scholarly journals Spatiotemporal Pattern Networks of Heavy Rain among Automatic Weather Stations and Very-Short-Term Heavy-Rain Prediction

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Yong-Hyuk Kim ◽  
Yourim Yoon
Keyword(s):  
South Korea ◽  
Heavy Rain ◽  
Spatiotemporal Pattern ◽  
Precipitation Data ◽  
Short Term ◽  
Hourly Precipitation ◽  
Weather Stations

Spatiotemporal pattern networks of heavy rain among automatic weather stations, which reflect the mobility of heavy rain, were constructed and analyzed based on the hourly precipitation data over the last ten years (from 2003 to 2012) in South Korea. Moreover, a new algorithm applying the constructed heavy-rain pattern networks to very-short-term heavy-rain prediction was developed, and significant prediction results could be obtained.

scholarly journals Estimating Drought Conditions for Regions with Limited Precipitation Data

2011 ◽  
Vol 50 (3) ◽  
pp. 548-559 ◽  
Author(s):  
Jinyoung Rhee ◽  
Gregory J. Carbone
Keyword(s):  
Spatial Interpolation ◽  
Drought Index ◽  
Daily Precipitation ◽  
Tropical Rainfall Measuring Mission ◽  
Precipitation Data ◽  
Short Term ◽  
Daily Precipitation Data ◽  
Weather Stations ◽  
Better Than

Abstract Three closely related issues that affect drought estimation in regions with limited precipitation data are addressed by investigating methods for filling missing daily precipitation data, handling short-term records, and deriving drought information for unsampled locations. The analysis yields three general conclusions: 1) it is better to conduct spatial interpolation prior to calculating drought index values, 2) using weather stations with moderate lengths of records (usually at least 10 years) improves the spatial–temporal characterization of drought, and 3) alternative precipitation sources of the National Weather Service multisensor precipitation rainfall estimates and the Tropical Rainfall Measuring Mission (TRMM) satellite monthly rainfall product (3B43) do not outperform spatially interpolated daily precipitation data in most regions, except in the western United States where the TRMM-based precipitation data work better than the spatially interpolated values for drought monitoring.

scholarly journals GIST-PM-Asia v1: development of a numerical system to improve particulate matter forecasts in South Korea using geostationary satellite-retrieved aerosol optical data over Northeast Asia

2016 ◽  
Vol 9 (1) ◽  
pp. 17-39 ◽  
Author(s):  
S. Lee ◽  
C. H. Song ◽  
R. S. Park ◽  
M. E. Park ◽  
K. M. Han ◽  
...  
Keyword(s):  
Particulate Matter ◽  
South Korea ◽  
Northeast Asia ◽  
Initial Distribution ◽  
Low Earth Orbit ◽  
Optical Data ◽  
Future Research ◽  
Observation Data ◽  
Forecast System ◽  
Short Term

Abstract. To improve short-term particulate matter (PM) forecasts in South Korea, the initial distribution of PM composition, particularly over the upwind regions, is primarily important. To prepare the initial PM composition, the aerosol optical depth (AOD) data retrieved from a geostationary equatorial orbit (GEO) satellite sensor, GOCI (Geostationary Ocean Color Imager) which covers a part of Northeast Asia (113–146° E; 25–47° N), were used. Although GOCI can provide a higher number of AOD data in a semicontinuous manner than low Earth orbit (LEO) satellite sensors, it still has a serious limitation in that the AOD data are not available at cloud pixels and over high-reflectance areas, such as desert and snow-covered regions. To overcome this limitation, a spatiotemporal-kriging (STK) method was used to better prepare the initial AOD distributions that were converted into the PM composition over Northeast Asia. One of the largest advantages in using the STK method in this study is that more observed AOD data can be used to prepare the best initial AOD fields compared with other methods that use single frame of observation data around the time of initialization. It is demonstrated in this study that the short-term PM forecast system developed with the application of the STK method can greatly improve PM10 predictions in the Seoul metropolitan area (SMA) when evaluated with ground-based observations. For example, errors and biases of PM10 predictions decreased by  ∼  60 and  ∼  70 %, respectively, during the first 6 h of short-term PM forecasting, compared with those without the initial PM composition. In addition, the influences of several factors on the performances of the short-term PM forecast were explored in this study. The influences of the choices of the control variables on the PM chemical composition were also investigated with the composition data measured via PILS-IC (particle-into-liquid sampler coupled with ion chromatography) and low air-volume sample instruments at a site near Seoul. To improve the overall performances of the short-term PM forecast system, several future research directions were also discussed and suggested.

scholarly journals Heavy Rain Types for Triggering Shallow Landslides in South Korea

2015 ◽  
Vol 60 (1) ◽  
pp. 243-249 ◽  
Author(s):  
Suk-Woo Kim ◽  
Kun-Woo Chun ◽  
Kyoichi Otsuki ◽  
Yoshinori Shinohara ◽  
Man–Il Kim ◽  
...  
Keyword(s):  
South Korea ◽  
Heavy Rain ◽  
Shallow Landslides

scholarly journals Evaluation of Rainfall Forecasts with Heavy Rain Types in the High-Resolution Unified Model over South Korea

2019 ◽  
Vol 34 (5) ◽  
pp. 1277-1293 ◽  
Author(s):  
Hwan-Jin Song ◽  
Byunghwan Lim ◽  
Sangwon Joo
Keyword(s):  
High Resolution ◽  
South Korea ◽  
Korean Peninsula ◽  
Heavy Rainfall ◽  
Rainfall Intensity ◽  
Heavy Rain ◽  
Unified Model ◽  
Moderate Intensity ◽  
Local Data ◽  
Total Rainfall

Abstract Heavy rainfall events account for most socioeconomic damages caused by natural disasters in South Korea. However, the microphysical understanding of heavy rain is still lacking, leading to uncertainties in quantitative rainfall prediction. This study is aimed at evaluating rainfall forecasts in the Local Data Assimilation and Prediction System (LDAPS), a high-resolution configuration of the Unified Model over the Korean Peninsula. The rainfall of LDAPS forecasts was evaluated with observations based on two types of heavy rain events classified from K-means clustering for the relationship between surface rainfall intensity and cloud-top height. LDAPS forecasts were characterized by more heavy rain cases with high cloud-top heights (cold-type heavy rain) in contrast to observations showing frequent moderate-intensity rain systems with relatively lower cloud-top heights (warm-type heavy rain) over South Korea. The observed cold-type and warm-type events accounted for 32.7% and 67.3% of total rainfall, whereas LDAPS forecasts accounted for 65.3% and 34.7%, respectively. This indicates severe overestimation and underestimation of total rainfall for the cold-type and warm-type forecast events, respectively. The overestimation of cold-type heavy rainfall was mainly due to its frequent occurrence, whereas the underestimation of warm-type heavy rainfall was affected by both its low occurrence and weak intensity. The rainfall forecast skill for the warm-type events was much lower than for the cold-type events, due to the lower rainfall intensity and smaller rain area of the warm-type. Therefore, cloud parameterizations for warm-type heavy rain should be improved to enhance rainfall forecasts over the Korean Peninsula.

scholarly journals Effects of meteorology and emissions on urban air quality: a quantitative statistical approach to long-term records (1999–2016) in Seoul, South Korea

2018 ◽  
Vol 18 (21) ◽  
pp. 16121-16137 ◽  
Author(s):  
Jihoon Seo ◽  
Doo-Sun R. Park ◽  
Jin Young Kim ◽  
Daeok Youn ◽  
Yong Bin Lim ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
South Korea ◽  
Meteorological Conditions ◽  
Meteorological Variables ◽  
Urban Air ◽  
Short Term ◽  
Long Term Trends ◽  
Local Emissions

Abstract. Together with emissions of air pollutants and precursors, meteorological conditions play important roles in local air quality through accumulation or ventilation, regional transport, and atmospheric chemistry. In this study, we extensively investigated multi-timescale meteorological effects on the urban air pollution using the long-term measurements data of PM10, SO2, NO2, CO, and O3 and meteorological variables over the period of 1999–2016 in Seoul, South Korea. The long-term air quality data were decomposed into trend-free short-term components and long-term trends by the Kolmogorov–Zurbenko filter, and the effects of meteorology and emissions were quantitatively isolated using a multiple linear regression with meteorological variables. In terms of short-term variability, intercorrelations among the pollutants and meteorological variables and composite analysis of synoptic meteorological fields exhibited that the warm and stagnant conditions in the migratory high-pressure system are related to the high PM10 and primary pollutant, while the strong irradiance and low NO2 by high winds at the rear of a cyclone are related to the high O3. In terms of long-term trends, decrease in PM10 (−1.75 µg m−3 yr−1) and increase in O3 (+0.88 ppb yr−1) in Seoul were largely contributed by the meteorology-related trends (−0.94 µg m−3 yr−1 for PM10 and +0.47 ppb yr−1 for O3), which were attributable to the subregional-scale wind speed increase. Comparisons with estimated local emissions and socioeconomic indices like gross domestic product (GDP) growth and fuel consumptions indicate probable influences of the 2008 global economic recession as well as the enforced regulations from the mid-2000s on the emission-related trends of PM10 and other primary pollutants. Change rates of local emissions and the transport term of long-term components calculated by the tracer continuity equation revealed a decrease in contributions of local emissions to the primary pollutants including PM10 and an increase in contributions of local secondary productions to O3. The present results not only reveal an important role of synoptic meteorological conditions on the episodic air pollution events but also give insights into the practical effects of environmental policies and regulations on the long-term air pollution trends. As a complementary approach to the chemical transport modeling, this study will provide a scientific background for developing and improving effective air quality management strategy in Seoul and its metropolitan area.

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