Articles | Volume 15, issue 22
https://doi.org/10.5194/gmd-15-8541-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-8541-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
22 Nov 2022
Development and technical paper |
| 22 Nov 2022
| 22 Nov 2022
Optimization of snow-related parameters in the Noah land surface model (v3.4.1) using a micro-genetic algorithm (v1.7a)
Sujeong Lim
Center for Climate/Environment Change Prediction Research, Ewha Womans University, Seoul, 03760, Republic of Korea
Severe Storm Research Center, Ewha Womans University, Seoul, 03760, Republic of Korea
Hyeon-Ju Gim
Korea Institute of Atmospheric Prediction System (KIAPS), Seoul, 07071, Republic of Korea
Ebony Lee
Center for Climate/Environment Change Prediction Research, Ewha Womans University, Seoul, 03760, Republic of Korea
Severe Storm Research Center, Ewha Womans University, Seoul, 03760, Republic of Korea
Department of Climate and Energy System Engineering, Ewha Womans University, Seoul, 03760, Republic of Korea
Seungyeon Lee
Center for Climate/Environment Change Prediction Research, Ewha Womans University, Seoul, 03760, Republic of Korea
Severe Storm Research Center, Ewha Womans University, Seoul, 03760, Republic of Korea
Department of Climate and Energy System Engineering, Ewha Womans University, Seoul, 03760, Republic of Korea
Won Young Lee
Center for Climate/Environment Change Prediction Research, Ewha Womans University, Seoul, 03760, Republic of Korea
Severe Storm Research Center, Ewha Womans University, Seoul, 03760, Republic of Korea
Yong Hee Lee
High Impact Weather Research Department, National Institute of Meteorological Sciences, Gangneung, 25457, Republic of Korea
Claudio Cassardo
Department of Physics and NatRisk Centre, University of Turin, Turin, 10125, Italy
Center for Climate/Environment Change Prediction Research, Ewha Womans University, Seoul, 03760, Republic of Korea
Severe Storm Research Center, Ewha Womans University, Seoul, 03760, Republic of Korea
Department of Climate and Energy System Engineering, Ewha Womans University, Seoul, 03760, Republic of Korea
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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.
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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.
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Sojung Park and Seon Ki Park
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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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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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Model evaluations against ground observations are usually unfair. The former simulates mean status over coarse grids and the latter the surrounding atmosphere. To solve this, we proposed the new land-use-based representative (LUBR) operator that considers intra-grid variance. The LUBR operator is validated to provide insights that align with satellite measurements. The results highlight the importance of considering fine-scale urban–rural differences when comparing models and observation.
Mijie Pang, Jianbing Jin, Arjo Segers, Huiya Jiang, Wei Han, Batjargal Buyantogtokh, Ji Xia, Li Fang, Jiandong Li, Hai Xiang Lin, and Hong Liao
Geosci. Model Dev., 17, 8223–8242, https://doi.org/10.5194/gmd-17-8223-2024, https://doi.org/10.5194/gmd-17-8223-2024, 2024
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The ensemble Kalman filter (EnKF) improves dust storm forecasts but faces challenges with position errors. The valid time shifting EnKF (VTS-EnKF) addresses this by adjusting for position errors, enhancing accuracy in forecasting dust storms, as proven in tests on 2021 events, even with smaller ensembles and time intervals.
Prabhakar Namdev, Maithili Sharan, Piyush Srivastava, and Saroj Kanta Mishra
Geosci. Model Dev., 17, 8093–8114, https://doi.org/10.5194/gmd-17-8093-2024, https://doi.org/10.5194/gmd-17-8093-2024, 2024
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Inadequate representation of surface–atmosphere interaction processes is a major source of uncertainty in numerical weather prediction models. Here, an effort has been made to improve the Weather Research and Forecasting (WRF) model version 4.2.2 by introducing a unique theoretical framework under convective conditions. In addition, to enhance the potential applicability of the WRF modeling system, various commonly used similarity functions under convective conditions have also been installed.
Andrew Gettelman, Richard Forbes, Roger Marchand, Chih-Chieh Chen, and Mark Fielding
Geosci. Model Dev., 17, 8069–8092, https://doi.org/10.5194/gmd-17-8069-2024, https://doi.org/10.5194/gmd-17-8069-2024, 2024
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Supercooled liquid clouds (liquid clouds colder than 0°C) are common at higher latitudes (especially over the Southern Ocean) and are critical for constraining climate projections. We compare a single-column version of a weather model to observations with two different cloud schemes and find that both the dynamical environment and atmospheric aerosols are important for reproducing observations.
Yujuan Wang, Peng Zhang, Jie Li, Yaman Liu, Yanxu Zhang, Jiawei Li, and Zhiwei Han
Geosci. Model Dev., 17, 7995–8021, https://doi.org/10.5194/gmd-17-7995-2024, https://doi.org/10.5194/gmd-17-7995-2024, 2024
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This study updates the CESM's aerosol schemes, focusing on dust, marine aerosol emissions, and secondary organic aerosol (SOA) . Dust emission modifications make deflation areas more continuous, improving results in North America and the sub-Arctic. Humidity correction to sea-salt emissions has a minor effect. Introducing marine organic aerosol emissions, coupled with ocean biogeochemical processes, and adding aqueous reactions for SOA formation advance the CESM's aerosol modelling results.
Lucas A. McMichael, Michael J. Schmidt, Robert Wood, Peter N. Blossey, and Lekha Patel
Geosci. Model Dev., 17, 7867–7888, https://doi.org/10.5194/gmd-17-7867-2024, https://doi.org/10.5194/gmd-17-7867-2024, 2024
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Marine cloud brightening (MCB) is a climate intervention technique to potentially cool the climate. Climate models used to gauge regional climate impacts associated with MCB often assume large areas of the ocean are uniformly perturbed. However, a more realistic representation of MCB application would require information about how an injected particle plume spreads. This work aims to develop such a plume-spreading model.
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 7915–7962, https://doi.org/10.5194/gmd-17-7915-2024, https://doi.org/10.5194/gmd-17-7915-2024, 2024
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Data-driven models are becoming a viable alternative to physics-based models for weather forecasting up to 15 d into the future. However, it is unclear whether they are as reliable as physics-based models when forecasting weather extremes. We evaluate their performance in forecasting near-surface cold, hot, and windy extremes globally. We find that data-driven models can compete with physics-based models and that the choice of the best model mainly depends on the region and type of extreme.
David C. Wong, Jeff Willison, Jonathan E. Pleim, Golam Sarwar, James Beidler, Russ Bullock, Jerold A. Herwehe, Rob Gilliam, Daiwen Kang, Christian Hogrefe, George Pouliot, and Hosein Foroutan
Geosci. Model Dev., 17, 7855–7866, https://doi.org/10.5194/gmd-17-7855-2024, https://doi.org/10.5194/gmd-17-7855-2024, 2024
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This work describe how we linked the meteorological Model for Prediction Across Scales – Atmosphere (MPAS-A) with the Community Multiscale Air Quality (CMAQ) air quality model to form a coupled modelling system. This could be used to study air quality or climate and air quality interaction at a global scale. This new model scales well in high-performance computing environments and performs well with respect to ground surface networks in terms of ozone and PM2.5.
Giulio Mandorli and Claudia J. Stubenrauch
Geosci. Model Dev., 17, 7795–7813, https://doi.org/10.5194/gmd-17-7795-2024, https://doi.org/10.5194/gmd-17-7795-2024, 2024
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In recent years, several studies focused their attention on the disposition of convection. Lots of methods, called indices, have been developed to quantify the amount of convection clustering. These indices are evaluated in this study by defining criteria that must be satisfied and then evaluating the indices against these standards. None of the indices meet all criteria, with some only partially meeting them.
Kerry Anderson, Jack Chen, Peter Englefield, Debora Griffin, Paul A. Makar, and Dan Thompson
Geosci. Model Dev., 17, 7713–7749, https://doi.org/10.5194/gmd-17-7713-2024, https://doi.org/10.5194/gmd-17-7713-2024, 2024
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The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that predicts smoke and carbon emissions from wildland fires. The model calculates emissions from the ground up based on satellite-detected fires, modelled weather and fire characteristics. Unlike other global models, GFFEPS uses daily weather conditions to capture changing burning conditions on a day-to-day basis. GFFEPS produced lower carbon emissions due to the changing weather not captured by the other models.
Samiha Binte Shahid, Forrest G. Lacey, Christine Wiedinmyer, Robert J. Yokelson, and Kelley C. Barsanti
Geosci. Model Dev., 17, 7679–7711, https://doi.org/10.5194/gmd-17-7679-2024, https://doi.org/10.5194/gmd-17-7679-2024, 2024
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The Next-generation Emissions InVentory expansion of Akagi (NEIVA) v.1.0 is a comprehensive biomass burning emissions database that allows integration of new data and flexible querying. Data are stored in connected datasets, including recommended averages of ~1500 constituents for 14 globally relevant fire types. Individual compounds were mapped to common model species to allow better attribution of emissions in modeling studies that predict the effects of fires on air quality and climate.
Lucie Bakels, Daria Tatsii, Anne Tipka, Rona Thompson, Marina Dütsch, Michael Blaschek, Petra Seibert, Katharina Baier, Silvia Bucci, Massimo Cassiani, Sabine Eckhardt, Christine Groot Zwaaftink, Stephan Henne, Pirmin Kaufmann, Vincent Lechner, Christian Maurer, Marie D. Mulder, Ignacio Pisso, Andreas Plach, Rakesh Subramanian, Martin Vojta, and Andreas Stohl
Geosci. Model Dev., 17, 7595–7627, https://doi.org/10.5194/gmd-17-7595-2024, https://doi.org/10.5194/gmd-17-7595-2024, 2024
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Computer models are essential for improving our understanding of how gases and particles move in the atmosphere. We present an update of the atmospheric transport model FLEXPART. FLEXPART 11 is more accurate due to a reduced number of interpolations and a new scheme for wet deposition. It can simulate non-spherical aerosols and includes linear chemical reactions. It is parallelised using OpenMP and includes new user options. A new user manual details how to use FLEXPART 11.
Jaroslav Resler, Petra Bauerová, Michal Belda, Martin Bureš, Kryštof Eben, Vladimír Fuka, Jan Geletič, Radek Jareš, Jan Karel, Josef Keder, Pavel Krč, William Patiño, Jelena Radović, Hynek Řezníček, Matthias Sühring, Adriana Šindelářová, and Ondřej Vlček
Geosci. Model Dev., 17, 7513–7537, https://doi.org/10.5194/gmd-17-7513-2024, https://doi.org/10.5194/gmd-17-7513-2024, 2024
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Detailed modeling of urban air quality in stable conditions is a challenge. We show the unprecedented sensitivity of a large eddy simulation (LES) model to meteorological boundary conditions and model parameters in an urban environment under stable conditions. We demonstrate the crucial role of boundary conditions for the comparability of results with observations. The study reveals a strong sensitivity of the results to model parameters and model numerical instabilities during such conditions.
Jorge E. Pachón, Mariel A. Opazo, Pablo Lichtig, Nicolas Huneeus, Idir Bouarar, Guy Brasseur, Cathy W. Y. Li, Johannes Flemming, Laurent Menut, Camilo Menares, Laura Gallardo, Michael Gauss, Mikhail Sofiev, Rostislav Kouznetsov, Julia Palamarchuk, Andreas Uppstu, Laura Dawidowski, Nestor Y. Rojas, María de Fátima Andrade, Mario E. Gavidia-Calderón, Alejandro H. Delgado Peralta, and Daniel Schuch
Geosci. Model Dev., 17, 7467–7512, https://doi.org/10.5194/gmd-17-7467-2024, https://doi.org/10.5194/gmd-17-7467-2024, 2024
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Latin America (LAC) has some of the most populated urban areas in the world, with high levels of air pollution. Air quality management in LAC has been traditionally focused on surveillance and building emission inventories. This study performed the first intercomparison and model evaluation in LAC, with interesting and insightful findings for the region. A multiscale modeling ensemble chain was assembled as a first step towards an air quality forecasting system.
David Ho, Michał Gałkowski, Friedemann Reum, Santiago Botía, Julia Marshall, Kai Uwe Totsche, and Christoph Gerbig
Geosci. Model Dev., 17, 7401–7422, https://doi.org/10.5194/gmd-17-7401-2024, https://doi.org/10.5194/gmd-17-7401-2024, 2024
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Atmospheric model users often overlook the impact of the land–atmosphere interaction. This study accessed various setups of WRF-GHG simulations that ensure consistency between the model and driving reanalysis fields. We found that a combination of nudging and frequent re-initialization allows certain improvement by constraining the soil moisture fields and, through its impact on atmospheric mixing, improves atmospheric transport.
Phuong Loan Nguyen, Lisa V. Alexander, Marcus J. Thatcher, Son C. H. Truong, Rachael N. Isphording, and John L. McGregor
Geosci. Model Dev., 17, 7285–7315, https://doi.org/10.5194/gmd-17-7285-2024, https://doi.org/10.5194/gmd-17-7285-2024, 2024
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We use a comprehensive approach to select a subset of CMIP6 models for dynamical downscaling over Southeast Asia, taking into account model performance, model independence, data availability and the range of future climate projections. The standardised benchmarking framework is applied to assess model performance through both statistical and process-based metrics. Ultimately, we identify two independent model groups that are suitable for dynamical downscaling in the Southeast Asian region.
Ingrid Super, Tia Scarpelli, Arjan Droste, and Paul I. Palmer
Geosci. Model Dev., 17, 7263–7284, https://doi.org/10.5194/gmd-17-7263-2024, https://doi.org/10.5194/gmd-17-7263-2024, 2024
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Monitoring greenhouse gas emission reductions requires a combination of models and observations, as well as an initial emission estimate. Each component provides information with a certain level of certainty and is weighted to yield the most reliable estimate of actual emissions. We describe efforts for estimating the uncertainty in the initial emission estimate, which significantly impacts the outcome. Hence, a good uncertainty estimate is key for obtaining reliable information on emissions.
Álvaro González-Cervera and Luis Durán
Geosci. Model Dev., 17, 7245–7261, https://doi.org/10.5194/gmd-17-7245-2024, https://doi.org/10.5194/gmd-17-7245-2024, 2024
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RASCAL is an open-source Python tool designed for reconstructing daily climate observations, especially in regions with complex local phenomena. It merges large-scale weather patterns with local weather using the analog method. Evaluations in central Spain show that RASCAL outperforms ERA20C reanalysis in reconstructing precipitation and temperature. RASCAL offers opportunities for broad scientific applications, from short-term forecasts to local-scale climate change scenarios.
Sun-Young Park, Kyo-Sun Sunny Lim, Kwonil Kim, Gyuwon Lee, and Jason A. Milbrandt
Geosci. Model Dev., 17, 7199–7218, https://doi.org/10.5194/gmd-17-7199-2024, https://doi.org/10.5194/gmd-17-7199-2024, 2024
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We enhance the WDM6 scheme by incorporating predicted graupel density. The modification affects graupel characteristics, including fall velocity–diameter and mass–diameter relationships. Simulations highlight changes in graupel distribution and precipitation patterns, potentially influencing surface snow amounts. The study underscores the significance of integrating predicted graupel density for a more realistic portrayal of microphysical properties in weather models.
Christos I. Efstathiou, Elizabeth Adams, Carlie J. Coats, Robert Zelt, Mark Reed, John McGee, Kristen M. Foley, Fahim I. Sidi, David C. Wong, Steven Fine, and Saravanan Arunachalam
Geosci. Model Dev., 17, 7001–7027, https://doi.org/10.5194/gmd-17-7001-2024, https://doi.org/10.5194/gmd-17-7001-2024, 2024
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We present a summary of enabling high-performance computing of the Community Multiscale Air Quality Model (CMAQ) – a state-of-the-science community multiscale air quality model – on two cloud computing platforms through documenting the technologies, model performance, scaling and relative merits. This may be a new paradigm for computationally intense future model applications. We initiated this work due to a need to leverage cloud computing advances and to ease the learning curve for new users.
Peter A. Bogenschutz, Jishi Zhang, Qi Tang, and Philip Cameron-Smith
Geosci. Model Dev., 17, 7029–7050, https://doi.org/10.5194/gmd-17-7029-2024, https://doi.org/10.5194/gmd-17-7029-2024, 2024
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Using high-resolution and state-of-the-art modeling techniques we simulate five atmospheric river events for California to test the capability to represent precipitation for these events. We find that our model is able to capture the distribution of precipitation very well but suffers from overestimating the precipitation amounts over high elevation. Increasing the resolution further has no impact on reducing this bias, while increasing the domain size does have modest impacts.
David Patoulias, Kalliopi Florou, and Spyros N. Pandis
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-145, https://doi.org/10.5194/gmd-2024-145, 2024
Revised manuscript accepted for GMD
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The effect of the assumed atmospheric nucleation mechanism on particle number concentrations and size distribution was investigated. Two quite different mechanisms involving sulfuric acid and ammonia or a biogenic organic vapor gave quite similar results which were consistent with measurements in 26 measurement stations across Europe. The number of larger particles that serve as cloud condensation nuclei showed little sensitivity to the assumed nucleation mechanism.
Manu Anna Thomas, Klaus Wyser, Shiyu Wang, Marios Chatziparaschos, Paraskevi Georgakaki, Montserrat Costa-Surós, Maria Gonçalves Ageitos, Maria Kanakidou, Carlos Pérez García-Pando, Athanasios Nenes, Twan van Noije, Philippe Le Sager, and Abhay Devasthale
Geosci. Model Dev., 17, 6903–6927, https://doi.org/10.5194/gmd-17-6903-2024, https://doi.org/10.5194/gmd-17-6903-2024, 2024
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Aerosol–cloud interactions occur at a range of spatio-temporal scales. While evaluating recent developments in EC-Earth3-AerChem, this study aims to understand the extent to which the Twomey effect manifests itself at larger scales. We find a reduction in the warm bias over the Southern Ocean due to model improvements. While we see footprints of the Twomey effect at larger scales, the negative relationship between cloud droplet number and liquid water drives the shortwave radiative effect.
Kai Cao, Qizhong Wu, Lingling Wang, Hengliang Guo, Nan Wang, Huaqiong Cheng, Xiao Tang, Dongxing Li, Lina Liu, Dongqing Li, Hao Wu, and Lanning Wang
Geosci. Model Dev., 17, 6887–6901, https://doi.org/10.5194/gmd-17-6887-2024, https://doi.org/10.5194/gmd-17-6887-2024, 2024
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AMD’s heterogeneous-compute interface for portability was implemented to port the piecewise parabolic method solver from NVIDIA GPUs to China's GPU-like accelerators. The results show that the larger the model scale, the more acceleration effect on the GPU-like accelerator, up to 28.9 times. The multi-level parallelism achieves a speedup of 32.7 times on the heterogeneous cluster. By comparing the results, the GPU-like accelerators have more accuracy for the geoscience numerical models.
Ruyi Zhang, Limin Zhou, Shin-ichiro Shima, and Huawei Yang
Geosci. Model Dev., 17, 6761–6774, https://doi.org/10.5194/gmd-17-6761-2024, https://doi.org/10.5194/gmd-17-6761-2024, 2024
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Solar activity weakly ionises Earth's atmosphere, charging cloud droplets. Electro-coalescence is when oppositely charged droplets stick together. We introduce an analytical expression of electro-coalescence probability and use it in a warm-cumulus-cloud simulation. Results show that charge cases increase rain and droplet size, with the new method outperforming older ones. The new method requires longer computation time, but its impact on rain justifies inclusion in meteorology models.
Hilda Sandström and Patrick Rinke
EGUsphere, https://doi.org/10.48550/arXiv.2406.18171, https://doi.org/10.48550/arXiv.2406.18171, 2024
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Machine learning has the potential to aid the identification organic molecules involved in aerosol formation. Yet, progress is stalled by a lack of curated atmospheric molecular datasets. Here, we compared atmospheric compounds with large molecular datasets used in machine learning and found minimal overlap with similarity algorithms. Our result underlines the need for collaborative efforts to curate atmospheric molecular data to facilitate machine learning model in atmospheric sciences.
Jenna Ritvanen, Seppo Pulkkinen, Dmitri Moisseev, and Daniele Nerini
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-99, https://doi.org/10.5194/gmd-2024-99, 2024
Revised manuscript accepted for GMD
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Nowcasting models struggle with the rapid evolution of heavy rain, and common verification methods are unable to describe how accurately the models predict the growth and decay of heavy rainfall. We propose a framework to assess model performance. In the framework, convective cells are identified and tracked in the forecasts and observations, and then the model skill is evaluated by comparing differences between forecast and observed cells. We demonstrate the framework with 4 open-source models.
Máté Mile, Stephanie Guedj, and Roger Randriamampianina
Geosci. Model Dev., 17, 6571–6587, https://doi.org/10.5194/gmd-17-6571-2024, https://doi.org/10.5194/gmd-17-6571-2024, 2024
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Satellite observations provide crucial information about atmospheric constituents in a global distribution that helps to better predict the weather over sparsely observed regions like the Arctic. However, the use of satellite data is usually conservative and imperfect. In this study, a better spatial representation of satellite observations is discussed and explored by a so-called footprint function or operator, highlighting its added value through a case study and diagnostics.
Hynek Bednář and Holger Kantz
Geosci. Model Dev., 17, 6489–6511, https://doi.org/10.5194/gmd-17-6489-2024, https://doi.org/10.5194/gmd-17-6489-2024, 2024
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The forecast error growth of atmospheric phenomena is caused by initial and model errors. When studying the initial error growth, it may turn out that small-scale phenomena, which contribute little to the forecast product, significantly affect the ability to predict this product. With a negative result, we investigate in the extended Lorenz (2005) system whether omitting these phenomena will improve predictability. A theory explaining and describing this behavior is developed.
Giorgio Veratti, Alessandro Bigi, Sergio Teggi, and Grazia Ghermandi
Geosci. Model Dev., 17, 6465–6487, https://doi.org/10.5194/gmd-17-6465-2024, https://doi.org/10.5194/gmd-17-6465-2024, 2024
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In this study, we present VERT (Vehicular Emissions from Road Traffic), an R package designed to estimate transport emissions using traffic estimates and vehicle fleet composition data. Compared to other tools available in the literature, VERT stands out for its user-friendly configuration and flexibility of user input. Case studies demonstrate its accuracy in both urban and regional contexts, making it a valuable tool for air quality management and transport scenario planning.
Joël Thanwerdas, Antoine Berchet, Lionel Constantin, Aki Tsuruta, Michael Steiner, Friedemann Reum, Stephan Henne, and Dominik Brunner
EGUsphere, https://doi.org/10.5194/egusphere-2024-2197, https://doi.org/10.5194/egusphere-2024-2197, 2024
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The Community Inversion Framework (CIF) brings together methods for estimating greenhouse gas fluxes from atmospheric observations. The initial ensemble method implemented in CIF was found to be incomplete and could hardly be compared to other ensemble methods employed in the inversion community. In this paper, we present and evaluate a more efficient implementation of the serial and batch versions of the Ensemble Square Root Filter (EnSRF) algorithm in CIF.
Sam P. Raj, Puna Ram Sinha, Rohit Srivastava, Srinivas Bikkina, and Damu Bala Subrahamanyam
Geosci. Model Dev., 17, 6379–6399, https://doi.org/10.5194/gmd-17-6379-2024, https://doi.org/10.5194/gmd-17-6379-2024, 2024
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A Python successor to the aerosol module of the OPAC model, named AeroMix, has been developed, with enhanced capabilities to better represent real atmospheric aerosol mixing scenarios. AeroMix’s performance in modeling aerosol mixing states has been evaluated against field measurements, substantiating its potential as a versatile aerosol optical model framework for next-generation algorithms to infer aerosol mixing states and chemical composition.
Angeline G. Pendergrass, Michael P. Byrne, Oliver Watt-Meyer, Penelope Maher, and Mark J. Webb
Geosci. Model Dev., 17, 6365–6378, https://doi.org/10.5194/gmd-17-6365-2024, https://doi.org/10.5194/gmd-17-6365-2024, 2024
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The width of the tropical rain belt affects many aspects of our climate, yet we do not understand what controls it. To better understand it, we present a method to change it in numerical model experiments. We show that the method works well in four different models. The behavior of the width is unexpectedly simple in some ways, such as how strong the winds are as it changes, but in other ways, it is more complicated, especially how temperature increases with carbon dioxide.
Tianning Su and Yunyan Zhang
Geosci. Model Dev., 17, 6319–6336, https://doi.org/10.5194/gmd-17-6319-2024, https://doi.org/10.5194/gmd-17-6319-2024, 2024
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Using 2 decades of field observations over the Southern Great Plains, this study developed a deep-learning model to simulate the complex dynamics of boundary layer clouds. The deep-learning model can serve as the cloud parameterization within reanalysis frameworks, offering insights into improving the simulation of low clouds. By quantifying biases due to various meteorological factors and parameterizations, this deep-learning-driven approach helps bridge the observation–modeling divide.
Siyuan Chen, Yi Zhang, Yiming Wang, Zhuang Liu, Xiaohan Li, and Wei Xue
Geosci. Model Dev., 17, 6301–6318, https://doi.org/10.5194/gmd-17-6301-2024, https://doi.org/10.5194/gmd-17-6301-2024, 2024
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This study explores strategies and techniques for implementing mixed-precision code optimization within an atmosphere model dynamical core. The coded equation terms in the governing equations that are sensitive (or insensitive) to the precision level have been identified. The performance of mixed-precision computing in weather and climate simulations was analyzed.
Sam O. Owens, Dipanjan Majumdar, Chris E. Wilson, Paul Bartholomew, and Maarten van Reeuwijk
Geosci. Model Dev., 17, 6277–6300, https://doi.org/10.5194/gmd-17-6277-2024, https://doi.org/10.5194/gmd-17-6277-2024, 2024
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Designing cities that are resilient, sustainable, and beneficial to health requires an understanding of urban climate and air quality. This article presents an upgrade to the multi-physics numerical model uDALES, which can simulate microscale airflow, heat transfer, and pollutant dispersion in urban environments. This upgrade enables it to resolve realistic urban geometries more accurately and to take advantage of the resources available on current and future high-performance computing systems.
Felipe Cifuentes, Henk Eskes, Folkert Boersma, Enrico Dammers, and Charlotte Bryan
EGUsphere, https://doi.org/10.5194/egusphere-2024-2225, https://doi.org/10.5194/egusphere-2024-2225, 2024
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We tested the capability of the flux divergence approach (FDA) to reproduce known NOX emissions using synthetic NO2 satellite column retrievals derived from high-resolution model simulations. The FDA accurately reproduced NOX emissions when column observations were limited to the boundary layer and when the variability of NO2 lifetime, NOX:NO2 ratio, and NO2 profile shapes were correctly modeled. This introduces a strong model dependency, reducing the simplicity of the original FDA formulation.
Allison A. Wing, Levi G. Silvers, and Kevin A. Reed
Geosci. Model Dev., 17, 6195–6225, https://doi.org/10.5194/gmd-17-6195-2024, https://doi.org/10.5194/gmd-17-6195-2024, 2024
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This paper presents the experimental design for a model intercomparison project to study tropical clouds and climate. It is a follow-up from a prior project that used a simplified framework for tropical climate. The new project adds one new component – a specified pattern of sea surface temperatures as the lower boundary condition. We provide example results from one cloud-resolving model and one global climate model and test the sensitivity to the experimental parameters.
Astrid Kerkweg, Timo Kirfel, Doung H. Do, Sabine Griessbach, Patrick Jöckel, and Domenico Taraborrelli
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-117, https://doi.org/10.5194/gmd-2024-117, 2024
Revised manuscript accepted for GMD
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This article introduces the MESSy DWARF. Usually, the Modular Earth Submodel System (MESSy) is linked to full dynamical models to build chemistry climate models. However, due to the modular concept of MESSy, and the newly developed DWARF component, it is now possible to create simplified models containing just one or some process descriptions. This renders very useful for technical optimisation (e.g., GPU porting) and can be used to create less complex models, e.g., a chemical box model.
Philip G. Sansom and Jennifer L. Catto
Geosci. Model Dev., 17, 6137–6151, https://doi.org/10.5194/gmd-17-6137-2024, https://doi.org/10.5194/gmd-17-6137-2024, 2024
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Weather fronts bring a lot of rain and strong winds to many regions of the mid-latitudes. We have developed an updated method of identifying these fronts in gridded data that can be used on new datasets with small grid spacing. The method can be easily applied to different datasets due to the use of open-source software for its development and shows improvements over similar previous methods. We present an updated estimate of the average frequency of fronts over the past 40 years.
Cited articles
Anderson, E. A.: National Weather Service River Forecast System: Snow Accumulation and Ablation Model, Tech. Mem., US Department of Commerce, National Oceanic and Atmospheric Administration, National Weather Service, vol. 17, https://repository.library.noaa.gov/view/noaa/13507 (last access: 24 October 2022), 1973. a, b
Annan, J. D. and Hargreaves, J. C.: Efficient parameter estimation for a highly
chaotic system, Tellus A, 56, 520–526, 2004. a
Bonekamp, P. N. J., Collier, E., and Immerzeel, W. W.: The impact of spatial
resolution, land use, and spinup time on resolving spatial precipitation
patterns in the Himalayas, J. Hydrometeorol., 19, 1565–1581, 2018. a
Carroll, D. L.: Fortran Genetic Algorithm Front-End Driver Code, CU Aerospace [code], https://cuaerospace.com/products-services/genetic-algorithm/ga-drive-free-download, last access: 24 October 2022. a
Chen, F., Mitchell, K., Schaake, J., Xue, Y., Pan, H.-L., Koren, V., Duan,
Q. Y., Ek, M., and Betts, A.: Modeling of land surface evaporation by four
schemes and comparison with FIFE observations, J. Geophys. Res.-Atmos.,
101, 7251–7268, 1996. a
Cheong, S.-H., Byun, K.-Y., and Lee, T.-Y.: Classification of snowfalls over
the Korean Peninsula based on developing mechanism, Atmosphere, 16, 33–48,
2006 (in Korean with English abstract). a
Chinta, S. and Balaji, C.: Calibration of WRF model parameters using
multiobjective adaptive surrogate model-based optimization to improve the
prediction of the Indian summer monsoon, Clim. Dynam., 55, 631–650, 2020. a
Cosgrove, B. A., Lohmann, D., Mitchell, K. E., Houser, P. R., Wood, E. F.,
Schaake, J. C., Robock, A., Sheffield, J., Duan, Q., Luo, L., Higgins, R. W.,
Pinker, R. T., and Tarpley, J. D.: Land surface model spin-up behavior in
the North American Land Data Assimilation System (NLDAS), J. Geophys.
Res.-Atmos., 108, 8845, https://doi.org/10.1029/2002JD003316, 2003. a
Duan, Q., Schaake, J., Andréassian, V., Franks, S., Goteti, G., Gupta, H. V.,
Gusev, Y. M., Habets, F., Hall, A., Hay, L., Hogue, T., Huang, M., Leavesley, G., Liang, X., Nasonova, O. N., Noilhan, J., Oudin, L., Sorooshian, S., Wagener, T., and Wood, E. F.: Model Parameter Estimation
Experiment (MOPEX): An overview of science strategy and major results from
the second and third workshops, J. Hydrol., 320, 3–17, 2006. a, b
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. a
Ek, M. B., Mitchell, K. E., Lin, Y., Rogers, E., Grunmann, P., Koren, V.,
Gayno, G., and Tarpley, J. D.: Implementation of Noah land surface model
advances in the National Centers for Environmental Prediction operational
mesoscale Eta model, J. Geophys. Res.-Atmos., 108, 8851,
https://doi.org/10.1029/2002JD003296, 2003. a, b, c, d, e
Folberth, C., Elliott, J., Müller, C., Balkovič, J.,
Chryssanthacopoulos, J., Izaurralde, R. C., Jones, C. D., Khabarov, N., Liu,
W., Reddy, A., Schmid, E., Skalský, R., Yang, H., Arneth, A., Ciais, P.,
Deryng, D., Lawrence, P. J., Olin, S., Pugh, T. A. M., Ruane, A. C., and
Wang, X.: Parameterization-induced uncertainties and impacts of crop
management harmonization in a global gridded crop model ensemble, PLoS One,
14, e0221862, https://doi.org/10.1371/journal.pone.0221862, 2019. a
Friedl, M. and Sulla-Menashe, D.: MCD12C1 MODIS/Terra+Aqua Land Cover Type
Yearly L3 Global 0.05 Deg CMG V006, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MCD12C1.006, 2015. a
Golberg, D. E.: Genetic Algorithms in Search, Optimization, and Machine
Learning, 1st edn., Addion-Wesley, Reading, MA, USA, 372 pp., ISBN 978-0-201-15767-3, 1989. a
Gubler, S., Gruber, S., and Purves, R. S.: Uncertainties of parameterized surface downward clear-sky shortwave and all-sky longwave radiation., Atmos. Chem. Phys., 12, 5077–5098, https://doi.org/10.5194/acp-12-5077-2012, 2012. a
Günther, D., Marke, T., Essery, R., and Strasser, U.: Uncertainties in
snowpack simulations – Assessing the impact of model structure, parameter
choice, and forcing data error on point-scale energy balance snow model
performance, Water Resour. Res., 55, 2779–2800, 2019. a
Hall, D. K. and Riggs, G. A.: Accuracy assessment of the MODIS snow products,
Hydrol. Process., 21, 1534–1547, 2007. a
Hall, D. K. and Riggs, G. A.: MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid, Version 61. NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, Colorado USA [data set], https://doi.org/10.5067/MODIS/MOD10A1.061, 2021. a, b
Hall, D. K., Riggs, G. A., and Salomonson, V. V.: MODIS/Terra Snow Cover 5-Min
L2 Swath 500m, Version 5. NASA National Snow and Ice Data Center Distributed
Active Archive Center, Boulder, Colorado, USA [data set], https://doi.org/10.5067/ACYTYZB9BEOS,
2006. a
Holland, J. H.: Genetic algorithms and the optimal allocation of trials, SIAM
J. Comput., 2, 88–105, 1973. a
Holland, J. H.: Adaptation in Natural and Artificial Systems: An Introductory
Analysis with Applications to Biology, Control, and Artificial Intelligence,
University of Michigan Press, Ann Arbor, MI, 1975. a
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. a, b
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, 2015. a
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. a
Jiang, Y., Chen, F., Gao, Y., He, C., Barlage, M., and Huang, W.: Assessment of
uncertainty sources in snow cover simulation in the Tibetan Plateau, J.
Geophys. Res.-Atmos., 125, e2020JD032674,
https://doi.org/10.1029/2020JD032674, 2020. a
Jin, J. and Miller, N. L.: Analysis of the impact of snow on daily weather
variability in mountainous regions using MM5, J. Hydrometeorol., 8, 245–258,
2007. a
Jonas, T., Marty, C., and Magnusson, J.: Estimating the snow water equivalent
from snow depth measurements in the Swiss Alps, J. Hydrol., 378, 161–167,
2009. a
Jun, S., Park, J.-H., Boo, K.-O., and Kang, H.-S.: Analyzing off-line Noah land
surface model spin-up behavior for initialization of global numerical weather
prediction model, Journal of Korea Water Resources Association, 53, 181–191, 2020 (in Korean with English abstract). a
Jung, S.-H., Im, E.-S., and Han, S.-O.: The effect of topography and sea
surface temperature on heavy snowfall in the Yeongdong region: A case study
with high resolution WRF simulation, Asia-Pac. J. Atmos. Sci., 48, 259–273,
2012. a
Kim, D.-E. and Park, S. K.: Uncertainty in predicting the Eurasian snow: Intercomparison of land surface models coupled to a regional climate model, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2019-15, 2019. a, b, c
Kim, G., Joo, H., and Kim, H.: The study for damage effect factors of heavy
snowfall disasters: Focused on heavy snowfall disasters during the period of
2005 to 2014, Journal of the Korea Academia-Industrial cooperation Society,
19, 125–136, 2018 (in Korean with English abstract). a
Koo, M.-S., Baek, S., Seol, K.-H., and Cho, K.: Advances in land modeling of
KIAPS based on the Noah land surface model, Asia-Pac. J. Atmos. Sci., 53,
361–373, 2017. a
Korea Meteorological Administration: Automated Synoptic Observing System (ASOS), Open MET Data Portal [data set], https://data.kma.go.kr/data/grnd/selectAsosRltmList.do?pgmNo=36, last access: 24 October 2022. a
Kotsuki, S., Terasaki, K., Yashiro, H., Tomita, H., Satoh, M., and Miyoshi, T.:
Online model parameter estimation With ensemble data assimilation in the real
global atmosphere: A case With the nonhydrostatic icosahedral atmospheric
model (NICAM) and the global satellite mapping of precipitation data, J.
Geophys. Res.-Atmos., 123, 7375–7392, 2018. a
Krishnakumar, K.: Micro-genetic algorithms for stationary and non-stationary function optimization, in: Intelligent Control and Adaptive Systems, 1989 Symposium on Visual Communications, Image Processing, and Intelligent Robotics Systems, 1989, Philadelphia, PA, United States, vol. 1196, International Society for Optics and Photonics, 289–296, https://doi.org/10.1117/12.969927, 1990. a, b
Lee, J., Kim, S.-M., Park, H.-S., and Woo, B.-H.: Optimum design of cold-formed
steel channel beams using micro Genetic Algorithm, Eng. Struct., 27, 17–24,
2005. a
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. a
Li, J., Duan, Q., Wang, Y.-P., Gong, W., Gan, Y., and Wang, C.: Parameter
optimization for carbon and water fluxes in two global land surface models
based on surrogate modelling, Int. J. Climatol., 38, e1016–e1031,
https://doi.org/10.1002/joc.5428, 2018. a
Li, J., Chen, F., Lu, X., Gong, W., Zhang, G., and Gan, Y.: Quantifying
contributions of uncertainties in physical parameterization schemes and model
parameters to overall errors in Noah-MP dynamic vegetation modeling, J. Adv.
Model. Earth Sy., 12, e2019MS001914, https://doi.org/10.1029/2019MS001914, 2020. a
Lim, S., Gim, H.-J., Lee, E., Lee, S.-Y., Lee, W. Y., Lee, Y. H., Cassardo, C.,
and Park, S. K.: Code and Data: Optimization of Snow-Related Parameters in
Noah Land Surface Model (v3.4.1) Using Micro-Genetic Algorithm (v1.7a),
Zenodo [code and data set], https://doi.org/10.5281/zenodo.6873384, 2021. a
Mallet, V. and Sportisse, B.: Uncertainty in a chemistry-transport model due to
physical parameterizations and numerical approximations: An ensemble approach
applied to ozone modeling, J. Geophys. Res.-Atmos., 111, D01302,
https://doi.org/10.1029/2005JD006149, 2006. a
Mitchell, K. E., Lohmann, D., Houser, P. R., Wood, E. F., Schaake, J. C.,
Robock, A., Cosgrove, B. A., Sheffield, J., Duan, Q., Luo, L., Higgins,
R. W., Pinker, R. T., Tarpley, J. D., Lettenmaier, D. P., Marshall, C. H.,
Entin, J. K., Pan, M., Shi, W., Koren, V., Meng, J., Ramsay, B. H., and
Bailey, A. A.: The multi-institution North American Land Data Assimilation
System (NLDAS): Utilizing multiple GCIP products and partners in a
continental distributed hydrological modeling system, J. Geophys.
Res.-Atmos., 109, D07S90, https://doi.org/10.1029/2003jd003823, 2004. a, b
Muñoz-Sabater, J.: ERA5-Land hourly data from 1981 to present, Copernicus
Climate Change Service (C3S) Climate Data Store (CDS) [data set],
https://doi.org/10.24381/cds.e2161bac, 2019. a, b
National Center for Atmosphere Research: Unified Noah LSM, NCAR [code], https://ral.ucar.edu/solutions/products/unified-wrf-noah-lsm, last access: 24 October 2022. a
Neelin, J. D., Bracco, A., Luo, H., McWilliams, J. C., and Meyerson, J. E.:
Considerations for parameter optimization and sensitivity in climate models,
P. Natl. Acad. Sci., 107, 21349–21354, 2010. a
Olafsson, H. and Bao, J.-W.: Uncertainties in Numerical Weather
Prediction, 1st edn., Elsevier, 364 pp., ISBN 9780128154915, 2020. a
Pan, M., Sheffield, J., Wood, E. F., Mitchell, K. E., Houser, P. R., Schaake,
J. C., Robock, A., Lohmann, D., Cosgrove, B., Duan, Q., Luo, L., Higgins, R. W., Pinker R. T., and Tarpley J. D.: Snow process
modeling in the North American Land Data Assimilation System (NLDAS): 2.
Evaluation of model simulated snow water equivalent, J. Geophys. Res.-Atmos.,
108, 8850, https://doi.org/10.1029/2003JD003994, 2003. a
Park, S. and Park, S. K.: 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, Geosci. Model Dev., 14, 6241–6255, https://doi.org/10.5194/gmd-14-6241-2021, 2021. a
Pathak, R., Sahany, S., and Mishra, S. K.: Uncertainty quantification based
cloud parameterization sensitivity analysis in the NCAR community atmosphere
model, Sci. Rep., 10, 17499, https://doi.org/10.1038/s41598-020-74441-x, 2020. a
Robinson, D. A. and Kukla, G.: Maximum surface albedo of seasonally
snow-covered lands in the Northern Hemisphere, J. Appl. Meteorol. Clim., 24,
402–411, 1985. a
Rosolem, R., Gupta, H. V., Shuttleworth, W. J., de Gonçalves, L. G. G.,
and Zeng, X.: Towards a Comprehensive Approach to Parameter Estimation in
Land Surface Parameterization Schemes, Hydrol. Process., 27, 2075–2097,
2013. a
Rudnaya, S. and Santosa, F.: Application of a micro-genetic algorithm in
optimal design of a diffractive optical element, in: System Modelling and
Optimization: Methods, Theory and Applications. CSMO 1999, IFIPAICT, vol. 46,
edited by: Powell, M. J. D. and Scholtes, S., Springer, Boston,
MA, USA, 251–267, https://doi.org/10.1007/978-0-387-35514-6_12, 2000. a
Saha, S. K., Sujith, K., Pokhrel, S., Chaudhari, H. S., and Hazra, A.: Effects
of multilayer snow scheme on the simulation of snow: Offline Noah and coupled
with NCEP CFS v2, J. Adv. Model. Earth Sy., 9, 271–290, 2017. a
Sheffield, J., Pan, M., Wood, E. F., Mitchell, K. E., Houser, P. R., Schaake,
J. C., Robock, A., Lohmann, D., Cosgrove, B., Duan, Q., Luo, L., Higgins, R. W., Pinker, R. T., Tarpley, J. D. and Ramsay, B. H.: Snow process
modeling in the North American Land Data Assimilation System (NLDAS): 1.
Evaluation of model-simulated snow cover extent, J. Geophys. Res.-Atmos.,
108, 8849, https://doi.org/10.1029/2002JD003274, 2003. a
Shutts, G. and Pallarès, A. C.: Assessing parametrization uncertainty
associated with horizontal resolution in numerical weather prediction models,
Philos. T. R. Soc. A., 372, 20130284, https://doi.org/10.1098/rsta.2013.0284, 2014. a
Souza, A. N., Wagner, G. L., Ramadhan, A., Allen, B., Churavy, V., Schloss, J.,
Campin, J., Hill, C., Edelman, A., Marshall, J., Flierl, G., and Ferrari, R.:
Uncertainty quantification of ocean parameterizations: Application to the
K-profile-parameterization for penetrative convection, J. Adv. Model. Earth
Sy., 12, e2020MS002108, https://doi.org/10.1029/2020MS002108, 2020. a
Sulla-Menashe, D. and Friedl, M. A.: User Guide to Collection 6 MODIS Land Cover (MCD12Q1 and MCD12C1) Product, USGS, Reston, VA, USA, 1, 18 pp., 2018. a
Sultana, R., Hsu, K.-L., Li, J., and Sorooshian, S.: Evaluating the Utah Energy Balance (UEB) snow model in the Noah land-surface model, Hydrol. Earth Syst. Sci., 18, 3553–3570, https://doi.org/10.5194/hess-18-3553-2014, 2014. a
USACE: Snow Hydrology: Summary Report of the Snow Investigations,
Tech. Rep., US Army Corps of Engineers, North Pacific
Division, Portland, Orgeon, USA, 437 pp., 1956. a
Wang, Q., Fang, H., and Zou, X.-K.: Application of Micro-GA for optimal cost
base isolation design of bridges subject to transient earthquake loads,
Struct. Multidiscip. O., 41, 765–777, 2010. a
Wang, Z. and Zeng, X.: Evaluation of snow albedo in land models for weather and climate studies, J. Appl. Meteorol. Clim., 49, 363–380, https://doi.org/10.1175/2009JAMC2134.1, 2010. a
Warren, S. G.: Optical properties of snow, Rev. Geophys., 20, 67–89, 1982. a
Warren, S. G. and Wiscombe, W. J.: A model for the spectral albedo of snow. II:
Snow containing atmospheric aerosols, J. Atmos. Sci., 37, 2734–2745, 1980. a
Xu, Y., Jones, A., and Rhoades, A.: A quantitative method to decompose SWE
differences between regional climate models and reanalysis datasets, Sci.
Rep., 9, 16520, https://doi.org/10.1038/s41598-019-52880-5, 2019. a
Yan, J., Xu, Z., Yu, Y., Xu, H., and Gao, K.: Application of a hybrid optimized
BP network model to estimate water quality parameters of Beihai Lake in
Beijing, Appl. Sci., 9, 1863, https://doi.org/10.3390/app9091863, 2019. a
Yoon, J. W., Lim, S., and Park, S. K.: Combinational optimization of the WRF
physical parameterization schemes to improve numerical sea breeze prediction
using micro-genetic algorithm, Appl. Sci., 11, 11221, https://doi.org/10.3390/app112311221, 2021. a, b, c
Zhang, X., Zhang, S., Liu, Z., Wu, X., and Han, G.: Parameter optimization in
an intermediate coupled climate model with biased physics, J. Climate, 28,
1227–1247, 2015. a
Zhao, W. and Li, A.: A review on land surface processes modelling over complex
terrain, Adv. Meteorol., 2015, 607187,
https://doi.org/10.1155/2015/607181, 2015.
a
Zhu, J., Shu, J., and Yu, X.: Improvement of typhoon rainfall prediction based
on optimization of the Kain-Fritsch convection parameterization scheme using
a micro-genetic algorithm, Front. Earth Sci., 13, 721–732, 2019. a
Short summary
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.
The land surface model (LSM) contains various uncertain parameters, which are obtained by the...
