Summertime wind climate in Yerevan: valley wind systems

Gevorgyan, Artur

doi:10.1007/s00382-016-3175-7

Cited by 8 publications

(10 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The valley winds of 4 July 2015 were characterized by great strength and longer lifetime. Thus, maximum wind gusts reached to 21 m s −1 in the evening hours at Yerevan‐Zvartnots station which is well above the threshold of strong wind gust events (17 m s −1 ) defined by Gevorgyan (). In general, valley winds start to blow at around 1500 UTC, slightly before the local sunset, while those decay during nighttime, around 2100 UTC (Gevorgyan, ).…”

Section: Methods and Datamentioning

confidence: 65%

“…It is worth noting that Yerevan‐Zvartnots station is located at 854 m above the sea level (the green triangle in Figure b), and observations from this station were also used in previous studies focused on valley winds in Yerevan (Gevorgyan, ; Gevorgyan & Melkonyan, ). On the other hand, the observed profiles of potential temperature and wind were derived from the Yerevan‐Aerological station.…”

Section: Methods and Datamentioning

confidence: 99%

“…The study area presented in Figure includes the mountain ranges and intermountain plateaus of the Armenian Highland covering the western and central parts of the region, the Greater Caucasus range is extending from northwest to southeast (northern to Armenia), and the low elevated Kura‐Araks plain is in the east. A few recent studies investigated the intricate and intriguing mesoscale plain‐plateau circulation arising over the Armenian Highland and South Caucasus region as well as local wind systems over Armenia induced by local terrain channeling effects (Gevorgyan, ; Gevorgyan & Melkonyan, ). Gevorgyan and Melkonyan () demonstrated that during summer seasons (July–August) the mountain massifs and plateaus strongly heat air within a daytime planetary boundary layer (PBL) leading to formation of significant horizontal temperature gradients in the lower and middle troposphere between the Armenian Highland and Kura‐Araks plain.…”

Section: Introductionmentioning

confidence: 99%

“…It should be noted that net heating of the sloping terrain is the main driver of the initiation of significant mesoscale low‐level jets (LLJs) over Great Plains and Middle East (Dezfuli et al, ; Parish & Clark, ). Gevorgyan () demonstrated that most notably, pronounced mountain‐valley winds are observed in Yerevan city, capital of Armenia, due to channeling effects of Hrazdan River flowing through Yerevan from northeast to southwest (Figure b). The climatological analysis of near‐surface meteorological variables in Yerevan over period 1992–2014 revealed that valley winds significantly impact the diurnal variations of wind, temperature, and humidity during July and August.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Gevorgyan

2018

JGR Atmospheres

Self Cite

View full text Add to dashboard Cite

Capabilities of high‐resolution (3 km) Weather Research and Forecasting (WRF) simulations to reproduce topographically induced mountain‐valley winds and low‐level jets (LLJs) in Yerevan have been evaluated using high‐frequency observational and modeled data. High sensitivities of simulations of near‐surface winds and LLJ characteristics observed on 4 July 2015 to both boundary layer and initial and lateral boundary conditions setup have been demonstrated. Among the nine tested planetary boundary layer (PBL) parameterization schemes the MYJ, QNSE, and TEMF PBL schemes showed greater skill in simulation of near‐surface valley winds over Yerevan, while the other PBL schemes tend to significantly underestimate the strength of valley winds, with the BouLac PBL scheme being the worst performer. Most of PBL schemes simulate well‐defined LLJs in Yerevan associated with evening valley winds. The simulated jet cores are mostly located between 150 and 250 m above ground with magnitudes varying from 12 to 21 m s−1. However, the intensity of the observed nocturnal LLJ in Yerevan (located at 110 m above ground) is strongly underestimated by most of the WRF runs while the Shin and Hong and YSU PBL schemes simulate nocturnal LLJs higher than the observed LLJ. The WRF runs initiated with newly released European Centre for Medium‐Range Weather Forecasts ERA‐5 data set showed improved simulation of near‐surface winds and nighttime potential temperatures in Yerevan relative to those forced by the Global Forecast System fields.

show abstract

Section: Methods and Datamentioning

confidence: 65%

Section: Methods and Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Gevorgyan

2018

JGR Atmospheres

Self Cite

View full text Add to dashboard Cite

show abstract

“…The observation data received from the meteorological stations and data received from a global model used as inputs and outputs to the high-resolution numerical weather prediction models to produce outputs of temperature, precipitation, and other meteorological elements from the ground to the top of the atmosphere [1,2].…”

mentioning

confidence: 99%

Weather Data Visualization and Analytical Platform

Astsatryan

Grogoryan

Gyulgyulyan

et al. 2018

SCPE

View full text Add to dashboard Cite

This article aims to present a web-based interactive visualization and analytical platform for weather data in Armenia by integrating the three existing infrastructures for observational data, numerical weather prediction, and satellite image processing. The weather data used in the platform consists of near-surface atmospheric elements including air temperature, pressure, relative humidity, wind and precipitation. The visualization and analytical platform has been implemented for 2-m surface temperature. The platform gives Armenian State Hydrometeorological and Monitoring Service analytical capabilities to analyze the in-situ observations, model and satellite image data per station and region for a given period.

show abstract