GyuWon Lee

Asymmetrically interacting spreading dynamics on complex layered networks

The spread of disease through a physical-contact network and the spread of information about the disease on a communication network are two intimately related dynamical processes. We investigate the asymmetrical interplay between the two types of spreading dynamics, each occurring on its own layer, by focusing on the two fundamental quantities underlying any spreading process: epidemic threshold and the final infection ratio. We find that an epidemic outbreak on the contact layer can induce an outbreak on the communication layer, and information spreading can effectively raise the epidemic threshold. When structural correlation exists between the two layers, the information threshold remains unchanged but the epidemic threshold can be enhanced, making the contact layer more resilient to epidemic outbreak. We develop a physical theory to understand the intricate interplay between the two types of spreading dynamics.

Evaluation of WRF Cloud Microphysics Schemes Using Radar Observations

AbstractThe Korea Meteorological Administration (KMA) implemented a 10-yr project to develop its own global model (GM) by 2020. To reflect the complex topography and unique weather characteristics of the Korean Peninsula, a high-resolution model with accurate physics and input data is required. The WRF single-moment 6-class microphysics scheme (WSM6) and WRF double-moment 6-class microphysics scheme (WDM6) that will be implemented in the Korea GM (KGM) are evaluated. Comparisons of the contoured frequency by altitude diagram (CFAD), time–height cross sections, and vertical profiles of hydrometeors are utilized to assess the two schemes in simulating summer monsoon and convective precipitation cases over the Korean Peninsula during 2011. The results show that WSM6 and WDM6 overestimate the height of the melting level and bright band as compared to radar observations. However, the accuracy of WDM6 is in better agreement with radar observations. This is attributed to the difference in the sedimentation proce...

The Adjustment of Radar Precipitation Estimation Based on the Kriging Method

Quantitative precipitation estimation (QPE) is one of the most important elements in meteorological and hydrological applications. In this study, we adjusted the QPE from an S-band weather radar based on co-kriging method using the geostatistical structure function of error distribution of radar rainrate. In order to estimate the accurate quantitative precipitation, the error of radar rainrate which is a primary variable of co-kriging was determined by the difference of rain rates from rain gauge and radar. Also, the gauge rainfield, a secondary variable of co-kriging is derived from the ordinary kriging based on raingauge network. The error distribution of radar rain rate was produced by co-kriging with the derived theoretical variogram determined by experimental variogram. The error of radar rain rate was then applied to the radar estimated precipitation field. Locally heavy rainfall case during 6-7 July 2009 is chosen to verify this study. Correlation between adjusted one-hour radar rainfall accumulation and rain gauge rainfall accumulation improved from 0.55 to 0.84 when compared to prior adjustment of radar error with the adjustment of root mean square error from 7.45 to 3.93 mm.

Classification of precipitation types using fall velocity-diameter relationships from 2D-video distrometer measurements

Fall velocity-diameter relationships for four different snowflake types (dendrite, plate, needle, and graupel) were investigated in northeastern South Korea, and a new algorithm for classifying hydrometeors is proposed for distrometric measurements based on the new relationships. Falling ice crystals (approximately 40 000 particles) were measured with a two-dimensional video disdrometer (2DVD) during a winter experiment from 15 January to 9 April 2010. The fall velocity-diameter relationships were derived for the four types of snowflakes based on manual classification by experts using snow photos and 2DVD measurements: the coefficients (exponents) for different snowflake types were 0.82 (0.24) for dendrite, 0.74 (0.35) for plate, 1.03 (0.71) for needle, and 1.30 (0.94) for graupel, respectively. These new relationships established in the present study (PS) were compared with those from two previous studies. Hydrometeor types were classified with the derived fall velocity-diameter relationships, and the classification algorithm was evaluated using 3× 3 contingency tables for one rain-snow transition event and three snowfall events. The algorithm showed good performance for the transition event: the critical success indices (CSIs) were 0.89, 0.61 and 0.71 for snow, wet-snow and rain, respectively. For snow events, the algorithm performance for dendrite and plate (CSIs = 1.0 and 1.0, respectively) was better than for needle and graupel (CSIs = 0.67 and 0.50, respectively).

A Fuzzy Logic Method for Lightning Prediction Using Thermodynamic and Kinematic Parameters from Radio Sounding Observations in South Korea

AbstractLightning is one of the most troubling weather phenomena for weather forecasters at space centers. In this study, proximity sounding and lightning data were used to evaluate the utility of thermodynamic and kinematic parameters for forecasting lightning prior to launch operations. Various parameters from 4138 radio sounding observations at five sites and cloud-to-ground (CG) stroke data from the Korea Meteorological Administration’s Lightning Detection Network (KLDN) over South Korea during 2004–09 were used. To support launch operations, forecasts of the total membership function for lightning (TMF) were derived from the combination of membership functions of selected thermodynamic and kinematic parameters with each objective weight using a fuzzy logic algorithm. The forecast skill of TMF was evaluated by computing several skill statistics, which include probability of detection (POD), false alarm rate (FAR), percent correct (PC), critical success index (CSI), and the true skill statistic (TSS). ...

Temporal Statistical Downscaling of Precipitation and Temperature Forecasts Using a Stochastic Weather Generator

Statistical downscaling is based on the fact that the large-scale climatic state and regional/local physiographic features control the regional climate. In the present paper, a stochastic weather generator is applied to seasonal precipitation and temperature forecasts produced by the International Research Institute for Climate and Society (IRI). In conjunction with the GLM (generalized linear modeling) weather generator, a resampling scheme is used to translate the uncertainty in the seasonal forecasts (the IRI format only specifies probabilities for three categories: below normal, near normal, and above normal) into the corresponding uncertainty for the daily weather statistics. The method is able to generate potentially useful shifts in the probability distributions of seasonally aggregated precipitation and minimum and maximum temperature, as well as more meaningful daily weather statistics for crop yields, such as the number of dry days and the amount of precipitation on wet days. The approach is extended to the case of climate change scenarios, treating a hypothetical return to a previously observed drier regime in the Pampas.

Incorporation of parameter uncertainty into spatial interpolation using Bayesian trans-Gaussian kriging

Quantitative precipitation estimation (QPE) plays an important role in meteorological and hydrological applications. Ground-based telemetered rain gauges are widely used to collect precipitation measurements. Spatial interpolation methods are commonly employed to estimate precipitation fields covering non-observed locations. Kriging is a simple and popular geostatistical interpolation method, but it has two known problems: uncertainty underestimation and violation of assumptions. This paper tackles these problems and seeks an optimal spatial interpolation for QPE in order to enhance spatial interpolation through appropriately assessing prediction uncertainty and fulfilling the required assumptions. To this end, several methods are tested: transformation, detrending, multiple spatial correlation functions, and Bayesian kriging. In particular, we focus on a short-term and time-specific rather than a long-term and event-specific analysis. This paper analyzes a stratiform rain event with an embedded convection linked to the passing monsoon front on the 23 August 2012. Data from a total of 100 automatic weather stations are used, and the rainfall intensities are calculated from the difference of 15 minute accumulated rainfall observed every 1 minute. The one-hour average rainfall intensity is then calculated to minimize the measurement random error. Cross-validation is carried out for evaluating the interpolation methods at regional and local levels. As a result, transformation is found to play an important role in improving spatial interpolation and uncertainty assessment, and Bayesian methods generally outperform traditional ones in terms of the criteria.

Identification and removal of non-meteorological echoes in dual-polarization radar data based on a fuzzy logic algorithm

A major issue in radar quantitative precipitation estimation is the contamination of radar echoes by non-meteorological targets such as ground clutter, chaff, clear air echoes etc. In this study, a fuzzy logic algorithm for the identification of non-meteorological echoes is developed using optimized membership functions and weights for the dual-polarization radar located at Mount Sobaek. For selected precipitation and non-meteorological events, the characteristics of the precipitation and non-meteorological echo are derived by the probability density functions of five fuzzy parameters as functions of reflectivity values. The membership functions and weights are then determined by these density functions. Finally, the nonmeteorological echoes are identified by combining the membership functions and weights. The performance is qualitatively evaluated by long-term rain accumulation. The detection accuracy of the fuzzy logic algorithm is calculated using the probability of detection (POD), false alarm rate (FAR), and clutter-signal ratio (CSR). In addition, the issues in using filtered dual-polarization data are alleviated.

Stochastic precipitation generator with hidden state covariates

Time series of daily weather such as precipitation, minimum temperature and maximum temperature are commonly required for various fields. Stochastic weather generators constitute one of the techniques to produce synthetic daily weather. The recently introduced approach for stochastic weather generators is based on generalized linear modeling (GLM) with covariates to account for seasonality and teleconnections (e.g., with the El Niño). In general, stochastic weather generators tend to underestimate the observed interannual variance of seasonally aggregated variables. To reduce this overdispersion, we incorporated time series of seasonal dry/wet indicators in the GLM weather generator as covariates. These seasonal time series were local (or global) decodings obtained by a hidden Markov model of seasonal total precipitation and implemented in the weather generator. The proposed method is applied to time series of daily weather from Seoul, Korea and Pergamino, Argentina. This method provides a straightforward translation of the uncertainty of the seasonal forecast to the corresponding conditional daily weather statistics.

Attenuation Correction Effects in Rainfall Estimation at X-Band Dual-Polarization Radar: Evaluation with a Dense Rain Gauge Network

The effects of attenuation correction in rainfall estimation with X-band dual-polarization radar were investigated with a dense rain gauge network. The calibration bias in reflectivity ( ) was corrected using a self-consistency principle. The attenuation correction of and the differential reflectivity ( ) were performed by a path integration method. After attenuation correction, and were significantly improved, and their scatter plots matched well with the theoretical relationship between and . The comparisons between the radar rainfall estimation and the rain gauge rainfall were investigated using the bulk statistics with different temporal accumulations and spatial averages. The bias significantly improves from 70% to 0% with . However, the improvement with was relatively small, from 3% to 1%. This indicated that rainfall estimation using a polarimetric variable was more robust at attenuation than was a single polarimetric variable method. The bias did not show improvement in comparisons between the temporal accumulations or the spatial averages in either rainfall estimation method. However, the random error improved from 68% to 25% with different temporal accumulations or spatial averages. This result indicates that temporal accumulation or spatial average (aggregation) is important to reduce random error.