model Dev., 16, 2215–2233, https://doi.org/10.5194/gmd-16-2215-2023, https://doi.org/10.5194/gmd-16-2215-2023, 2023
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This study proposes (i) the structural k-means (S k-means) algorithm for clustering spatiotemporally structured climate data and (ii) the clustering uncertainty evaluation framework (CUEF) based on the mutual-information concept.
Xinlei He, Yanping Li, Shaomin Liu, Tongren Xu, Fei Chen, Zhenhua Li, Zhe Zhang, Rui Liu, Lisheng Song, Ziwei Xu, Zhixing Peng, and Chen Zheng
Hydrol. Earth Syst. Sci., 27, 1583–1606, https://doi.org/10.5194/hess-27-1583-2023, https://doi.org/10.5194/hess-27-1583-2023, 2023
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This study highlights the role of integrating vegetation and multi-source soil moisture observations in regional climate models via a hybrid data assimilation and machine learning method. In particular, we show that this approach can improve land surface fluxes, near-surface atmospheric conditions, and land–atmosphere interactions by implementing detailed land characterization information in basins with complex underlying surfaces.
Dalei Hao, Gautam Bisht, Karl Rittger, Edward Bair, Cenlin He, Huilin Huang, Cheng Dang, Timbo Stillinger, Yu Gu, Hailong Wang, Yun Qian, and L. Ruby Leung
Geosci. Model Dev., 16, 75–94, https://doi.org/10.5194/gmd-16-75-2023, https://doi.org/10.5194/gmd-16-75-2023, 2023
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Snow with the highest albedo of land surface plays a vital role in Earth’s surface energy budget and water cycle. This study accounts for the impacts of snow grain shape and mixing state of light-absorbing particles with snow on snow albedo in the E3SM land model. The findings advance our understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offer guidance for improving snow simulations and radiative forcing estimates in Earth system models.
Huilin Huang, Yun Qian, Ye Liu, Cenlin He, Jianyu Zheng, Zhibo Zhang, and Antonis Gkikas
Atmos. Chem. Phys., 22, 15469–15488, https://doi.org/10.5194/acp-22-15469-2022, https://doi.org/10.5194/acp-22-15469-2022, 2022
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Using a clustering method developed in the field of artificial neural networks, we identify four typical dust transport patterns across the Sierra Nevada, associated with the mesoscale and regional-scale wind circulations. Our results highlight the connection between dust transport and dominant weather patterns, which can be used to understand dust transport in a changing climate.
Chaman Gul, Shichang Kang, Siva Praveen Puppala, Xiaokang Wu, Cenlin He, Yangyang Xu, Inka Koch, Sher Muhammad, Rajesh Kumar, and Getachew Dubache
Atmos. Chem. Phys., 22, 8725–8737, https://doi.org/10.5194/acp-22-8725-2022, https://doi.org/10.5194/acp-22-8725-2022, 2022
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This work aims to understand concentrations, spatial variability, and potential source regions of light-absorbing impurities (black carbon aerosols, dust particles, and organic carbon) in the surface snow of central and western Himalayan glaciers and their impact on snow albedo and radiative forcing.
Mark G. Flanner, Julian B. Arnheim, Joseph M. Cook, Cheng Dang, Cenlin He, Xianglei Huang, Deepak Singh, S. McKenzie Skiles, Chloe A. Whicker, and Charles S. Zender
Geosci. Model Dev., 14, 7673–7704, https://doi.org/10.5194/gmd-14-7673-2021, https://doi.org/10.5194/gmd-14-7673-2021, 2021
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We present the technical formulation and evaluation of a publicly available code and web-based model to simulate the spectral albedo of snow. Our model accounts for numerous features of the snow state and ambient conditions, including the the presence of light-absorbing matter like black and brown carbon, mineral dust, volcanic ash, and snow algae. Carbon dioxide snow, found on Mars, is also represented. The model accurately reproduces spectral measurements of clean and contaminated snow.
Quang-Van Doan, Hiroyuki Kusaka, Takuto Sato, and Fei Chen
Geosci. Model Dev., 14, 2097–2111, https://doi.org/10.5194/gmd-14-2097-2021, https://doi.org/10.5194/gmd-14-2097-2021, 2021
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This study proposes a novel structural self-organizing map (S-SOM) algorithm. The superiority of S-SOM is that it can better recognize the difference (or similarity) among spatial (or temporal) data used for training and thus improve the clustering quality compared to traditional SOM algorithms.
Michael Matiu, Alice Crespi, Giacomo Bertoldi, Carlo Maria Carmagnola, Christoph Marty, Samuel Morin, Wolfgang Schöner, Daniele Cat Berro, Gabriele Chiogna, Ludovica De Gregorio, Sven Kotlarski, Bruno Majone, Gernot Resch, Silvia Terzago, Mauro Valt, Walter Beozzo, Paola Cianfarra, Isabelle Gouttevin, Giorgia Marcolini, Claudia Notarnicola, Marcello Petitta, Simon C. Scherrer, Ulrich Strasser, Michael Winkler, Marc Zebisch, Andrea Cicogna, Roberto Cremonini, Andrea Debernardi, Mattia Faletto, Mauro Gaddo, Lorenzo Giovannini, Luca Mercalli, Jean-Michel Soubeyroux, Andrea Sušnik, Alberto Trenti, Stefano Urbani, and Viktor Weilguni
The Cryosphere, 15, 1343–1382, https://doi.org/10.5194/tc-15-1343-2021, https://doi.org/10.5194/tc-15-1343-2021, 2021
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The first Alpine-wide assessment of station snow depth has been enabled by a collaborative effort of the research community which involves more than 30 partners, 6 countries, and more than 2000 stations. It shows how snow in the European Alps matches the climatic zones and gives a robust estimate of observed changes: stronger decreases in the snow season at low elevations and in spring at all elevations, however, with considerable regional differences.
Julián Gelman Constantin, Lucas Ruiz, Gustavo Villarosa, Valeria Outes, Facundo N. Bajano, Cenlin He, Hector Bajano, and Laura Dawidowski
The Cryosphere, 14, 4581–4601, https://doi.org/10.5194/tc-14-4581-2020, https://doi.org/10.5194/tc-14-4581-2020, 2020
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We present the results of two field campaigns and modeling activities on the impact of atmospheric particles on Alerce Glacier (Argentinean Andes). We found that volcanic ash remains at different snow layers several years after eruption, increasing light absorption on the glacier surface (with a minor contribution of soot). This leads to 36 % higher annual glacier melting. We find remarkably that volcano eruptions in 2011 and 2015 have a relevant effect on the glacier even in 2016 and 2017.
Wenfu Tang, Benjamin Gaubert, Louisa Emmons, Yonghoon Choi, Joshua P. DiGangi, Glenn S. Diskin, Xiaomei Xu, Cenlin He, Helen Worden, Simone Tilmes, Rebecca Buchholz, Hannah S. Halliday, and Avelino F. Arellano
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-864, https://doi.org/10.5194/acp-2020-864, 2020
Revised manuscript not accepted
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A specific demonstration of the potential use of correlative information from carbon monoxide to refine estimates of regional carbon dioxide emissions from fossil fuel combustion.
Zhe Zhang, Yanping Li, Michael Barlage, Fei Chen, Gonzalo Miguez-Macho, Andrew Ireson, and Zhenhua Li
Hydrol. Earth Syst. Sci., 24, 655–672, https://doi.org/10.5194/hess-24-655-2020, https://doi.org/10.5194/hess-24-655-2020, 2020
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The groundwater regime in cold regions is strongly impacted by the soil freeze–thaw processes and semiarid climatic conditions. In this paper, we incorporate groundwater dynamics in the Noah-MP land surface model to simulate the water exchange between the unsaturated soil zone and an unconfined aquifer in the Prairie Pothole Region. The water table dynamics are reasonably simulated. The water budget of groundwater aquifer under current and future climate are also investigated.
Marco Falocchi, Werner Tirler, Lorenzo Giovannini, Elena Tomasi, Gianluca Antonacci, and Dino Zardi
Earth Syst. Sci. Data, 12, 277–291, https://doi.org/10.5194/essd-12-277-2020, https://doi.org/10.5194/essd-12-277-2020, 2020
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This paper describes a dataset of tracer concentrations and meteorological measurements collected during the Bolzano Tracer EXperiment (BTEX) to evaluate the pollutant dispersion from a waste incinerator close to Bolzano (Italian Alps).
BTEX represents one of the few experiments available in the literature performed over complex mountainous terrain to evaluate dispersion processes by means of controlled tracer releases. This dataset represents a useful benchmark for testing dispersion models.
Wei Pu, Jiecan Cui, Tenglong Shi, Xuelei Zhang, Cenlin He, and Xin Wang
Atmos. Chem. Phys., 19, 9949–9968, https://doi.org/10.5194/acp-19-9949-2019, https://doi.org/10.5194/acp-19-9949-2019, 2019
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LAPs (light-absorbing particles) deposited on snow can decrease snow albedo and increase the absorption of solar radiation. Radiative forcing by LAPs will affect the regional hydrological cycle and climate. We use MODIS observations to retrieve the radiative forcing by LAPs in snow across northeastern China (NEC). The results of radiative forcing present distinct spatial variability. We find that the biases are negatively correlated with LAP concentrations and range from
~ 5 % to ~ 350 %.
Cenlin He, Mark G. Flanner, Fei Chen, Michael Barlage, Kuo-Nan Liou, Shichang Kang, Jing Ming, and Yun Qian
Atmos. Chem. Phys., 18, 11507–11527, https://doi.org/10.5194/acp-18-11507-2018, https://doi.org/10.5194/acp-18-11507-2018, 2018
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Snow albedo plays a key role in the Earth and climate system. It can be affected by impurities and snow properties. This study implements new parameterizations into a widely used snow model to account for effects of snow shape and black carbon–snow mixing state on snow albedo reduction in the Tibetan Plateau. This study points toward an imperative need for extensive measurements and improved model characterization of snow grain shape and aerosol–snow mixing state in Tibet and elsewhere.
Yingying Yan, Jintai Lin, and Cenlin He
Atmos. Chem. Phys., 18, 1185–1202, https://doi.org/10.5194/acp-18-1185-2018, https://doi.org/10.5194/acp-18-1185-2018, 2018
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Examining observed and simulated ozone at about 1000 sites during 1990–2014, we find a clear diurnal cycle both in the magnitude of ozone trends and in the relative importance of climate variability versus anthropogenic emissions to ozone changes, which has policy implications to mitigate ozone at night and other non-peak hours.
Bin Zhao, Kuo-Nan Liou, Yu Gu, Jonathan H. Jiang, Qinbin Li, Rong Fu, Lei Huang, Xiaohong Liu, Xiangjun Shi, Hui Su, and Cenlin He
Atmos. Chem. Phys., 18, 1065–1078, https://doi.org/10.5194/acp-18-1065-2018, https://doi.org/10.5194/acp-18-1065-2018, 2018
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The interactions between aerosols and ice clouds represent one of the largest uncertainties among anthropogenic forcings on climate change. We find that the responses of ice crystal effective radius, a key parameter determining ice clouds' net radiative effect, to aerosol loadings are modulated by water vapor amount and vary from a significant negative correlation in moist conditions (consistent with the “Twomey effect” for liquid clouds) to a strong positive correlation in dry conditions.
Ling Qi, Qinbin Li, Daven K. Henze, Hsien-Liang Tseng, and Cenlin He
Atmos. Chem. Phys., 17, 9697–9716, https://doi.org/10.5194/acp-17-9697-2017, https://doi.org/10.5194/acp-17-9697-2017, 2017
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We find that Asian anthropogenic sources are the largest contributors (~ 40 %) to surface BC in spring in the Arctic, inconsistent with previous studies which repeatedly identified sources of surface BC as anthropogenic emissions from Europe and Russia. It takes 12–17 days for Asian anthropogenic emissions to be transported to the Arctic surface. Additionally, a large fraction (40–65 %) of Asian contribution is in the form of chronic pollution on 1- to 2-month timescales.
Ling Qi, Qinbin Li, Cenlin He, Xin Wang, and Jianping Huang
Atmos. Chem. Phys., 17, 7459–7479, https://doi.org/10.5194/acp-17-7459-2017, https://doi.org/10.5194/acp-17-7459-2017, 2017
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Black carbon (BC) is the second only to CO2 in heating the planet, but the simulation of BC is associated with large uncertainties. BC burden is largely underestimated over land and overestimated over ocean. Our study finds that a missing process in current Wegener–Bergeron–Findeisen models largely explains the discrepancy in BC simulation over land. We call for more observations of BC in mixed-phase clouds to understand this process and improve the simulation of global BC.
Ling Qi, Qinbin Li, Yinrui Li, and Cenlin He
Atmos. Chem. Phys., 17, 1037–1059, https://doi.org/10.5194/acp-17-1037-2017, https://doi.org/10.5194/acp-17-1037-2017, 2017
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The Arctic is the most vulnerable region for climate change. Black carbon (BC) in air and deposited on snow and ice warms the Arctic substantially, but simulations of BC climate effects are associated with large uncertainties. To reduce this uncertainty, it is imperative to improve the simulation of BC distribution in the Arctic. We evaluate the effects of controlling factors (emissions, dry and wet deposition) on BC distribution and call for more observations to constrain these processes.
Liang Chen, Yanping Li, Fei Chen, Alan Barr, Michael Barlage, and Bingcheng Wan
Atmos. Chem. Phys., 16, 8375–8387, https://doi.org/10.5194/acp-16-8375-2016, https://doi.org/10.5194/acp-16-8375-2016, 2016
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This work is the first time that Noah-MP is used to investigate the impact of parameterizing organic soil at a boreal forest site. Including an organic soil parameterization significantly improved performance of the model in surface energy and hydrology simulations due to the lower thermal conductivity and greater porosity of the organic soil. It substantially modified the partition between direct soil evaporation and vegetation transpiration in the simulation.
Bin Zhao, Kuo-Nan Liou, Yu Gu, Cenlin He, Wee-Liang Lee, Xing Chang, Qinbin Li, Shuxiao Wang, Hsien-Liang R. Tseng, Lai-Yung R. Leung, and Jiming Hao
Atmos. Chem. Phys., 16, 5841–5852, https://doi.org/10.5194/acp-16-5841-2016, https://doi.org/10.5194/acp-16-5841-2016, 2016
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We examine the impact of buildings on surface solar fluxes in Beijing by accounting for their 3-D structures. We find that inclusion of buildings changes surface solar fluxes by within ±1 W m−2, ±1–10 W m−2, and up to ±100 W m−2 at grid resolutions of 4 km, 800 m, and 90 m, respectively. We can resolve pairs of positive-negative flux deviations on different sides of buildings at ≤ 800 m resolutions. We should treat building-effect on solar fluxes differently in models with different resolutions.
Cenlin He, Qinbin Li, Kuo-Nan Liou, Ling Qi, Shu Tao, and Joshua P. Schwarz
Atmos. Chem. Phys., 16, 3077–3098, https://doi.org/10.5194/acp-16-3077-2016, https://doi.org/10.5194/acp-16-3077-2016, 2016
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Blarck carbon aging significantly affects its global distribution and thus climatic effects. This study develops a microphysics-based BC aging scheme in a global model, which substantially improves model simulations of BC over the remote Pacific. The microphysical scheme shows fast aging over source regions and much slower aging in remote regions. The microphysical aging significantly reduces global BC burden and lifetime, showing important implications for the estimate of BC radiative effects.
X. Cai, Z.-L. Yang, J. B. Fisher, X. Zhang, M. Barlage, and F. Chen
Geosci. Model Dev., 9, 1–15, https://doi.org/10.5194/gmd-9-1-2016, https://doi.org/10.5194/gmd-9-1-2016, 2016
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A terrestrial nitrogen dynamics model is integrated into Noah-MP. The new model performs well in capturing the major nitrogen state/flux variables (e.g., soil nitrate and nitrate leaching). The addition of nitrogen dynamics improves the modeling of net primary productivity and evapotranspiration. This improvement advances the capability of Noah-MP to simultaneously predict weather and water quality.
C. He, K.-N. Liou, Y. Takano, R. Zhang, M. Levy Zamora, P. Yang, Q. Li, and L. R. Leung
Atmos. Chem. Phys., 15, 11967–11980, https://doi.org/10.5194/acp-15-11967-2015, https://doi.org/10.5194/acp-15-11967-2015, 2015
Y. H. Mao, Q. B. Li, D. K. Henze, Z. Jiang, D. B. A. Jones, M. Kopacz, C. He, L. Qi, M. Gao, W.-M. Hao, and K.-N. Liou
Atmos. Chem. Phys., 15, 7685–7702, https://doi.org/10.5194/acp-15-7685-2015, https://doi.org/10.5194/acp-15-7685-2015, 2015
L. Laiti, D. Zardi, M. de Franceschi, G. Rampanelli, and L. Giovannini
Atmos. Chem. Phys., 14, 9771–9786, https://doi.org/10.5194/acp-14-9771-2014, https://doi.org/10.5194/acp-14-9771-2014, 2014
C. He, Q. B. Li, K. N. Liou, J. Zhang, L. Qi, Y. Mao, M. Gao, Z. Lu, D. G. Streets, Q. Zhang, M. M. Sarin, and K. Ram
Atmos. Chem. Phys., 14, 7091–7112, https://doi.org/10.5194/acp-14-7091-2014, https://doi.org/10.5194/acp-14-7091-2014, 2014
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Improved definition of prior uncertainties in CO2 and CO fossil fuel fluxes and its impact on multi-species inversion with GEOS-Chem (v12.5)
RASCAL v1.0: an open-source tool for climatological time series reconstruction and extension
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
Introducing graupel density prediction in Weather Research and Forecasting (WRF) double-moment 6-class (WDM6) microphysics and evaluation of the modified scheme during the ICE-POP field campaign
Enabling high-performance cloud computing for the Community Multiscale Air Quality Model (CMAQ) version 5.3.3: performance evaluation and benefits for the user community
Atmospheric-river-induced precipitation in California as simulated by the regionally refined Simple Convective Resolving E3SM Atmosphere Model (SCREAM) Version 0
Recent improvements and maximum covariance analysis of aerosol and cloud properties in the EC-Earth3-AerChem model
GPU-HADVPPM4HIP V1.0: using the heterogeneous-compute interface for portability (HIP) to speed up the piecewise parabolic method in the CAMx (v6.10) air quality model on China's domestic GPU-like accelerator
Preliminary evaluation of the effect of electro-coalescence with conducting sphere approximation on the formation of warm cumulus clouds using SCALE-SDM version 0.2.5–2.3.0
Similarity-Based Analysis of Atmospheric Organic Compounds for Machine Learning Applications
Cell tracking -based framework for assessing nowcasting model skill in reproducing growth and decay of convective rainfall
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.
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.
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.
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.
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.
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.
Hongyi Li, Ting Yang, Lars Nerger, Dawei Zhang, Di Zhang, Guigang Tang, Haibo Wang, Yele Sun, Pingqing Fu, Hang Su, and Zifa Wang
Geosci. Model Dev., 17, 8495–8519, https://doi.org/10.5194/gmd-17-8495-2024, https://doi.org/10.5194/gmd-17-8495-2024, 2024
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To accurately characterize the spatiotemporal distribution of particulate matter <2.5 µm chemical components, we developed the Nested Air Quality Prediction Model System with the Parallel Data Assimilation Framework (NAQPMS-PDAF) v2.0 for chemical components with non-Gaussian and nonlinear properties. NAQPMS-PDAF v2.0 has better computing efficiency, excels when used with a small ensemble size, and can significantly improve the simulation performance of chemical components.
T. Nash Skipper, Christian Hogrefe, Barron H. Henderson, Rohit Mathur, Kristen M. Foley, and Armistead G. Russell
Geosci. Model Dev., 17, 8373–8397, https://doi.org/10.5194/gmd-17-8373-2024, https://doi.org/10.5194/gmd-17-8373-2024, 2024
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Chemical transport model simulations are combined with ozone observations to estimate the bias in ozone attributable to US anthropogenic sources and individual sources of US background ozone: natural sources, non-US anthropogenic sources, and stratospheric ozone. Results indicate a positive bias correlated with US anthropogenic emissions during summer in the eastern US and a negative bias correlated with stratospheric ozone during spring.
Li Fang, Jianbing Jin, Arjo Segers, Ke Li, Ji Xia, Wei Han, Baojie Li, Hai Xiang Lin, Lei Zhu, Song Liu, and Hong Liao
Geosci. Model Dev., 17, 8267–8282, https://doi.org/10.5194/gmd-17-8267-2024, https://doi.org/10.5194/gmd-17-8267-2024, 2024
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Model evaluations against ground observations are usually unfair. The former simulates mean status over coarse grids and the latter the surrounding atmosphere. To solve this, we proposed the new land-use-based representative (LUBR) operator that considers intra-grid variance. The LUBR operator is validated to provide insights that align with satellite measurements. The results highlight the importance of considering fine-scale urban–rural differences when comparing models and observation.
Mijie Pang, Jianbing Jin, Arjo Segers, Huiya Jiang, Wei Han, Batjargal Buyantogtokh, Ji Xia, Li Fang, Jiandong Li, Hai Xiang Lin, and Hong Liao
Geosci. Model Dev., 17, 8223–8242, https://doi.org/10.5194/gmd-17-8223-2024, https://doi.org/10.5194/gmd-17-8223-2024, 2024
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The ensemble Kalman filter (EnKF) improves dust storm forecasts but faces challenges with position errors. The valid time shifting EnKF (VTS-EnKF) addresses this by adjusting for position errors, enhancing accuracy in forecasting dust storms, as proven in tests on 2021 events, even with smaller ensembles and time intervals.
Prabhakar Namdev, Maithili Sharan, Piyush Srivastava, and Saroj Kanta Mishra
Geosci. Model Dev., 17, 8093–8114, https://doi.org/10.5194/gmd-17-8093-2024, https://doi.org/10.5194/gmd-17-8093-2024, 2024
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Inadequate representation of surface–atmosphere interaction processes is a major source of uncertainty in numerical weather prediction models. Here, an effort has been made to improve the Weather Research and Forecasting (WRF) model version 4.2.2 by introducing a unique theoretical framework under convective conditions. In addition, to enhance the potential applicability of the WRF modeling system, various commonly used similarity functions under convective conditions have also been installed.
Andrew Gettelman, Richard Forbes, Roger Marchand, Chih-Chieh Chen, and Mark Fielding
Geosci. Model Dev., 17, 8069–8092, https://doi.org/10.5194/gmd-17-8069-2024, https://doi.org/10.5194/gmd-17-8069-2024, 2024
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Supercooled liquid clouds (liquid clouds colder than 0°C) are common at higher latitudes (especially over the Southern Ocean) and are critical for constraining climate projections. We compare a single-column version of a weather model to observations with two different cloud schemes and find that both the dynamical environment and atmospheric aerosols are important for reproducing observations.
Yujuan Wang, Peng Zhang, Jie Li, Yaman Liu, Yanxu Zhang, Jiawei Li, and Zhiwei Han
Geosci. Model Dev., 17, 7995–8021, https://doi.org/10.5194/gmd-17-7995-2024, https://doi.org/10.5194/gmd-17-7995-2024, 2024
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This study updates the CESM's aerosol schemes, focusing on dust, marine aerosol emissions, and secondary organic aerosol (SOA) . Dust emission modifications make deflation areas more continuous, improving results in North America and the sub-Arctic. Humidity correction to sea-salt emissions has a minor effect. Introducing marine organic aerosol emissions, coupled with ocean biogeochemical processes, and adding aqueous reactions for SOA formation advance the CESM's aerosol modelling results.
Lucas A. McMichael, Michael J. Schmidt, Robert Wood, Peter N. Blossey, and Lekha Patel
Geosci. Model Dev., 17, 7867–7888, https://doi.org/10.5194/gmd-17-7867-2024, https://doi.org/10.5194/gmd-17-7867-2024, 2024
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Marine cloud brightening (MCB) is a climate intervention technique to potentially cool the climate. Climate models used to gauge regional climate impacts associated with MCB often assume large areas of the ocean are uniformly perturbed. However, a more realistic representation of MCB application would require information about how an injected particle plume spreads. This work aims to develop such a plume-spreading model.
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 7915–7962, https://doi.org/10.5194/gmd-17-7915-2024, https://doi.org/10.5194/gmd-17-7915-2024, 2024
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Data-driven models are becoming a viable alternative to physics-based models for weather forecasting up to 15 d into the future. However, it is unclear whether they are as reliable as physics-based models when forecasting weather extremes. We evaluate their performance in forecasting near-surface cold, hot, and windy extremes globally. We find that data-driven models can compete with physics-based models and that the choice of the best model mainly depends on the region and type of extreme.
David C. Wong, Jeff Willison, Jonathan E. Pleim, Golam Sarwar, James Beidler, Russ Bullock, Jerold A. Herwehe, Rob Gilliam, Daiwen Kang, Christian Hogrefe, George Pouliot, and Hosein Foroutan
Geosci. Model Dev., 17, 7855–7866, https://doi.org/10.5194/gmd-17-7855-2024, https://doi.org/10.5194/gmd-17-7855-2024, 2024
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This work describe how we linked the meteorological Model for Prediction Across Scales – Atmosphere (MPAS-A) with the Community Multiscale Air Quality (CMAQ) air quality model to form a coupled modelling system. This could be used to study air quality or climate and air quality interaction at a global scale. This new model scales well in high-performance computing environments and performs well with respect to ground surface networks in terms of ozone and PM2.5.
Giulio Mandorli and Claudia J. Stubenrauch
Geosci. Model Dev., 17, 7795–7813, https://doi.org/10.5194/gmd-17-7795-2024, https://doi.org/10.5194/gmd-17-7795-2024, 2024
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In recent years, several studies focused their attention on the disposition of convection. Lots of methods, called indices, have been developed to quantify the amount of convection clustering. These indices are evaluated in this study by defining criteria that must be satisfied and then evaluating the indices against these standards. None of the indices meet all criteria, with some only partially meeting them.
Kerry Anderson, Jack Chen, Peter Englefield, Debora Griffin, Paul A. Makar, and Dan Thompson
Geosci. Model Dev., 17, 7713–7749, https://doi.org/10.5194/gmd-17-7713-2024, https://doi.org/10.5194/gmd-17-7713-2024, 2024
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The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that predicts smoke and carbon emissions from wildland fires. The model calculates emissions from the ground up based on satellite-detected fires, modelled weather and fire characteristics. Unlike other global models, GFFEPS uses daily weather conditions to capture changing burning conditions on a day-to-day basis. GFFEPS produced lower carbon emissions due to the changing weather not captured by the other models.
Jianyu Lin, Tie Dai, Lifang Sheng, Weihang Zhang, Shangfei Hai, and Yawen Kong
EGUsphere, https://doi.org/10.5194/egusphere-2024-3321, https://doi.org/10.5194/egusphere-2024-3321, 2024
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The effectiveness of assimilation system and its sensitivity to ensemble member size and length of assimilation window have been investigated. This study advances our understanding about the selection of basic parameters in the four-dimension local ensemble transform Kalman filter assimilation system and the performance of ensemble simulation in a particulate matter polluted environment.
Yi-Ning Shi, Jun Yang, Wei Han, Lujie Han, Jiajia Mao, Wanlin Kan, and Fuzhong Weng
EGUsphere, https://doi.org/10.5194/egusphere-2024-2884, https://doi.org/10.5194/egusphere-2024-2884, 2024
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Assimilating Ground-based microwave radiometers' observations into numerical weather prediction models holds significant promise for enhancing forecast accuracy. Radiative transfer models (RTM) are crucial for direct data assimilation. We propose a new RTM capable of simulating brightness temperatures observed by GMRs and their Jacobians. Several improvements are introduced to achieve higher accuracy.The RTM align with RTTOV-gb well and can achieve smaller STD in water vapor absorption channels.
Samiha Binte Shahid, Forrest G. Lacey, Christine Wiedinmyer, Robert J. Yokelson, and Kelley C. Barsanti
Geosci. Model Dev., 17, 7679–7711, https://doi.org/10.5194/gmd-17-7679-2024, https://doi.org/10.5194/gmd-17-7679-2024, 2024
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The Next-generation Emissions InVentory expansion of Akagi (NEIVA) v.1.0 is a comprehensive biomass burning emissions database that allows integration of new data and flexible querying. Data are stored in connected datasets, including recommended averages of ~1500 constituents for 14 globally relevant fire types. Individual compounds were mapped to common model species to allow better attribution of emissions in modeling studies that predict the effects of fires on air quality and climate.
Lucie Bakels, Daria Tatsii, Anne Tipka, Rona Thompson, Marina Dütsch, Michael Blaschek, Petra Seibert, Katharina Baier, Silvia Bucci, Massimo Cassiani, Sabine Eckhardt, Christine Groot Zwaaftink, Stephan Henne, Pirmin Kaufmann, Vincent Lechner, Christian Maurer, Marie D. Mulder, Ignacio Pisso, Andreas Plach, Rakesh Subramanian, Martin Vojta, and Andreas Stohl
Geosci. Model Dev., 17, 7595–7627, https://doi.org/10.5194/gmd-17-7595-2024, https://doi.org/10.5194/gmd-17-7595-2024, 2024
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Computer models are essential for improving our understanding of how gases and particles move in the atmosphere. We present an update of the atmospheric transport model FLEXPART. FLEXPART 11 is more accurate due to a reduced number of interpolations and a new scheme for wet deposition. It can simulate non-spherical aerosols and includes linear chemical reactions. It is parallelised using OpenMP and includes new user options. A new user manual details how to use FLEXPART 11.
Jaroslav Resler, Petra Bauerová, Michal Belda, Martin Bureš, Kryštof Eben, Vladimír Fuka, Jan Geletič, Radek Jareš, Jan Karel, Josef Keder, Pavel Krč, William Patiño, Jelena Radović, Hynek Řezníček, Matthias Sühring, Adriana Šindelářová, and Ondřej Vlček
Geosci. Model Dev., 17, 7513–7537, https://doi.org/10.5194/gmd-17-7513-2024, https://doi.org/10.5194/gmd-17-7513-2024, 2024
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Detailed modeling of urban air quality in stable conditions is a challenge. We show the unprecedented sensitivity of a large eddy simulation (LES) model to meteorological boundary conditions and model parameters in an urban environment under stable conditions. We demonstrate the crucial role of boundary conditions for the comparability of results with observations. The study reveals a strong sensitivity of the results to model parameters and model numerical instabilities during such conditions.
Matthieu Dabrowski, José Mennesson, Jérôme Riedi, Chaabane Djeraba, and Pierre Nabat
EGUsphere, https://doi.org/10.5194/egusphere-2024-2676, https://doi.org/10.5194/egusphere-2024-2676, 2024
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This work focuses on the prediction of aerosol concentration values at ground level, which are a strong indicator of air quality, using Artificial Neural Networks. A study of different variables and their efficiency as inputs for these models is also proposed, and reveals that the best results are obtained when using all of them. Comparison of networks architectures and information fusion methods allows the extraction of knowledge on the most efficient methods in the context of this study.
Jorge E. Pachón, Mariel A. Opazo, Pablo Lichtig, Nicolas Huneeus, Idir Bouarar, Guy Brasseur, Cathy W. Y. Li, Johannes Flemming, Laurent Menut, Camilo Menares, Laura Gallardo, Michael Gauss, Mikhail Sofiev, Rostislav Kouznetsov, Julia Palamarchuk, Andreas Uppstu, Laura Dawidowski, Nestor Y. Rojas, María de Fátima Andrade, Mario E. Gavidia-Calderón, Alejandro H. Delgado Peralta, and Daniel Schuch
Geosci. Model Dev., 17, 7467–7512, https://doi.org/10.5194/gmd-17-7467-2024, https://doi.org/10.5194/gmd-17-7467-2024, 2024
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Latin America (LAC) has some of the most populated urban areas in the world, with high levels of air pollution. Air quality management in LAC has been traditionally focused on surveillance and building emission inventories. This study performed the first intercomparison and model evaluation in LAC, with interesting and insightful findings for the region. A multiscale modeling ensemble chain was assembled as a first step towards an air quality forecasting system.
David Ho, Michał Gałkowski, Friedemann Reum, Santiago Botía, Julia Marshall, Kai Uwe Totsche, and Christoph Gerbig
Geosci. Model Dev., 17, 7401–7422, https://doi.org/10.5194/gmd-17-7401-2024, https://doi.org/10.5194/gmd-17-7401-2024, 2024
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Atmospheric model users often overlook the impact of the land–atmosphere interaction. This study accessed various setups of WRF-GHG simulations that ensure consistency between the model and driving reanalysis fields. We found that a combination of nudging and frequent re-initialization allows certain improvement by constraining the soil moisture fields and, through its impact on atmospheric mixing, improves atmospheric transport.
Jie Gao, Yi Huang, Jonathon S. Wright, Ke Li, Tao Geng, and Qiurun Yu
EGUsphere, https://doi.org/10.5194/egusphere-2024-2815, https://doi.org/10.5194/egusphere-2024-2815, 2024
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The aerosol in the upper troposphere and stratosphere is highly variable, and its radiative effect is poorly understood. To estimate that 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 and consists well with radiative model calculations and can be applied to atmospheric models with speed requirements.
Phuong Loan Nguyen, Lisa V. Alexander, Marcus J. Thatcher, Son C. H. Truong, Rachael N. Isphording, and John L. McGregor
Geosci. Model Dev., 17, 7285–7315, https://doi.org/10.5194/gmd-17-7285-2024, https://doi.org/10.5194/gmd-17-7285-2024, 2024
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We use a comprehensive approach to select a subset of CMIP6 models for dynamical downscaling over Southeast Asia, taking into account model performance, model independence, data availability and the range of future climate projections. The standardised benchmarking framework is applied to assess model performance through both statistical and process-based metrics. Ultimately, we identify two independent model groups that are suitable for dynamical downscaling in the Southeast Asian region.
Ingrid Super, Tia Scarpelli, Arjan Droste, and Paul I. Palmer
Geosci. Model Dev., 17, 7263–7284, https://doi.org/10.5194/gmd-17-7263-2024, https://doi.org/10.5194/gmd-17-7263-2024, 2024
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Monitoring greenhouse gas emission reductions requires a combination of models and observations, as well as an initial emission estimate. Each component provides information with a certain level of certainty and is weighted to yield the most reliable estimate of actual emissions. We describe efforts for estimating the uncertainty in the initial emission estimate, which significantly impacts the outcome. Hence, a good uncertainty estimate is key for obtaining reliable information on emissions.
Álvaro González-Cervera and Luis Durán
Geosci. Model Dev., 17, 7245–7261, https://doi.org/10.5194/gmd-17-7245-2024, https://doi.org/10.5194/gmd-17-7245-2024, 2024
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RASCAL is an open-source Python tool designed for reconstructing daily climate observations, especially in regions with complex local phenomena. It merges large-scale weather patterns with local weather using the analog method. Evaluations in central Spain show that RASCAL outperforms ERA20C reanalysis in reconstructing precipitation and temperature. RASCAL offers opportunities for broad scientific applications, from short-term forecasts to local-scale climate change scenarios.
Matti Niskanen, Aku Seppänen, Henri Oikarinen, Miska Olin, Panu Karjalainen, Santtu Mikkonen, and Kari Lehtinen
EGUsphere, https://doi.org/10.5194/egusphere-2024-1898, https://doi.org/10.5194/egusphere-2024-1898, 2024
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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.
Sun-Young Park, Kyo-Sun Sunny Lim, Kwonil Kim, Gyuwon Lee, and Jason A. Milbrandt
Geosci. Model Dev., 17, 7199–7218, https://doi.org/10.5194/gmd-17-7199-2024, https://doi.org/10.5194/gmd-17-7199-2024, 2024
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We enhance the WDM6 scheme by incorporating predicted graupel density. The modification affects graupel characteristics, including fall velocity–diameter and mass–diameter relationships. Simulations highlight changes in graupel distribution and precipitation patterns, potentially influencing surface snow amounts. The study underscores the significance of integrating predicted graupel density for a more realistic portrayal of microphysical properties in weather models.
Christos I. Efstathiou, Elizabeth Adams, Carlie J. Coats, Robert Zelt, Mark Reed, John McGee, Kristen M. Foley, Fahim I. Sidi, David C. Wong, Steven Fine, and Saravanan Arunachalam
Geosci. Model Dev., 17, 7001–7027, https://doi.org/10.5194/gmd-17-7001-2024, https://doi.org/10.5194/gmd-17-7001-2024, 2024
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We present a summary of enabling high-performance computing of the Community Multiscale Air Quality Model (CMAQ) – a state-of-the-science community multiscale air quality model – on two cloud computing platforms through documenting the technologies, model performance, scaling and relative merits. This may be a new paradigm for computationally intense future model applications. We initiated this work due to a need to leverage cloud computing advances and to ease the learning curve for new users.
Peter A. Bogenschutz, Jishi Zhang, Qi Tang, and Philip Cameron-Smith
Geosci. Model Dev., 17, 7029–7050, https://doi.org/10.5194/gmd-17-7029-2024, https://doi.org/10.5194/gmd-17-7029-2024, 2024
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Using high-resolution and state-of-the-art modeling techniques we simulate five atmospheric river events for California to test the capability to represent precipitation for these events. We find that our model is able to capture the distribution of precipitation very well but suffers from overestimating the precipitation amounts over high elevation. Increasing the resolution further has no impact on reducing this bias, while increasing the domain size does have modest impacts.
Manu Anna Thomas, Klaus Wyser, Shiyu Wang, Marios Chatziparaschos, Paraskevi Georgakaki, Montserrat Costa-Surós, Maria Gonçalves Ageitos, Maria Kanakidou, Carlos Pérez García-Pando, Athanasios Nenes, Twan van Noije, Philippe Le Sager, and Abhay Devasthale
Geosci. Model Dev., 17, 6903–6927, https://doi.org/10.5194/gmd-17-6903-2024, https://doi.org/10.5194/gmd-17-6903-2024, 2024
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Aerosol–cloud interactions occur at a range of spatio-temporal scales. While evaluating recent developments in EC-Earth3-AerChem, this study aims to understand the extent to which the Twomey effect manifests itself at larger scales. We find a reduction in the warm bias over the Southern Ocean due to model improvements. While we see footprints of the Twomey effect at larger scales, the negative relationship between cloud droplet number and liquid water drives the shortwave radiative effect.
Kai Cao, Qizhong Wu, Lingling Wang, Hengliang Guo, Nan Wang, Huaqiong Cheng, Xiao Tang, Dongxing Li, Lina Liu, Dongqing Li, Hao Wu, and Lanning Wang
Geosci. Model Dev., 17, 6887–6901, https://doi.org/10.5194/gmd-17-6887-2024, https://doi.org/10.5194/gmd-17-6887-2024, 2024
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AMD’s heterogeneous-compute interface for portability was implemented to port the piecewise parabolic method solver from NVIDIA GPUs to China's GPU-like accelerators. The results show that the larger the model scale, the more acceleration effect on the GPU-like accelerator, up to 28.9 times. The multi-level parallelism achieves a speedup of 32.7 times on the heterogeneous cluster. By comparing the results, the GPU-like accelerators have more accuracy for the geoscience numerical models.
Ruyi Zhang, Limin Zhou, Shin-ichiro Shima, and Huawei Yang
Geosci. Model Dev., 17, 6761–6774, https://doi.org/10.5194/gmd-17-6761-2024, https://doi.org/10.5194/gmd-17-6761-2024, 2024
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Solar activity weakly ionises Earth's atmosphere, charging cloud droplets. Electro-coalescence is when oppositely charged droplets stick together. We introduce an analytical expression of electro-coalescence probability and use it in a warm-cumulus-cloud simulation. Results show that charge cases increase rain and droplet size, with the new method outperforming older ones. The new method requires longer computation time, but its impact on rain justifies inclusion in meteorology models.
Hilda Sandström and Patrick Rinke
EGUsphere, https://doi.org/10.48550/arXiv.2406.18171, https://doi.org/10.48550/arXiv.2406.18171, 2024
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Machine learning has the potential to aid the identification 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 model in atmospheric sciences.
Jenna Ritvanen, Seppo Pulkkinen, Dmitri Moisseev, and Daniele Nerini
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-99, https://doi.org/10.5194/gmd-2024-99, 2024
Revised manuscript accepted for GMD
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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 rainfall. We propose a framework to assess model performance. In the framework, convective cells are identified and tracked in the forecasts and observations, and then the model skill is evaluated by comparing differences between forecast and observed cells. We demonstrate the framework with 4 open-source models.
Cited articles
Alqahtani, A., Shah, M. I., Aldrees, A., and Javed, M. F.: Comparative Assessment of Individual and Ensemble Machine Learning Models for Efficient Analysis of River Water Quality, Sustainability, 14, 1183, https://doi.org/10.3390/su14031183, 2022. a
Antoniadis, A., Lambert-Lacroix, S., and Poggi, J.-M.: Random forests for global sensitivity analysis: A selective review, Reliability Engineering & System Safety, 206, 107312, https://doi.org/10.1016/j.ress.2020.107312, 2021. a
Antonogeorgos, G., Panagiotakos, D. B., Priftis, K. N., and Tzonou, A.: Logistic Regression and Linear Discriminant Analyses in Evaluating Factors Associated with Asthma Prevalence among 10- to 12-Years-Old Children: Divergence and Similarity of the Two Statistical Methods, International J. Pediatrics, 2009, 952042, https://doi.org/10.1155/2009/952042, 2009. a
Arpaci, A., Malowerschnig, B., Sass, O., and Vacik, H.: Using multi variate data mining techniques for estimating fire susceptibility of Tyrolean forests, Appl. Geogr., 53, 258–270, https://doi.org/10.1016/j.apgeog.2014.05.015, 2014. a
Arsenault, K. R., Nearing, G. S., Wang, S., Yatheendradas, S., and Peters-Lidard, C. D.: Parameter Sensitivity of the Noah-MP Land Surface Model with Dynamic Vegetation, J. Hydrometeorol., 19, 815–830, https://doi.org/10.1175/jhm-d-17-0205.1, 2018. a
Baki, H., Chinta, S., C Balaji, and Srinivasan, B.: Determining the sensitive parameters of the Weather Research and Forecasting (WRF) model for the simulation of tropical cyclones in the Bay of Bengal using global sensitivity analysis and machine learning, Geosci. Model Dev., 15, 2133–2155, https://doi.org/10.5194/gmd-15-2133-2022, 2022. a
Bar Massada, A., Syphard, A. D., Stewart, S. I., and Radeloff, V. C.: Wildfire ignition-distribution modelling: a comparative study in the Huron–Manistee National Forest, Michigan, USA, Int. J. Wildland Fire, 22, 174–183, https://doi.org/10.1071/WF11178, 2013. a
Bocquet, M., Brajard, J., Carrassi, A., and Bertino, L.: Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization, Foundations of Data Science, 2, 55–80, https://doi.org/10.3934/fods.2020004, 2020. a
Bonavita, M. and Laloyaux, P.: Machine Learning for Model Error Inference and Correction, J. Adv. Model. Earth Sy., 12, e2020MS002232, https://doi.org/10.1029/2020MS002232, 2020. a
Boser, B. E., Guyon, I. M., and Vapnik, V. N.: A training algorithm for optimal margin classifiers, p. 144–152, https://doi.org/10.1145/130385.130401, 1992. a
Box, G. E. and Tiao, G. C.: John Wiley & Sons, Ltd, ISBN 9781118033197, https://doi.org/10.1002/9781118033197, 1992. a
Bratley, P. and Fox, B. L.: Algorithm 659: Implementing Sobol's quasirandom sequence generator, ACM Trans. Math. Softw., 14, 88–100, https://doi.org/10.1145/42288.214372, 1988. a
Breiman, L.: Random Forests, Machine Learning, 45, 5–32, 2001. a
Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J.: Classification and Regression Trees, Biometrics, 40, 874 pp., https://api.semanticscholar.org/CorpusID:29458883 (last access: 1 March 2024), 1984. a
Brunton, S. L. and Kutz, J. N.: Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control, Cambridge University Press, https://doi.org/10.1017/9781108380690, 2019. a
Campolongo, F., Cariboni, J., and Saltelli, A.: An effective screening design for sensitivity analysis of large models, Environ. Modell. Softw., 22, 1509–1518, https://doi.org/10.1016/j.envsoft.2006.10.004, 2007. a
Campolongo, F., Saltelli, A., and Cariboni, J.: From screening to quantitative sensitivity analysis. A unified approach, Comput. Phys. Commun., 182, 978–988, https://doi.org/10.1016/j.cpc.2010.12.039, 2011. a
Catani, F., Lagomarsino, D., Segoni, S., and Tofani, V.: Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues, Nat. Hazards Earth Syst. Sci., 13, 2815–2831, https://doi.org/10.5194/nhess-13-2815-2013, 2013. a
Chase, R. J., Harrison, D. R., Burke, A., Lackmann, G. M., and McGovern, A.: A Machine Learning Tutorial for Operational Meteorology. Part I: Traditional Machine Learning, Weather Forecast., 37, 1509–1529, https://doi.org/10.1175/WAF-D-22-0070.1, 2022. a
Chase, R. J., Harrison, D. R., Lackmann, G. M., and McGovern, A.: A Machine Learning Tutorial for Operational Meteorology, Part II: Neural Networks and Deep Learning, Weather Forecast., 38, 1271–1293, https://doi.org/10.1175/WAF-D-22-0187.1, 2023. a
Chen, T. and Guestrin, C.: XGBoost: A Scalable Tree Boosting System, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '16, ACM, https://doi.org/10.1145/2939672.2939785, 2016. a
Chen, T., Zhu, L., Niu, R.-Q., Trinder, C. J., Peng, L., and Lei, T.: Mapping landslide susceptibility at the Three Gorges Reservoir, China, using gradient boosting decision tree, random forest and information value models, J. Mountain Sci., 17, 670–685, https://doi.org/10.1007/s11629-019-5839-3, 2020. a
Cinquegrana, D., Zollo, A. L., Montesarchio, M., and Bucchignani, E.: A Metamodel-Based Optimization of Physical Parameters of High Resolution NWP ICON-LAM over Southern Italy, Atmosphere, 14, 788, https://doi.org/10.3390/atmos14050788, 2023. a, b
Clark, M. P., Kavetski, D., and Fenicia, F.: Pursuing the method of multiple working hypotheses for hydrological modeling, Water Resour. Res., 47, W09301, https://doi.org/10.1029/2010WR009827, 2011. a
Cortes, C. and Vapnik, V. N.: Support-Vector Networks, Mach. Learn., 20, 273–297, https://api.semanticscholar.org/CorpusID:52874011 (last access: 1 March 2024), 1995. a
Couvreux, F., Hourdin, F., Williamson, D., Roehrig, R., Volodina, V., Villefranque, N., Rio, C., Audouin, O., Salter, J., Bazile, E., Brient, F., Favot, F., Honnert, R., Lefebvre, M.-P., Madeleine, J.-B., Rodier, Q., and Xu, W.: Process-Based Climate Model Development Harnessing Machine Learning: I. A Calibration Tool for Parameterization Improvement, J. Adv. Model. Earth Sy., 13, e2020MS002217, https://doi.org/10.1029/2020MS002217, 2021. a, b
Cui, C. and Wang, D.: High dimensional data regression using Lasso model and neural networks with random weights, Information Sci., 372, 505–517, https://doi.org/10.1016/j.ins.2016.08.060, 2016. a
Cuntz, M., Mai, J., Zink, M., Thober, S., Kumar, R., Schäfer, D., Schrön, M., Craven, J., Rakovec, O., Spieler, D., Prykhodko, V., Dalmasso, G., Musuuza, J., Langenberg, B., Attinger, S., and Samaniego, L.: Computationally inexpensive identification of noninformative model parameters by sequential screening, Water Resour. Res., 51, 6417–6441, https://doi.org/10.1002/2015WR016907, 2015. a, b
Cuntz, M., Mai, J., Samaniego, L., Clark, M., Wulfmeyer, V., Branch, O., Attinger, S., and Thober, S.: The impact of standard and hard-coded parameters on the hydrologic fluxes in the Noah-MP land surface model, J. Geophys. Res.-Atmos., 121, 10676–10700, https://doi.org/10.1002/2016JD025097, 2016. a, b
Dagon, K., Sanderson, B. M., Fisher, R. A., and Lawrence, D. M.: A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5, Adv. Stat. Clim. Meteorol. Oceanogr., 6, 223–244, https://doi.org/10.5194/ascmo-6-223-2020, 2020. a, b
Daviran, M., Shamekhi, M., Ghezelbash, R., and Maghsoudi, A.: Landslide susceptibility prediction using artificial neural networks, SVMs and random forest: hyperparameters tuning by genetic optimization algorithm, Int. J. Environ. Sci. Technol., 20, 259–276, https://doi.org/10.1007/s13762-022-04491-3, 2023. a
Dey, A., Sahoo, D. P., Kumar, R., and Remesan, R.: A multimodel ensemble machine learning approach for CMIP6 climate model projections in an Indian River basin, Int. J. Climatol., 42, 9215–9236, https://doi.org/10.1002/joc.7813, 2022. a
Di Santo, D.: ML-AMPSIT/datasetFlatSeabreeze: datasetFlatSeabreeze, Zenodo [data set], https://doi.org/10.5281/zenodo.14051616, 2024a. a
Di Santo, D.: ML-AMPSIT, Zenodo [code], https://doi.org/10.5281/zenodo.10789930, 2024b. a
Dudhia, J.: Numerical Study of Convection Observed during the Winter Monsoon Experiment Using a Mesoscale Two-Dimensional Model, J. Atmos. Sci., 46, 3077–3107, https://doi.org/10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2, 1989. a
Ek, M. B., Mitchell, K. E., Lin, Y., Rogers, E., Grunmann, P., Koren, V., Gayno, G., and Tarpley, J. D.: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model, J. Geophys. Res.-Atmos., 108, 8851, https://doi.org/10.1029/2002JD003296, 2003. a
Elia, L., Castellaro, S., Dahal, A., and Lombardo, L.: Assessing multi-hazard susceptibility to cryospheric hazards: Lesson learnt from an Alaskan example, Sci. Total Environ., 898, 165289, https://doi.org/10.1016/j.scitotenv.2023.165289, 2023. a
Engelbrecht, A. P., Cloete, I., and Zurada, J. M.: Determining the significance of input parameters using sensitivity analysis, in: From Natural to Artificial Neural Computation, edited by: Mira, J. and Sandoval, F., IWANN 1995, Lecture Notes in Computer Science, vol 930, Springer, Berlin, Heidelberg, 82–388, https://doi.org/10.1007/3-540-59497-3_199, 1995. a
Farooq, F., Nasir Amin, M., Khan, K., Rehan Sadiq, M., Faisal Javed, M., Aslam, F., and Alyousef, R.: A Comparative Study of Random Forest and Genetic Engineering Programming for the Prediction of Compressive Strength of High Strength Concrete (HSC), Appl. Sci., 10, 7330, https://doi.org/10.3390/app10207330, 2020. a
Fernández-Godino, M. G., Park, C., Kim, N., and Haftka, R.: Review of multi-fidelity models, Adv. Comput. Sci. Eng., 1, 351–400, https://doi.org/10.3934/acse.2023015, 2017. a
Fischer, M., Knippertz, P., van der Linden, R., Lemburg, A., Pante, G., Proppe, C., and Marsham, J. H.: Quantifying uncertainty in simulations of the West African monsoon with the use of surrogate models, Weather Clim. Dynam., 5, 511–536, https://doi.org/10.5194/wcd-5-511-2024, 2024. a
Forrester, A., Sobester, A., and Keane, A.: Engineering Design Via Surrogate Modelling: A Practical Guide, ISBN 978-0-470-06068-1, https://doi.org/10.1002/9780470770801, 2008. a
Fowler, H. J., Blenkinsop, S., and Tebaldi, C.: Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling, Int. J. Climatol., 27, 1547–1578, https://doi.org/10.1002/joc.1556, 2007. a
Gholampour, A., Gandomi, A. H., and Ozbakkaloglu, T.: New formulations for mechanical properties of recycled aggregate concrete using gene expression programming, Construct. Build. Mater., 130, 122–145, https://doi.org/10.1016/j.conbuildmat.2016.10.114, 2017. a
Gigović, L., Pourghasemi, H. R., Drobnjak, S., and Bai, S.: Testing a New Ensemble Model Based on SVM and Random Forest in Forest Fire Susceptibility Assessment and Its Mapping in Serbia’s Tara National Park, Forests, 10, 408, https://doi.org/10.3390/f10050408, 2019. a
Grundner, A., Beucler, T., Gentine, P., Iglesias-Suarez, F., Giorgetta, M. A., and Eyring, V.: Deep Learning Based Cloud Cover Parameterization for ICON, J. Adv. Model. Earth Sy., 14, e2021MS002959, https://doi.org/10.1029/2021MS002959, 2022. a
Haghiabi, A. H., Nasrolahi, A. H., and Parsaie, A.: Water quality prediction using machine learning methods, Water Qual. Res. J., 53, 3–13, https://doi.org/10.2166/wqrj.2018.025, 2018. a
Han, Y., Zhang, G. J., Huang, X., and Wang, Y.: A Moist Physics Parameterization Based on Deep Learning, J. Adv. Model. Earth Sy., 12, e2020MS002076, https://doi.org/10.1029/2020MS002076, 2020. a
He, C., P., V., Barlage, M., Chen, F., Gochis, D., Cabell, R., Schneider, T., Rasmussen, R., Niu, G.-Y., Yang, Z.-L., Niyogi, D., and Ek, M.: The Community Noah-MP Land Surface Modeling System Technical Description Version 5.0, No. NCAR/TN-575+STR, https://doi.org/10.5065/ew8g-yr95, 2023. a
Herman, J. D., Kollat, J. B., Reed, P. M., and Wagener, T.: Technical Note: Method of Morris effectively reduces the computational demands of global sensitivity analysis for distributed watershed models, Hydrol. Earth Syst. Sci., 17, 2893–2903, https://doi.org/10.5194/hess-17-2893-2013, 2013. a
Hong, S.-Y., Noh, Y., and Dudhia, J.: A New Vertical Diffusion Package with an Explicit Treatment of Entrainment Processes, Mon. Weather Rev., 134, 2318–2341, https://doi.org/10.1175/MWR3199.1, 2006. a
Kalantar, B., Pradhan, B., Naghibi, S. A., Motevalli, A., and Mansor, S.: Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN), Geomat. Nat. Haz. Risk, 9, 49–69, https://doi.org/10.1080/19475705.2017.1407368, 2018. a
Kim, S. and Boukouvala, F.: Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques, Optim. Lett., 14, 989–1010, https://doi.org/10.1007/s11590-019-01428-7, 2020. a
Kok, Z. H., Mohamed Shariff, A. R., Alfatni, M. S. M., and Khairunniza-Bejo, S.: Support Vector Machine in Precision Agriculture: A review, Comput. Electron. Agr., 191, 106546, https://doi.org/10.1016/j.compag.2021.106546, 2021. a
Lamberti, G. and Gorlé, C.: A multi-fidelity machine learning framework to predict wind loads on buildings, J. Wind Eng. Ind. Aerod., 214, 104647, https://doi.org/10.1016/j.jweia.2021.104647, 2021. a
Lee, J.-H., Sameen, M. I., Pradhan, B., and Park, H.-J.: Modeling landslide susceptibility in data-scarce environments using optimized data mining and statistical methods, Geomorphology, 303, 284–298, https://doi.org/10.1016/j.geomorph.2017.12.007, 2018. a
Lei, T., Ng, S., and Siu, S.: Application of ANN, XGBoost, and Other ML Methods to Forecast Air Quality in Macau, Sustainability, 15, 5341, https://doi.org/10.3390/su15065341, 2023. a
Leinonen, J., Nerini, D., and Berne, A.: Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields With a Generative Adversarial Network, IEEE T. Geosci. Remote, 59, 7211–7223, https://doi.org/10.1109/TGRS.2020.3032790, 2021. a
Li, J., Chen, F., Zhang, G., Barlage, M., Gan, Y., Xin, Y., and Wang, C.: Impacts of Land Cover and Soil Texture Uncertainty on Land Model Simulations Over the Central Tibetan Plateau, J. Adv. Model. Earth Sy., 10, 2121–2146, https://doi.org/10.1029/2018MS001377, 2018. a
Liu, Y., Xu, P., Cao, C., Shan, B., Zhu, K., Ma, Q., Zhang, Z., and Yin, H.: A comparative evaluation of machine learning algorithms and an improved optimal model for landslide susceptibility: a case study, Geomat. Nat. Haz. Risk, 12, 1973–2001, https://doi.org/10.1080/19475705.2021.1955018, 2021. a
Longo, R., Bellemans, A., Derudi, M., and Parente, A.: A multi-fidelity framework for the estimation of the turbulent Schmidt number in the simulation of atmospheric dispersion, Build. Environ., 185, 107066, https://doi.org/10.1016/j.buildenv.2020.107066, 2020. a
Maleki, H., Sorooshian, A., Goudarzi, G., Baboli, Z., Tahmasebi Birgani, Y., and Rahmati, M.: Air pollution prediction by using an artificial neural network model, Clean Technol. Environ., 21, 1341–1352, https://doi.org/10.1007/s10098-019-01709-w, 2019. a
Maraun, D. and Widmann, M.: Statistical Downscaling and Bias Correction for Climate Research, Cambridge University Press, https://doi.org/10.1017/9781107588783, 2018. a
Maroco, J., Silva, D., Rodrigues, A., Guerreiro, M., Santana, I., and de Mendonça, A.: Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests, BMC Research Notes, 4, 299, https://doi.org/10.1186/1756-0500-4-299, 2011. a
Mckay, M., Beckman, R., and Conover, W.: A Comparison of Three Methods for Selecting Vales of Input Variables in the Analysis of Output From a Computer Code, Technometrics, 21, 239–245, https://doi.org/10.1080/00401706.1979.10489755, 1979. a
Meenal, R., Binu, D., Ramya, K. C., Michael, P. A., Vinoth Kumar, K., Rajasekaran, E., and Sangeetha, B.: Weather Forecasting for Renewable Energy System: A Review, Arch. Comput. Methods E., 29, 2875–2891, https://doi.org/10.1007/s11831-021-09695-3, 2022. a
Mendoza, P. A., Clark, M. P., Barlage, M., Rajagopalan, B., Samaniego, L., Abramowitz, G., and Gupta, H.: Are we unnecessarily constraining the agility of complex process-based models?, Water Resour. Res., 51, 716–728, https://doi.org/10.1002/2014WR015820, 2015. a
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S. A.: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave, J. Geophys. Res.-Atmos., 102, 16663–16682, https://doi.org/10.1029/97JD00237, 1997. a
Mooers, G., Pritchard, M., Beucler, T., Ott, J., Yacalis, G., Baldi, P., and Gentine, P.: Assessing the Potential of Deep Learning for Emulating Cloud Superparameterization in Climate Models With Real-Geography Boundary Conditions, J. Adv. Model. Earth Sy., 13, e2020MS002385, https://doi.org/10.1029/2020MS002385, 2021. a
Morris, M. D.: Factorial sampling plans for preliminary computational experiments, Quality Eng., 37, 307–310, 1991. a
Murti, M. A., Junior, R., Ahmed, A. N., and Elshafie, A.: Earthquake multi-classification detection based velocity and displacement data filtering using machine learning algorithms, Sci. Rep., 12, 21200, https://doi.org/10.1038/s41598-022-25098-1, 2022. a
Muthukrishnan, R. and Rohini, R.: LASSO: A feature selection technique in predictive modeling for machine learning, in: 2016 IEEE International Conference on Advances in Computer Applications (ICACA), 18–20, https://doi.org/10.1109/ICACA.2016.7887916, 2016. a
Niu, G.-Y. and Yang, Z.-L.: Effects of vegetation canopy processes on snow surface energy and mass balances, J. Geophys. Res.-Atmos., 109, D23111, https://doi.org/10.1029/2004JD004884, 2004. a
Niu, G.-Y., Yang, Z.-L., Dickinson, R. E., Gulden, L. E., and Su, H.: Development of a simple groundwater model for use in climate models and evaluation with Gravity Recovery and Climate Experiment data, J. Geophys. Res.-Atmos., 112, D07103, https://doi.org/10.1029/2006JD007522, 2007. a
Niu, G.-Y., Yang, Z.-L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements, J. Geophys. Res.-Atmos., 116, D12109, https://doi.org/10.1029/2010JD015139, 2011. a
Oliveira, S., Oehler, F., San-Miguel-Ayanz, J., Camia, A., and Pereira, J. M.: Modeling spatial patterns of fire occurrence in Mediterranean Europe using Multiple Regression and Random Forest, Forest Ecol. Manage., 275, 117–129, https://doi.org/10.1016/j.foreco.2012.03.003, 2012. a
O'Hagan, A.: Bayesian analysis of computer code outputs: A tutorial, Reliab. Eng. Syst. Safe., 91, 1290–1300, https://doi.org/10.1016/j.ress.2005.11.025, 2006. a
Palani, S., Liong, S.-Y., and Tkalich, P.: An ANN application for water quality forecasting, Mar. Pollut. Bull., 56, 1586–1597, https://doi.org/10.1016/j.marpolbul.2008.05.021, 2008. a
Pourtaghi, Z. S., Pourghasemi, H. R., Aretano, R., and Semeraro, T.: Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques, Ecol. Indic., 64, 72–84, https://doi.org/10.1016/j.ecolind.2015.12.030, 2016. a
Pradhan, B.: A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS, Comput. Geosci., 51, 350–365, https://doi.org/10.1016/j.cageo.2012.08.023, 2013. a
Queipo, N. V., Haftka, R. T., Shyy, W., Goel, T., Vaidyanathan, R., and Kevin Tucker, P.: Surrogate-based analysis and optimization, Prog. Aerospace Sci., 41, 1–28, https://doi.org/10.1016/j.paerosci.2005.02.001, 2005. a
Rahmati, O., Pourghasemi, H. R., and Melesse, A. M.: Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: A case study at Mehran Region, Iran, CATENA, 137, 360–372, https://doi.org/10.1016/j.catena.2015.10.010, 2016. a
Rasmussen, C. E. and Williams, C. K. I.: Gaussian Processes for Machine Learning, The MIT Press, ISBN 9780262256834, https://doi.org/10.7551/mitpress/3206.001.0001, 2005. a
Rasp, S., Pritchard, M. S., and Gentine, P.: Deep learning to represent subgrid processes in climate models, P. Natl. Acad. Sci. USA, 115, 9684–9689, https://doi.org/10.1073/pnas.1810286115, 2018. a
Ren, Y., Zhang, L., and Suganthan, P.: Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article], IEEE Comput. Intell. M., 11, 41–53, https://doi.org/10.1109/MCI.2015.2471235, 2016. a
Rodriguez-Galiano, V., Mendes, M. P., Garcia-Soldado, M. J., Chica-Olmo, M., and Ribeiro, L.: Predictive modeling of groundwater nitrate pollution using Random Forest and multisource variables related to intrinsic and specific vulnerability: A case study in an agricultural setting (Southern Spain), Sci. Total Environ., 476–477, 189–206, https://doi.org/10.1016/j.scitotenv.2014.01.001, 2014. a
Ross, A., Li, Z., Perezhogin, P., Fernandez-Granda, C., and Zanna, L.: Benchmarking of Machine Learning Ocean Subgrid Parameterizations in an Idealized Model, J. Adv. Model. Earth Sy., 15, e2022MS003258, https://doi.org/10.1029/2022MS003258, 2023. a
Sakaguchi, K. and Zeng, X.: Effects of soil wetness, plant litter, and under-canopy atmospheric stability on ground evaporation in the Community Land Model (CLM3.5), J. Geophys. Res.-Atmos., 114, D01107, https://doi.org/10.1029/2008JD010834, 2009. a
Salmasi, F., Nouri, M., Sihag, P., and Abraham, J.: Application of SVM, ANN, GRNN, RF, GP and RT models for predicting discharge coefficients of oblique sluice gates using experimental data, Water Supply, 21, 232–248, https://doi.org/10.2166/ws.2020.226, 2020. a
Saltelli, A.: Elementary Effects Method, Chap. 3, John Wiley & Sons, Ltd, 109–154, ISBN 9780470725184, https://doi.org/10.1002/9780470725184.ch3, 2007. a
Saltelli, A., Annoni, P., Azzini, I., Campolongo, F., Ratto, M., and Tarantola, S.: Variance based sensitivity analysis of model output, Design and estimator for the total sensitivity index, Comput. Phys. Commun., 181, 259–270, https://doi.org/10.1016/j.cpc.2009.09.018, 2010. a, b
Sarkar, A. and Pandey, P.: River Water Quality Modelling Using Artificial Neural Network Technique, Aquatic Procedia, 4, 1070–1077, https://doi.org/10.1016/j.aqpro.2015.02.135, 2015. a
Satir, O., Berberoglu, S., and Donmez, C.: Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem, Geomat. Nat. Haz. Risk, 7, 1645–1658, https://doi.org/10.1080/19475705.2015.1084541, 2016. a
Schaake, J. C., Koren, V. I., Duan, Q.-Y., Mitchell, K., and Chen, F.: Simple water balance model for estimating runoff at different spatial and temporal scales, J. Geophys. Res.-Atmos., 101, 7461–7475, https://doi.org/10.1029/95JD02892, 1996. a
Schneider, R., Bonavita, M., Geer, A., Arcucci, R., Dueben, P., Vitolo, C., Le Saux, B., Demir, B., and Mathieu, P.-P.: ESA-ECMWF Report on recent progress and research directions in machine learning for Earth System observation and prediction, npj Clim. Atmos. Sci., 5, 51, https://doi.org/10.1038/s41612-022-00269-z, 2022. a
Schultz, M. G., Betancourt, C., Gong, B., Kleinert, F., Langguth, M., Leufen, L. H., Mozaffari, A., and Stadtler, S.: Can deep learning beat numerical weather prediction?, Philos. T. Roy. Soc. A-Math., 379, 20200097, https://doi.org/10.1098/rsta.2020.0097, 2021. a
Schölkopf, B. and Smola, A.: Support Vector Machines and Kernel Algorithms, Encyclopedia of Biostatistics, 5328–5335 (2005), ISBN 0-470-84907-X, 2002. a
Sekulić, A., Kilibarda, M., Heuvelink, G. B., Nikolić, M., and Bajat, B.: Random Forest Spatial Interpolation, Remote Sens., 12, 1687, https://doi.org/10.3390/rs12101687, 2020. a
Shah, M. I., Alaloul, W. S., Alqahtani, A., Aldrees, A., Musarat, M. A., and Javed, M. F.: Predictive Modeling Approach for Surface Water Quality: Development and Comparison of Machine Learning Models, Sustainability, 13, https://doi.org/10.3390/su13147515, 2021. a
Sharma, A., Paliwal, K. K., Imoto, S., and Miyano, S.: A feature selection method using improved regularized linear discriminant analysis, Mach. Vision Appl., 25, 775–786, https://doi.org/10.1007/s00138-013-0577-y, 2014. a
Shen, K.-Q., Ong, C.-J., Li, X.-P., and Wilder-Smith, E. P. V.: Feature selection via sensitivity analysis of SVM probabilistic outputs, Mach. Learn., 70, 1–20, https://doi.org/10.1007/s10994-007-5025-7, 2008. a
Sihag, P., Kumar, V., Afghan, F. R., Pandhiani, S. M., and Keshavarzi, A.: Predictive modeling of PM2.5 using soft computing techniques: case study—Faridabad, Haryana, India, Air Quality, Atmos. Health, 12, 1511–1520, https://doi.org/10.1007/s11869-019-00755-z, 2019. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner, J., Wang, W., Powers, J. G., Duda, M. G., Barker, D. M., and Huang, X.-Y.: A Description of the Advanced Research WRF Version 4, NCAR Tech. Note NCAR/TN-556+STR, 145 pp., https://doi.org/10.5065/1dfh-6p97, 2019. a
Skamarock, W. C., Klemp, J., Dudhia, J., Gill, D., Liu, Z., Berner, J., Wanh, W., Powers, J., Duda, M., Barker, D., and Huang, X.-Y.: A Description of the Advanced Research WRF Model Version 4.3, No. NCAR/TN-556+STR, https://doi.org/10.5065/1dfh-6p97, 2021. a, b
Sridhara, S., Manoj, K. N., Gopakkali, P., Kashyap, G. R., Das, B., Singh, K. K., and Srivastava, A. K.: Evaluation of machine learning approaches for prediction of pigeon pea yield based on weather parameters in India, Int. J.f Biometeorol., 67, 165–180, https://doi.org/10.1007/s00484-022-02396-x, 2023. a
Stein, M. L.: Interpolation of spatial data: some theory for kriging, Springer Science & Business Media, https://doi.org/10.1007/978-1-4612-1494-6, 1999. a, b
Suárez Sánchez, A., García Nieto, P., Riesgo Fernández, P., del Coz Díaz, J., and Iglesias-Rodríguez, F.: Application of an SVM-based regression model to the air quality study at local scale in the Avilés urban area (Spain), Math. Comput. Model., 54, 1453–1466, https://doi.org/10.1016/j.mcm.2011.04.017, 2011. a
Tian, W.: A review of sensitivity analysis methods in building energy analysis, Renew. Sust. Energ. Rev., 20, 411–419, https://doi.org/10.1016/j.rser.2012.12.014, 2013. a
Tibshirani, R.: Regression Shrinkage and Selection Via the Lasso, J. Roy. Stat. Soc. Ser. B, 58, 267–288, https://doi.org/10.1111/j.2517-6161.1996.tb02080.x, 1996. a
Torres, M.: A Machine Learning Method for Parameter Estimation and Sensitivity Analysis, 330–343, ISBN 978-3-030-77976-4, https://doi.org/10.1007/978-3-030-77977-1_26, 2021. a
Trabelsi, F. and Bel Hadj Ali, S.: Exploring Machine Learning Models in Predicting Irrigation Groundwater Quality Indices for Effective Decision Making in Medjerda River Basin, Tunisia, Sustainability, 14, 2341, https://doi.org/10.3390/su14042341, 2022. a
Vapnik, V. N. and Chervonenkis, A.: On a class of algorithms of learning pattern recognition, Avtomat. i Telemekh, 25, 112–120, 1963. a
Watson-Parris, D., Williams, A., Deaconu, L., and Stier, P.: Model calibration using ESEm v1.1.0 – an open, scalable Earth system emulator, Geosci. Model Dev., 14, 7659–7672, https://doi.org/10.5194/gmd-14-7659-2021, 2021. a, b
Wicker, L. and Skamarock, W.: Time-Splitting Methods for Elastic Models Using Forward Time Schemes, Mon. Weather Rev., 130, 2088–2097, https://doi.org/10.1175/1520-0493(2002)130<2088:TSMFEM>2.0.CO;2, 2002. a
Williamson, D., Goldstein, M., Allison, L., Blaker, A., Challenor, P., Jackson, L., and Yamazaki, K.: History matching for exploring and reducing climate model parameter space using observations and a large perturbed physics ensemble, Clim. Dynam., 41, 1703–1729, https://doi.org/10.1007/s00382-013-1896-4, 2013. a
Wolff, B., Kramer, O., and Heinemann, D.: Selection of Numerical Weather Forecast Features for PV Power Predictions with Random Forests, in: Data Analytics for Renewable Energy Integration, edited by: Woon, W. L., Aung, Z., Kramer, O., and Madnick, S., Springer International Publishing, Cham, 78–91, https://doi.org/10.1007/978-3-319-50947-1, 2017. a
Wu, W., Li, M.-F., Xu, X., Tang, X.-P., Yang, C., and Liu, H.-B.: The transferability of random forest and support vector machine for estimating daily global solar radiation using sunshine duration over different climate zones, Theor. Appl. Climatol., 146, 45–55, https://doi.org/10.1007/s00704-021-03726-6, 2021. a
Wu, Z., Cui, N., Gong, D., Zhu, F., Xing, L., Zhu, B., Chen, X., Wen, S., and Liu, Q.: Simulation of daily maize evapotranspiration at different growth stages using four machine learning models in semi-humid regions of northwest China, J. Hydrol., 617, 128947, https://doi.org/10.1016/j.jhydrol.2022.128947, 2023. a
Yang, W.-X., Wang, F.-F., Pan, Y.-Y., Xie, J.-Q., Lu, M.-H., and You, C.-G.: Comparison of ischemic stroke diagnosis models based on machine learning, Front. Neurol., 13, 1014346, https://doi.org/10.3389/fneur.2022.1014346, 2022. a
Yang, Z.-L., Niu, G.-Y., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Longuevergne, L., Manning, K., Niyogi, D., Tewari, M., and Xia, Y.: The community Noah land surface model with multiparameterization options (Noah-MP): 2. Evaluation over global river basins, J. Geophys. Res.-Atmos., 116, D12110, https://doi.org/10.1029/2010JD015140, 2011. a
Yilmaz, I.: Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine, Environ. Earth Sci., 61, 821–836, https://doi.org/10.1007/s12665-009-0394-9, 2010. a
Youssef, A. M., Pourghasemi, H. R., Pourtaghi, Z. S., and Al-Katheeri, M. M.: Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia, Landslides, 13, 839–856, https://doi.org/10.1007/s10346-015-0614-1, 2016. a
Yu, R., Yang, Y., Yang, L., Han, G., and Move, O. A.: RAQ–A Random Forest Approach for Predicting Air Quality in Urban Sensing Systems, Sensors, 16, https://doi.org/10.3390/s16010086, 2016. a
Yu, Z., Dai, H., Yang, J., Zhu, Y., and Yuan, S.: Global sensitivity analysis with deep learning-based surrogate models for unraveling key parameters and processes governing redox zonation in riparian zone, J. Hydrol., 638, 131442, https://doi.org/10.1016/j.jhydrol.2024.131442, 2024. a
Yuval, J. and O'Gorman, P. A.: Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions, Nat. Commun., 11, 3295, https://doi.org/10.1038/s41467-020-17142-3, 2020. a
Zhang, X., Srinivasan, R., and Van Liew, M.: Approximating SWAT Model Using Artificial Neural Network and Support Vector Machine1, JAWRA J. Am. Water Resour. Assoc., 45, 460–474, https://doi.org/10.1111/j.1752-1688.2009.00302.x, 2009. a
Zhou, C., Yin, K., Cao, Y., Ahmed, B., Li, Y., Catani, F., and Pourghasemi, H. R.: Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China, Comput. Geosci., 112, 23–37, https://doi.org/10.1016/j.cageo.2017.11.019, 2018. a
Zouhri, W., Homri, L., and Dantan, J.-Y.: Handling the impact of feature uncertainties on SVM: A robust approach based on Sobol sensitivity analysis, Expert Syst. Appl., 189, 115691, https://doi.org/10.1016/j.eswa.2021.115691, 2022. a
Short summary
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.
This paper presents the Machine Learning-based Automated Multi-method Parameter Sensitivity and...
