Deep Work: ULAT P-POTEKA Network Visualizes Typhoon Ambo
The project Understanding Lightning and Thunderstorms (ULAT) aims to examine behaviors of extreme weather events using lightning and weather devices called POTEKA. One of the activities is identifying trends of weather events by comparing POTEKA data against various sources to validate its accuracy and reliability.
Shown in Figure 1 is the data for Typhoon Ambo (Typhoon Vongfong) which passed through Metro Manila on 15 May 2020. The researchers wanted to (1) detect the typhoon’s proximity in a given timeframe; (2) identify parts of Metro Manila receiving the most rainfall; and (3) identify flooded areas in relation to the rainfall activity. Using the data from 35 P-POTEKA stations installed throughout Metro Manila, researchers were able to visualize Typhoon Ambo’s movements and the distribution of rainfall as it passed through the city.
Figure 1: Visualization of the rainfall data trend from the 35 P-POTEKA stations on 15 May 2020.
The first step was to visualize the behavior of the rainfall data from the P-POTEKA network. It was observed that rainfall activity peaked at 8:00 PM (20:00) to 10:00 PM (22:00). The DOST- Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA) issued a red heavy rainfall warning for Metro Manila at the same time from 8:00 PM (20:00) to 10:00 PM (22:00). This confirms that the rainfall behavior observed with the ULAT network was similar to PAGASA’s forecast.
The next step is to identify the parts of Metro Manila that received the most rainfall. Using interpolation, the team generated a 16-hour rainfall timelapse of Metro Manila (Figure 2). The time-lapse revealed that the northern part of Metro Manila received the most amount of rainfall with the likelihood of flooding. Reports from the National Disaster Risk Reduction and Management Council (NDRRMC) warned that prolonged rainfall by the typhoon may result in flooding in highly susceptible areas.
Figure 2: Rainfall timelapse of Metro Manila showing the most affected area during the onslaught of Typhoon Ambo.
This small-scale yet complex experiment validates the accuracy of rainfall and weather data generated by the ULAT P-POTEKA network. This demonstrates that the network may be used to measure rainfall and opens doors for other opportunities to monitor localized weather activities.