Physical and dynamic factors that drove the heavy rainfall event over the middle Korean Peninsula on 26-27 July 2011

Lee, Jooyoung; Kim, Wonsu; Lee, Tae Young

doi:10.1007/s13143-017-0009-4

Cited by 10 publications

(11 citation statements)

References 27 publications

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“…Owing to this extended tongue, a convectively unstable layer appears above the maximum-θ e level, below which a convectively stable surface layer exists. Similar structure was also found in a previous study (Lee et al 2017;Fig. 8).…”

Section: Methodssupporting

confidence: 91%

Atmospheric Structure for Convective Development in the Events of Cloud Clusters over the Korean Peninsula

Lee

Shin

Park

2020

Asia-Pacific J Atmos Sci

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This study examines atmospheric structures causing convective development in the events of cloud cluster (CC) over the Korean peninsula using the analysis and forecast data of National Centers for Environmental Prediction (NCEP) climate forecast system reanalysis (CFSR) and observation data. Two CC types-CCs associated with meso-α-scale lows (CCMLs) and mesoscale troughs (CCMTs)-were investigated. The common atmospheric structure for convective development in CC events is comprised of i) a strong southwesterly band (SWB; a region with southwesterly wind speeds >12.5 m s −1) in the lower troposphere upstream of CCs with a mesoscale convergence zone in its exit area, ii) a layer of high-θ e air in the lower troposphere near the surface extending from the southwest to SWB exit, iii) elevated height of maximum θ e in the lower troposphere near and over the convergence zone, above which a convectively unstable layer exists. Generality of the above-described structure has been demonstrated via examination of composite fields. SWB plays a major role in producing the structure for convective development in CC events over the Korean peninsula mainly through i) advection of high-θ e air from the southwest, and ii) significant horizontal convergence in the exit area, which can facilitate convection initiation. The two types of CC show notable differences in atmospheric structure across the boundary between high-θ e air from the southwest and low-θ e air in the northeast and in the mode of high-θ e air transport to the region of convective development. The boundary is generally tilted northeastward with height for CCML cases, whereas it is nearly vertical for the majority of CCMT cases. This study indicates that, despite the abovementioned differences, convective developments in both CC types can be considered as elevated convection that occurs as air parcels in an elevated layer of convective instability are lifted by upward motion in the convergence zone. For both types of CC, differential θ e advection plays the key role for the occurrence of elevated layer of convective instability. And θ e front in CCML events indicates the presence of elevated convective instability above it and the possibility of elevated convection provided that a lifting mechanism is available.

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Section: Methodssupporting

confidence: 91%

Atmospheric Structure for Convective Development in the Events of Cloud Clusters over the Korean Peninsula

Lee

Shin

Park

2020

Asia-Pacific J Atmos Sci

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“…You et al (2010) observed that deep advection of warm air is related to a longer precipitation period and a higher precipitation rate. Large-scale tropical and monsoon related circulation systems (together with underlying lower boundary conditions) may also cause long-lasting precipitation periods (Lee et al, 2017a;Liu and Wu, 2016). Liu and Wu (2016) conclude that the duration of precipitation events depends on the duration of specific circulation phenomena.…”

Section: Moisture and Its Large-scale Transportmentioning

confidence: 99%

“…Cold pools can be generated by evaporation and melting of precipitation in sub-saturated air and cooling due to subsequent downdrafts below precipitation forming clouds or by low-level jets that transport cold air masses from cold air source regions. The cooling of low-level air can create a ridge and the development of a mid-level ridge and trough pair (Lee et al, 2017a). The stronger the evaporative cooling, the stronger and larger the cold pool (Tu et al, 2017).…”

Section: Liftingmentioning

confidence: 99%

Meteorological aspects of heavy precipitation in relation to floods – An overview

Breugem

Wesseling

Oostindie

et al. 2020

Earth-Science Reviews

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“…Many studies have been done to improve the quantitative precipitation forecast using numerical weather prediction models and satellite or radar data (e.g., Fritsch et al, 1998;Lee et al, 2006;Kim and Oh, 2010;Yerong and David, 2006;Yu et al, 2013). : Based on these precipitation observations, the mesoscale structures of precipitation as well as the hydrologic budgets in Korea have been extensively studied (e.g., Kim and Lee, 2006;Cassardo et al, 2009;Jeong et al, 2012;Jung and Lee, 2013;Lee et al, 2017a). Capturing the characteristic :::::::::::: spatiotemporal features of precipitation systems out of :::: from the observation networks is essential to runoff forecast, especially at the catchment scale and for the flooding cases , for accurate and reliable spatiotemporal estimates of precipitation are crucial to the successful forecast of catchment response (Volkmann et al, 2010).…”

Section: =⇒ Asmentioning

confidence: 99%

Geostatistical assessment of summertime rainfall observations in Korea based on composite precipitation and satellite water vapor data

Park

Lee

Park

2018

Preprint

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Abstract. Among the meteorological disasters, heavy rainfalls cause the second largest damage in Korea, following typhoons. To confront with the potential disasters due to heavy rainfalls, understanding the observational characteristics of precipitation is of utmost importance. In this study, we investigate the spatial and temporal characteristics of summertime precipitation in Korea, according to the precipitation types, by conducting the geostatistical analyses such as autocorrelogram, Moran's I and general G, on the composite (radar + station) precipitation data. The e-folding distance of precipitation ranges from 15 to 35 km, depending on the spatial distribution, rather than intensity, of precipitation, whereas the e-folding time ranges from 1 to 2 h. The directional analyses revealed that the summertime precipitation in Korea has high spatial correlations in the southwest–northeast and west–east directions, mainly due to frontal rainfalls during the monsoon season. Furthermore, the cluster versus dispersion patterns and the hot versus cold spots are analyzed through Moran's I and general G, respectively. Water vapor, represented by the brightness temperature, from three Himawari-8 water vapor bands also show similar characteristics with precipitation but with strong spatial correlation over much longer distance (~ 100 km), possibly due to the continuity of water vapor. We found that, under the e-folding-based standard, the current observation network of Korea is sufficient to capture the characteristics of most precipitation systems; however, under a strict standard (e.g., autocorrelation of 0.6), a higher-resolution observation network is essentially required – especially in local areas with frequent heavy rainfalls – depending on the directional features of precipitation systems. Establishing such an observation network based on the characteristics of precipitation enables us to improve monitoring/tracking/prediction skills of high-impact weather phenomena as well as to enhance the utilization of numerical weather prediction.

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Physical and dynamic factors that drove the heavy rainfall event over the middle Korean Peninsula on 26-27 July 2011

Cited by 10 publications

References 27 publications

Atmospheric Structure for Convective Development in the Events of Cloud Clusters over the Korean Peninsula

Atmospheric Structure for Convective Development in the Events of Cloud Clusters over the Korean Peninsula

Meteorological aspects of heavy precipitation in relation to floods – An overview

Geostatistical assessment of summertime rainfall observations in Korea based on composite precipitation and satellite water vapor data

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