• Water Engineering Research
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• v.3 no.2
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• pp.113-122
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• 2002

Accurate quantitative forecasting of rainfall for basins with a short response time is essential to predict flash floods. In this study, a Quantitative Flood Forecasting (QFF) model was developed by incorporating the evolving structure and frequency of intense weather systems and by using neural network approach. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters as input. The convective classification and tracking system (CCATS) was used to identify and quantify storm properties such as lifetime, area, eccentricity, and track. As in standard expert prediction systems, the fundamental structure of the neural network model was learned from the hydroclimatology of the relationships between weather system, rainfall production and streamflow response in the study area. All these processes stretched leadtime up to 18 hours. The QFF model will be applied to the mid-Atlantic region of United States in a forthcoming paper.

• Atmosphere
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• v.21 no.3
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• pp.243-256
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• 2011

This paper presents the development of new algorithm for identifying and tracking the convective cells in three dimensional reflectivity fields in Cartesian coordinates. First, the radar volume data in spherical coordinate system has been converted into Cartesian coordinate system by the bilinear interpolation. The three-dimensional convective cell has then been identified as a group of spatially consecutive grid points using reflectivity and volume thresholds. The tracking algorithm utilizes a fuzzy logic with four membership functions and their weights. The four fuzzy parameters of speed, area change ratio, reflectivity change ratio, and axis transformation ratio have been newly defined. In order to make their membership functions, the normalized frequency distributions are calculated using the pairs of manually matched cells in the consecutive radar reflectivity fields. The algorithms have been verified for two convective events in summer season. Results show that the algorithms have properly identified storm cells and tracked the same cells successively. The developed algorithms may provide useful short-term forecasting or nowcasting capability of convective storm cells and provide the statistical characteristics of severe weather.

• Journal of Korea Water Resources Association
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• v.36 no.3 s.134
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• pp.495-505
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• 2003

Convective weather systems such as organized mesoscale convective systems (Mesoscale Convective Complex, MCC and Convective Cloud Clusters, CCC) and much weaker Disorganized Short-lived Convection (DSC) in the region of India and Nepal were analyzed using the Meteosat-5 IR imagery. The diurnal march and propagation of patterns of convective activity in the Himalayas and Northern Indian subcontinent were examined. Results indicate that infrared satellite images of Northern India and along the southern flank of the Himalayas reveal a strong presence of convective weather systems during the 1999 and 2000 monsoons, especially in the afternoon and during the night. The typical MCCs have life-times of about 11 hours, and areal extent about $300,000km^2$. Although the core of MCC activity remains generally away from the Middle Himalayan range, the occurrence of heavy precipitation events in this region can be directly linked to MCCs that venture into the Lesser Himalayan region and remain within the region bounded by $25^{\circ}-30^{\circ}N$. One principal feature in the spatial organization of convection is the dichotomy between the Tibetan Plateau and the Northern Indian Plains: CCCs and DSCs begin in the Tibetan Plateau in the mid-afternoon into the evening; while they are most active in the mid-night and early morning in the Gangetic Plains and along the southern facing flanks of the Himalayas. Furthermore, these data are consistent with the daily cycle of rainfall documented for a network of 20 hydrometeorological stations in Central Nepal, which show strong nocturnal peaks of intense rainfall consistent with the close presence of Convective Weather Systems (CWSs) in the Gangetic Plains (Barros et al. 2000).

• Journal of the Korean earth science society
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• v.33 no.5
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• pp.377-394
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• 2012

To elucidate the mechanism associated with the development of heavy precipitation system, a field experiment was carried out in Jejudo (or Jeju Island) and Marado, Korea from 22 June to 12 July 2006. The synoptic atmospheric conditions were analyzed using the National Centers for Environmental Prediction-National Center for Atmospheric Research's (NCEP/NCAR) reanalyzed data, weather maps, and sounding data. The kinematic characteristics of each precipitation system were investigated by dual Doppler radar analysis. During the field experiment, data of four precipitation events with more than 20 mm rainfall were collected. In F case (frontal precipitation), a typical Changma front was dominant and the observation field was fully saturated. However there was no convective instability near the surface. LF case (low pressure accompanied with Changma front) showed strong convective instability near the surface, while a strong convergence corresponded to the low pressure from China accompanied with Changma front. In FT case (Changma front indirectly influenced by typhoon), the presence of a convective instability indicated the transport of near surface, strong additional moisture from the typhoon 'EWINIAR'. The convergence wind field was ground to be located at a low level. The convective instability was not significant in T case (precipitation of the typhoon 'EWINIAR'), since the typhoon passed through Jejudo and the Changma front was disappeared toward the northeastern region of the Korean peninsula. The kinematic (convergence and divergence) characteristics of wind fields, convective instability, and additional moisture inflow played important roles in the formation and development of heavy precipitation.

• Atmosphere
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• v.15 no.3
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• pp.155-165
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• 2005

The characteristics of six precipitation systems occurred around Cheongju in 2002 are analyzed after the convective/stratiform radar echo classification using radar reflectivity from the Meteorological Research Institute"s X-band Doppler weather radar. The Biggerstaff and Listemaa (2000) algorithm is applied for the classification and reveals a physical characteristics of the convective and stratiform rain diagnosed from the three-dimensional structure of the radar reflectivity. The area satisfying the vertical profile of radar reflectivity is well classified, while the area near the radar site and the topography-shielded area show a mis-classification. The seasonal characteristics of the precipitation system are also analyzed using the contoured frequency by altitude diagrams (CFADs). The heights of maximum reflectivity are 4 km and 5.5 km in spring and summer, respectively, and the vertical gradient of radar reflectivity from 1.5 km to the melting layer in spring is larger than in summer.

• Korean Journal of Remote Sensing
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• v.25 no.6
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• pp.465-474
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• 2009

Convective/stratiform radar echo classification schemes by Steiner et al. (1995) and Biggerstaff and Listemaa (2000) are examined on a monsoonal front during the summer monsoon-Changma period, which is organized as a cloud cluster with mesoscale convective complex. Target radar is S-band with wavelength of 10cm, spatial resolution of 1km, elevation angle interval of 0.5-1.0 degree, and minimum elevation angle of 0.19 degree at Jindo over the Korean Peninsula. For verification of rainfall amount retrieved from the echo classification, ground-based rain gauge observations (Automatic Weather Stations) are examined, converting the radar echo grid data to the station values using the inverse distance weighted method. Improvement from the echo classification is evaluated based on the correlation coefficient and the scattered diagram. Additionally, an optimal use method was designed to produce combined rainfalls from the radar echo and Tropical Rainfall Measuring Mission Precipitation Radar (TRMM/PR) data. Optimal values for the radar rain and TRMM/PR rain are inversely weighted according to the error variance statistics for each single station. It is noted how the rainfall distribution during the summer monsoon frontal system is improved from the classification of convective/stratiform echo and the use of the optimal use technique.

• Atmosphere
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• v.24 no.4
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• pp.457-470
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• 2014

Thermodynamic conditions related with localized torrential rainfall in the middle west region of Korean peninsula are examined using radar rain rate and radiosonde observational data. Localized torrential rainfall events in this study are defined by three criteria base on 1) any one of Automated Synoptic Observing System (ASOS) hourly rainfall exceeds $30mmhr^{-1}$ around Osan, 2) the rain (> $1mmhr^{-1}$) area estimated from radar reflectivity is less than $20,000km^2$, and 3) the rain (> $10mmhr^{-1}$) cell is detected clearly and duration is short than 24 hr. As a result, 13 cases were selected during the summer season of 10 years (2004-13). It was found that the duration, the maximum rain area, and the maximum volumetric rain rate of convective cells (> $30mmhr^{-1}$) are less than 9hr, smaller than $1,000km^2$, and $15,000{\sim}60,000m^3s^{-1}$ in these cases. And a majority of cases shows the following thermodynamic characteristics: 1) Convective Available Potential Energy (CAPE) > $800Jkg^{-1}$, 2) Convective Inhibition (CIN) < $40Jkg^{-1}$, 3) Total Precipitable Water (TPW) ${\approx}$ 55 mm, and 4) Storm Relative Helicity (SRH) < $120m^2s^{-2}$. These cases mostly occurred in the afternoon. These thermodynamic conditions indicated that these cases were caused by strong atmospheric instability, lifting to overcome CIN, and sufficient moisture. The localized torrential rainfall occurred with deep moisture convection result from the instability caused by convective heating.

• Atmosphere
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• v.26 no.1
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• pp.97-109
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• 2016

Heavy rainfall (over $80mm\;hr^{-1}$) system associated with unstable atmospheric conditions occurred over the Seoul metropolitan area on 27 July 2011. To investigate the heavy rainfall system, we used three-dimensional data from Korea Local Analysis and Prediction System (KLAPS) reanalysis data and analysed the structure of the precipitation system, kinematic characteristics, thermodynamic properties, and Meteorological condition. The existence of Upper-Level Jet (ULJ) and Low-Level Jet (LLJ) are accelerated the heavy rainfall. Convective cloud developed when a strong southwesterly LLJ and strong moisture convergence occurring around the time of the heavy rainfall is consistent with the results of previous studies on such continuous production. Environmental conditions included high equivalent potential temperature of over 355 K at low levels, and low equivalent potential temperature of under 330 K at middle levels, causing vertical instability. The tip of the band shaped precipitation system was made up of line-shaped convective systems (LSCSs) that caused flooding and landslides, and the LSCSs were continuously enhanced by merging between new cells and the pre-existing cell. Difference of wind direction between low and middle levels has also been considered an important factor favouring the occurrence of precipitation systems similar to LSCSs. Development of LSCs from the wind direction difference at heights of the severe precipitation occurrence area was also identified. This study can contribute to the identification of production and development mechanisms of heavy rainfall and can be used in applied research for prediction of severe weather.

• Proceedings of the Korea Water Resources Association Conference
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• 2002.05a
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• pp.43-50
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• 2002

Accurate quantitative forecasting of rainfall for basins with a short response time is essential to predict streamflow and flash floods. Previously, neural networks were used to develop a Quantitative Precipitation Forecasting (QPF) model that highly improved forecasting skill at specific locations in Pennsylvania, using both Numerical Weather Prediction (NWP) output and rainfall and radiosonde data. The objective of this study was to improve an existing artificial neural network model and incorporate the evolving structure and frequency of intense weather systems in the mid-Atlantic region of the United States for improved flood forecasting. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters as input. The convective classification and tracking system (CCATS) was used to identify and quantify storm properties such as life time, area, eccentricity, and track. As in standard expert prediction systems, the fundamental structure of the neural network model was learned from the hydroclimatology of the relationships between weather system, rainfall production and streamflow response in the study area. The new Quantitative Flood Forecasting (QFF) model was applied to predict streamflow peaks with lead-times of 18 and 24 hours over a five year period in 4 watersheds on the leeward side of the Appalachian mountains in the mid-Atlantic region. Threat scores consistently above .6 and close to 0.8 ∼ 0.9 were obtained fur 18 hour lead-time forecasts, and skill scores of at least 4％ and up to 6％ were attained for the 24 hour lead-time forecasts. This work demonstrates that multisensor data cast into an expert information system such as neural networks, if built upon scientific understanding of regional hydrometeorology, can lead to significant gains in the forecast skill of extreme rainfall and associated floods. In particular, this study validates our hypothesis that accurate and extended flood forecast lead-times can be attained by taking into consideration the synoptic evolution of atmospheric conditions extracted from the analysis of large-area remotely sensed imagery While physically-based numerical weather prediction and river routing models cannot accurately depict complex natural non-linear processes, and thus have difficulty in simulating extreme events such as heavy rainfall and floods, data-driven approaches should be viewed as a strong alternative in operational hydrology. This is especially more pertinent at a time when the diversity of sensors in satellites and ground-based operational weather monitoring systems provide large volumes of data on a real-time basis.

• Atmosphere
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• v.21 no.4
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• pp.391-404
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• 2011

This study investigates the impact of cumulus parameterization scheme (CPS) with different horizontal grid sizes on the simulation of the local heavy rainfall case over the Korean Peninsula. The Weather Research and Forecasting (WRF)-based real-time forecast system of the Joint Center for High-impact Weather and Climate Research (JHWC) is used. Three CPSs are used for sensitivity experiments: the BMJ (Betts-Miller-Janjic), GD (Grell-Devenyi ensemble), and KF (Kain-Fritsch) CPSs. The heavy rainfall case selected in this study is characterized by low-level jet and low-level transport of warm and moist air. In 27-km simulations (DM1), simulated precipitation is overestimated in the experiment with BMJ scheme, and it is underestimated with GD scheme. The experiment with KF scheme shows well-developed precipitation cells in the southern and the central region of the Korean Peninsula, which are similar to the observations. All schemes show wet bias and cold bias in the lower troposphere. The simulated rainfall in 27-km horizontal resolution has influence on rainfall forecast in 9-km horizontal resolution, so the statements on 27-km horizontal resolution can be applied to 9-km horizontal resolution. In the sensitivity experiments of CPS for DM3 (3-km resolution), the experiment with BMJ scheme shows better heavy rainfall forecast than the other experiments. The experiments with CPS in 3-km horizontal resolution improve rainfall forecasts compared to the experiments without CPS, especially in rainfall distribution. The experiments with CPS show lower LCL(Lifted Condensation Level) than those without CPS at the maximum rainfall point, and weaker vertical velocity is simulated in the experiments with CPS compared to the experiments without CPS. It means that CPS suppresses convective instability and influences mainly convective rainfall. Consequently, heavy rainfall simulation with BMJ CPS is better than the other CPSs, and even in 3-km horizontal resolution, CPS should be applied to control convective instability. This conclusion can be generalized by conducting more experiments for a variety of cases over the Korean Peninsula.