• Journal of the Korean Society of Fisheries and Ocean Technology
• /
• v.15 no.1
• /
• pp.23-33
• /
• 1979

This paper was analysed based on the oceanographic and meteorological data complied from 1971 to 1977 for that search relationships among the fluctuation of sea condition and weather condition, and the catch of anchovy. In the year when heat loss from the sea surface in winter was maximum(in 1974, 658 Iy), temperature of midwater in summer was lower 2~4\ulcornerC than normal year. While heat loss was minimum (in1973, 487 Iy), temperature of mid water was higher 2\ulcornerC. When temperature of mid water of southern coast from June to August was higher than normal year, anchovy was caught good deal, but that was lower than normal year was bad fishing. When it had much precipitation (in 1973, 256mm), plankton was checked maximum (12cc) and also the catch of anchovy too (11, OOOm/t). While precipitation was minimum (in 1976, 123mm), plankton (3cc) and anchovy (2, 800m/t) was a litle. If we calcalate heat budget in winter, we can forecast temperature of mid-water in summer of following year. Therefore we may be able to forecast catch anchovy.

• Atmosphere
• /
• v.25 no.4
• /
• pp.577-589
• /
• 2015

An unusual long-period and heavy snowfall occurred in the Yeongdong region from 6 to 14 February 2014. This event produced snowfall total of 194.8 cm and the recordbreaking 9-day snowfall duration in the 103-year local record at Gangneung. In this study, satellite-derived cloud-top brightness temperatures from the infrared channel in the atmospheric window ($10{\mu}m{\sim}11{\mu}m$) are examined to find out the characteristics of clouds related with this heavy snowfall event. The analysis results reveal that a majority of precipitation is related with the low-level stratiform clouds whose cloud-top brightness temperatures are distributed from -15 to $-20^{\circ}C$ and their standard deviations over the analysis domain (${\sim}1,000km^2$, 37 satellite pixels) are less than $2^{\circ}C$. It is also found that in the above temperature range precipitation intensity tends to increase with colder temperature. When the temperatures are warmer than $-15^{\circ}C$, there is no precipitation or light precipitation. Furthermore this relation is confirmed from the examination of some other heavy snowfall events and light precipitation events which are related with the low-level stratiform clouds. This precipitation-brightness temperature relation may be explained by the combined effect of ice crystal growth processes: the maximum in dendritic ice-crystal growth occurs at about $-15^{\circ}C$ and the activation of ice nuclei begins below temperatures from approximately -7 to $-16^{\circ}C$, depending on the composition of the ice nuclei.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.2 no.2
• /
• pp.69-78
• /
• 1999

In this paper, a neural network approach to forecast Korean regional precipitation is presented. We first analyze the characteristics of the conventional models for time series prediction, and then propose a new model and its learning method for the precipitation forecast. The proposed model is a layered network in which the outputs of a layer are buffered within a given period time and then fed fully connected to the upper layer. This study adopted the dual connections between two layers for the model. The network behavior and learning algorithm for the model are also described. The dual connection structure plays the role of the bias of the ordinary Multi-Layer Perceptron(MLP), and reflects the relationships among the features effectively. From these advantageous features, the model provides the learning efficiency in comparison with the FIR network, which is the most popular model for time series prediction. We have applied the model to the monthly and seasonal forecast of precipitation. The precipitation data and SST(Sea Surface Temperature) data for several decades are used as the learning pattern for the neural network predictor. The experimental results have shown the validity of the proposed model.

• Atmosphere
• /
• v.29 no.3
• /
• pp.257-267
• /
• 2019

This study is to assess the applicability of the Extreme Forecast Index (EFI) algorithm of the ECMWF seasonal forecast system to the Global Seasonal Forecasting System version 5 (GloSea5), operational seasonal forecast system of the Korea Meteorological Administration (KMA). The EFI is based on the difference between Cumulative Distribution Function (CDF) curves of the model's climate data and the current ensemble forecast distribution, which is essential to diagnose the predictability in the extreme cases. To investigate its applicability, the experiment was conducted during the heat-wave cases (the year of 1994 and 2003) and compared GloSea5 hindcast data based EFI with anomaly data of ERA-Interim. The data also used to determine quantitative estimates of Probability Of Detection (POD), False Alarm Ratio (FAR), and spatial pattern correlation. The results showed that the area of ERA-Interim indicating above 4-degree temperature corresponded to the area of EFI 0.8 and above. POD showed high ratio (0.7 and 0.9, respectively), when ERA-Interim anomaly data were the highest (on Jul. 11, 1994 (> $5^{\circ}C$) and Aug. 8, 2003 (> $7^{\circ}C$), respectively). The spatial pattern showed a high correlation in the range of 0.5~0.9. However, the correlation decreased as the lead time increased. Furthermore, the case of Korea heat wave in 2018 was conducted using GloSea5 forecast data to validate EFI showed successful prediction for two to three weeks lead time. As a result, the EFI forecasts can be used to predict the probability that an extreme weather event of interest might occur. Overall, we expected these results to be available for extreme weather forecasting.

• Journal of Korea Water Resources Association
• /
• v.44 no.5
• /
• pp.339-350
• /
• 2011

This study developed a climate informed Bayesian nonstationary frequency model which allows us to forecast seasonal summer rainfall at Nakdong River. We constructed a 37-year summer rainfall data set from 10 weather stations within Nakdong river basin, and two climate indices from sea surface temperature (SST) and outgoing longwave radiation (OLR) were derived through correlation analysis. The selected SST and OLR have been widely acknowledged as a climate driver for summer rainfall. The developed model was applied first to the 2010-year summer rainfall (888.1 mm) in order to assure ourself. We demonstrated model performance by comparing posterior distributions. It was confirmed that the proposed model is able to produce a reasonable forecast. The forecasted value is about 858.2 mm, and the difference between forecast and observation is about 30 mm. As the second case study, 2011-year summer rainfall forecast was made using an observed winter SSTs and an assumed 50% value of OLRs. The forecasted value is 967.7 mm and associated exceedance probability over average summer rainfall 680 mm is 92.9%. In addition, 50-year return period for summer rainfall was projected through the nonstationary frequency model. An exceedance probability over 1,400 mm corresponding to the 50-year return level is about 73.7%.

• Proceedings of the KIEE Conference
• /
• 1993.11a
• /
• pp.82-84
• /
• 1993

A method for simulating general characteristics and temperature characteristics of magnetizing fixture coil of the capacitor discharge impulse magnetizer-magnetizing fixture system using SPICE is presented. This method has been developed which can aid the design, understanding and inexpensive, time-saving of magnetizing circuit. As the detailed characteristics of magnetizing circuit can be obtained, the efficient design of the magnetizing circuit which produce desired magnet will be possible using our SPICE modeling. Especially, The knowledge of the temperature of the magnetizing fixture is very important to forecast the characteristics of the magnetizing circuits tinder different conditions. The capacitor voltage was not raised above 810[V] to protect the magnetizing fixture from excessive heating. The temperature estimation method uses multi-lumped model with equivalent thermal resistance and thermal capacitance.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.58 no.9
• /
• pp.1695-1699
• /
• 2009

This paper analyzed the characteristics of the demand of electric power in Jeju by year, day. For this analysis, this research used the correlation between the changes in the temperature and the demand of electric power in summer, and cleaned the data of the characteristics of the temperatures, using the coefficient of correlation as the standard. And it proposed the algorithm of forecasting the short-term electric power demand in Jeju, Therefore, in the case of summer, the data by each cleaned temperature section were used. Based on the data, this paper forecasted the short-term electric power demand in the exponential smoothing method. Through the forecast of the electric power demand, this paper verified the excellence of the proposed technique by comparing with the monthly report of Jeju power system operation result made by Korea Power Exchange-Jeju.

• International Journal of Highway Engineering
• /
• v.20 no.2
• /
• pp.127-135
• /
• 2018

PURPOSES : This study develops various models that can estimate the pattern of road surface temperature changes using machine learning methods. METHODS : Both a thermal mapping system and weather forecast information were employed in order to collect data for developing the models. In previous studies, the authors defined road surface temperature data as a response, while vehicular ambient temperature, air temperature, and humidity were considered as predictors. In this research, two additional factors-road type and weather forecasts-were considered for the estimation of the road surface temperature change pattern. Finally, a total of six models for estimating the pattern of road surface temperature changes were developed using the MATLAB program, which provides the classification learner as a machine learning tool. RESULTS : Model 5 was considered the most superior owing to its high accuracy. It was seen that the accuracy of the model could increase when weather forecasts (e.g., Sky Status) were applied. A comparison between Models 4 and 5 showed that the influence of humidity on road surface temperature changes is negligible. CONCLUSIONS : Even though Models 4, 5, and 6 demonstrated the same performance in terms of average absolute error (AAE), Model 5 can be considered the optimal one from the point of view of accuracy.

• The Korean Journal of Applied Statistics
• /
• v.21 no.4
• /
• pp.705-716
• /
• 2008

The objectives of this study are the detection and forecast of climate change signal in the annual mean of surface temperature data, which are generated by MRI/JMA CGCM over the Korean Peninsula. MRI/JMA CGCM outputs consist of control run data(experiment with no change of $CO_2$ concentration) and scenario run data($CO_2$ 1%/year increase experiment to quadrupling) during 142 years for surface temperature and precipitation. And ECMWF reanalysis data during 43 years are used as observations. All data have the same spatial structure which consists of 42 grid points. Two statistical models, the Bayesian fingerprint method and the regression model with autoregressive error(AUTOREG model), are separately applied to detect the climate change signal. The forecasts up to 2100 are generated by the estimated AUTOREG model only for detected grid points.

• Atmosphere
• /
• v.20 no.3
• /
• pp.229-238
• /
• 2010

The 1,000~500 hPa thickness and the $0^{\circ}C$ isotherm at 850 hPa have been used as the traditional predictors for wintertime precipitation-type forecasts. New approaches are taking on added significance as preexistence method of determination for wintertime precipitation-type exhibits more or less prevalent false alarms. Moreover thicknesses and thermodynamic profiles from ordinary upper-air observation were not adequate to monitor the atmospheric structure. In this regard, Microwave radiometric profiler microwave radiometer is useful in wintertime precipitation-type forecasts because radiometric measurements provide soundings at high temporal resolution. In this study, the determination and the predictability of wintertime precipitation-type were examined by using the calculated thicknesses, temperature of 850 hPa (T850) from a microwave radiometer, and surface observation at National Center for Intensive Observation of severe weather (NCIO) located at Haenam, Korea. The critical values for traditional predictors (thickness of 1000~500 hPa and T850) were evaluated and adjusted to Haenam region because snow rarely occurred with a 1000-500 hPa thickness > 5,300 m and T850 > $-10^{\circ}C$. Three thicknesses (e.g., 1,000~850, 1000~700, and 850~700 hPa thickness), T850, surface air temperature, and wet-bulb temperature were also evaluated as the additional predictors. A simple nomogram and a flow chart were finally designed to determine the wintertime precipitation-type using the microwave radiometer. The skill scores for the predictability of precipitation-type determination are considerably improved and the predictors showed the temporal variations in 12 hours before precipitation. We can monitor the hit and run snowfall in winter successful by realtime watch of the predictors, especially in commutes of big cities.