• 대기
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• 제20권4호
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• pp.451-466
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• 2010

This edition has continued since 2006 tropical cyclone season our effort to provide standard tropical cyclone summaries by the western North Pacific basin and detailed reviews of operationally or meteorologically significant tropical cyclones to document significant challenges and shortfalls in the tropical cyclone warning system to serve as a focal point for research and development efforts. The tropical cyclone season of 2009 in the western North Pacific basin is summarized and the main characteristics of general atmospheric circulation are described. Also, the official track and intensity forecasts of these cyclones are verified. The total number is less than 59-year (1951~2009) average frequency of 26.4. The 2009 western North Pacific season was an inactive one, in which 22 tropical storms generated. Of these, 13 TCs reached typhoon (TY) intensity, while the rest 9 TCs only reached severe tropical storm (STS) and tropical storm (TS) intensity - three STS and six TS storms. On average of 22 TCs in 2009, the Korea Meteorological Administration official track forecast error for 48 hours was 219 km. There was a big challenge for individual cyclones such as 0902 CHAN-HOM, 0909 ETAU, and 0920 LUPIT resulting in significant forecast error, with both intricate tracks and irregular moving speed. There was no tropical cyclone causing significant direct impact to the country. The tropical cyclone season in 2009 began in May with the formation of KUJIRA (0901). In September and October, ten TSs formed in the western North Pacific in response to enhanced convective activity. On the other hand, the TC activity was very weak from June to July. It is found that the unusual anti-cyclonic circulation in the lower level and weak convection near the Philippines are dominant during summertime. The convection and atmospheric circulation in the western North Pacific contributed unfavorable condition for TC activity in the 2009 summertime. Year 2009 has continued the below normal condition since mid 1990s which is apparent in the decadal variability in TC activity.

• 대기
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• 제27권1호
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• pp.93-104
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• 2017

The effect of Advanced Microwave Sounding Unit-A (AMSU-A) observations on the short-range forecast in East Asia (EA) was investigated for the Northern Hemispheric (NH) summer and winter months, using the Forecast Sensitivity to Observations (FSO) method. For both periods, the contribution of radiosonde (TEMP) to the EA forecast was largest, followed by AIRCRAFT, AMSU-A, Infrared Atmospheric Sounding Interferometer (IASI), and the atmospheric motion vector of Communication, Ocean and Meteorological Satellite (COMS) or Multi-functional Transport Satellite (MTSAT). The contribution of AMSU-A sensor was largely originated from the NOAA 19, NOAA 18, and MetOp-A (NOAA 19 and 18) satellites in the NH summer (winter). The contribution of AMSU-A sensor on the MetOp-A (NOAA 18 and 19) satellites was large at 00 and 12 UTC (06 and 18 UTC) analysis times, which was associated with the scanning track of four satellites. The MetOp-A provided the radiance data over the Korea Peninsula in the morning (08:00~11:30 LST), which was important to the morning forecast. In the NH summer, the channel 5 observations on MetOp-A, NOAA 18, 19 along the seaside (along the ridge of the subtropical high) increased (decreased) the forecast error slightly (largely). In the NH winter, the channel 8 observations on NOAA 18 (NOAA 15 and MetOp-A) over the Eastern China (Tibetan Plateau) decreased (increased) the forecast error. The FSO provides useful information on the effect of each AMSU-A sensor on the EA forecasts, which leads guidance to better use of AMSU-A observations for EA regional numerical weather prediction.

• 대기
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• 제19권2호
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• pp.183-198
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• 2009

The purpose of this study is to summarize the tropical cyclone (TC) activity of 2008 over the western North Pacific including the verification of the official track and intensity forecast errors of these TCs. The TC activity - frequency, Normalized Typhoon Activity (NTA), and life span - was lower than 58-year (1951-2008) average. 22 tropical cyclones of tropical storm (TS) intensity or higher formed in the western North Pacific and the South China Sea in 2008. The total number is less than 58-year average frequency of 26.4. Out of 22 tropical cyclones, 11 TCs reached typhoon (TY) intensity, while the rest 11 TCs only reached severe tropical storm (STS) and tropical storm (TS) intensity - six STS and five TS storms. One typhoon KALMAEGI (0807) among them affected the Korea peninsula. However, no significant impact - casualty or property damage - was reported. On average of 22 TCs in 2008, the Korea Meteorological Administration (KMA) official track forecast error for 48 hours was 229 km. There was a big challenge for individual cyclones such as 0806 FENGSHEN and 0817 HIGOS presenting significant forecast error, with both intricate tracks and irregular moving speed. The tropical cyclone season in 2008 began in April with the formation of NEOGURI (0801). In May, four TCs formed in the western North Pacific in response to enhanced convective activity. On the other hand, the TC activity was very weak from June to August. It is found that the unusual anti-cyclonic circulation in the lower level and weak convection near the Philippines are dominant during summertime. The convection and atmospheric circulation in the western North Pacific contributed unfavorable condition for TC activity in the 2008 summertime. The 2008 TC activity has continued the below normal state since mid 1990s which is apparent the decadal variability in TC activity.

• 대기
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• 제24권3호
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• pp.283-301
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• 2014

In 2010, only 14 tropical cyclones (TCs) were generated over the western North Pacific (WNP), which was the smallest since 1951. This study summarizes characteristics of TCs generated in 2010 over the WNP and investigates the causes of the record-breaking TC genesis. A long-term variation of TC activity in the WNP and verification of official track forecast in 2010 are also examined. Monthly tropical sea surface temperature (SST) anomaly data reveal that El Ni$\tilde{n}$o/Southern Oscillation (ENSO) event in 2010 was shifted from El Ni$\tilde{n}$o to La Ni$\tilde{n}$a in June and the La Ni$\tilde{n}$a event was strong and continued to the end of the year. We found that these tropical environments leaded to unfavorable conditions for TC formation at main TC development area prior to May and at tropics east of $140^{\circ}E$ during summer mostly due to low SST, weak convection, and strong vertical wind shear in those areas. The similar ENSO event (in shifting time and La Ni$\tilde{n}$a intensity) also occurred in 1998, which was the second smallest TC genesis year (16 TCs) since 1951. The common point of the two years suggests that the ENSO episode shifting from El Ni$\tilde{n}$o to strong La Ni$\tilde{n}$a in summer leads to extremely low TC genesis during La Ni$\tilde{n}$a although more samples are needed for confidence. In 2010, three TCs, DIANMU (1004), KOMPASU (1007) and MALOU (1009), influenced the Korean Peninsula (KP) in spite of low total TC genesis. These TCs were all generated at high latitude above $20^{\circ}N$ and arrived over the KP in short time. Among them, KOMPASU (1007) brought the most serious damage to the KP due to strong wind. For 14 TCs in 2010, mean official track forecast error of the Korea Meteorological Administration (KMA) for 48 hours was 215 km, which was the highest among other foreign agencies although the errors are generally decreasing for last 10 years, suggesting that more efforts are needed to improve the forecast skill.

• 한국지구과학회지
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• 제30권3호
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• pp.294-304
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• 2009

북서태평양에서 발생한 태풍에 대해 발생 후 5일 동안 12시간 간격으로 태풍의 강도 및 진로를 예측할 수 있는 인공신경망 모델을 개발하였다. 사용되어진 예측인지는 CLIPER(발생 위치 강도 일자), 운동학적 파라미터(연직바람시어, 상층발산, 하층상대와도), 열적 파라미터(상층 상당온위, ENSO, 상층온도, 중층 상대습도)로 구성되어졌다. 예측인자의 특성에 따라 일곱개의 인공신경망 모델들이 개발되었으며, CLIPER와 열적 파라미터가 조합된(CLIPER-THERM) 모델이 가장 좋은 예측성능을 보였다. 이 CLIPER-THERM 모델은 강도 및 진로 모두에서 동절기보다 하절기에 더 나은 예측성능을 나타내었다. 또한 태풍의 발생이 아열대 서태평양의 남동쪽에 위치할수록 강도예측에서는 큰 오차를 보였고, 진로예측에서는 아열대 서태평양의 북서쪽에서 발생할수록 큰 오차를 보였다. 이후 인공신경망 모델의 예측성능을 검증하기 위해 같은 예측인자들을 이용하여 다중선형회귀모델을 개발하였으며, 결과로서 비선형 통계기법인 인공신경망 모델이 다중선형회귀모형보다는 더 나은 예측성능을 보였다.

• 한국방재학회:학술대회논문집
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• 한국방재학회 2007년도 정기총회 및 학술발표대회
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• pp.327-331
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• 2007

23 tropical cyclones of tropical storm(TS) intensity or higher formed in the western North Pacific and the South China Sea in 2006. The total number is less than the 30-year $(1971{\sim}2000)$ average frequency of 26.7, Out of 23, 15 cyclones reached typhoon(TY) intensity, three severe tropical storm(STS) intensity, and five TS intensity. The tropical cyclone season in 2006 began in May with the formation of CHANCHU(0601). While convective activity was slightly inactive around the Philippines from late June to early August. In addition, subtropical high was more enhanced than normal over the south of Japan from May to early August. Consequently, most tropical cyclones formed over the sea east of the Philippines after late June, and many of them moved westwards to China. CHANCHU(0601), BILIS(0604), KAEMI(0605), PRAPIROON(0606) and SAOMI(0608) brought damage to China, the Philippines, and Vietnam. On the other hand, EWINIAR(0603) moved northwards and hit the Republic of Korea, causing damage to the country From late August to early September, convective activity was temporarily inactive over the sea east of the Philippines. However, it turned active again after late September. Subtropical high was weak over the south of Japan after late August. Therefore, most tropical cyclones formed over the sea east of the Philippines and moved northwards. WUKONG(0610) and SHANSHAN(0613) hit Japan to bring damage to the country. On the other hand, XANGSANE(0615) and CIMARON(0619) moved westwards in the South China Sea, causing damage to the Philippines, Thailand, and Vietnam. In addition, IOKE(0612) was the first namded cyclone formed in the central North Pacific and moved westwards across longitude 180 degrees east after HUKO(0224).

• 한국지구과학회지
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• 제38권2호
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• pp.105-114
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• 2017

Two dimensional finite element method with quadrilateral basis functions was applied to the spherical high order filter on the spherical surface limited area domain. The basis function consists of four shape functions which are defined on separate four grid boxes sharing the same gridpoint. With the basis functions, the first order derivative was expressed as an algebraic equation associated with nine point stencil. As the theory depicts, the convergence rate of the error for the spherical Laplacian operator was found to be fourth order, while it was the second order for the spherical Laplacian operator. The accuracy of the new high order filter was shown to be almost the same as those of Fourier finite element high order filter. The two-dimension finite element high order filter was incorporated in the weather research and forecasting (WRF) model as a hyper viscosity. The effect of the high order filter was compared with the built-in viscosity scheme of the WRF model. It was revealed that the high order filter performed better than the built in viscosity scheme did in providing a sharper cutoff of small scale disturbances without affecting the large scale field. Simulation of the tropical cyclone track and intensity with the high order filter showed a forecast performance comparable to the built in viscosity scheme. However, the predicted amount and spatial distribution of the rainfall for the simulation with the high order filter was closer to the observed values than the case of built in viscosity scheme.

• International Journal of Computer Science & Network Security
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• 제23권9호
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.