Muzayanah et a l Extreme Rainfall Analysis Padang 16 June 2016 EAR2016 2
International Symposium on the 15th Anniversary of the Equatorial Atmosphere Radar (EAR)
Extreme Rainfall Analysis using Radar-based Rainfall Estimates, Ground
Observation and Model Simulation in West Sumatra (Case Study: Padang
Floods on June 16, 2016)
Linda F Muzayanah1*), Donaldi S Permana1*), Alfan S Praja1, Eka S P Wulandari2, Wido Hanggoro1
1
2
Research and Development Center, Indonesian Agency for Meteorology Climatology and Geophysics (BMKG)
Minangkabau Meteorological Station, Indonesian Agency for Meteorology Climatology and Geophysics (BMKG)
*)
E-mail:[email protected]; [email protected]
ABSTRACT - This case study evaluates and compares the suitability of radar rainfall estimation, ground observed
rainfall and model simulation during the extreme rainfall event on June 16, 2016 in West Sumatra which caused
flooding in Padang and closed of Minangkabau Airport due to minimum visibility. Radar-based rainfall estimation was
produced from reflectivity (dBZ) that had been observed by C-band radar located in Minangkabau station
(100.3°E;0.79°S;24 magl). Radar data processing and rainfall estimation were conducted using the open source library
wradlib. Radar rainfall estimation were calculated using two reflectivity-rainfall rate (Z-R) relationships of MarshallPalmer (MP) and Rosenfeld (RO). Three-hourly rainfall data from radar rainfall estimates and observed data were
compared at two meteorological stations in Minangkabau and Teluk Bayur (~25 km from radar site) during the day
event. Extreme rainfall were measured at these two stations with intensity of 384.1 mm/day and 379 mm/day,
respectively, possibly due to local interaction with mountain ranges. The results show that both of radar rainfall
estimates generally underestimated the observation data with RO relationship was better than MP. Furthermore, radar
rainfall estimations were better in Teluk bayur than in Minangkabau. The Mean Absolute Error (MAE) values for RO
relationship is 11- 14.2 mm/hr and MP relationship is 18.3-19.3 mm/hr and 21 - 22 mm/hr for RO and MP
relationships, respectively.
Keywords: extreme rainfall, floods, radar rainfall estimation, Z-R relationship, padang, WRF
1.
INTRODUCTION
On June 16 2016, extreme weather and heavy rainfall have occurred in West Sumatra province which
resulted in flooding in Padang city and its surrounding areas. Extreme rainfall were measured at two BMKG
stations in Minangkabau and Teluk Bayur with rainfall intensity of 384.1 mm/day and 379 mm/day,
respectively (Wulandari and Nugraha, 2016). There have been several factors that may cause this event,
which include a positive anomaly of sea surface temperature (SST) by 0.5-1.5°C and an active Madden
Julian Oscillation (MJO) in the eastern Indian Ocean, a combination of low pressure area in the western part
of West Sumatra and an eddy in the Karimata strait, high relative humidity at surface up to 500 mb level, and
also cooler top cloud temperatures over West Sumatra (Figure 1; Wulandari and Nugraha, 2016). This
extreme event has inundated most of Padang city with about 30-60 cm of water level and impacted various
sectors. For instance, during the event, Minangkabau airport has been closed 4 times at 09:57-11:00, 11:54 to
12:30, 13:53 to 14:15 and 14:48 to 15:35 UTC due to minimum visibility based on information issued by
Airnav. As a results, there were five air planes had to be diverted to Pekanbaru and Medan airports.
Extreme events have been a big challenge for BMKG in providing fast, right and accurate information to
the public. Therefore, BMKG has installed 40 weather radar across Indonesia (http://radar.bmkg.go.id) since
2006 including in Padang city. The advantages of weather radar are able to detect a very short-term weather
conditions and have a high resolution. Weather radars measure the electromagnetic radiation backscattered
by cloud raindrops. They have the potential to estimate rainfall rates (R) by exploiting the reflectivity (Z)
1
International Symposium on the 15th Anniversary of the Equatorial Atmosphere Radar (EAR)
values via empirical power-law Z-R relationships. This study presents the analyses and comparison of radarbased rainfall estimates from Padang weather radar data and ground rainfall observation at two BMKG
stations in Minangkabau and Teluk Bayur. Rainfall rates estimations were derived from two common Z-R
relationships of Marshall-Palmer (MP) with Z = 200R1.6 for general stratiform precipitation (Marshall et al.,
1947; Rinehart, 2010) and Rosenfeld (RO) with Z = 250R1.2 for tropical convective rain (Rosenfeld et al.,
1993). In addition, WRF model simulation are also performed during this extreme event.
A
B
C
D
Figure 1. The global ocean and atmospheric conditions during the extreme rainfall on June 16, 2016 in West Sumatra,
Indonesia. (A). SST anomaly (B). Sea level pressure and streamline (C). Relative humidity at 500 mb level and (D).
Infrared channel from HIMAWARI Satellite represent top cloud temperatures during the event.
2.
DATA AND METHOD
For this study, data collected from C-band radar in Minangkabau station (100.3°E; 0.79°S; 24 meter
above ground level) during June 16, 2016 are used. The radar has a maximum horizontal coverage of 240
km, however, in this study the reflectivity data for up to 120 km radius only is used. This is to avoid the low
quality reflectivity data, because at a great distance from radar, the signal received by radar is influenced by
noise (Sebastianelli et al., 2010). Data are archived every 10 minutes in volumetric format which consist of
10 Plan Position Indicator (PPI) scans (0.5, 1.6, 2.9, 4.5, 6.4, 8.8, 11.8, 15.3, 19.7 and 25.0 degree elevation)
(Figure 2A) and each of them contains the reflectivity values in decibel (dBZ with dBZ = 10log10Z).
On the other hand, ground rainfall observation were accumulatively measured every 3 hours at BMKG
stations in Minangkabau (100.3°E; 0.79°S; with distance of 1-2 km from the radar site) and Teluk Bayur
(100.37°E; 0.99°S; with distance of ~25 km from the radar site) during the day event (Figure 2B). Rainfall
data at 00 to 21 UTC on June 16, 2016 were available in Minangkabau and Teluk Bayur.
2
International Symposium on the 15th Anniversary of the Equatorial Atmosphere Radar (EAR)
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 km/pixel. 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.id/wrf).
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 mm/day as the extreme rainfall. This
condition applied in Minangkabau and Teluk Bayur stations on June 16, 2016 with intensity of 384.1
mm/day and 379 mm/day, 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 mm/hr 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.
3
International Symposium on the 15th Anniversary of the Equatorial Atmosphere Radar (EAR)
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.
4
International Symposium on the 15th Anniversary of the Equatorial Atmosphere Radar (EAR)
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 mm/hr, respectively, while in Minangkabau, they are 19.8 and 17.4 mm/hr, 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 mm/hr) and MAE (11 - 22 mm/hr) 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 mm/hr)
Marshall-Palmer
RMSE
Minangkabau
Teluk Bayur
MAE
Minangkabau
Teluk Bayur
R_ave
29.6
23.8
R_ave
22.0
19.3
R_max
28.0
21.6
R_max
21.0
18.3
Rosenfeld
R_ave
26.2
15.9
R_ave
19.8
14.2
R_max
22.4
16.2
R_max
17.4
11.0
5
International Symposium on the 15th Anniversary of the Equatorial Atmosphere Radar (EAR)
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 mm/hr and wind speed of 10-18 m/s. This predicted rainfall rate is still in comparison with radarbased rainfall estimates but underestimates the ground observation data (Figure 5). This may be caused by
low spatial resolution of WRF model (~27km).
4.
CONCLUSIONS
The extreme rainfall event on June 16, 2016 in West Sumatra which caused flooding in Padang and its
surrounding area has been evaluated for its possible global and local causes. The meteorological data
gathered during the event from radar reflectivity data in Padang city, ground observed rainfall at
Minangkabau and Teluk Bayur stations and WRF model simulation have been used to investigate the event
and to perform two reflectivity-rainfall rate relationships (Marshall-Palmer and Rosenfeld) in generating
radar-based rainfall estimates. The results show that radar-based rainfall estimates generally underestimated
the ground observation data with Rosenfeld relationship was better than Marshall-Palmer relationshio. The
rainfall estimations were better in Teluk Bayur than in Minangkabau, possibly due to further distance from
radar site which associated with more scanning data from the radar. This study suggests a follow-up research
study to derive a new Z-R relationship that suits with radar reflectivity data in Padang, but this will need a
longer dataset. In addition, WRF model were also performed to forecast this event.
5.
ACKNOWLEDGEMENTS
The authors would like to thank the Meteorological Division of Center for Research and Development
Center of BMKG for supporting this study. The authors would also like to acknowledge the Minangkabau
and Teluk Bayur meteorological stations in West Sumatra for the provision of weather radar data and ground
rainfall observation.
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International Symposium on the 15th Anniversary of the Equatorial Atmosphere Radar (EAR)
6.
REFERENCES
Febri, D. H., Hidayat, R., and Hanggoro, W. (2016). Sensitivity of WRF-EMS Model to Predict Rainfall Event on Wet
and Dry Seasons Over West Sumatra. Procedia Environmental Sciences, 33, 140-154.
Heistermann, M., Jacobi, S., and Pfaff, T. (2013). Technical Note: An open source library for processing weather radar
data (wradlib). Hydrology and Earth System Sciences, 17(2), 863-871.
Kamaruzaman M. A. and Subramaniam M. (2012). Rainfall estimation from radar data, Malaysia Meteorological
Department (MMD), Ministry of Science, Technoloy and Innovation (MOSTI), Research publication no. 6/2012,
pp. 1-16.
Mapiam, P. P., and Sriwongsitanon, N. (2008). Climatological ZR relationship for radar rainfall estimation in the upper
Ping river basin. ScienceAsia J, 34(2), 215-222.
Marshall, J. S., Langille, R. C., and Palmer, W. M. K. (1947). Measurement of rainfall by radar. Journal of
Meteorology, 4(6), 186-192.
Rinehart, R.E. (2010). Radar For Meteorologists (Fifth Edition), Rinehart Publications, 5, 136-139.
Rosenfeld, D., Wolff, D. B., and Atlas, D. (1993). General probability-matched relations between radar reflectivity and
rain rate. Journal of applied meteorology, 32(1), 50-72.
Sebastianelli, S., Russo, F., Napolitano, F., and Baldini, L. (2010). Comparison between radar and rain gauges data at
different distances from radar and correlation existing between the rainfall values in the adjacent pixels. Hydrology
and Earth System Sciences Discussions, 7(4), 5171-5212.
Wulandari, E, S, P., and Nugraha, Y. (2016). Analisis cuaca terkait kejadian hujan ekstrim di Sumatera Barat
mengakibatkan banjir dan genangan air di kota Padang tanggal 16 Juni 2016, accessed on July 20, 2016 from
http://eoffice.bmkg.go.id/Dokumen/Artikel/Artikel_20160628154826_eavde8_Analisis-Cuaca-Terkait-KejadianHujan-Ekstrim-di-Sumatera-Barat-16-Juni-2016.pdf
7
Extreme Rainfall Analysis using Radar-based Rainfall Estimates, Ground
Observation and Model Simulation in West Sumatra (Case Study: Padang
Floods on June 16, 2016)
Linda F Muzayanah1*), Donaldi S Permana1*), Alfan S Praja1, Eka S P Wulandari2, Wido Hanggoro1
1
2
Research and Development Center, Indonesian Agency for Meteorology Climatology and Geophysics (BMKG)
Minangkabau Meteorological Station, Indonesian Agency for Meteorology Climatology and Geophysics (BMKG)
*)
E-mail:[email protected]; [email protected]
ABSTRACT - This case study evaluates and compares the suitability of radar rainfall estimation, ground observed
rainfall and model simulation during the extreme rainfall event on June 16, 2016 in West Sumatra which caused
flooding in Padang and closed of Minangkabau Airport due to minimum visibility. Radar-based rainfall estimation was
produced from reflectivity (dBZ) that had been observed by C-band radar located in Minangkabau station
(100.3°E;0.79°S;24 magl). Radar data processing and rainfall estimation were conducted using the open source library
wradlib. Radar rainfall estimation were calculated using two reflectivity-rainfall rate (Z-R) relationships of MarshallPalmer (MP) and Rosenfeld (RO). Three-hourly rainfall data from radar rainfall estimates and observed data were
compared at two meteorological stations in Minangkabau and Teluk Bayur (~25 km from radar site) during the day
event. Extreme rainfall were measured at these two stations with intensity of 384.1 mm/day and 379 mm/day,
respectively, possibly due to local interaction with mountain ranges. The results show that both of radar rainfall
estimates generally underestimated the observation data with RO relationship was better than MP. Furthermore, radar
rainfall estimations were better in Teluk bayur than in Minangkabau. The Mean Absolute Error (MAE) values for RO
relationship is 11- 14.2 mm/hr and MP relationship is 18.3-19.3 mm/hr and 21 - 22 mm/hr for RO and MP
relationships, respectively.
Keywords: extreme rainfall, floods, radar rainfall estimation, Z-R relationship, padang, WRF
1.
INTRODUCTION
On June 16 2016, extreme weather and heavy rainfall have occurred in West Sumatra province which
resulted in flooding in Padang city and its surrounding areas. Extreme rainfall were measured at two BMKG
stations in Minangkabau and Teluk Bayur with rainfall intensity of 384.1 mm/day and 379 mm/day,
respectively (Wulandari and Nugraha, 2016). There have been several factors that may cause this event,
which include a positive anomaly of sea surface temperature (SST) by 0.5-1.5°C and an active Madden
Julian Oscillation (MJO) in the eastern Indian Ocean, a combination of low pressure area in the western part
of West Sumatra and an eddy in the Karimata strait, high relative humidity at surface up to 500 mb level, and
also cooler top cloud temperatures over West Sumatra (Figure 1; Wulandari and Nugraha, 2016). This
extreme event has inundated most of Padang city with about 30-60 cm of water level and impacted various
sectors. For instance, during the event, Minangkabau airport has been closed 4 times at 09:57-11:00, 11:54 to
12:30, 13:53 to 14:15 and 14:48 to 15:35 UTC due to minimum visibility based on information issued by
Airnav. As a results, there were five air planes had to be diverted to Pekanbaru and Medan airports.
Extreme events have been a big challenge for BMKG in providing fast, right and accurate information to
the public. Therefore, BMKG has installed 40 weather radar across Indonesia (http://radar.bmkg.go.id) since
2006 including in Padang city. The advantages of weather radar are able to detect a very short-term weather
conditions and have a high resolution. Weather radars measure the electromagnetic radiation backscattered
by cloud raindrops. They have the potential to estimate rainfall rates (R) by exploiting the reflectivity (Z)
1
International Symposium on the 15th Anniversary of the Equatorial Atmosphere Radar (EAR)
values via empirical power-law Z-R relationships. This study presents the analyses and comparison of radarbased rainfall estimates from Padang weather radar data and ground rainfall observation at two BMKG
stations in Minangkabau and Teluk Bayur. Rainfall rates estimations were derived from two common Z-R
relationships of Marshall-Palmer (MP) with Z = 200R1.6 for general stratiform precipitation (Marshall et al.,
1947; Rinehart, 2010) and Rosenfeld (RO) with Z = 250R1.2 for tropical convective rain (Rosenfeld et al.,
1993). In addition, WRF model simulation are also performed during this extreme event.
A
B
C
D
Figure 1. The global ocean and atmospheric conditions during the extreme rainfall on June 16, 2016 in West Sumatra,
Indonesia. (A). SST anomaly (B). Sea level pressure and streamline (C). Relative humidity at 500 mb level and (D).
Infrared channel from HIMAWARI Satellite represent top cloud temperatures during the event.
2.
DATA AND METHOD
For this study, data collected from C-band radar in Minangkabau station (100.3°E; 0.79°S; 24 meter
above ground level) during June 16, 2016 are used. The radar has a maximum horizontal coverage of 240
km, however, in this study the reflectivity data for up to 120 km radius only is used. This is to avoid the low
quality reflectivity data, because at a great distance from radar, the signal received by radar is influenced by
noise (Sebastianelli et al., 2010). Data are archived every 10 minutes in volumetric format which consist of
10 Plan Position Indicator (PPI) scans (0.5, 1.6, 2.9, 4.5, 6.4, 8.8, 11.8, 15.3, 19.7 and 25.0 degree elevation)
(Figure 2A) and each of them contains the reflectivity values in decibel (dBZ with dBZ = 10log10Z).
On the other hand, ground rainfall observation were accumulatively measured every 3 hours at BMKG
stations in Minangkabau (100.3°E; 0.79°S; with distance of 1-2 km from the radar site) and Teluk Bayur
(100.37°E; 0.99°S; with distance of ~25 km from the radar site) during the day event (Figure 2B). Rainfall
data at 00 to 21 UTC on June 16, 2016 were available in Minangkabau and Teluk Bayur.
2
International Symposium on the 15th Anniversary of the Equatorial Atmosphere Radar (EAR)
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 km/pixel. 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.id/wrf).
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 mm/day as the extreme rainfall. This
condition applied in Minangkabau and Teluk Bayur stations on June 16, 2016 with intensity of 384.1
mm/day and 379 mm/day, 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 mm/hr 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.
3
International Symposium on the 15th Anniversary of the Equatorial Atmosphere Radar (EAR)
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.
4
International Symposium on the 15th Anniversary of the Equatorial Atmosphere Radar (EAR)
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 mm/hr, respectively, while in Minangkabau, they are 19.8 and 17.4 mm/hr, 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 mm/hr) and MAE (11 - 22 mm/hr) 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 mm/hr)
Marshall-Palmer
RMSE
Minangkabau
Teluk Bayur
MAE
Minangkabau
Teluk Bayur
R_ave
29.6
23.8
R_ave
22.0
19.3
R_max
28.0
21.6
R_max
21.0
18.3
Rosenfeld
R_ave
26.2
15.9
R_ave
19.8
14.2
R_max
22.4
16.2
R_max
17.4
11.0
5
International Symposium on the 15th Anniversary of the Equatorial Atmosphere Radar (EAR)
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 mm/hr and wind speed of 10-18 m/s. This predicted rainfall rate is still in comparison with radarbased rainfall estimates but underestimates the ground observation data (Figure 5). This may be caused by
low spatial resolution of WRF model (~27km).
4.
CONCLUSIONS
The extreme rainfall event on June 16, 2016 in West Sumatra which caused flooding in Padang and its
surrounding area has been evaluated for its possible global and local causes. The meteorological data
gathered during the event from radar reflectivity data in Padang city, ground observed rainfall at
Minangkabau and Teluk Bayur stations and WRF model simulation have been used to investigate the event
and to perform two reflectivity-rainfall rate relationships (Marshall-Palmer and Rosenfeld) in generating
radar-based rainfall estimates. The results show that radar-based rainfall estimates generally underestimated
the ground observation data with Rosenfeld relationship was better than Marshall-Palmer relationshio. The
rainfall estimations were better in Teluk Bayur than in Minangkabau, possibly due to further distance from
radar site which associated with more scanning data from the radar. This study suggests a follow-up research
study to derive a new Z-R relationship that suits with radar reflectivity data in Padang, but this will need a
longer dataset. In addition, WRF model were also performed to forecast this event.
5.
ACKNOWLEDGEMENTS
The authors would like to thank the Meteorological Division of Center for Research and Development
Center of BMKG for supporting this study. The authors would also like to acknowledge the Minangkabau
and Teluk Bayur meteorological stations in West Sumatra for the provision of weather radar data and ground
rainfall observation.
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International Symposium on the 15th Anniversary of the Equatorial Atmosphere Radar (EAR)
6.
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