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.
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
Related authors
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.
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
Short summary
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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
Short summary
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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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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
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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.
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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.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
Lucas Schauer, Michael J. Schmidt, Nicholas B. Engdahl, Stephen D. Pankavich, David A. Benson, and Diogo Bolster
Geosci. Model Dev., 16, 833–849, https://doi.org/10.5194/gmd-16-833-2023, https://doi.org/10.5194/gmd-16-833-2023, 2023
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We develop a multi-dimensional, parallelized domain decomposition strategy for mass-transfer particle tracking methods in two and three dimensions, investigate different procedures for decomposing the domain, and prescribe an optimal tiling based on physical problem parameters and the number of available CPU cores. For an optimally subdivided diffusion problem, the parallelized algorithm achieves nearly perfect linear speedup in comparison with the serial run-up to thousands of cores.
John Mern and Jef Caers
Geosci. Model Dev., 16, 289–313, https://doi.org/10.5194/gmd-16-289-2023, https://doi.org/10.5194/gmd-16-289-2023, 2023
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In this work, we formulate the sequential geoscientific data acquisition problem as a problem that is similar to playing chess against nature, except the pieces are not fully observed. Solutions to these problems are given in AI and rarely used in geoscientific data planning. We illustrate our approach to a simple 2D problem of mineral exploration.
Ziqi Gao, Yifeng Wang, Petros Vasilakos, Cesunica E. Ivey, Khanh Do, and Armistead G. Russell
Geosci. Model Dev., 15, 9015–9029, https://doi.org/10.5194/gmd-15-9015-2022, https://doi.org/10.5194/gmd-15-9015-2022, 2022
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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.
Zhihao Wang, Jason Goetz, and Alexander Brenning
Geosci. Model Dev., 15, 8765–8784, https://doi.org/10.5194/gmd-15-8765-2022, https://doi.org/10.5194/gmd-15-8765-2022, 2022
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A lack of inventory data can be a limiting factor in developing landslide predictive models, which are crucial for supporting hazard policy and decision-making. We show how case-based reasoning and domain adaptation (transfer-learning techniques) can effectively retrieve similar landslide modeling situations for prediction in new data-scarce areas. Using cases in Italy, Austria, and Ecuador, our findings support the application of transfer learning for areas that require rapid model development.
Till Sachau, Haibin Yang, Justin Lang, Paul D. Bons, and Louis Moresi
Geosci. Model Dev., 15, 8749–8764, https://doi.org/10.5194/gmd-15-8749-2022, https://doi.org/10.5194/gmd-15-8749-2022, 2022
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Knowledge of the internal structures of the major continental ice sheets is improving, thanks to new investigative techniques. These structures are an essential indication of the flow behavior and dynamics of ice transport, which in turn is important for understanding the actual impact of the vast amounts of water trapped in continental ice sheets on global sea-level rise. The software studied here is specifically designed to simulate such structures and their evolution.
Keith J. Roberts, Alexandre Olender, Lucas Franceschini, Robert C. Kirby, Rafael S. Gioria, and Bruno S. Carmo
Geosci. Model Dev., 15, 8639–8667, https://doi.org/10.5194/gmd-15-8639-2022, https://doi.org/10.5194/gmd-15-8639-2022, 2022
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Finite-element methods (FEMs) permit the use of more flexible unstructured meshes but are rarely used in full waveform inversions (FWIs), an iterative process that reconstructs velocity models of earth’s subsurface, due to computational and memory storage costs. To reduce those costs, novel software is presented allowing the use of high-order mass-lumped FEMs on triangular meshes, together with a material-property mesh-adaptation performance-enhancing strategy, enabling its use in FWIs.
Konstantinos Papadakis, Yann Pfau-Kempf, Urs Ganse, Markus Battarbee, Markku Alho, Maxime Grandin, Maxime Dubart, Lucile Turc, Hongyang Zhou, Konstantinos Horaites, Ivan Zaitsev, Giulia Cozzani, Maarja Bussov, Evgeny Gordeev, Fasil Tesema, Harriet George, Jonas Suni, Vertti Tarvus, and Minna Palmroth
Geosci. Model Dev., 15, 7903–7912, https://doi.org/10.5194/gmd-15-7903-2022, https://doi.org/10.5194/gmd-15-7903-2022, 2022
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Vlasiator is a plasma simulation code that simulates the entire near-Earth space at a global scale. As 6D simulations require enormous amounts of computational resources, Vlasiator uses adaptive mesh refinement (AMR) to lighten the computational burden. However, due to Vlasiator’s grid topology, AMR simulations suffer from grid aliasing artifacts that affect the global results. In this work, we present and evaluate the performance of a mechanism for alleviating those artifacts.
Artur Safin, Damien Bouffard, Firat Ozdemir, Cintia L. Ramón, James Runnalls, Fotis Georgatos, Camille Minaudo, and Jonas Šukys
Geosci. Model Dev., 15, 7715–7730, https://doi.org/10.5194/gmd-15-7715-2022, https://doi.org/10.5194/gmd-15-7715-2022, 2022
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Reconciling the differences between numerical model predictions and observational data is always a challenge. In this paper, we investigate the viability of a novel approach to the calibration of a three-dimensional hydrodynamic model of Lake Geneva, where the target parameters are inferred in terms of distributions. We employ a filtering technique that generates physically consistent model trajectories and implement a neural network to enable bulk-to-skin temperature conversion.
Colin Grudzien and Marc Bocquet
Geosci. Model Dev., 15, 7641–7681, https://doi.org/10.5194/gmd-15-7641-2022, https://doi.org/10.5194/gmd-15-7641-2022, 2022
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Iterative optimization techniques, the state of the art in data assimilation, have largely focused on extending forecast accuracy to moderate- to long-range forecast systems. However, current methodology may not be cost-effective in reducing forecast errors in online, short-range forecast systems. We propose a novel optimization of these techniques for online, short-range forecast cycles, simultaneously providing an improvement in forecast accuracy and a reduction in the computational cost.
Yangyang Yu, Shaoqing Zhang, Haohuan Fu, Lixin Wu, Dexun Chen, Yang Gao, Zhiqiang Wei, Dongning Jia, and Xiaopei Lin
Geosci. Model Dev., 15, 6695–6708, https://doi.org/10.5194/gmd-15-6695-2022, https://doi.org/10.5194/gmd-15-6695-2022, 2022
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To understand the scientific consequence of perturbations caused by slave cores in heterogeneous computing environments, we examine the influence of perturbation amplitudes on the determination of the cloud bottom and cloud top and compute the probability density function (PDF) of generated clouds. A series of comparisons of the PDFs between homogeneous and heterogeneous systems show consistently acceptable error tolerances when using slave cores in heterogeneous computing environments.
Vijay S. Mahadevan, Jorge E. Guerra, Xiangmin Jiao, Paul Kuberry, Yipeng Li, Paul Ullrich, David Marsico, Robert Jacob, Pavel Bochev, and Philip Jones
Geosci. Model Dev., 15, 6601–6635, https://doi.org/10.5194/gmd-15-6601-2022, https://doi.org/10.5194/gmd-15-6601-2022, 2022
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Coupled Earth system models require transfer of field data between multiple components with varying spatial resolutions to determine the correct climate behavior. We present the Metrics for Intercomparison of Remapping Algorithms (MIRA) protocol to evaluate the accuracy, conservation properties, monotonicity, and local feature preservation of four different remapper algorithms for various unstructured mesh problems of interest. Future extensions to more practical use cases are also discussed.
Yilin Fang, L. Ruby Leung, Ryan Knox, Charlie Koven, and Ben Bond-Lamberty
Geosci. Model Dev., 15, 6385–6398, https://doi.org/10.5194/gmd-15-6385-2022, https://doi.org/10.5194/gmd-15-6385-2022, 2022
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Accounting for water movement in the soil and water transport within the plant is important for plant growth in Earth system modeling. We implemented different numerical approaches for a plant hydrodynamic model and compared their impacts on the simulated aboveground biomass (AGB) at single points and globally. We found care should be taken when discretizing the number of soil layers for numerical simulations as it can significantly affect AGB if accuracy and computational costs are of concern.
Andrew M. Bradley, Peter A. Bosler, and Oksana Guba
Geosci. Model Dev., 15, 6285–6310, https://doi.org/10.5194/gmd-15-6285-2022, https://doi.org/10.5194/gmd-15-6285-2022, 2022
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Tracer transport in atmosphere models can be computationally expensive. We describe a flexible and efficient interpolation semi-Lagrangian method, the Islet method. It permits using up to three grids that share an element grid: a dynamics grid for computing quantities such as the wind velocity; a physics parameterizations grid; and a tracer grid. The Islet method performs well on a number of verification problems and achieves high performance in the E3SM Atmosphere Model version 2.
Léo Pujol, Pierre-André Garambois, and Jérôme Monnier
Geosci. Model Dev., 15, 6085–6113, https://doi.org/10.5194/gmd-15-6085-2022, https://doi.org/10.5194/gmd-15-6085-2022, 2022
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This contribution presents a new numerical model for representing hydraulic–hydrological quantities at the basin scale. It allows modeling large areas at a low computational cost, with fine zooms where needed. It allows the integration of local and satellite measurements, via data assimilation methods, to improve the model's match to observations. Using this capability, good matches to in situ observations are obtained on a model of the complex Adour river network with fine zooms on floodplains.
Ludovic Räss, Ivan Utkin, Thibault Duretz, Samuel Omlin, and Yuri Y. Podladchikov
Geosci. Model Dev., 15, 5757–5786, https://doi.org/10.5194/gmd-15-5757-2022, https://doi.org/10.5194/gmd-15-5757-2022, 2022
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Continuum mechanics-based modelling of physical processes at large scale requires huge computational resources provided by massively parallel hardware such as graphical processing units. We present a suite of numerical algorithms, implemented using the Julia language, that efficiently leverages the parallelism. We demonstrate that our implementation is efficient, scalable and robust and showcase applications to various geophysical problems.
Meriem Krouma, Pascal Yiou, Céline Déandreis, and Soulivanh Thao
Geosci. Model Dev., 15, 4941–4958, https://doi.org/10.5194/gmd-15-4941-2022, https://doi.org/10.5194/gmd-15-4941-2022, 2022
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We evaluated the skill of a stochastic weather generator (SWG) to forecast precipitation at different time scales and in different areas of western Europe from analogs of Z500 hPa. The SWG has the skill to simulate precipitation for 5 and 10 d. We found that forecast weaknesses can be associated with specific weather patterns. The comparison with ECMWF forecasts confirms the skill of our model. This work is important because it provides information about weather forecasts over specific areas.
Piotr Dziekan and Piotr Zmijewski
Geosci. Model Dev., 15, 4489–4501, https://doi.org/10.5194/gmd-15-4489-2022, https://doi.org/10.5194/gmd-15-4489-2022, 2022
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Detailed computer simulations of clouds are important for understanding Earth's atmosphere and climate. The paper describes how the UWLCM has been adapted to work on supercomputers. A distinctive feature of UWLCM is that air flow is calculated by processors at the same time as cloud droplets are modeled by graphics cards. Thanks to this, use of computing resources is maximized and the time to complete simulations of large domains is not affected by communications between supercomputer nodes.
Hynek Bednář and Holger Kantz
Geosci. Model Dev., 15, 4147–4161, https://doi.org/10.5194/gmd-15-4147-2022, https://doi.org/10.5194/gmd-15-4147-2022, 2022
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A scale-dependent error growth described by a power law or by a quadratic hypothesis is studied in Lorenz’s system with three spatiotemporal levels. The validity of power law is extended by including a saturation effect. The quadratic hypothesis can only serve as a first guess. In addition, we study the initial error growth for the ECMWF forecast system. Fitting the parameters, we conclude that there is an intrinsic limit of predictability after 22 days.
Michael A. Olesik, Jakub Banaśkiewicz, Piotr Bartman, Manuel Baumgartner, Simon Unterstrasser, and Sylwester Arabas
Geosci. Model Dev., 15, 3879–3899, https://doi.org/10.5194/gmd-15-3879-2022, https://doi.org/10.5194/gmd-15-3879-2022, 2022
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In systems such as atmospheric clouds, droplets undergo growth through condensation of vapor. The broadness of the resultant size spectrum of droplets influences precipitation likelihood and the radiative properties of clouds. One of the inherent limitations of simulations of the problem is the so-called numerical diffusion causing overestimation of the spectrum width, hence the term numerical broadening. In the paper, we take a closer look at one of the algorithms used in this context: MPDATA.
Navjot Kukreja, Jan Hückelheim, Mathias Louboutin, John Washbourne, Paul H. J. Kelly, and Gerard J. Gorman
Geosci. Model Dev., 15, 3815–3829, https://doi.org/10.5194/gmd-15-3815-2022, https://doi.org/10.5194/gmd-15-3815-2022, 2022
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Full waveform inversion (FWI) is a partial-differential equation (PDE)-constrained optimization problem that is notorious for its high computational load and memory footprint. In this paper we present a method that combines recomputation with lossy compression to accelerate the computation with minimal loss of precision in the results. We show this using experiments running FWI with a variety of compression settings on a popular academic dataset.
Richard Scalzo, Mark Lindsay, Mark Jessell, Guillaume Pirot, Jeremie Giraud, Edward Cripps, and Sally Cripps
Geosci. Model Dev., 15, 3641–3662, https://doi.org/10.5194/gmd-15-3641-2022, https://doi.org/10.5194/gmd-15-3641-2022, 2022
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This paper addresses numerical challenges in reasoning about geological models constrained by sensor data, especially models that describe the history of an area in terms of a sequence of events. Our method ensures that small changes in simulated geological features, such as the position of a boundary between two rock layers, do not result in unrealistically large changes to resulting sensor measurements, as occur presently using several popular modeling packages.
Romit Maulik, Vishwas Rao, Jiali Wang, Gianmarco Mengaldo, Emil Constantinescu, Bethany Lusch, Prasanna Balaprakash, Ian Foster, and Rao Kotamarthi
Geosci. Model Dev., 15, 3433–3445, https://doi.org/10.5194/gmd-15-3433-2022, https://doi.org/10.5194/gmd-15-3433-2022, 2022
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In numerical weather prediction, data assimilation is frequently utilized to enhance the accuracy of forecasts from equation-based models. In this work we use a machine learning framework that approximates a complex dynamical system given by the geopotential height. Instead of using an equation-based model, we utilize this machine-learned alternative to dramatically accelerate both the forecast and the assimilation of data, thereby reducing need for large computational resources.
Hiromasa Yoshimura
Geosci. Model Dev., 15, 2561–2597, https://doi.org/10.5194/gmd-15-2561-2022, https://doi.org/10.5194/gmd-15-2561-2022, 2022
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This paper proposes a new double Fourier series (DFS) method on a sphere that improves the numerical stability of a model compared with conventional DFS methods. The shallow-water model and the advection model using the new DFS method give stable results without the appearance of high-wavenumber noise near the poles. The model using the new DFS method is faster than the model using spherical harmonics (especially at high resolutions) and gives almost the same results.
Mirko Mälicke
Geosci. Model Dev., 15, 2505–2532, https://doi.org/10.5194/gmd-15-2505-2022, https://doi.org/10.5194/gmd-15-2505-2022, 2022
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I preset SciKit-GStat, a well-documented and tested Python package for variogram estimation. The variogram is the core means of geostatistics, which almost all other methods rely on. Geostatistical interpolation and field generation are widely spread in geoscience, i.e., for data assimilation or modeling.
While SciKit-GStat focuses on effective and intuitive variogram estimation, it can interface with other prominent packages and make its variograms available for a multitude of methods.
Christopher J. L. D'Amboise, Michael Neuhauser, Michaela Teich, Andreas Huber, Andreas Kofler, Frank Perzl, Reinhard Fromm, Karl Kleemayr, and Jan-Thomas Fischer
Geosci. Model Dev., 15, 2423–2439, https://doi.org/10.5194/gmd-15-2423-2022, https://doi.org/10.5194/gmd-15-2423-2022, 2022
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The term gravitational mass flow (GMF) covers various natural hazard processes such as snow avalanches, rockfall, landslides, and debris flows. Here we present the open-source GMF simulation tool Flow-Py. The model equations are based on simple geometrical relations in three-dimensional terrain. We show that Flow-Py is an educational, innovative GMF simulation tool with three computational experiments: 1. validation of implementation, 2. performance, and 3. expandability.
Evan Baker, Anna B. Harper, Daniel Williamson, and Peter Challenor
Geosci. Model Dev., 15, 1913–1929, https://doi.org/10.5194/gmd-15-1913-2022, https://doi.org/10.5194/gmd-15-1913-2022, 2022
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We have adapted machine learning techniques to build a model of the land surface in Great Britain. The model was trained using data from a very complex land surface model called JULES. Our model is faster at producing simulations and predictions and can investigate many different scenarios, which can be used to improve our understanding of the climate and could also be used to help make local decisions.
Daichun Wang, Wei You, Zengliang Zang, Xiaobin Pan, Yiwen Hu, and Yanfei Liang
Geosci. Model Dev., 15, 1821–1840, https://doi.org/10.5194/gmd-15-1821-2022, https://doi.org/10.5194/gmd-15-1821-2022, 2022
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This paper presents a 3D variational data assimilation system for aerosol optical properties, including aerosol optical thickness (AOT) retrievals and lidar-based aerosol profiles, which was developed for a size-resolved sectional model in WRF-Chem. To directly assimilate aerosol optical properties, an observation operator based on the Mie scattering theory was designed. The results show that Himawari-8 AOT assimilation can significantly improve model aerosol analyses and forecasts.
Kevin Bulthuis and Eric Larour
Geosci. Model Dev., 15, 1195–1217, https://doi.org/10.5194/gmd-15-1195-2022, https://doi.org/10.5194/gmd-15-1195-2022, 2022
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We present and implement a stochastic solver to sample spatially and temporal varying uncertain input parameters in the Ice-sheet and Sea-level System Model, such as ice thickness or surface mass balance. We represent these sources of uncertainty using Gaussian random fields with Matérn covariance function. We generate random samples of this random field using an efficient computational approach based on solving a stochastic partial differential equation.
Urmas Raudsepp and Ilja Maljutenko
Geosci. Model Dev., 15, 535–551, https://doi.org/10.5194/gmd-15-535-2022, https://doi.org/10.5194/gmd-15-535-2022, 2022
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A model's ability to reproduce the state of a simulated object is always a subject of discussion. A new method for the multivariate assessment of numerical model skills uses the K-means algorithm for clustering model errors. All available data that fall into the model domain and simulation period are incorporated into the skill assessment. The clustered errors are used for spatial and temporal analysis of the model accuracy. The method can be applied to different types of geoscientific models.
Emmanuel Wyser, Yury Alkhimenkov, Michel Jaboyedoff, and Yury Y. Podladchikov
Geosci. Model Dev., 14, 7749–7774, https://doi.org/10.5194/gmd-14-7749-2021, https://doi.org/10.5194/gmd-14-7749-2021, 2021
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We propose an implementation of the material point method using graphical processing units (GPUs) to solve elastoplastic problems in three-dimensional configurations, such as the granular collapse or the slumping mechanics, i.e., landslide. The computational power of GPUs promotes fast code executions, compared to a traditional implementation using central processing units (CPUs). This allows us to study complex three-dimensional problems tackling high spatial resolution.
Rafael Lago, Thomas Gastine, Tilman Dannert, Markus Rampp, and Johannes Wicht
Geosci. Model Dev., 14, 7477–7495, https://doi.org/10.5194/gmd-14-7477-2021, https://doi.org/10.5194/gmd-14-7477-2021, 2021
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In this work we discuss a two-dimensional distributed parallelization of MagIC, an open-source code for the numerical solution of the magnetohydrodynamics equations. Such a parallelization involves several challenges concerning the distribution of work and data. We detail our algorithm and compare it with the established, optimized, one-dimensional distribution in the context of the dynamo benchmark and discuss the merits of both implementations.
Moritz Lange, Henri Suominen, Mona Kurppa, Leena Järvi, Emilia Oikarinen, Rafael Savvides, and Kai Puolamäki
Geosci. Model Dev., 14, 7411–7424, https://doi.org/10.5194/gmd-14-7411-2021, https://doi.org/10.5194/gmd-14-7411-2021, 2021
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This study aims to replicate computationally expensive high-resolution large-eddy simulations (LESs) with regression models to simulate urban air quality and pollutant dispersion. The model development, including feature selection, model training and cross-validation, and detection of concept drift, has been described in detail. Of the models applied, log-linear regression shows the best performance. A regression model can replace LES unless high accuracy is needed.
Hynek Bednář, Aleš Raidl, and Jiří Mikšovský
Geosci. Model Dev., 14, 7377–7389, https://doi.org/10.5194/gmd-14-7377-2021, https://doi.org/10.5194/gmd-14-7377-2021, 2021
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Forecast errors in numerical weather prediction systems grow in time. To quantify the impacts of this growth, parametric error growth models may be employed. This study recalculates and newly defines parameters for several statistic models approximating error growth in the ECMWF forecasting system. Accurate values of parameters are important because they are used to evaluate improvements of the forecasting systems or to estimate predictability.
Denise Degen, Cameron Spooner, Magdalena Scheck-Wenderoth, and Mauro Cacace
Geosci. Model Dev., 14, 7133–7153, https://doi.org/10.5194/gmd-14-7133-2021, https://doi.org/10.5194/gmd-14-7133-2021, 2021
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In times of worldwide energy transitions, an understanding of the subsurface is increasingly important to provide renewable energy sources such as geothermal energy. To validate our understanding of the subsurface we require data. However, the data are usually not distributed equally and introduce a potential misinterpretation of the subsurface. Therefore, in this study we investigate the influence of measurements on temperature distribution in the European Alps.
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...