A Description of the Advanced Research WRF Model Version 4

Skamarock, C.; Klemp, B.; Dudhia, Jimy; Gill, O.; Liu, Zhiquan; Berner, Judith; Wang, Wei; Powers, G.; Dūda, Gintaras; Barker, Dale; Huang, Xiang‐Yu

doi:10.5065/1dfh-6p97

Cited by 529 publications

(413 citation statements)

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“…The Advanced Research WRF (ARW) model version 4.1.2 8 is used in this study, with four different microphysics schemes described below. WRF solves fully compressible, non-hydrostatic equations, and it also has a hydrostatic option.…”

Section: Model and Simulations Descriptionmentioning

confidence: 99%

“…To provide weather forecasts, BDMS runs two regional numerical weather prediction (NWP) models, namely the Weather Research and Forecasting (WRF) 8 and Consortium for Small-Scale Modeling (COSMO) 9 systems, and these models have been running since 2010 and 2016, respectively. The resolutions for WRF and COSMO models are 14km and 20km respectively, and the two models run on a BDMS server.…”

Section: Introductionmentioning

confidence: 99%

“…WRF is a community model and provides a wide range of physics options, with over 15 microphysics schemes 8 . In this study we test the performance of four different microphysics schemes with comparable levels of complexity in simulating a heavy rainfall event that occurred in February 2017.…”

Section: Introductionmentioning

confidence: 99%

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Sensitivity of Botswana Ex-Tropical Cyclone Dineo rainfall simulations to cloud microphysics scheme

Molongwane¹,

Bopape

Fridlind

et al. 2020

AAS Open Res

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Background: Numerical weather and climate models rely on the use of microphysics schemes to simulate clouds and produce precipitation at convective scales. It is important that we understand how different microphysics schemes perform when simulating high impact weather to inform operational forecasting. Methods: Simulations a heavy rainfall event from 17-20 February 2017 over Botswana were made with the Weather Research and Forecasting (WRF) model using four different microphysics schemes. The schemes used were the Weather Research and Forecasting Single Moment 6-class scheme (WSM6); Weather Research and Forecasting Single Moment 5-class scheme (WSM5); Stony Brook University scheme (SBU-YLIN); and Thompson scheme. WSM5 is considered as the least sophisticated of the four schemes, while Thompson is the most sophisticated. Simulations were initialized and forced by the Global Forecast System (GFS), and configured with a grid spacing of 9km over an outer domain and 3km for a nested inner domain without the convection parameterization. The simulations were produced using the University of Botswana and the Centre for High Performance Computing (CHPC) High Performance Computing (HPC) systems. Results: WSM5 and WSM6 simulations are mostly similar; the presence of graupel in WSM6 did not result in large differences in the rainfall simulations. SBU-YLIN simulated the least amount of rainfall, followed by Thompson. All the schemes captured the north-south rainfall gradient observed on 17 February, but with all simulations rainfall is simulated slightly south of where it was observed. All the schemes overestimated rainfall on 18 February over the central parts of Botswana, and underestimated rainfall on 19 February over most of Botswana. Conclusions: Simulations with different microphysics looked more similar to each other, than to observations. Future studies will test WRF configurations including a single nest over Botswana to determine the best configuration for operational forecasting by the Botswana Department of Meteorological Services.

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Section: Model and Simulations Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sensitivity of Botswana Ex-Tropical Cyclone Dineo rainfall simulations to cloud microphysics scheme

Molongwane¹,

Bopape

Fridlind

et al. 2020

AAS Open Res

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“…The WRF V4 (Skamarock et al, 2019) setup is driven by 3-hourly GFS data (NCEP, 2017) in two domains (Fig. 3) focusing on central Europe and the USCB, respectively.…”

Section: Wrf Wind Fieldsmentioning

confidence: 99%

Observational constraints on methane emissions from Polish coal mines using a ground-based remote sensing network

Luther

Kostinek

Kleinschek

et al. 2021

Preprint

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Abstract. Given its abundant coal mining activities, the Upper Silesian Coal Basin (USCB) in southern Poland is one of the largest sources for anthropogenic methane (CH4) emissions in Europe. Here, we report on CH4 emission estimates for coal mine ventilation facilities in the USCB. Our estimates are driven by pair-wise upwind-downwind observations of the column-average dry-air mole fractions of CH4 (XCH4) by a network of four portable, ground-based, sun-viewing Fourier Transform Spectrometers of the type EM27/SUN operated during the CoMet campaign in May/June 2018. The EM27/SUN were deployed in the four cardinal directions around the USCB in approx. 50 km distance to the center of the basin. We report on six case studies for which we inferred emissions by evaluating the mismatch between the observed downwind enhancements and simulations based on trajectory calculations releasing particles out of the ventilation shafts using the Lagrangian particle dispersion model FLEXPART. The latter was driven by wind fields calculated by WRF (Weather Research and Forecasting model) under assimilation of vertical wind profile measurements of three co-deployed wind lidars. For emission estimation, we use a Phillips-Tikhonov regularization scheme with the L-curve criterion. Diagnosed by the averaging kernels, we find that, depending on the catchment area of the downwind measurements, our ad-hoc network can resolve individual facilities or groups of ventilation facilities but that inspecting the averaging kernels is essential to detected correlated estimates. Generally, our instantaneous emission estimates range between 80 and 133 kt CH4 a−1 for the south-eastern part of the USCB and between 414 and 790 kt CH4 a−1 for various larger parts of the basin, suggesting higher emissions than expected from the annual emissions reported by the E-PRTR (European Pollutant Release and Transfer Register). Uncertainties range between 23 and 36 % dominated by the error contribution from uncertain wind fields.

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“…The calculation code WRF-ARW (WRF -Weather Research and Forecasting, ARW -Advanced Research WRF) is actively used to predict the capacity of the wind farm. The WRF-ARW model is a model of a regional weather forecast, for the construction of which it needs boundary and initial conditions [1,2]. Data sources in this case can be global models, observational data, or forecast reanalysis data.…”

Section: Mathematical Model In Wrf-arw Codementioning

confidence: 99%

On the Problem of Choosing the Optimal Parameters for the Wind Farm in the Arctic Town of Tiksi

Strijhak

Gergel

Ivanov

et al. 2021

Communications in Computer and Information Science

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A Description of the Advanced Research WRF Model Version 4

Cited by 529 publications

References 0 publications

Sensitivity of Botswana Ex-Tropical Cyclone Dineo rainfall simulations to cloud microphysics scheme

Sensitivity of Botswana Ex-Tropical Cyclone Dineo rainfall simulations to cloud microphysics scheme

Observational constraints on methane emissions from Polish coal mines using a ground-based remote sensing network

On the Problem of Choosing the Optimal Parameters for the Wind Farm in the Arctic Town of Tiksi

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