Articles | Volume 15, issue 2
Methods for assessment of models
| Highlight paper
26 Jan 2022
Methods for assessment of models | Highlight paper | 26 Jan 2022
Numerically consistent budgets of potential temperature, momentum, and moisture in Cartesian coordinates: application to the WRF model
Matthias Göbel et al.
No articles found.
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.
Hetal Dabhi, Mathias Rotach, and Michael Oberguggenberger
Hydrol. Earth Syst. Sci. Discuss.,
Revised manuscript under review for HESSShort summary
High-resolution precipitation data consistent both in space and time for current and future climate are required for climate change impact assessments, but it is very challenging for complex topography. We present a model that generates synthetic gridded data of daily precipitation at 1 km spatial resolution using observed meteorological station data as input and provides data where historical observations are not available. We evaluate this model for a mountainous region in the European Alps.
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,
Related subject area
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Youhua Tang, Patrick C. Campbell, Pius Lee, Rick Saylor, Fanglin Yang, Barry Baker, Daniel Tong, Ariel Stein, Jianping Huang, Ho-Chun Huang, Li Pan, Jeff McQueen, Ivanka Stajner, Jose Tirado-Delgado, Youngsun Jung, Melissa Yang, Ilann Bourgeois, Jeff Peischl, Tom Ryerson, Donald Blake, Joshua Schwarz, Jose-Luis Jimenez, James Crawford, Glenn Diskin, Richard Moore, Johnathan Hair, Greg Huey, Andrew Rollins, Jack Dibb, and Xiaoyang Zhang
Geosci. Model Dev., 15, 7977–7999,Short summary
This paper compares two meteorological datasets for driving a regional air quality model: a regional meteorological model using WRF (WRF-CMAQ) and direct interpolation from an operational global model (GFS-CMAQ). In the comparison with surface measurements and aircraft data in summer 2019, these two methods show mixed performance depending on the corresponding meteorological settings. Direct interpolation is found to be a viable method to drive air quality models.
Zhiquan Liu, Chris Snyder, Jonathan J. Guerrette, Byoung-Joo Jung, Junmei Ban, Steven Vahl, Yali Wu, Yannick Trémolet, Thomas Auligné, Benjamin Ménétrier, Anna Shlyaeva, Stephen Herbener, Emily Liu, Daniel Holdaway, and Benjamin T. Johnson
Geosci. Model Dev., 15, 7859–7878,Short summary
JEDI-MPAS 1.0.0, a new data assimilation (DA) system for the MPAS model, was publicly released for community use. This article describes JEDI-MPAS's implementation of the ensemble–variational DA technique and demonstrates its robustness and credible performance by incrementally adding three types of microwave radiances (clear-sky AMSU-A, all-sky AMSU-A, clear-sky MHS) to a non-radiance DA experiment. We intend to periodically release new and improved versions of JEDI-MPAS in upcoming years.
Li Fang, Jianbing Jin, Arjo Segers, Hai Xiang Lin, Mijie Pang, Cong Xiao, Tuo Deng, and Hong Liao
Geosci. Model Dev., 15, 7791–7807,Short summary
This study proposes a regional feature selection-based machine learning system to predict short-term air quality in China. The system has a tool that can figure out the importance of input data for better prediction. It provides large-scale air quality prediction that exhibits improved interpretability, fewer training costs, and higher accuracy compared with a standard machine learning system. It can act as an early warning for citizens and reduce exposure to PM2.5 and other air pollutants.
Stella E. I. Manavi and Spyros N. Pandis
Geosci. Model Dev., 15, 7731–7749,Short summary
The paper describes the first step towards the development of a simulation framework for the chemistry and secondary organic aerosol production of intermediate-volatility organic compounds (IVOCs). These compounds can be a significant source of organic particulate matter. Our approach treats IVOCs as lumped compounds that retain their chemical characteristics. Estimated IVOC emissions from road transport were a factor of 8 higher than emissions used in previous applications.
Peter Bräuer and Matthias Tesche
Geosci. Model Dev., 15, 7557–7572,Short summary
This paper presents a tool for (i) finding temporally and spatially resolved intersections between two- or three-dimensional geographical tracks (trajectories) and (ii) extracting of data in the vicinity of intersections to achieve the optimal combination of various data sets.
Benjamin Zanger, Jia Chen, Man Sun, and Florian Dietrich
Geosci. Model Dev., 15, 7533–7556,Short summary
Gaussian priors (GPs) used in least squares inversion do not reflect the true distributions of greenhouse gas emissions well. A method that does not rely on GPs is sparse reconstruction (SR). We show that necessary conditions for SR are satisfied for cities and that the application of a wavelet transform can further enhance sparsity. We apply the theory of compressed sensing to SR. Our results show that SR needs fewer measurements and is superior for assessing unknown emitters compared to GPs.
Paul Konopka, Mengchu Tao, Marc von Hobe, Lars Hoffmann, Corinna Kloss, Fabrizio Ravegnani, C. Michael Volk, Valentin Lauther, Andreas Zahn, Peter Hoor, and Felix Ploeger
Geosci. Model Dev., 15, 7471–7487,Short summary
Pure trajectory-based transport models driven by meteorology derived from reanalysis products (ERA5) take into account only the resolved, advective part of transport. That means neither mixing processes nor unresolved subgrid-scale advective processes like convection are included. The Chemical Lagrangian Model of the Stratosphere (CLaMS) includes these processes. We show that isentropic mixing dominates unresolved transport. The second most important transport process is unresolved convection.
Youngseob Kim, Lya Lugon, Alice Maison, Thibaud Sarica, Yelva Roustan, Myrto Valari, Yang Zhang, Michel André, and Karine Sartelet
Geosci. Model Dev., 15, 7371–7396,Short summary
This paper presents the latest version of the street-network model MUNICH, v2.0. The description of MUNICH v1.0, which models gas-phase pollutants in a street network, was published in GMD in 2018. Since then, major modifications have been made to MUNICH. The comprehensive aerosol model SSH-aerosol is now coupled to MUNICH to simulate primary and secondary aerosol concentrations. New parameterisations have also been introduced. Test cases are defined to illustrate the new model functionalities.
Yongbo Zhou, Yubao Liu, Zhaoyang Huo, and Yang Li
Geosci. Model Dev., 15, 7397–7420,Short summary
The study evaluates the performance of the Data Assimilation Research Testbed (DART), equipped with the recently added forward operator Radiative Transfer for TOVS (RTTOV), in assimilating FY-4A visible images into the Weather Research and Forecasting (WRF) model. The ability of the WRF-DART/RTTOV system to improve the forecasting skills for a tropical storm over East Asia and the Western Pacific is demonstrated in an Observing System Simulation Experiment framework.
Dánnell Quesada-Chacón, Klemens Barfus, and Christian Bernhofer
Geosci. Model Dev., 15, 7353–7370,Short summary
We improved the performance of past perfect prognosis statistical downscaling methods while achieving full model repeatability with GPU-calculated deep learning models using the TensorFlow, climate4R, and VALUE frameworks. We employed the ERA5 reanalysis as predictors and ReKIS (eastern Ore Mountains, Germany, 1 km resolution) as precipitation predictand, while incorporating modern deep learning architectures. The achieved repeatability is key to accomplish further milestones with deep learning.
Petri Clusius, Carlton Xavier, Lukas Pichelstorfer, Putian Zhou, Tinja Olenius, Pontus Roldin, and Michael Boy
Geosci. Model Dev., 15, 7257–7286,Short summary
Atmospheric chemistry and aerosol processes form a dynamic and sensitively balanced system, and solving problems regarding air quality or climate requires detailed modelling and coupling of the processes. The models involved are often very complex to use. We have addressed this problem with the new ARCA box model. It puts much of the current knowledge of the nano- and microscale aerosol dynamics and chemistry into usable software and has the potential to become a valuable tool in the community.
Adam Milsom, Amy Lees, Adam M. Squires, and Christian Pfrang
Geosci. Model Dev., 15, 7139–7151,Short summary
MultilayerPy is a Python-based framework facilitating the creation, running and optimisation of state-of-the-art kinetic multi-layer models of aerosol and film processes. Models can be fit to data with local and global optimisation algorithms along with a statistical sampling algorithm, which quantifies the uncertainty in optimised model parameters. This “modelling study in a box” enables more reproducible and reliable results, with model code and outputs produced in a human-readable way.
Johan F. de Haan, Ping Wang, Maarten Sneep, J. Pepijn Veefkind, and Piet Stammes
Geosci. Model Dev., 15, 7031–7050,Short summary
We present an overview of the DISAMAR radiative transfer code, highlighting the novel semi-analytical derivatives for the doubling–adding formulae and the new DISMAS technique for weak absorbers. DISAMAR includes forward simulations and retrievals for satellite spectral measurements from 270 to 2400 nm to determine instrument specifications for passive remote sensing. It has been used in various Sentinel-4/5P/5 projects and in the TROPOMI aerosol layer height and ozone profile products.
Ivette H. Banos, Will D. Mayfield, Guoqing Ge, Luiz F. Sapucci, Jacob R. Carley, and Louisa Nance
Geosci. Model Dev., 15, 6891–6917,Short summary
A prototype data assimilation system for NOAA’s next-generation rapidly updated, convection-allowing forecast system, or Rapid Refresh Forecast System (RRFS) v0.1, is tested and evaluated. The impact of using data assimilation with a convective storm case study is examined. Although the convection in RRFS tends to be overestimated in intensity and underestimated in extent, the use of data assimilation proves to be crucial to improve short-term forecasts of storms and precipitation.
Andrew Geiss, Sam J. Silva, and Joseph C. Hardin
Geosci. Model Dev., 15, 6677–6694,Short summary
This work demonstrates the use of modern machine learning techniques to enhance the resolution of atmospheric chemistry simulations. We evaluate the schemes for an 8 x 10 increase in resolution and find that they perform substantially better than conventional methods. Methods are introduced to target machine learning methods towards this type of problem, most notably by ensuring they do not break known physical constraints.
Daniel C. Anderson, Melanie B. Follette-Cook, Sarah A. Strode, Julie M. Nicely, Junhua Liu, Peter D. Ivatt, and Bryan N. Duncan
Geosci. Model Dev., 15, 6341–6358,Short summary
The hydroxyl radical (OH) is the most important chemical in the atmosphere for removing certain pollutants, including methane, the second-most-important greenhouse gas. We present a methodology to create an easily modifiable parameterization that can calculate OH concentrations in a computationally efficient way. The parameterization, which predicts OH within 5 %, can be integrated into larger climate models to allow for calculation of the interactions between OH, methane, and other chemicals.
Akshay Sridhar, Yassine Tissaoui, Simone Marras, Zhaoyi Shen, Charles Kawczynski, Simon Byrne, Kiran Pamnany, Maciej Waruszewski, Thomas H. Gibson, Jeremy E. Kozdon, Valentin Churavy, Lucas C. Wilcox, Francis X. Giraldo, and Tapio Schneider
Geosci. Model Dev., 15, 6259–6284,Short summary
ClimateMachine is a new open-source Julia-language atmospheric modeling code. We describe its limited-area configuration and the model equations, and we demonstrate applicability through benchmark problems, including atmospheric flow in the shallow cumulus regime. We show that the discontinuous Galerkin numerics and model equations allow global conservation of key variables (up to sources and sinks). We assess CPU strong scaling and GPU weak scaling to show its suitability for large simulations.
Joshua Chun Kwang Lee, Javier Amezcua, and Ross Noel Bannister
Geosci. Model Dev., 15, 6197–6219,Short summary
In this article, we implement a novel data assimilation method for the ABC–DA system which combines traditional data assimilation approaches in a hybrid approach. We document the technical development and test the hybrid approach in idealised experiments within a tropical framework of the ABC–DA system. Our findings indicate that the hybrid approach outperforms individual traditional approaches. Its potential benefits have been highlighted and should be explored further within this framework.
Vincent Huijnen, Philippe Le Sager, Marcus O. Köhler, Glenn Carver, Samuel Rémy, Johannes Flemming, Simon Chabrillat, Quentin Errera, and Twan van Noije
Geosci. Model Dev., 15, 6221–6241,Short summary
We report on the first implementation of atmospheric chemistry and aerosol as part of the OpenIFS model, based on the CAMS global model. We give an overview of the model and evaluate two reference model configurations, with and without the stratospheric chemistry extension, against a variety of observational datasets. This OpenIFS version with atmospheric composition components is open to the scientific user community under a standard OpenIFS license.
Xueyin Ruan, Chun Zhao, Rahul A. Zaveri, Pengzhen He, Xinming Wang, Jingyuan Shao, and Lei Geng
Geosci. Model Dev., 15, 6143–6164,Short summary
Accurate prediction of aerosol pH in chemical transport models is essential to aerosol modeling. This study examines the performance of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) on aerosol pH predictions and the sensitivities to emissions of nonvolatile cations and NH3, aerosol-phase state assumption, and heterogeneous sulfate production. Temporal evolution of aerosol pH during haze cycles in Beijing and the driving factors are also presented and discussed.
Ping Wang, Kebiao Mao, Fei Meng, Zhihao Qin, Shu Fang, and Sayed M. Bateni
Geosci. Model Dev., 15, 6059–6083,Short summary
In order to obtain the key parameters of high-temperature spatial–temporal variation analysis, this study proposed a daily highest air temperature (Tmax) estimation frame to build a Tmax dataset in China from 1979 to 2018. We found that the annual and seasonal mean Tmax in most areas of China showed an increasing trend. The abnormal temperature changes mainly occurred in El Nin~o years or La Nin~a years. IOBW had a stronger influence on China's warming events than other factors.
Vanessa Simone Rieger and Volker Grewe
Geosci. Model Dev., 15, 5883–5903,Short summary
Road traffic emissions of nitrogen oxides, volatile organic compounds and carbon monoxide produce ozone in the troposphere and thus influence Earth's climate. To assess the ozone response to a broad range of mitigation strategies for road traffic, we developed a new chemistry–climate response model called TransClim. It is based on lookup tables containing climate–response relations and thus is able to quickly determine the climate response of a mitigation option.
Josué Bock, Jan Kaiser, Max Thomas, Andreas Bott, and Roland von Glasow
Geosci. Model Dev., 15, 5807–5828,Short summary
MISTRA-v9.0 is an atmospheric boundary layer chemistry model. The model includes a detailed particle description with regards to the microphysics, gas–particle interactions, and liquid phase chemistry within particles. Version 9.0 is the first release of MISTRA as an open-source community model. This paper presents a thorough description of the model characteristics and components. We show some examples of simulations reproducing previous studies with MISTRA with good consistency.
Daniel J. Varon, Daniel J. Jacob, Melissa Sulprizio, Lucas A. Estrada, William B. Downs, Lu Shen, Sarah E. Hancock, Hannah Nesser, Zhen Qu, Elise Penn, Zichong Chen, Xiao Lu, Alba Lorente, Ashutosh Tewari, and Cynthia A. Randles
Geosci. Model Dev., 15, 5787–5805,Short summary
Reducing atmospheric methane emissions is critical to slow near-term climate change. Globally surveying satellite instruments like the TROPOspheric Monitoring Instrument (TROPOMI) have unique capabilities for monitoring atmospheric methane around the world. Here we present a user-friendly cloud-computing tool that enables researchers and stakeholders to quantify methane emissions across user-selected regions of interest using TROPOMI satellite observations.
Haochen Sun, Jimmy C. H. Fung, Yiang Chen, Zhenning Li, Dehao Yuan, Wanying Chen, and Xingcheng Lu
Geosci. Model Dev. Discuss.,
Revised manuscript accepted for GMDShort summary
This study developed a novel deep-learning layer, the broadcasting layer, to build an end-to-end LSTM based deep-learning model for regional air pollution forecast. By combining the ground observation, WRF-CMAQ simulation, and the Broadcasting-LSTM deep-learning model, forecast accuracy has been significantly improved when compared to other methods. The broadcasting layer and its variants can also be applied in other research areas to supersede the traditional numerical interpolation methods.
Taewon Cho, Julianne Chung, Scot M. Miller, and Arvind K. Saibaba
Geosci. Model Dev., 15, 5547–5565,Short summary
Atmospheric inverse modeling describes the process of estimating greenhouse gas fluxes or air pollution emissions at the Earth's surface using observations of these gases collected in the atmosphere. The launch of new satellites, the expansion of surface observation networks, and a desire for more detailed maps of surface fluxes have yielded numerous computational and statistical challenges. This article describes computationally efficient methods for large-scale atmospheric inverse modeling.
Zhiqiang Liu, Ning Zeng, Yun Liu, Eugenia Kalnay, Ghassem Asrar, Bo Wu, Qixiang Cai, Di Liu, and Pengfei Han
Geosci. Model Dev., 15, 5511–5528,Short summary
We described the application of a constrained ensemble Kalman filter (CEnKF) in a joint CO2 and surface carbon fluxes estimation study. By assimilating the pseudo-surface and OCO-2 observations, the annual global flux estimation is significantly biased without mass conservation. With the additional CEnKF process, the CO2 mass is strictly constrained, and the estimation of annual fluxes is significantly improved.
Zheng Zhang, Chuyao Luo, Shanshan Feng, Rui Ye, Yunming Ye, and Xutao Li
Geosci. Model Dev., 15, 5407–5419,Short summary
In this paper, we develop a model to predict radar echo sequences and apply it in the precipitation nowcasting field. Different from existing models, we propose two new attention modules. By introducing them, the performance of RAP-Net outperforms other models, especially in those regions with moderate and heavy rainfall. Considering that these regions cause more threats to human activities, the research in our work is significant for preventing natural disasters caused by heavy rainfall.
Fabian Maier, Christoph Gerbig, Ingeborg Levin, Ingrid Super, Julia Marshall, and Samuel Hammer
Geosci. Model Dev., 15, 5391–5406,Short summary
We show that the default representation of point source emissions in WRF–STILT leads to large overestimations when modelling fossil fuel CO2 concentrations for a 30 m high observation site during stable atmospheric conditions. We therefore introduce a novel point source modelling approach in WRF-STILT that takes into account their effective emission heights and results in a much better agreement with observations.
Ivo Suter, Tom Grylls, Birgit S. Sützl, Sam O. Owens, Chris E. Wilson, and Maarten van Reeuwijk
Geosci. Model Dev., 15, 5309–5335,Short summary
Cities are increasingly moving to the fore of climate and air quality research due to their central role in the population’s health and well-being, while suitable models remain scarce. This article describes the development of a new urban LES model, which allows examining the effects of various processes, infrastructure and vegetation on the local climate and air quality. Possible applications are demonstrated and a comparison to an experiment is shown.
Li Zhang, Raffaele Montuoro, Stuart A. McKeen, Barry Baker, Partha S. Bhattacharjee, Georg A. Grell, Judy Henderson, Li Pan, Gregory J. Frost, Jeff McQueen, Rick Saylor, Haiqin Li, Ravan Ahmadov, Jun Wang, Ivanka Stajner, Shobha Kondragunta, Xiaoyang Zhang, and Fangjun Li
Geosci. Model Dev., 15, 5337–5369,Short summary
The NOAA’s air quality predictions contribute to protecting lives and health in the US, which requires sustainable development and improvement of forecast systems. GEFS-Aerosols v1 has been developed in a collaboration between the NOAA research laboratories for operational forecast since September 2020 in the NCEP. The predictions demonstrate substantial improvements for both composition and variability of aerosol distributions over those from the former operational system.
Xuanli Li, Jason B. Roberts, Jayanthi Srikishen, Jonathan L. Case, Walter A. Petersen, Gyuwon Lee, and Christopher R. Hain
Geosci. Model Dev., 15, 5287–5308,Short summary
This research assimilated the Global Precipitation Measurement (GPM) satellite-retrieved ocean surface meteorology data into the Weather Research and Forecasting (WRF) model with the Gridpoint Statistical Interpolation (GSI) system. This was for two snowstorms during the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic Winter Games' (ICE-POP 2018) field experiments. The results indicated a positive impact of the data for short-term forecasts for heavy snowfall.
Philippe Thunis, Alain Clappier, Enrico Pisoni, Bertrand Bessagnet, Jeroen Kuenen, Marc Guevara, and Susana Lopez-Aparicio
Geosci. Model Dev., 15, 5271–5286,Short summary
In this work, we propose a screening method to improve the quality of emission inventories, which are responsible for large uncertainties in air-quality modeling. The first step of screening consists of keeping only emission contributions that are relevant enough. In a second step, the method identifies large differences that provide evidence of methodological divergence or errors. We used the approach to compare two versions of the CAMS-REG European-scale inventory over 150 European cities.
Julian Quimbayo-Duarte, Johannes Wagner, Norman Wildmann, Thomas Gerz, and Juerg Schmidli
Geosci. Model Dev., 15, 5195–5209,Short summary
The ultimate objective of this model evaluation is to improve boundary layer flow representation over complex terrain. The numerical model is tested against observations retrieved during the Perdigão 2017 field campaign (moderate complex terrain). We observed that the inclusion of a forest parameterization in the numerical model significantly improves the representation of the wind field in the atmospheric boundary layer.
Walter Hannah and Kyle Pressel
A multiscale modelling framework couples two models of the atmosphere that each cover different scale ranges. Traditionally, fluctuations in the small-scale model are not transported by the flow on the large-scale model grid, but this is hypothesized to be responsible for a persistent, unphysical checkerboard pattern. A method is presented to facilitate the transport of these small-scale fluctuations, analogous to how small-scale clouds and turbulence are transported in the real atmosphere.
Peter Hitchcock, Amy Butler, Andrew Charlton-Perez, Chaim I. Garfinkel, Tim Stockdale, James Anstey, Dann Mitchell, Daniela I. V. Domeisen, Tongwen Wu, Yixiong Lu, Daniele Mastrangelo, Piero Malguzzi, Hai Lin, Ryan Muncaster, Bill Merryfield, Michael Sigmond, Baoqiang Xiang, Liwei Jia, Yu-Kyung Hyun, Jiyoung Oh, Damien Specq, Isla R. Simpson, Jadwiga H. Richter, Cory Barton, Jeff Knight, Eun-Pa Lim, and Harry Hendon
Geosci. Model Dev., 15, 5073–5092,Short summary
This paper describes an experimental protocol focused on sudden stratospheric warmings to be carried out by subseasonal forecast modeling centers. These will allow for inter-model comparisons of these major disruptions to the stratospheric polar vortex and their impacts on the near-surface flow. The protocol will lead to new insights into the contribution of the stratosphere to subseasonal forecast skill and new approaches to the dynamical attribution of extreme events.
Daiwen Kang, Nicholas Heath, Robert Gilliam, Tanya Spero, and Jonathan Pleim
Lightning assimilation implemented in the WRF model's Kain-Fritsch convective scheme is updated and applied to simulations from regional to hemispheric scales using observed lightning flashes from ground-based lightning detection networks. Different user-toggled options associated with the Kain-Fritsch scheme on simulations with and without lightning assimilation are assessed. The model's performance is improved significantly by lightning assimilation, but it is sensitive to various factors.
Reimar Bauer, Jens-Uwe Grooß, Jörn Ungermann, May Bär, Markus Geldenhuys, and Lars Hoffmann
Geosci. Model Dev. Discuss.,
Revised manuscript accepted for GMDShort summary
The Mission Support System (MSS) is an open source software package that has been used for planning flight tracks of scientific aircraft in multiple measurements campaigns during the last decade. Here, we describe the MSS software and its use during the SouthTRAC measurement campaign in 2019. As example for how the MSS software is used in conjunction with many data sets, we describe the planning of a single flight probing orographic gravity waves propagating up into the lower mesosphere.
Joël Thanwerdas, Marielle Saunois, Antoine Berchet, Isabelle Pison, Bruce H. Vaughn, Sylvia Englund Michel, and Philippe Bousquet
Geosci. Model Dev., 15, 4831–4851,Short summary
Estimating CH4 sources by exploiting observations within an inverse modeling framework is a powerful approach. Here, a new system designed to assimilate δ13C(CH4) observations together with CH4 observations is presented. By optimizing both the emissions and associated source signatures of multiple emission categories, this new system can efficiently differentiate the co-located emission categories and provide estimates of CH4 sources that are consistent with isotopic data.
Bok H. Baek, Rizzieri Pedruzzi, Minwoo Park, Chi-Tsan Wang, Younha Kim, Chul-Han Song, and Jung-Hun Woo
Geosci. Model Dev., 15, 4757–4781,Short summary
The Comprehensive Automobile Research System (CARS) is an open-source Python-based automobile emissions inventory model designed to efficiently estimate high-quality emissions. The CARS is designed to utilize the local vehicle activity database, such as vehicle travel distance, road-link-level network information, and vehicle-specific average speed by road type, to generate a temporally and spatially enhanced inventory for policymakers, stakeholders, and the air quality modeling community.
Joonatan Ala-Könni, Kukka-Maaria Kohonen, Matti Leppäranta, and Ivan Mammarella
Geosci. Model Dev., 15, 4739–4755,Short summary
Properties of seasonally ice-covered lakes are not currently sufficiently included in global climate models. To fill this gap, this study evaluates three models that could be used to quantify the amount of heat that moves from and into the lake by the air above it and through evaporation of the ice cover. The results show that the complex nature of the surrounding environment as well as difficulties in accurately measuring the surface temperature of ice introduce errors to these models.
Jason E. Williams, Vincent Huijnen, Idir Bouarar, Mehdi Meziane, Timo Schreurs, Sophie Pelletier, Virginie Marécal, Beatrice Josse, and Johannes Flemming
Geosci. Model Dev., 15, 4657–4687,Short summary
The global CAMS air quality model is used for providing tropospheric ozone information to end users. This paper updates the chemical mechanism employed (CBA) and compares it against two other mechanisms (MOCAGE, MOZART) and a multi-decadal dataset based on a previous version of CBA. We perform extensive validation for the US using multiple surface and aircraft datasets, providing an assessment of biases and the extent of correlation across different seasons during 2014.
Martin Vojta, Andreas Plach, Rona L. Thompson, and Andreas Stohl
In light of recent global warming, we aim to improve the methods for modeling greenhouse gas emissions in order to support the successful implementation of the Paris Agreement. In this study, we investigate certain aspects of a Bayesian inversion method that uses computer simulations and atmospheric observations to improve estimates of greenhouse gas emissions. We explore method limitations, discuss problems, and suggest improvements.
Sudhanshu Pandey, Sander Houweling, and Arjo Segers
Geosci. Model Dev., 15, 4555–4567,Short summary
Inversions are used to calculate methane emissions using atmospheric mole-fraction measurements. Multidecadal inversions are needed to extract information from the long measurement records of methane. However, multidecadal inversion computations can take months to finish. Here, we demonstrate an order of magnitude improvement in wall clock time for an iterative multidecadal inversion by physical parallelization of chemical transport model.
Jeong-Su Ko, Kyo-Sun Sunny Lim, Kwonil Kim, Gyuwon Lee, Gregory Thompson, and Alexis Berne
Geosci. Model Dev., 15, 4529–4553,Short summary
This study evaluates the performance of the four microphysics parameterizations, the WDM6, WDM7, Thompson, and Morrison schemes, in simulating snowfall events during the ICE-POP 2018 field campaign. Eight snowfall events are selected and classified into three categories (cold-low, warm-low, and air–sea interaction cases). The evaluation focuses on the simulated hydrometeors, microphysics budgets, wind fields, and precipitation using the measurement data.
Christoph Fischer, Andreas H. Fink, Elmar Schömer, Roderick van der Linden, Michael Maier-Gerber, Marc Rautenhaus, and Michael Riemer
Geosci. Model Dev., 15, 4447–4468,Short summary
Potential vorticity (PV) analysis plays a central role in studying atmospheric dynamics. For example, anomalies in the PV field near the tropopause are linked to extreme weather events. In this study, an objective strategy to identify these anomalies is presented and evaluated. As a novel concept, it can be applied to three-dimensional (3-D) data sets. Supported by 3-D visualizations, we illustrate advantages of this new analysis over existing studies along a case study.
Joseph Mouallem, Lucas Harris, and Rusty Benson
Geosci. Model Dev., 15, 4355–4371,Short summary
The single-nest capability in GFDL's dynamical core, FV3, is upgraded to support multiple same-level and telescoping nests. Grid nesting adds a refined grid over an area of interest to better resolve small-scale flow features necessary to accurately predict special weather events such as severe storms and hurricanes. This work allows concurrent execution of multiple same-level and telescoping multi-level nested grids in both global and regional setups.
Shunji Kotsuki, Takemasa Miyoshi, Keiichi Kondo, and Roland Potthast
Geosci. Model Dev. Discuss.,
Revised manuscript accepted for GMDShort summary
Data assimilation plays an important role in numerical weather prediction (NWP) to combine forecasted states and millions of observations. While data assimilation methods in NWP usually assume the Gaussian error distribution, some variables in the atmosphere are known to have non-Gaussian error statistics such as precipitation. This study extended a widely used ensemble data assimilation algorithm for enabling to assimilate more observation with non-Gaussian error characteristics.
Clara Betancourt, Timo T. Stomberg, Ann-Kathrin Edrich, Ankit Patnala, Martin G. Schultz, Ribana Roscher, Julia Kowalski, and Scarlet Stadtler
Geosci. Model Dev., 15, 4331–4354,Short summary
Ozone is a toxic greenhouse gas with high spatial variability. We present a machine-learning-based ozone-mapping workflow generating a transparent and reliable product. Going beyond standard mapping methods, this work combines explainable machine learning with uncertainty assessment to increase the integrity of the produced map.
Huan Fang and Greg Michalski
Geosci. Model Dev., 15, 4239–4258,Short summary
A new emission input dataset that incorporates nitrogen isotopes has been used in the CMAQ (Community Multiscale Air Quality) modeling system simulation to qualitatively analyze the changes in δ15N values, due to the dispersion, mixing, and transport of the atmospheric NOx emitted from different sources. The dispersion, mixing, and transport of the atmospheric NOx were based on the meteorology files generated from the WRF (Weather Research and Forecasting) model.
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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...