A Vertically Flow-Following Icosahedral Grid Model for Medium-Range and Seasonal Prediction. Part I: Model Description

Bleck, Rainer; Bao, Jian‐Wen; Benjamin, Stanley G.; Brown, John M.; Fiorino, Michael; Henderson, Thomas B.; Lee, Jin-Luen; MacDonald, Alexander E.; Madden, Paul; Middlecoff, Jacques; Rosiński, J.; Smirnova, Tanya; Sun, Shan; Wang, Ning

doi:10.1175/mwr-d-14-00300.1

Cited by 19 publications

(23 citation statements)

References 91 publications

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“…Research teams from National Center for Atmospheric Research (NCAR)'s Community Earth System Model (CESM) (Kim et al 2013), Weather Research and Forecasting (WRF) Model (Michalakes et al 2016), and the FV3 (Nguyen et al 2013) reported little to no performance gain compared to the CPU. A more comprehensive parallelization for the MIC with National Oceanic and Atmospheric Administration (NOAA)'s Flow-Following Finite-Volume Icosahedral Model (FIM) (Bleck et al 2015) included dynamics and physics running on the MIC (Rosinski 2015). Execution of the entire model on the KNC gave no performance benefit compared to the CPU.…”

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confidence: 99%

Parallelization and Performance of the NIM Weather Model on CPU, GPU, and MIC Processors

Govett

Rosinski

Middlecoff

et al. 2017

Bulletin of the American Meteorological Society

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Next-generation supercomputers containing millions of processors will require weather prediction models to be designed and developed by scientists and software experts to ensure portability and efficiency on increasingly diverse HPC systems. Intel, Cray, PGI, and NVIDIA who were responsible for fixing bugs and providing access to the latest hardware and compilers. Thanks also to the staff at ORNL and TACC for providing system resources and helping to resolve system issues. This work was also supported in part by the Disaster Relief Appropriations Act of 2013 and the NOAA HPCC program.

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confidence: 99%

Parallelization and Performance of the NIM Weather Model on CPU, GPU, and MIC Processors

Govett

Rosinski

Middlecoff

et al. 2017

Bulletin of the American Meteorological Society

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“…FIM‐Chem model is used to generate the nature run. FIM is a global Flow‐following finite‐volume Icosahedral Model (http://fim.noaa.gov/) [ Bleck et al ., ] developed in the Global Systems Division of NOAA Earth System Research Laboratory. By integrating FIM and GOCART aerosol chemistry scheme [ Chin et al ., , ], FIM‐Chem simulates and forecasts aerosol and gas‐phase species at various spatial resolutions and with different levels of complexity [ Grell et al ., ; Zhang et al ., ].…”

Section: Methodsmentioning

confidence: 99%

Sense size‐dependent dust loading and emission from space using reflected solar and infrared spectral measurements: An observation system simulation experiment

Wang

et al. 2017

JGR Atmospheres

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The Climate Absolute Radiance and Refractivity Observatory (CLARREO) satellite mission observes hyperspectral Earth reflected solar (RS) and emitted infrared radiance (IR). Such measurements span an additional dimension on spectrally dependent scattering and absorption of dust, the critical signals for particle size. Through a suite of observation system simulation experiments (OSSEs), this study assesses the capability of CLARREO's measurements for recovering size‐dependent dust emissions in GEOS‐Chem chemistry transport model (CTM). To this end, another CTM (Flow‐following finite‐volume Icosahedral Model‐Chem, or FIM‐Chem) is used for the nature run to simulate CLARREO spectral radiances. The spectral signals are then used for analyzing the sensitivities and error characteristics of dust optical depth (DOD) under three observations scenarios (IR only, RS only, and combined IR and RS) using an optimal estimation technique. Next, these synthetic data are assimilated into GEOS‐Chem adjoint model to constrain dust emissions of four particle sizes with radii from 0.1 μm to 6.0 μm. The OSSEs results indicate (1) the IR spectra are most sensitive to dust of the third size bin (1.8–3.0 μm) and least sensitive to the smallest bin (0.1–1.0 μm); (2) the RS spectra are most sensitive to dust of the smallest size bin and the sensitivity decreases as dust size increases; (3) combining IR and RS spectra can fully characterize DOD across all sizes, providing the best constraints for size‐resolved dust emissions; and (4) CLARREO data fail to constrain the spatial distribution of dust sources due to its narrow swath and joint observations from CLARREO‐calibrated sensors with wide swath are desirable.

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“…Conventionally, such a construction is referred to as layer‐averaged discretization, which was used by some isopycnal models (e.g., Bleck, ; Oberhuber, ) or the Arbitrarily Lagrangian Eulerian framework (e.g., Adcroft & Campin, ; Ringler et al, ) in the ocean modeling community. For atmospheric modeling, it is also convenient for some vertically Lagrangian models (e.g., Chen et al, ; Lin, ) or quasi‐Lagrangian models (e.g., Bleck et al ) to adopt such a construction. In a quasi‐Lagrangian approach, for example, an isentropic coordinate for the atmosphere,

\overset{η}{true}

remains zero in the adiabatic condition so that no flux goes across the coordinate face.…”

Section: Model Descriptionmentioning

confidence: 99%

A Layer‐Averaged Nonhydrostatic Dynamical Framework on an Unstructured Mesh for Global and Regional Atmospheric Modeling: Model Description, Baseline Evaluation, and Sensitivity Exploration

Zhang

et al. 2019

J Adv Model Earth Syst

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This paper describes the development and evaluation of a new nonhydrostatic dynamical framework for global and regional atmospheric modeling, with an emphasis on the numerical performance of dry dynamics. The model is formulated in a layer‐averaged manner using a generalized hybrid sigma‐mass vertical coordinate and an unstructured mesh. The mass‐based equations allow a flexible and effective switch between the hydrostatic and nonhydrostatic solvers. The unstructured mesh treats the conventional icosahedral grid and the more general Voronoi polygon in a consistent manner, allowing a flexible switch between quasi‐uniform and variable‐resolution modeling. The horizontal discretization and vertical discretization are formulated in an explicit Eulerian approach, while those terms describing the vertically propagating fast waves are solved implicitly. The model is equipped with physically based Smagorinsky diffusion as a tuning tool. A suite of multiscale test cases from hydrostatic to nonhydrostatic regimes is used to assess the model performance. The general strategies for evaluation focus on two aspects: (i) the nonhydrostatic solver should behave similarly to its hydrostatic counterpart under the hydrostatic regime and (ii) the nonhydrostatic solver should produce unique nonhydrostatic responses under the nonhydrostatic regime. In the context of model evaluation, model sensitivity to numerical configurations is further explored to understand the impact of isolated components, helping to identify appropriate configurations for realistic modeling applications. The present framework is a prototype toward a Global‐Regional Integrated forecast SysTem (GRIST).

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A Vertically Flow-Following Icosahedral Grid Model for Medium-Range and Seasonal Prediction. Part I: Model Description

Cited by 19 publications

References 91 publications

Parallelization and Performance of the NIM Weather Model on CPU, GPU, and MIC Processors

Parallelization and Performance of the NIM Weather Model on CPU, GPU, and MIC Processors

Sense size‐dependent dust loading and emission from space using reflected solar and infrared spectral measurements: An observation system simulation experiment

A Layer‐Averaged Nonhydrostatic Dynamical Framework on an Unstructured Mesh for Global and Regional Atmospheric Modeling: Model Description, Baseline Evaluation, and Sensitivity Exploration

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