Synoptic and dynamic analysis of a flash flood-inducing heavy rainfall event in arid and semi-arid central-northern Iran and its simulation using the WRF model

Khansalari, Sakineh; Ranjbar-Saadatabadi, A.; Fazel-Rastgar, Farahnaz; Raziei, Tayeb

doi:10.1016/j.dynatmoce.2020.101198

Cited by 12 publications

(4 citation statements)

References 28 publications

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“…Several studies (e.g., Ekström et al, 2004;Tao et al, 2006;Karami et al, 2021b;Khansalari et al, 2021;al-Abbasi et al, 2023) considered large-scale synoptic features that contribute to dust storms. These studies often identify characteristics such as upper troughs and pressure gradients at the surface (between low and high-pressure systems) and the passage of a cold front without precipitation for these synoptic patterns.…”

Section: Synoptic-scale and Local Atmospheric Circulationsmentioning

confidence: 99%

Simulation and synoptic investigation of a severe dust storm originated from the Urmia Lake in the Middle East

Hossein Hamzeh,

Ranjbar Saadat Abadi,

Abdukhakimovich Shukurov

et al. 2024

atm

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Dried lake beds are one of the largest sources of dust in the world, causing environmental problems in the surrounding areas. In this study, the desiccated Urmia Lake was the primary source of dust for all nearby synoptic stations during the April 24-25, 2017 dust episode. Synoptic analysis revealed that the heavy dust storm was triggered by a strong Black Sea cyclone and a low-pressure system over central Iraq in conjunction with a vast high-pressure system. HYSPLIT-based trajectory analysis showed that high PM10 recorded over the Urmia Lake region on April 23-26, 2017, influenced western Azerbaijan, the south of the Caspian Sea, southwestern Kazakhstan, northwestern Uzbekistan, and western Turkmenistan. The dustiest air masses (PM10 > 400 µg m–3) affected the south of the Caspian Sea and western Azerbaijan. Furthermore, the WRF-Chem model was run to evaluate the spatial distribution of dust particles in the study region. The vertical profile revealed that the simulated dust concentration ascended to 5 km from the lake. The WRF-Chem dust schemes accurately simulated dust propagation and the vertical dust profile over Urmia Lake; however, the AFWA and GOCART dust schemes showed that PM10 fluctuating changes were earlier than the measured surface PM10 at five stations around Urmia Lake on April 23-26, 2017. Furthermore, the maximum amount anticipated by the model simulation was 12 h earlier than the maximum surface mass concentration of measured PM10 at the stations throughout the period.

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Section: Synoptic-scale and Local Atmospheric Circulationsmentioning

confidence: 99%

Simulation and synoptic investigation of a severe dust storm originated from the Urmia Lake in the Middle East

Hossein Hamzeh,

Ranjbar Saadat Abadi,

Abdukhakimovich Shukurov

et al. 2024

atm

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“…Considerable spatiotemporal heterogeneity, high volatility, and nonlinear properties are the main obstacles in extreme precipitation prediction. Currently, the Weather Research and Forecasting (WRF; Skamarock et al, 2019) mesoscale meteorological model considers an indispensable dynamic downscaling method to research and predict such heavy precipitation associated with extreme weather events (Khansalari et al, 2021; Letson et al, 2020; Powers et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…In this context, Madala et al (2013) studied the performance of different CPSs in simulating pre‐monsoon thunderstorm events, Haghroosta et al (2014) in simulating rainfall associated with typhoons, Cruz and Narisma (2016) in predicting rainfall associated with tropical cyclones, Das et al (2019) in predicting rainfall associated with monsoon lows, Sarkar et al (2020) in predicting rainfall associated with westerly disturbances, Liu et al (2020) in simulating heavy rainfall, Castorina et al (2021) in forecasting weather for complex orographic areas. Recently, Khansalari et al (2021) showed that Grell‐Freitas CPS performs better than Kain‐Fritsch, Tiedtke, and Grell‐3 CPSs when convective uplift causes precipitation but fails in representing precipitation associated with frontal uplift in the northern part of Iran. Their results indicate that the selection of CPS in the model configuration should be made considering the study's objectives, and although a unique CPS is not suitable for making accurate predictions for all atmospheric conditions, the best ones from the options available in the WRF could be explored.…”

Section: Introductionmentioning

confidence: 99%

WRF prediction of an atmospheric river‐related precipitation event: Sensitivity to cumulus parameterization schemes

Maddah,

Mostamandi

2024

Meteorological Applications

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The present study aimed to evaluate performance sensitivity to the Cumulus Parameterization Scheme (CPS) used for the Weather Research and Forecasting (WRF) model to predict the atmospheric river‐related precipitation (ARP) event with 206 and 57 mm, highest and area‐averaged precipitation (AAP) per 24‐h, respectively, that occurred over the central mountainous basins of Iran on 31 March 2019. For this purpose, experiments were designed using the 12 (almost all) CPSs available in WRF v4. To verify the predicted precipitation (from the inner 4‐km domain), both point‐scale and grid‐scale comparisons were performed against gauge‐ and satellite‐based observational data at three accumulation time‐scales (12‐, 18‐, and 24‐h) and in three distinct sub‐regions. All scores obtained from the different statistical metrics used, are in complete agreement with a strongly dependent performance of WRF on the CPS used. In addition, the use of Kain‐Fritsch, KF‐CuP, and Grell‐3 CPSs could provide a realistic picture of impending heavy precipitation for WRF. Contrary, the New SAS, Tiedtke, and Zhang‐McFarlane CPSs did not perform satisfactorily in predicting the ARP event. As a result, CPSs with the “momentum transport” option in their modification mechanism are unlikely to adequately simulate the conversion of incoming low‐level moisture from atmospheric river to precipitation. However, precipitation predictions are more accurate at the 24‐h accumulation time‐scale than at the 12‐ and 18‐h. Also, a dry bias in the predictions is expected as the terrain elevation and accumulation time‐scale decrease and the distance from the core of the precipitation field increases.

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“…Numerical weather prediction at the regional level, on the other hand, can properly simulate large-scale weather phenomena, resulting in better representation of convection. For this reason, regional models are increasingly being used to investigate rainfall scenarios [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Weather Research and Forecasting (WRF) Physical Schemes Parameterization to Predict Moderate to Extreme Rainfall in Poorly Gauged Basin

et al. 2022

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Incomplete hydro-meteorological data and insufficient rainfall gauges have caused difficulties in establishing a reliable flood forecasting system. This study attempted to adopt the remotely sensed hydro-meteorological data as an alternative to the incomplete observed rainfall data in the poorly gauged Kuantan River Basin (KRB), the main city at the east coast of Peninsula Malaysia. Performance of Weather Research and Forecasting (WRF) schemes’ combinations, including eight microphysics (MP) and six cumulus, were evaluated to determine the most suitable combination of WRF MPCU in simulating rainfall over KRB. All the obtained results were validated against observed moderate to extreme rainfall events. Among all, the combination scheme Stony Brook University and Betts–Miller–Janjic (SBUBMJ) was found to be the most suitable to capture both spatial and temporal rainfall, with average percentage error of about ±17.5% to ±25.2% for heavy and moderate rainfall. However, the estimated PE ranges of −58.1% to 68.2% resulted in uncertainty while simulating extreme rainfall events, requiring more simulation tests for the schemes’ combinations using different boundary layer conditions and domain configurations. Findings also indicate that for the region where hydro-meteorological data are limited, WRF, as an alternative approach, can be used to achieve more sustainable water resource management and reliable hydrological forecasting.

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Synoptic and dynamic analysis of a flash flood-inducing heavy rainfall event in arid and semi-arid central-northern Iran and its simulation using the WRF model

Cited by 12 publications

References 28 publications

Simulation and synoptic investigation of a severe dust storm originated from the Urmia Lake in the Middle East

Simulation and synoptic investigation of a severe dust storm originated from the Urmia Lake in the Middle East

WRF prediction of an atmospheric river‐related precipitation event: Sensitivity to cumulus parameterization schemes

Assessment of Weather Research and Forecasting (WRF) Physical Schemes Parameterization to Predict Moderate to Extreme Rainfall in Poorly Gauged Basin

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