• Atmosphere
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• v.24 no.3
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• pp.343-364
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• 2014

This study presents local and synoptic conditions associated with extreme heavy snowfall events in the Yeongdong region, as well as the temporal and spatial variability of these conditions. During the last 12 years (2001~2012), 3 extreme snowfall events occurred in the Yeongdong region, which recorded daily snowfall greater than 50 cm, respectively. In these events, one of the noticeable features is the occurrence of heavy hourly snowfall greater than 10 cm. It was reported from satellite analysis that these heavy snowfall may be closely related to mesoscale convective clouds. In this paper the 3 extreme events are examined on their synoptic environments associated with the developments of mesoscale convective system using numerical model output. These 3 events all occurred in strongly forced synoptic environments where 500 and 300 hPa troughs and 500 hPa thermal troughs were evident. From the analysis of diagnostic variables, it was found in all 3 events that absolute vorticity and cold air advection were dominant in the Yeongdong region and its surrounding sea at upper levels, especially at around 500 hPa (absolute vorticity: $20{\sim}60{\times}10^{-5}s^{-1}$, cold air advection: $-10{\sim}-20^{\circ}C$ $12hr^{-1}$). Moreover, the spatial distributions of cold advection showed mostly the shape of a narrow band along the eastern coast of Korea. These features of absolute vorticity and cold advection at 500 hPa were sustained for about 10 hours before the occurrence of maximum hourly snowfall.

• Korean Journal of Remote Sensing
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• v.29 no.4
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• pp.423-441
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• 2013

On 21 September 2010, one of Chuseok holidays in Korea, localized heavy rainfalls occurred over the midwestern region of the Korean peninsula. In this study MTSAT-2 infrared and water vapor channel imagery are examined to find out some features which are obvious in each stage of the life cycle of convective cell for this heavy rain event. Also the kinematic and thermodynamic features probably associated with them are investigated. The first clouds related with the Chuseok heavy rain are detected as low-level multicell cloud (brightness temperature: $-15{\sim}0^{\circ}C$) in the middle of the Yellow sea at 1630~1900 UTC on 20 Sept., which are probably associated with the convergence at 1000 hPa. Convective cells are initiated in the vicinity of Shantung peninsula at 1933 UTC 20, which have developed around the edge of the dark region in water vapor images. At two times of 0033 and 0433 UTC 21 the merging of two convective cells happens near midwestern coast of the peninsula and then they have developed rapidly. From 0430 to 1000 UTC 21, key features of convective cell include repeated formation of secondary cell, slow horizontal cloud motion, persistence of lower brightness temperature ($-75{\sim}-65^{\circ}C$), and relatively small cloud size (${\leq}-50^{\circ}C$) of about $30,000km^2$. Radar analysis showed that this heavy rain is featured by a narrow line-shaped rainband with locally heavy rainrate (${\geq}50$ mm/hr), which is located in the south-western edge of the convective cell. However there are no distinct features in the associated synoptic-scale dynamic forcing. After 1000 UTC 21 the convective cell grows up quickly in cloud size and then is dissipated. These satellite features may be employed for very short range forecast and nowcasting of mesoscale heavy rain system.

• Journal of Korea Water Resources Association
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• v.33 no.2
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• pp.219-227
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• 2000

In Korea, heavy rainfall is mainly induced by the Changma front or frontal system passed over Korea periodically. Both its unknown mesoscale characteristics and the lack of direct measurements make it difficult to predict precipitation reasonably. To understand its 3-dimensional structure, initiation and development mechanism of precipitation in that system will be very helpful to forecast it more accurately. A meteorological radar is specially useful because it produces direct measurement with high resolution in time and space. In this study, representative frontal system is selected and analyzed specially focused on its vertical structure using radar data. Results shows that there are convective cells with horizontal scale of 10 - 20 km in precipitation system. Melting layer located between 3 and 5 km height, maximum fall speeds of rain drops were seen just below bright band.

• Korean Journal of Agricultural and Forest Meteorology
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• v.5 no.2
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• pp.138-149
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• 2003

Korean regional network of tower flux sites, KoFlux, has been initiated to better understand $CO_2$, water and energy exchange between ecosystems and the atmosphere, and to contribute to regional, continental, and global observation networks such as FLUXNET and CEOP. Due to heterogeneous surface characteristics, most of KoFlux towers are located in non-ideal sites. In order to quantify carbon and energy exchange and to scale them up from plot scales to a region scale, applications of various methods combining measurement and modeling are needed. In an attempt to infer regional-scale flux, four methods (i.e., tower flux, convective boundary layer (CBL) budget method, MM5 mesoscale model, and NCAR/NCEP reanalysis data) were employed to estimate sensible heat flux representing different surface areas. Our preliminary results showed that (1) sensible heat flux from the tower in Haenam farmland revealed heterogeneous surface characteristics of the site; (2) sensible heat flux from CBL method was sensitive to the estimation of advection; and (3) MM5 mesoscale model produced regional fluxes that were comparable to tower fluxes. In view of the spatial heterogeneity of the site and inherent differences in spatial scale between the methods, however, the spatial representativeness of tower flux need to be quantified based on footprint climatology, geographic information system, and the patch scale analysis of satellite images of the study site.

• Journal of Korean Society for Atmospheric Environment
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• v.16 no.2
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• pp.141-150
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• 2000

In order to genralize the vertical dispersion of plume at long distances on mesoscale over complex terrain dispersion coefficients data have been obtained systematically according to lapsed time after release by using a composite turbulence water tank that simulates convective boundary layer. Dispersion experiments have been carried out for various combined conditions of thermal turbulence intensity mechanical turbulence intensity and plume release height at slightly to moderately unstable conditions. Results of tracer dispersion experiments conducted using water tank camera and image processing system have been converted into atmospheric dispersion data through the application of similarity law. The equation $\sigma$z/Zi=aX/(b+c X2)0.5 where $\sigma$2; vertical dispersion coefficient zi : mixing height X : dimen-sionaless downwind distance was confirmed to be an appropriate and general equation for expressing $\sigma$2 variation with turbulence intensity and plume release height, The value of "a" was found to be principally affected by mechanical turbulence intensity and that of "b" by mechanical turbulence intensity and release height. It was confirmed that the magnitude of "c" varies with release height. Results of water tank experiments on the relationship of $\sigma$2 vs downwind distance x have been compared with actual atmospheric dispersion data such as CONDORS data and Bowne's nomogram Operating conditions of a composite turbulence water tank for simulating the field turbulence situations of CONDORS experiments and Bowne's $\sigma$2(x) nomogram for suburban area have also been investigated in terms of water temperature difference between convection water tank and bottom plate heating tank grid plate stroke mixing water depth length scale and velocity scale. Moreover the effect of mechanical turbulence intensity on vertical dispersion has been discussed in the light of release height and downwind distance. height and downwind distance.

• Atmosphere
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• v.17 no.1
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• pp.69-86
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• 2007

The characteristics of the dual-Doppler wind retrieval method based on a three dimensional variational (3DVAR) conception were investigated from the following four points of view; the sensitivity of the number of iteration, the effect of the weak constraint term, the effect of the smoothness term, and the sensitivity of the error mixing ratio of the radial velocities. In the experiment, the radial velocities relative to the Gosan and Jindo radar sites of the Korea Meteorological Administration (KMA) were calculated from the forecasting of the WRF (Weather Research and Forecast; Skamarock, 2004) model at 1330 UTC 30 June 2006, which is the one and half hour forecast from the initial time, 1200 UTC on that day. The results showed that the retrieval performance of the horizontal wind field was robust, but that of the vertical wind was sensitive to the external conditions, such as iteration number and the on/off of the weak constraint term. The sensitivity of error mixing ratio was so large that even the horizontal wind retrieval efficiency was reduced a lot. But the sensitivity of the smooth term was not so large. When we applied this method to the real mesoscale convective system (MCS) between the Gosan and Jindo radar pair at 1430 UTC 30 June 2006, the wind structure of the convective cells in the MCS was consistently retrieved relative to the reflectivity factor structure. By comparing the vertical wind structure of this case with that of 10 minutes after, 1440 UTC 30 June 2006, we got the physical consistency of our method.

• 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.17 no.4
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• pp.435-453
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• 2007

KEOP (Korea Enhanced Observing Period)-2004 intensive summer observation was carried out from 20 June to 5 July 2004 over the Southwestern part of the Korean peninsula. In this study, the effects of KEOP-2004 intensive observation data on the simulation of precipitation system are investigated using KLAPS (Korea Local Analysis and Prediction System) and PSU/NCAR MM5. Three precipitation cases during the intensive observation are selected for detailed analysis. In addition to the control experiments using the traditional data for its initial and boundary conditions, two sensitivity experiments using KEOP data with and without Jindo radar are performed. Although it is hard to find a clear and consistent improvement in the verification score (threat score), it is found that the KEOP data play a role in improving the position and intensity of the simulated precipitation system. The experiments started at 00 and 12 UTC show more positive effect than those of 06 and 18 UTC. The effect of Jindo radar is dependent on the case. It plays a significant role in the heavy rain cases related to a mesoscale low over Changma front and the landing of a Typhoon. KEOP data produce more strong difference in the 06/18 UTC experiments than in 00/12 UTC, but give more positive effects in 00/12 UTC experiments. One of the possible explanations for this is that : KEOP data could properly correct the atmosphere around them when there are certain amounts of data, while gives excessive effect to the atmospheric field when there are few data. CRA analysis supports this reasoning. According to the CRA (Contiguous Rain Area) analysis, KEOP data in 00/12 UTC experiments improve only the surrounding area, resulting in essentially same precipitation system so the effects remain only in each convective cell rather than the system itself. On the other hand, KEOP data modify the precipitation system itself in 06/18 UTC experiments. Therefore the effects become amplified with time integration.

• Atmosphere
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• v.23 no.3
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• pp.293-305
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• 2013

Nonhydrostatic effects on convectively forced mesoscale flows in two dimensions are numerically investigated using a nondimensional model. An elevated heating that represents convective heating due to deep cumulus convection is specified in a uniform basic flow with constant stability, and numerical experiments are performed with different values of the nonlinearity factor and nonhydrostaticity factor. The simulation result in a linear system is first compared to the analytic solution. The simulated vertical velocity field is very similar to the analytic one, confirming the high accuracy of nondimensional model's solutions. When the nonhydrostaticity factor is small, alternating regions of upward and downward motion above the heating top appear. On the other hand, when the nonhydrostaticity factor is relatively large, alternating updraft and downdraft cells appear downwind of the main updraft region. These features according to the nonhydrostaticity factor appear in both linear and nonlinear flow systems. The location of the maximum vertical velocity in the main updraft region differs depending on the degrees of nonlinearity and nonhydrostaticity. Using the Taylor-Goldstein equation in a linear, steady-state, invscid system, it is analyzed that evanescent waves exist for a given nonhydrostaticity factor. The critical wavelength of an evanescent wave is given by ${\lambda}_c=2{\pi}{\beta}$, where ${\beta}$ is the nonhydrostaticity factor. Waves whose wavelengths are smaller than the critical wavelength become evanescent. The alternating updraft and downdraft cells are formed by the superposition of evanescent waves and horizontally propagating parts of propagating waves. Simulation results show that the horizontal length of the updraft and downdraft cells is the half of the critical wavelength (${\pi}{\beta}$) in a linear flow system and larger than ${\pi}{\beta}$ in a weakly nonlinear flow system.

• Journal of the Korean earth science society
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• v.43 no.6
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• pp.712-736
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• 2022

To understand the characteristics of low-level clouds (CLs), environmental variables are composited on each CL using individual surface observations and six-hourly upper-air meteorologies around the globe. Individual CLs has its own distinct environmental conditions. Over the eastern subtropical and western North Pacific Ocean in JJA, stratocumulus (CL5) has a colder sea surface temperature (SST), stronger and lower inversion, and more low-level cloud amount (LCA) than the climatology whereas cumulus (CL12) has the opposite characteristics. Over the eastern subtropical Pacific, CL5 and CL12 are influenced by cold and warm advection within the PBL, respectively but have similar cold advection over the western North Pacific. This indicates that the fundamental physical process distinguishing CL5 and CL12 is not the horizontal temperature advection but the interaction with the underlying sea surface, i.e., the deepening-decoupling of PBL and the positive feedback between shortwave radiation and SST. Over the western North Pacific during JJA, sky-obscuring fog (CL11), no low-level cloud (CL0), and fair weather stratus (CL6) are associated with anomalous warm advection, surface-based inversion, mean upward flow, and moist mid-troposphere with the strongest anomalies for CL11 followed by CL0. Over the western North Pacific during DJF, bad weather stratus (CL7) occurs in the warm front of the extratropical cyclone with anomalous upward flow while cumulonimbus (CL39) occurs on the rear side of the cold front with anomalous downward flow. Over the tropical oceans, CL7 has strong positive (negative) anomalies of temperature in the upper troposphere (PBL), relative humidity, and surface wind speed in association with the mesoscale convective system while CL12 has the opposite anomalies and CL39 is in between.