## 2 Study Area and Data
### Gauge and Radar Data  
Although physical rain gauges can be used to obtain rainfall information, their limited distribution and point-source data make it impossible to obtain accurate rainfall information for specific accident locations. In contrast, radar-derived rainfall data can provide two-dimensional high-resolution (250 m to 1 km) coverage that can be tied to the accident location and provides a significantly more detailed picture of rainfall distribution than rain gauges [19; 20].  
The Korea Meteorological Administration (KMA) operates 30 automatic weather stations (AWSs) in or near Seoul (Figure 1). These AWSs collect 1-min rainfall observation data that we converted into 1-h cumulative rainfall data for further analysis. Radar-derived rainfall data were collected from two KMA weather radar sites to investigate the three-dimensional structure of rainfall. The Kwanaksan weather radar site is located in Seoul, where a cone of silence causes some observation gaps, and the Gwangdeoksan weather radar site is located approximately 100 km away and is affected by beam attenuation and topography because the city is surrounded by mountains. The data from the two sites were composited to compensate for these issues. Both radars are S-band with a beam width of 1\({}^{\circ}\) and a gate size of 250 m; radar is provided in Universal Format (UF). Volume data were extracted from the radar reflectivity data to obtain constant altitude plan position indicator (CAPPI) data at a height of 1.5 km using a bilinear interpolation program based on the algorithm suggested by [PERSON] [21]. The spherical coordinates of the volume data were then transformed into Cartesian coordinates. The spatial and temporal resolutions of the extracted CAPPI data were 250 \(\times\) 250 m\({}^{2}\) and 10 min accumulated [22]. To use radar data, the raw data (dBZ) were converted into rainfall intensity data using the Z-R power-law relation (Z = aR\({}^{\text{b}}\)), with a = 200 and b = 1.6.  
In general, it is known that the adjustment of the radar rainfall value is quantitatively estimated by calculating the bias of the ground rainfall gauge. Then, the calibrated radar data are generally used to replace ungauged locations. However, it is not an appropriate calibration process for the radar data  
Figure 1: Locations of selected traffic accidents on rainy days, April to October 2008–2017; automatic weather stations (AWSs); and weather radar grid in Seoul.  
for this study, because the radar and gauge station data should be directly compared and each paired with the accident locations. Therefore, when analyzing the effect of rainfall on traffic accidents in this study, a separate radar rainfall correction technique was not applied.  
The interpolated rainfall data were used to obtain rainfall information corresponding to the accident location in some previous studies [14; 23]. To consider a spatial distribution of rainfall using a point rain gauge, we also used kriging to interpolate the rain gauge data into a distributed data set, then used Geographic Information System (GIS) spatial analysis to compare the gauge- and radar-derived rainfall to identify the rain gauges nearest the accident site and rainfall distribution at the time of the accident (Figure 1).