Global Data Assimilation And Prediction System Research Articles

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.

Surface wind gust prediction over Incheon international airport using the unified model

The unified model (Version: UM10.4) wind gust prediction system using rose nesting suite at 300 m resolution over the Incheon international airport (IIA) is carried out, and the prediction results of multiple domain nesting with input initial and lateral boundary conditions from the Korea meteorological administration (KMA) operational global data assimilation and prediction system (GDAPS) are compared with the operational local data assimilation and prediction system and the observed automated weather stations (AWS) dataset. The 300 m nested prediction system over the IIA is built from the 17 km GDAPS output in KMA; it is downscaled with a multiple nesting domains to 300 m resolution from the GDAPS, here after called as IIA-300 m. The model results are validated against the AWS to see the accuracy of the IIA-300 m model’s skill in predicting the wind gust over the IIA. The nesting suite is run with the default ancillary files created by the central ancillary program 9.0 (CAP 9.0) as well as modified ancillary files created by modifying the land points over IIA region using an ancillary editor. The UM model wind prediction is done with both ancillary files to see the improvements achieved by adding additional land points over the IIA domain. Further the vegetation fraction from the international geosphere-biosphere program (IGBP) data classified as 9 tiles in the ancillary file is replaced by the environmental-geographic information system (E-GIS) data to see the effect on the UM model’s wind gust predictions. The prediction of two wind gust events on 30-August-2016 and 02-October-2016 reveals only modest improvements in the prediction with respect to AWS observations, even after the modification of ancillaries with land points over the IIA domain, and only minor improvements are noted for the vegetation fraction replacement from IGBP to E-GIS vegetation dataset over the Incheon International Airport and exhibited only a marginal wind speed decrease due to warming up of the land part, particularly on the peripheries of the IIA-domain.

Development of the Global-Korean Aviation Turbulence Guidance (Global-KTG) System Using the Global Data Assimilation and Prediction System (GDAPS) of the Korea Meteorological Administration (KMA)

Development of the Global-Korean Aviation Turbulence Guidance (Global-KTG) System Using the Global Data Assimilation and Prediction System (GDAPS) of the Korea Meteorological Administration (KMA)

A drought outlook study in Korea

Techniques are proposed for developing a monthly and weekly drought outlook and the drought outlook components are evaluated. A drought index, the surface water supply index (SWSI) was modified and used for the drought outlook. A water balance model (abcd) was successfully calibrated using a regional regression, including monthly and weekly factors, and was used to convert meteorology to hydrology. For the monthly drought outlook, an ensemble technique was applied, both with and without monthly industrial meteorology information (MIMI). For the weekly drought outlook, a deterministic forecasting technique was applied employing the Global Data Assimilation and Prediction System (GDAPS). The methodologies were applied to the Geum River basin in Korea. While only the weekly outlook using the GDAPS has sufficient forecasting capability to suggest it might be useful, the accuracy of the monthly drought outlook is expected to improve as the climate forecast accuracy increases. Editor Z.W. Kundzewicz; Associate editor D. Hughes Citation Kim, Y.-O., Lee, J.-K., and Palmer, R.N., 2012. A drought outlook study in Korea. Hydrological Sciences Journal, 57 (6), 1141–1153.

Bayesian regression model for seasonal forecast of precipitation over Korea

In this paper, we apply three different Bayesian methods to the seasonal forecasting of the precipitation in a region around Korea (32.5°N–42.5°N, 122.5°E-132.5°E). We focus on the precipitation of summer season (June–July–August; JJA) for the period of 1979–2007 using the precipitation produced by the Global Data Assimilation and Prediction System (GDAPS) as predictors. Through cross-validation, we demonstrate improvement for seasonal forecast of precipitation in terms of root mean squared error (RMSE) and linear error in probability space score (LEPS). The proposed methods yield RMSE of 1.09 and LEPS of 0.31 between the predicted and observed precipitations, while the prediction using GDAPS output only produces RMSE of 1.20 and LEPS of 0.33 for CPC Merged Analyzed Precipitation (CMAP) data. For station-measured precipitation data, the RMSE and LEPS of the proposed Bayesian methods are 0.53 and 0.29, while GDAPS output is 0.66 and 0.33, respectively. The methods seem to capture the spatial pattern of the observed precipitation. The Bayesian paradigm incorporates the model uncertainty as an integral part of modeling in a natural way. We provide a probabilistic forecast integrating model uncertainty.

RDAPS 모델의 강수량과 실측강수량의 비교를 통한 적용성 검토

최근 우리나라뿐만 아니라 전 세계적으로 기후변화에 따른 국지성 호우의 증가로 피해가 증가하고 있고 그에 따른 대책으로 단기예보의 중요성이 높아지고 있다. 우리나라의 기상청에서는 전구 모형인 GDAPS와 지역 모델인 RDAPS를 이용하여 정량적 예보인 수치 예보를 하고 있다. RDAPS 모델을 이용하여 생성된 자료는 3시간의 누적 강수량으로 48시간에 대한 예측 자료를 12시간 간격으로 00UTC와 12UTC를 생성한다. 본 연구에서는 2005년의 RDAPS의 결과를 9가지의 경우에 대하여 일강수량으로 변환하였으며, 금강유역을 대상으로 면적평균강수량 (MAP)을 산정하였다. 또한, 기상청 강우관측소의 관측강우량과 절대상대오차평균 (AARE)을 산정하여 가장 정확한 변환의 방법을 제시하고 적용성을 검토하였다. The climate change has been observed in Korea as well as in the entire world recently. The rainstorm has been gradually increased and then the damage has been grown. It is getting important to predict short-term rainfall. The Korea Meteorological Administration (KMA) generates numerical model outputs which are computed by Global Data Assimilation and Prediction System (GDAPS) and Regional Data Assimilation and Prediction System (RDAPS). The KMA predicts rainfall using RDAPS results. RDAPS model generates 48 hours data which is organized 3 hours data accumulated at 00UTC and 12UTC. RDAPS results which are organized 3 hours time scale are converted into daily rainfall to compare observed daily rainfall. In this study, 9 cases are applied to convert RDAPS results to daily rainfall data. The MAP (mean areal precipitation) in Geum river basin are computed by using KMA which are 2005 are used. Finally, the best case which gives the close value to the observed rainfall data is obtained using the average absolute relative error (AARE) especially for the Geum River basin.

Construction of the Regional Prediction System using a Regional Climate Model and Validation of its Wintertime Forecast

A dynamical downscaling system for seasonal forecast has been constructed based on a regional climate model, and its predictability was investigated for 10 years' wintertime (December-January-February; DJF) climatology in East Asia. Initial and lateral boundary conditions were obtained from the operational seasonal forecasting data, which are realtime output of the Global Data Assimilation and Prediction System (GDAPS) at Korea Meteorological Administration (KMA). Sea surface temperature was also obtained from the operational forecasts, i.e., KMA El-Nino and Global Sea Surface Temperature Forecast System. In order to determine the better configuration of the regional climate model for East Asian regions, two sensitivity experiments were carried out for one winter season (97/98 DJF): One is for the topography blending and the other is for the cumulus parameterization scheme. After determining the proper configuration, the predictability of the regional forecasting system was validated with respect to 850 hPa temperature and precipitation. The results showed that mean fields error and other verification statistics were generally decreased compared to GDAPS, most evident in 500 hPa geopotential heights. These improved simulation affected season prediction, and then HSS was better 36% and 11% about 850 hPa temperature and precipitation, respectively.

Beta gyres in global analysis fields

A three-component decomposition is applied to global analysis data to show the existence of a beta gyre, which causes Tropical Cyclone (TC) to drift from a large-scale environmental steering current. Analyses from the Global Data Assimilation and Prediction System (GDAPS) of the Korea Meteorological Administration (KMA), the Global Forecast System (GFS) of NCEP, and the Navy Operational Global Atmospheric Prediction System (NOGAPS) are used in this study.

Multistage stochastic linear programming model for daily coordinated multi-reservoir operation

Operation planning for a coordinated multi-reservoir is a complex and challenging task due to the inherent uncertainty in inflow. In this study, we suggest the use of a new, multi-stage and scenario-based stochastic linear program with a recourse model incorporating the meteorological weather prediction information for daily, coordinated, multi-reservoir operation planning. Stages are defined as prediction lead-time spans of the weather prediction system. The multi-stage scenarios of the stochastic model are formed considering the reliability of rainfall prediction for each lead-time span. Future inflow scenarios are generated by a rainfall–runoff model based on the rainfall forecast. For short-term stage (2 days) scenarios, the regional data assimilation and prediction system (RDAPS) information is employed, and for mid-term stage (more than 2 days) scenarios, precipitation from the global data assimilation and prediction system (GDAPS) is used as an input for the rainfall–runoff model. After the 10th day (third stage), the daily historical rainfall data are used following the ensemble streamflow prediction (ESP) procedure. The model is applied to simulate the daily reservoir operation of the Nakdong River basin in Korea in a real-time operational environment. The expected benefit of the stochastic model is markedly superior to that of the deterministic model with average rainfall information. Our study results confirm the effectiveness of the stochastic model in real-time operation with meteorological forecasts and the presence of inflow uncertainty.

태풍-중위도 종관 시스템 상호작용 연구: 루사(0215), 매미(0314) 사례분석

The impact of midlatitude synoptic system (upper-level trough) on typhoon intensity change was investigated by analyzing the spatial and temporal characteristics of vertical wind shear (VWS), relative eddy momentum flux convergence (REFC), and potential vorticity (PV). These variables were computed over the radial mean from the typhoon center by using GDAPS (Global Data Assimilation and Prediction System) data provided by the Korea Meteorological Administration (KMA). The selected cases in this study are typhoons Rusa (0215) and Maemi (0314), causing much damage in life and property in Korea. Results show that the threshold value of VWS indicating typhoon intensity change (typhoon to severe tropical storm) is approximately 15 m/s and of REFC ranges 6 to 6.5 in both cases, respectively. During the period with the intensity of typhoon class, PVs with 3 to 3.5 PVU are present in 360K surface-PV field in the cases. In addition, there is a time-lag of 24 hours between central pressure of typhoon and minimum value of VWS, meaning that the midlatitude upper-level trough interacts with the edge of typhoon with a horizontal distance less than 2,000 km between trough and typhoon. That is, strong midlatitude upper-level divergence above the edge of the typhoon provides a good condition for strengthening the vertical circulation associated with the typhoons. In particular, when the distance between typhoon and midlatitude upper-level trough is less than 1,000 km, the typhoons tend to weaken to STS (Severe Tropical Storm). It might be mentioned that midlatitude synoptic system affects the intensity change of typhoons Rusa (0215) and Maemi (0314) while they moves northward. Thus, these variables are useful for diagnosing the intensity change of typhoon approaching to the Korean peninsula.

An Evaluation of a Mass-Flux Cumulus Parameterization Scheme in the KMA Global Forecast System

A mass-flux type cumulus parameterization scheme is evaluated in the Korea Meteorological Administration (KMA)’s Global Data Assimilation and Prediction System (GDAPS). Two sets of parallel assimilation runs, with a horizontal resolution corresponding to the spectral truncation of T213, approximately 50 km, are designed, one with the operational Kuo convection scheme, and another with a simplified Arakawa-Schubert (SAS) convection scheme, for the month of July 2001, which was a typical summer monsoon period in Korea. An additional sensitivity experiment, that excludes the moist downdrafts in the SAS scheme, is conducted. A 10-day forecast cycle is run, within a data assimilation window, beginning at every 1200 UTC in July 2001. A heavy rainfall event, that occurred in the Korean peninsula on 14-15 July 2001, is investigated in detail.For the heavy rainfall case, the SAS scheme tends to reduce the precipitation amount, as compared to the Kuo scheme. It is found that the exclusion of moist downdrafts in the SAS scheme, increases drying and heating rates, resulting in enhanced rainfall, as shown in the Kuo scheme. The monthly verification demonstrates that the systematic warm bias in the lower troposphere in the operational model is nearly absent when the SAS scheme is employed. In terms of statistical measure of the skill for the predicted sea-level pressure, and that for the upper level patterns, the experiment with the SAS (Kuo) scheme out-performs in the northern (southern) hemisphere. In the case of precipitation forecasts, the SAS scheme effectively corrects the spurious excessive precipitation over the tropical land area, due to the inclusion of moist downdrafts. However, the reduction in precipitation in the SAS scheme, deteriorates the forecast skill for high precipitation categories.

중장기 유량예측 향상을 위한 국내 기후정보의 이용

중장기 기후예보는 기후역학모형의 비약적인 발전과 ENSO등의 기후현상에 대한 규명으로, 전세계적으로 정확성이 크게 향상되고 있어 중장기 유량예측의 중요한 실마리가 되고 있다. 본 연구에서는 우선 중장기 유량예측 향상을 위하여 국내에서 사용 가능한 기후정보, 즉 월간산업기상정보와 GDAPS(Global Data Assimilation and Prediction System)를 조사하고 그 정확성을 평가하였다. 월간산업기상정보와 GDAPS의 순별 예보에서 모두 초보예측보다 정확하였고 특히 갈수기보다는 홍수기에 정확성이 더 높게 나와 이 기간에는 기후예보로서 유효함을 확인하였다. 다음으로 기후예보를 이용하여 충주댐 유역에 대하여 유량예측을 수행하였다. 월간산업기상정보에서는 전체 시나리오, 교집합 시나리오, 합집합 시나리오로 나누어 유량예측에 적용하였다. 세 경우 모두 초보예측보다 평균예측점수가 높아 예측으로서 유효하였으며, 특히 홍수기에 교집합 및 합집합 시나리오의 평균예측점수가 전체 시나리오보다 높게 나타났다. GDAPS를 이용한 순별 유량예측의 경우에도 역시 갈수기보다 홍소기에 더 높은 정확성이 나타났다. 따라서 본 연구에서는 홍수기에 보다 정확한 기후예보를 사용하여 기상학적 불확실성을 줄인다면 월 유량예측의 정확성을 향상시킬 수 있음을 증명하였다. Since the accuracy of climate forecast information has improved from better understanding of the climatic system, particularly, from the better understanding of ENSO and the improvement in meteorological models, the forecasted climate information is becoming the important clue for streamflow prediction. This study investigated the available climate forecast information to improve the extended streamflow prediction in Korea, such as MIMI(Monthly Industrial Meteorological Information) and GDAPS(Global Data Assimilation and Prediction) and measured their accuracies. Both MIMI and the 10-day forecast of GDAPS were superior to a naive forecasts and peformed better for the flood season than for the dry season, thus it was proved that such climate forecasts would be valuable for the flood season. This study then forecasted the monthly inflows to Chungju Dam by using MIMI and GDAPS. For MIMI, we compared three cases: All, Intersection, Union. The accuracies of all three cases are better than the naive forecast and especially, Extended Streamflow Predictions(ESPs) with the Intersection and with Union scenarios were superior to that with the All scenarios for the flood season. For GDAPS, the 10-day ahead streamflow prediction also has the better accuracy for the flood season than for the dry season. Therefore, this study proved that using the climate information such as MIMI and GDAPS to reduce the meteorologic uncertainty can improve the accuracy of the extended streamflow prediction for the flood season.