Suppression of very low intensity precipitation in Korea

Yum, Seong Soo; Wan, Joo

doi:10.1016/j.atmosres.2010.06.006

Cited by 13 publications

(10 citation statements)

References 28 publications

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“…At MP, the snow N(D) was similar between Mtype and C-type around 0.6 to 1 mm diameter but the N(D) of M-type was higher than that of C-type at the larger diameter range. These results suggest that DG is mainly affected by the orographic effect around 0.6 to 1 mm diameter while the effect was weak for the same diameters at MP (Yum and Cha 2010;Cha et al 2009). However, the difference of snow N(D) between M-type and C-type at MP was larger than that at DG for the 5-10 mm diameter range.…”

Section: Temperature Wind Speed and Wind Direction Effects On Dsdmentioning

confidence: 74%

“…The 'snow' occurrence frequency was higher at DG with 21% than at MP with 12% because DG is at northern latitude and higher altitude than MP. These results indicate that the mountainous site (DG) is affected by the orographic effect that can more easily induce precipitation (Blanchard 1953;Yum and Cha 2010). Supportively, Sun-Hee et al 2012showed precipitation enhancement in the DG site area by the orographic effect using numerical modeling.…”

Section: Analysis Of Hc Characteristicsmentioning

confidence: 80%

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Characteristics of Precipitation Particles Measured by PARSIVEL Disdrometer at a Mountain and a Coastal Site in Korea

Wan

Yum

2020

Asia-Pacific J Atmos Sci

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This study analyzed HC (Hydrometeor Classification), rain rate, and DSD (Drop Size Distribution) observed using an optical disdrometer, parsivel, at a mountain (Daegwallyeong, DG) and a coastal site (Mokpo, MP) over 2 years (2010-2011). The HC accuracies of the drizzle, rain, and snow were about 95%, 87%, and 80% respectively. The R 2 (coefficient of determination) of the parsivel with TB (Tipping Bucket) rain gauge was 0.91 at MP and 0.96 at DG. The shapes of drizzle and rain DSD observed at DG and MP were similar while the variation in snow DSD was easily influenced by weather condition such as temperature and wind speed. The relatively warm temperature (-5°C ∼-0°C) increased the snow particle number concentration at around 0.6~1 mm diameter and the relatively cold temperature (-15°C ∼-10°C) decreased it above 2 mm diameter. Although wind speed was not a strong factor in snow DSD, larger particles were apt to form in relatively strong wind conditions. Due to different wind directions for the maritime and continental regions, snow particle number density (N(D)) at MP and DG exhibited large differences in terms of snow DSD shape and the number concentration. For instance, in the maritime precipitation, snow DSD shape at MP was broader than that at DG and small-size snow particles were observed at DG more frequently than at MP. In addition, camera-observed snow particle type measurement was carried out at DG in January to March 2010. During the measurement at DG, a mixed type of plate and column was the most frequent and an aggregation of plate type frequently occurred at lower temperatures.

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Section: Temperature Wind Speed and Wind Direction Effects On Dsdmentioning

confidence: 74%

Section: Analysis Of Hc Characteristicsmentioning

confidence: 80%

Characteristics of Precipitation Particles Measured by PARSIVEL Disdrometer at a Mountain and a Coastal Site in Korea

Wan

Yum

2020

Asia-Pacific J Atmos Sci

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“…Yum and Cha [] proposed a parameter called the precipitation efficiency of orographic clouds for better accounting for the suppression of light rain by air pollution. It is defined as the frequency of precipitation (Fp) divided by the frequency of orographic clouds (Fc).…”

Section: Resultsmentioning

confidence: 99%

“…It is useful to study trends in different rain intensities, in addition to total rainfall, given their different roles in the hydrological cycle and in extreme events such as floods and droughts [ Goswami et al , ]. Similar to Yum and Cha [], rain intensity is classified into four categories: very low (VL; ≤ 0.6 mm h −1 ), low (LO; 0.6–2 mm h −1 ), moderate (MO; 2–8 mm h −1 ), and high (HI; > 8 mm h −1 ).…”

Section: Methodsmentioning

confidence: 99%

Precipitation and air pollution at mountain and plain stations in northern China: Insights gained from observations and modeling

et al. 2014

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We analyzed 40 year data sets of daily average visibility (a proxy for surface aerosol concentration) and hourly precipitation at seven weather stations, including three stations located on the Taihang Mountains, during the summertime in northern China. There was no significant trend in summertime total precipitation at almost all stations. However, light rain decreased, whereas heavy rain increased as visibility decreased over the period studied. The decrease in light rain was seen in both orographic-forced shallow clouds and mesoscale stratiform clouds. The consistent trends in observed changes in visibility, precipitation, and orographic factor appear to be a testimony to the effects of aerosols. The potential impact of large-scale environmental factors, such as precipitable water, convective available potential energy, and vertical wind shear, on precipitation was investigated. No direct links were found. To validate our observational hypothesis about aerosol effects, Weather Research and Forecasting model simulations with spectral-bin microphysics at the cloud-resolving scale were conducted. Model results confirmed the role of aerosol indirect effects in reducing the light rain amount and frequency in the mountainous area for both orographic-forced shallow clouds and mesoscale stratiform clouds and in eliciting a different response in the neighboring plains. The opposite response of light rain to the increase in pollution when there is no terrain included in the model suggests that orography is likely a significant factor contributing to the opposite trends in light rain seen in mountainous and plain areas.

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“…Globally, there is a partial agreement between the disdrometer and the rain masking algorithm: After 1815 UTC rain intensity drops so much that the disdrometer is unable to detect the precipitation, while after 2200 UTC, the rain masking algorithm fails to detect the rainfall up to the ground. Precipitation events from lidar data are then necessary to fill a gap in detecting very low precipitation intensity events (<0.05 mm h −1 ) that are crucial to study the aerosol effects and interactions on clouds and rainfall [33].…”

Section: A Simple Case: 22 April 2016mentioning

confidence: 99%

Overview of the New Version 3 NASA Micro-Pulse Lidar Network (MPLNET) Automatic Precipitation Detection Algorithm

et al. 2019

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Precipitation modifies atmospheric column thermodynamics through the process of evaporation and serves as a proxy for latent heat modulation. For this reason, a correct precipitation parameterization (especially for low-intensity precipitation) within global scale models is crucial. In addition to improving our modeling of the hydrological cycle, this will reduce the associated uncertainty of global climate models in correctly forecasting future scenarios, and will enable the application of mitigation strategies. In this manuscript we present a proof of concept algorithm to automatically detect precipitation from lidar measurements obtained from the National Aeronautics and Space Administration Micropulse lidar network (MPLNET). The algorithm, once tested and validated against other remote sensing instruments, will be operationally implemented into the network to deliver a near real time (latency <1.5 h) rain masking variable that will be publicly available on MPLNET website as part of the new Version 3 data products. The methodology, based on an image processing technique, detects only light precipitation events (defined by intensity and duration) such as light rain, drizzle, and virga. During heavy rain events, the lidar signal is completely extinguished after a few meters in the precipitation or it is unusable because of water accumulated on the receiver optics. Results from the algorithm, in addition to filling a gap in light rain, drizzle, and virga detection by radars, are of particular interest for the scientific community as they help to fully characterize the aerosol cycle, from emission to deposition, as precipitation is a crucial meteorological phenomenon accelerating atmospheric aerosol removal through the scavenging effect. Algorithm results will also help the understanding of long term aerosol–cloud interactions, exploiting the multi-year database from several MPLNET permanent observational sites across the globe. The algorithm is also applicable to other lidar and/or ceilometer network infrastructures in the framework of the Global Aerosol Watch (GAW) aerosol lidar observation network (GALION).

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Suppression of very low intensity precipitation in Korea

Cited by 13 publications

References 28 publications

Characteristics of Precipitation Particles Measured by PARSIVEL Disdrometer at a Mountain and a Coastal Site in Korea

Characteristics of Precipitation Particles Measured by PARSIVEL Disdrometer at a Mountain and a Coastal Site in Korea

Precipitation and air pollution at mountain and plain stations in northern China: Insights gained from observations and modeling

Overview of the New Version 3 NASA Micro-Pulse Lidar Network (MPLNET) Automatic Precipitation Detection Algorithm

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