• The Journal of Internal Korean Medicine
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• v.30 no.1
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• pp.200-211
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• 2009

There are many reports about correlations between meteorological elements and stroke. In Oriental medicine, it is recognized that the weather affects the human body and diseases, but there are few studies about the correlation between meteorological elements and pattern identification of stroke. 105 stroke patients classified into fire-heat pattern or dampress-phlegm pattern were registered during the study period. We took the measurement of each meteorological element (atmospheric pressure, temperature, humidity, wind speed) according to pattern identification and analyzed pattern identification into two groups according to mean of each meteorological element during the study period. Mean temperature was higher with the heat-fire pattern than with the dampness-phlegm pattern. Heat-fire pattern also had higher frequency when temperature was higher than mean temperature. There was no correlation between atmospheric pressure, relative humidity, or wind speed and pattern identification.

• Journal of Digital Convergence
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• v.14 no.6
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• pp.457-466
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• 2016

To achieve eco-friendly hospitals it is necessary to empirically verify the effect of meteorological factors on the power consumption of the hospital. Using daily meteorological big data from 2009 to 2013, we studied the weather conditions impact to power consumption and analyzed the patterns of power consumption of two hospitals. R analysis revealed that temperature among the meteorological factors had the greatest impact on the hospital power consumption, and was a significant factor regardless of hospital size. The pattern of hospital power consumption differed considerably depending on the hospital size. The larger hospital had a linear pattern of power consumption and the smaller hospital had a quadratic nonlinear pattern. A typical pattern of increasing power consumption during a hot summer and a cold winter was evident for both hospitals. The results of this study suggest that a hospital's functional specificity and meteorological factors should be considered to improve energy savings and eco-friendly building.

• Atmosphere
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• v.29 no.4
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• pp.451-461
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• 2019

Medium-range forecast is highly dependent on ensemble forecast data. However, operational weather forecasters have not enough time to digest all of detailed features revealed in ensemble forecast data. To utilize the ensemble data effectively in medium-range forecasting, representative weather patterns in East Asia in this study are defined. The k-means clustering analysis is applied for the objectivity of weather patterns. Input data used daily Mean Sea Level Pressure (MSLP) anomaly of the ECMWF ReAnalysis-Interim (ERA-Interim) during 1981~2010 (30 years) provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). Using the Explained Variance (EV), the optimal study area is defined by 20~60°N, 100~150°E. The number of clusters defined by Explained Cluster Variance (ECV) is thirty (k = 30). 30 representative weather patterns with their frequencies are summarized. Weather pattern #1 occurred all seasons, but it was about 56% in summer (June~September). The relatively rare occurrence of weather pattern (#30) occurred mainly in winter. Additionally, we investigate the relationship between weather patterns and extreme weather events such as heat wave, cold wave, and heavy rainfall as well as snowfall. The weather patterns associated with heavy rainfall exceeding 110 mm day^-1 were #1, #4, and #9 with days (%) of more than 10%. Heavy snowfall events exceeding 24 cm day^-1 mainly occurred in weather pattern #28 (4%) and #29 (6%). High and low temperature events (> 34℃ and < -14℃) were associated with weather pattern #1~4 (14~18%) and #28~29 (27~29%), respectively. These results suggest that the classification of various weather patterns will be used as a reference for grouping all ensemble forecast data, which will be useful for the scenario-based medium-range ensemble forecast in the future.

• Journal of Korean Society for Atmospheric Environment
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• v.11 no.1
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• pp.45-56
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• 1995

We considered that characteristics of surface ozone continuous and important meteorological parameters from the data measured 7 air quality continuous monitoring stations during 2 years (1990, 1993) in pusan. The diurnal ozone variation showed a primary peak near 1500LST and a secondary peak of the DP(double peaked) pattern. The episode day was defined when an ozone peak higher than 60 ppb was observed at least one station. The frequency of episode day was 100 (298 hours, 69 days). The frequency of the episode day was higher at Meongryundong and Daeyeondong than other sites and highest in August under control of pacific subtropical high. The high temporatant meteorological parameters accompanying the high episode days. The favorable synoptic environment accompanying ozone episode was distributed to 7 different pattern. These pattern can be taken as a nesessary but not an absolute indicator for predicting the occerrence of an episode.

• Atmosphere
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• v.16 no.3
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• pp.225-234
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• 2006

Convective systems propagate eastward with a persistent pattern in the longitude-time space. The characteristic structure and fluctuation of convective system is helpful in determining its predictability. In this study, convective index (CI) was defined as a difference between GOES-9 window and water vapor channel brightness temperatures following Mosher (2001). Then the temporal-spatial scales and variational characteristics of the summer convective systems in the East Asia were analyzed. It is found that the average moving speed of the convective system is about 14 m/s which is much faster than the low pressure system in the summer. Their average duration is about 12 hours and the average length of the cloud streak is about 750km. These characteristics are consistent with results from other studies. Although the convective systems are forced by the synoptic system and are mostly developed in the eastern edge of the Tibetan Plateau, they have a persistent pattern, i.e., appearance of the maximum intensity of convective systems, as they approach the Korean Peninsula. The consistency of the convective systems, i.e., the eastward propagation, suggests that there exists an intrinsic predictability.

• Journal of Environmental Science International
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• v.24 no.7
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• pp.893-905
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• 2015

This study analyzes the correlation between Western Pacific (WP) teleconnection pattern index (WPI) in April during 1954-2008 and rainfall amounts in the same month. Based on the results, it is identified that there have been strong positive correlations between central China, Korea and the southwestern part of Japan in the East Asian region. Through differences between 10 positive WP years and 10 negative WP years selected from the April WPI excluding ENSO years, it is found that rainfall amounts increase in April of positive WP years due to the following characteristics. Increases in rainfall amounts are evident in the East Asian middle latitudinal region where the positive correlation between the two variables is the highest and this is because anomalous southwesterlies are strengthened in the East Asian middle latitudinal region due to the spatial pattern of a south-low-north-high anomalous pressure system centered on this region that is made by anomalous anticyclones centered on the southeastern side of the region and other anomalous anticyclones centered on the northeastern side of the region. In addition, anomalous westerlies (jet) are strengthen in the upper troposphere of the East Asian middle latitudinal region and as a result, anomalous upward flows are strengthened in this region and thus anomalous warm air temperatures are formed in the entire level of the troposphere in the region. In addition to atmospheric environments, anomalous warm sea surface temperatures are formed in the seas in the East Asian middle latitudinal region to help the rainfall amount increases in the East Asian middle latitudinal region.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.7
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• pp.1114-1124
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• 2015

In this paper, precipitation / non-precipitation pattern classification of meteorological radar data is conducted by using neuro-fuzzy algorithm. Structure expression of meteorological radar data information is analyzed in order to effectively classify precipitation and non-precipitation. Also diverse input variables for designing pattern classifier could be considered by exploiting the quantitative as well as qualitative characteristic of meteorological radar data information and then each characteristic of input variables is analyzed. Preferred pattern classifier can be designed by essential input variables that give a decisive effect on output performance as well as model architecture. As the proposed model architecture, neuro-fuzzy algorithm is designed by using FCM-based radial basis function neural network(RBFNN). Two parts of classifiers such as instance classifier part and echo classifier part are designed and carried out serially in the entire system architecture. In the instance classifier part, the pattern classifier identifies between precipitation and non-precipitation data. In the echo classifier part, because precipitation data information identified by the instance classifier could partially involve non-precipitation data information, echo classifier is considered to classify between them. The performance of the proposed classifier is evaluated and analyzed when compared with existing QC method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.9
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• pp.1337-1346
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• 2015

In this paper, pattern classifier is designed to classify precipitation and non-precipitation events from weather radar data. The proposed classifier is based on Fuzzy Neural Network(FNN) and consists of three FNNs which operate in parallel. In the proposed network, the connection weights of the consequent part of fuzzy rules are expressed as two polynomial types such as constant or linear polynomial function, and their coefficients are learned by using Least Square Estimation(LSE). In addition, parametric as well as structural factors of the proposed classifier are optimized through Differential Evolution(DE) algorithm. After event classification between precipitation and non-precipitation echo, non-precipitation event is to get rid of all echo, while precipitation event including non-precipitation echo is to get rid of non-precipitation echo by classifier that is also based on Fuzzy Neural Network. Weather radar data obtained from meteorological office is to analysis and discuss performance of the proposed event and echo patter classifier, result of echo pattern classifier compare to QC(Quality Control) data obtained from meteorological office.

• Journal of Environmental Science International
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• v.22 no.4
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• pp.443-451
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• 2013

Spring rainfall events were comprehensively analyzed based on the distribution of precipitation amount and the related synoptic weather between 2008~2012. Forty-eight cases are selected among the rain events of the entire country, and each distribution of the 24-hour accumulated precipitation amount is classified into three types: evenly distributed rain(Type 1), more rain in the southern area and south coast region (Type 2), and more rain in the central region (Type 3), respectively. Type 1 constitutes the largest part(35 cases, 72.9%) with mean precipitation amount of 19.4 mm, and the 17 cases of Type 1 are observed in March. Although Type B and C constitutes small parts (11 cases, 22.9% and 2 cases, 4.2%), respectively. The precipitation amount of these types is greater than 34.5 mm and occurred usually in April. The main synoptic weather patterns affecting precipitation distribution are classified into five patterns according to the pathway of low pressures. The most influential pattern is type 4, and this usually occurs in March, April, and May (Low pressures from the north and the ones from the west and south consecutively affect South Korea, 37.5%). The pattern 3(Low pressures from the south affect South Korea, 25%) happens mostly in April, and the average precipitation is usually greater than 30 mm. This value is relatively higher than the values in any other patterns.

• Atmosphere
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• v.27 no.2
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• pp.163-175
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• 2017

The global terrestrial ecosystems have shown a large spatial variability in recent decades and represented a carbon sink pattern at mid-to-high latitude in Northern Hemisphere. However, there are many uncertainties in magnitude and spatial distribution of terrestrial carbon fluxes due to the effect of climate factors. So, it needs to accurately understand the spatio-temporal variations on carbon exchange flux with climate. This study focused on the effects of climate factors, .i.e. temperature, precipitation, and solar radiation, to terrestrial biosphere carbon flux. We used the terrestrial carbon flux that is simulated by a CarbonTracker, which performs data assimilation of global atmospheric $CO_2$ mole fraction measurements. We demonstrated significant interactions between Net Ecosystem Production (NEP) and climate factors by using the partial correlation analysis. NEP showed positive correlation with temperature at mid-to-high latitude in Northern Hemisphere but showed negative correlation pattern at $0-30^{\circ}N$. Also, NEP represented mostly negative correlation with precipitation at $60^{\circ}S-30^{\circ}N$. Solar radiation affected NEP positively at all latitudes and percentage of positive correlation at tropical regions was relatively lower than other latitudes. Spring and summer warming had potentially positive effect on NEP in Northern Hemisphere. On the other hand as increasing the temperature in autumn, NEP was largely reduced in most northern terrestrial ecosystems. The NEP variability that depends on climate factors also differently represented with the type of vegetation. Especially in crop regions, land carbon sinks had positive correlation with temperature but showed negative correlation with precipitation.