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Radar data processing and rainfall estimation were conducted using the open source library wradlib based on Python Heistermann et al., 2013. Constant Altitude PPI CAPPI values were calculated for every 0.5
km from altitudes 0.5 to 5 km with horizontal resolution of 0.5 kmpixel. BMKG usually uses a parameter CAPPI-CMAX maximum CAPPI values in altitude column to analyze the extreme weather events.
Therefore, we utilize dBZ values from CAPPI-CMAX to derive the rainfall rate estimates. Specifically, we extract dBZ values from 9 grid points closest to the ground rainfall stations and calculate their average values
dBZave and maximum values dBZmax. The instantaneous dBZave and dBZmax values at every 10 minutes interval are averaged into hourly and three-hourly values for each station. Rainfall rates estimation
from the MP and RO relationships are compared with ground measured rainfall at the two stations to investigate their performances by calculating Root Mean Squared Error RMSE and Mean Absolute Error
MAE. In this study, the rainfall field is assumed to remain stationary in space and intensity during the sampling interval, where the raindrop is assumed to fall vertically downward Mapiam et al., 2008. In
addition, this event were also simulated by WRF model run by Center for Research and Development Center of BMKG http:www.puslitbang.bmkg.go.idwrf.
A B
Figure 2. A PPI scan strategy of weather radar in Padang. B The Padang radar site black triangle and BMKG
stations circles in West Sumatera.
3. RESULTS AND DISCUSSION
BMKG has defined the rainfall with intensity more than 100 mmday as the extreme rainfall. This condition applied in Minangkabau and Teluk Bayur stations on June 16, 2016 with intensity of 384.1
mmday and 379 mmday, respectively. Ground measured rainfall at these stations indicate that rainfall began at 06-09 UTC, peak at 09-12 UTC and then decreased and stopped around 21 UTC on June 16, 2016
Figure 3A. This suggests that, on average, the rainfall rates at these stations are ~28 mmhr on June 16, 2016. This observation was also supported by weather radar data in Padang as shown in Figure 3B. Rainfall
started earlier in Teluk Bayur than in Minangkabau at 06 UTC and increased at 08 UTC with reflectivity values of ~40 dBZ. While in Minangkabau, rainfall began at 07 UTC and increased at 09 UTC with
reflectivity values of 30-35 dBZ. Both stations have rainfall peak at 10 UTC with reflectivity values of 40-50 dBZ. This explains the high accumulation rainfall observed at both stations between 09-12 UTC. After that,
rainfall stayed at high rates until 16 UTC and gradually decreased afterward. Spatial reflectivity values of CAPPI-CMAX at 08, 09, 10 and 16 UTC are given in Figure 4. Figure 4 shows that most of rainfall was
initially formed inland close to the edge of mountainous chain Bukit Barisan. This indicates the role of mountain ranges in Sumatra on the formation of orographic rainfall during the event. Therefore, the main
cause of this extreme rainfall was not only due to global conditions but also strengthened by local interactions with mountain ranges in Sumatra. In addition, at 16 UTC, rainfall were equally distributed in
radar range area with intensification in the ocean.
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A B
Figure 3. A Ground measured rainfall and B reflectivity radar data at Minangkabau and Teluk Bayur stations.
Figure 4. Reflectivity data dBZ from CAPPI-CMAX of Padang weather radar at 08, 09, 10 and 16 UTC on June 16,
2016. Brown color indicates topography contour using ETOPO2 with 2-minute resolution. Each contour line represents an increase of 0.5km elevation.
Rainfall rates estimation were derived from MP and RO relationships with data input Zave and Zmax. Comparison between the ground observation rainfall and these estimated rainfall rates are given in Figure 5.
The results show that all of radar rainfall estimates generally underestimate the ground observation data at both stations, except for 09 UTC at Teluk Bayur station. The RO relationship has a better results than the MP
relationship at both stations Figure 5 as indicated by smaller RMSE and MAE values Table 1. This is possibly because RO relationship is more suitable for tropical convective area like West Sumatra.
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Furthermore, radar-based rainfall estimations were closer to the observed values in Teluk Bayur than in Minangkabau. For instance, MAE values for R_ave and R_max with Rosenfeld relationship in Teluk Bayur
are 14.2 and 11.0 mmhr, respectively, while in Minangkabau, they are 19.8 and 17.4 mmhr, respectively. This may be caused by the different distance between the stations and the radar site. Minangkabau station is
located very close ~1-2 km to the radar site where is poorly observed by the radar because there is no radar scanning on that range Figure 2A. In contrast, Teluk Bayur station is located at ~25 km to the south of the
radar site. This distance range is well observed by the radar through 10 PPI scans Figure 2A. In addition, Table 1 also shows that Zmax is better than Zave in estimation of rainfall rates. The result show that both of
MP and RO relationships still have large RMSE 15.9 - 29.6 mmhr and MAE 11 - 22 mmhr values. Therefore, this study suggests to conduct a follow-up research study to derive a new Z-R relationship that
suits with radar reflectivity data in Padang. However, this will need a longer radar and ground observation dataset Kamaruzaman and Subramaniam, 2012.
Figure 5. Comparison between ground observation rainfall and radar-based rainfall rate estimates at Minangkabau and
Teluk Bayur stations. Note: R_ave_MP represents rainfall estimates derived from MP relationship with input Zave.
Table 1. RMSE and MAE between ground observation rainfall and radar-based rainfall estimates in mmhr
Marshall-Palmer Rosenfeld
RMSE R_ave
R_max R_ave
R_max Minangkabau
29.6 28.0
26.2 22.4
Teluk Bayur 23.8
21.6 15.9
16.2 MAE
R_ave R_max
R_ave R_max
Minangkabau 22.0
21.0 19.8
17.4 Teluk Bayur
19.3 18.3
14.2 11.0
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Figure 6. Model forecasts valid at 16 UTC on June 16, 2016 using WRF model run by Center for Research
and Development of BMKG In addition to the comparison between radar-based rainfall estimates and ground observation rainfall,
WRF model are also performed to forecast this extreme event. Sensitivity of WRF model has been performed to predict rainfall over West Sumatra Febri et al., 2016. For this study, Figure 6 shows the WRF
model forecast at 16 UTC on June 16, 2016 with initial condition at 12 UTC on June 16, 2016 for parameter wind and hourly precipitation. Comparing to Figure 4, WRF prediction at 16 UTC also shows a similar
spatial rainfall pattern which mainly distributed over the ocean in the west of West Sumatra with intensity of 12 - 16 mmhr and wind speed of 10-18 ms. This predicted rainfall rate is still in comparison with radar-
based rainfall estimates but underestimates the ground observation data Figure 5. This may be caused by low spatial resolution of WRF model ~27km.
4. CONCLUSIONS
