Articles | Volume 15, issue 2
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Numerically consistent budgets of potential temperature, momentum, and moisture in Cartesian coordinates: application to the WRF model
Department of Atmospheric and Cryospheric Sciences, University of Innsbruck, Innsbruck, Austria
Department of Meteorology and Geophysics, University of Vienna, Vienna, Austria
Mathias W. Rotach
Department of Atmospheric and Cryospheric Sciences, University of Innsbruck, Innsbruck, Austria
No articles found.
Matthias Göbel, Stefano Serafin, and Mathias W. Rotach
Weather Clim. Dynam., 4, 725–745,Short summary
On summer days over mountains, upslope winds transport moist air towards mountain tops and beyond, making local rain showers more likely. We use idealized simulations to investigate how mountain steepness affects this mechanism. We find that steeper mountains lead to a delayed onset and lower intensity of the storms, because less moisture accumulates over the ridges and the thermal updraft zone at the top is narrower and thus more prone to the intrusion of dry air from the environment.
Hetal P. Dabhi, Mathias W. Rotach, and Michael Oberguggenberger
Hydrol. Earth Syst. Sci., 27, 2123–2147,Short summary
Spatiotemporally consistent high-resolution precipitation data on climate are needed for climate change impact assessments, but obtaining these data is challenging for areas with complex topography. We present a model that generates synthetic gridded daily precipitation data at a 1 km spatial resolution using observed meteorological station data as input, thereby providing data where historical observations are unavailable. We evaluate this model for a mountainous region in the European Alps.
Cornelius Immanuel Weiß, Alexander Gohm, Mathias Walter Rotach, and Thomas Torora Minda
Weather Clim. Dynam., 3, 1003–1019,Short summary
Two gap flow events in the Great Rift Valley in Ethiopia were investigated based on observations, ERA5 reanalysis data, and simulations with the numerical weather prediction model WRF. The main focus was on strong winds in the area around Lake Abaya since the winds may generate waves on the lake which help to sustain the lake's ecology. That is important in terms of food supply for the local population. The gap winds exhibit a diurnal cycle and a seasonal dependence.
Helen Claire Ward, Mathias Walter Rotach, Alexander Gohm, Martin Graus, Thomas Karl, Maren Haid, Lukas Umek, and Thomas Muschinski
Atmos. Chem. Phys., 22, 6559–6593,Short summary
This study examines how cities and their surroundings influence turbulent exchange processes responsible for weather and climate. Analysis of a 4-year observational dataset for the Alpine city of Innsbruck reveals several similarities with other (flat) city centre sites. However, the mountain setting leads to characteristic daily and seasonal flow patterns (valley winds) and downslope windstorms that have a marked effect on temperature, wind speed, turbulence and pollutant concentration.
Johannes Horak, Marlis Hofer, Ethan Gutmann, Alexander Gohm, and Mathias W. Rotach
Geosci. Model Dev., 14, 1657–1680,Short summary
This process-based evaluation of the atmospheric model ICAR is conducted to derive recommendations to increase the likelihood of its results being correct for the right reasons. We conclude that a different diagnosis of the atmospheric background state is necessary, as well as a model top at an elevation of at least 10 km. Alternative boundary conditions at the top were not found to be effective in reducing this model top elevation. The results have wide implications for future ICAR studies.
Bettina Richter, Alec van Herwijnen, Mathias W. Rotach, and Jürg Schweizer
Nat. Hazards Earth Syst. Sci., 20, 2873–2888,Short summary
We investigated the sensitivity of modeled snow instability to uncertainties in meteorological input, typically found in complex terrain. The formation of the weak layer was very robust due to the long dry period, indicated by a widespread avalanche problem. Once a weak layer has formed, precipitation mostly determined slab and weak layer properties and hence snow instability. When spatially assessing snow instability for avalanche forecasting, accurate precipitation patterns have to be known.
Bettina Richter, Jürg Schweizer, Mathias W. Rotach, and Alec van Herwijnen
The Cryosphere, 13, 3353–3366,Short summary
Information on snow stability is important for avalanche forecasting. To improve the stability estimation in the snow cover model SNOWPACK, we suggested an improved parameterization for the critical crack length. We compared 3 years of field data to SNOWPACK simulations. The match between observed and modeled critical crack lengths greatly improved, and critical weak layers appear more prominently in the modeled vertical profile of critical crack length.
Christian Mallaun, Andreas Giez, Georg J. Mayr, and Mathias W. Rotach
Atmos. Chem. Phys., 19, 9769–9786,Short summary
This study presents airborne measurements in shallow convection over land to investigate the dynamic properties of clouds focusing on possible narrow downdraughts in the surrounding of the clouds. A characteristic narrow downdraught region (
subsiding shell) is found directly outside the cloud borders for the mean vertical wind distribution. The
subsiding shellresults from the distribution of the highly variable updraughts and downdraughts in the near vicinity of the cloud.
Johannes Horak, Marlis Hofer, Fabien Maussion, Ethan Gutmann, Alexander Gohm, and Mathias W. Rotach
Hydrol. Earth Syst. Sci., 23, 2715–2734,Short summary
This study presents an in-depth evaluation of the Intermediate Complexity Atmospheric Research (ICAR) model for high-resolution precipitation fields in complex topography. ICAR is evaluated with data from weather stations located in the Southern Alps of New Zealand. While ICAR underestimates rainfall amounts, it clearly improves over a coarser global model and shows potential to generate precipitation fields for long-term impact studies focused on the local impact of a changing global climate.
Daniel Leukauf, Alexander Gohm, and Mathias W. Rotach
Atmos. Chem. Phys., 16, 13049–13066,Short summary
Since populated valleys suffer often from poor air quality, it is of interest to better understand the various mechanisms relevant to remove pollutants from the valley atmosphere. One mechanism is the transport by along-slope flows, which are generated during fair-weather days. In this study we quantify the amount of tracer that is removed from a valley atmosphere and the amount that is re-circulated within the valleys. For this study we are using the numerical weather model WRF.
N. Kljun, P. Calanca, M. W. Rotach, and H. P. Schmid
Geosci. Model Dev., 8, 3695–3713,Short summary
Flux footprint models describe the surface area of influence of a flux measurement. They are used for designing flux tower sites, and for interpretation of flux measurements. The two-dimensional footprint parameterisation (FFP) presented here is suitable for processing large data sets, and, unlike other fast footprint models, FFP is applicable to daytime or night-time measurements, fluxes from short masts over grassland to tall towers over mature forests, and even to airborne flux measurements.
G. Massaro, I. Stiperski, B. Pospichal, and M. W. Rotach
Atmos. Meas. Tech., 8, 3355–3367,
J. S. Wagner, A. Gohm, and M. W. Rotach
Atmos. Chem. Phys., 15, 6589–6603,
K. Zink, A. Pauling, M. W. Rotach, H. Vogel, P. Kaufmann, and B. Clot
Geosci. Model Dev., 6, 1961–1975,
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Li Fang, Jianbing Jin, Arjo Segers, Hong Liao, Ke Li, Bufan Xu, Wei Han, Mijie Pang, and Hai Xiang Lin
Geosci. Model Dev., 16, 4867–4882,Short summary
Machine learning models have gained great popularity in air quality prediction. However, they are only available at air quality monitoring stations. In contrast, chemical transport models (CTM) provide predictions that are continuous in the 3D field. Owing to complex error sources, they are typically biased. In this study, we proposed a gridded prediction with high accuracy by fusing predictions from our regional feature selection machine learning prediction (RFSML v1.0) and a CTM prediction.
Manu Goudar, Juliëtte C. S. Anema, Rajesh Kumar, Tobias Borsdorff, and Jochen Landgraf
Geosci. Model Dev., 16, 4835–4852,Short summary
A framework was developed to automatically detect plumes and compute emission estimates with cross-sectional flux method (CFM) for biomass burning events in TROPOMI CO datasets using Visible Infrared Imaging Radiometer Suite active fire data. The emissions were more reliable when changing plume height in downwind direction was used instead of constant injection height. The CFM had uncertainty even when the meteorological conditions were accurate; thus there is a need for better inversion models.
Drew C. Pendergrass, Daniel J. Jacob, Hannah Nesser, Daniel J. Varon, Melissa Sulprizio, Kazuyuki Miyazaki, and Kevin W. Bowman
Geosci. Model Dev., 16, 4793–4810,Short summary
We have built a tool called CHEEREIO that allows scientists to use observations of pollutants or gases in the atmosphere, such as from satellites or surface stations, to update supercomputer models that simulate the Earth. CHEEREIO uses the difference between the model simulations of the atmosphere and real-world observations to come up with a good guess for the actual composition of our atmosphere, the true emissions of various pollutants, and whatever else they may want to study.
Geosci. Model Dev., 16, 4749–4766,Short summary
The Earth's atmosphere can support various types of global-scale waves. Some waves propagate eastward and others westward, and they can have different zonal wavenumbers. The Fourier–wavelet analysis is a useful technique for identifying different components of global-scale waves and their temporal variability. This paper introduces an easy-to-implement method to derive Fourier–wavelet spectra from 2-D space–time data. Application examples are presented using atmospheric models.
Bok H. Baek, Carlie Coats, Siqi Ma, Chi-Tsan Wang, Yunyao Li, Jia Xing, Daniel Tong, Soontae Kim, and Jung-Hun Woo
Geosci. Model Dev., 16, 4659–4676,Short summary
To enable the direct feedback effects of aerosols and local meteorology in an air quality modeling system without any computational bottleneck, we have developed an inline meteorology-induced emissions coupler module within the U.S. Environmental Protection Agency’s Community Multiscale Air Quality modeling system to dynamically model the complex MOtor Vehicle Emission Simulator (MOVES) on-road mobile emissions inline without a separate dedicated emissions processing model like SMOKE.
Christoph Neuhauser, Maicon Hieronymus, Michael Kern, Marc Rautenhaus, Annika Oertel, and Rüdiger Westermann
Geosci. Model Dev., 16, 4617–4638,Short summary
Numerical weather prediction models rely on parameterizations for sub-grid-scale processes, which are a source of uncertainty. We present novel visual analytics solutions to analyze interactively the sensitivities of a selected prognostic variable to multiple model parameters along trajectories regarding similarities in temporal development and spatiotemporal relationships. The proposed workflow is applied to cloud microphysical sensitivities along coherent strongly ascending trajectories.
Yingqi Zheng, Minttu Havu, Huizhi Liu, Xueling Cheng, Yifan Wen, Hei Shing Lee, Joyson Ahongshangbam, and Leena Järvi
Geosci. Model Dev., 16, 4551–4579,Short summary
The performance of the Surface Urban Energy and Water Balance Scheme (SUEWS) is evaluated against the observed surface exchanges (fluxes) of heat and carbon dioxide in a densely built neighborhood in Beijing. The heat flux modeling is noticeably improved by using the observed maximum conductance and by optimizing the vegetation phenology modeling. SUEWS also performs well in simulating carbon dioxide flux.
Simone Dietmüller, Sigrun Matthes, Katrin Dahlmann, Hiroshi Yamashita, Abolfazl Simorgh, Manuel Soler, Florian Linke, Benjamin Lührs, Maximilian M. Meuser, Christian Weder, Volker Grewe, Feijia Yin, and Federica Castino
Geosci. Model Dev., 16, 4405–4425,Short summary
Climate-optimized aircraft trajectories avoid atmospheric regions with a large climate impact due to aviation emissions. This requires spatially and temporally resolved information on aviation's climate impact. We propose using algorithmic climate change functions (aCCFs) for CO2 and non-CO2 effects (ozone, methane, water vapor, contrail cirrus). Merged aCCFs combine individual aCCFs by assuming aircraft-specific parameters and climate metrics. Technically this is done with a Python library.
Andreas A. Beckert, Lea Eisenstein, Annika Oertel, Tim Hewson, George C. Craig, and Marc Rautenhaus
Geosci. Model Dev., 16, 4427–4450,Short summary
We investigate the benefit of objective 3-D front detection with modern interactive visual analysis techniques for case studies of extra-tropical cyclones and comparisons of frontal structures between different numerical weather prediction models. The 3-D frontal structures show agreement with 2-D fronts from surface analysis charts and augment them in the vertical dimension. We see great potential for more complex studies of atmospheric dynamics and for operational weather forecasting.
Zhenxin Liu, Yuanhao Chen, Yuhang Wang, Cheng Liu, Shuhua Liu, and Hong Liao
Geosci. Model Dev., 16, 4385–4403,Short summary
The heterogeneous layout of urban buildings leads to the complex wind field in and over the urban canopy. Large discrepancies between the observations and the current simulations result from misunderstanding the character of the wind field. The Inhomogeneous Wind Scheme in Urban Street (IWSUS) was developed to simulate the heterogeneity of the wind speed in a typical street and then improve the simulated energy budget in the lower atmospheric layer over the urban canopy.
Kai Cao, Qizhong Wu, Lingling Wang, Nan Wang, Huaqiong Cheng, Xiao Tang, Dongqing Li, and Lanning Wang
Geosci. Model Dev., 16, 4367–4383,Short summary
Offline performance experiment results show that the GPU-HADVPPM on a V100 GPU can achieve up to 1113.6 × speedups to its original version on an E5-2682 v4 CPU. A series of optimization measures are taken, and the CAMx-CUDA model improves the computing efficiency by 128.4 × on a single V100 GPU card. A parallel architecture with an MPI plus CUDA hybrid paradigm is presented, and it can achieve up to 4.5 × speedup when launching eight CPU cores and eight GPU cards.
Geosci. Model Dev., 16, 4265–4281,Short summary
This study analyzes forecasts that were made in 2021 to help trigger measurements during the CADDIWA experiment. The WRF and CHIMERE models were run each day, and the first goal is to quantify the variability of the forecast as a function of forecast leads and forecast location. The possibility of using the different leads as an ensemble is also tested. For some locations, the correlation scores are better with this approach. This could be tested on operational forecast chains in the future.
Emily de Jong, John Ben Mackay, Oleksii Bulenok, Anna Jaruga, and Sylwester Arabas
Geosci. Model Dev., 16, 4193–4211,Short summary
In clouds, collisional breakup occurs when two colliding droplets splinter into new, smaller fragments. Particle-based modeling approaches often do not represent breakup because of the computational demands of creating new droplets. We present a particle-based breakup method that preserves the computational efficiency of these methods. In a series of simple demonstrations, we show that this representation alters cloud processes in reasonable and expected ways.
Caiyi Jin, Qiangqiang Yuan, Tongwen Li, Yuan Wang, and Liangpei Zhang
Geosci. Model Dev., 16, 4137–4154,Short summary
The semi-empirical physical approach derives PM2.5 with strong physical significance. However, due to the complex optical characteristic, the physical parameters are difficult to express accurately. Thus, combining the atmospheric physical mechanism and machine learning, we propose an optimized model. It creatively embeds the random forest model into the physical PM2.5 remote sensing approach to simulate a physical parameter. Our method shows great optimized performance in the validations.
Cyril Caram, Sophie Szopa, Anne Cozic, Slimane Bekki, Carlos A. Cuevas, and Alfonso Saiz-Lopez
Geosci. Model Dev., 16, 4041–4062,Short summary
We studied the role of halogenated compounds (containing chlorine, bromine and iodine), emitted by natural processes (mainly above the oceans), in the chemistry of the lower layers of the atmosphere. We introduced this relatively new chemistry in a three-dimensional climate–chemistry model and looked at how this chemistry will disrupt the ozone. We showed that the concentration of ozone decreases by 22 % worldwide and that of the atmospheric detergent, OH, by 8 %.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Marc Bocquet, Jinghui Lian, Grégoire Broquet, Gerrit Kuhlmann, Alexandre Danjou, and Thomas Lauvaux
Geosci. Model Dev., 16, 3997–4016,Short summary
Monitoring of CO2 emissions is key to the development of reduction policies. Local emissions, from cities or power plants, may be estimated from CO2 plumes detected in satellite images. CO2 plumes generally have a weak signal and are partially concealed by highly variable background concentrations and instrument errors, which hampers their detection. To address this problem, we propose and apply deep learning methods to detect the contour of a plume in simulated CO2 satellite images.
Min-Seop Ahn, Paul A. Ullrich, Peter J. Gleckler, Jiwoo Lee, Ana C. Ordonez, and Angeline G. Pendergrass
Geosci. Model Dev., 16, 3927–3951,Short summary
We introduce a framework for regional-scale evaluation of simulated precipitation distributions with 62 climate reference regions and 10 metrics and apply it to evaluate CMIP5 and CMIP6 models against multiple satellite-based precipitation products. The common model biases identified in this study are mainly associated with the overestimated light precipitation and underestimated heavy precipitation. These biases persist from earlier-generation models and have been slightly improved in CMIP6.
Christine Wiedinmyer, Yosuke Kimura, Elena C. McDonald-Buller, Louisa K. Emmons, Rebecca R. Buchholz, Wenfu Tang, Keenan Seto, Maxwell B. Joseph, Kelley C. Barsanti, Annmarie G. Carlton, and Robert Yokelson
Geosci. Model Dev., 16, 3873–3891,Short summary
The Fire INventory from NCAR (FINN) provides daily global estimates of emissions from open fires based on satellite detections of hot spots. This version has been updated to apply MODIS and VIIRS satellite fire detection and better represents both large and small fires. FINNv2.5 generates more emissions than FINNv1 and is in general agreement with other fire emissions inventories. The new estimates are consistent with satellite observations, but uncertainties remain regionally and by pollutant.
Lichao Yang, Wansuo Duan, and Zifa Wang
Geosci. Model Dev., 16, 3827–3848,Short summary
An approach is proposed to refine a ground meteorological observation network to improve the PM2.5 forecasts in the Beijing–Tianjin–Hebei region. A cost-effective observation network is obtained and makes the relevant PM2.5 forecasts assimilate fewer observations but achieve the forecasting skill comparable to or higher than that obtained by assimilating all ground station observations, suggesting that many of the current ground stations can be greatly scattered to avoid much unnecessary work.
Abhishekh Kumar Srivastava, Paul Aaron Ullrich, Deeksha Rastogi, Pouya Vahmani, Andrew Jones, and Richard Grotjahn
Geosci. Model Dev., 16, 3699–3722,Short summary
Stakeholders need high-resolution regional climate data for applications such as assessing water availability and mountain snowpack. This study examines 3 h and 24 h historical precipitation over the contiguous United States in the 12 km WRF version 4.2.1-based dynamical downscaling of the ERA5 reanalysis. WRF improves precipitation characteristics such as the annual cycle and distribution of the precipitation maxima, but it also displays regionally and seasonally varying precipitation biases.
Haixia Xiao, Yaqiang Wang, Yu Zheng, Yuanyuan Zheng, Xiaoran Zhuang, Hongyan Wang, and Mei Gao
Geosci. Model Dev., 16, 3611–3628,Short summary
Due to the small-scale and nonstationary nature of convective wind gusts (CGs), reliable CG nowcasting has remained unattainable. Here, we developed a deep learning model — namely CGsNet — for 0—2 h of quantitative CG nowcasting, first achieving minute—kilometer-level forecasts. Based on the CGsNet model, the average surface wind speed (ASWS) and peak wind gust speed (PWGS) predictions are obtained. Experiments indicate that CGsNet exhibits higher accuracy than the traditional method.
Maria Krutova, Mostafa Bakhoday-Paskyabi, Joachim Reuder, and Finn Gunnar Nielsen
Geosci. Model Dev., 16, 3553–3564,Short summary
Local refinement of the grid is a powerful method allowing us to reduce the computational time while preserving the accuracy in the area of interest. Depending on the implementation, the local refinement may introduce unwanted numerical effects into the results. We study the wind speed common to the wind turbine operational speeds and confirm strong alteration of the result when the heat fluxes are present, except for the specific refinement scheme used.
Sylvia Sullivan, Behrooz Keshtgar, Nicole Albern, Elzina Bala, Christoph Braun, Anubhav Choudhary, Johannes Hörner, Hilke Lentink, Georgios Papavasileiou, and Aiko Voigt
Geosci. Model Dev., 16, 3535–3551,Short summary
Clouds absorb and re-emit infrared radiation from Earth's surface and absorb and reflect incoming solar radiation. As a result, they change atmospheric temperature gradients that drive large-scale circulation. To better simulate this circulation, we study how the radiative heating and cooling from clouds depends on model settings like grid spacing; whether we describe convection approximately or exactly; and the level of detail used to describe small-scale processes, or microphysics, in clouds.
James Weber, James A. King, Katerina Sindelarova, and Maria Val Martin
Geosci. Model Dev., 16, 3083–3101,Short summary
The emissions of volatile organic compounds from vegetation (BVOCs) influence atmospheric composition and contribute to certain gases and aerosols (tiny airborne particles) which play a role in climate change. BVOC emissions are likely to change in the future due to changes in climate and land use. Therefore, accurate simulation of BVOC emission is important, and this study describes an update to the simulation of BVOC emissions in the United Kingdom Earth System Model (UKESM).
Koichi Sakaguchi, L. Ruby Leung, Colin M. Zarzycki, Jihyeon Jang, Seth McGinnis, Bryce E. Harrop, William C. Skamarock, Andrew Gettelman, Chun Zhao, William J. Gutowski, Stephen Leak, and Linda Mearns
Geosci. Model Dev., 16, 3029–3081,Short summary
We document details of the regional climate downscaling dataset produced by a global variable-resolution model. The experiment is unique in that it follows a standard protocol designed for coordinated experiments of regional models. We found negligible influence of post-processing on statistical analysis, importance of simulation quality outside of the target region, and computational challenges that our model code faced due to rapidly changing super computer systems.
Xueying Liu, Yuxuan Wang, Shailaja Wasti, Wei Li, Ehsan Soleimanian, James Flynn, Travis Griggs, Sergio Alvarez, John T. Sullivan, Maurice Roots, Laurence Twigg, Guillaume Gronoff, Timothy Berkoff, Paul Walter, Mark Estes, Johnathan W. Hair, Taylor Shingler, Amy Jo Scarino, Marta Fenn, and Laura Judd
With a comprehensive suite of ground-based and airborne remote sensing measurements during the 2021 Tracking Aerosol Convection Experiment Air Quality (TRACER-AQ) campaign in Houston, this study evaluates the simulation of the planetary boundary layer (PBL) height and the ozone vertical profile by a high-resolution (1.33 km) 3-D photochemical model Weather Research and Forecasting-driven GEOS-Chem (WRF-GC).
Xiaohan Li, Yi Zhang, Xindong Peng, Baiquan Zhou, Jian Li, and Yiming Wang
Geosci. Model Dev., 16, 2975–2993,Short summary
The weather and climate physics suites used in GRIST-A22.7.28 are compared using single-column modeling. The source of their discrepancies in terms of modeling cloud and precipitation is explored. Convective parameterization is found to be a key factor responsible for the differences. The two suites also have intrinsic differences in the interaction between microphysics and other processes, resulting in different cloud features and time step sensitivities.
Virginie Marécal, Ronan Voisin-Plessis, Tjarda Jane Roberts, Alessandro Aiuppa, Herizo Narivelo, Paul David Hamer, Béatrice Josse, Jonathan Guth, Luke Surl, and Lisa Grellier
Geosci. Model Dev., 16, 2873–2898,Short summary
We implemented a halogen volcanic chemistry scheme in a one-dimensional modelling framework preparing for further use in a three-dimensional global chemistry-transport model. The results of the simulations for an eruption of Mt Etna in 2008, including various sensitivity tests, show a good consistency with previous modelling studies.
Zhe Feng, Joseph Hardin, Hannah C. Barnes, Jianfeng Li, L. Ruby Leung, Adam Varble, and Zhixiao Zhang
Geosci. Model Dev., 16, 2753–2776,Short summary
PyFLEXTRKR is a flexible atmospheric feature tracking framework with specific capabilities to track convective clouds from a variety of observations and model simulations. The package has a collection of multi-object identification algorithms and has been optimized for large datasets. This paper describes the algorithms and demonstrates applications for tracking deep convective cells and mesoscale convective systems from observations and model simulations at a wide range of scales.
Yan Ji, Bing Gong, Michael Langguth, Amirpasha Mozaffari, and Xiefei Zhi
Geosci. Model Dev., 16, 2737–2752,Short summary
Formulating short-term precipitation forecasting as a video prediction task, a novel deep learning architecture (convolutional long short-term memory generative adversarial network, CLGAN) is proposed. A benchmark dataset is built on minute-level precipitation measurements. Results show that with the GAN component the model generates predictions sharing statistical properties with observations, resulting in it outperforming the baseline in dichotomous and spatial scores for heavy precipitation.
Aleksander Lacima, Hervé Petetin, Albert Soret, Dene Bowdalo, Oriol Jorba, Zhaoyue Chen, Raúl F. Méndez Turrubiates, Hicham Achebak, Joan Ballester, and Carlos Pérez García-Pando
Geosci. Model Dev., 16, 2689–2718,Short summary
Understanding how air pollution varies across space and time is of key importance for the safeguarding of human health. This work arose in the context of the project EARLY-ADAPT, for which the Barcelona Supercomputing Center developed an air pollution database covering all of Europe. Through different statistical methods, we compared two global pollution models against measurements from ground stations and found significant discrepancies between the observed and the modeled surface pollution.
Andrew Geiss, Po-Lun Ma, Balwinder Singh, and Joseph C. Hardin
Geosci. Model Dev., 16, 2355–2370,Short summary
Atmospheric aerosols play a critical role in Earth's climate, but it is too computationally expensive to directly model their interaction with radiation in climate simulations. This work develops a new neural-network-based parameterization of aerosol optical properties for use in the Energy Exascale Earth System Model that is much more accurate than the current one; it also introduces a unique model optimization method that involves randomly generating neural network architectures.
Joey C. Y. Lam, Amos P. K. Tai, Jason A. Ducker, and Christopher D. Holmes
Geosci. Model Dev., 16, 2323–2342,Short summary
We developed a new component within an atmospheric chemistry model to better simulate plant ecophysiological processes relevant for ozone air quality. We showed that it reduces simulated biases in plant uptake of ozone in prior models. The new model enables us to explore how future climatic changes affect air quality via affecting plants, examine ozone–vegetation interactions and feedbacks, and evaluate the impacts of changing atmospheric chemistry and climate on vegetation productivity.
Qian Shu, Sergey L. Napelenok, William T. Hutzell, Kirk R. Baker, Barron H. Henderson, Benjamin N. Murphy, and Christian Hogrefe
Geosci. Model Dev., 16, 2303–2322,Short summary
Source attribution methods are generally used to determine culpability of precursor emission sources to ambient pollutant concentrations. However, source attribution of secondarily formed pollutants such as ozone and its precursors cannot be explicitly measured, making evaluation of source apportionment methods challenging. In this study, multiple apportionment approach comparisons show common features but still reveal wide variations in predicted sector contribution and species dependency.
Rüdiger Brecht, Lucie Bakels, Alex Bihlo, and Andreas Stohl
Geosci. Model Dev., 16, 2181–2192,Short summary
We use neural-network-based single-image super-resolution to improve the upscaling of meteorological wind fields to be used for particle dispersion models. This deep-learning-based methodology improves the standard linear interpolation typically used in particle dispersion models. The improvement of wind fields leads to substantial improvement in the computed trajectories of the particles.
Alvaro Criado, Jan Mateu Armengol, Hervé Petetin, Daniel Rodriguez-Rey, Jaime Benavides, Marc Guevara, Carlos Pérez García-Pando, Albert Soret, and Oriol Jorba
Geosci. Model Dev., 16, 2193–2213,Short summary
This work aims to derive and evaluate a general statistical post-processing tool specifically designed for the street scale that can be applied to any urban air quality system. Our data fusion methodology corrects NO2 fields based on continuous hourly observations and experimental campaigns. This study enables us to obtain exceedance probability maps of air quality standards. In 2019, 13 % of the Barcelona area had a 70 % or higher probability of exceeding the annual legal NO2 limit of 40 µg/m3.
Liang Wang, Bingcheng Wan, Shaohui Zhou, Haofei Sun, and Zhiqiu Gao
Geosci. Model Dev., 16, 2167–2179,Short summary
The past 24 h TC trajectories and meteorological field data were used to forecast TC tracks in the northwestern Pacific from hours 6–72 based on GRU_CNN, which we proposed in this paper and which has better prediction results than traditional single deep-learning methods. The historical steering flow of cyclones has a significant effect on improving the accuracy of short-term forecasting, while, in long-term forecasting, the SST and geopotential height will have a particular impact.
Roland Eichinger, Sebastian Rhode, Hella Garny, Peter Preusse, Petr Pisoft, Aleš Kuchar, Patrick Jöckel, Astrid Kerkweg, and Bastian Kern
Dynamical model biases result from the columnar approach of gravity wave (GW) schemes, but parallel decomposition makes horizontal GW propagation computationally unfeasible. In the global model EMAC, we approximate it by GW redistribution at one altitude using tailor-made redistribution maps generated with a ray-tracer. More spread-out GW drag helps reconciling the model with observations and closing the 60S GW gap. Polar vortex dynamics are improved, enhancing climate model credibility.
Thibaud Sarica, Alice Maison, Yelva Roustan, Matthias Ketzel, Steen Solvang Jensen, Youngseob Kim, Christophe Chaillou, and Karine Sartelet
Geosci. Model Dev. Discuss.,
Revised manuscript accepted for GMDShort summary
A new version of the Model of Urban Network of Intersecting Canyons and Highways (MUNICH) is developed to represent heterogeneities of concentrations in streets. The street volume is discretized vertically and horizontally to limit the artificial dilution of emissions and concentrations. This new version is applied to street networks in Copenhagen and Paris. The comparisons to observations are improved, with higher concentrations of pollutants emitted by traffic at the bottom of the street.
Thomas Berkemeier, Matteo Krüger, Aryeh Feinberg, Marcel Müller, Ulrich Pöschl, and Ulrich K. Krieger
Geosci. Model Dev., 16, 2037–2054,Short summary
Kinetic multi-layer models (KMs) successfully describe heterogeneous and multiphase atmospheric chemistry. In applications requiring repeated execution, however, these models can be too expensive. We trained machine learning surrogate models on output of the model KM-SUB and achieved high correlations. The surrogate models run orders of magnitude faster, which suggests potential applicability in global optimization tasks and as sub-modules in large-scale atmospheric models.
Elena Fillola, Raul Santos-Rodriguez, Alistair Manning, Simon O'Doherty, and Matt Rigby
Geosci. Model Dev., 16, 1997–2009,Short summary
Lagrangian particle dispersion models are used extensively for the estimation of greenhouse gas (GHG) fluxes using atmospheric observations. However, these models do not scale well as data volumes increase. Here, we develop a proof-of-concept machine learning emulator that can produce outputs similar to those of the dispersion model, but 50 000 times faster, using only meteorological inputs. This works demonstrates the potential of machine learning to accelerate GHG estimations across the globe.
Stijn Van Leuven, Pieter De Meutter, Johan Camps, Piet Termonia, and Andy Delcloo
Precipitation collects airborne particles and deposits these on the ground. This process is called wet deposition and greatly determines how airborne radioactive particles (released routinely or accidental) contaminate the surface. In this work we present a new method to improve the calculation of wet deposition in computer models. We apply this method to the existing model FLEXPART by simulating the Fukushima nuclear accident (2011), and show that it improves the simulation of wet deposition.
Maria J. Chinita, Mikael Witte, Marcin J. Kurowski, Joao Teixeira, Kay Suselj, Georgios Matheou, and Peter Bogenschutz
Geosci. Model Dev., 16, 1909–1924,Short summary
Low clouds are one of the largest sources of uncertainty in climate prediction. In this paper, we introduce the first version of the unified turbulence and shallow convection parameterization named SHOC+MF developed to improve the representation of shallow cumulus clouds in the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM). Here, we also show promising preliminary results in a single-column model framework for two benchmark cases of shallow cumulus convection.
Kun Wang, Chao Gao, Kai Wu, Kaiyun Liu, Haofan Wang, Mo Dan, Xiaohui Ji, and Qingqing Tong
Geosci. Model Dev., 16, 1961–1973,Short summary
This study establishes an easy-to-use and integrated framework for a model-ready emission inventory for the Weather Research and Forecasting (WRF)–Air Quality Numerical Model (AQM). A free tool called the ISAT (Inventory Spatial Allocation Tool) was developed based on this framework. ISAT helps users complete the workflow from the WRF nested-domain configuration to a model-ready emission inventory for AQM with a regional emission inventory and a shapefile for the target region.
Jagat S. H. Bisht, Prabir K. Patra, Masayuki Takigawa, Takashi Sekiya, Yugo Kanaya, Naoko Saitoh, and Kazuyuki Miyazaki
Geosci. Model Dev., 16, 1823–1838,Short summary
In this study, we estimated CH4 fluxes using an advanced 4D-LETKF method. The system was tested and optimized using observation system simulation experiments (OSSEs), where a known surface emission distribution is retrieved from synthetic observations. The availability of satellite measurements has increased, and there are still many missions focused on greenhouse gas observations that have not yet launched. The technique being referred to has the potential to improve estimates of CH4 fluxes.
Ruizi Shi and Fanghua Xu
Geosci. Model Dev., 16, 1839–1856,Short summary
Based on the Gaussian quadrature method, a fast algorithm of sea-spray-mediated heat flux is developed. Compared with the widely used single-radius algorithm, the new fast algorithm shows a better agreement with the full spectrum integral of spray flux. The new fast algorithm is evaluated in a coupled modeling system, and the simulations of sea surface temperature, wind speed and wave height are improved. Thereby, the new fast algorithm has great potential to be used in coupled modeling systems.
Forwood Wiser, Bryan K. Place, Siddhartha Sen, Havala O. T. Pye, Benjamin Yang, Daniel M. Westervelt, Daven K. Henze, Arlene M. Fiore, and V. Faye McNeill
Geosci. Model Dev., 16, 1801–1821,Short summary
We developed a reduced model of atmospheric isoprene oxidation, AMORE-Isoprene 1.0. It was created using a new Automated Model Reduction (AMORE) method designed to simplify complex chemical mechanisms with minimal manual adjustments to the output. AMORE-Isoprene 1.0 has improved accuracy and similar size to other reduced isoprene mechanisms. When included in the CRACMM mechanism, it improved the accuracy of EPA’s CMAQ model predictions for the northeastern USA compared to observations.
Jonathan D. Labriola, Jeremy A. Gibbs, and Louis J. Wicker
Geosci. Model Dev., 16, 1779–1799,Short summary
Observing system simulation experiments (OSSEs) are simulated case studies used to understand how different assimilated weather observations impact forecast skill. This study introduces the methods used to create an OSSE for a tornadic quasi-linear convective system event. These steps provide an opportunity to simulate a realistic high-impact weather event and can be used to encourage a more diverse set of OSSEs.
Mike Bush, Ian Boutle, John Edwards, Anke Finnenkoetter, Charmaine Franklin, Kirsty Hanley, Aravindakshan Jayakumar, Huw Lewis, Adrian Lock, Marion Mittermaier, Saji Mohandas, Rachel North, Aurore Porson, Belinda Roux, Stuart Webster, and Mark Weeks
Geosci. Model Dev., 16, 1713–1734,Short summary
Building on the baseline of RAL1, the RAL2 science configuration is used for regional modelling around the UM partnership and in operations at the Met Office. RAL2 has been tested in different parts of the world including Australia, India and the UK. RAL2 increases medium and low cloud amounts in the mid-latitudes compared to RAL1, leading to improved cloud forecasts and a reduced diurnal cycle of screen temperature. There is also a reduction in the frequency of heavier precipitation rates.
Daniel Yazgi and Tinja Olenius
We present flexible tools to implement aerosol formation rate predictions in climate and chemical transport models. New-particle formation is a significant but uncertain factor affecting aerosol numbers, and an active field within molecular modeling which provides data for assessing formation rates for different chemical species. We introduce tools to generate and interpolate formation rate look-up tables for user-defined data, thus enabling easy inclusion and testing of formation schemes.
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Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner, J., Wang, W., Powers, J. G., Duda, M. G., Barker, D. M., and Huang, X.-Y.: A Description of the Advanced Research WRF Model Version 4, Tech. rep., UCAR/NCAR, Boulder, Colorado, USA, https://doi.org/10.5065/1DFH-6P97, 2019. a, b
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Xiao, H., Endo, S., Wong, M., Skamarock, W. C., Klemp, J. B., Fast, J. D., Gustafson, W. I., Vogelmann, A. M., Wang, H., Liu, Y., and Lin, W.: Modifications to WRF's Dynamical Core to Improve the Treatment of Moisture for Large-Eddy Simulations, J. Adv. Model. Earth Sy., 7, 1627–1642, https://doi.org/10/gg5jgn, 2015. a
We present WRFlux, an open-source software that allows numerically consistent, time-averaged budget evaluation of prognostic variables for the numerical weather prediction model WRF as well as the transformation of the budget equations from the terrain-following grid of the model to the Cartesian coordinate system. We demonstrate the performance and a possible application of WRFlux and illustrate the detrimental effects of approximations that are inconsistent with the model numerics.
We present WRFlux, an open-source software that allows numerically consistent, time-averaged...