Articles | Volume 17, issue 12
https://doi.org/10.5194/gmd-17-4891-2024
© Author(s) 2024. 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-17-4891-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
21 Jun 2024
Development and technical paper |
| 21 Jun 2024
| 21 Jun 2024
A parameterization scheme for the floating wind farm in a coupled atmosphere–wave model (COAWST v3.7)
Shaokun Deng
Institute for Ocean Engineering, Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
Shengmu Yang
College of Meteorology and Oceanography, National University of Defense Technology, Changsha, China
Shengli Chen
CORRESPONDING AUTHOR
Institute for Ocean Engineering, Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
Daoyi Chen
Institute for Ocean Engineering, Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
Xuefeng Yang
Institute for Ocean Engineering, Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
Shanshan Cui
Institute for Ocean Engineering, Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
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Wei Liu, Xuefeng Yang, Shengli Chen, Shaokun Deng, Peining Yu, and Jiuxing Xing
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-174, https://doi.org/10.5194/gmd-2023-174, 2023
Revised manuscript not accepted
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Assessing environmental impacts of wind farms requires an accurate parameterization of wind farms in atmospheric models, which, in our study, is improved considering the wind turbine wake. Based on an engineering wake model of a turbine, a wake superposition coefficient and an angle correction coefficient are proposed, calculated and added in the model. Sensitivity experiments reveal that, with enlarged grid size and shortened turbine spacing, the new scheme shows more advantages.
Yu Zhang, Jintao Gu, Shengli Chen, Jianyu Hu, Jinyu Sheng, and Jiuxing Xing
EGUsphere, https://doi.org/10.5194/egusphere-2024-3457, https://doi.org/10.5194/egusphere-2024-3457, 2024
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Current observations at two moorings in the northern South China Sea reveal that mesoscale eddies can transfer energy with near-inertial oscillations (NIOs). Numerical experiments are conducted to investigate important parameters affecting energy transfer between mesoscale eddies and NIOs, which demonstrate that the energy transferred by mesoscale eddies is larger with stronger winds and higher strength of the mesoscale eddy. Anticyclonic eddies can transfer more energy than cyclonic eddies.
Wei Liu, Xuefeng Yang, Shengli Chen, Shaokun Deng, Peining Yu, and Jiuxing Xing
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-174, https://doi.org/10.5194/gmd-2023-174, 2023
Revised manuscript not accepted
Short summary
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Assessing environmental impacts of wind farms requires an accurate parameterization of wind farms in atmospheric models, which, in our study, is improved considering the wind turbine wake. Based on an engineering wake model of a turbine, a wake superposition coefficient and an angle correction coefficient are proposed, calculated and added in the model. Sensitivity experiments reveal that, with enlarged grid size and shortened turbine spacing, the new scheme shows more advantages.
Shengmu Yang, Jiuxing Xing, Shengli Chen, Jiwei Tian, and Daoyi Chen
Ocean Sci. Discuss., https://doi.org/10.5194/os-2018-39, https://doi.org/10.5194/os-2018-39, 2018
Revised manuscript not accepted
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Motivated by the recent field observations of the eddy tilting structure in the South China Sea, a simple theoretical analysis and a numerical model (MITgcm) are used to investigate the mechanism of the vertical structure tilt of a mesoscale eddy on the β-plane. The model results correspond well with the theoretical analysis and the results indicate that the β-effect, nonlinear advection, and ocean stratification are important factors in controlling the vertical structure of a mesoscale eddy.
Shengmu Yang, Jiuxing Xing, Daoyi Chen, and Shengli Chen
Ocean Sci., 13, 837–849, https://doi.org/10.5194/os-13-837-2017, https://doi.org/10.5194/os-13-837-2017, 2017
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Motivated by eddy-splitting near Dongsha island, the eddy's trajectory and the effect of topography on eddy evolution were explored using the MITgcm. Warm eddies propagate to the southwest while cold eddies propagate to the northwest in open oceans. The results of the model indicate that the eddy would split in a qualitative range, and the location of the eddy split-off is related to the island size. In addition, eddy-splitting is an important way to transform energy between different scales.
Shengli Chen, Daoyi Chen, and Jiuxing Xing
Ocean Sci., 13, 829–836, https://doi.org/10.5194/os-13-829-2017, https://doi.org/10.5194/os-13-829-2017, 2017
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Simulations are used to examine the response of a shallow closed basin to a wind pulse. Features and differences of inertial oscillations and near-inertial internal waves are explored. The horizontal distribution of near-inertial energy is primarily controlled by the boundary effect on inertial oscillations, and the near-inertial internal wave makes a secondary effect. Near-inertial energy is reduced for cases with smaller water depths, due to energy transferred to seiches by barotropic waves.
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Geosci. Model Dev., 17, 8639–8664, https://doi.org/10.5194/gmd-17-8639-2024, https://doi.org/10.5194/gmd-17-8639-2024, 2024
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Geosci. Model Dev., 17, 8373–8397, https://doi.org/10.5194/gmd-17-8373-2024, https://doi.org/10.5194/gmd-17-8373-2024, 2024
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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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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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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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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.
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.
Lukas Pfitzenmaier, Pavlos Kollias, Nils Risse, Imke Schirmacher, Bernat Puigdomenech Treserras, and Katia Lamer
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-129, https://doi.org/10.5194/gmd-2024-129, 2024
Revised manuscript accepted for GMD
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Orbital-radar is a Python tool transferring sub-orbital radar data (ground-based, airborne, and forward-simulated NWP) into synthetical space-borne cloud profiling radar data mimicking the platform characteristics, e.g. EarthCARE or CloudSat CPR. The novelty of orbital-radar is the simulation platform characteristic noise floors and errors. By this long time data sets can be transformed into synthetic observations for Cal/Valor sensitivity studies for new or future satellite missions.
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.
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.
Kelly M. Núñez Ocasio and Zachary L. Moon
Geosci. Model Dev., 17, 6035–6049, https://doi.org/10.5194/gmd-17-6035-2024, https://doi.org/10.5194/gmd-17-6035-2024, 2024
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TAMS is an open-source Python-based package for tracking and classifying mesoscale convective systems that can be used to study observed and simulated systems. Each step of the algorithm is described in this paper with examples showing how to make use of visualization and post-processing tools within the package. A unique and valuable feature of this tracker is its support for unstructured grids in the identification stage and grid-independent tracking.
Irene C. Dedoussi, Daven K. Henze, Sebastian D. Eastham, Raymond L. Speth, and Steven R. H. Barrett
Geosci. Model Dev., 17, 5689–5703, https://doi.org/10.5194/gmd-17-5689-2024, https://doi.org/10.5194/gmd-17-5689-2024, 2024
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Atmospheric model gradients provide a meaningful tool for better understanding the underlying atmospheric processes. Adjoint modeling enables computationally efficient gradient calculations. We present the adjoint of the GEOS-Chem unified chemistry extension (UCX). With this development, the GEOS-Chem adjoint model can capture stratospheric ozone and other processes jointly with tropospheric processes. We apply it to characterize the Antarctic ozone depletion potential of active halogen species.
Sylvain Mailler, Sotirios Mallios, Arineh Cholakian, Vassilis Amiridis, Laurent Menut, and Romain Pennel
Geosci. Model Dev., 17, 5641–5655, https://doi.org/10.5194/gmd-17-5641-2024, https://doi.org/10.5194/gmd-17-5641-2024, 2024
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We propose two explicit expressions to calculate the settling speed of solid atmospheric particles with prolate spheroidal shapes. The first formulation is based on theoretical arguments only, while the second one is based on computational fluid dynamics calculations. We show that the first method is suitable for virtually all atmospheric aerosols, provided their shape can be adequately described as a prolate spheroid, and we provide an implementation of the first method in AerSett v2.0.2.
Hejun Xie, Lei Bi, and Wei Han
Geosci. Model Dev., 17, 5657–5688, https://doi.org/10.5194/gmd-17-5657-2024, https://doi.org/10.5194/gmd-17-5657-2024, 2024
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A radar operator plays a crucial role in utilizing radar observations to enhance numerical weather forecasts. However, developing an advanced radar operator is challenging due to various complexities associated with the wave scattering by non-spherical hydrometeors, radar beam propagation, and multiple platforms. In this study, we introduce a novel radar operator named the Accurate and Efficient Radar Operator developed by ZheJiang University (ZJU-AERO) which boasts several unique features.
Jonathan J. Day, Gunilla Svensson, Barbara Casati, Taneil Uttal, Siri-Jodha Khalsa, Eric Bazile, Elena Akish, Niramson Azouz, Lara Ferrighi, Helmut Frank, Michael Gallagher, Øystein Godøy, Leslie M. Hartten, Laura X. Huang, Jareth Holt, Massimo Di Stefano, Irene Suomi, Zen Mariani, Sara Morris, Ewan O'Connor, Roberta Pirazzini, Teresa Remes, Rostislav Fadeev, Amy Solomon, Johanna Tjernström, and Mikhail Tolstykh
Geosci. Model Dev., 17, 5511–5543, https://doi.org/10.5194/gmd-17-5511-2024, https://doi.org/10.5194/gmd-17-5511-2024, 2024
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The YOPP site Model Intercomparison Project (YOPPsiteMIP), which was designed to facilitate enhanced weather forecast evaluation in polar regions, is discussed here, focussing on describing the archive of forecast data and presenting a multi-model evaluation at Arctic supersites during February and March 2018. The study highlights an underestimation in boundary layer temperature variance that is common across models and a related inability to forecast cold extremes at several of the sites.
Cited articles
Abkar, M. and Porté-Agel, F.: A new wind-farm parameterization for large-scale atmospheric models, J. Renew. Sustain. Energ., 7, 013121, https://doi.org/10.1063/1.4907600, 2015.
Alari, V. and Raudsepp, U.: Simulation of wave damping near coast due to offshore wind farms, J. Coast. Res., 279, 143–148, https://doi.org/10.2112/JCOASTRES-D-10-00054.1, 2012.
AlSam, A., Szasz, R., and Revstedt, J.: The influence of sea waves on offshore wind turbine aerodynamics, J. Energ. Resour. Technol., Transactions of the ASME, 137, 1–10, https://doi.org/10.1115/1.4031005, 2015.
Archer, C., Wu, S., Ma, Y., and Jiménez, P.: Two corrections for turbulent kinetic energy generated by wind farms in the WRF model, Mon. Weather Rev., 148, 4823–4835, https://doi.org/10.1175/MWR-D-20-0097.1, 2020.
Battjes, J. and Janssen, J.: Energy loss and set-up due to breaking of random waves, Costal Eng. Proc., 1, 32, https://doi.org/10.9753/icce.v16.32, 1978.
Booij, N., Ris, R., and Holthuijsen, L.: A third-generation wave model for coastal regions: 1. model description and validation, J. Geophys. Res.-Atmos., 104, 7649–7666, https://doi.org/10.1029/98JC02622, 1999.
Carvalho, D., Rocha, A., Santos, C., and Pereira, R.: Wind resource modelling in complex terrain using different mesoscale-microscale coupling techniques, Appl. Energ., 108, 493–504, https://doi.org/10.1016/j.apenergy.2013.03.074, 2013.
Dalrymple, R., Kirby, J., and Hwang, P.: Wave diffraction due to areas of energy dissipation, J. Waterw. Port C, 110, 67–79, https://doi.org/10.1061/(ASCE)0733-950X(1984)110:1(67), 1984.
Deng, S.: A Parameterization Scheme for the Floating Wind Farm, Zenodo [data set], https://doi.org/10.5281/zenodo.12180181, 2024.
Desmond, C., Murphy, J., Blonk, L., and Haans, W.: Description of an 8 MW reference wind turbine, J. Phys. Conf. Ser., 753, 092013, https://doi.org/10.1088/1742-6596/753/9/092013, 2016.
Diaz, H. and Guedes Soares, C.: Review of the current status, technology and future trends of offshore wind farms, Ocean Eng., 209, 107381, https://doi.org/10.1016/j.oceaneng.2020.107381, 2020.
Du, J., Bolanos, R., and Guo Larsen, X.: The use of wave boundary layer model in SWAN, J. Geophys. Res.-Oceans, 122, 42–62, https://doi.org/10.1002/2016JC012104, 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.
Dudhia, J.: A multi-layer soil temperature model for MM5, Sixth PSU/NCAR Mesoscale Model Users Workshop 1996, Boulder, CO, PSU/NCAR, 4950, 1996.
Emeis, S.: A simple analytical wind park model considering atmospheric stability, Wind Energy, 13, 459–469, https://doi.org/10.1002/we.367, 2010.
Emeis, S., Siedersleben, S., Lampert, A., Platis, A., Bange, J., Djath, B., Schulz-Stellenfleth, J., and Neumann, T.: Exploring the wakes of large offshore wind farms, J. Phys. Conf. Ser., 753, 092014, https://doi.org/10.1088/1742-6596/753/9/092014, 2016.
Fitch, A.: Climate impacts of large-scale wind farms as parameterized in a global climate model, J. Climate, 28, 6160–6180, https://doi.org/10.1175/JCLI-D-14-00245.1, 2015.
Fitch, A., Olson, J., Lundquist, J., Dudhia, J., Gupta, A., Michalakes, J., and Barstad, I.: Local and mesoscale impacts of wind farms as parameterized in a mesoscale NWP Model, Mon. Weather Rev., 140, 3017–3038, https://doi.org/10.1175/MWR-D-11-00352.1, 2012.
Fitch, A., Olson, J., and Lundquist, J.: Parameterization of wind farms in climate models, J. Climate, 26, 6439–6458, https://doi.org/10.1175/JCLI-D-12-00376.1, 2013.
Gettelman, A., Gagne, D., Chen, C., Christensen, M., Lebo, Z., Morrison, H., and Gantos, G.: Machine learning the warm rain process, J. Adv. Model. Earth Sy., 13, e2020MS002268, https://doi.org/10.1029/2020MS002268, 2020.
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.
Hong, S. and Lim, J.: WRF single-moment 6-class microphysics scheme (WSM6), J. Korean Meteorol. Soc., 42, 129–151 ,2006.
Hong, S., 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.
Isaacson, M.: Wave-induced forces in the diffraction regime, in: Mechanics of wave-induced forces on cylinders, edited by: Shwa, T. L., Pitman Publishing, London, 68–89, 1979.
Janjic, Z.: Nonsingular implementation of the Mellor-Yamada level 2.5 scheme in the NCEP Meso model, NCEP Office note #437 2001, 61, 2001.
Jenkins, A., Paskyabi, M., Fer, I., Gupta, A., and Adakudlu, M.: Modelling the effect of ocean waves on the atmospheric and ocean boundary layers, Energ. Proced., 24, 166–175, https://doi.org/10.1016/j.egypro.2012.06.098, 2012.
Kalvig, S., Manger, E., Hjertager, B., and Jakobsen, J.: Wave influenced wind and the effect on offshore wind turbine performance, Energ. Proced., 53, 202–213, https://doi.org/10.1016/j.egypro.2014.07.229, 2014.
Kobayashi, N., Raichle, A., and Asano, T.: Wave attenuation by vegetation. J. Waterw. Port C., 119, 30–48, https://doi.org/10.1061/(ASCE)0733-950X(1993)119:1(30), 1993.
Komen, G., Hasselmann, S., and Hasselmann, K.: On the existence of a fully developed wind-sea spectrum, J. Phys. Oceanogr., 14, 1271–1285, https://doi.org/10.1175/1520-0485(1984)014<1271:OTEOAF>2.0.CO;2, 1984.
Lillibridge, J.: US DOC/NOAA/NESDIS > Office of Satellite Data Processing and Distribution, Jason-3 Level-2 Operational, Interim and Final Geophysical Data Records (X-GDR), 2016 to present (NCEI Accession 0122595), NOAA National Centers for Environmental Information [data set], https://www.ncei.noaa.gov/archive/accession/0122595 (last access: 25 January 2024), 2019.
Lundquist, J., DuVivier, K., Kaffine, D., and Tomaszewski, J.: Costs and consequences of wind turbine wake effects arising from uncoordinated wind energy development, Nature Energy, 4, 26–34, https://doi.org/10.1038/s41560-018-0281-2, 2019.
Ma, Y., Archer, C., and Vasel-Be-Hagh, A.: Comparison of individual versus ensemble wind farm parameterizations inclusive of sub-grid wakes for the WRF model, Wind Energy, 25, 1573–1595, https://doi.org/10.1002/we.2758, 2022a.
Ma, Y., Archer, C. L., and Vasel-Be-Hagh, A.: The Jensen wind farm parameterization, Wind Energ. Sci., 7, 2407–2431, https://doi.org/10.5194/wes-7-2407-2022, 2022b.
Madsen, O., Poon, Y., and Graber, H.: Spectral wave attenuation by bottom friction: Theory, Proceedings of the 21st International Conference Coastal Engineering, 492–504, 1988.
McCombs, M., Mulligan, R., and Boegman, L.: Offshore wind farm impacts on surface waves and circulation in Eastern Lake Ontario, Coastal Eng., 93, 32–38, https://doi.org/10.1016/j.coastaleng.2014.08.001, 2014.
Mendez, F. and Losada, I.: An empirical model to estimate the propagation of random breaking and nonbreaking waves over vegetation fields, Coastal Eng., 51, 103–118, https://doi.org/10.1016/j.coastaleng.2003.11.003, 2004.
Mlawer, E., Taubman, S., Brown, P., Iacono, M., and Clough, S.: 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.
Molen, J., Smith, H., Lepper, P., Limpenny, S., and Rees, J.: Predicting the large-scale consequence of offshore wind turbine array development on a North Sea ecosystem, Cont. Shelf Res., 85, 60–72, https://doi.org/10.1016/j.csr.2014.05.018, 2014.
Morison, J., Johnson, J., and Schaaf, S.: The force exerted by surface waves on piles, J. Petrol. Technol., 2, 149–154, https://doi.org/10.2118/950149-G, 1950.
Nakanishi, M. and Niino, H.: Development of an improved turbulence closure model for the atmospheric boundary layer, J. Meteorol. Soc. Jpn. Ser., 87, 895–912, https://doi.org/10.2151/jmsj.87.895, 2009.
National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce (NCEP): NCEP GDAS/FNL 0.25 Degree Global Tropospheric Analyses and Forecast Grids, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://rda.ucar.edu/datasets/ds083.3/ (last access: 20 June 2024), 2015.
O'Gorman, P. and Dwyer, J.: Using machine learning to parameterize moist convection: Potential for modeling of climate, climate change, and extreme events, J. Adv. Model. Earth Syst., 10, 2548–2563, https://doi.org/10.1029/2018MS001351, 2018.
Pan, Y. and Archer, C.: A hybrid wind-farm parametrization for mesoscale and climate models, Bound.-Lay. Meteorol., 168, 469–495, https://doi.org/10.1007/s10546-018-0351-9, 2018.
Paskyabi, M., Zieger, S., Jenkins, A., Babanin, A., and Chalikov, D.: Sea surface gravity wave-wind interaction in the marine atmospheric boundary layer, Energ. Proced., 53, 184–192, https://doi.org/10.1016/j.egypro.2014.07.227, 2014.
Ponce de León, S., Bettencourt, J., and Kjerstad, N.: Simulation of irregular waves in an offshore wind farm with a spectral wave model, Cont. Shelf Res., 31, 1541–1557, https://doi.org/10.1016/j.csr.2011.07.003, 2011.
Porchetta, S., Munoz-Esparza, D., Munters, W., Beeck, J., and Lipzig, N.: Impact of ocean waves on offshore wind farm power production, Renew. Energy, 180, 1179–1193, https://doi.org/10.1016/j.renene.2021.08.111, 2021.
Pryor, S., Barthelmie, R., Bukovsky, M., Ruby Leung, L., and Sakaguchi, K.: Climate change impacts on wind power generation, Nat. Rev. Earth Environ., 1, 1–17, https://doi.org/10.1038/s43017-020-0101-7, 2020.
Redfern, S., Olson, J., Lundquist, J., and Clack, C.: Incorporation of the rotor-equivalent wind speed into the weather research and forecasting model's wind farm parameterization, Mon. Weather Rev., 147, 1029–1046, https://doi.org/10.1175/MWR-D-18-0194.1, 2019.
Roddier, D., Cermelli, C., Aubault, A., and Weinstein, A.: Wind Float: A floating foundation for offshore wind turbines, J. Renew. Sustain. Energ., 2, 033104, https://doi.org/10.1063/1.3435339, 2010.
Santoni, C., García-Cartagena, E., Ciri, U., Iungo, V., and Leonardi, S.: Coupling of mesoscale Weather Research and Forecasting model to a high fidelity Large Eddy Simulation, J. Phys. Conf. Ser., 1037, 062010, https://doi.org/10.1088/1742-6596/1037/6/062010, 2018.
Seifert, A. and Rasp, S.: Potential and limitations of machine learning for modeling warm-rain cloud microphysical processes, J. Adv. Model. Earth Sy., 12, e2020MS002301, https://doi.org/10.1029/2020MS002301, 2020.
Taylor, P. and Yelland, M.: The dependence of sea surface roughness on the height and steepness of the waves, J. Phys. Oceanogr., 31, 572–590, https://doi.org/10.1175/1520-0485(2001)031<0572:TDOSSR>2.0.CO;2, 2001.
Temel, O., Brictexu, L., and Beeck, J.: Coupled WRF-OpenFOAM study of wind flow over complex terrain, J. Wind Eng. Ind. Aerodyn., 174, 152–169, https://doi.org/10.1016/j.jweia.2018.01.002, 2018.
Vanderwende, B. and Lundquist, J.: Could crop height affect the wind resource at agriculturally productive wind farm sites?, Bound.-Lay. Meteorol., 158, 409–428, https://doi.org/10.1007/s10546-015-0102-0, 2016.
Volker, P. J. H., Badger, J., Hahmann, A. N., and Ott, S.: The Explicit Wake Parametrisation V1.0: a wind farm parametrisation in the mesoscale model WRF, Geosci. Model Dev., 8, 3715–3731, https://doi.org/10.5194/gmd-8-3715-2015, 2015.
Warner, J., Armstrong, B., He, R., and Zambon, J.: Development of a coupled ocean-atmosphere-wave-sediment transport (COAWST) modeling system, Ocean Model., 35, 230–244, https://doi.org/10.1016/j.ocemod.2010.07.010, 2010 (code available at: https://github.com/DOI-USGS/COAWST, last access: 20 June 2024).
WW3DG: User manual and system documentation of Wavewatch, (R) version 6.07, NCEI [data set], https://www.ncei.noaa.gov/thredds-ocean/catalog/ncep/nww3/catalog.html (last access: 25 January 2024), 2019.
Wu, L., Shao, M., and Sahlée, E.: Impact of air-wave-sea coupling on the simulation of offshore wind and wave energy potentials, Atmosphere, 11, 327, https://doi.org/10.3390/atmos11040327, 2020.
Yang, D., Meneveau, C., and Shen, L.: Effect of downwind swells on offshore wind energy harvesting-A large-eddy simulation study, Renew. Energy, 70, 11–23, https://doi.org/10.1016/j.renene.2014.03.069, 2014.
Zou, Z., Zhao, D., Zhang, J., Li, S., Cheng, Y., Lv, H., and Ma, X.: The influence of swell on the atmospheric boundary layer under nonneutral conditions, J. Phys. Oceanogr., 48, 925–936, https://doi.org/10.1175/JPO-D-17-0195.1, 2018.
Short summary
Global offshore wind power development is moving from offshore to deeper waters, where floating offshore wind turbines have an advantage over bottom-fixed turbines. However, current wind farm parameterization schemes in mesoscale models are not applicable to floating turbines. We propose a floating wind farm parameterization scheme that accounts for the attenuation of the significant wave height by floating turbines. The results indicate that it has a significant effect on the power output.
Global offshore wind power development is moving from offshore to deeper waters, where floating...
