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Department of Urban and Environmental Engineering (Disaster Management Engineering)clos

Comparison of Regional Climate Model Performances for Different Types of Heat Waves over South Korea

South Korea's heat wave events over 39 years (1980-2018) were defined by spatiotemporal criteria, and their quantitative characteristics were analyzed. The duration and intensity of these events ranked highest in 2016 and 2018. An examination of synoptic conditions of heat wave events in 2016 and 2018 based on a reanalysis dataset revealed a positive anomaly of 500-hPa geopotential height, which could have induced warm conditions over the Korean Peninsula in both years. However, a difference prevailed in that there was a blocking high over the Kamchatka Peninsula and a continental thermal high over northern China in 2016, while the expansion of the western North Pacific subtropical high was mainly associated with 2018 heat wave events. Numerical experiments using the Weather Research and Forecasting (WRF) Model were conducted to 1) evaluate how distinct meteorological characteristics of heat wave events in 2016 and 2018 were reproduced by the model, and 2) investigate how they affect extreme temperature events. Typical synoptic features of the 2016 heat wave events (i.e., Kamchatka blocking and continental thermal high) were not captured well by the WRF Model, while those of 2018 were reasonably reproduced. On the contrary, the heat wave event during late August 2016 related to the Kamchatka blocking high was realistically simulated when the blocking was artificially sustained by applying spectral nudging. In conclusion, the existence of a blocking high over the Kamchatka region (i.e., northern Pacific region) is an important feature to accurately predict long-lasting heat waves in East Asia

A novel ensemble learning for post-processing of NWP Model's next-day maximum air temperature forecast in summer using deep learning and statistical approaches

A reliable and accurate extreme air temperature in summer is necessary to prepare for and respond to thermal disasters such as heatstroke and power outages. The numerical weather prediction (NWP) model is commonly used to forecast air temperature using dynamic mechanisms. Because of its high uncertainty from coarse spatial resolution and unstable parameterization, however, it requires post-processing. Recent studies have proposed advanced post-processing methods using machine learning and deep learning techniques. This study compared various individual post-processing models-multi-linear regression (MLR), support vector regression (SVR), gated recurrent units (GRU), and convolutional neural network (CNN). It also proposed a novel multi-model ensemble (MMESS) that aggregates individual post-processing models based on the skill score (SS) for the Local Data Assimilation and Prediction System (LDAPS, a local NWP model over Korea) model's next-day maximum air temperature (Tmax) forecast data in two different domains: South Korea and Seoul. The pressure and surface data of the present-day analysis and next-day forecast fields of LDAPS were used as input variables. As a result of hindcast validation, CNN showed good overall performance (root mean square error (RMSE) of 1.41 (?)degrees C in South Korea and 1.50 C in Seoul) among individual models. We found that CNN demonstrated lower RMSE (1.17-1.58 ?degrees C) than other post-processing models (1.43-2.17 C) at stations where the bias of LDAPS changes, using surrounding spatial information. The proposed MMESS exhibited more reliable, robust results than the individual models did. A further comparison to the simple average ensemble and the constrained linear squares-based MMEsupported the proposed MMESS as a more suitable ensemble method for next-day Tmax forecast, considering the relative significance of the individual models

Impacts of the East Asian Winter Monsoon and Local Sea Surface Temperature on Heavy Snowfall over the Yeongdong Region

This research investigates the impact of local sea surface temperature (SST) on the 2-month (January and February) accumulated snowfall over the Yeongdong (YD) region. The YD region is strongly affected by synoptic-scale factors such as the East Asian winter monsoon (EAWM). The relationships of snowfall over the YD region to the EAWM and local SST are examined based on observational analyses and sensitivity experiments using a regional climate model. In the sensitivity experiments, local SST is replaced with the 33-yr mean winter SST (1982???2014). The observational analysis shows that both the synoptic environment and local SST are important factors for the occurrence of anomalous heavy snowfall over the YD region. The favorable synoptic environments can be characterized by eastward expansion of the Siberian high over Manchuria and corresponding enhancement of easterly anomalies over the YD region. These conditions are more frequently observed during the weak EAWM years than during the strong EAWM. Furthermore, warm SST over the East Sea contributes to heavy snowfall over the YD region by providing heat and moisture in the lower troposphere, which are important sources of energy for the formation of heavy snowfall. Warm SST anomalies over the East Sea enhance low-level moisture convergence over the YD region, while cold SST anomalies lead to reduced moisture convergence. Sensitivity experiments indicate that local SST can significantly affect snowfall amount over the YD region when the synoptic environments are favorable. However, without these synoptic conditions (expansion of the Siberian high and easterly inflow), the impact of local SST on the snowfall over the YD region is not significant

Development of model output statistics based on the least absolute shrinkage and selection operator regression for forecasting next-day maximum temperature in South Korea

Regression models for model output statistics (MOS) based on least absolute shrinkage and selection operator methods were developed to forecast next-day maximum surface air temperature (TMAX) during the warm season in South Korea. The forecast fields from the operational numerical weather prediction (NWP) system of the Korean Meteorological Administration for global and local forecasts and the observed TMAX data in 225 observation stations were used as input variables for the MOS. The training period was July and August (JA) from 2015 to 2018, and the regression models were tested using data from JA 2019. As a result of hindcasting for the test period, the MOS models performed significantly better for next-day TMAX forecasting over South Korea than the numerical models during JA 2019. The mean TMAX errors were reduced by over 1 degrees C in MOSs compared to those in the numerical models. However, the TMAX forecast performance was generally lower in the higher-resolution NWP Local Data Assimilation and Prediction System (LDAPS)-based MOS (LMOS) than in the lower-resolution NWP Global Data Assimilation and Prediction System (GDAPS)-based MOS. This pattern was dominant when LDAPS simulated the TMAX more accurately than average. In particular, the random TMAX error of LDAPS was larger than that of GDAPS during the training period, and a positive random error of TMAX was magnified in LMOS. Because the other predictors forecasted from LDAPS can be associated with lower TMAX forecast performance of LMOS, in addition to TMAX effects as a predictor, a new MOS was developed using both LDAPS and GDAPS outputs. The forecast accuracy was improved by up to 0.3 degrees C when the forecast fields from the GDAPS substituted several LMOS predictors, even though TMAX was the primary predictor for LMOS