A Study on Estimation Technique of Extreme Precipitation Diameter

Transcription

1 Vol.125 (Art, Culture, Game, Graphics, Broadcasting and Digital Contents 2016), pp A Study on Estimation Technique of Extreme Precipitation Diameter Jin Woo Choi 1, Tae Min Kim 2, Taeg Keun Whangbo 2* 1 Culture Technology Institute, 2 Department of Computer Engineering, Gachon University 1342 Seongnamdaero, Sujeong-gu, Seongnam-si, Gyeonggi-do, , Korea {cjw49, scc0309, * tkwhangbo}@gachon.ac.kr Abstract. Recently, meteorological information is processed through diversified sensors and researching meteorological pattern by analyzing this information is very important work. At the time of extreme precipitation, cloud features could be modeled by analyzing precipitation pattern. This modeling is implemented by extracting extreme precipitation data by each AWS position and analyzing surrounding precipitation distribution contour diameter relevant to rainfall days and large capacity operation and statistical analysis are required to be automated. In this study, its objective is to research on a technique of being able to estimate max. precipitation diameter by each 5mm unit automatically. Keywords: Extreme precipitation, Contour diameter, AWS 1 Introduction Today, meteorological climate information is being utilized in diversified fields and its accurate and effective use is an important work. By identifying and predicting atmospheric environmental condition, its favorable effect could be exerted to public convenience and overall industry. Therefore, prediction of environmental meteorology that could be also utilized as a simple information by modeling regional, temporal meteorological condition is required to be developed most urgently as an essential condition [5]. Meteorological disaster by local meteorological disturbance such as severe heavy rainfall, seasonal rain front, suddenly changed severe weather brought forth heavy damage of human life and property and such economic damage was increased by over 30% for the last 10 years ( ) [1]. Recently, in case of guerilla type heavy rain being frequently taken place in summertime, it induces landslide and flood by heavy rainfall in short period of time and severe damage by this rainfall has been taken place [5]. Occurrence frequency of extreme precipitation is estimated by collecting meteorological observation data for the past 30 years and analyzing it statistically and general cloud feature is analyzed by estimating information for date and region of such frequency. ISSN: ASTL Copyright 2016 SERSC

2 Cloud feature classification is enabled through precipitation distribution analysis at the time of extreme precipitation by each AWS position. In this study, precipitation distribution data of precipitation extreme days is converted to gridded contour data by using Voxler 3D visualized program [2] that may generate 3D image and model such as meteorological data and GIS data without difficulty after estimating precipitation extreme value by each AWS position. Afterwards, feature of extreme precipitation zone is analyzed by developing a technique that may estimate max. diameter of precipitation contour of 5mm unit by each AWS position 2 Extreme precipitation Data used for determining extreme precipitation is rainfall observation data for 1 hour having been observed for 31 years from 1980 to 2010 being observed at 91 positions among ground observation positions being operated by installing it nationwide by Korea Meteorological Administration. Among these, as observation position at 8 points (172, 217, 258, 259, 263, 264, 276, 283) being composed of observation data below 2 years was excluded, total analysis position is observation data of 83 positions [3]. Extreme precipitation estimation method being used in this study is frequency estimation of each extreme precipitation in monthly dataset. Occurrence frequency of extreme precipitation is estimated by 4 rank conditions of 0.1, 0.5, 1, and 5%. In observation data of 1 hour precipitation for 30 years, one dataset by each month is composed (app. 720ea). In this dataset, temporal/spatial information relevant to 0.1, 0.5, 1, 5% is estimated respectively. Through mutual comparison of extreme precipitation occurrence frequency of 0.1, 0.5, 1, 5% being estimated in monthly dataset for 30 years, one extreme precipitation with most severe rainfall is determined (Table 1). Table 1. Precipitation extreme data in certain position of AWS Position Date 0.1% Date 0.5% Date 1% Date 5% Copyright 2016 SERSC73 73

3 In monthly dataset of 0.1% extreme precipitation, date and position value corresponding to 0.74, 0.72, 0.696, is determined by extreme precipitation and in extreme precipitation of 0.5%, a value corresponding to 3.72, 3.6, 3.48, 3.36, in 1%, 7.44, 7.2, 6.96, 6.72 and in that of 5%, 37.2, 36, 34.8, 33.6 are determined as extreme precipitation. 3 Estimation of contour data by each extreme precipitation position Precipitation data of AWS position relevant to extreme rainfall days by each AWS position is converted to input data form of Voxler and composed as shown on Table 2. Table % Extreme Precipitation distribution data at No. 90 position of AWS Longitude Latitude Precipitation(mm) Copyright 2016 SERSC

4 In order to express input data of Table 2 as contour, it is required to be converted to formulated grid data. If applying inverse distance weighted method (IDW) by using each AWS position and precipitation, gridded rainfall data could be generated. Gridded rainfall data could be visualized in a form of contour as shown on Fig. 1 through Voxler. Fig. 1. Visualization of AWS rainfall data contour 4 Estimation technique of rainfall contour max. diameter In order to estimate diameter of contour by each 5mm unit in specific area based on AWS position of rainfall contour as shown on Fig. 2, position value of max. diameter (both end point) is required. In order to identify position of both ends, position value of each image pixel is required to be estimated. Each pixel of image being extracted from Voxler does not include position (longitude, latitude) value. In order to assign position value to each pixel, reference outline is required to be expressed as shown on Fig. 2 through a function of drawing polygon of Voxler. Copyright 2016 SERSC75 75

5 Fig. 2. Rainfall contour image including reference outline In order to obtain longitude, latitude difference by each pixel, number of longitudinal, traverse outline pixel is calculated and max. longitude, latitude difference of outline is also calculated. Through ratio between calculated outline pixel number and longitude, latitude difference value, longitude, latitude difference value per 1 pixel could be obtained. When longitude, latitude value of each pixel is assigned in above method, max. diameter of precipitation by 5mm unit in specific area by each AWS position could be extracted. Table 2. Illustration of rainfall contour diameter information around AWS No. 112 position (0.1%, 32mm, ) Precipitation diameter(km) Latitude1 Longitude1 Latitude2 Longitude2 5mm mm mm mm mm mm mm mm Extreme precipitation of Rank 0.1% in AWS No. 112 position is 32mm and observation time is 19 hours, July 20, Diameter information of rainfall contour 76 Copyright 2016 SERSC

6 by 5mm around AWS No. 112 position is as shown on Table 2. Latitude, longitude1 & 2 are both ends of max. diameter and max. diameter (km) could be obtained by calculating the distance of two points. 5 Conclusion In order to analyze cloud features during extreme precipitation, feature survey like precipitation distribution around extreme position by each AWS position is required. By calculating extreme precipitation frequency of 83 AWS positions of which precipitation was measured for 30 years, precipitation date and quantity of 0.1%, 0.5%, 1%, 5% were estimated. Gridded distribution data of surrounding AWS precipitation on relevant rainfall date was made by using Voxler that is 3D visualizing tool and through such data, contour image was generated. As a lot of time is required and error size is likely to be magnified if manually estimating max. diameter of precipitation by 5mm unit of contour image being generated like this, we developed a technique of being able to calculate precipitation diameter automatically. By adding reference outline information to precipitation contour image being generated through Voxler, position value was assigned to all the pixels of precipitation contour image and through each pixel value having position information, max. diameter of precipitation zone by 5mm unit in specific area defined by user by each AWS position could be estimated. Through this study, it is expected that general cloud features could be analyzed by modeling regional and daily precipitation intensity and its zone size. Acknowledgments. This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(NRF-2013R1A1A ) References 1. Statistics Korea, 2. Voxler Related Information, 3. Choi, Y., Kim, M.G., Kim, Y.J., Park, C.Y.: Characteristics and Changes of Extreme Precipitation Events in the Republic of Korea, 1954~2010: Their Magnitude, Frequency, and Percent to Total Precipitation: vol.6, pp Journal of Climate Research (2011) 4. Nam, J.Y., Lee, Y.H., Ha, J.C., Jung, G.Y. : Generation of precipitation re-analysis data through development of synthesized precipitation production technique in Korean peninsula: pp Conference Proceedings of Korean Meteorological Society (2012) 5. AGENCY FOR DEFENSE DEVELOPMENT : Meteorological data processing and analysis services, AGENCY FOR DEFENSE DEVELOPMENT Report (2014) 6. Tae, H.U., Kim, H.I., Park, K.D.: Development of a Virtual Reference Station-based Correction Generation Technique Using Enhanced Inverse Distance Weighting: vol.4 no.2 pp Journal of Positioning, Navigation, and Timing (2015) Copyright 2016 SERSC77 77