Articles | Volume 16, issue 6
https://doi.org/10.5194/gmd-16-1779-2023
© Author(s) 2023. 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-16-1779-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
29 Mar 2023
Development and technical paper |
| 29 Mar 2023
| 29 Mar 2023
A method for generating a quasi-linear convective system suitable for observing system simulation experiments
National Severe Storms Laboratory, Norman, OK 73072, USA
National Research Council, Washington, DC 20001, USA
Jeremy A. Gibbs
National Severe Storms Laboratory, Norman, OK 73072, USA
Louis J. Wicker
National Severe Storms Laboratory, Norman, OK 73072, USA
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Fabien Margairaz, Balwinder Singh, Jeremy A. Gibbs, Loren Atwood, Eric R. Pardyjak, and Rob Stoll
Geosci. Model Dev., 16, 5729–5754, https://doi.org/10.5194/gmd-16-5729-2023, https://doi.org/10.5194/gmd-16-5729-2023, 2023
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The Quick Environmental Simulation (QES) tool is a low-computational-cost fast-response framework. It provides high-resolution wind and concentration information to study complex problems, such as spore or smoke transport, urban pollution, and air quality. This paper presents the particle dispersion model and its validation against analytical solutions and wind-tunnel data for a mock-urban setting. In all cases, the model provides accurate results with competitive computational performance.
Related subject area
Atmospheric sciences
Exploring the footprint representation of microwave radiance observations in an Arctic limited-area data assimilation system
Analysis of model error in forecast errors of extended atmospheric Lorenz 05 systems and the ECMWF system
Description and validation of Vehicular Emissions from Road Traffic (VERT) 1.0, an R-based framework for estimating road transport emissions from traffic flows
AeroMix v1.0.1: a Python package for modeling aerosol optical properties and mixing states
Impact of ITCZ width on global climate: ITCZ-MIP
Deep-learning-driven simulations of boundary layer clouds over the Southern Great Plains
Mixed-precision computing in the GRIST dynamical core for weather and climate modelling
A conservative immersed boundary method for the multi-physics urban large-eddy simulation model uDALES v2.0
RCEMIP-II: mock-Walker simulations as phase II of the radiative–convective equilibrium model intercomparison project
Objective identification of meteorological fronts and climatologies from ERA-Interim and ERA5
TAMS: a tracking, classifying, and variable-assigning algorithm for mesoscale convective systems in simulated and satellite-derived datasets
Development of the adjoint of the unified tropospheric–stratospheric chemistry extension (UCX) in GEOS-Chem adjoint v36
New explicit formulae for the settling speed of prolate spheroids in the atmosphere: theoretical background and implementation in AerSett v2.0.2
ZJU-AERO V0.5: an Accurate and Efficient Radar Operator designed for CMA-GFS/MESO with the capability to simulate non-spherical hydrometeors
The Year of Polar Prediction site Model Intercomparison Project (YOPPsiteMIP) phase 1: project overview and Arctic winter forecast evaluation
Evaluating CHASER V4.0 global formaldehyde (HCHO) simulations using satellite, aircraft, and ground-based remote-sensing observations
Global variable-resolution simulations of extreme precipitation over Henan, China, in 2021 with MPAS-Atmosphere v7.3
The CHIMERE chemistry-transport model v2023r1
tobac v1.5: introducing fast 3D tracking, splits and mergers, and other enhancements for identifying and analysing meteorological phenomena
Merged Observatory Data Files (MODFs): an integrated observational data product supporting process-oriented investigations and diagnostics
Simulation of marine stratocumulus using the super-droplet method: numerical convergence and comparison to a double-moment bulk scheme using SCALE-SDM 5.2.6-2.3.1
WRF-Comfort: simulating microscale variability in outdoor heat stress at the city scale with a mesoscale model
Representing effects of surface heterogeneity in a multi-plume eddy diffusivity mass flux boundary layer parameterization
Can TROPOMI NO2 satellite data be used to track the drop in and resurgence of NOx emissions in Germany between 2019–2021 using the multi-source plume method (MSPM)?
A spatiotemporally separated framework for reconstructing the sources of atmospheric radionuclide releases
A parameterization scheme for the floating wind farm in a coupled atmosphere–wave model (COAWST v3.7)
RoadSurf 1.1: open-source road weather model library
Calibrating and validating the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) urban cooling model: case studies in France and the United States
The ddeq Python library for point source quantification from remote sensing images (version 1.0)
Incorporating Oxygen Isotopes of Oxidized Reactive Nitrogen in the Regional Atmospheric Chemistry Mechanism, version 2 (ICOIN-RACM2)
A general comprehensive evaluation method for cross-scale precipitation forecasts
Implementation of a Simple Actuator Disk for Large-Eddy Simulation in the Weather Research and Forecasting Model (WRF-SADLES v1.2) for wind turbine wake simulation
WRF-PDAF v1.0: implementation and application of an online localized ensemble data assimilation framework
Implementation and evaluation of diabatic advection in the Lagrangian transport model MPTRAC 2.6
An improved and extended parameterization of the CO2 15 µm cooling in the middle and upper atmosphere (CO2_cool_fort-1.0)
Development of a multiphase chemical mechanism to improve secondary organic aerosol formation in CAABA/MECCA (version 4.7.0)
Application of regional meteorology and air quality models based on the microprocessor without interlocked piped stages (MIPS) and LoongArch CPU platforms
Investigating ground-level ozone pollution in semi-arid and arid regions of Arizona using WRF-Chem v4.4 modeling
An objective identification technique for potential vorticity structures associated with African easterly waves
Importance of microphysical settings for climate forcing by stratospheric SO2 injections as modeled by SOCOL-AERv2
Assessment of surface ozone products from downscaled CAMS reanalysis and CAMS daily forecast using urban air quality monitoring stations in Iran
Open boundary conditions for atmospheric large-eddy simulations and their implementation in DALES4.4
Efficient and stable coupling of the SuperdropNet deep-learning-based cloud microphysics (v0.1.0) with the ICON climate and weather model (v2.6.5)
Three-dimensional variational assimilation with a multivariate background error covariance for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 2.0.0-beta)
FUME 2.0 – Flexible Universal processor for Modeling Emissions
DEUCE v1.0: a neural network for probabilistic precipitation nowcasting with aleatoric and epistemic uncertainties
Evaluation of multi-season convection-permitting atmosphere – mixed-layer ocean simulations of the Maritime Continent
RASCAL v1.0.0: An Open Source Tool for Climatological Time Series Reconstruction and Extension
Selecting CMIP6 GCMs for CORDEX Dynamical Downscaling over Southeast Asia Using a Standardised Benchmarking Framework
Investigating the impact of coupling HARMONIE-WINS50 (cy43) meteorology to LOTOS-EUROS (v2.2.002) on a simulation of NO2 concentrations over the Netherlands
Máté Mile, Stephanie Guedj, and Roger Randriamampianina
Geosci. Model Dev., 17, 6571–6587, https://doi.org/10.5194/gmd-17-6571-2024, https://doi.org/10.5194/gmd-17-6571-2024, 2024
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Satellite observations provide crucial information about atmospheric constituents in a global distribution that helps to better predict the weather over sparsely observed regions like the Arctic. However, the use of satellite data is usually conservative and imperfect. In this study, a better spatial representation of satellite observations is discussed and explored by a so-called footprint function or operator, highlighting its added value through a case study and diagnostics.
Hynek Bednář and Holger Kantz
Geosci. Model Dev., 17, 6489–6511, https://doi.org/10.5194/gmd-17-6489-2024, https://doi.org/10.5194/gmd-17-6489-2024, 2024
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The forecast error growth of atmospheric phenomena is caused by initial and model errors. When studying the initial error growth, it may turn out that small-scale phenomena, which contribute little to the forecast product, significantly affect the ability to predict this product. With a negative result, we investigate in the extended Lorenz (2005) system whether omitting these phenomena will improve predictability. A theory explaining and describing this behavior is developed.
Giorgio Veratti, Alessandro Bigi, Sergio Teggi, and Grazia Ghermandi
Geosci. Model Dev., 17, 6465–6487, https://doi.org/10.5194/gmd-17-6465-2024, https://doi.org/10.5194/gmd-17-6465-2024, 2024
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In this study, we present VERT (Vehicular Emissions from Road Traffic), an R package designed to estimate transport emissions using traffic estimates and vehicle fleet composition data. Compared to other tools available in the literature, VERT stands out for its user-friendly configuration and flexibility of user input. Case studies demonstrate its accuracy in both urban and regional contexts, making it a valuable tool for air quality management and transport scenario planning.
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.
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.
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.
Hossain Mohammed Syedul Hoque, Kengo Sudo, Hitoshi Irie, Yanfeng He, and Md Firoz Khan
Geosci. Model Dev., 17, 5545–5571, https://doi.org/10.5194/gmd-17-5545-2024, https://doi.org/10.5194/gmd-17-5545-2024, 2024
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Using multi-platform observations, we validated global formaldehyde (HCHO) simulations from a chemistry transport model. HCHO is a crucial intermediate in the chemical catalytic cycle that governs the ozone formation in the troposphere. The model was capable of replicating the observed spatiotemporal variability in HCHO. In a few cases, the model's capability was limited. This is attributed to the uncertainties in the observations and the model parameters.
Zijun Liu, Li Dong, Zongxu Qiu, Xingrong Li, Huiling Yuan, Dongmei Meng, Xiaobin Qiu, Dingyuan Liang, and Yafei Wang
Geosci. Model Dev., 17, 5477–5496, https://doi.org/10.5194/gmd-17-5477-2024, https://doi.org/10.5194/gmd-17-5477-2024, 2024
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In this study, we completed a series of simulations with MPAS-Atmosphere (version 7.3) to study the extreme precipitation event of Henan, China, during 20–22 July 2021. We found the different performance of two built-in parameterization scheme suites (mesoscale and convection-permitting suites) with global quasi-uniform and variable-resolution meshes. This study holds significant implications for advancing the understanding of the scale-aware capability of MPAS-Atmosphere.
Laurent Menut, Arineh Cholakian, Romain Pennel, Guillaume Siour, Sylvain Mailler, Myrto Valari, Lya Lugon, and Yann Meurdesoif
Geosci. Model Dev., 17, 5431–5457, https://doi.org/10.5194/gmd-17-5431-2024, https://doi.org/10.5194/gmd-17-5431-2024, 2024
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A new version of the CHIMERE model is presented. This version contains both computational and physico-chemical changes. The computational changes make it easy to choose the variables to be extracted as a result, including values of maximum sub-hourly concentrations. Performance tests show that the model is 1.5 to 2 times faster than the previous version for the same setup. Processes such as turbulence, transport schemes and dry deposition have been modified and updated.
G. Alexander Sokolowsky, Sean W. Freeman, William K. Jones, Julia Kukulies, Fabian Senf, Peter J. Marinescu, Max Heikenfeld, Kelcy N. Brunner, Eric C. Bruning, Scott M. Collis, Robert C. Jackson, Gabrielle R. Leung, Nils Pfeifer, Bhupendra A. Raut, Stephen M. Saleeby, Philip Stier, and Susan C. van den Heever
Geosci. Model Dev., 17, 5309–5330, https://doi.org/10.5194/gmd-17-5309-2024, https://doi.org/10.5194/gmd-17-5309-2024, 2024
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Building on previous analysis tools developed for atmospheric science, the original release of the Tracking and Object-Based Analysis (tobac) Python package, v1.2, was open-source, modular, and insensitive to the type of gridded input data. Here, we present the latest version of tobac, v1.5, which substantially improves scientific capabilities and computational efficiency from the previous version. These enhancements permit new uses for tobac in atmospheric science and potentially other fields.
Taneil Uttal, Leslie M. Hartten, Siri Jodha Khalsa, Barbara Casati, Gunilla Svensson, Jonathan Day, Jareth Holt, Elena Akish, Sara Morris, Ewan O'Connor, Roberta Pirazzini, Laura X. Huang, Robert Crawford, Zen Mariani, Øystein Godøy, Johanna A. K. Tjernström, Giri Prakash, Nicki Hickmon, Marion Maturilli, and Christopher J. Cox
Geosci. Model Dev., 17, 5225–5247, https://doi.org/10.5194/gmd-17-5225-2024, https://doi.org/10.5194/gmd-17-5225-2024, 2024
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A Merged Observatory Data File (MODF) format to systematically collate complex atmosphere, ocean, and terrestrial data sets collected by multiple instruments during field campaigns is presented. The MODF format is also designed to be applied to model output data, yielding format-matching Merged Model Data Files (MMDFs). MODFs plus MMDFs will augment and accelerate the synergistic use of model results with observational data to increase understanding and predictive skill.
Chongzhi Yin, Shin-ichiro Shima, Lulin Xue, and Chunsong Lu
Geosci. Model Dev., 17, 5167–5189, https://doi.org/10.5194/gmd-17-5167-2024, https://doi.org/10.5194/gmd-17-5167-2024, 2024
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We investigate numerical convergence properties of a particle-based numerical cloud microphysics model (SDM) and a double-moment bulk scheme for simulating a marine stratocumulus case, compare their results with model intercomparison project results, and present possible explanations for the different results of the SDM and the bulk scheme. Aerosol processes can be accurately simulated using SDM, and this may be an important factor affecting the behavior and morphology of marine stratocumulus.
Alberto Martilli, Negin Nazarian, E. Scott Krayenhoff, Jacob Lachapelle, Jiachen Lu, Esther Rivas, Alejandro Rodriguez-Sanchez, Beatriz Sanchez, and José Luis Santiago
Geosci. Model Dev., 17, 5023–5039, https://doi.org/10.5194/gmd-17-5023-2024, https://doi.org/10.5194/gmd-17-5023-2024, 2024
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Here, we present a model that quantifies the thermal stress and its microscale variability at a city scale with a mesoscale model. This tool can have multiple applications, from early warnings of extreme heat to the vulnerable population to the evaluation of the effectiveness of heat mitigation strategies. It is the first model that includes information on microscale variability in a mesoscale model, something that is essential for fully evaluating heat stress.
Nathan P. Arnold
Geosci. Model Dev., 17, 5041–5056, https://doi.org/10.5194/gmd-17-5041-2024, https://doi.org/10.5194/gmd-17-5041-2024, 2024
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Earth system models often represent the land surface at smaller scales than the atmosphere, but surface–atmosphere coupling uses only aggregated surface properties. This study presents a method to allow heterogeneous surface properties to modify boundary layer updrafts. The method is tested in single column experiments. Updraft properties are found to reasonably covary with surface conditions, and simulated boundary layer variability is enhanced over more heterogeneous land surfaces.
Enrico Dammers, Janot Tokaya, Christian Mielke, Kevin Hausmann, Debora Griffin, Chris McLinden, Henk Eskes, and Renske Timmermans
Geosci. Model Dev., 17, 4983–5007, https://doi.org/10.5194/gmd-17-4983-2024, https://doi.org/10.5194/gmd-17-4983-2024, 2024
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Nitrogen dioxide (NOx) is produced by sources such as industry and traffic and is directly linked to negative impacts on health and the environment. The current construction of emission inventories to keep track of NOx emissions is slow and time-consuming. Satellite measurements provide a way to quickly and independently estimate emissions. In this study, we apply a consistent methodology to derive NOx emissions over Germany and illustrate the value of having such a method for fast projections.
Yuhan Xu, Sheng Fang, Xinwen Dong, and Shuhan Zhuang
Geosci. Model Dev., 17, 4961–4982, https://doi.org/10.5194/gmd-17-4961-2024, https://doi.org/10.5194/gmd-17-4961-2024, 2024
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Recent atmospheric radionuclide leakages from unknown sources have posed a new challenge in nuclear emergency assessment. Reconstruction via environmental observations is the only feasible way to identify sources, but simultaneous reconstruction of the source location and release rate yields high uncertainties. We propose a spatiotemporally separated reconstruction strategy that avoids these uncertainties and outperforms state-of-the-art methods with respect to accuracy and uncertainty ranges.
Shaokun Deng, Shengmu Yang, Shengli Chen, Daoyi Chen, Xuefeng Yang, and Shanshan Cui
Geosci. Model Dev., 17, 4891–4909, https://doi.org/10.5194/gmd-17-4891-2024, https://doi.org/10.5194/gmd-17-4891-2024, 2024
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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.
Virve Eveliina Karsisto
Geosci. Model Dev., 17, 4837–4853, https://doi.org/10.5194/gmd-17-4837-2024, https://doi.org/10.5194/gmd-17-4837-2024, 2024
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RoadSurf is an open-source library that contains functions from the Finnish Meteorological Institute’s road weather model. The evaluation of the library shows that it is well suited for making road surface temperature forecasts. The evaluation was done by making forecasts for about 400 road weather stations in Finland with the library. Accurate forecasts help road authorities perform salting and plowing operations at the right time and keep roads safe for drivers.
Perrine Hamel, Martí Bosch, Léa Tardieu, Aude Lemonsu, Cécile de Munck, Chris Nootenboom, Vincent Viguié, Eric Lonsdorf, James A. Douglass, and Richard P. Sharp
Geosci. Model Dev., 17, 4755–4771, https://doi.org/10.5194/gmd-17-4755-2024, https://doi.org/10.5194/gmd-17-4755-2024, 2024
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The InVEST Urban Cooling model estimates the cooling effect of vegetation in cities. We further developed an algorithm to facilitate model calibration and evaluation. Applying the algorithm to case studies in France and in the United States, we found that nighttime air temperature estimates compare well with reference datasets. Estimated change in temperature from a land cover scenario compares well with an alternative model estimate, supporting the use of the model for urban planning decisions.
Gerrit Kuhlmann, Erik Koene, Sandro Meier, Diego Santaren, Grégoire Broquet, Frédéric Chevallier, Janne Hakkarainen, Janne Nurmela, Laia Amorós, Johanna Tamminen, and Dominik Brunner
Geosci. Model Dev., 17, 4773–4789, https://doi.org/10.5194/gmd-17-4773-2024, https://doi.org/10.5194/gmd-17-4773-2024, 2024
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We present a Python software library for data-driven emission quantification (ddeq). It can be used to determine the emissions of hot spots (cities, power plants and industry) from remote sensing images using different methods. ddeq can be extended for new datasets and methods, providing a powerful community tool for users and developers. The application of the methods is shown using Jupyter notebooks included in the library.
Wendell W. Walters, Masayuki Takeuchi, Nga L. Ng, and Meredith G. Hastings
Geosci. Model Dev., 17, 4673–4687, https://doi.org/10.5194/gmd-17-4673-2024, https://doi.org/10.5194/gmd-17-4673-2024, 2024
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The study introduces a novel chemical mechanism for explicitly tracking oxygen isotope transfer in oxidized reactive nitrogen and odd oxygen using the Regional Atmospheric Chemistry Mechanism, version 2. This model enhances our ability to simulate and compare oxygen isotope compositions of reactive nitrogen, revealing insights into oxidation chemistry. The approach shows promise for improving atmospheric chemistry models and tropospheric oxidation capacity predictions.
Bing Zhang, Mingjian Zeng, Anning Huang, Zhengkun Qin, Couhua Liu, Wenru Shi, Xin Li, Kefeng Zhu, Chunlei Gu, and Jialing Zhou
Geosci. Model Dev., 17, 4579–4601, https://doi.org/10.5194/gmd-17-4579-2024, https://doi.org/10.5194/gmd-17-4579-2024, 2024
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By directly analyzing the proximity of precipitation forecasts and observations, a precipitation accuracy score (PAS) method was constructed. This method does not utilize a traditional contingency-table-based classification verification; however, it can replace the threat score (TS), equitable threat score (ETS), and other skill score methods, and it can be used to calculate the accuracy of numerical models or quantitative precipitation forecasts.
Hai Bui, Mostafa Bakhoday-Paskyabi, and Mohammadreza Mohammadpour-Penchah
Geosci. Model Dev., 17, 4447–4465, https://doi.org/10.5194/gmd-17-4447-2024, https://doi.org/10.5194/gmd-17-4447-2024, 2024
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We developed a new wind turbine wake model, the Simple Actuator Disc for Large Eddy Simulation (SADLES), integrated with the widely used Weather Research and Forecasting (WRF) model. WRF-SADLES accurately simulates wind turbine wakes at resolutions of a few dozen meters, aligning well with idealized simulations and observational measurements. This makes WRF-SADLES a promising tool for wind energy research, offering a balance between accuracy, computational efficiency, and ease of implementation.
Changliang Shao and Lars Nerger
Geosci. Model Dev., 17, 4433–4445, https://doi.org/10.5194/gmd-17-4433-2024, https://doi.org/10.5194/gmd-17-4433-2024, 2024
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This paper introduces and evaluates WRF-PDAF, a fully online-coupled ensemble data assimilation (DA) system. A key advantage of the WRF-PDAF configuration is its ability to concurrently integrate all ensemble states, eliminating the need for time-consuming distribution and collection of ensembles during the coupling communication. The extra time required for DA amounts to only 20.6 % per cycle. Twin experiment results underscore the effectiveness of the WRF-PDAF system.
Jan Clemens, Lars Hoffmann, Bärbel Vogel, Sabine Grießbach, and Nicole Thomas
Geosci. Model Dev., 17, 4467–4493, https://doi.org/10.5194/gmd-17-4467-2024, https://doi.org/10.5194/gmd-17-4467-2024, 2024
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Lagrangian transport models simulate the transport of air masses in the atmosphere. For example, one model (CLaMS) is well suited to calculating transport as it uses a special coordinate system and special vertical wind. However, it only runs inefficiently on modern supercomputers. Hence, we have implemented the benefits of CLaMS into a new model (MPTRAC), which is already highly efficient on modern supercomputers. Finally, in extensive tests, we showed that CLaMS and MPTRAC agree very well.
Manuel López-Puertas, Federico Fabiano, Victor Fomichev, Bernd Funke, and Daniel R. Marsh
Geosci. Model Dev., 17, 4401–4432, https://doi.org/10.5194/gmd-17-4401-2024, https://doi.org/10.5194/gmd-17-4401-2024, 2024
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The radiative infrared cooling of CO2 in the middle atmosphere is crucial for computing its thermal structure. It requires one however to include non-local thermodynamic equilibrium processes which are computationally very expensive, which cannot be afforded by climate models. In this work, we present an updated, efficient, accurate and very fast (~50 µs) parameterization of that cooling able to cope with CO2 abundances from half the pre-industrial values to 10 times the current abundance.
Felix Wieser, Rolf Sander, Changmin Cho, Hendrik Fuchs, Thorsten Hohaus, Anna Novelli, Ralf Tillmann, and Domenico Taraborrelli
Geosci. Model Dev., 17, 4311–4330, https://doi.org/10.5194/gmd-17-4311-2024, https://doi.org/10.5194/gmd-17-4311-2024, 2024
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The chemistry scheme of the atmospheric box model CAABA/MECCA is expanded to achieve an improved aerosol formation from emitted organic compounds. In addition to newly added reactions, temperature-dependent partitioning of all new species between the gas and aqueous phases is estimated and included in the pre-existing scheme. Sensitivity runs show an overestimation of key compounds from isoprene, which can be explained by a lack of aqueous-phase degradation reactions and box model limitations.
Zehua Bai, Qizhong Wu, Kai Cao, Yiming Sun, and Huaqiong Cheng
Geosci. Model Dev., 17, 4383–4399, https://doi.org/10.5194/gmd-17-4383-2024, https://doi.org/10.5194/gmd-17-4383-2024, 2024
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There is relatively limited research on the application of scientific computing on RISC CPU platforms. The MIPS architecture CPUs, a type of RISC CPUs, have distinct advantages in energy efficiency and scalability. The air quality modeling system can run stably on the MIPS and LoongArch platforms, and the experiment results verify the stability of scientific computing on the platforms. The work provides a technical foundation for the scientific application based on MIPS and LoongArch.
Yafang Guo, Chayan Roychoudhury, Mohammad Amin Mirrezaei, Rajesh Kumar, Armin Sorooshian, and Avelino F. Arellano
Geosci. Model Dev., 17, 4331–4353, https://doi.org/10.5194/gmd-17-4331-2024, https://doi.org/10.5194/gmd-17-4331-2024, 2024
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This research focuses on surface ozone (O3) pollution in Arizona, a historically air-quality-challenged arid and semi-arid region in the US. The unique characteristics of this kind of region, e.g., intense heat, minimal moisture, and persistent desert shrubs, play a vital role in comprehending O3 exceedances. Using the WRF-Chem model, we analyzed O3 levels in the pre-monsoon month, revealing the model's skill in capturing diurnal and MDA8 O3 levels.
Christoph Fischer, Andreas H. Fink, Elmar Schömer, Marc Rautenhaus, and Michael Riemer
Geosci. Model Dev., 17, 4213–4228, https://doi.org/10.5194/gmd-17-4213-2024, https://doi.org/10.5194/gmd-17-4213-2024, 2024
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This study presents a method for identifying and tracking 3-D potential vorticity structures within African easterly waves (AEWs). Each identified structure is characterized by descriptors, including its 3-D position and orientation, which have been validated through composite comparisons. A trough-centric perspective on the descriptors reveals the evolution and distinct characteristics of AEWs. These descriptors serve as valuable statistical inputs for the study of AEW-related phenomena.
Sandro Vattioni, Andrea Stenke, Beiping Luo, Gabriel Chiodo, Timofei Sukhodolov, Elia Wunderlin, and Thomas Peter
Geosci. Model Dev., 17, 4181–4197, https://doi.org/10.5194/gmd-17-4181-2024, https://doi.org/10.5194/gmd-17-4181-2024, 2024
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We investigate the sensitivity of aerosol size distributions in the presence of strong SO2 injections for climate interventions or after volcanic eruptions to the call sequence and frequency of the routines for nucleation and condensation in sectional aerosol models with operator splitting. Using the aerosol–chemistry–climate model SOCOL-AERv2, we show that the radiative and chemical outputs are sensitive to these settings at high H2SO4 supersaturations and how to obtain reliable results.
Najmeh Kaffashzadeh and Abbas-Ali Aliakbari Bidokhti
Geosci. Model Dev., 17, 4155–4179, https://doi.org/10.5194/gmd-17-4155-2024, https://doi.org/10.5194/gmd-17-4155-2024, 2024
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This paper assesses the capability of two state-of-the-art global datasets in simulating surface ozone over Iran using a new methodology. It is found that the global model data need to be downscaled for regulatory purposes or policy applications at local scales. The method can be useful not only for the evaluation but also for the prediction of other chemical species, such as aerosols.
Franciscus Liqui Lung, Christian Jakob, A. Pier Siebesma, and Fredrik Jansson
Geosci. Model Dev., 17, 4053–4076, https://doi.org/10.5194/gmd-17-4053-2024, https://doi.org/10.5194/gmd-17-4053-2024, 2024
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Traditionally, high-resolution atmospheric models employ periodic boundary conditions, which limit simulations to domains without horizontal variations. In this research open boundary conditions are developed to replace the periodic boundary conditions. The implementation is tested in a controlled setup, and the results show minimal disturbances. Using these boundary conditions, high-resolution models can be forced by a coarser model to study atmospheric phenomena in realistic background states.
Caroline Arnold, Shivani Sharma, Tobias Weigel, and David S. Greenberg
Geosci. Model Dev., 17, 4017–4029, https://doi.org/10.5194/gmd-17-4017-2024, https://doi.org/10.5194/gmd-17-4017-2024, 2024
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In atmospheric models, rain formation is simplified to be computationally efficient. We trained a machine learning model, SuperdropNet, to emulate warm-rain formation based on super-droplet simulations. Here, we couple SuperdropNet with an atmospheric model in a warm-bubble experiment and find that the coupled simulation runs stable and produces reasonable results, making SuperdropNet a viable ML proxy for droplet simulations. We also present a comprehensive benchmark for coupling architectures.
Byoung-Joo Jung, Benjamin Ménétrier, Chris Snyder, Zhiquan Liu, Jonathan J. Guerrette, Junmei Ban, Ivette Hernández Baños, Yonggang G. Yu, and William C. Skamarock
Geosci. Model Dev., 17, 3879–3895, https://doi.org/10.5194/gmd-17-3879-2024, https://doi.org/10.5194/gmd-17-3879-2024, 2024
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We describe the multivariate static background error covariance (B) for the JEDI-MPAS 3D-Var data assimilation system. With tuned B parameters, the multivariate B gives physically balanced analysis increment fields in the single-observation test framework. In the month-long cycling experiment with a global 60 km mesh, 3D-Var with static B performs stably. Due to its simple workflow and minimal computational requirements, JEDI-MPAS 3D-Var can be useful for the research community.
Michal Belda, Nina Benešová, Jaroslav Resler, Peter Huszár, Ondřej Vlček, Pavel Krč, Jan Karlický, Pavel Juruš, and Kryštof Eben
Geosci. Model Dev., 17, 3867–3878, https://doi.org/10.5194/gmd-17-3867-2024, https://doi.org/10.5194/gmd-17-3867-2024, 2024
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For modeling atmospheric chemistry, it is necessary to provide data on emissions of pollutants. These can come from various sources and in various forms, and preprocessing of the data to be ingestible by chemistry models can be quite challenging. We developed the FUME processor to use a database layer that internally transforms all input data into a rigid structure, facilitating further processing to allow for emission processing from the continental to the street scale.
Bent Harnist, Seppo Pulkkinen, and Terhi Mäkinen
Geosci. Model Dev., 17, 3839–3866, https://doi.org/10.5194/gmd-17-3839-2024, https://doi.org/10.5194/gmd-17-3839-2024, 2024
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Probabilistic precipitation nowcasting (local forecasting for 0–6 h) is crucial for reducing damage from events like flash floods. For this goal, we propose the DEUCE neural-network-based model which uses data and model uncertainties to generate an ensemble of potential precipitation development scenarios for the next hour. Trained and evaluated with Finnish precipitation composites, DEUCE was found to produce more skillful and reliable nowcasts than established models.
Emma Howard, Steven Woolnough, Nicholas Klingaman, Daniel Shipley, Claudio Sanchez, Simon C. Peatman, Cathryn E. Birch, and Adrian J. Matthews
Geosci. Model Dev., 17, 3815–3837, https://doi.org/10.5194/gmd-17-3815-2024, https://doi.org/10.5194/gmd-17-3815-2024, 2024
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This paper describes a coupled atmosphere–mixed-layer ocean simulation setup that will be used to study weather processes in Southeast Asia. The set-up has been used to compare high-resolution simulations, which are able to partially resolve storms, to coarser simulations, which cannot. We compare the model performance at representing variability of rainfall and sea surface temperatures across length scales between the coarse and fine models.
Álvaro González-Cervera and Luis Durán
EGUsphere, https://doi.org/10.5194/egusphere-2024-958, https://doi.org/10.5194/egusphere-2024-958, 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 of broad scientific applications, from short-term forecasts to local-scale climate change scenarios.
Phuong Loan Nguyen, Lisa V. Alexander, Marcus J. Thatcher, Son C. H. Truong, Rachael N. Isphording, and John L. McGregor
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-84, https://doi.org/10.5194/gmd-2024-84, 2024
Revised manuscript accepted for GMD
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We apply a comprehensive approach to select a subset of CMIP6 that is suitable for dynamical downscaling over Southeast Asia by considering model performance, model independence, data availability, and future climate change spread. The standardised benchmarking framework is applied to identify a subset of models through two stages of assessment: statistical-based and process-based metrics. We finalize a sub-set of two independent models for dynamical downscaling over Southeast Asia.
Andrés Yarce Botero, Michiel van Weele, Arjo Segers, Pier Siebesma, and Henk Eskes
Geosci. Model Dev., 17, 3765–3781, https://doi.org/10.5194/gmd-17-3765-2024, https://doi.org/10.5194/gmd-17-3765-2024, 2024
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HARMONIE WINS50 reanalysis data with 0.025° × 0.025° resolution from 2019 to 2021 were coupled with the LOTOS-EUROS Chemical Transport Model. HARMONIE and ECMWF meteorology configurations against Cabauw observations (52.0° N, 4.9° W) were evaluated as simulated NO2 concentrations with ground-level sensors. Differences in crucial meteorological input parameters (boundary layer height, vertical diffusion coefficient) between the hydrostatic and non-hydrostatic models were analysed.
Cited articles
Aksoy, A., Dowell, D. C., and Snyder, C.: A multicase comparative assessment
of the ensemble Kalman filter for assimilation of radar observations. Part
II: Short-range ensemble forecasts, Mon. Weather Rev., 138, 1273–1292,
https://doi.org/10.1175/2009MWR3086.1, 2010. a
Anderson, J., Hoar, T., Raeder, K., Liu, H., Collins, N., Torn, R., and Avellano, A.: The Data Assimilation Research Testbed: A Community Facility. B. Am. Meteorol. Soc., 90, 1283–1296, https://doi.org/10.1175/2009BAMS2618.1, 2009. a
Anderson, J. L.: An Ensemble Adjustment Kalman Filter for Data Assimilation, Mon. Weather Rev., 129, 2884–2903, https://doi.org/10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2, 2001. a
Anderson, J. L. and Collins, N.: Scalable implementations of ensemble filter
algorithms for data assimilation, J. Atmos. Ocean.
Tech., 24, 1452–1463, https://doi.org/10.1175/JTECH2049.1, 2007. a, b
Brier, G. W.: Verification of forecasts expersses in terms of probaility,
Mon. Weather Rev., 78, 1–3, https://doi.org/10.1126/science.27.693.594, 1950. a
Bryan, G. H. and Fritsch, J. M.: A benchmark simulation for moist
nonhydrostatic numerical models, Mon. Weather Rev., 130, 2917–2928,
https://doi.org/10.1175/1520-0493(2002)130<2917:ABSFMN>2.0.CO;2, 2002. a
Bryan, G. H. and Morrison, H.: Sensitivity of a Simulated Squall Line to
Horizontal Resolution and Parameterization of Microphysics, Mon. Weather
Rev., 140, 202–225, https://doi.org/10.1175/mwr-d-11-00046.1, 2012. a, b
Bryan, G. H. and Rotunno, R.: Evaluation of an analytical model for the
maximum intensity of tropical cyclones, J. Atmos. Sci.,
66, 3042–3060, https://doi.org/10.1175/2009JAS3038.1, 2009. a
Buehner, M. and Jacques, D.: Non-Gaussian deterministic assimilation of
radar-derived precipitation accumulations, Mon. Weather Rev., 148,
783–808, https://doi.org/10.1175/MWR-D-19-0199.1, 2020. a
Carlin, J. T., Gao, J., Snyder, J. C., and Ryzhkov, A. V.: Assimilation of ZDR
Columns for Improving the Spin-Up and Forecast of Convective Storms in
Storm-Scale Models: Proof-of-Concept Experiments, Mon. Weather Rev.,
145, 5033–5057, https://doi.org/10.1175/MWR-D-17-0103.1, 2017. a
Chipilski, H. G., Wang, X., and Parsons, D. B.: Impact of Assimilating PECAN
Profilers on the Prediction of Bore-Driven Nocturnal Convection: A Multiscale
Forecast Evaluation for the 6 July 2015 Case Study, Mon. Weather Rev.,
148, 1147–1175, https://doi.org/10.1175/mwr-d-19-0171.1, 2020. a
Cintineo, R. M. and Stensrud, D. J.: On the predictability of supercell
thunderstorm evolution, J. Atmos. Sci., 70, 1993–2011,
https://doi.org/10.1175/JAS-D-12-0166.1, 2013. a
Cintineo, R. M., Otkin, J. A., Jones, T. A., Koch, S., and Stensrud, D. J.:
Assimilation of synthetic GOES-R ABI infrared brightness temperatures and
WSR-88D radar observations in a high-resolution OSSE, Mon. Weather Rev., 144, 3159–3180, https://doi.org/10.1175/MWR-D-15-0366.1, 2016. a
Clark, A. J., Kain, J. S., Marsh, P. T., Correia, J., Xue, M., and Kong, F.:
Forecasting Tornado Pathlengths Using a Three-Dimensional Object
Identification Algorithm Applied to Convection-Allowing Forecasts, Weather Forecast., 27, 1090–1113, https://doi.org/10.1175/WAF-D-11-00147.1, 2012. a
Cohen, A. E., Cavallo, S. M., Coniglio, M. C., and Brooks, H. E.: A review of
planetary boundary layer parameterization schemes and their sensitivity in
simulating southeastern U.S. cold season severe weather environments,
Weather Forecast., 30, 591–612, https://doi.org/10.1175/WAF-D-14-00105.1, 2015. a
Coniglio, M. C., Correia, J., Marsh, P. T., and Kong, F.: Verification of
convection-allowing WRF model forecasts of the planetary boundary layer using
sounding observations, Weather Forecast., 28, 842–862,
https://doi.org/10.1175/WAF-D-12-00103.1, 2013. a
Coniglio, M. C., Romine, G. S., Turner, D. D., and Torn, R. D.: Impacts of
targeted AERI and doppler lidar wind retrievals on short-term forecasts of
the initiation and early evolution of thunderstorms, Mon. Weather Rev.,
147, 1149–1170, https://doi.org/10.1175/MWR-D-18-0351.1, 2019. a
Cressman, G.: An Operational Objective Analysis System, Mon. Weather Rev., 87, 367–374, 1959. a
Crum, T. D., Alberty, R. L., and Burgess, D. W.: Recording, archiving, and
using WSR-88D data, B. Am. Meteorol. Soc., 74,
645–653, https://doi.org/10.1175/1520-0477(1993)074<0645:raauwd>2.0.co;2, 1993. a
Dawson, D. T. I., Wicker, L. J., Mansell, E. R., and Tanamachi, R. L.: Impact
of the Environmental Low-Level Wind Profile on Ensemble Forecasts of the 4
May 2007 Greensburg, Kansas, Tornadic Storm and Associated Mesocyclones,
Mon. Weather Rev., 140, 696–716, https://doi.org/10.1175/MWR-D-11-00008.1, 2012. a, b
Deardorff, J. W.: Stratocumulus-Capped Mixed Layers Derived From a
Three-Dimensional Model, Bound.-Lay. Meteorol., 18, 495–527,
https://doi.org/10.1007/BF00119502, 1980. a
Degelia, S. K., Wang, X., and Stensrud, D. J.: An Evaluation of the Impact of
Assimilating AERI Retrievals, Kinematic Profilers, Rawinsondes, and Surface
Observations on a Forecast of a Nocturnal Convection Initiation Event during
the PECAN Field Campaign, Mon. Weather Rev., 147, 2739–2764,
https://doi.org/10.1175/mwr-d-18-0423.1, 2019. a
Degelia, S. K., Wang, X., Stensrud, D. J., and Turner, D. D.: Systematic
evaluation of the impact of assimilating a network of ground-based remote
sensing profilers for forecasts of nocturnal convection initiation during
PECAN, Mon. Weather Rev., 148, 4703–4728,
https://doi.org/10.1175/MWR-D-20-0118.1, 2020. a
Dowell, D. C. and Wicker, L. J.: Additive noise for storm-scale ensemble data
assimilation, J. Atmos. Ocean. Tech., 26, 911–927,
https://doi.org/10.1175/2008JTECHA1156.1, 2009. a
Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic
model using Monte Carlo methods to forecast error statistics, J.
Geophys. Res., 99, 143–162, 1994. a
Evensen, G.: The Ensemble Kalman Filter : theoretical formulation and
practical implementation, Ocean Dynam., 53, 343–367,
https://doi.org/10.1007/s10236-003-0036-9, 2003. a
Flora, M. L., Potvin, C. K., and Wicker, L. J.: Practical predictability of
supercells: Exploring ensemble forecast sensitivity to initial condition
spread, Mon. Weather Rev., 146, 2361–2379,
https://doi.org/10.1175/MWR-D-17-0374.1, 2018. a
Flora, M. L., Skinner, P. S., Potvin, C. K., Reinhart, A. E., Jones, T. A.,
Yussouf, N., and Knopfmeier, K. H.: Object-based verification of short-term,
storm-scale probabilistic mesocyclone guidance from an
experimentalWarn-on-Forecast system, Weather Forecast., 34,
1721–1739, https://doi.org/10.1175/waf-d-19-0094.1, 2019. a
Gallo, B. T., Clark, A. J., and Dembek, S. R.: Forecasting Tornadoes Using
Convection-Permitting Ensembles, Weather Forecast., 31, 273–295,
https://doi.org/10.1175/WAF-D-15-0134.1, 2016. a
Gallo, B. T., Wilson, K. A., Choate, J., Knopfmeier, K., Skinner, P., Roberts,
B., Heinselman, P., Jirak, I., and Clark, A. J.: Exploring the
Watch-to-Warning Space: Experimental Outlook Performance during the 2019
Spring Forecasting Experiment in NOAA's Hazardous Weather Testbed, Weather Forecast., 37, 1–38, https://doi.org/10.1175/WAF-D-21-0171.1, 2022. a
Gallus, W. A., Snook, N. A., and Johnson, E. V.: Spring and summer severe
weather reports over the midwest as a function of convective mode: A
preliminary study, Weather Forecast., 23, 101–113,
https://doi.org/10.1175/2007WAF2006120.1, 2008. a
Gao, J. and Stensrud, D. J.: Some observing system simulation experiments with
a hybrid 3DEnVAR system for storm-scale radar data assimilation, Mon. Weather Rev., 142, 3326–3346, https://doi.org/10.1175/MWR-D-14-00025.1, 2014. a
Gaspari, G. and Cohn, S. E.: Construction of correlation functions in two and
three dimensions, Q. J. Roy. Meteor. Soc., 125,
723–757, 1999. a
Guyer, J. L. and Dean, A. R.: Tornadoes Within Weak CAPE Environments Across
the Continental United States, in: 25th Conference on Severe Local Storms, 11–14 October 2010,
Denver, CO, https://ams.confex.com/ams/pdfpapers/175725.pdf (last access: 13 February 2023),
2010. a
Hodyss, D. and Nichols, N.: The error of representation: Basic understanding,
Tellus A, 67, 24822,
https://doi.org/10.3402/tellusa.v67.24822, 2015. a
Hoffman, R. N. and Atlas, R.: Future observing system simulation experiments,
B. Am. Meteorol. Soc., 97, 1601–1616,
https://doi.org/10.1175/BAMS-D-15-00200.1, 2016. a
Hong, S. Y., Noh, Y., and Dudhia, J.: A new vertical diffusion package with an
explicit treatment of entrainment processes, Mon. Weather Rev., 134,
2318–2341, https://doi.org/10.1175/MWR3199.1, 2006. a
Janjić, T. and Cohn, S. E.: Treatment of observation error due to
unresolved scales in atmospheric data assimilation, Mon. Weather Rev.,
134, 2900–2915, https://doi.org/10.1175/MWR3229.1, 2006. a
Jiménez, P. A., Dudhia, J., González-Rouco, J. F., Navarro, J.,
Montávez, J. P., and García-Bustamante, E.: A revised scheme for
the WRF surface layer formulation, Mon. Weather Rev., 140, 898–918,
https://doi.org/10.1175/MWR-D-11-00056.1, 2012. a
Johnson, A. and Wang, X.: Design and Implementation of a GSI-Based
Convection-Allowing Ensemble Data Assimilation and Forecast System for the
PECAN Field Experiment. Part I: Optimal Configurations for Nocturnal
Convection Prediction Using Retrospective Cases, Weather Forecast.,
32, 289–315, https://doi.org/10.1175/waf-d-16-0102.1, 2017. a
Johnson, A., Wang, X., Kong, F., and Xue, M.: Object-based evaluation of the
impact of horizontal grid spacing on convection-allowing forecasts, Mon. Weather Rev., 141, 3413–3425, https://doi.org/10.1175/MWR-D-13-00027.1, 2013. a
Jones, T. A., Knopfmeier, K. H., Wheatley, D. M., Creager, G. J., Minnis, P.,
and Palikonda, R.: Storm-Scale Data Assimilation and Ensemble Forecasting
with the NSSL Experimental Warn-on-Forecast System. Part II: Combined Radar
and Satellite Data Experiments, Weather Forecast., 31, 297–327,
https://doi.org/10.1175/WAF-D-15-0107.1, 2016. a, b
Jung, Y., Xue, M., Zhang, G., and Straka, J. M.: Assimilation of Simulated
Polarimetric Radar Data for a Convective Storm Using the Ensemble Kalman
Filter. Part I: Observation Operators for Reflectivity and Polarimetric
Variables, Mon. Weather Rev., 136, 2228–2245,
https://doi.org/10.1175/2007MWR2288.1, 2008. a, b
Jung, Y., Xue, M., Wang. Y., Zhu, K., and Pan, Y.: Tests of a cycled EnKF
data assimilation and forecasts for the 10 May 2010 tornado outbreak in the
central US domain, in: 25th Conference on Severe and Local Storms, 11–14 October 2010, Amer.
Meteor. Soc., Nashville, TN, 2012. a
Kain, J. S., Weiss, S. J., Bright, D. R., Baldwin, M. E., Levit, J. J., Carbin,
G. W., Schwartz, C. S., Weisman, M. L., Droegemeier, K. K., Weber, D. B., and
Thomas, K. W.: Some Practical Considerations Regarding Horizontal Resolution
in the First Generation of Operational Convection-Allowing NWP, Weather Forecast., 23, 931–952, https://doi.org/10.1175/2008WAF2007106.1, 2008. a, b
Kain, J. S., Dembek, S. R., Weiss, S. J., Case, J. L., Levit, J. J., and
Sobash, R. A.: Extracting Unique Information from High-Resolution Forecast
Models : Monitoring Selected Fields and Phenomena Every Time Step, Weather Forecast., 25, 1536–1542, https://doi.org/10.1175/2010WAF2222430.1, 2010. a
Kalnay, E.: Atmospheric modeling, data assimilation, and predictability,
Cambridge University Press, https://doi.org/10.1256/00359000360683511, 2002. a
Kerr, C. A., Stensrud, D. J., and Wang, X.: Assimilation of cloud-top
temperature and radar observations of an idealized splitting supercell using
an observing system simulation experiment, Mon. Weather Rev., 143,
1018–1034, https://doi.org/10.1175/MWR-D-14-00146.1, 2015. a, b
Labriola, J. D. and Wicker, L. J.: Creating Physically‐Coherent and
Spatially‐Correlated Perturbations to Initialize High‐Resolution
Ensembles of Simulated Convection, Q. J. Roy.
Meteor. Soc., 148, 1–21, https://doi.org/10.1002/qj.4348, 2022. a, b, c, d
Labriola, J., Gibbs, J., and Wicker, L.: A Method for Generating a Quasi-Linear Convective System Suitable for Observing System Simulation Experiments (v1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.7109050, 2022a. a
Labriola, J., Gibbs, J., and Wicker, L.: A Method for Generating a Quasi-Linear Convective System Suitable for Observing System Simulation Experiments: Dataset (v1.0.0), Zenodo [data set], https://doi.org/10.5281/zenodo.7126769, 2022b. a
Loken, E. D., Clark, A. J., Xue, M., and Kong, F.: Comparison of Next-Day
Probabilistic Severe Weather Forecasts from Coarse- and Fine-Resolution CAMs
and a Convection-Allowing Ensemble, Weather Forecast., 32, 1403–1421,
https://doi.org/10.1175/WAF-D-16-0200.1, 2017. a
Markowski, P. M.: An idealized numerical simulation investigation of the
effects of surface drag on the development of near-surface vertical vorticity
in supercell thunderstorms, J. Atmos. Sci., 73,
4349–4385, https://doi.org/10.1175/JAS-D-16-0150.1, 2016. a
Markowski, P. M.: What is the intrinsic predictability of tornadic supercell
thunderstorms?, Mon. Weather Rev., 148, 3157–3180,
https://doi.org/10.1175/MWR-D-20-0076.1, 2020. a, b
Melhauser, C. and Zhang, F.: Practical and intrinsic predictability of severe
and convective weather at the mesoscales, J. Atmos.
Sci., 69, 3350–3371, https://doi.org/10.1175/JAS-D-11-0315.1, 2012. a
Miller, W. J., Potvin, C. K., Flora, M. L., Gallo, B. T., Wicker, L. J., Jones,
T. A., Skinner, P. S., Matilla, B. C., and Knopfmeier, K. H.: Exploring the
Usefulness of Downscaling Free Forecasts from the Warn-on-Forecast System,
Weather Forecast., 37, 181–203, https://doi.org/10.1175/WAF-D-21-0079.1, 2022. a
Morrison, H., Curry, J. A., Shupe, M. D., and Zuidema, P.: A New Double-Moment
Microphysics Parameterization for Application in Cloud and Climate Models.
Part II: Single-Column Modeling of Arctic Clouds, J. Atmos.
Sci., 62, 1678–1693, https://doi.org/10.1175/JAS3447.1, 2005. a
Morrison, H., Thompson, G., and Tatarskii, V.: Impact of Cloud Microphysics on
the Development of Trailing Stratiform Precipitation in a Simulated Squall
Line: Comparison of One- and Two-Moment Schemes, Mon. Weather Rev., 137,
991–1007, https://doi.org/10.1175/2008MWR2556.1, 2009. a
Murphy, A. H.: A New Vector Partition of the Probability Score, J.
Appl. Meteorol., 12, 595–600,
https://doi.org/10.1175/1520-0450(1973)012<0595:ANVPOT>2.0.CO;2, 1973. a
Nowotarski, C. J., Markowski, P. M., Richardson, Y. P., and Bryan, G. H.:
Supercell low-level mesocyclones in simulations with a sheared convective
boundary layer, Mon. Weather Rev., 143, 272–297,
https://doi.org/10.1175/MWR-D-14-00151.1, 2015. a
Poterjoy, J.: Implications of Multivariate Non-Gaussian Data Assimilation for
Multi-scale Weather Prediction, Mon. Weather Rev., 150, 1475–1493,
https://doi.org/10.1175/mwr-d-21-0228.1, 2022. a
Poterjoy, J., Sobash, R. A., and Anderson, J. L.: Convective-scale data
assimilation for the weather research and forecasting model using the local
particle filter, Mon. Weather Rev., 145, 1897–1918,
https://doi.org/10.1175/MWR-D-16-0298.1, 2017. a
Potvin, C. K. and Wicker, L. J.: Comparison between dual-doppler and enkf
storm-scale wind analyses: Observing system simulation experiments with a
supercell thunderstorm, Mon. Weather Rev., 140, 3972–3991,
https://doi.org/10.1175/MWR-D-12-00044.1, 2012. a
Potvin, C. K., Wicker, L. J., Biggerstaff, M. I., Betten, D., and Shapiro, A.:
Comparison between dual-doppler and EnKF storm-scale wind analyses: The
29–30 May 2004 Geary, Oklahoma, supercell thunderstorm, Mon. Weather Rev., 141, 1612–1628, https://doi.org/10.1175/MWR-D-12-00308.1, 2013. a, b
Potvin, C. K., Carley, J. R., Clark, A. J., Wicker, L. J., Skinner, P. S.,
Reinhart, A. E., Gallo, B. T., Kain, J. S., Romine, G. S., Aligo, E. A.,
Brewster, K. A., Dowell, D. C., Harris, L. M., Jirak, I. L., Kong, F.,
Supinie, T. A., Thomas, K. W., Wang, X., Wang, Y., and Xue, M.: Systematic
comparison of convection-allowing models during the 2017 NOAA HWT spring
forecasting experiment, Weather Forecast., 34, 1395–1416,
https://doi.org/10.1175/WAF-D-19-0056.1, 2019. a
Reames, L. J. and Stensrud, D. J.: Sensitivity of simulated urban-atmosphere
interactions in Oklahoma city to urban parameterization, J. Appl.
Meteorol. Climatol., 56, 1405–1430, https://doi.org/10.1175/JAMC-D-16-0223.1,
2017. a
Reames, L. J. and Stensrud, D. J.: Influence of a great plains urban
environment on a simulated supercell, Mon. Weather Rev., 146,
1437–1462, https://doi.org/10.1175/MWR-D-17-0284.1, 2018. a
Robert, S., Leuenberger, D., and Künsch, H. R.: A local ensemble
transform Kalman particle filter for convective-scale data assimilation,
Q. J. Roy. Meteor. Soc., 144, 1279–1296,
https://doi.org/10.1002/qj.3116, 2018. a
Romine, G. S., Schwartz, C. S., Snyder, C., Anderson, J. L., and Weisman,
M. L.: Model bias in a continuously cycled assimilation system and its
influence on convection-permitting forecasts, Mon. Weather Rev., 141,
1263–1284, https://doi.org/10.1175/MWR-D-12-00112.1, 2013. a, b
Schneider, R. S., Dean, A. R., Weiss, S. J., and Bothwell, P. D.: Analysis of
estimated environments for 2004 and 2005 severe convective storm reports,
in: 23rd Conf. on Severe Local Storms, p. 3.5, Amer. Meteor. Soc., St. Louis,
MO,
https://ams.confex.com/ams/23SLS/techprogram/paper_115246.html (last access: 13 February 2023),
2006. a
Schwartz, C. S. and Sobash, R. A.: Generating Probabilistic Forecasts from
Convection-Allowing Ensembles Using Neighborhood Approaches: A Review and
Recommendations, Mon. Weather Rev., 145, 3397–3418,
https://doi.org/10.1175/MWR-D-16-0400.1, 2017. a
Sherburn, K. D. and Parker, M. D.: Climatology and ingredients of significant
severe convection in high-shear, low-CAPE environments, Weather Forecast., 29, 854–877, https://doi.org/10.1175/WAF-D-13-00041.1, 2014. a, b
Sherburn, K. D. and Parker, M. D.: The development of severe vortices within
simulated high-shear, Low-CAPE convection, Mon. Weather Rev., 147,
2189–2216, https://doi.org/10.1175/MWR-D-18-0246.1, 2019. a, b
Sherburn, K. D., Parker, M. D., King, J. R., and Lackmann, G. M.: Composite
environments of severe and nonsevere high-shear, low-CAPE convective events,
Weather Forecast., 31, 1899–1927, https://doi.org/10.1175/WAF-D-16-0086.1, 2016. a
Skinner, P. S., Wheatley, D. M., Knopfmeier, K. H., Reinhart, A. E., Choate,
J. J., Jones, T. A., Creager, G. J., Dowell, D. C., Alexander, C. R., Ladwig,
T. T., Wicker, L. J., Heinselman, P. L., Minnis, P., and Palikonda, R.:
Object-based verification of a prototype Warn-on-Forecast system, Weather Forecast., 33, 1225–1250, https://doi.org/10.1175/WAF-D-18-0020.1, 2018. a, b
Snook, N. and Xue, M.: Effects of microphysical drop size distribution on
tornadogenesis in supercell thunderstorms, Geophys. Res. Lett., 35, L24803,
https://doi.org/10.1029/2008GL035866, 2008. a
Snook, N., Kong, F., Brewster, K. A., Xue, M., Thomas, K. W., Supinie, T. A.,
Perfater, S., and Albright, B.: Evaluation of Convection-Permitting
Precipitation Forecast Products Using WRF, NMMB, and FV3 for the 2016–17
NOAA Hydrometeorology Testbed Flash Flood and Intense Rainfall Experiments,
Weather Forecast., 34, 781–804, https://doi.org/10.1175/waf-d-18-0155.1, 2019. a
Snook, N. A., Xue, M., and Jung, Y.: Analysis of a Tornadic Mesoscale
Convective Vortex Based on Ensemble Kalman Filter Assimilation of CASA X-Band
and WSR-88D Radar Data, Mon. Weather Rev., 139, 3446–3468,
https://doi.org/10.1175/MWR-D-10-05053.1, 2011. a
Sobash, R. A. and Stensrud, D. J.: The impact of covariance localization for
radar data on EnKF analyses of a developing MCS: Observing system simulation
experiments, Mon. Weather Rev., 141, 3691–3709,
https://doi.org/10.1175/MWR-D-12-00203.1, 2013. a, b
Sobash, R. A., Kain, J. S., Bright, D. R., Dean, A. R., Coniglio, M. C., and
Weiss, S. J.: Probabilistic Forecast Guidance for Severe Thunderstorms Based
on the Identification of Extreme Phenomena in Convection-Allowing Model
Forecasts, Weather Forecast., 26, 714–728,
https://doi.org/10.1175/WAF-D-10-05046.1, 2011. a
Sobash, R. A., Schwartz, C. S., Romine, G. S., Fossell, K. R., and Weisman,
M. L.: Severe Weather Prediction Using Storm Surrogates from an Ensemble
Forecasting System, Weather Forecast., 31, 255–271,
https://doi.org/10.1175/WAF-D-15-0138.1, 2016. a, b
Stratman, D. R., Potvin, C. K., and Wicker, L. J.: Correcting storm
displacement errors in ensembles using the feature alignment technique
(FAT), Mon. Weather Rev., 146, 2125–2145,
https://doi.org/10.1175/MWR-D-17-0357.1, 2018. a, b
Tong, M. and Xue, M.: Ensemble Kalman Filter Assimilation of Doppler Radar
Data with a Comprssible Nonhydrostatic Model: OSS Experiments, Mon. Weather Rev., 133, 1789–1807, https://doi.org/10.1175/MWR2898.1, 2005. a
VandenBerg, M. A., Coniglio, M. C., Clark, A. J., VandenBerg, M. A., Coniglio,
M. C., and Clark, A. J.: Comparison of Next-Day Convection-Allowing
Forecasts of Storm motion on 1- and 4-km Grids, Weather Forecast., 29,
878–893, https://doi.org/10.1175/WAF-D-14-00011.1, 2014. a
Verrelle, A., Ricard, D., and Lac, C.: Sensitivity of high-resolution
idealized simulations of thunderstorms to horizontal resolution and
turbulence parametrization, Q. J. Roy. Meteor.
Soc., 141, 433–448, https://doi.org/10.1002/qj.2363, 2015. a
Wheatley, D. M., Knopfmeier, K. H., Jones, T. A., and Creager, G. J.:
Storm-Scale Data Assimilation and Ensemble Forecasting with the NSSL
Experimental Warn-on-Forecast System. Part I: Radar Data Experiments,
Weather Forecast., 30, 1795–1817, https://doi.org/10.1175/WAF-D-15-0043.1, 2015.
a, b
Wilson, K. A., Gallo, B. T., Skinner, P., Clark, A., Heinselman, P., and
Choate, J. J.: Analysis of End User Access of Warn-on-Forecast Guidance
Products during an Experimental Forecasting Task, Weather, Climate, and
Society, 859–874, https://doi.org/10.1175/wcas-d-20-0175.1, 2021. a
Xue, M., Tong, M., and Droegemeier, K. K.: An OSSE framework based on the
ensemble square root Kalman filter for evaluating the impact of data from
radar networks on thunderstorm analysis and forecasting, J.
Atmos. Ocean. Tech., 23, 46–66, https://doi.org/10.1175/JTECH1835.1,
2006. a, b
Yang, L., Li, Q., Yuan, H., Niu, Z., and Wang, L.: Impacts of Urban Canopy on
Two Convective Storms With Contrasting Synoptic Conditions Over Nanjing,
China, J. Geophys. Res.-Atmos., 126, e2020JD034509,
https://doi.org/10.1029/2020JD034509, 2021. a
Yussouf, N. and Stensrud, D. J.: Impact of phased-array radar observations
over a short assimilation period: Observing system simulation experiments
using an ensemble Kalman filter, Mon. Weather Rev., 138, 517–538,
https://doi.org/10.1175/2009MWR2925.1, 2010. a
Zeng, Y., Janjić, T., de Lozar, A., Welzbacher, C. A., Blahak, U., and
Seifert, A.: Assimilating radar radial wind and reflectivity data in an
idealized setup of the COSMO-KENDA system, Atmos. Res., 249,
105282, https://doi.org/10.1016/j.atmosres.2020.105282, 2021. a
Zhang, F., Snyder, C., and Sun, J.: Impacts of Initial Estimate and
Observation Availability on Convective-Scale Data Assimilation with an
Ensemble Kalman Filter, Mon. Weather Rev., 132, 1238–1253, 2004. a
Zhang, Y., Zhang, F., Stensrud, D. J., and Meng, Z.: Practical predictability
of the 20 May 2013 tornadic thunderstorm event in Oklahoma: sensitivity to
synoptic timing and topographical influence, Mon. Weather Rev., 143,
2973–2997, https://doi.org/10.1175/MWR-D-14-00394.1, 2015. a
Zhao, J., Gao, J., Jones, T. A., and Hu, J.: Impact of Assimilating
High-Resolution Atmospheric Motion Vectors on Convective Scale Short-Term
Forecasts: 1. Observing System Simulation Experiment (OSSE), J.
Adv. Model. Earth Sy., 13, e2021MS002486, https://doi.org/10.1029/2021MS002484, 2021. a
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.
Observing system simulation experiments (OSSEs) are simulated case studies used to understand...
