In this study, the temperature bias of aircraft observation was estimated through comparison with sonde observation prior to developing the temperature bias correction method at the Korea Institute of Atmospheric Prediction Systems (KIAPS). First, we tried to compare aircraft temperature with collocated sonde observations at 0000 UTC on June 22, 2012. However, it was difficult to estimate the temperature bias due to the lack of samples and the uncertainty of the sonde position at high altitudes. Second, we attempted a background innovation comparison for sonde and aircraft using KIAPS Package for Observation Processing (KPOP). The one month averaged background innovation shows the aircraft temperature have a warm bias against sonde for all levels. In particular, there is a globally distinct warm bias about 0.4 K between 200 hPa and 300 hPa corresponding to flight level. Spatially, most of the areas showed the warm bias except for below 300 hPa in some part of China at 0000 and 1200 UTC and below 850 hPa in Australia at 0000 UTC. In general, the temperature bias was larger at 1200 UTC than 0000 UTC. Based on the estimated temperature bias, we have applied the static bias correction method to the aircraft temperature observation. As a result, the warm bias of the aircraft temperature has decreased at most levels, but a slight cold bias has occurred in some areas.

Planetary boundary layer height (PBLH), produced by the Local Data Assimilation and Prediction System (LDAPS), was verified using RawinSonde (RS) data obtained from observation at Daegwallyeong (DGW) and Sokcho (SCW) during the International Collaborative Experiments for Pyeongchang 2018 Olympic and Paralympic winter games (ICE-POP 2018). The PBLH was calculated using RS data by applying the bulk Richardson number and the parcel method. This calculated PBLH was then compared to the values produced by LDAPS. The PBLH simulations for DGW and SCW were generally underestimation. However, the PBLH was an overestimation from surface to 200 m and 450 m at DGW and SCW, respectively; this result of model's failure to correctly simulate the Surface Boundary Layer (SBL) and the Mixing Layer (ML) as the PBLH. When the accuracy of the PBLH simulation is low, large errors are seen in the mid- and low-level humidity. The highest frequencies of Planetary boundary layer (PBL) types, calculated by the LDAPS at DGW and SCW, were presented as types Ι and II, respectively. Analysis of meteorological factors according to the PBL types indicate that the PBLH of the existing stratocumulus were overestimated when the mid- and low-level humidity errors were large. If the instabilities of the surface and vertical mixing into clouds are considered important factors affecting the estimation of PBLH into model, then mid- and low-level humidity should also be considered important factors influencing PBLH simulation performance.

This study investigates synoptic characteristics of cold surges over South Korea during winter season (December-February). A total of 63 cold events are selected by quantile regression analysis using daily mean temperature observations from 11 KMA stations for 38 years (1979/80-2016/17). Large-scale pressure pattern during the cold surges is well characterized by high over Siberia and low over Aleutian regions, which elucidates cold advection over the Korean peninsula. However, the large-scale pattern cannot successfully explain the observed sudden decrease of temperature during the cold surges. Composite analyses reveal that a synoptic-scale cyclone developing over the northern Japan is a key feature that significantly contribute to the enhancement of cold advection by increasing pressure gradient over the Korean peninsula. Enhanced sensible and latent heat fluxes are observed over the southern ocean of Korea and Japan during the cold surges due to temperature and humidity differences between the near surface and the lower atmosphere over the ocean. The evaporated water vapor transported toward the center of the surface cyclone and condenses in the lower-to-middle troposphere. The released energy likely promotes the development of the surface cyclone by inducing positive PV near the surface of the heating region.

This study investigated future changes in the Arctic permafrost features and related biogeochemical alterations under global warming. The Community Land Model (CLM) with biogeochemistry (BGC) was run for the period 2005 to 2099 with projected future climate based on the Special Report on Emissions Scenarios (SRES) A2 scenario. Under global warming, over the Arctic land except for the permafrost region, the rise in soil temperature led to an increase in soil liquid and decrease in soil ice. Also, the Arctic ground obtained carbon dioxide from the atmosphere due to the increase in photosynthesis of vegetation. On the other hand, over the permafrost region, the microbial respiration was increased due to thawing permafrost, resulting in increased carbon dioxide emissions. Methane emissions associated with total water storage have increased over most of Arctic land, especially in the permafrost region. Methane releases were predicted to be greatly increased especially near the rivers and lakes associated with an increased chance of flooding. In conclusion, at the end of $21^{st}$ century, except for permafrost region, the Arctic ground is projected to be the sink of carbon dioxide, and only permafrost region the source of carbon dioxide. This study suggests that thawing permafrost can further to accelerate global warming significantly.

Predictability of Northern Hemisphere blocking in the Korea Meteorological Administration (KMA) Global Data Assimilation and Prediction System (GDAPS) is evaluated for the period of July 2016 to May 2017. Using the operational model output, blocking is defined by a meridional gradient reversal of 500-hPa geopotential height as Tibaldi-Molteni Index. Its predictability is quantified by computing the critical success index and bias score against ERA-Interim data. It turns out that Northwest Pacific blockings, among others, are reasonably well predicted with a forecast lead time of 2~3 days. The highest prediction skill is found in spring with 3.5 lead days, whereas the lowest prediction skill is observed in autumn with 2.25 lead days. Although further analyses are needed with longer dataset, this result suggests that Northern Hemisphere blocking is not well predicted in the operational weather prediction model beyond a short-term weather prediction limit. In the spring, summer, and autumn periods, there was a tendency to overestimate the Western North Pacific blocking.

The roles of atmospheric heating formation and distribution on the global circulation are of utmost importance, and those are directly related to not only spatial but also temporal characteristics of monsoon system. In this study, before we clarify the characteristics of apparent heat source <$Q_1$> and moisture sink <$Q_2$>, comparisons of three reanalysis datasets (NCEP2, ERA-Interim, and JRA-55) in its global or regional patterns are performed to clearly evaluate differences among datasets. Considering inter-hemispheric difference of global monsoon regions, seasonal means of June-July-August and December-January-February, which is summer (winter) and winter (summer) in the Northern (Southern) Hemisphere are employed respectively. Here we show the characteristics of eight different regional monsoon regions and find contributions of <$Q_2$> to <$Q_1$> for the regional monsoon regions. Each term in apparent heat source and moisture sink is shown to come from the ERA-Interim dataset, since the ERA-Interim could be representative of three datasets. The NCEP2 data has a different characteristic in the ratio of <$Q_2$> and <$Q_1$> because it overestimates <$Q_1$> compared to the other two different datasets. The Australia monsoon has been performing better over time, while some regional monsoons (South America, North America, and North Africa) have been showing increasing data inconsistency. In addition, the three reanalysis datasets are getting different marching with time, in particular since the early 2000s over South America, North America, and North Africa monsoon regions. The recent inconsistency among the three datasets that may be associated with the global warming hiatus remains unexplored.

This study examines the impact of the urban parameterization scheme and the land cover change on simulated near surface temperature using Unified Model (UM) over the Seoul metropolitan area. We perform four simulations by varying the land cover and the urban parameterization scheme, and then compare the model results with 46 AWS observation data from 2 to 9 August 2016. Four simulations were performed with different combination of two urban parameterization schemes and two land cover data. Two schemes are Best scheme and MORUSES (Met Office Reading Urban Surface Exchange Scheme) and two land cover data are IGBP (International Geosphere and Biosphere Programme) and EGIS (Environmental Geographic information service) land cover data. When land use data change from IGBP to EGIS, urban ratio over the study area increased by 15.9%. The results of the study showed that the higher change in urban fraction between IGBP and EGIS, the higher the improvement in temperature performance, and the higher the urban fraction, the higher the effect of improving temperature performance of the urban parameterization scheme. 1.5-m temperature increased rapidly during the early morning due to increase of sensible heat flux in EXP2 compared to CTL. The MORUSES with EGIS (EXP3) provided best agreement with observations and represents a reasonable option for simulating the near surface temperature of urban area.

Nowcasting algorithms using weather radar data are mostly based on extrapolating the radar echoes. We estimate the echo motion vectors that are used to extrapolate the echo properly. Therefore, understanding the general characteristics of these motion vectors is important to improve the performance of nowcasting. General characteristics of radar-based motions are analyzed for warm season precipitation over Jeju region. Three-year summer season data (June~August, 2011~2013) from two radars (GSN, SSP) in Jeju are used to obtain echo motion vectors that are retrieved by Variational Echo Tracking (VET) method which is widely used in nowcasting. The highest frequency occurs in precipitation motion toward east-northeast with the speed of $15{\sim}16m\;s^{-1}$ during the warm season. Precipitation system moves faster and eastward in June-July while it moves slower and northeastward in August. The maximum frequency of speed appears in $10{\sim}20m\;s^{-1}$ and $5{\sim}10m\;s^{-1}$ in June~July and August respectively while average speed is about $14{\sim}15m\;s^{-1}$ in June~July and $8m\;s^{-1}$ in August. In addition, the direction of precipitation motion is highly variable in time in August. The speed of motion in Lee side of the island is smaller than that of the windward side.

This study investigated the variability of spring (March-May) forest fire risk in Korea for the period 1991~2017 and analyzed its relationship with large-scale climate factors. The Forest Weather Index (FWI) representing the meteorological risk for forest fire occurrences calculated based on observational data and its relationship with large-scale climate factors were analyzed. We performed the empirical orthogonal function (EOF) analysis on the spring FWI. The leading EOF mode of FWI accounting for about 70% of total variability was found to be highly correlated with total number of forest fire occurrences in Korea. The high FWI, forest fire occurrence risk, in Korea, is associated with warmer atmosphere temperature in midwest Eurasia-China-Korea peninsula, cyclonic circulation anomaly in northeastern China-Korea peninsula-northwest pacific, westerly wind anomaly in central China-Korea peninsula, and low humidity in Korea. These are further related with warmer sea surface temperature and enhanced outgoing longwave radiation over Western Pacific, which represents a typical condition for a La $Ni\tilde{n}a$ episode. This suggests that large-scale climate factors over East Asia and ENSO could have a significant influence on the occurrence of spring forest fires in Korea.

We performed the comparison of observed $PM_{10}$ and $PM_{2.5}$ at both the Yonsei University and the AIRKOREA site in the same Seodaemun-gu district, Seoul from March to December 2016. Generally, the moderate correlations between two sites were found for both $PM_{10}$ and $PM_{2.5}$, but monthly difference was somewhat occurred, implying that the measurement situation is not equally maintained even in a closely located area. Particularly correlations became weaker in June and July, which seems the impact of rainy conditions. Correlations between two stations were higher for $PM_{10}$ compared to $PM_{2.5}$, probably indicating the spatially larger difference of fine mode particle. Monthly mean variation was similar between two sites showing a maximum in March and minimum in August. Diurnal variation was somewhat different: morning peak at Yonsei University but evening peak at the Seodaemun-gu AIRKOREA site, reflecting the difference of local air condition. We also compared the extent of $PM_{10}$ and $PM_{2.5}$ according to the local wind speed and direction. In general, the level of particulate matter was high when the wind is blowing from the northwestern area with low wind speed, meaning the high accumulation effect of transported air particles. Findings of this study can be usefully considered for the investigation about the discrepancy of aerosol measurement in a local scale.

This study assesses the prediction skill of regional scale model for the mean temperature anomaly over South Korea produced by Pusan National University Coupled General Circulation Model (PNU CGCM)-Weather Research and Forecasting (WRF) chain. The initial and boundary conditions of WRF are derived from PNU CGCM. The hindcast period is 11 years from 2007 to 2017. The model's prediction skill of mean temperature anomaly is evaluated in terms of the temporal correlation coefficient (TCC), root mean square error (RMSE) and skill scores which are Heidke skill score (HSS), hit rate (HR), false alarm rate (FAR). The predictions of WRF and PNU CGCM are overall similar to observation (OBS). However, TCC of WRF with OBS is higher than that of PNU CGCM and the variation of mean temperature is more comparable to OBS than that of PNU CGCM. The prediction skill of WRF is higher in March and April but lower in October to December. HSS is as high as above 0.25 and HR (FAR) is as high (low) as above (below) 0.35 in 2-month lead time. According to the spatial distribution of HSS, predictability is not concentrated in a specific region but homogeneously spread throughout the whole region of South Korea.

This study collected and analyzed records related to the Asian dust phenomenon in Korean history books. I collected a wide range of records from diverse sources and identified data for analysis. 290 records were selected through this process. These records provide a variety of information including the occurrence time and duration of the phenomenon, other natural phenomenon occurring at the same date, descriptions of the phenomenon, and so on. Reports on, Asian dust in the historical records was not continuous or uniform. However, their appearance in certain months of the year is similar to that observed in modern times. In addition, I collected 90 records showing it that can be largely divided into two viewpoints which they were a natural calamity or an unusual phenomenon. Generally, natural phenomenons in historical books are mostly recorded in viewpoint of unusual phenomenon. Nevertheless, research result verified the record as actual observational data. Consequentially, it also indicates that this data is important for research from a meteorological point of view. I think that the data collected through this study can be helpful to modern meteorological studies related to the Asian dust phenomenon.

The performance of the newly designed Pusan National University Coupled General Circulation Model (PNU CGCM) Ensemble Forecast System which produce 40 ensemble members for 12-month lead prediction is evaluated and analyzed in terms of boreal winter temperature over South Korea (S. Korea). The influence of ensemble size on prediction skill is examined with 40 ensemble members and the result shows that spreads of predictability are larger when the size of ensemble member is smaller. Moreover, it is suggested that more than 20 ensemble members are required for better prediction of statistically significant inter-annual variability of wintertime temperature over S. Korea. As for the ensemble average (ENS), it shows superior forecast skill compared to each ensemble member and has significant temporal correlation with Automated Surface Observing System (ASOS) temperature at 99% confidence level. In addition to forecast skill for inter-annual variability of wintertime temperature over S. Korea, winter climatology around East Asia and synoptic characteristics of warm (above normal) and cold (below normal) winters are reasonably captured by PNU CGCM. For the categorical forecast with $3{\times}3$ contingency table, the deterministic forecast generally shows better performance than probabilistic forecast except for warm winter (hit rate of probabilistic forecast: 71%). It is also found that, in case of concentrated distribution of 40 ensemble members to one category out of the three, the probabilistic forecast tends to have relatively high predictability. Meanwhile, in the case when the ensemble members distribute evenly throughout the categories, the predictability becomes lower in the probabilistic forecast.