A Case Study of Low‐Level Jets in Yerevan Simulated by the WRF Model

Gevorgyan, Artur

doi:10.1002/2017jd027629

Cited by 21 publications

(13 citation statements)

References 53 publications

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“…The latter leads to more accurate modeling of low‐level orographically induced flows, convergence centers, and moisture which are very important for initiation and organization of deep convection over a mountain terrain (Bennett et al, ; Demko & Geerts, ; Wang et al, ). It is worth noting that the results from another recent study also demonstrated that the WRF runs initiated with the ERA5 analysis have higher skills in simulation of low‐level jets (mountain valley wind systems) and potential temperatures in Armenia relative to those forced by the GFS fields (Gevorgyan, ).…”

Section: Disscussions and Conclusionmentioning

confidence: 99%

“…Initiation of those convective cells may be induced by low‐level convergence centers due to interaction between cold pool outflows and well‐known valley wind systems with northerly and northeasterly directions blowing to the west and east from Talin station. The valley wind systems in Armenia are induced by regional‐scale heat‐driven plain plateau circulation (Gevorgyan & Melkonyan, ), and those are further enhanced due to funneling effect of river valleys leading to formation of low‐level jets (Gevorgyan, , ). As successfully simulated by the WRF model the valley winds become stronger in the afternoon and evening hours with near‐surface wind speeds reaching up to 10 m/s (Figures h, c, d, g, and h).…”

Section: Synoptic Analysis Of the Extreme Rainfall Eventmentioning

confidence: 99%

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Convection‐Permitting Simulation of a Heavy Rainfall Event in Armenia Using the WRF Model

Gevorgyan

2018

JGR Atmospheres

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On 2 July 2016 afternoon a heavy rainfall (52.8 mm) was observed over southwestern part of Armenia, at Talin station. High-frequency radar observations and Weather Research and Forecasting (WRF) model output show that initiation of earliest convection occurred over Aragats mountain massif around noon due to low-level convergence of thermally induced upslope winds. Further convective development was affected by generation of secondary convection as a result of interaction between cold pool outflows from developed convective cells and upslope winds. The high-resolution WRF run (3 km) using NSSL two-moment cloud microphysics parametrization and the European Centre for Medium-Range Weather Forecasts operational model forcing data best reproduces the location, timing, magnitude, and microphysical structure of the observed convective rainfall among six WRF microphysical schemes tested in this study. Radar observations show that cold cloud process typical for continental-type deep convection was observed in Armenia. The NSSL includes the double-moment microphysics schemes for both warm and cold cloud processes, which might be a reason for improved simulation of observed heavy rainfall event in Armenia. Using the coarser resolution ERA5 analysis forcing data in the WRF model leads to simulation of earlier rainfall peaks at Talin station and spurious convective rainfall areas. The WRF model forced by the Global Data Assimilation System Final analysis from the National Centers for Environmental Prediction, even run at 3-km resolution, is not able to reproduce the accurate location of convection and rainfall over the study area. GEVORGYAN11,008

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Section: Disscussions and Conclusionmentioning

confidence: 99%

Section: Synoptic Analysis Of the Extreme Rainfall Eventmentioning

confidence: 99%

Convection‐Permitting Simulation of a Heavy Rainfall Event in Armenia Using the WRF Model

Gevorgyan

2018

JGR Atmospheres

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“…ERA5 has been validated and shown to give good agreement with radiosonde profiles (Bindu et al, 2018). In additional tests, ERA5 showed improved simulation of near-surface winds at a site near Yerevan, Armenia, with significantly reduced root-mean-square error (RMSE) relative to those forced by the Global Forecast System fields when using ERA5 initial and boundary conditions (Gevorgyan, 2018). Motivated by these good results, we used ERA5 as initial and boundary conditions to simulate fog, because in our tests, fog was sensitive to the near surface southern wind, which brings warm moist air masses over cold water.…”

Section: Model Configurationmentioning

confidence: 99%

Boundary Layer Parameterizations to Simulate Fog Over Atlantic Canada Waters

Chen

Zhang

Perrie

et al. 2020

Earth and Space Science

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In this study, a series of fog events that occurred near Halifax, Canada, during 20 June to 31 July 2016 are investigated using the Weather Research and Forecasting Model Version 3.8.1 (WRF), in comparison with in situ and satellite remotely sensed observations from the Moderate Resolution Imaging Spectroradiometer. We evaluate five planetary boundary layer (PBL) schemes available in WRF. Results show that these five PBL schemes lead to overestimates in liquid water content, especially the nonlocal schemes, and that they are biased early, in terms of the predicting the onset of fog, and late, in terms of fog dissipation, although their spatial patterns of fog are in good agreement with those suggested by Moderate Resolution Imaging Spectroradiometer imagery. The Kunkel equation is used to calculate visibility, based on WRF modeling of liquid water content. Comparisons with observed visibility show that this methodology sometimes fails to predict fog dissipation. We present a modification of this formulation for visibility that shows improved agreement with observations and more accurate fog dissipation. Continued improvements in the PBL scheme and visibility parameterization are needed for more accurate fog prediction.

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“…The entire structure of the simulated atmospheric boundary layer could also depend on the details of PBL schemes and their parameterization of turbulent fluxes of momentum, thereby also potentially affecting the simulation of LLJs. Widely used schemes like the Yonsei University (YSU) and the Mellor‐Yamada‐Janic (MYJ) might produce appropriate representations of low‐level winds and LLJs, with other schemes having similar or even better performance in different situations (Dimitrova et al., 2016; Gevorgyan, 2018; Gómez‐Navarro et al., 2015; Hu et al., 2010; Santos & Nascimento, 2016; Tyagi et al., 2018). For example, Smith et al.…”

Section: Introductionmentioning

confidence: 99%

The Orinoco Low‐Level Jet and the Cross‐Equatorial Moisture Transport Over Tropical South America: Lessons From Seasonal WRF Simulations

et al. 2022

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A study of the simulation of the Orinoco Low‐Level Jet (OLLJ) and the cross‐equatorial moisture transport over tropical South America using the Weather Research and Forecasting (WRF) model is presented. The focus is on the diurnal cycle and monthly means during one December‐January‐February (DJF) season (2003–2004). The sensitivity of the OLLJ and cross‐equatorial moisture transport to the representation of surface fluxes and turbulence is explored by using two different Land Surface Models (LSM) and three Planetary Boundary Layer (PBL) schemes. Different LSMs produce large differences in the sensible heat flux over the Orinoco basin, but no substantial differences in the flow at the entrance of the OLLJ. However, these changes in surface fluxes were associated with a change in the low‐level pressure gradient between the Orinoco and Amazon basins, and with up to 10% change in the simulated cross‐equatorial moisture transport. The largest effects from different PBL schemes were found in the low‐level flow near the Andes cordillera, and on the low‐level southward cross‐equatorial flow during the daytime. One of the PBL schemes exhibited a reduced moisture transport on the eastern flank of the Andes but enhanced southward transport around the core of the cross‐equatorial moisture flux. Another PBL scheme produced a stronger net cross‐equatorial moisture transport, with enhanced precipitation over the Andes‐Amazon region by up to 30% during the afternoon and up to 13% in the daily mean. Improved observations over the region are needed to evaluate which combination best represents the flow.

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A Case Study of Low‐Level Jets in Yerevan Simulated by the WRF Model

Cited by 21 publications

References 53 publications

Convection‐Permitting Simulation of a Heavy Rainfall Event in Armenia Using the WRF Model

Convection‐Permitting Simulation of a Heavy Rainfall Event in Armenia Using the WRF Model

Boundary Layer Parameterizations to Simulate Fog Over Atlantic Canada Waters

The Orinoco Low‐Level Jet and the Cross‐Equatorial Moisture Transport Over Tropical South America: Lessons From Seasonal WRF Simulations

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