STUDY ON EFFICIENCY IMPROVEMENT OF TRAIN DISPATCH UNDER SEVERE RAINFALL BY USING RADAR

Transcription

1 STUDY ON EFFICIENCY IMPROVEMENT OF TRAIN DISPATCH UNDER SEVERE RAINFALL BY USING RADAR TOMOKI OSHIRO (1) & SATORU OISHI (2) (1) Department of Civil Engineering, Graduate School of Engineering, Kobe University, Kobe, Japan, (2) Research Center for Urban Safety and Security, Kobe University, Kobe, Japan, tetsu@phoenix.kobe-u.ac.jp ABSTRACT Recently, the frequency of severe rainfall increases in Japan. The average of annual frequency of severe rainfall which is defined as rainfall having more than 50mm within one hour has become increase from 168 times per year (TPY) during the year of to 226 TPY during (Japan Meteorological Agency, 2012). The increasing of frequency of severe rain raise risk of sediment related disaster and inundation disaster. Trains especially local train run on reclaimed land and mountainous area have risk of such disasters when severe rain might happen. Therefore, railway companies take many kinds of countermeasures. The present study deals with the dispatching problem under severe rainfall. Each train dispatching system has defined a threshold of rainfall amount where dispatchers stop trains or they command to reduce the speed of trains. A Japanese railway company has measured the rainfall amount by their own rain gauges that locate almost every 13km and it is very difficult to change the measurement system because the company has a historical rainfall data by which the threshold of rainfall amount has been defined. On the other hand, the company has an interest for introducing radar rainfall information for dispatching trains because of increasing of severe rainfall having small spatial scale and efficiency improvement for collecting rainfall data. Many railway companies have the similar contradiction. The present study shows a statistical analysis of the uncertainty to use radar for capturing severe rainfall by using probability function of difference between rainfall amount measured by rain gauge and one by radar. Keywords: local severe rainfall, Radar/Raingauge-Analyzed Precipitation, train dispatch, non exceeding probability 1. INTRODUCTION Recently, frequency of local severe rainfall has increased to damage transportation and urban infrastructure. According to the Japan Meteorological Agency (JMA), the number of rainfall events exceeding 50mm in one hour might be increasing when 11 year average were taken; average number of rain more than 50mm in one hour from year of 1976 to 1986 was 168 times per year; average from 1987 to 1998 was 195 times; average from 1999 to 2010 was 226 times(japan Meteorological Agency, 2012). Therefore, the risk of sediment related disasters like land slide, flush flood and collapse of cliff should be increasing. The infrastructure of railway also exposes the risk by increasing local severe rainfall. Especially, local train (not bullet train called shinkansen ) uses soil based infrastructure like embankment, reclaimed land and mountainous tunnel. Railway companies have strategies to avoid accidents by local severe rain. The strategies include civil works as hard type countermeasure and dispatching process as soft type countermeasure. The present study deals with the monitoring of rainfall for dispatching trains. Radar/Raingauge-Analyzed Precipitation (RRAP) data issued by Japan Meteorological Agency is proposed to be used for dispatching trains with keeping consistency of historical dispatching rules based on histrical recorded train raingauge system data. The present study applied the stochastic procedure to use RRAP data which is different from train raingauge system data. 1

2 , 2. PRINCIPAL DATA FOR MONITORING RAINFALL FOR DISPATCHING TRAINS 2.1 Precipitation data in Japan Automated Meteorological Data Acquisition System (AMeDAS) Japan Meteorological Agency (JMA) that is the government authority to collect meteorological data, to predict weather and to give official alert for public has Automated Meteorological Data Acquisition System (AMeDAS). AMeDAS is the system of automated ground based meteorological data sensors which regularly collect data with 10 minutes interval and send them to center server immediately after they collect the data. Each AMeDAS observation site has a raingauge of tipping bucket type. In Japan, there are approximately 1300 AMeDAS observation sites. In other words, AMeDAS sites locates approximately every 17km in spatial Radar/Raingauge-Analyzed Precipitation data JMA has 20 C-Band weather radars as of February Radar/Raingauge-Analyzed Precipitation data is quantitative precipitation information with higher resolution grid type format. JMA combines C-Band weather radar information and raingauge precipitation information that consists of AMeDAS and precipitation information of other institution like Ministry of Land Infrastructure, Transport and Tourism (MLIT) to integrate them into 1km resolution hourly precipitation data. JMA releases Radar/Raingauge-Analyzed Precipitation data every 30minutes. In the following study, the Radar/Raingauge-Analyzed Precipitation data is used for train dispatch in the present study Precipitation Nowcasts Precipitation Nowcasts are composite weather radar echoes and precipitation forecasts up to 60 minutes ahead are displayed in 1 km x 1 km resolution every 5 minutes, respectively. Precipitation Nowcasts provide precipitation intensity forecasts of swiftly growing convections with a spatial resolution of 1 km up to an hour ahead to assist with disaster prevention activities High-resolution Precipitation Nowcasts JMA combines C-Band weather radar information with X-Band Multi-Parameter weather radar information which is managed by MLIT, raingauge of JMA, MLIT, local governments, wind profiler data and radiosonde data to analyze the interior mechanism of precipitation cloud in order to predict precipitation within 30 minutes with 250m resolution every 5 minutes. 2.2 Precipitation data used for train dispatch Basically railway companies have their own precipitation monitoring system that basically consists only of raingauge up to now. It is called as train raingauge (TR) system. TR system of each railway company has longer historical record of precipitation and the company utilizes the record for making their own dispatching rule. It is impossible to take over the TR system by radar raingauge system even the latter has strong advantage of maintenance, real time operation and finer resolution. However, some railway companies have interests to improve the system using radar based precipitation data because the accuracy of radar based precipitation has been improved recently as well as the efficiency of collecting data by radar. A railway company which operates trains in Kinki region in Japan, where Osaka, Kobe and Kyoto are included, has their own TR system. Hereinafter, the railway company is shown as A-rail. In the TR system of A-rail, each raingauge is deployed every about 13km as Figure 1 and the data obtained by the TR is transmitted to the company s information center every one minute. Figure 1. Schematic image of the train raingauge system. The A-train also has interests to use radar based rainfall information with keeping consistency of dispatching rule made from their own TR system. Therefore, the present study deals with the stochastic approach to investigate the impact of difference between radar based rainfall information and one of TR system. 2

3 2.3 Information system for train dispatch There is a threshold of one hour rainfall amount and accumulated rainfall amount for dispatching trains. When one hour rainfall amount of a raingauge or accumulated rainfall amount of the raingauge exceeds its threshold, a dispatcher should stop the train during the area of which the raingauge have in charge. As written in section 2.2, the TR system of A-Rail has approximately 13km interval, where local severe rain could not be detected well by raingauges. Moreover, frequency of local severe rain increases recently. Therefore, it is necessary to use radar based rainfall information to overcome the problem of missing local severe rain and to avoid accident regarding severe rainfall. 3. UTILIZATION SIMULATION OF RADAR/RAINGAUGE-ANALYZED PRECIPITATION DATA FOR TRAIN DISPATCH 3.1 Advantage and disadvantage to use Radar/Raingauge-Analyzed Precipitation data The present study uses the Radar/Raingauge-Analyzed Precipitation data (RRAP) because JMA publishes the RRAP record though an agency; where as record of Precipitation Nowcasts has not been available; High-resolution Precipitation Nowcasts is still newly developed data. RRAP data is grid based one hour rainfall data with 1km grid scale, where as a raingauge in a train raingauge (TR) system locates at a station. Therefore, grids that include railway in RRAP data is used for the analysis as shown in Figure 2. Figure 2. Schematic Image of girds of RRAP in use. Although RRAP data has enough accuracy and very fine resolution in terms of time and space, it is different from data obtained by the TR system. Moreover, the dispatching rule was established by records of the TR system. Therefore, the consistency of the RRAP data and the TR system data should be analyzed. As shown in Figure 2, RRAP data has several grid points within an area where a raingauge of the TR system in charge. Therefore, the difference of RRAP data and the TR system data is dealt with stochastically. In other words, the difference is represented as a stochastic variable and the probability to exceed the threshold gives certainty of RRAP data. 3.2 Area and period The data of a TR system in A-train has been used in the present study. Data of five raingauges have been provided from A-train. The specification of the raingauges of the TR system are shown on Table 1. Table 1. Specification of the raingauges(rg) of the TR system in use, RG NUMBER OF STATION LENGTH [KM] NUMBER OF GRIDS A B C D E The period of analysis were summer (June to September) of years from 2009 to The one hour accumulated rainfall of RRAP data has been used. Therefore, the rainfall data means one hour accumulated rainfall data in the following description. 3

4 , 3.3 Analysis process First, the difference between RRAP and TR should be defined. It is assumed that TR is true value in the present study. Then, the difference between RRAP and TR can be assumed as error of RRAP from TR. Therefore, the RRAP error ( ) is defined as Eq. [1], (x A,t) r t (x A,t) r A (x A,t) [1] where, (x A ) is grid coordinate of a raingauge point A in TR, t, time, r t, TR rainfall data and r A, RRAP rainfall data at the grid which includes point A in TR. Then, the histogram of the RRAP error is shown as Figure 3 and it can be assumed as Gaussian distribution. Figure 3. An example of histogram and probabilistic density function of error of RRAP. Second, the estimated rainfall data at point B where no TR raingauge measures is represented as Eq.[2]. Then, the non exceeding probability that defines as probability of that estimated rainfall data at point B does not exceed the threshold ( r r ) defined by TR rain at point A. [2] The equation is transformed into, P( ( ) r r r A (x B, y B,t)) It means that probability that the RRAP error is less than difference of threshold and RRAP. Third, the areal total non exceeding probability is defined n P all P( ( ) r r A (x i, y i,t)) [3] i The procedure has been applied to five TR raingauges. 3.4 Area and period First, the table of average, standard deviation (Table 2) has been created from the histogram (Figure 3). RG Table 2. Statistics of error of RRAP, AVERAGE [mm] STDEV [mm] VARIANCE A B C D E Then, the exceeding probability of the area that each five TR raingauge was calculated. The threshold for dispatch is assumed as 40mm in one hour because A-Train does not publish the threshold. The result of 28 rainfall events that were defined that at least one grid in RRAP exceeded 30mm in one hour is shown on Figure 4. All except one show almost 1.0 or 0.0 probability of non exceeding probability, which means that dispatcher does not have difficulty to judge the train operation. Dots at around 1.0 in vertical axis show that they were not exceeding the 4

5 threshold with almost 1.0 probability and the dots at bottom around 0.0 in vertical axis show that it is reasonable to assume that they exceeded the threshold. Figure 4. Result of non exceeding probability of 28 rainfall events. The seven dots at the bottom and left hand side of the broken vertical line are the most important points in the present study. They mean that TR raingauge data missed the exceeding of threshold because the rain was so local but severe. However, the RRAP gave certainty that it exceeded the threshold. Four dots at the bottom and right hand side of the broken vertical line of the figure mean that they exceeded threshold in TR as well as certainly they assumed to have exceeded the threshold from RRAP. The most difficult one to judge exceeding of threshold is one which has almost 0.5 probability. In the TR raingauge, it was 14mm in one hour and there was a grid that showed 41mm in one hour and the other grids had less than 35mm in one hour. The probability of non exceeding from threshold (40mm in one hour) is 0.5 when RRAP is 41mm, and 1.0 when RRAP is less than 35mm. Therefore, total non exceeding probability was almost CONCLUSIONS In the present study, Radar/Raingauge-Analyzed Precipitation (RRAP) data has been applied to train dispatch under severe local rainfall condition with keeping consistency of dispatching rule made by a train raingauge (TR) system. The recorded 28 events of summer (June to September) from 2009 to 2011 have been used for analysis, then the following things are found. 1. RRAP had no miss rain events which were larger than threshold in TR system. For dispatching trains with RRAP, the proposed process that uses total non exceeding probability is useful; 2. RRAP caught seven local severe rain events that TR system missed; 3. Even each grid of RRAP has different from TR raingauge data, the proposed process kept consistency of threshold. ACKNOWLEDGMENTS It is gratefully acknowledged that the present study has been supported by West Japan Railway Company. REFERENCES Japan Meteorological Agency (2006). Report of Climate Variability. 5