Local Data Assimilation And Prediction System Research Articles

Lower tropospheric states revealed in high vertical-resolution radiosonde data in Korea and synoptic patterns for instability based on a self-organizing map

Seasonal and spatial characterizations of wind and stability in the lower troposphere below 1500 m are investigated using 1 s high-resolution operational radiosonde data observed at five stations (Baengnyeongdo, Heuksando, National Typhoon Center (NTC), Bukgangneung, and Pohang) in South Korea for 4 years (July 2016–June 2020), concentrating on four variables: horizontal wind speed (HWS), vertical wind shear (VWS), squared Brunt–Väisälä frequency (N2), and Richardson number (Ri). Significant seasonal variations are identified in these variables due to the heterogeneity of the geographical locations as well as seasonal variations in the synoptic patterns. VWS is high in spring and summer at two island stations on the west of the Korean Peninsula, Baengnyeongdo, and Heuksando, whereas it is high in winter at NTC, Bukgangneung, and Pohang, leading to frequent Kelvin–Helmholtz (KH) instability (KHI; 0 ≤ Ri < 1/4). Below z = 1000 m, static stability is lower in winter than in summer at all stations, resulting in a higher convective instability (Ri < 0) in winter, which is attributed to the inflow of the marine boundary layer. The frequent convective instability observed in spring at Bukgangneung suggests a potential relationship with mountain wave breaking and downslope windstorms. The microstructures of the lower tropospheric instabilities revealed in the current radiosonde observations are not properly represented in the high-resolution local numerical weather prediction and analysis system, namely local Data Assimilation and Prediction System (LDAPS) of Korea Meteorological Administration. In particular, the observed convective and KH instabilities, which are approximately 45% for the whole period, are rarely (approximately 15%) represented in LDAPS. The synoptic patterns favorable for the convective and KH instabilities cases are identified based on a self-organizing map clustering algorithm: (1) west-high and east-low patterns with extended Siberian high in winter, (2) strong low-pressure systems susceptible to KHI from spring to fall, (3) weak high-pressure systems with potential convective instability, accompanying northerly or north-westerly flows with cold advection at 925 hPa.

Informer-Based Temperature Prediction Using Observed and Numerical Weather Prediction Data.

This paper proposes an Informer-based temperature prediction model to leverage data from an automatic weather station (AWS) and a local data assimilation and prediction system (LDAPS), where the Informer as a variant of a Transformer was developed to better deal with time series data. Recently, deep-learning-based temperature prediction models have been proposed, demonstrating successful performances, such as conventional neural network (CNN)-based models, bi-directional long short-term memory (BLSTM)-based models, and a combination of both neural networks, CNN-BLSTM. However, these models have encountered issues due to the lack of time data integration during the training phase, which also lead to the persistence of a long-term dependency problem in the LSTM models. These limitations have culminated in a performance deterioration when the prediction time length was extended. To overcome these issues, the proposed model first incorporates time-periodic information into the learning process by generating time-periodic information and inputting it into the model. Second, the proposed model replaces the LSTM with an Informer as an alternative to mitigating the long-term dependency problem. Third, a series of fusion operations between AWS and LDAPS data are executed to examine the effect of each dataset on the temperature prediction performance. The performance of the proposed temperature prediction model is evaluated via objective measures, including the root-mean-square error (RMSE) and mean absolute error (MAE) over different timeframes, ranging from 6 to 336 h. The experiments showed that the proposed model relatively reduced the average RMSE and MAE by 0.25 °C and 0.203 °C, respectively, compared with the results of the CNN-BLSTM-based model.

Short-Term Visibility Prediction Using Tree-Based Machine Learning Algorithms and Numerical Weather Prediction Data

Abstract Accurate visibility prediction is imperative in the interests of human and environmental health. However, the existing numerical models for visibility prediction are characterized by low prediction accuracy and high computational cost. Thus, in this study, we predicted visibility using tree-based machine learning algorithms and numerical weather prediction data determined by the local data assimilation and prediction system (LDAPS) of the Korea Meteorological Administration. We then evaluated the accuracy of visibility prediction for Seoul, South Korea, through a comparative analysis using observed visibility from the automated synoptic observing system. The visibility predicted by machine learning algorithm was compared with the visibility predicted by LDAPS. The LDAPS data employed to construct the visibility prediction model were divided into learning, validation, and test sets. The optimal machine learning algorithm for visibility prediction was determined using the learning and validation sets. In this study, the extreme gradient boosting (XGB) algorithm showed the highest accuracy for visibility prediction. Comparative results using the test sets revealed lower prediction error and higher correlation coefficient for visibility predicted by the XGB algorithm (bias: −0.62 km, MAE: 2.04 km, RMSE: 2.94 km, and R: 0.88) than for that predicted by LDAPS (bias: −0.32 km, MAE: 4.66 km, RMSE: 6.48 km, and R: 0.40). Moreover, the mean equitable threat score (ETS) also indicated higher prediction accuracy for visibility predicted by the XGB algorithm (ETS: 0.5–0.6 for visibility ranges) than for that predicted by LDAPS (ETS: 0.1–0.2).

Short-term prediction of particulate matter (PM10 and PM2.5) in Seoul, South Korea using tree-based machine learning algorithms

In this study, highly accurate particulate matter (PM10 and PM2.5) predictions were obtained using meteorological prediction data from the local data assimilation and prediction system (LDAPS) and tree-based machine learning (ML). The study area was Seoul, South Korea, and data from July 2018 to June 2021 as well as LDAPS 36-h predictions with 1-h intervals 4 times a day were used. The predicted PM values were then compared with the observed PM measurements to evaluate the prediction accuracy. The PM prediction performance of the Community Multi-Scale Air Quality (CMAQ)-based chemical transport model (CTM) was compared with that reported by this study. The experimental results report that, among tree-based ML algorithms, light gradient boosting (LGB) is the most suitable for PM prediction. The PM prediction results of the LGB algorithm for the hourly test data were: bias = −0.10 μg/m3, root mean square error (RMSE) = 13.15 μg/m3, and R2 = 0.86 for PM10 and bias = −0.02 μg/m3, RMSE = 7.48 μg/m3, and R2 = 0.83 for PM2.5, and for daily mean were: RMSE ≤1.16 μg/m3 and R2 = 0.996. The relative RMSE (%RMSE) is 21% lower than the results of the CTM model, and R2 is 0.20 higher. Even in the high PM concentration case prediction results, the algorithm showed good predictive performance with %RMSE = 8.91%–20.43% and R2 = 0.89–0.97. Therefore, in addition to the CTM, high-accuracy PM prediction results using ML can also be used for air quality monitoring and improvement.

Towards observation- and atmospheric model-based early warning systems for meteotsunami mitigation: A case study of Korea

A traveling air pressure disturbance of several hPa over a short period (5–10 min) can generate tsunami-like waves in coastal areas owing to a multi-resonant mechanism. In recent years, pressure-forced meteotsunamis have been regularly reported over the Korean Peninsula. However, the Korean meteotsunami early warning system does not always provide sufficient lead time. The early warning system comprises two-segmented zones for tracking the intensity and propagation of air pressure disturbances via 89 meteorological stations in the precaution (offshore islands) and warning zones (coastline and inland areas). To address the lead time problem in the current observation-based monitoring system, we used an atmospheric model with a horizontal resolution of 1.5 km, the Korea Meteorological Administration's local data assimilation and prediction system (LDAPS). The ability of the LDAPS to detect air pressure disturbances was tested for a widespread and destructive meteotsunami event that occurred on March 4, 2018. The detection capability of significant air pressure disturbances (>1.5 hPa/10 min) was 67% in the precaution zone, and the propagation pattern (direction, speed, and spatial scale) was reasonably consistent with the monitoring results. Based on LDAPS prediction and the established observation-based monitoring system, monitoring operators can determine the potential meteotsunami risk from the open Yellow Sea beyond the observation system with sufficient lead time. This case study contributes to developing observation- and atmospheric model-based early warning systems for meteotsunami mitigation. An early warning of tsunamigenic disturbances in the Korean Peninsula is expected to be provided with the LDAPS-based modeling system using a high-resolution atmosphere-ocean coupled model shortly.

All-Sky 1 km MODIS Land Surface Temperature Reconstruction Considering Cloud Effects Based on Machine Learning

A high spatio-temporal resolution land surface temperature (LST) is necessary for various research fields because LST plays a crucial role in the energy exchange between the atmosphere and the ground surface. The moderate-resolution imaging spectroradiometer (MODIS) LST has been widely used, but it is not available under cloudy conditions. This study proposed a novel approach for reconstructing all-sky 1 km MODIS LST in South Korea during the summer seasons using various data sources, considering the cloud effects on LST. In South Korea, a Local Data Assimilation and Prediction System (LDAPS) with a relatively high spatial resolution of 1.5 km has been operated since 2013. The LDAPS model’s analysis data, binary MODIS cloud cover, and auxiliary data were used as input variables, while MODIS LST and cloudy-sky in situ LST were used together as target variables based on the light gradient boosting machine (LightGBM) approach. As a result of spatial five-fold cross-validation using MODIS LST, the proposed model had a coefficient of determination (R2) of 0.89–0.91 with a root mean square error (RMSE) of 1.11–1.39 °C during the daytime, and an R2 of 0.96–0.97 with an RMSE of 0.59–0.60 °C at nighttime. In addition, the reconstructed LST under the cloud was evaluated using leave-one-station-out cross-validation (LOSOCV) using 22 weather stations. From the LOSOCV results under cloudy conditions, the proposed LightGBM model had an R2 of 0.55–0.63 with an RMSE of 2.41–3.00 °C during the daytime, and an R2 of 0.70–0.74 with an RMSE of 1.31–1.36 °C at nighttime. These results indicated that the reconstructed LST has higher accuracy than the LDAPS model. This study also demonstrated that cloud cover information improved the cloudy-sky LST estimation accuracy by adequately reflecting the heterogeneity of the relationship between LST and input variables under clear and cloudy skies. The reconstructed all-sky LST can be used in a variety of research applications including weather monitoring and forecasting.

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

AbstractRegression 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°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°C when the forecast fields from the GDAPS substituted several LMOS predictors, even though TMAX was the primary predictor for LMOS.

Visibility Prediction over South Korea Based on Random Forest

In this study, the visibility of South Korea was predicted (VISRF) using a random forest (RF) model based on ground observation data from the Automated Synoptic Observing System (ASOS) and air pollutant data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Copernicus Atmosphere Monitoring Service (CAMS) model. Visibility was predicted and evaluated using a training set for the period 2017–2018 and a test set for 2019. VISRF results were compared and analyzed using visibility data from the ASOS (VISASOS) and the Unified Model (UM) Local Data Assimilation and Prediction System (LDAPS) (VISLDAPS) operated by the Korea Meteorological Administration (KMA). Bias, root mean square error (RMSE), and correlation coefficients (R) for the VISASOS and VISLDAPS datasets were 3.67 km, 6.12 km, and 0.36, respectively, compared to 0.14 km, 2.84 km, and 0.81, respectively, for the VISASOS and VISRF datasets. Based on these comparisons, the applied RF model offers significantly better predictive performance and more accurate visibility data (VISRF) than the currently available VISLDAPS outputs. This modeling approach can be implemented by authorities to accurately estimate visibility and thereby reduce accidents, risks to public health, and economic losses, as well as inform on urban development policies and environmental regulations.

Characteristics of LDAPS-Predicted Surface Wind Speed and Temperature at Automated Weather Stations with Different Surrounding Land Cover and Topography in Korea

We investigated the characteristics of surface wind speeds and temperatures predicted by the local data assimilation and prediction system (LDAPS) operated by the Korean Meteorological Administration. First, we classified automated weather stations (AWSs) into four categories (urban flat (Uf), rural flat (Rf), rural mountainous (Rm), and rural coastal (Rc) terrains) based on the surrounding land cover and topography, and selected 25 AWSs representing each category. Then we calculated the mean bias error of wind speed (WE) and temperature (TE) using AWS observations and LDAPS predictions for the 25 AWSs in each category for a period of 1 year (January–December 2015). We found that LDAPS overestimated wind speed (average WE = 1.26 m s−1) and underestimated temperature (average TE = −0.63 °C) at Uf AWSs located on flat terrain in urban areas because it failed to reflect the drag and local heating caused by buildings. At Rf, located on flat terrain in rural areas, LDAPS showed the best performance in predicting surface wind speed and temperature (average WE = 0.42 m s−1, average TE = 0.12 °C). In mountainous rural terrain (Rm), WE and TE were strongly correlated with differences between LDAPS and actual altitude. LDAPS underestimated (overestimated) wind speed (temperature) for LDAPS altitudes that were lower than actual altitude, and vice versa. In rural coastal terrain (Rc), LDAPS temperature predictions depended on whether the grid was on land or sea, whereas wind speed did not depend on grid location. LDAPS underestimated temperature at grid points on the sea, with smaller TE obtained for grid points on sea than on land.

Wind speed estimation in urban areas based on the relationships between background wind speeds and morphological parameters

This study proposes a method for estimating wind speeds. The method determined the rate of change in wind speed associated with the morphological parameters in the urban or suburban districts by conducting multiple regression analyses. Then, it estimated wind speeds by combining the change rate and inflow wind speeds in Gangnam-gu (GD), Yangcheon-gu (YD), and Pyeongtaek City (PD). Compared to the local data assimilation and prediction system (LDAPS), the estimation method decreased the root mean square error (RMSE) of wind speeds at the automatic weather stations for 1 year (56.2% in GD and 25.6% in YD). For the selected period (4–13 Feb. 2017), the RMSEs in GD (0.66 ​m ​s−1) and PD (0.80 ​m ​s−1) of the wind speeds estimated by the proposed methods were comparable to, or even better than, those (0.72 ​m ​s−1 in GD and 1.00 ​m ​s−1 in PD) of the computational fluid dynamics (CFD) model coupled to LDAPS. The wind speed distribution estimated by the proposed method was reasonably well correlated with that simulated by LDAPS-CFD coupled model (R ​= ​0.69–0.92). We need to improve the method for districts with spatially uneven morphological parameters.

Exploring solar and wind energy resources in North Korea with COMS MI geostationary satellite data coupled with numerical weather prediction reanalysis variables

Despite their potential as a naturally-available clean energy option, the renewable energy resources of the Democratic People's Republic of Korea (i.e., North Korea) have rarely been evaluated. Therefore, to estimate the availability of land surface solar irradiance necessary for solar applications and to model available energy potential, physically-based models drawing on Communication, Ocean and Meteorological Satellite (COMS) data and associated statistics for key atmospheric constituents, were employed. To assess wind energy resources, model output statistics (MOS) were integrated from post-processed Local Data Assimilation and Prediction System (LDAPS) variables, thereby removing any systematic bias arising from long-term regression methods. The root mean square error (RMSE) and mean bias error (MBE) served to compare pyranometer- and satellite-sourced solar radiation, under instantaneous (87.90 W m−2 and 16.84 W m−2, respectively) and daily ‘all sky conditions’ (624.98 Wh m−2 d−1 and 13.89 Wh m−2 d−1, respectively). These low values indicate that satellite-based solar irradiance is sufficiently accurate to be used to model future land surface solar energy in North Korea. In the evaluation of wind energy resources, daily wind speeds obtained from Numerical Weather Prediction (NWP) reanalysis fields showed good accuracy compared to a meteorological tower measurement (RMSE = 0.37 m s−1 and MBE = 0.24 m s−1). In the study region, mean wind energy potential (from 2013–2015) was 3.44 kWh m−2 d−1, whereas solar energy potential was slightly lower at 3.36 kWh m−2 d−1; this can be attributed to the nation's mountainous terrain and high latitude. Although the region's mountainous terrain may be an obstacle for future development of renewable energy infrastructure, these initial annual mean solar and wind power density results illustrate the significant renewable energy potential of North Korea. This situates the country in a position to promote the United Nations Sustainable Development Goal (SDG #7) of integrating cleaner and more sustainable energy resources through solar and wind power.

Temperature Prediction Using the Missing Data Refinement Model Based on a Long Short-Term Memory Neural Network

In this paper, we propose a new temperature prediction model based on deep learning by using real observed weather data. To this end, a huge amount of model training data is needed, but these data should not be defective. However, there is a limitation in collecting weather data since it is not possible to measure data that have been missed. Thus, the collected data are apt to be incomplete, with random or extended gaps. Therefore, the proposed temperature prediction model is used to refine missing data in order to restore missed weather data. In addition, since temperature is seasonal, the proposed model utilizes a long short-term memory (LSTM) neural network, which is a kind of recurrent neural network known to be suitable for time-series data modeling. Furthermore, different configurations of LSTMs are investigated so that the proposed LSTM-based model can reflect the time-series traits of the temperature data. In particular, when a part of the data is detected as missing, it is restored by using the proposed model’s refinement function. After all the missing data are refined, the LSTM-based model is retrained using the refined data. Finally, the proposed LSTM-based temperature prediction model can predict the temperature through three time steps: 6, 12, and 24 h. Furthermore, the model is extended to predict 7 and 14 day future temperatures. The performance of the proposed model is measured by its root-mean-squared error (RMSE) and compared with the RMSEs of a feedforward deep neural network, a conventional LSTM neural network without any refinement function, and a mathematical model currently used by the meteorological office in Korea. Consequently, it is shown that the proposed LSTM-based model employing LSTM-refinement achieves the lowest RMSEs for 6, 12, and 24 h temperature prediction as well as for 7 and 14 day temperature prediction, compared to other DNN-based and LSTM-based models with either no refinement or linear interpolation. Moreover, the prediction accuracy of the proposed model is higher than that of the Unified Model (UM) Local Data Assimilation and Prediction System (LDAPS) for 24 h temperature predictions.

PREDICTION AND MAPPING OF COCHLODINIUM POLYKRIKOIDES RED TIDE USING MACHINE LEARNING UNDER IMBALANCED DATA

Abstract. Cochlodinium polykrikoides (C. polykrikoides) is a phytoplankton that causes red tides every year in the middle of the South Sea of Korea. C. polykrikoides is a harmful Algae that has migratory ability and causes the fisheries damage over a long period of wide sea area if it causes red tide once. To minimize red tide damage, it is important to anticipate and prepare the red tide occurrence timing and location in advance. In this study, we predicted the occurrence of red tide of C. polykrikoides using machine learning techniques and compared the results of each algorithm. Logistic regression model, decision tree model, and multilayer neural network model were used for prediction of red tide occurrence. To produce the data set for model learning, we used the red tide occurrence map provided by the National Institute of Fisheries Science, the Local Data Assimilation and Prediction System (LDAPS) provided by the Korea Meteorological Agency, and the G1SST provided by the National Oceanic and Atmospheric Administration (NOAA). The feature vectors used for modeling consisted of 59 elements, which were made by using temperature, water temperature, precipitation, solar radiation, wind direction and wind speed. Only a very small number of red tide cases can be collected compared to the case of no red tide cases. Thus, an imbalance data problem arises in the data set. To overcome this imbalanced data problem, we used adding noise after oversampling to data of red tide occurrence to solve the difference of data between two classes.The data set is divided into 8 : 2 to prevent over-fitting and 80% is used as the learning data. The remaining 20% was used to evaluate the performance of each model. As a result of evaluating the prediction performance of each model, the multilayer neural network model showed the highest prediction accuracy.

Evaluation of Rainfall Forecasts with Heavy Rain Types in the High-Resolution Unified Model over South Korea

Abstract Heavy rainfall events account for most socioeconomic damages caused by natural disasters in South Korea. However, the microphysical understanding of heavy rain is still lacking, leading to uncertainties in quantitative rainfall prediction. This study is aimed at evaluating rainfall forecasts in the Local Data Assimilation and Prediction System (LDAPS), a high-resolution configuration of the Unified Model over the Korean Peninsula. The rainfall of LDAPS forecasts was evaluated with observations based on two types of heavy rain events classified from K-means clustering for the relationship between surface rainfall intensity and cloud-top height. LDAPS forecasts were characterized by more heavy rain cases with high cloud-top heights (cold-type heavy rain) in contrast to observations showing frequent moderate-intensity rain systems with relatively lower cloud-top heights (warm-type heavy rain) over South Korea. The observed cold-type and warm-type events accounted for 32.7% and 67.3% of total rainfall, whereas LDAPS forecasts accounted for 65.3% and 34.7%, respectively. This indicates severe overestimation and underestimation of total rainfall for the cold-type and warm-type forecast events, respectively. The overestimation of cold-type heavy rainfall was mainly due to its frequent occurrence, whereas the underestimation of warm-type heavy rainfall was affected by both its low occurrence and weak intensity. The rainfall forecast skill for the warm-type events was much lower than for the cold-type events, due to the lower rainfall intensity and smaller rain area of the warm-type. Therefore, cloud parameterizations for warm-type heavy rain should be improved to enhance rainfall forecasts over the Korean Peninsula.

An approach for improving the capability of a coupled meteorological and hydrological model for rainfall and flood forecasts

This study proposes a method for improving the capability of rainfall and flood forecasts by generating ensemble precipitation predictions (EPPs) associated with radar-based rainfall prediction by considering spatial rainfall errors. The EPPs are generated as a kind of Monte-Carlo simulation based on the performance of the numerical weather prediction (NWP) model in the previous time step window. The generated EPPs continue to be blended with radar-based rainfall predictions to produce hybrid rainfall forecasts that perform better than each system could perform individually. The hybrid forecasts are then adjusted by reducing the spatial rainfall errors, which have considerable contributions to the accuracy of the flood forecasts. To validate the performance of the proposed method, this method was applied to improve the capability of the coupled Local Data Assimilation and Prediction system (LDAPS) and Sejong University Rainfall – Runoff (SURR) model for rainfall and flood forecasts during two flood events that occurred in 2013 and 2016 in the Yeongwol watershed. EPPs were generated from the deterministic LDAPS rainfall events and were then blended with the McGill algorithm for precipitation nowcasting by Lagrangian extrapolation (MAPLE) rainfall predictions to produce hybrid rainfall forecasts. Each ensemble member of the hybrid model was then corrected before it was forced as an input for the SURR model to obtain ensemble streamflow predictions. The results showed that the capability of the coupled model was improved sustainably step by step and exhibited the best skills after applying the final step. For rainfall forecasts, the proportion correct (PC), root mean square error (RMSE), correlation coefficient (CC), and Brier score (BS) were improved dramatically by 59%, 32%, 32% and 48% for flood events in 2013 and by 39%, 15%, 33% and 37% for flood events in 2016, respectively. For flood forecasting, the Nash – Sutcliffe efficiency (NSE) and absolute relative error in volume (AREV) were improved substantially by 63% and 24% for flood events in 2013 and by 42% and 53% for flood events in 2016, respectively. The success of this case study proved the viability of the method proposed in this study.

Radiation Component Calculation and Energy Budget Analysis for the Korean Peninsula Region

In this study, a radiation component calculation algorithm was developed using channel data from the Himawari-8 Advanced Himawari Imager (AHI) and meteorological data from the Unified Model (UM) Local Data Assimilation and Prediction System (LDAPS). In addition, the energy budget of the Korean Peninsula region in 2016 was calculated and its regional differences were analyzed. Radiation components derived using the algorithm were calibrated using the broadband radiation component data from the Clouds and the Earth’s Radiant Energy System (CERES) to improve their accuracy. The calculated radiation components and the CERES data showed an annual mean percent bias of less than 3.5% and a high correlation coefficient of over 0.98. The energy budget of the Korean Peninsula region was −2.4 Wm−2 at the top of the atmosphere (RT), −14.5 Wm−2 at the surface (RS), and 12.1 Wm−2 in the atmosphere (RA), with regional energy budget differences. The Seoul region had a high surface temperature (289.5 K) and a RS of −33.4 Wm−2 (surface emission), whereas the Sokcho region had a low surface temperature (284.7 K) and a RS of 5.0 Wm−2 (surface absorption), for a difference of 38.5 Wm−2. In short, regions with relatively high surface temperatures tended to show energy emission, and regions with relatively low surface temperatures tended to show energy absorption. Such regional energy imbalances can cause weather and climate changes and bring about meteorological disasters, and thus research on detecting energy budget changes must be continued.

Verification of Planetary Boundary Layer Height for Local Data Assimilation and Prediction System (LDAPS) Using the Winter Season Intensive Observation Data during ICE-POP 2018

Verification of Planetary Boundary Layer Height for Local Data Assimilation and Prediction System (LDAPS) Using the Winter Season Intensive Observation Data during ICE-POP 2018

토지 피복별 차등 가열이 도시 지역의 흐름과 기온에 미치는 영향

본 연구에서는 기상청 현업 국지기상모델(Local Data Assimilation and Prediction System, LDAPS)과 전산유체역학(Computational Fluid Dynamics, CFD) 모델을 접합하여, 서울 종로구 송월동에 위치한 지동기상관측소(서울 ASOS) 주변 지역의 기상 환경을 분석하였다. 토지 피복별 차등 가열이 도시 지역의 대기 흐름과 기온에 미치는 영향을 분석하기 위하여, 시간 변화에 따른 토지 피복별 지표면 온도와 그림자 영역에 대한 지표면 온도 감소 효과를 고려하였다. LDAPS 모델은 상세한 건물, 지형, 지표면 가열 효과를 고려하지 못하기 때문에, 풍속을 과대모의 하고 기온을 과소 모의하였다. 건물과 지형의 마찰 효과와 태양 복사에 의한 지표면 가열을 고려할 수 있는 LDAPS-CFD 접합 모델은 서울 ASOS 지점의 관측 풍속과 유사한 풍속을 모의하였고, 관측 기온을 잘 재현하였다. 주로 동풍이 부는 오전 시간대에는 LDAPS-CFD 접합 모델 또한 기온을 과소모의 하였는데, 이는 서울 ASOS 지점의 풍상측(동쪽)에 위치한 경희궁 주변 지역에 주로 수목이 분포하고 있고, 표면 온도가 상대적으로 낮기 때문인 것으로 판단된다. 그러나, 주로 남동풍 계열의 바람이 부는 오후 시간대에는 풍상측에 위치한 건물의 표면 가열의 효과로 인해 서울 ASOS 지점의 관측 기온을 상대적으로 잘 모의하였다. In this study, the effects of differential heating by land-use types on flow and air temperature at an Seoul Automated Synoptic Observing Systems (ASOS) located at Songwol-dong, Jongno-gu, Seoul was analyzed. For this, a computation fluid dynamics (CFD) model was coupled to the local data assimilation and prediction system (LDAPS) for reflecting the local meteorological characteristics at the boundaries of the CFD model domain. Time variation of temperatures on solid surfaces was calculated using observation data at El-Oued, Algeria of which latitude is similar to that of the target area. Considering land-use type and shadow, surface temperatures were prescribed in the LDAPS-CFD coupled model. The LDAPS overestimated wind speeds and underestimated air temperature compared to the observations. However, a coupled LDAPS-CFD model relatively well reproduced the observed wind speeds and air temperature, considering complicated flows and surface temperatures in the urban area. In the morning when the easterly was dominant around the target area, both the LDAPS and coupled LDAPS-CFD model underestimated the observed temperatures at the Seoul ASOS. This is because the Kyunghee Palace located at the upwind region was composed of green area and its surface temperature was relatively low. However, in the afternoon when the southeasterly was dominant, the LDAPS still underestimated, on the while, the coupled LDAPS-CFD model well reproduced the observed temperatures at the Seoul ASOS by considering the building-surface heating.

The Sensitivity Analyses of Initial Condition and Data Assimilation for a Fog Event using the Mesoscale Meteorological Model

The accurate simulation of micro-scale weather phenomena such as fog using the mesoscale meteorological models is a very complex task. Especially, the uncertainty arisen from initial input data of the numerical models has a decisive effect on the accuracy of numerical models. The data assimilation is required to reduce the uncertainty of initial input data. In this study, the limitation of the mesoscale meteorological model was verified by WRF (Weather Research and Forecasting) model for a summer fog event around the Nakdong river in Korea. The sensitivity analyses of simulation accuracy from the numerical model were conducted using two different initial and boundary conditions: KLAPS (Korea Local Analysis and Prediction System) and LDAPS (Local Data Assimilation and Prediction System) data. In addition, the improvement of numerical model performance by FDDA (Four-Dimensional Data Assimilation) using the observational data from AWS (Automatic Weather System) was investigated. The result of sensitivity analysis showed that the accuracy of simulated air temperature, dew point temperature, and relative humidity with LDAPS data was higher than those of KLAPS, but the accuracy of the wind speed of LDAPS was lower than that of KLAPS. Significant difference was found in case of relative humidity where RMSE (Root Mean Square Error) for LDAPS and KLAPS was 15.7 and 35.6%, respectively. The RMSE for air temperature, wind speed, and relative humidity was improved by approximately , , and 2.2%, respectively after incorporating the FDDA.

기상청 고해상도 지역예보모델을 이용한 한반도 영역 한국형 항공난류 예측시스템(한반도-KTG) 개발

Korean Peninsula has high potential for occurrence of aviation turbulence. A Korean aviation Turbulence Guidance (KTG) system focused on the Korean Peninsula, named Korean-Peninsula KTG (KP-KTG) system, is developed using the high resolution (horizontal grid spacing of 1.5 km) Local Data Assimilation and Prediction System (LDAPS) of the Korea Meteorological Administration (KMA). The KP-KTG system is constructed first by selection of 15 best diagnostics of aviation turbulence using the method of probability of detection (POD) with pilot reports (PIREPs) and the LDAPS analysis data. The 15 best diagnostics are combined into an ensemble KTG predictor, named KP-KTG, with their weighting scores computed by the values of area under curve (AUC) of each diagnostics. The performance of the KP-KTG, represented by AUC, is larger than 0.84 in the recent two years (June 2012~May 2014), which is very good considering relatively small number of PIREPs. The KP-KTG can provide localized turbulence forecasting in Korean Peninsula, and its skill score is as good as that of the operational-KTG conducting in East Asia.