Articles | Volume 15, issue 24
https://doi.org/10.5194/gmd-15-9015-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-9015-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
16 Dec 2022
Model description paper |
| 16 Dec 2022
| 16 Dec 2022
Predicting peak daily maximum 8 h ozone and linkages to emissions and meteorology in Southern California using machine learning methods (SoCAB-8HR V1.0)
Ziqi Gao
CORRESPONDING AUTHOR
School of Civil and Environmental Engineering, Georgia Institute of
Technology, Atlanta, GA 30332, USA
Yifeng Wang
School of Civil and Environmental Engineering, Georgia Institute of
Technology, Atlanta, GA 30332, USA
Petros Vasilakos
School of Civil and Environmental Engineering, Georgia Institute of
Technology, Atlanta, GA 30332, USA
Cesunica E. Ivey
Department of Chemical and Environmental Engineering, University of
California, Riverside, Riverside, CA 92521, USA
now at: Department of Civil and Environmental Engineering,
University of California, Berkeley, Berkeley, CA 94720, USA
Khanh Do
Department of Chemical and Environmental Engineering, University of
California, Riverside, Riverside, CA 92521, USA
Center for Environmental Research and Technology, University of
California, Riverside, Riverside, CA 92521,
USA
Armistead G. Russell
School of Civil and Environmental Engineering, Georgia Institute of
Technology, Atlanta, GA 30332, USA
Related authors
No articles found.
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
Short summary
Short summary
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.
Bingqing Zhang, Huizhong Shen, Pengfei Liu, Hongyu Guo, Yongtao Hu, Yilin Chen, Shaodong Xie, Ziyan Xi, T. Nash Skipper, and Armistead G. Russell
Atmos. Chem. Phys., 21, 8341–8356, https://doi.org/10.5194/acp-21-8341-2021, https://doi.org/10.5194/acp-21-8341-2021, 2021
Short summary
Short summary
Extended ground-level measurements are coupled with model simulations to comprehensively compare the aerosol acidity in China and the United States. Aerosols in China are significantly less acidic than those in the United States, with pH values 1–2 units higher. Higher aerosol mass concentrations and the abundance of ammonia and ammonium in China, compared to the United States, are leading causes of the pH difference between these two countries.
Athanasios Nenes, Spyros N. Pandis, Maria Kanakidou, Armistead G. Russell, Shaojie Song, Petros Vasilakos, and Rodney J. Weber
Atmos. Chem. Phys., 21, 6023–6033, https://doi.org/10.5194/acp-21-6023-2021, https://doi.org/10.5194/acp-21-6023-2021, 2021
Short summary
Short summary
Ecosystems and air quality are affected by the dry deposition of inorganic reactive nitrogen (Nr, the sum of ammonium and nitrate). Its large variability is driven by the large difference in deposition velocity of N when in the gas or particle phase. Here we show that aerosol liquid water and acidity, by affecting gas–particle partitioning, modulate the dry deposition velocity of NH3, HNO3, and Nr worldwide. These effects explain the rapid accumulation of nitrate aerosol during haze events.
Yilin Chen, Huizhong Shen, Jennifer Kaiser, Yongtao Hu, Shannon L. Capps, Shunliu Zhao, Amir Hakami, Jhih-Shyang Shih, Gertrude K. Pavur, Matthew D. Turner, Daven K. Henze, Jaroslav Resler, Athanasios Nenes, Sergey L. Napelenok, Jesse O. Bash, Kathleen M. Fahey, Gregory R. Carmichael, Tianfeng Chai, Lieven Clarisse, Pierre-François Coheur, Martin Van Damme, and Armistead G. Russell
Atmos. Chem. Phys., 21, 2067–2082, https://doi.org/10.5194/acp-21-2067-2021, https://doi.org/10.5194/acp-21-2067-2021, 2021
Short summary
Short summary
Ammonia (NH3) emissions can exert adverse impacts on air quality and ecosystem well-being. NH3 emission inventories are viewed as highly uncertain. Here we optimize the NH3 emission estimates in the US using an air quality model and NH3 measurements from the IASI satellite instruments. The optimized NH3 emissions are much higher than the National Emissions Inventory estimates in April. The optimized NH3 emissions improved model performance when evaluated against independent observation.
Shunliu Zhao, Matthew G. Russell, Amir Hakami, Shannon L. Capps, Matthew D. Turner, Daven K. Henze, Peter B. Percell, Jaroslav Resler, Huizhong Shen, Armistead G. Russell, Athanasios Nenes, Amanda J. Pappin, Sergey L. Napelenok, Jesse O. Bash, Kathleen M. Fahey, Gregory R. Carmichael, Charles O. Stanier, and Tianfeng Chai
Geosci. Model Dev., 13, 2925–2944, https://doi.org/10.5194/gmd-13-2925-2020, https://doi.org/10.5194/gmd-13-2925-2020, 2020
Dong Gao, Krystal J. Godri Pollitt, James A. Mulholland, Armistead G. Russell, and Rodney J. Weber
Atmos. Chem. Phys., 20, 5197–5210, https://doi.org/10.5194/acp-20-5197-2020, https://doi.org/10.5194/acp-20-5197-2020, 2020
Short summary
Short summary
This study provides a direct intercomparison between two assays for quantifying oxidative potential (OP) of ambient particles: the synthetic respiratory-tract-lining fluid (RTLF) assay and the dithiothreitol (DTT) assay. The results suggest that the DTT assay and the ascorbic acid depletion in RTLF are associated with organic species, transition metal ions, and antagonistic interactions between species. The glutathione depletion in RTLF is strongly dependent on water-soluble copper.
Athanasios Nenes, Spyros N. Pandis, Rodney J. Weber, and Armistead Russell
Atmos. Chem. Phys., 20, 3249–3258, https://doi.org/10.5194/acp-20-3249-2020, https://doi.org/10.5194/acp-20-3249-2020, 2020
Short summary
Short summary
We show that aerosol acidity (pH) and liquid water content naturally emerge as previously ignored parameters that drive particulate matter formation in the atmosphere, and its sensitivity to emissions of ammonia and nitric acid. The simple framework presented is easily applied to ambient measurements or model output, and it provides the
chemical regimeof PM sensitivity to ammonia and nitric acid availability.
Petros Vasilakos, Armistead Russell, Rodney Weber, and Athanasios Nenes
Atmos. Chem. Phys., 18, 12765–12775, https://doi.org/10.5194/acp-18-12765-2018, https://doi.org/10.5194/acp-18-12765-2018, 2018
Short summary
Short summary
In this work, we investigated the role of emission reductions on aerosol acidity and particulate nitrate. We found that models exhibit positive biases in pH predictions, attributed to very high levels of crustal elements (Mg, Ca, K) in model simulations, which in turn led to an increasing aerosol pH trend over the past decade and allowed nitrate to become an important component of aerosol, which is inconsistent with the measurements, highlighting the importance of accurate pH prediction.
Theodora Nah, Hongyu Guo, Amy P. Sullivan, Yunle Chen, David J. Tanner, Athanasios Nenes, Armistead Russell, Nga Lee Ng, L. Gregory Huey, and Rodney J. Weber
Atmos. Chem. Phys., 18, 11471–11491, https://doi.org/10.5194/acp-18-11471-2018, https://doi.org/10.5194/acp-18-11471-2018, 2018
Short summary
Short summary
We present measurements from a field study conducted in an agriculturally intensive region in the southeastern US during the fall of 2016 to investigate how NH3 affects particle acidity and SOA formation via gas–particle partitioning of semi-volatile organic acids. For this study, higher NH3 concentrations relative to what has been measured in the region in previous studies had minor effects on PM1 organic acids and their influence on the overall organic aerosol and PM1 mass concentrations.
Petros Vasilakos, Yong-Ηa Kim, Jeffrey R. Pierce, Sotira Yiacoumi, Costas Tsouris, and Athanasios Nenes
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-96, https://doi.org/10.5194/gmd-2017-96, 2017
Revised manuscript not accepted
Short summary
Short summary
Radioactive charging can significantly impact the way radioactive aerosols behave, and as a result their lifetime, but such effects are neglected in predictive model studies of radioactive plumes. We extend a well-established model that simulates the evolution of atmospheric particulate matter to account for radioactive charging effects in an accurate and computationally efficient way. It is shown that radioactivity can strongly impact the deposition patterns of aerosol.
Havala O. T. Pye, Benjamin N. Murphy, Lu Xu, Nga L. Ng, Annmarie G. Carlton, Hongyu Guo, Rodney Weber, Petros Vasilakos, K. Wyat Appel, Sri Hapsari Budisulistiorini, Jason D. Surratt, Athanasios Nenes, Weiwei Hu, Jose L. Jimenez, Gabriel Isaacman-VanWertz, Pawel K. Misztal, and Allen H. Goldstein
Atmos. Chem. Phys., 17, 343–369, https://doi.org/10.5194/acp-17-343-2017, https://doi.org/10.5194/acp-17-343-2017, 2017
Short summary
Short summary
We use a chemical transport model to examine how organic compounds in the atmosphere interact with water present in particles. Organic compounds themselves lead to water uptake, and organic compounds interact with water associated with inorganic compounds in the rural southeast atmosphere. Including interactions of organic compounds with water requires a treatment of nonideality to more accurately represent aerosol observations during the Southern Oxidant and Aerosol Study (SOAS) 2013.
Amelia F. Longo, David J. Vine, Laura E. King, Michelle Oakes, Rodney J. Weber, Lewis Gregory Huey, Armistead G. Russell, and Ellery D. Ingall
Atmos. Chem. Phys., 16, 13389–13398, https://doi.org/10.5194/acp-16-13389-2016, https://doi.org/10.5194/acp-16-13389-2016, 2016
Short summary
Short summary
New synchrotron-based techniques were applied to characterize the oxidation state and composition of sulfur in ambient aerosol and emission sources. Individual particles were found to contain surprisingly high levels of elemental sulfur, a form of sulfur found in only one of the emission sources analyzed. We also show metal sulfates as a key component of urban aerosols. These metal sulfate phases are highly soluble and are indicative of acidic processes transforming metals in the environment.
Ting Fang, Vishal Verma, Josephine T. Bates, Joseph Abrams, Mitchel Klein, Matthew J. Strickland, Stefanie E. Sarnat, Howard H. Chang, James A. Mulholland, Paige E. Tolbert, Armistead G. Russell, and Rodney J. Weber
Atmos. Chem. Phys., 16, 3865–3879, https://doi.org/10.5194/acp-16-3865-2016, https://doi.org/10.5194/acp-16-3865-2016, 2016
Short summary
Short summary
Ascorbic acid (AA) and Dithiothreitol (DTT) assay measures of water-soluble PM2.5 oxidative potential (OP) are compared in terms of spatiotemporal trends, chemical selectivity, sources, and health impacts based on an epidemiological study with backcast estimated OP. Both assays point to metals from brake/tire wear, but only the DTT assay also identifies organics from combustion. DTT is associated with emergency department visits for asthma/wheeze and congestive heart failure, whereas AA is not.
Karoline K. Johnson, Michael H. Bergin, Armistead G. Russell, and Gayle S. W. Hagler
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2015-331, https://doi.org/10.5194/amt-2015-331, 2016
Revised manuscript not accepted
Short summary
Short summary
Low-cost air quality sensors were evaluated in Atlanta, GA, in Hyderabad, India and also in lab experiments. Freeway emissions were also quantified in Atlanta. The performance of these sensors were evaluated against current measurement instruments. Results show potential usefulness for these sensors in polluted areas. The ability to inexpensively measure air pollution will enable researches, citizens, and policy makers to make informed decisions to reduce health impacts from air pollution.
C. E. Ivey, H. A. Holmes, Y. T. Hu, J. A. Mulholland, and A. G. Russell
Geosci. Model Dev., 8, 2153–2165, https://doi.org/10.5194/gmd-8-2153-2015, https://doi.org/10.5194/gmd-8-2153-2015, 2015
Short summary
Short summary
An integral part of air quality management is knowledge of the impact of pollutant sources on ambient concentrations of particulate matter (PM). This work presents a novel spatiotemporal source apportionment method that generates source impacts for the continental USA. Key sources presented include fossil fuel combustion, biomass burning, dust, sea salt, as well as agricultural activities, biogenics, and aircraft.
V. Verma, T. Fang, H. Guo, L. King, J. T. Bates, R. E. Peltier, E. Edgerton, A. G. Russell, and R. J. Weber
Atmos. Chem. Phys., 14, 12915–12930, https://doi.org/10.5194/acp-14-12915-2014, https://doi.org/10.5194/acp-14-12915-2014, 2014
Short summary
Short summary
The major emission sources of the reactive oxygen species (ROS) associated with ambient particulate matter in the southeastern United States were identified. The study shows biomass burning and secondary aerosol formation as the major sources contributing to the ROS-generating capability of ambient particles. The ubiquitous nature of these two sources suggests widespread population exposures to the toxic aerosol components.
Y. Hu, S. Balachandran, J. E. Pachon, J. Baek, C. Ivey, H. Holmes, M. T. Odman, J. A. Mulholland, and A. G. Russell
Atmos. Chem. Phys., 14, 5415–5431, https://doi.org/10.5194/acp-14-5415-2014, https://doi.org/10.5194/acp-14-5415-2014, 2014
M. Trail, A. P. Tsimpidi, P. Liu, K. Tsigaridis, Y. Hu, A. Nenes, and A. G. Russell
Geosci. Model Dev., 6, 1429–1445, https://doi.org/10.5194/gmd-6-1429-2013, https://doi.org/10.5194/gmd-6-1429-2013, 2013
Related subject area
Numerical methods
The Measurement Error Proxy System Model: MEPSM v0.2
Numerical stabilization methods for level-set-based ice front migration
Modelling chemical advection during magma ascent
Consistent point data assimilation in Firedrake and Icepack
A Joint Reconstruction and Model Selection Approach for Large Scale Inverse Modeling
A computationally efficient parameterization of aerosol, cloud and precipitation pH for application at global and regional scale (EQSAM4Clim-v12)
Assessing the benefits of approximately exact step sizes for Picard and Newton solver in simulating ice flow (FEniCS-full-Stokes v.1.3.2)
Assessing effects of climate and technology uncertainties in large natural resource allocation problems
VISIR-2: ship weather routing in Python
Incremental analysis update (IAU) in the Model for Prediction Across Scales coupled with the Joint Effort for Data assimilation Integration (MPAS–JEDI 2.0.0)
Decision-making strategies implemented in SolFinder 1.0 to identify eco-efficient aircraft trajectories: application study in AirTraf 3.0
Developing meshing workflows in Gmsh v4.11 for the geologic uncertainty assessment of high-temperature aquifer thermal energy storage
Development and preliminary validation of a land surface image assimilation system based on the Common Land Model
NorSand4AI: a comprehensive triaxial test simulation database for NorSand constitutive model materials
Explicit stochastic advection algorithms for the regional scale particle-resolved atmospheric aerosol model WRF-PartMC (v1.0)
ParticleDA.jl v.1.0: a distributed particle-filtering data assimilation package
HETerogeneous vectorized or Parallel (HETPv1.0): an updated inorganic heterogeneous chemistry solver for the metastable-state NH4+–Na+–Ca2+–K+–Mg2+–SO42−–NO3−–Cl−–H2O system based on ISORROPIA II
Three-dimensional geological modelling of igneous intrusions in LoopStructural v1.5.10
Estimating volcanic ash emissions using retrieved satellite ash columns and inverse ash transport modeling using VolcanicAshInversion v1.2.1, within the operational eEMEP (emergency European Monitoring and Evaluation Programme) volcanic plume forecasting system (version rv4_17)
Accounting for uncertainties in forecasting tropical-cyclone-induced compound flooding
An automatic mesh generator for coupled 1D–2D hydrodynamic models
Numerical coupling of aerosol emissions, dry removal, and turbulent mixing in the E3SM Atmosphere Model version 1 (EAMv1) – Part 1: Dust budget analyses and the impacts of a revised coupling scheme
Numerical coupling of aerosol emissions, dry removal, and turbulent mixing in the E3SM Atmosphere Model version 1 (EAMv1) – Part 2: A semi-discrete error analysis framework for assessing coupling schemes
jsmetrics v0.2.0: a Python package for metrics and algorithms used to identify or characterise atmospheric jet streams
The Paleochrono-1.1 probabilistic model to derive optimized and consistent chronologies for several paleoclimatic sites
P3D-BRNS v1.0.0: a three-dimensional, multiphase, multicomponent, pore-scale reactive transport modelling package for simulating biogeochemical processes in subsurface environments
MinVoellmy v1: a lightweight model for simulating rapid mass movements based on a modified Voellmy rheology
Scalable Feature Extraction and Tracking (SCAFET): a general framework for feature extraction from large climate data sets
Sweep interpolation: a cost-effective semi-Lagrangian scheme in the Global Environmental Multiscale model
CHONK 1.0: landscape evolution framework: cellular automata meets graph theory
Perspectives of physics-based machine learning strategies for geoscientific applications governed by partial differential equations
Calibration of absorbing boundary layers for geoacoustic wave modeling in pseudo-spectral time-domain methods
Assimilation of snow water equivalent from AMSR2 and IMS satellite data utilizing the local ensemble transform Kalman filter
GeoINR 1.0: an implicit neural network approach to three-dimensional geological modelling
A comparison of Eulerian and Lagrangian methods for vertical particle transport in the water column
AutoQS v1: automatic parametrization of QuickSampling based on training images analysis
Implementation and application of ensemble optimal interpolation on an operational chemistry weather model for improving PM2.5 and visibility predictions
A dynamical core based on a discontinuous Galerkin method for higher-order finite-element sea ice modeling
GStatSim V1.0: a Python package for geostatistical interpolation and conditional simulation
Leveraging Google's Tensor Processing Units for tsunami-risk mitigation planning in the Pacific Northwest and beyond
An improved subgrid channel model with upwind-form artificial diffusion for river hydrodynamics and floodplain inundation simulation
A model instability issue in the National Centers for Environmental Prediction Global Forecast System version 16 and potential solutions
A comparison of 3-D spherical shell thermal convection results at low to moderate Rayleigh number using ASPECT (version 2.2.0) and CitcomS (version 3.3.1)
LISFLOOD-FP 8.1: new GPU-accelerated solvers for faster fluvial/pluvial flood simulations
Fast approximate Barnes interpolation: illustrated by Python-Numba implementation fast-barnes-py v1.0
Strategies for conservative and non-conservative monotone remapping on the sphere
Modeling large‐scale landform evolution with a stream power law for glacial erosion (OpenLEM v37): benchmarking experiments against a more process-based description of ice flow (iSOSIA v3.4.3)
A mixed finite-element discretisation of the shallow-water equations
Multifidelity Monte Carlo estimation for efficient uncertainty quantification in climate-related modeling
Massively parallel modeling and inversion of electrical resistivity tomography data using PFLOTRAN
Matt J. Fischer
Geosci. Model Dev., 17, 6745–6760, https://doi.org/10.5194/gmd-17-6745-2024, https://doi.org/10.5194/gmd-17-6745-2024, 2024
Short summary
Short summary
In paleoclimate research, proxy system models (PSMs) model chemical or biological systems which receive environmental input and output (e.g., geochemical signals). The environmental inputs are rarely noiseless, which causes problems when calibrating multi-input PSMs. Here a PSM is developed which includes generalized noise in both model inputs and outputs and prior information. A quasi-Bayesian method enhances the stability of the solution of the Measurement Error Proxy System Model.
Gong Cheng, Mathieu Morlighem, and G. Hilmar Gudmundsson
Geosci. Model Dev., 17, 6227–6247, https://doi.org/10.5194/gmd-17-6227-2024, https://doi.org/10.5194/gmd-17-6227-2024, 2024
Short summary
Short summary
We conducted a comprehensive analysis of the stabilization and reinitialization techniques currently employed in ISSM and Úa for solving level-set equations, specifically those related to the dynamic representation of moving ice fronts within numerical ice sheet models. Our results demonstrate that the streamline upwind Petrov–Galerkin (SUPG) method outperforms the other approaches. We found that excessively frequent reinitialization can lead to exceptionally high errors in simulations.
Hugo Dominguez, Nicolas Riel, and Pierre Lanari
Geosci. Model Dev., 17, 6105–6122, https://doi.org/10.5194/gmd-17-6105-2024, https://doi.org/10.5194/gmd-17-6105-2024, 2024
Short summary
Short summary
Predicting the behaviour of magmatic systems is important for understanding Earth's matter and heat transport. Numerical modelling is a technique that can predict complex systems at different scales of space and time by solving equations using various techniques. This study tests four algorithms to find the best way to transport the melt composition. The "weighted essentially non-oscillatory" algorithm emerges as the best choice, minimising errors and preserving system mass well.
Reuben W. Nixon-Hill, Daniel Shapero, Colin J. Cotter, and David A. Ham
Geosci. Model Dev., 17, 5369–5386, https://doi.org/10.5194/gmd-17-5369-2024, https://doi.org/10.5194/gmd-17-5369-2024, 2024
Short summary
Short summary
Scientists often use models to study complex processes, like the movement of ice sheets, and compare them to measurements for estimating quantities that are hard to measure. We highlight an approach that ensures accurate results from point data sources (e.g. height measurements) by evaluating the numerical solution at true point locations. This method improves accuracy, aids communication between scientists, and is well-suited for integration with specialised software that automates processes.
Malena Sabaté Landman, Julianne Chung, Jiahua Jiang, Scot Miller, and Arvind Saibaba
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-90, https://doi.org/10.5194/gmd-2024-90, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Making an informed decision about what prior information to incorporate or discard in an inverse model is important yet very challenging, as it is often not straightforward to distinguish between informative and non-informative variables. In this study, we develop a new approach for incorporating prior information in an inverse model using predictor variables, while simultaneously selecting the relevant predictor variables for the estimation of the unknown quantity of interest.
Swen Metzger, Samuel Rémy, Jason E. Williams, Vincent Huijnen, and Johannes Flemming
Geosci. Model Dev., 17, 5009–5021, https://doi.org/10.5194/gmd-17-5009-2024, https://doi.org/10.5194/gmd-17-5009-2024, 2024
Short summary
Short summary
EQSAM4Clim has recently been revised to provide an accurate and efficient method for calculating the acidity of atmospheric particles. It is based on an analytical concept that is sufficiently fast and free of numerical noise, which makes it attractive for air quality forecasting. Version 12 allows the calculation of aerosol composition based on the gas–liquid–solid and the reduced gas–liquid partitioning with the associated water uptake for both cases, including the acidity of the aerosols.
Niko Schmidt, Angelika Humbert, and Thomas Slawig
Geosci. Model Dev., 17, 4943–4959, https://doi.org/10.5194/gmd-17-4943-2024, https://doi.org/10.5194/gmd-17-4943-2024, 2024
Short summary
Short summary
Future sea-level rise is of big significance for coastal regions. The melting and acceleration of glaciers plays a major role in sea-level change. Computer simulation of glaciers costs a lot of computational resources. In this publication, we test a new way of simulating glaciers. This approach produces the same results but has the advantage that it needs much less computation time. As simulations can be obtained with fewer computation resources, higher resolution and physics become affordable.
Jevgenijs Steinbuks, Yongyang Cai, Jonas Jaegermeyr, and Thomas W. Hertel
Geosci. Model Dev., 17, 4791–4819, https://doi.org/10.5194/gmd-17-4791-2024, https://doi.org/10.5194/gmd-17-4791-2024, 2024
Short summary
Short summary
This paper applies a cutting-edge numerical method, SCEQ, to show how uncertain climate change and technological progress affect the future utilization of the world's scarce land resources. The paper's key insight is to illustrate how much global cropland will expand when future crop yields are unknown. The study finds the range of outcomes for land use change to be smaller when using this novel method compared to existing deterministic models.
Gianandrea Mannarini, Mario Leonardo Salinas, Lorenzo Carelli, Nicola Petacco, and Josip Orović
Geosci. Model Dev., 17, 4355–4382, https://doi.org/10.5194/gmd-17-4355-2024, https://doi.org/10.5194/gmd-17-4355-2024, 2024
Short summary
Short summary
Ship weather routing has the potential to reduce CO2 emissions, but it currently lacks open and verifiable research. The Python-refactored VISIR-2 model considers currents, waves, and wind to optimise routes. The model was validated, and its computational performance is quasi-linear. For a ferry sailing in the Mediterranean Sea, VISIR-2 yields the largest percentage emission savings for upwind navigation. Given the vessel performance curve, the model is generalisable across various vessel types.
Soyoung Ha, Jonathan J. Guerrette, Ivette Hernández Baños, William C. Skamarock, and Michael G. Duda
Geosci. Model Dev., 17, 4199–4211, https://doi.org/10.5194/gmd-17-4199-2024, https://doi.org/10.5194/gmd-17-4199-2024, 2024
Short summary
Short summary
To mitigate the imbalances in the initial conditions, this study introduces our recent implementation of the incremental analysis update (IAU) in the Model for Prediction Across Scales – Atmospheric (MPAS-A) component coupled with the Joint Effort for Data assimilation Integration (JEDI) through the cycling system. A month-long cycling run demonstrates the successful implementation of the IAU capability in the MPAS–JEDI cycling system.
Federica Castino, Feijia Yin, Volker Grewe, Hiroshi Yamashita, Sigrun Matthes, Simone Dietmüller, Sabine Baumann, Manuel Soler, Abolfazl Simorgh, Maximilian Mendiguchia Meuser, Florian Linke, and Benjamin Lührs
Geosci. Model Dev., 17, 4031–4052, https://doi.org/10.5194/gmd-17-4031-2024, https://doi.org/10.5194/gmd-17-4031-2024, 2024
Short summary
Short summary
We introduce SolFinder 1.0, a decision-making tool to select trade-offs between different objective functions for optimal aircraft trajectories, including fuel use, flight time, NOx emissions, contrail distance, and climate impact. The module is included in the AirTraf 3.0 submodel and uses weather conditions simulated by the EMAC atmospheric model. This paper focuses on the ability of SolFinder to identify eco-efficient trajectories, reducing a flight's climate impact at limited cost penalties.
Ali Dashti, Jens C. Grimmer, Christophe Geuzaine, Florian Bauer, and Thomas Kohl
Geosci. Model Dev., 17, 3467–3485, https://doi.org/10.5194/gmd-17-3467-2024, https://doi.org/10.5194/gmd-17-3467-2024, 2024
Short summary
Short summary
This study developed new meshing workflows to enable the automatic generation of meshes that follow geological models. The workflow allows for importing several geological models as input for Gmsh and later exporting the same number of high-quality meshes. This way, geological uncertainty is directly included in the numerical simulations. This study evaluates the impact of the geological uncertainty on thermohydraulic performance of two reservoirs for high-temperature heat storage applications.
Wangbin Shen, Zhaohui Lin, Zhengkun Qin, and Juan Li
Geosci. Model Dev., 17, 3447–3465, https://doi.org/10.5194/gmd-17-3447-2024, https://doi.org/10.5194/gmd-17-3447-2024, 2024
Short summary
Short summary
In this study, a land surface image assimilation system capable of optimizing the spatial structure of the background field is constructed by introducing the curvelet analysis method and taking the similarity of image structure as a weak constraint. The findings demonstrate that the assimilation of surface soil moisture observation images effectively and reasonably enhances the spatial structure of soil moisture analysis field.
Luan Carlos de Sena Monteiro Ozelim, Michéle Dal Toé Casagrande, and André Luís Brasil Cavalcante
Geosci. Model Dev., 17, 3175–3197, https://doi.org/10.5194/gmd-17-3175-2024, https://doi.org/10.5194/gmd-17-3175-2024, 2024
Short summary
Short summary
The paper addresses synthetic dataset challenges in nonlinear constitutive modeling of soils, providing two datasets: one with 2000 soil types, 40 test conditions each (160 000 triaxial tests), and a second with 2048 soil types, 42 test conditions each (172 032 triaxial tests). Each dataset is a 4000×10 matrix applicable for multivariate forecasting and geotechnical simulations. In addition, a new Python code is introduced, empowering researchers to leverage Python packages for NorSand analyses.
Jeffrey Henry Curtis, Nicole Riemer, and Matthew West
EGUsphere, https://doi.org/10.5194/egusphere-2024-825, https://doi.org/10.5194/egusphere-2024-825, 2024
Short summary
Short summary
This paper introduces a numerical method for simulating particle-based aerosol transport in atmospheric models. We detail the various advection order method’s numerical properties and demonstrate its implementation in a 3D weather prediction model (WRF) for the first time. Particle-based techniques improve the accuracy of aerosol size and composition predictions, which are key for aerosol-cloud and aerosol-radiation interactions.
Daniel Giles, Matthew M. Graham, Mosè Giordano, Tuomas Koskela, Alexandros Beskos, and Serge Guillas
Geosci. Model Dev., 17, 2427–2445, https://doi.org/10.5194/gmd-17-2427-2024, https://doi.org/10.5194/gmd-17-2427-2024, 2024
Short summary
Short summary
Digital twins of physical and human systems informed by real-time data are becoming ubiquitous across a wide range of settings. Progress for researchers is currently limited by a lack of tools to run these models effectively and efficiently. A key challenge is the optimal use of high-performance computing environments. The work presented here focuses on a developed open-source software platform which aims to improve this usage, with an emphasis placed on flexibility, efficiency, and scalability.
Stefan J. Miller, Paul A. Makar, and Colin J. Lee
Geosci. Model Dev., 17, 2197–2219, https://doi.org/10.5194/gmd-17-2197-2024, https://doi.org/10.5194/gmd-17-2197-2024, 2024
Short summary
Short summary
This work outlines a new solver written in Fortran to calculate the partitioning of metastable aerosols at thermodynamic equilibrium based on the forward algorithms of ISORROPIA II. The new code includes numerical improvements that decrease the computational speed (compared to ISORROPIA II) while improving the accuracy of the partitioning solution.
Fernanda Alvarado-Neves, Laurent Ailleres, Lachlan Grose, Alexander R. Cruden, and Robin Armit
Geosci. Model Dev., 17, 1975–1993, https://doi.org/10.5194/gmd-17-1975-2024, https://doi.org/10.5194/gmd-17-1975-2024, 2024
Short summary
Short summary
Previous work has demonstrated that adding geological knowledge to modelling methods creates more accurate and reliable models. Following this reasoning, we added constraints from magma emplacement mechanisms into existing modelling frameworks to improve the 3D characterisation of igneous intrusions. We tested the method on synthetic and real-world case studies, and the results show that our method can reproduce intrusion morphologies with no manual processing and using realistic datasets.
André R. Brodtkorb, Anna Benedictow, Heiko Klein, Arve Kylling, Agnes Nyiri, Alvaro Valdebenito, Espen Sollum, and Nina Kristiansen
Geosci. Model Dev., 17, 1957–1974, https://doi.org/10.5194/gmd-17-1957-2024, https://doi.org/10.5194/gmd-17-1957-2024, 2024
Short summary
Short summary
It is vital to know the extent and concentration of volcanic ash in the atmosphere during a volcanic eruption. Whilst satellite imagery may give an estimate of the ash right now (assuming no cloud coverage), we also need to know where it will be in the coming hours. This paper presents a method for estimating parameters for a volcanic eruption based on satellite observations of ash in the atmosphere. The software package is open source and applicable to similar inversion scenarios.
Kees Nederhoff, Maarten van Ormondt, Jay Veeramony, Ap van Dongeren, José Antonio Álvarez Antolínez, Tim Leijnse, and Dano Roelvink
Geosci. Model Dev., 17, 1789–1811, https://doi.org/10.5194/gmd-17-1789-2024, https://doi.org/10.5194/gmd-17-1789-2024, 2024
Short summary
Short summary
Forecasting tropical cyclones and their flooding impact is challenging. Our research introduces the Tropical Cyclone Forecasting Framework (TC-FF), enhancing cyclone predictions despite uncertainties. TC-FF generates global wind and flood scenarios, valuable even in data-limited regions. Applied to cases like Cyclone Idai, it showcases potential in bettering disaster preparation, marking progress in handling cyclone threats.
Younghun Kang and Ethan J. Kubatko
Geosci. Model Dev., 17, 1603–1625, https://doi.org/10.5194/gmd-17-1603-2024, https://doi.org/10.5194/gmd-17-1603-2024, 2024
Short summary
Short summary
Models used to simulate the flow of coastal and riverine waters, including flooding, require a geometric representation (or mesh) of geographic features that exhibit a range of disparate spatial scales from large, open waters to small, narrow channels. Representing these features in an accurate way without excessive computational overhead presents a challenge. Here, we develop an automatic mesh generation tool to help address this challenge. Our results demonstrate the efficacy of our approach.
Hui Wan, Kai Zhang, Christopher J. Vogl, Carol S. Woodward, Richard C. Easter, Philip J. Rasch, Yan Feng, and Hailong Wang
Geosci. Model Dev., 17, 1387–1407, https://doi.org/10.5194/gmd-17-1387-2024, https://doi.org/10.5194/gmd-17-1387-2024, 2024
Short summary
Short summary
Sophisticated numerical models of the Earth's atmosphere include representations of many physical and chemical processes. In numerical simulations, these processes need to be calculated in a certain sequence. This study reveals the weaknesses of the sequence of calculations used for aerosol processes in a global atmosphere model. A revision of the sequence is proposed and its impacts on the simulated global aerosol climatology are evaluated.
Christopher J. Vogl, Hui Wan, Carol S. Woodward, and Quan M. Bui
Geosci. Model Dev., 17, 1409–1428, https://doi.org/10.5194/gmd-17-1409-2024, https://doi.org/10.5194/gmd-17-1409-2024, 2024
Short summary
Short summary
Generally speaking, accurate climate simulation requires an accurate evolution of the underlying mathematical equations on large computers. The equations are typically formulated and evolved in process groups. Process coupling refers to how the evolution of each group is combined with that of other groups to evolve the full set of equations for the whole atmosphere. This work presents a mathematical framework to evaluate methods without the need to first implement the methods.
Tom Keel, Chris Brierley, and Tamsin Edwards
Geosci. Model Dev., 17, 1229–1247, https://doi.org/10.5194/gmd-17-1229-2024, https://doi.org/10.5194/gmd-17-1229-2024, 2024
Short summary
Short summary
Jet streams are an important control on surface weather as their speed and shape can modify the properties of weather systems. Establishing trends in the operation of jet streams may provide some indication of the future of weather in a warming world. Despite this, it has not been easy to establish trends, as many methods have been used to characterise them in data. We introduce a tool containing various implementations of jet stream statistics and algorithms that works in a standardised manner.
Frédéric Parrenin, Marie Bouchet, Christo Buizert, Emilie Capron, Ellen Corrick, Russell Drysdale, Kenji Kawamura, Amaëlle Landais, Robert Mulvaney, Ikumi Oyabu, and Sune Rasmussen
EGUsphere, https://doi.org/10.5194/egusphere-2023-2911, https://doi.org/10.5194/egusphere-2023-2911, 2024
Short summary
Short summary
The Paleochrono1 probablistic dating model allows to derive a common and optimized chronology for several paleoclimatic sites from various archives (ice cores, speleothems, marine cores, lake cores, etc.). It combines prior sedimentation scenarios with chronological information such as dated horizons, dated intervals, stratigraphic links and (for ice cores) Delta-depth observations. Paleochrono1 is available under the MIT open-source license.
Amir Golparvar, Matthias Kästner, and Martin Thullner
Geosci. Model Dev., 17, 881–898, https://doi.org/10.5194/gmd-17-881-2024, https://doi.org/10.5194/gmd-17-881-2024, 2024
Short summary
Short summary
Coupled reaction transport modelling is an established and beneficial method for studying natural and synthetic porous material, with applications ranging from industrial processes to natural decompositions in terrestrial environments. Up to now, a framework that explicitly considers the porous structure (e.g. from µ-CT images) for modelling the transport of reactive species is missing. We presented a model that overcomes this limitation and represents a novel numerical simulation toolbox.
Stefan Hergarten
Geosci. Model Dev., 17, 781–794, https://doi.org/10.5194/gmd-17-781-2024, https://doi.org/10.5194/gmd-17-781-2024, 2024
Short summary
Short summary
The Voellmy rheology has been widely used for simulating snow and rock avalanches. Recently, a modified version of this rheology was proposed, which turned out to be able to predict the observed long runout of large rock avalanches theoretically. The software MinVoellmy presented here is the first numerical implementation of the modified rheology. It consists of MATLAB and Python classes, where simplicity and parsimony were the design goals.
Arjun Babu Nellikkattil, Danielle Lemmon, Travis Allen O'Brien, June-Yi Lee, and Jung-Eun Chu
Geosci. Model Dev., 17, 301–320, https://doi.org/10.5194/gmd-17-301-2024, https://doi.org/10.5194/gmd-17-301-2024, 2024
Short summary
Short summary
This study introduces a new computational framework called Scalable Feature Extraction and Tracking (SCAFET), designed to extract and track features in climate data. SCAFET stands out by using innovative shape-based metrics to identify features without relying on preconceived assumptions about the climate model or mean state. This approach allows more accurate comparisons between different models and scenarios.
Mohammad Mortezazadeh, Jean-François Cossette, Ashu Dastoor, Jean de Grandpré, Irena Ivanova, and Abdessamad Qaddouri
Geosci. Model Dev., 17, 335–346, https://doi.org/10.5194/gmd-17-335-2024, https://doi.org/10.5194/gmd-17-335-2024, 2024
Short summary
Short summary
The interpolation process is the most computationally expensive step of the semi-Lagrangian (SL) approach. In this paper we implement a new interpolation scheme into the semi-Lagrangian approach which has the same computational cost as a third-order polynomial scheme but with the accuracy of a fourth-order interpolation scheme. This improvement is achieved by using two third-order backward and forward polynomial interpolation schemes in two consecutive time steps.
Boris Gailleton, Luca C. Malatesta, Guillaume Cordonnier, and Jean Braun
Geosci. Model Dev., 17, 71–90, https://doi.org/10.5194/gmd-17-71-2024, https://doi.org/10.5194/gmd-17-71-2024, 2024
Short summary
Short summary
This contribution presents a new method to numerically explore the evolution of mountain ranges and surrounding areas. The method helps in monitoring with details on the timing and travel path of material eroded from the mountain ranges. It is particularly well suited to studies juxtaposing different domains – lakes or multiple rock types, for example – and enables the combination of different processes.
Denise Degen, Daniel Caviedes Voullième, Susanne Buiter, Harrie-Jan Hendricks Franssen, Harry Vereecken, Ana González-Nicolás, and Florian Wellmann
Geosci. Model Dev., 16, 7375–7409, https://doi.org/10.5194/gmd-16-7375-2023, https://doi.org/10.5194/gmd-16-7375-2023, 2023
Short summary
Short summary
In geosciences, we often use simulations based on physical laws. These simulations can be computationally expensive, which is a problem if simulations must be performed many times (e.g., to add error bounds). We show how a novel machine learning method helps to reduce simulation time. In comparison to other approaches, which typically only look at the output of a simulation, the method considers physical laws in the simulation itself. The method provides reliable results faster than standard.
Carlos Spa, Otilio Rojas, and Josep de la Puente
Geosci. Model Dev., 16, 7237–7252, https://doi.org/10.5194/gmd-16-7237-2023, https://doi.org/10.5194/gmd-16-7237-2023, 2023
Short summary
Short summary
This paper develops a calibration methodology of all absorbing techniques typically used by Fourier pseudo-spectral time-domain (PSTD) methods for geoacoustic wave simulations. The main contributions of the paper are (a) an implementation and quantitative comparison of all absorbing techniques available for PSTD methods through a simple and robust numerical experiment, and (b) a validation of these absorbing techniques in several 3-D seismic scenarios with gradual heterogeneity complexities.
Joonlee Lee, Myong-In Lee, Sunlae Tak, Eunkyo Seo, and Yong-Keun Lee
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-221, https://doi.org/10.5194/gmd-2023-221, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
We developed a snow assimilation system using satellite data based on a land surface model. The snow states produced by the assimilation system demonstrate high performance in all regions, including transition regions, compared to the satellite data and land model. As snow significantly influences energy and water balance at the atmosphere-land boundary, this approach allows for a more accurate prediction of atmospheric conditions by realistically representing atmosphere-land interactions.
Michael Hillier, Florian Wellmann, Eric A. de Kemp, Boyan Brodaric, Ernst Schetselaar, and Karine Bédard
Geosci. Model Dev., 16, 6987–7012, https://doi.org/10.5194/gmd-16-6987-2023, https://doi.org/10.5194/gmd-16-6987-2023, 2023
Short summary
Short summary
Neural networks can be used effectively to model three-dimensional geological structures from point data, sampling geological interfaces, units, and structural orientations. Existing neural network approaches for this type of modelling are advanced by the efficient incorporation of unconformities, new knowledge inputs, and improved data fitting techniques. These advances permit the modelling of more complex geology in diverse geological settings, different-sized areas, and various data regimes.
Tor Nordam, Ruben Kristiansen, Raymond Nepstad, Erik van Sebille, and Andy M. Booth
Geosci. Model Dev., 16, 5339–5363, https://doi.org/10.5194/gmd-16-5339-2023, https://doi.org/10.5194/gmd-16-5339-2023, 2023
Short summary
Short summary
We describe and compare two common methods, Eulerian and Lagrangian models, used to simulate the vertical transport of material in the ocean. They both solve the same transport problems but use different approaches for representing the underlying equations on the computer. The main focus of our study is on the numerical accuracy of the two approaches. Our results should be useful for other researchers creating or using these types of transport models.
Mathieu Gravey and Grégoire Mariethoz
Geosci. Model Dev., 16, 5265–5279, https://doi.org/10.5194/gmd-16-5265-2023, https://doi.org/10.5194/gmd-16-5265-2023, 2023
Short summary
Short summary
Multiple‐point geostatistics are widely used to simulate complex spatial structures based on a training image. The use of these methods relies on the possibility of finding optimal training images and parametrization of the simulation algorithms. Here, we propose finding an optimal set of parameters using only the training image as input. The main advantage of our approach is to remove the risk of overfitting an objective function.
Siting Li, Ping Wang, Hong Wang, Yue Peng, Zhaodong Liu, Wenjie Zhang, Hongli Liu, Yaqiang Wang, Huizheng Che, and Xiaoye Zhang
Geosci. Model Dev., 16, 4171–4191, https://doi.org/10.5194/gmd-16-4171-2023, https://doi.org/10.5194/gmd-16-4171-2023, 2023
Short summary
Short summary
Optimizing the initial state of atmospheric chemistry model input is one of the most essential methods to improve forecast accuracy. Considering the large computational load of the model, we introduce an ensemble optimal interpolation scheme (EnOI) for operational use and efficient updating of the initial fields of chemical components. The results suggest that EnOI provides a practical and cost-effective technique for improving the accuracy of chemical weather numerical forecasts.
Thomas Richter, Véronique Dansereau, Christian Lessig, and Piotr Minakowski
Geosci. Model Dev., 16, 3907–3926, https://doi.org/10.5194/gmd-16-3907-2023, https://doi.org/10.5194/gmd-16-3907-2023, 2023
Short summary
Short summary
Sea ice covers not only the pole regions but affects the weather and climate globally. For example, its white surface reflects more sunlight than land. The oceans around the poles are therefore kept cool, which affects the circulation in the oceans worldwide. Simulating the behavior and changes in sea ice on a computer is, however, very difficult. We propose a new computer simulation that better models how cracks in the ice change over time and show this by comparing to other simulations.
Emma J. MacKie, Michael Field, Lijing Wang, Zhen Yin, Nathan Schoedl, Matthew Hibbs, and Allan Zhang
Geosci. Model Dev., 16, 3765–3783, https://doi.org/10.5194/gmd-16-3765-2023, https://doi.org/10.5194/gmd-16-3765-2023, 2023
Short summary
Short summary
Earth scientists often have to fill in spatial gaps in measurements. This gap-filling or interpolation can be accomplished with geostatistical methods, where the statistical relationships between measurements are used to inform how these gaps should be filled. Despite the broad utility of these methods, there are few freely available geostatistical software applications. We present GStatSim, a Python package for performing different geostatistical interpolation methods.
Ian Madden, Simone Marras, and Jenny Suckale
Geosci. Model Dev., 16, 3479–3500, https://doi.org/10.5194/gmd-16-3479-2023, https://doi.org/10.5194/gmd-16-3479-2023, 2023
Short summary
Short summary
To aid risk managers who may wish to rapidly assess tsunami risk but may lack high-performance computing infrastructure, we provide an accessible software package able to rapidly model tsunami inundation over real topography by leveraging Google's Tensor Processing Unit, a high-performance hardware. Minimally trained users can take advantage of the rapid modeling abilities provided by this package via a web browser thanks to the ease of use of Google Cloud Platform.
Youtong Rong, Paul Bates, and Jeffrey Neal
Geosci. Model Dev., 16, 3291–3311, https://doi.org/10.5194/gmd-16-3291-2023, https://doi.org/10.5194/gmd-16-3291-2023, 2023
Short summary
Short summary
A novel subgrid channel (SGC) model is developed for river–floodplain modelling, allowing utilization of subgrid-scale bathymetric information while performing computations on relatively coarse grids. By including adaptive artificial diffusion, potential numerical instability, which the original SGC solver had, in low-friction regions such as urban areas is addressed. Evaluation of the new SGC model through structured tests confirmed that the accuracy and stability have improved.
Xiaqiong Zhou and Hann-Ming Henry Juang
Geosci. Model Dev., 16, 3263–3274, https://doi.org/10.5194/gmd-16-3263-2023, https://doi.org/10.5194/gmd-16-3263-2023, 2023
Short summary
Short summary
The National Centers for Environmental Prediction Global Forecast System version 16 experienced model instability failures in real-time runs resolved by increasing the minimum thickness depth parameter. Further investigation revealed that the issue was caused by the advection of geopotential heights at the model's layer interfaces. By replacing high-order boundary conditions with zero-gradient boundary conditions for interface-wind reconstruction, the instability was effectively addressed.
Grant T. Euen, Shangxin Liu, Rene Gassmöller, Timo Heister, and Scott D. King
Geosci. Model Dev., 16, 3221–3239, https://doi.org/10.5194/gmd-16-3221-2023, https://doi.org/10.5194/gmd-16-3221-2023, 2023
Short summary
Short summary
Due to the increasing availability of high-performance computing over the past few decades, numerical models have become an important tool for research. Here we test two geodynamic codes that produce such models: ASPECT, a newer code, and CitcomS, an older one. We show that they produce solutions that are extremely close. As methods and codes become more complex over time, showing reproducibility allows us to seamlessly link previously known information to modern methodologies.
Mohammad Kazem Sharifian, Georges Kesserwani, Alovya Ahmed Chowdhury, Jeffrey Neal, and Paul Bates
Geosci. Model Dev., 16, 2391–2413, https://doi.org/10.5194/gmd-16-2391-2023, https://doi.org/10.5194/gmd-16-2391-2023, 2023
Short summary
Short summary
This paper describes a new release of the LISFLOOD-FP model for fast and efficient flood simulations. It features a new non-uniform grid generator that uses multiwavelet analyses to sensibly coarsens the resolutions where the local topographic variations are smooth. Moreover, the model is parallelised on the graphical processing units (GPUs) to further boost computational efficiency. The performance of the model is assessed for five real-world case studies, noting its potential applications.
Bruno K. Zürcher
Geosci. Model Dev., 16, 1697–1711, https://doi.org/10.5194/gmd-16-1697-2023, https://doi.org/10.5194/gmd-16-1697-2023, 2023
Short summary
Short summary
We present a novel algorithm to efficiently compute Barnes interpolation, which is a method for transforming data values recorded at irregularly spaced points into a corresponding regular grid. In contrast to naive implementations with an algorithmic complexity that depends on the product of the number of sample points and the number of grid points, our approach reduces this dependency to their sum.
David H. Marsico and Paul A. Ullrich
Geosci. Model Dev., 16, 1537–1551, https://doi.org/10.5194/gmd-16-1537-2023, https://doi.org/10.5194/gmd-16-1537-2023, 2023
Short summary
Short summary
Climate models involve several different components, such as the atmosphere, ocean, and land models. Information needs to be exchanged, or remapped, between these models, and devising algorithms for performing this exchange is important for ensuring the accuracy of climate simulations. In this paper, we examine the efficacy of several traditional and novel approaches to remapping on the sphere and demonstrate where our approaches offer improvement.
Moritz Liebl, Jörg Robl, Stefan Hergarten, David Lundbek Egholm, and Kurt Stüwe
Geosci. Model Dev., 16, 1315–1343, https://doi.org/10.5194/gmd-16-1315-2023, https://doi.org/10.5194/gmd-16-1315-2023, 2023
Short summary
Short summary
In this study, we benchmark a topography-based model for glacier erosion (OpenLEM) with a well-established process-based model (iSOSIA). Our experiments show that large-scale erosion patterns and particularly the transformation of valley length geometry from fluvial to glacial conditions are very similar in both models. This finding enables the application of OpenLEM to study the influence of climate and tectonics on glaciated mountains with reasonable computational effort on standard PCs.
James Kent, Thomas Melvin, and Golo Albert Wimmer
Geosci. Model Dev., 16, 1265–1276, https://doi.org/10.5194/gmd-16-1265-2023, https://doi.org/10.5194/gmd-16-1265-2023, 2023
Short summary
Short summary
This paper introduces the Met Office's new shallow water model. The shallow water model is a building block towards the Met Office's new atmospheric dynamical core. The shallow water model is tested on a number of standard spherical shallow water test cases, including flow over mountains and unstable jets. Results show that the model produces similar results to other shallow water models in the literature.
Anthony Gruber, Max Gunzburger, Lili Ju, Rihui Lan, and Zhu Wang
Geosci. Model Dev., 16, 1213–1229, https://doi.org/10.5194/gmd-16-1213-2023, https://doi.org/10.5194/gmd-16-1213-2023, 2023
Short summary
Short summary
This work applies a novel technical tool, multifidelity Monte Carlo (MFMC) estimation, to three climate-related benchmark experiments involving oceanic, atmospheric, and glacial modeling. By considering useful quantities such as maximum sea height and total (kinetic) energy, we show that MFMC leads to predictions which are more accurate and less costly than those obtained by standard methods. This suggests MFMC as a potential drop-in replacement for estimation in realistic climate models.
Piyoosh Jaysaval, Glenn E. Hammond, and Timothy C. Johnson
Geosci. Model Dev., 16, 961–976, https://doi.org/10.5194/gmd-16-961-2023, https://doi.org/10.5194/gmd-16-961-2023, 2023
Short summary
Short summary
We present a robust and highly scalable implementation of numerical forward modeling and inversion algorithms for geophysical electrical resistivity tomography data. The implementation is publicly available and developed within the framework of PFLOTRAN (http://www.pflotran.org), an open-source, state-of-the-art massively parallel subsurface flow and transport simulation code. The paper details all the theoretical and implementation aspects of the new capabilities along with test examples.
Cited articles
Agarwal, R. and Sen, S.: Creators of Mathematical and Computational
Sciences, https://doi.org/10.1007/978-3-319-10870-4, 2014.
Ainsworth, E. A., Yendrek, C. R., Sitch, S., Collins, W. J., and Emberson,
L. D.: The Effects of Tropospheric Ozone on Net Primary Productivity and
Implications for Climate Change, Annu. Rev. Plant Biol., 63,
637–661, https://doi.org/10.1146/annurev-arplant-042110-103829, 2012.
Aldrin, M. and Haff, I.: Generalised additive modelling of air pollution,
traffic volume and meteorology, Atmos. Environ., 39, 2145–2155,
https://doi.org/10.1016/j.atmosenv.2004.12.020, 2005.
Alduchov, O. A. and Eskridge, R. E.: Improved Magnus Form Approximation of
Saturation Vapor Pressure, J. Appl. Meteorol., 35, 601–609,
https://doi.org/10.1175/1520-0450(1996)035<0601:imfaos>2.0.co;2, 1996.
Aw, J. and Kleeman, M. J.: Evaluating the first-order effect of intraannual
temperature variability on urban air pollution, J. Geophys.
Res., 108, 4365, https://doi.org/10.1029/2002jd002688, 2003.
Banta, J. E.: Sir William Petty: Modern epidemiologist (1623–1687), J. Commun. He., 12, 185–198, https://doi.org/10.1007/bf01323480, 1987.
Benirschke, K.: Francis Galton: Pioneer of Heredity and Biometry, J.
Heredity, 95, 273–273, https://doi.org/10.1093/jhered/esh039, 2004.
Blanchard, C. L., Hidy, G. M., and Tanenbaum, S.: Ozone in the southeastern
United States: An observation-based model using measurements from the SEARCH
network, Atmos. Environ., 88, 192–200, https://doi.org/10.1016/j.atmosenv.2014.02.006, 2014.
Blanchard, C. L., Shaw, S. L., Edgerton, E. S., and Schwab, J. J.: Emission
influences on air pollutant concentrations in New York State: I. ozone,
Atmos. Environ. X, 3, 100033, https://doi.org/10.1016/j.aeaoa.2019.100033, 2019.
Camalier, L., Cox, W., and Dolwick, P.: The effects of meteorology on ozone
in urban areas and their use in assessing ozone trends, Atmos.
Environ., 41, 7127–7137, https://doi.org/10.1016/j.atmosenv.2007.04.061, 2007.
CARB: Air Quality and Meteorological Information System (AQMIS), https://www.arb.ca.gov/aqmis2/aqdselect.php,
last access: 27 May 2020.
Chang, C.-C. and Lin, C.-J.: LIBSVM, ACM Transactions on Intelligent Systems
and Technology, 2, 1–27, https://doi.org/10.1145/1961189.1961199, 2011.
Cox, P., Delao, A., Komorniczak, A., and Weller, R.: The California Almanac of Emissions and Air Quality – 2009 edition, Planning and Technical Support Division California Air Resources Board,
2009.
Cox, P., Delao, A., and Komorniczak, A.: The California Almanac of Emissions and Air Quality – 2013 edition, Air Quality Planning and Science Division California Air Resources Board,
2013.
Drucker, H., Burges, C. J. C., Kaufman, L., Smola, A., and Vapnik, V.:
Support vector regression machines, Proceedings of the 9th International
Conference on Neural Information Processing Systems, Denver, Colorado, 1996.
Fan, R.-E., Chen, P.-H., and Lin, C.-J.: Working Set Selection Using Second
Order Information for Training Support Vector Machines, J. Mach. Learn.
Res., 6, 1889–1918, 2005.
Fisher, R. A.: On the mathematical foundations of theoretical statistics,
Philos. T. Roy. Soc. Lond. A, 222, 309–368,
https://doi.org/10.1098/rsta.1922.0009, 1922.
Flynn, M. T., Mattson, E. J., Jaffe, D. A., and Gratz, L. E.: Spatial
patterns in summertime surface ozone in the Southern Front Range of the U.S.
Rocky Mountains, Elementa: Science of the Anthropocene, 9, 00104,
https://doi.org/10.1525/elementa.2020.00104, 2021.
Friedman, J. H.: Multivariate Adaptive Regression Splines, Ann.
Stat., 19, 1–67, https://doi.org/10.1214/aos/1176347963, 1991.
Friedman, J. H. and Silverman, B. W.: Flexible Parsimonious Smoothing and
Additive Modeling, Technometrics, 31, 3–21, https://doi.org/10.2307/1270359, 1989.
Galton, F.: Co-Relations and Their Measurement, Chiefly from Anthropometric
Data, P. Roy. Soc. Lond., 45, 135–145, 1888.
Galton, F. S.: Natural inheritance, Macmillan, London, https://doi.org/10.5962/bhl.title.32181, 1889.
Gao, Z.: Predicting peak daily maximum 8-hour ozone, and linkages to emissions and meteorology, in Southern California using machine learning methods, Zenodo [code], https://doi.org/10.5281/zenodo.6892066, 2022a.
Gao, Z.: Predicting peak daily maximum 8-hour ozone, and linkages to emissions and meteorology, in Southern California using machine learning methods, Zenodo [data set], https://doi.org/10.5281/zenodo.6892062, 2022b.
Gao, Z., Ivey, C. E., Blanchard, C. L., Do, K., Lee, S.-M., and Russell, A.
G.: Separating emissions and meteorological impacts on peak ozone
concentrations in Southern California using generalized additive modeling,
Environ. Pollut., 307, 119503, https://doi.org/10.1016/j.envpol.2022.119503, 2022.
García Nieto, P. J. and Álvarez Antón, J. C.: Nonlinear air
quality modeling using multivariate adaptive regression splines in Gijón
urban area (Northern Spain) at local scale, Appl. Math.
Comput., 235, 50–65, https://doi.org/10.1016/j.amc.2014.02.096, 2014.
Gong, X., Kaulfus, A., Nair, U., and Jaffe, D. A.: Quantifying O3 Impacts in
Urban Areas Due to Wildfires Using a Generalized Additive Model,
Environ. Sci. Technol., 51, 13216–13223,
https://doi.org/10.1021/acs.est.7b03130, 2017.
Gong, X., Hong, S., and Jaffe, D. A.: Ozone in China: Spatial Distribution
and Leading Meteorological Factors Controlling O3 in 16 Chinese Cities,
Aerosol Air Qual. Res., 18, 2287–2300, https://doi.org/10.4209/aaqr.2017.10.0368,
2018.
Gorai, A. K., Tuluri, F., Tchounwou, P. B., and Ambinakudige, S.: Influence
of local meteorology and NO2 conditions on ground-level ozone concentrations
in the eastern part of Texas, USA, Air Quality, Atmos. He., 8,
81–96, https://doi.org/10.1007/s11869-014-0276-5, 2015.
Harrell, F. E.: General Aspects of Fitting Regression Models, Springer
International Publishing, 13–44, https://doi.org/10.1007/978-3-319-19425-7_2,
2015.
Hastie, T.: Generalized additive models, Chapter 7, in: Statistical Models in S, edited by: Chambers, J. M. and Hastie,
T. J., Wadsworth & Brooks/Cole, 1991.
Hastie, T. and Tibshirani, R.: Generalized Additive Models, Stat.
Sci., 1, 297–310, https://doi.org/10.1214/ss/1177013604, 1986.
Hastie, T. and Tibshirani, R.: Generalized additive models, Chapman &
Hall/CRC, London, 1990.
Hastie, T. and Tibshirani, R.: Discriminant Analysis by Gaussian Mixtures,
J. Roy. Stat. Soc. B, 58,
155–176, 1996.
Hastie, T., Tibshirani, R., and Friedman, J. H.: The elements of statistical
learning: data mining, inference, and prediction, 2nd edn.,
Springer, New York, 2009.
Henneman, L. R. F., Holmes, H. A., Mulholland, J. A., and Russell, A. G.:
Meteorological detrending of primary and secondary pollutant concentrations:
Method application and evaluation using long-term (2000–2012) data in
Atlanta, Atmos. Environ., 119, 201–210, https://doi.org/10.1016/j.atmosenv.2015.08.007, 2015.
Hogrefe, C., Rao, S. T., Zurbenko, I. G., and Porter, P. S.: Interpreting
the Information in Ozone Observations and Model Predictions Relevant to
Regulatory Policies in the Eastern United States, B. Am.
Meteorol. Soc., 81, 2083–2106, https://doi.org/10.1175/1520-0477(2000)081<2083:itiioo>2.3.co;2, 2000.
Hong, C., Mueller, N. D., Burney, J. A., Zhang, Y., AghaKouchak, A., Moore,
F. C., Qin, Y., Tong, D., and Davis, S. J.: Impacts of ozone and climate
change on yields of perennial crops in California, Nature Food, 1, 166–172,
https://doi.org/10.1038/s43016-020-0043-8, 2020.
Hu, C., Kang, P., Jaffe, D. A., Li, C., Zhang, X., Wu, K., and Zhou, M.:
Understanding the impact of meteorology on ozone in 334 cities of China,
Atmos. Environ., 248, 118221, https://doi.org/10.1016/j.atmosenv.2021.118221, 2021.
Huang, X. G., Shao, T. J., Zhao, J. B., Cao, J. J., and Lü, X. H.:
Influencing Factors of Ozone Concentration in Xi'an Based on Generalized
Additive Models, Huan Jing Ke Xue, 41, 1535–1543,
https://doi.org/10.13227/j.hjkx.201906067, 2020.
Jeong, Y., Lee, H. W., and Jeon, W.: Regional Differences of Primary
Meteorological Factors Impacting O3 Variability in South Korea, Atmosphere,
11, 74, https://doi.org/10.3390/atmos11010074, 2020.
Jin, L., Loisy, A., and Brown, N. J.: Role of meteorological processes in
ozone responses to emission controls in California's San Joaquin Valley,
J. Geophys. Res.-Atmos., 118, 8010–8022,
https://doi.org/10.1002/jgrd.50559, 2013.
Keller, C. A. and Evans, M. J.: Application of random forest regression to the calculation of gas-phase chemistry within the GEOS-Chem chemistry model v10, Geosci. Model Dev., 12, 1209–1225, https://doi.org/10.5194/gmd-12-1209-2019, 2019.
Kelley, M. C., Brown, M. M., Fedler, C. B., and Ardon-Dryer, K.: Long Term
Measurements of PM2.5 Concentrations in Lubbock, Texas, Aerosol Air
Qual. Res., 20, 1306–1318, https://doi.org/10.4209/aaqr.2019.09.0469, 2020.
Kleeman, M. J.: A preliminary assessment of the sensitivity of air quality
in California to global change, Clim. Change, 87, 273–292,
https://doi.org/10.1007/s10584-007-9351-3, 2008.
Lawrence, M. G.: The Relationship between Relative Humidity and the Dewpoint
Temperature in Moist Air: A Simple Conversion and Applications, B.
Am. Meteorol. Soc., 86, 225–234, https://doi.org/10.1175/BAMS-86-2-225,
2005.
Leathwick, J. R., Elith, J., and Hastie, T.: Comparative performance of
generalized additive models and multivariate adaptive regression splines for
statistical modelling of species distributions, Ecol. Model., 199,
188–196, https://doi.org/10.1016/j.ecolmodel.2006.05.022, 2006.
Liaw, A. and Wiener, M.: Classification and Regression by randomForest, R
News, 2, 18–22, 2002.
Ling, Z. H., Guo, H., Zheng, J. Y., Louie, P. K. K., Cheng, H. R., Jiang,
F., Cheung, K., Wong, L. C., and Feng, X. Q.: Establishing a conceptual
model for photochemical ozone pollution in subtropical Hong Kong,
Atmos. Environ., 76, 208–220, https://doi.org/10.1016/j.atmosenv.2012.09.051, 2013.
Liu, B.-C., Binaykia, A., Chang, P.-C., Tiwari, M. K., and Tsao, C.-C.:
Urban air quality forecasting based on multi-dimensional collaborative
Support Vector Regression (SVR): A case study of
Beijing-Tianjin-Shijiazhuang, PLOS ONE, 12, e0179763,
https://doi.org/10.1371/journal.pone.0179763, 2017.
Liu, T., Li, T. T., Zhang, Y. H., Xu, Y. J., Lao, X. Q., Rutherford, S.,
Chu, C., Luo, Y., Zhu, Q., Xu, X. J., Xie, H. Y., Liu, Z. R., and Ma, W. J.:
The short-term effect of ambient ozone on mortality is modified by
temperature in Guangzhou, China, Atmos. Environ., 76, 59–67,
https://doi.org/10.1016/j.atmosenv.2012.07.011, 2013.
Lu, R. and Turco, R. P.: Ozone distributions over the los angeles basin:
Three-dimensional simulations with the smog model, Atmos. Environ.,
30, 4155–4176, https://doi.org/10.1016/1352-2310(96)00153-7, 1996.
Lu, X., Zhang, L., and Shen, L.: Meteorology and Climate Influences on
Tropospheric Ozone: a Review of Natural Sources, Chemistry, and Transport
Patterns, Current Pollut. Rep., 5, 238–260, https://doi.org/10.1007/s40726-019-00118-3, 2019.
Luna, A. S., Paredes, M. L. L., De Oliveira, G. C. G., and Corrêa, S.
M.: Prediction of ozone concentration in tropospheric levels using
artificial neural networks and support vector machine at Rio de Janeiro,
Brazil, Atmos. Environ., 98, 98–104, https://doi.org/10.1016/j.atmosenv.2014.08.060,
2014.
Ma, Y., Ma, B., Jiao, H., Zhang, Y., Xin, J., and Yu, Z.: An analysis of the
effects of weather and air pollution on tropospheric ozone using a
generalized additive model in Western China: Lanzhou, Gansu, Atmos.
Environ., 224, 117342, https://doi.org/10.1016/j.atmosenv.2020.117342, 2020.
Mahmud, A., Hixson, M., Hu, J., Zhao, Z., Chen, S.-H., and Kleeman, M. J.: Climate impact on airborne particulate matter concentrations in California using seven year analysis periods, Atmos. Chem. Phys., 10, 11097–11114, https://doi.org/10.5194/acp-10-11097-2010, 2010.
McClure, C. D. and Jaffe, D. A.: Investigation of high ozone events due to
wildfire smoke in an urban area, Atmos. Environ., 194, 146–157,
https://doi.org/10.1016/j.atmosenv.2018.09.021, 2018.
McGlynn, D., Mao, H., Sive, B., and Sharac, T.: Understanding Long-Term
Variations in Surface Ozone in United States (U.S.) National Parks,
Atmosphere, 9, 125, https://doi.org/10.3390/atmos9040125, 2018.
Menne, M. J., Durre, I., Korzeniewski, B., McNeal, S., Thomas, K., Yin, X.,
Anthony, S., Ray, R., Vose, R. S., Gleason, B. E., and Houston, T. G.:
Global Historical Climatology Network - Daily (GHCN-Daily), Version 3,
https://doi.org/10.7289/V5D21VHZ, 2012a.
Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E., and Houston, T. G.: An
Overview of the Global Historical Climatology Network-Daily Database,
J. Atmos. Ocean. Tech., 29, 897–910,
https://doi.org/10.1175/jtech-d-11-00103.1, 2012b.
Milborrow, S.: Regression Splines,
FIM Forecast Model: 500 mb Wind Speed and 500 mb Height Contours – Real-time:
https://CRAN.R-project.org/package=earth (last access: 13 November 2021),
R program [code], 2021.
NOAA: FIM Forecast Model: 500 mb Wind Speed and 500 mb Height Contours – Real-time, https://sos.noaa.gov/datasets/fim-forecast-model-500mb-wind-speed-and-500mb-height-contours-real-time/ (last access: 23 May 2021), 2020.
Oduro, S. D., Metia, S., Duc, H., Hong, G., and Ha, Q. P.: Multivariate
adaptive regression splines models for vehicular emission prediction,
Visualization in Engineering, 3, 13, https://doi.org/10.1186/s40327-015-0024-4, 2015.
Oman, L. D., Ziemke, J. R., Douglass, A. R., Waugh, D. W., Lang, C.,
Rodriguez, J. M., and Nielsen, J. E.: The response of tropical tropospheric
ozone to ENSO, Geophys. Res. Lett., 38, 13706, https://doi.org/10.1029/2011GL047865, 2011.
Oman, L. D., Douglass, A. R., Ziemke, J. R., Rodriguez, J. M., Waugh, D. W.,
and Nielsen, J. E.: The ozone response to ENSO in Aura satellite
measurements and a chemistry-climate simulation, J. Geophys. Res.-Atmos., 118, 965–976,
https://doi.org/10.1029/2012jd018546, 2013.
Pearce, J. L., Beringer, J., Nicholls, N., Hyndman, R. J., and Tapper, N.
J.: Quantifying the influence of local meteorology on air quality using
generalized additive models, Atmos. Environ., 45, 1328–1336,
https://doi.org/10.1016/j.atmosenv.2010.11.051, 2011.
Pernak, R., Alvarado, M., Lonsdale, C., Mountain, M., Hegarty, J., and
Nehrkorn, T.: Forecasting Surface O3 in Texas Urban Areas Using Random
Forest and Generalized Additive Models, Aerosol Air Qual. Res., 9,
2815–2826, https://doi.org/10.4209/aaqr.2018.12.0464, 2019.
Pope, P. T. and Webster, J. T.: The Use of an F-Statistic in Stepwise
Regression Procedures, Technometrics, 14, 327–340, 1972.
Porter, T. M.: Social Interests and Statistical Theory: Statistics in
Britain, 1865–1930, Science, 214, 784–784,
https://doi.org/10.1126/science.214.4522.784.a, 1981.
Porter, T. M.: Trust in Numbers, Princeton University Press, ISBN 9780691208411, 1995.
Rao, S. T., Zurbenko, I. G., Neagu, R., Porter, P. S., Ku, J. Y., and Henry,
R. F.: Space and Time Scales in Ambient Ozone Data, B. Am.
Meteorol. Soc., 78, 2153–2166, https://doi.org/10.1175/1520-0477(1997)078<2153:satsia>2.0.co;2, 1997.
Rodríguez-Pérez, R., Vogt, M., and Bajorath, J.: Support Vector
Machine Classification and Regression Prioritize Different Structural
Features for Binary Compound Activity and Potency Value Prediction, ACS
Omega, 2, 6371–6379, https://doi.org/10.1021/acsomega.7b01079, 2017.
Roy, S. S., Pratyush, C., and Barna, C.: Predicting Ozone Layer
Concentration Using Multivariate Adaptive Regression Splines, Random Forest
and Classification and Regression Tree, Springer International
Publishing, 140–152, https://doi.org/10.1007/978-3-319-62524-9_11, 2018.
Rybarczyk, Y. and Zalakeviciute, R.: Machine Learning Approaches for Outdoor
Air Quality Modelling: A Systematic Review, Appl. Sci., 8, 2570,
https://doi.org/10.3390/app8122570, 2018.
Schölkopf, B. and Smola, A. J.: Learning with Kernels: Support Vector
Machines, Regularization, Optimization, and Beyond, MIT Press, ISBN 9780262536578, 2001.
Seinfeld, J. H. and Pandis, S. N.: Atmospheric Chemistry and Physics: From
Air Pollution to Climate Change, Wiley, ISBN 978-1-118-94740-1, 2016.
Smola, A. J. and Schölkopf, B.: A tutorial on support vector regression,
Stat. Comput., 14, 199–222, https://doi.org/10.1023/b:stco.0000035301.49549.88,
2004.
Sotomayor-Olmedo, A., Aceves-Fernández, M. A., Gorrostieta-Hurtado, E.,
Pedraza-Ortega, C., Ramos-Arreguín, J. M., and Vargas-Soto, J. E.:
Forecast Urban Air Pollution in Mexico City by Using Support Vector
Machines: A Kernel Performance Approach, International Journal of
Intelligence Science, 03, 126–135, https://doi.org/10.4236/ijis.2013.33014, 2013.
Stafoggia, M., Johansson, C., Glantz, P., Renzi, M., Shtein, A., De Hoogh,
K., Kloog, I., Davoli, M., Michelozzi, P., and Bellander, T.: A Random
Forest Approach to Estimate Daily Particulate Matter, Nitrogen Dioxide, and
Ozone at Fine Spatial Resolution in Sweden, Atmosphere, 11, 239,
https://doi.org/10.3390/atmos11030239, 2020.
Stephen, M. S.: Gauss and the Invention of Least Squares, Ann.
Stat., 9, 465–474, https://doi.org/10.1214/aos/1176345451, 1981.
Sun, L., Xue, L., Wang, Y., Li, L., Lin, J., Ni, R., Yan, Y., Chen, L., Li, J., Zhang, Q., and Wang, W.: Impacts of meteorology and emissions on summertime surface ozone increases over central eastern China between 2003 and 2015, Atmos. Chem. Phys., 19, 1455–1469, https://doi.org/10.5194/acp-19-1455-2019, 2019.
Tin Kam, H.: Random decision forests, Proceedings of 3rd International
Conference on Document Analysis and Recognition, 14–16 August 1995,
271, 278–282, https://doi.org/10.1109/ICDAR.1995.598994, 1995.
U.S. EPA:
Trends in Ozone Adjusted for Weather Conditions, https://www.epa.gov/air-trends/trends-ozone-adjusted-weather-conditions (last access: 13 November 2021),
U.S. EPA, 2016.
U.S. EPA: Integrated Science Assessment (ISA) for Ozone and Related Photochemical Oxidants (Final Report, Apr 2020), U.S. Environmental Protection Agency, Washington, D.C., EPA/600/R-20/012, 2020.
Vong, C.-M., Ip, W.-F., Wong, P.-K., and Yang, J.-Y.: Short-Term Prediction
of Air Pollution in Macau Using Support Vector Machines, J. Control
Sci. Eng., 2012, 1–11, https://doi.org/10.1155/2012/518032, 2012.
Wells, B., Dolwick, P., Eder, B., Evangelista, M., Foley, K., Mannshardt,
E., Misenis, C., and Weishampel, A.: Improved estimation of trends in U.S.
ozone concentrations adjusted for interannual variability in meteorological
conditions, Atmos. Environ., 248, 118234,
https://doi.org/10.1016/j.atmosenv.2021.118234, 2021.
Wikipedia Contributors: Multivariate adaptive regression spline, https://en.wikipedia.org/w/index.php?title=Multivariate_adaptive_regression_spline&oldid=1083057440, last access: 19 April 2022.
Wood, S. N.: Fast stable restricted maximum likelihood and marginal
likelihood estimation of semiparametric generalized linear models, J. Roy. Statist. Soc. B, 73,
3–36, https://doi.org/10.1111/j.1467-9868.2010.00749.x, 2011.
Wood, S. N.: Generalized Additive Models: An Introduction with R, 2nd edn.,
Chapman and Hall/CRC, ISBN 9781315370279, https://doi.org/10.1201/9781315370279, 2017.
Xu, L., Yu, J.-Y., Schnell, J. L., and Prather, M. J.: The Seasonality and
Geographic Dependence of ENSO Impacts on US Surface Ozone Variability, Geophys. Res. Lett.,
44, 3420–3428,
https://doi.org/10.1002/2017gl073044, 2017.
Zhan, Y., Luo, Y., Deng, X., Grieneisen, M. L., Zhang, M., and Di, B.:
Spatiotemporal prediction of daily ambient ozone levels across China using
random forest for human exposure assessment, Environ. Pollut., 233,
464–473, https://doi.org/10.1016/j.envpol.2017.10.029, 2018.
Short summary
While the national ambient air quality standard of ozone is based on the 3-year average of the fourth highest 8 h maximum (MDA8) ozone concentrations, these predicted extreme values using numerical methods are always biased low. We built four computational models (GAM, MARS, random forest and SVR) to predict the fourth highest MDA8 ozone in Southern California using precursor emissions, meteorology and climatological patterns. All models presented acceptable performance, with GAM being the best.
While the national ambient air quality standard of ozone is based on the 3-year average of the...
