Articles | Volume 14, issue 10
https://doi.org/10.5194/gmd-14-6241-2021
© Author(s) 2021. 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-14-6241-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
18 Oct 2021
Development and technical paper |
| 18 Oct 2021
| 18 Oct 2021
A micro-genetic algorithm (GA v1.7.1a) for combinatorial optimization of physics parameterizations in the Weather Research and Forecasting model (v4.0.3) for quantitative precipitation forecast in Korea
Sojung Park
Department of Climate and Energy Systems Engineering, Ewha Womans
University, Seoul, 03760, Korea
Department of Climate and Energy Systems Engineering, Ewha Womans
University, Seoul, 03760, Korea
Department of Environmental Science and Engineering, Ewha Womans
University, Seoul, 03760, Korea
Center for Climate/Environment Change Prediction Research, Ewha Womans
University, Seoul, 03760, Korea
Severe Storm Research Center, Ewha Womans University, Seoul, 03760,
Korea
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Sojung Park, Seon Ki Park, Jeung Whan Lee, and Yunho Park
Hydrol. Earth Syst. Sci., 22, 3435–3452, https://doi.org/10.5194/hess-22-3435-2018, https://doi.org/10.5194/hess-22-3435-2018, 2018
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Understanding the precipitation characteristics is essential to design an optimal observation network. We studied the spatial and temporal characteristics of summertime precipitation systems in Korea via geostatistical analyses on the ground-based precipitation and satellite water vapor data. We found that, under a strict standard, an observation network with higher resolution is required in local areas with frequent heavy rainfalls, depending on directional features of precipitation systems.
Sojung Park and Seon Ki Park
Geosci. Model Dev., 9, 1073–1085, https://doi.org/10.5194/gmd-9-1073-2016, https://doi.org/10.5194/gmd-9-1073-2016, 2016
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Snow albedo varies with snow grain size, snow cover thickness, etc. It also depends on the spatial characteristics of land cover and on the canopy density and structure. The Noah-MP model shows a bias error of albedo in winter due to no proper reflection of the vegetation effect. We developed new parameters, called leaf index and stem index, which reflect the vegetation effect on winter albedo. The Noah-MP's performance in albedo has prominently improved with about 69 % decrease in the RMSE.
Ji Won Yoon, Seungyeon Lee, Ebony Lee, and Seon Ki Park
Geosci. Model Dev., 18, 2303–2328, https://doi.org/10.5194/gmd-18-2303-2025, https://doi.org/10.5194/gmd-18-2303-2025, 2025
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This study evaluates the Weather Research and Forecasting Model (WRF) coupled with Chemistry (WRF-Chem) to predict a mega Asian dust storm (ADS) over South Korea on 28–29 March 2021. We assessed combinations of five dust emission and four land surface schemes by analyzing meteorological and air quality variables. The best scheme combination reduced the root mean square error (RMSE) for particulate matter 10 (PM10) by up to 29.6 %, demonstrating the highest performance.
Sungmin O, Ji Won Yoon, and Seon Ki Park
Atmos. Meas. Tech., 18, 1471–1484, https://doi.org/10.5194/amt-18-1471-2025, https://doi.org/10.5194/amt-18-1471-2025, 2025
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Air pollutants such as particulate matter with diameters of 10 µm and 2.5 µm or less (PM10 and PM2.5) can cause adverse public health and environment effects; therefore their regular monitoring is crucial to keep pollutant concentrations under control. Our study demonstrates the potential of high-resolution aerosol optical depth (AOD) data from the Geostationary Environment Monitoring Spectrometer (GEMS) satellite to estimate ground-level PM concentrations using machine learning models.
Sujeong Lim, Seon Ki Park, and Claudio Cassardo
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-28, https://doi.org/10.5194/gmd-2023-28, 2023
Revised manuscript not accepted
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The ensembles in the numerical weather prediction system are under-dispersed near the land surface; therefore, an inflation method is required to increase it. In this study, we perturbed soil temperature and soil moisture to represent the near-surface uncertainty. Perturbations were obtained by the optimization algorithm taking into account diurnal variations in soil states. Consequently, it indirectly inflated the temperature and water vapor mixing ratio in the planetary boundary layer.
Sujeong Lim, Hyeon-Ju Gim, Ebony Lee, Seungyeon Lee, Won Young Lee, Yong Hee Lee, Claudio Cassardo, and Seon Ki Park
Geosci. Model Dev., 15, 8541–8559, https://doi.org/10.5194/gmd-15-8541-2022, https://doi.org/10.5194/gmd-15-8541-2022, 2022
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The land surface model (LSM) contains various uncertain parameters, which are obtained by the empirical relations reflecting the specific local region and can be a source of uncertainty. To seek the optimal parameter values in the snow-related processes of the Noah LSM over South Korea, we have implemented an optimization algorithm, a micro-genetic algorithm using the observations. As a result, the optimized snow parameters improve snowfall prediction.
Won Young Lee, Hyeon-Ju Gim, and Seon Ki Park
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-319, https://doi.org/10.5194/tc-2021-319, 2021
Manuscript not accepted for further review
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Snow cover or snow albedo plays a vital role in the atmosphere and land surface interaction. Especially, direct observation of snow is difficult and scarce. That's why a reliable Land Surface Model (LSM), including snow physical processes, is significant. In this study, we tried to give meaningful insights for improving the LSM in the future by identifying the main variables or parameters used and examining the different formulas for snow-related processes of the eight LSMs.
Da-Eun Kim and Seon Ki Park
The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-15, https://doi.org/10.5194/tc-2019-15, 2019
Preprint withdrawn
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An accurate prediction of the Eurasian snow is essentially important in predicting the climate and weather phenomena in Asia. Regional climate models are mostly coupled with several land surface models (LSMs) in which the land surface process parameters are calculated under their own physical principles and parameterization schemes. We show that prediction of the Eurasian snow cover is sensitive to the choice of LSMs coupled to regional climate models, and hence the future climate projections.
Sojung Park, Seon Ki Park, Jeung Whan Lee, and Yunho Park
Hydrol. Earth Syst. Sci., 22, 3435–3452, https://doi.org/10.5194/hess-22-3435-2018, https://doi.org/10.5194/hess-22-3435-2018, 2018
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Understanding the precipitation characteristics is essential to design an optimal observation network. We studied the spatial and temporal characteristics of summertime precipitation systems in Korea via geostatistical analyses on the ground-based precipitation and satellite water vapor data. We found that, under a strict standard, an observation network with higher resolution is required in local areas with frequent heavy rainfalls, depending on directional features of precipitation systems.
Claudio Cassardo, Seon Ki Park, Marco Galli, and Sungmin O
Hydrol. Earth Syst. Sci., 22, 3331–3350, https://doi.org/10.5194/hess-22-3331-2018, https://doi.org/10.5194/hess-22-3331-2018, 2018
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Temperature and precipitation can have abnormal states due to climate change and exert a significant impact on the regional hydrologic cycle. We assess the hydrologic component changes in the Alps and northern Italy, on the basis of regional future climate (FC) conditions, using the UTOPIA land surface model. The annual mean number of dry (wet) days increase remarkably (slightly) in FCs, thus increasing the risk of severe droughts and slightly increasing the risk of floods coincidently.
Sojung Park and Seon Ki Park
Geosci. Model Dev., 9, 1073–1085, https://doi.org/10.5194/gmd-9-1073-2016, https://doi.org/10.5194/gmd-9-1073-2016, 2016
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Snow albedo varies with snow grain size, snow cover thickness, etc. It also depends on the spatial characteristics of land cover and on the canopy density and structure. The Noah-MP model shows a bias error of albedo in winter due to no proper reflection of the vegetation effect. We developed new parameters, called leaf index and stem index, which reflect the vegetation effect on winter albedo. The Noah-MP's performance in albedo has prominently improved with about 69 % decrease in the RMSE.
J. Kim and S. K. Park
Hydrol. Earth Syst. Sci., 20, 651–658, https://doi.org/10.5194/hess-20-651-2016, https://doi.org/10.5194/hess-20-651-2016, 2016
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This study examined the uncertainty in climatological precipitation in East Asia, calculated from five gridded analysis data sets based on in situ rain gauge observations from 1980 to 2007. It is found that the regions of large uncertainties are typically lightly populated and are characterized by severe terrain and/or very high elevations. Thus, care must be taken in using long-term trends calculated from gridded precipitation analysis data for climate studies over such regions in East Asia.
S. Lim, S. K. Park, and M. Zupanski
Atmos. Chem. Phys., 15, 10019–10031, https://doi.org/10.5194/acp-15-10019-2015, https://doi.org/10.5194/acp-15-10019-2015, 2015
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In this study, the impact of O3 observations on the tropical cyclone (TC) structure is examined using the WRF-Chem with an ensemble-based data assimilation (DA) system. For a TC case that occurred over East Asia, the ensemble forecast is reasonable and the O3 assimilation affects both chemical and atmospheric variables near the TC area. All measures indicate a positive impact of DA on the analysis – the cost function and root mean square error have decreased by 16.9% and 8.87%, respectively.
S. K. Park, S. Lim, and M. Zupanski
Geosci. Model Dev., 8, 1315–1320, https://doi.org/10.5194/gmd-8-1315-2015, https://doi.org/10.5194/gmd-8-1315-2015, 2015
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The structure of an ensemble-based coupled atmosphere-chemistry forecast error covariance is examined using the WRF-Chem, a coupled atmosphere-chemistry model. It is found that the coupled error covariance has important cross-variable components that allow a physically meaningful adjustment of all control variables. Additional benefit of the coupled error covariance is that a cross-component impact is allowed; e.g., atmospheric observations can exert impact on chemistry analysis, and vice versa.
S. Hong, X. Yu, S. K. Park, Y.-S. Choi, and B. Myoung
Geosci. Model Dev., 7, 2517–2529, https://doi.org/10.5194/gmd-7-2517-2014, https://doi.org/10.5194/gmd-7-2517-2014, 2014
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In geosciences, the reliance on numerical models necessitates the precise calibration of their parameters to effectively translate information from observed to unobserved settings. We introduce a generalizable framework for calibrating numerical models, with a case study of the geomorphological model CAESAR-Lisflood. This approach efficiently identifies the optimal set of parameters for a given numerical model, enabling retrospective and prospective analyses at various temporal resolutions.
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Geosci. Model Dev., 18, 725–762, https://doi.org/10.5194/gmd-18-725-2025, https://doi.org/10.5194/gmd-18-725-2025, 2025
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When considering future high-resolution atmospheric simulations with O(10–100 m) grid spacing, such as global large-eddy simulations, numerical errors with the conventional low-order dynamical cores can contaminate the effects of turbulent parameterization. To achieve high-order discretization, we developed a global atmospheric dynamical core using the discontinuous Galerkin method (DGM). By conducting several validation tests, we discussed the impact of a high-order DGM on atmospheric flows.
Malena Sabaté Landman, Julianne Chung, Jiahua Jiang, Scot M. Miller, and Arvind K. Saibaba
Geosci. Model Dev., 17, 8853–8872, https://doi.org/10.5194/gmd-17-8853-2024, https://doi.org/10.5194/gmd-17-8853-2024, 2024
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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.
Joonlee Lee, Myong-In Lee, Sunlae Tak, Eunkyo Seo, and Yong-Keun Lee
Geosci. Model Dev., 17, 8799–8816, https://doi.org/10.5194/gmd-17-8799-2024, https://doi.org/10.5194/gmd-17-8799-2024, 2024
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We developed an advanced snow water equivalent (SWE) data assimilation framework using satellite data based on a land surface model. The results of this study highlight the beneficial impact of data assimilation by effectively combining land surface model and satellite-derived data according to their relative uncertainty, thereby controlling not only transitional regions but also the regions with heavy snow accumulation that are difficult to detect by satellite.
Frédéric Parrenin, Marie Bouchet, Christo Buizert, Emilie Capron, Ellen Corrick, Russell Drysdale, Kenji Kawamura, Amaëlle Landais, Robert Mulvaney, Ikumi Oyabu, and Sune Olander Rasmussen
Geosci. Model Dev., 17, 8735–8750, https://doi.org/10.5194/gmd-17-8735-2024, https://doi.org/10.5194/gmd-17-8735-2024, 2024
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The Paleochrono-1.1 probabilistic dating model allows users 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) Δdepth observations. Paleochrono-1.1 is available under an open-source license.
Joshua Hope-Collins, Abdalaziz Hamdan, Werner Bauer, Lawrence Mitchell, and Colin Cotter
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Effectively using modern supercomputers requires massively parallel algorithms. Time-parallel algorithms calculate the system state (e.g. the atmosphere) at multiple times simultaneously and have exciting potential, but are tricky to implement and still require development. We have developed software to simplify implementing and testing the ParaDiag algorithm on supercomputers. We show that for some atmospheric problems it can enable faster or more accurate solutions than traditional techniques.
Jeffrey H. Curtis, Nicole Riemer, and Matthew West
Geosci. Model Dev., 17, 8399–8420, https://doi.org/10.5194/gmd-17-8399-2024, https://doi.org/10.5194/gmd-17-8399-2024, 2024
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This paper introduces a numerical method for simulating particle-based aerosol transport in atmospheric models. We detail the various numerical properties of the advection order method 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.
John Thuburn
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-153, https://doi.org/10.5194/gmd-2024-153, 2024
Revised manuscript accepted for GMD
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A new computational fluid dynamics code for simulating the atmospheric boundary layer and convection is presented. Moist thermodynamics is formulated via thermodynamic potentials, avoiding inconsistencies that can be introduced with conventional approaches. Numerical methods typical of weather and climate models are used, with no explicit subgrid scheme. Results highlight some advantages (e.g., large time steps) and disadvantages (e.g., weak vertical fluxes near the surface) of this approach.
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
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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
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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
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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
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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.
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
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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
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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
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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
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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
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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
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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.
Stefania Renna, Francesco Granella, Lara Aleluia Reis, and Paulina Estela Schulz-Antipa
EGUsphere, https://doi.org/10.2139/ssrn.4591252, https://doi.org/10.2139/ssrn.4591252, 2024
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The Country Level Air Quality Calculator (CLAQC) is a new fast modeling tool that predicts globally country-level monthly and annual concentrations of two major air pollutants, fine particulate matter (PM2.5) and tropospheric ozone (O3). It was designed to inform national and regional climate and pollution mitigation policies. It is easy to use and computationally efficient, allowing the simulation of a large number of policy scenarios for policy assessments and optimization frameworks.
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
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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
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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
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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.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
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
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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
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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
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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
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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
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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
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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
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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.
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
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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.
Ane Lopez de Gamiz-Zearra, Cecilie Hansen, Xavier Corrales, Iñaki Quincoces, Izaskun Preciado, and Eider Andonegi
EGUsphere, https://doi.org/10.5194/egusphere-2023-1368, https://doi.org/10.5194/egusphere-2023-1368, 2023
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This paper describes the development of the first calibrated end-to-end Atlantis model for the Bay of Biscay that will help us improve the comprehension of the spatial functioning of the Bay of Biscay ecosystem and help establishing management measures of human activities. Our results highlighted the importance of lower trophic levels to the pelagic system and demonstrate the importance of having accurate and precise data for biological processes.
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
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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
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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
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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
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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
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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
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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.
Cited articles
Angevine, W. M., Jiang, H., and Mauritsen, T.: Performance of an eddy
diffusivity-mass flux scheme for shallow cumulus boundary layers, Mon. Weather
Rev., 138, 2895–2912, https://doi.org/10.1175/2010MWR3142.1, 2010.
Azadivar, F. and Tompkins, G.: Simulation optimization with qualitative
variables and structural model changes: A genetic algorithm approach, Eur.
J. Oper. Res., 113, 169–182, 1999.
Babbar-Sebens, M. and Minsker, B.: A Case-Based Micro Interactive Genetic
Algorithm (CBMIGA) for interactive learning and search: Methodology and
application to groundwater monitoring design, Environ. Model. Softw., 25,
1176–1187, https://doi.org/10.1016/j.envsoft.2010.03.027, 2010.
Behzadian, K., Kapelan, Z., Savic, D., and Ardeshir, A.: Stochastic sampling
design using a multi-objective genetic algorithm and adaptive neural
networks, Environ. Model. Softw., 24, 530–541,
https://doi.org/10.1016/j.envsoft.2008.09.013, 2009.
Berg, L. K., Gustafson, W. I., Kassianov, E. I., and Deng, L.: Evaluation of
a modified scheme for shallow convection: Implementation of CuP and case
studies, Mon. Weather Rev., 141, 134–147, 2013.
Bougeault, P. and Lacarrère, P.: Parameterization of orography-induced
turbulence in a mesobeta-scale model, Mon. Weather Rev., 117, 1872–1890,
https://doi.org/10.1175/1520-0493(1989)117<1872:POOITI>2.0.CO;2,
1989.
Bretherton, C. S. and Park, S.: A new moist turbulence parameterization in
the Community Atmosphere Model, J. Climate, 22, 3422–3448,
https://doi.org/10.1175/2008JCLI2556.1, 2009.
Brown, A., Milton, S., Cullen, M., Golding, B., Mitchell, J., and Shelly,
A.: Unified modeling and prediction of weather and climate: A 25-year
journey, B. Am. Meteorol. Soc., 93, 1865–1877, 2012.
Chen, D., Leon, A. S., Engle, S. P., Fuentes, C., and Chen, Q.: Offline
training for improving online performance of a genetic algorithm based
optimization model for hourly multi-reservoir operation, Environ. Model.
Softw., 96, 46–57, https://doi.org/10.1016/j.envsoft.2017.06.038, 2017.
Chen, S.-H. and Sun, W.-Y.: A one-dimensional time dependent cloud model, J.
Meteorol. Soc. Jpn., 80, 99–118, https://doi.org/10.2151/jmsj.80.99, 2002.
Cohen, A. E., Cavallo, S. M., Coniglio, M. C., and Brooks, H. E.: A review
of planetary boundary layer parameterization schemes and their sensitivity
in simulating southeastern US cold season severe weather environments, Weather
Forecast., 30, 591–612, 2015.
Crétat, J., Pohl, B., Richard, Y., and Drobinski, P.: Uncertainties in
simulating regional climate of Southern Africa: sensitivity to physical
parameterizations using WRF, Clim. Dynam., 38, 613–634, 2012.
Cullen, M. J. P.: The unified forecast/climate model, Meteorol. Mag., 122,
81–94, 1993.
Dandy, G. C. and Engelhardt, M.: Optimal scheduling of pipe replacement
using genetic algorithms, J. Water Resour. Pl., 127, 214–223, 2001.
Davis, J. K., Gebrehiwot, T., Worku, M., Awoke, W., Mihretie, A., Nekorchuk,
D., and Wimberly, M. C.: A genetic algorithm for identifying
spatially-varying environmental drivers in a malaria time series model,
Environ. Model. Softw., 119, 275–284, https://doi.org/10.1016/j.envsoft.2019.06.010,
2019.
Di, Z., Duan, Q., Wang, C., Ye, A., Miao, C., and Gong, W.: Assessing the
applicability of WRF optimal parameters under the different precipitation
simulations in the Greater Beijing Area, Clim. Dynam., 50, 1927–1948, 2018.
Duan, Q., Di, Z., Quan, J., Wang, C., Gong, W., Gan, Y., Ye, A., Miao, C.,
Miao, S., Liang, X., and Fan, S.: Automatic model calibration: A new way to
improve numerical weather forecasting, B. Am. Meteorol. Soc., 98,
959–970, 2017.
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.
Eaton, B.: User's Guide to the Community Atmosphere Model CAM-5.1, available
at: http://www.cesm.ucar.edu/models/cesm1.0/cam (last access: 1 May 2021),
2011.
Evans, J. P., Ekström, M., and Ji, F.: Evaluating the performance of a
WRF physics ensemble over South-East Australia, Clim. Dynam., 39,
1241–1258, 2012.
Glotfelty, T., Alapaty, K., He, J., Hawbecker, P., Song, X., and Zhang, G.:
The Weather Research and Forecasting Model with Aerosol–Cloud Interactions
(WRF-ACI): development, evaluation, and initial application, Mon. Weather Rev.,
147, 1491–1511, 2019.
Grell, G. A.: Prognostic evaluation of assumptions used by cumulus
parameterizations, Mon. Weather Rev., 121, 764–787,
https://doi.org/10.1175/1520-0493(1993)121<0764:PEOAUB>2.0.CO;2,
1993.
Grell, G. A. and Devenyi, D.: A generalized approach to parameterizing
convection combining ensemble and data assimilation techniques, Geophys.
Res. Lett., 29, 38-1–38-4, https://doi.org/10.1029/2002GL015311, 2002.
Grell, G. A. and Freitas, S. R.: A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling, Atmos. Chem. Phys., 14, 5233–5250, https://doi.org/10.5194/acp-14-5233-2014, 2014.
Grenier, H. and Bretherton, C. S.: A moist PBL parameterization for
large-scale models and its application to subtropical cloud-topped marine
boundary layers, Mon. Weather Rev., 129, 357–377,
https://doi.org/10.1175/1520-0493(2001)129<0357:AMPPFL>2.0.CO;2,
2001.
Gupta, I., Gupta, A., and Khanna, P.: Genetic algorithm for optimization of
water distribution systems, Environ. Model. Softw., 14, 437–446, 1999.
Halhal, D., Walters, G. A., Ouazar, D., and Savic, D.A.: Water network
rehabilitation with structured messy genetic algorithms, J. Water Resour.
Pl., 123, 137–146, 1997.
Hamill, T. M.: Hypothesis tests for evaluating numerical precipitation
forecasts, Weather Forecast., 14, 155–167, 1999.
Han, J. and Pan, H.-L.: Revision of convection and vertical diffusion
schemes in the NCEP Global Forecast System, Weather Forecast., 26,
520–533, https://doi.org/10.1175/WAF-D-10-05038.1, 2011.
Hong, S., Yu, X., Park, S. K., Choi, Y.-S., and Myoung, B.: Assessing optimal set of implemented physical parameterization schemes in a multi-physics land surface model using genetic algorithm, Geosci. Model Dev., 7, 2517–2529, https://doi.org/10.5194/gmd-7-2517-2014, 2014.
Hong, S., Park, S. K., and Yu, X.: Scheme-based optimization of land surface
model using a micro-genetic algorithm: Assessment of its performance and
usability for regional applications, SOLA, 11, 129–133,
https://doi.org/10.2151/sola.2015-030, 2015.
Hong, S.-Y. and Lim, J.-O. J.: The WRF single-moment 6-class microphysics
scheme (WSM6), J. Korean Meteor. Soc., 42, 129–151, 2006.
Hong, S.-Y. and Pan, H.-L.: Nonlocal boundary layer vertical diffusion in a
medium-range forecast model, Mon. Weather Rev., 124, 2322–2339,
https://doi.org/10.1175/1520-0493(1996)124<2322:NBLVDI>2.0.CO;2,
1996.
Hong, S.-Y., Dudhia, J., and Chen, S.-H.: A revised approach to ice
microphysical processes for the bulk parameterization of clouds and
precipitation, Mon. Weather Rev., 132, 103–120,
https://doi.org/10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2,
2004.
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.
Hong, S. Y., Kwon, Y. C., Kim, T. H., Kim, J. E. E., Choi, S. J., Kwon, I.
H., Kim, J., Lee, E.-H., Park, R.-S., and Kim, D. I.: The Korean Integrated
Model (KIM) system for global weather forecasting, Asia-Pac. J. Atmos. Sci.,
54, 267–292, 2018.
Jamil, M. and Yang, X.-S.: A literature survey of benchmark functions for
global optimization problems, Int. Journal of Mathematical Modelling and
Numerical Optimisation, 4, 150–194, https://doi.org/10.1504/IJMMNO.2013.055204, 2013.
Janjic, Z. I.: The Step-mountain eta coordinate model: further developments
of the convection, viscous sublayer, and turbulence closure schemes, Mon.
Weather Rev., 122, 927–945, https://doi.org/10.1175/1520-0493(1994)122<0927:TSMECM>2.0.CO;2, 1994.
Janjic, Z. I.: The surface layer in the NCEP Eta Model, Eleventh Conference
on Numerical Weather Prediction, 19–23 August 1996, Norfolk, VA, Amer. Meteor.
Soc. 354–355, 1996.
Janjic, Z. I.: Nonsingular implementation of the Mellor–Yamada level 2.5
scheme in the NCEP meso model, National Centres for Environmental Prediction
(NCEP) Office Note, 437, Camp Springs, 61 pp., 2002.
Jimenez, P. A., Dudhia, J., Gonzalez-Rouco, J. F., Navarro, J., Montavez, J.
P., and Garcia-Bustamante, E.: A revised scheme for the WRF surface layer
formulation, Mon. Weather Rev., 140, 898–918, https://doi.org/10.1175/MWR-D-11-00056.1,
2012.
Kain, J. S.: The Kain-Fritsch convective parameterization: an update, J.
Appl. Meteorol., 43, 170–181, https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2, 2004.
Kain, J. S. and Fritsch, J. M.: A one-dimensional entraining/detraining
plume model and its application in convective parameterization, J. Atmos.
Sci., 47, 2784–2802, https://doi.org/10.1175/1520-0469(1990)047<2784:AODEPM>2.0.CO;2, 1990.
Kessler, E.: On the distribution and continuity of water substance in
atmospheric circulations, Meteorol. Monogr., 10, Amer. Meteor. Soc., Boston,
https://doi.org/10.1007/978-1-935704-36-2_1, 1969.
KMA: A report on damages by meteorological disasters in 2018, available at:
https://www.weather.go.kr/weather/lifenindustry/disaster_01.jsp (last access: 1 May 2021), 2020.
Krishnakumar, K.: Micro-genetic algorithms for stationary and nonstationary
function optimization, SPIE intelligent Control and Adaptive Systems, 1196,
289–296, 1989.
Koren, V., Schaake, J., Mitchell, K., Duan, Q. Y., Chen, F., and Baker, J.
M.: A parameterization of snowpack and frozen ground intended for NCEP
weather and climate models, J. Geophys. Res.-Atmos., 104, 19569–19585,
https://doi.org/10.1029/1999JD900232, 1999.
Kwon, Y.-C. and Hong, S.-Y.: A mass-flux cumulus parameterization scheme
across gray-zone resolutions, Mon. Weather Rev., 145, 585–598,
https://doi.org/10.1175/MWR-D-16-0034.1, 2017.
Lee, Y. H., Park, S. K., and Chang, D.-E.: Parameter estimation using the genetic algorithm and its impact on quantitative precipitation forecast, Ann. Geophys., 24, 3185–3189, https://doi.org/10.5194/angeo-24-3185-2006, 2006.
Lim, K.-S. S. and Hong, S.-Y.: Development of an effective double-moment
cloud microphysics scheme with prognostic cloud condensation nuclei (CCN)
for weather and climate models, Mon. Weather Rev., 138, 1587–1612,
https://doi.org/10.1175/2009MWR2968.1, 2010.
Lin, Y. and Colle, B. A.: A new bulk microphysical scheme that includes
riming intensity and temperature-dependent ice characteristics, Mon. Weather
Rev., 139, 1013–1035, https://doi.org/10.1175/2010MWR3293.1, 2011.
Mansell, E. R., Ziegler, C. L., and Bruning, E. C.: Simulated
electrification of a small thunderstorm with two-moment bulk microphysics,
J. Atmos. Sci., 67, 171–194, https://doi.org/10.1175/2009JAS2965.1, 2010.
Milbrandt, J. A. and Yau, M. K.: A multimoment bulk microphysics
parameterization. Part I: analysis of the role of the spectral shape
parameter, J. Atmos. Sci., 62, 3051–3064, https://doi.org/10.1175/JAS3534.1, 2005a.
Milbrandt, J. A. and Yau, M. K.: A multimoment bulk microphysics
parameterization. Part II: a proposed three-moment closure and scheme
description, J. Atmos. Sci., 62, 3065–3081, https://doi.org/10.1175/JAS3535.1, 2005b.
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.
Monin, A. S. and Obukhov, A. M.: Basic laws of turbulent mixing in the
surface layer of the atmosphere, Tr. Akad. Nauk SSSR Geophiz. Inst., 24,
163–187, 1954 (in Russian).
Morrison, H. and Milbrandt, J. A.: Parameterization of cloud microphysics
based on the prediction of bulk ice particle properties. Part I: scheme
description and idealized tests, J. Atmos. Sci., 72, 287–311,
https://doi.org/10.1175/JAS-D-14-0065.1, 2015.
Morrison, H., Thompson, G., and Tatarskii, V.: Impact of cloud
microphysics on the development of trailing stratiform precipitation in a
simulated squall line: comparison of one- and two-moment schemes, Mon. Weather
Rev., 137, 991–1007, https://doi.org/10.1175/2008MWR2556.1, 2009.
Nakanishi, M. and Niino, H.: An improved Mellor-Yamada level 3 model: its
numerical stability and application to a regional prediction of advecting
fog, Bound.-Lay. Meteorol., 119, 397–407, https://doi.org/10.1007/s10546-005-9030-8,
2006.
Nakanishi, M. and Niino, H.: Development of an improved turbulence closure
model for the atmospheric boundary layer, J. Meteorol. Soc. Jpn., 87,
895–912, https://doi.org/10.2151/jmsj.87.895, 2009.
NCEP (National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce): NCEP FNL Operational Model Global Tropospheric Analyses, continuing from July 1999, Research Data Archive at the National Center for Atmospheric Research [data set], Computational and Information Systems Laboratory, https://doi.org/10.5065/D6M043C6, 2000.
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
multi-parameterization options (Noah-MP): 1. Model description and
evaluation with local-scale measurements, J. Geophys. Res., 116, D12109,
https://doi.org/10.1029/2010JD015139, 2011.
NOAA: National Oceanic and Atmospheric Administration changes to the NCEP
meso eta analysis and forecast system: increase in resolution, new cloud
microphysics, modified precipitation assimilation, modified 3DVAR analysis,
available at: https://www.emc.ncep.noaa.gov/mmb/research/eta.log.html (last
access: 1 May 2021), 2001.
Olson, J. B., Kenyon, J. S., Angevine, W. M., Brown, J. M., Pagowski, M.,
and Sušelj, K.: A description of the MYNN-EDMF scheme and the coupling
to other components in WRF-ARW, NOAA Technical Memorandum OAR GSD, 61,
https://doi.org/10.25923/n9wm-be49, 2019.
Pan, H. L. and Wu, W. S.: Implementing a mass flux convective
parameterization package for the NMC medium range forecast model, NMC office
note, 409, 1–43, available at: https://repository.library.noaa.gov/view/noaa/11429 (last access: 1 May 2021), 1995.
Park, S. and Park, S. K.: Genetic Algorithm and WRF model v4.0.3, Zenodo [code],
https://doi.org/10.5281/zenodo.5076930, 2021.
Park, S., Park, S. K., Lee, J. W., and Park, Y.: Geostatistical assessment of warm-season precipitation observations in Korea based on the composite precipitation and satellite water vapor data, Hydrol. Earth Syst. Sci., 22, 3435–3452, https://doi.org/10.5194/hess-22-3435-2018, 2018.
Park, S. K. and Park, S.: On a Flood-Producing Coastal Mesoscale Convective
Storm Associated with the Kor'easterlies: Multi-Data Analyses Using
Remotely-Sensed and In-Situ Observations and Storm-Scale Model Simulations,
Remote Sens., 12, 1532, https://doi.org/10.3390/rs12091532, 2020.
Pleim, J. E.: A combined local and nonlocal closure model for the
atmospheric boundary layer. Part I: model description and testing, J. Appl.
Meteor. Clim., 46, 1383–1395, https://doi.org/10.1175/JAM2539.1, 2007.
Pilar, M., Adela, G. G., and Jose, L. A.: Water distribution network
optimization using a modified genetic algorithms, Water Resour. Res., 35,
3467–3473, 1999.
Rossa A., Nurmi, P., and Ebert, E: Overview of methods for the verification
of quantitative precipitation forecasts. Precipitation: Advances in
Measurement, Estimation and Prediction, Springer, Berlin, 419–452,
https://doi.org/10.1007/978-3-540-77655-0_16, 2008.
Savic, D. A. and Walters, G. A.: Genetic algorithms for least-cost design of
water distribution networks, J. Water Res. Pl., 123, 67–77, 1997.
Shin, H. H. and Hong, S.-Y.: Representation of the subgrid-scale turbulent
transport in convective boundary layers at gray-zone resolutions, Mon. Weather
Rev., 143, 250–271, https://doi.org/10.1175/MWR-D-14-00116.1, 2015.
Simpson, A. R., Dandy, G. C., and Murphy, L. J.: Genetic
algorithms compared to other techniques for pipe optimization, J. Water
Res. Pl., 120, 423–443, 1994.
Song, H. J. and Sohn, B. J.: An evaluation of WRF microphysics schemes for
simulating the warm-type heavy rain over the Korean peninsula, Asia-Pac. J.
Atmos. Sci., 54, 225–236, 2018.
Suk, M.-K., Chang, K.-H., Cha, J.-W., and Kim, K.-E.: Operational real-time
adjustment of radar rainfall estimation over the South Korea region, J.
Meteorol. Soc. Jpn., 91, 545–554, https://doi.org/10.2151/jmsj.2013-409, 2013.
Sukoriansky, S., Galperin, B., and Perov, V.: Application of a new spectral
model of stratified turbulence to the atmospheric boundary layer over sea
ice, Bound.-Lay. Meteorol., 117, 231–257, https://doi.org/10.1007/s10546-004-6848-4,
2005.
Tao, W.-K., Simpson, J., and McCumber, M.: An ice-water saturation
adjustment, Mon. Weather Rev., 117, 231–235,
https://doi.org/10.1175/1520-0493(1989)117<0231:AIWSA>2.0.CO;2,
1989.
Tao, W.-K., Wu, D., Lang, S., Chern, J.-D., Peters-Lidard, C., Fridlind, A.,
and Matsui, T.: High-resolution NU-WRF simulations of a deep
convective-precipitation system during MC3E: Further improvements and
comparisons between Goddard microphysics schemes and observations, J.
Geophys. Res.-Atmos., 121, 1278–1305, https://doi.org/10.1002/2015JD023986, 2016.
Thompson, G., Field, P. R., Rasmussen, R. M., and Hall, W. D.: Explicit
forecasts of winter precipitation using an improved bulk microphysics
scheme. Part II: implementation of a new snow parameterization, Mon. Weather
Rev., 136, 5095–5115, https://doi.org/10.1175/2008MWR2387.1, 2008.
Tiedtke, M.: A comprehensive mass flux scheme for cumulus parameterization
in large-scale models, Mon. Weather Rev., 117, 1779–1800,
https://doi.org/10.1175/1520-0493(1989)117<1779:ACMFSF>2.0.CO;2,
1989.
Weng, H. T. and Liaw, S. L.: Establishing an optimization model for sewer
system layout with applied genetic algorithm, J. Environ. Inform., 5, 26–35,
2005.
Yu, X., Park, S. K., Lee, Y. H., and Choi, Y.-S.: Quantitative precipitation
forecast of a tropical cyclone through optimal parameter estimation in a
convective parameterization, SOLA, 9, 36–39, 2013.
Zhang, C., Wang, Y., and Hamilton, K.: Improved representation of boundary
layer clouds over the southeast Pacific in ARW-WRF using a modified Tiedtke
cumulus parameterization scheme, Mon. Weather Rev., 139, 3489–3513,
https://doi.org/10.1175/MWR-D-10-05091.1, 2011.
Zheng, Y., Alapaty, K., Herwehe, J. A., Del Genio, A. D., and Niyogi, D.:
Improving high-resolution weather forecasts using the Weather Research and
Forecasting (WRF) Model with an updated Kain-Fritsch scheme, Mon. Weather Rev.,
114, 833–860, https://doi.org/10.1175/MWR-D-15-0005.1, 2016.
Short summary
One of the biggest uncertainties in numerical weather predictions (NWPs) comes from treating subgrid-scale physical processes. Physical processes, such as cumulus, microphysics, and planetary boundary layer processes, are parameterized in NWP models by empirical and theoretical backgrounds. We developed an interface between a micro-genetic algorithm and the WRF model for a combinatorial optimization of physics for heavy rainfall events in Korea. The system improved precipitation forecasts.
One of the biggest uncertainties in numerical weather predictions (NWPs) comes from treating...
