Indian Journal of Science and Technology, Vol 9(25), DOI: 10.17485/ijst/2016/v9i25/96627, July 2016 ISSN (Print) : 0974-6846 ISSN (Online) : 0974-5645 Analyses of Cloud Characteristic during Malaysian 2014 Flood Event Atikah Balqis Basri*, Ahmad Fadzil Ismail, Muhamad Haziq Khairolanuar, Nuurul Hudaa Mohd Sobli, Khairayu Badron, and Mohammad Kamrul Hasan Department of Electrical and Computer Engineering, Kulliyyah of Engineering, International Islamic University Malaysia (IIUM), Jln. Gombak, Selangor, Malaysia; atikahbalqis32@gmail.com, af_ismail@iium.edu.my, haziq@iium.edu.my, hudaa@iium.edu.my, khairayu@iium.edu.my, and hasankamrul@ieee.org Abstract Floods refers to the condition of great overflow of water over a dry land. In malaysia, monsoon flood which cause by the heavy rain in monsoon seasons regularly hits the country. A criteria of the cloud based on horizontal and vertical profile of radar reflectivity have been analyzed in this paper to estimate flood event. The values of the thickness and the size of the cloud are estimated from the analysis. In this paper, we analyze the river basin data and radar data in the duration of flooding time, T for the specific area covered by meteorological radar and rain gauge data. The procedure was applied to 14 days precipitation phenomenon observed in Kota Bharu, Kelantan (Malaysia) from 13 December 2014 until 26 December 2014. The objective of this paper is to analyse the distinctiveness of the cloud during flood events. The result shows that during the critical time of flood disaster, the cloud shows largest size of 13070.6 km2 and the thickness appeared to be the largest at almost 10.2 km during the beginning of the rain fall. The analysis helps us to understand the cloud characteristics hence in future flood estimation model can be constructed and modelled. Keywords: CAPPI, Flood Estimation Model, Flood Model, Radar, RHI 1. Introduction Flood hazard has become a major problem across the world. In Malaysia, flood is considered as one of the problems that regularly occur every year and known as one of the worst flood-affected countries in the world1. During the end of the year 2014, Malaysia experienced an overwhelming flood disaster and the most affected area is in Kelantan as shown in Figure 1 the recent flood happened in Kota Bharu, Kelantan. In Malaysia, flood has been categorized into two categories by the Malaysian Drainage and Irrigation Department which are known as flash flood and monsoon floods. From the hydrological perspectives, flash flood took only few hours to last while the monsoon flood can last for about one month. Malaysia is among the countries that solemnly vulnerable to the flood due to high precipitation rates throughout the years especially during the monsoon seasons3. * Author for correspondence Figure 1. Recent flood disaster in Kota Bharu2. In tropical region, there are difficulties in estimating the flood due to lack of information regarding type of tropical precipitation and climate characteristics such as rainfall intensity and the cloud size. In order to analyze this tropical precipitation, direct measurement approach is used by using rain gauge. Together with this direct measurement approach, radar is being used Analyses of Cloud Characteristic during Malaysian 2014 Flood Event as the indirect measurement method to evaluate the precipitation and the cloud size4. According to Tam et al.5 methods of estimating rainfall technique can be divided into two groups: indirect and direct method. Bouilloud et al.6 proposed a method for rainfall estimation based on post eventcreated on a digital terrain model of the region of interest characterized by dry weather clarifications and simulations. now casting method in order to forecast the motion of precipitation areas by using a nonlinear extrapolation to estimates the intensity and size of the raindrop. While the other method known as Storm Cell Identification and Tracking algorithm (SCIT) employs a mass centroid method with seven reflectivity thresholds. These paper highlights useful radar data information to estimate the thickness and cloud size for the development of tropical flood estimation technique9. The details of the data collection from the radar data for cloud characteristics are briefly explained in section 2. Afterwards, the data analyses and results for the cloud characteristics are discussed in section 3. Finally, section 4 draws some conclusion and future works to improve the techniques discussed. 2. Data Collection Radar data from a radar sensor located at PangkalanChepa, Kelantan, Malaysia were bought from Malaysia Meteorological Department for the use in this research. Whilst rain gauge data and river basin data were acquired from the Malaysian Drainage and Irrigation Department (DID). The location of these station is shown in Figure 3. Figure 2. Typical weather radar5. Weather radar as shown in Figure 2 function by measuring the electromagnetic radiation backscattered by cloud raindrops hence radar is possible to estimate the rainfall. Raincloud that backscattered higher electromagnetic radiation contains of larger droplets of rain and as the result, this type of cloud can possibly produce more rain6. Likewise, radar provides wider spatial coverage. Due to the limited number of rainfall measuring stations, and insufficient available data to characterize the highly variable rainfall rate especially applicable for developing countries like Malaysia, the use of weather radar as a measuring mechanism, is needed. Radar can accurately analyze the information in order to properly interpret the measured quantities7. Tsonis et al.8encompassed the single development of cells into a 2 Vol 9 (25) | July 2016 | www.indjst.org Figure 3. Radar and rain gauge location. The radar is positioned at 102.28°E, 6.17°N coordinate system. This radar has the displacement distance of 22.03km from the reference rain gauge station located at Salor, Kota Bharu. This type of radar functions as a single polarization radar operating at S-Band frequency and the radar are able to scan for the distance of 300km range. The radar is operating in a 360° azimuthal volume scan mode with steps in elevation angles from 0.0° to 32° during period of precipitation to happen. 313 samples of radar data from 13rd December until 26th December 2014 were Indian Journal of Science and Technology Atikah Balqis Basri, Ahmad Fadzil Ismail, Muhamad Haziq Khairolanuar, Nuurul Hudaa Mohd Sobli, Khairayu Badron, and Mohammad Kamrul Hasan used in this research. In order to extract the information from this particular data format, IRIS Vaisala Software was used to produce the reflectivity data and ProductX was employed to generate bin values in Microsoft® Excel. The measurement setup is shown in Figure 4 while the Table 1 tabulated the radar specifications. occurred every day for the duration of 14 days. Further observation shows that the longest rain event occurs at 17th December 2014 happened for 17hours and 30 minutes (14:30:00 to 8:00:00). From this evaluations, the time event is used as the point of reference for the analyses of the radar data. Since we need the verification of the flood event took place during this time, river basin data from Kelantan river station JetiKastam, Kota Bharu, is needed to confirm the condition of heavy rain occurred and the subsequently sudden high river flows as these will be the major causes of flooding. The distance from rain gauge station at Salor to Jeti Kastam is about 20.6 km. According to 9 only data from Jeti Kastam river basin are available during the disaster while the other station has been sink by the flood. Figure 5 demonstrate the river basin data from DID showing the rapid increasing of the water level from 0:00:00 (17/12/2014) to 23:00:00 (18/12/2014). The water level increased from 4.2 m to 7.1 m in few hours duration. Figure 4. Measurement setup. Table 1. Radar specifications Coordinate (Lat , Lon) Radar type (Model) Elevation height Tower height Wavelength Frequency Receiver IF frequency Antenna size (diameter) Type of antenna Beam width Specifications (6° 9’ 48.29 N, 102°18’ 51.18 E) EEC WSR 74 13 m 19.52 m 10 cm 2750 MHz Solid state 60 MHz 4.3 m Polarimetric 1.9 degree max. on axes 3. Data Analysis and Results In order to analyze the flood event, rain gauge station located at Salor is used to find the longest rain event and the time duration for the most critical event. Based on the rain gauge data, we are able to identify that the rain Vol 9 (25) | July 2016 | www.indjst.org Figure 5. River basin level. It is also proved that the water level exceeds the danger level, which more than five meter during the 17hours and 30min rain period according to DID standardization. The sudden change of the water level during that particular hour represent the heavy flood characteristic where the volume of water flowing in a river basin system exceeds its total water holding capacity10. Radar parameter can explicate the cloud characteristic involving its thickness and size. By identifying the size of the cloud during the flood event we broaden the analysis of clouds thickness using the spatial and temporal variation of radar vertical profile, focusing during the most intense rainfall at 17 December 2014. Figure 6 shows the illustration of the cloud at 1-km Indian Journal of Science and Technology 3 Analyses of Cloud Characteristic during Malaysian 2014 Flood Event Figure 6. Reflectivity profile as shown in IRIS Vaisala Software on 18th December 2014 at 06:32:25 for 1 km height. height of horizontal characteristic which can be explained using the radar product Constant Altitude Plan Position Indicator (CAPPI) viewed from the Kota Bharu, Kelantan. CAPPI radar data provide spatial variability information composed of the shape and size of the cloud or known as cloud characteristics. The reflectivity values can be further justifying in the attempt to classify the type of cloud11. Once the CAPPI product created, the ProductX was employed to generate bin values in Microsoft® Excel. Figure7 below is the example of the ProductX with all the information generated. Using the information given we are able to interpret radar reflectivity of the horizontal profile.By using Microsoft® Excel, the size of cloud can be estimated by calculating the bin that representing the definite dimension (347.22m x 347.22m) with reflectivity values associated with the cloud. Reflectivity is correlated to intensity of rainfall12. Values below 15 dBZ are typically associated with clouds13. In explaining precipitation characteristic, when the dBZ value reaches 15, light precipitation is present13. The size of the cloud is measured by calculating the bin’s that have values less than 15dBZ. Therefore, the size of the cloud at the particular time during the flood event can be estimated based on each ProductX generated file. Table 2 below shows the calculated cloud size during the duration of longest time of rainfall occur at 4 Vol 9 (25) | July 2016 | www.indjst.org 17/12/2014 from 14:32:21 to 18/12/14 at 7:32:21 for 1km height. Figure 8 illustrate the result. Table 2. Size of cloud from radar data Time 17/12 /2014 cloud size (km2) 14:32:21 9300.5 cloud size (km2) 23:32:24 10007.9 15:02:21 15:32:21 16:32:19 17:02:23 17:32:24 18:02:22 18:32:20 19:02:24 19:32:19 20:02:23 20:32:21 21:02:21 21:32:23 22:02:20 22:32:24 23:02:24 0:02:25 0:32:24 1:02:19 1:32:22 2:02:24 2:32:22 3:02:22 3:32:24 4:02:22 4:32:22 5:02:23 5:32:21 6:02:21 6:32:25 7:02:23 7:32:21 8657.4 18/12 /2014 8932.5 9907.5 10186.0 9504.0 9623.2 13045.4 13070.6 12891.2 11679.5 10783.8 10754.8 12343.5 9201.5 9227.8 9760.9 Time 9433.1 9878.7 9976.4 8609.3 8981.1 9107.0 9830.8 7916.8 7973.5 7968.8 7718.2 6647.7 6300.2 6491.5 7199.1 7400.0 Indian Journal of Science and Technology Atikah Balqis Basri, Ahmad Fadzil Ismail, Muhamad Haziq Khairolanuar, Nuurul Hudaa Mohd Sobli, Khairayu Badron, and Mohammad Kamrul Hasan Figure 8. Size of the cloud for 1 km height. In order to identify the thickness of the cloud, the product of the Range Height Indicator(RHI) is exploited. The thickness of the cloud for the whole duration of longest rain is measured. After the reflectivity value of more than 15dBZ is removed the cloud thickness can be estimated based on the conceptual model of the vertical profile develop to identify the bright band thickness as shown in Figure 9 14. Figure 9. Vertical profile for radar reflectivity at station salor on 17 December at 17:32:24 before and after eliminating dBZ>15. The approach used to extract the vertical profile for this event is by creating across section (XSect Product), a vertical slice through the volume scan of the radar. This product is similar to an RHI except that it is constructed from PPI data collected from elevation angle14. The resulting picture is a two-dimensional vertical slice as shown in Figure 10. Once the XSect product is generated ProductX was Figure 7. ProductX for Cappi. Vol 9 (25) | July 2016 | www.indjst.org Indian Journal of Science and Technology 5 Analyses of Cloud Characteristic during Malaysian 2014 Flood Event employed to generate bin values in Excel. Figure 11 is the example of the ProductX with all the information generate. With the information given we are able to produce the vertical profile for radar reflectivity15. Table 3 displays the thickness of the cloud associated with Salor station during the longest rain event. Figure 12 presented the information in a chart. The thickness of the cloud is proportionally decrease with the diameter (range) of the cloud at any height shown. Figure 10. XSect of station salor. Figure 11. ProductX from excel. 6 Vol 9 (25) | July 2016 | www.indjst.org Indian Journal of Science and Technology Atikah Balqis Basri, Ahmad Fadzil Ismail, Muhamad Haziq Khairolanuar, Nuurul Hudaa Mohd Sobli, Khairayu Badron, and Mohammad Kamrul Hasan Table 3. Thickness and diameter of the cloud 17/12/2014 17/12/2014 17/12/2014 17/12/2014 17/12/2014 17/12/2014 17/12/2014 17/12/2014 17/12/2014 17/12/2014 17/12/2014 17/12/2014 17/12/2014 17/12/2014 17/12/2014 17/12/2014 17/12/2014 17/12/2014 18/12/2014 18/12/2014 18/12/2014 18/12/2014 18/12/2014 18/12/2014 18/12/2014 18/12/2014 18/12/2014 18/12/2014 18/12/2014 18/12/2014 18/12/2014 18/12/2014 18/12/2014 18/12/2014 14:32:21 15:02:21 15:32:21 16:32:19 17:02:32 17:32:24 18:02:22 18:32:22 19:02:24 19:32:19 20:02:23 20:32:21 21:02:21 21:32:23 22:02:20 22:32:24 23:02:24 23:32:24 0:02:25 0:32:24 1:02:19 1:32:22 2:02:24 2:32:22 3:02:22 3:32:24 4:02:22 4:32:22 5:03:23 5:32:21 6:02:21 6:32:25 7:02:23 7:32:21 Thickness (km) 10.1 8.1 9.5 6.5 4.5 6.8 5.6 3.6 3.6 4.2 3.7 4.4 4.3 3.8 5.8 4.2 4.2 2.8 2.2 1.3 1.7 2.0 1.0 0.7 1.3 3.2 3.9 4.6 2.1 3.6 3.2 1.4 1.9 3.7 Diameter for 1km height 136.1 135.9 140.2 120.5 118.1 104.5 102.6 101.6 166.5 182.5 189.8 182.0 167.1 156.6 118.1 114.6 123.8 117.2 114.8 108.0 111.5 99.4 88.9 68.9 56.6 57.2 56.2 62.3 97.8 108.6 103.2 96.1 82.3 69.0 Diameter for 2km height 159.8 168.7 184.1 174.1 146.9 138.9 130.8 139.3 217.8 248.3 254.7 223.8 208.6 217.8 226.8 235.9 216.7 232.5 214.5 203.8 207.6 184.6 193.3 169.6 110.9 99.8 57.9 108.1 151.3 179.6 146.8 109.2 113.1 107.4 Di a meter for 2km hei ght Thi cknes s (km) 12 10 8 6 4 2 0 Thickness (km) 800 700 600 500 400 300 200 100 0 14:32:21 15:02:21 15:32:21 16:32:19 17:02:32 17:32:24 18:02:22 18:32:22 19:02:24 19:32:19 20:02:23 20:32:21 21:02:21 21:32:23 22:02:20 22:32:24 23:02:24 23:32:24 0:02:25 0:32:24 1:02:19 1:32:22 2:02:24 2:32:22 3:02:22 3:32:24 4:02:22 4:32:22 5:03:23 5:32:21 6:02:21 6:32:25 7:02:23 7:32:21 Diameter (km) Di a meter for 1km hei ght Di a meter for 3km hei ght Diameter for 3km height 205.4 208.9 221.1 215.2 171.0 197.5 272.9 279.2 306.6 285.7 281.1 262.8 210.1 261.1 274.8 239.7 248.6 261.1 246.8 247.7 259.7 244.6 225.6 283.2 282.7 276.7 165.8 141.5 24.1 116.0 112.0 161.5 118.8 109.0 12/17/2014 12/18/2014 Time (00:00:00) Figure 12. Graph of thickness and diameter of the cloud. Vol 9 (25) | July 2016 | www.indjst.org Indian Journal of Science and Technology 7 Analyses of Cloud Characteristic during Malaysian 2014 Flood Event 4. Conclusions The analyses observed in this research allow the estimation of the cloud characteristic based on rain gauge, river basin data and radar data during Malaysia worst flood in 2014. Details estimation is using horizontal profile and vertical profile of radar. Based on the observation, the cloud appears to be extreme in size during the flood event. The expectancy of the future work is basically to derive the coefficient for an algorithm or prediction technique that allows us to predict incoming flood so that we will be notified if the similar behaviour of the cloud exist. This will create an alarm system. Another set of radar data from the previous flood disaster in 2013 will be bought from Malaysia Meteorological Department (MMD) for algorithm formulation and validation. 5. Acknowledgement The authors recognize the Research Management Centre of the International Islamic University Malaysia (IIUM) and The Malaysian Ministry of Education for the financial assistance and support to the researcher. The reported research outcome is part of the deliverables for the research funded under IIUM’s Research University Initiatives. This research is being sponsored under Fundamental Research Grant Scheme (FRGS) Research Project by Malaysian Ministry of Education. 6. References 1. Worst floods in Kelantan [Internet]. [Cited 2015 May 02]. Available from: http://www.themalaymailonline.com/malaysia/article/worst­floods­in­kelantan. 2. Hujan, pembalakan dan banjir di Kelantan [Internet]. [Cited 2016 Jan 15]. Available from: http://www.detikdaily.net/ v6/modules.php?name=News&file=article&sid=36014. 3. Tangang FT, Juneng L, Salimun E, Vinayachandran PN, 8 Vol 9 (25) | July 2016 | www.indjst.org Seng YK, Reason CJC, Yasunari T. On the roles of the northeast cold surge, the Borneo vortex, the Madden‐Julian Oscillation, and the Indian Ocean Dipole during the extreme 2006/2007 flood in southern Peninsular Malaysia. Geophysical Research Letters. 2008; 35(14). 4. DID Manual, Volume 4 – Hydrology and water resources, department of irrigation and drainage, Government of Malaysia, Kuala Lumpur; 2009. 5. Tam T, Ibrahim A, Sa’ayou M. Statictical analysis of annual rainfall distribution for water resource in Peninsular Malaysia using Satellite TRMM algorihm. The 33rd Asian Conference on Remote Sensing 7; 2010. 6. Bouilloud L, Delrieu, G, Boudevillain B, Kirstetter PE. Radar rainfall estimation in the context of post-event analysis of flash-flood events. Journal of Hydrology. 2010; 394(1):17–27. 7. Zawadzki I.Factors affecting the precision of radar measurements of rain. Preprints 22nd Radar Meteorology Conference; 1984. 8. Tsonis AA, Austin GL. An evaluation of extrapolation techniques for the short‐term prediction of rain amounts. Atmosphere-Ocean. 1981; 19(1):54–65. 9. Johnson JT, MacKeen PL, Witt A, Mitchell EDW, Stumpf GJ, Eilts MD, Thomas KW. The storm cell identification and tracking algorithm: An enhanced WSR-88D algorithm. Weather and Forecasting. 1998; 13(2):263–76. 10. Kelantan [Internet]. [Cited 2016 Feb 03]. Available from: http://infobanjir2.water.gov.my/rainfall_page. cfm?state=KEL. 11. Weather radar [Internet]. [Cited 2016 Feb 04]. Available from: http://www.esands.com/products/Meteorology/3dradar.htm. 12. Poldirad Quicklooks, Martin Hagen [Internet]. [Cited 2016 Feb 05]. Available from: http://www.pa.op.dlr.de/cleocd/ poldirad/quickloo.htm. 13. Advisiory Circular 00-45G Aviation weather Services Section 4: Radar and Satellite Imagery; 2010.p. 4–6. 14. Romo JA, Maruri M, Fiel G, Fernandez I. Estimation of 0°C isotherm height from vertical profiles of reflectivity in raining earth-space paths.2009 3rd European Conference on Antennas and Propagation. 2009 Mar 23–27. p.3628–32. 15. Chumchean S. Improved estimation of radar rainfall for use in hydrological modeling: Ph.D. Thesis the University of New South Wales, Sydney: Australia; 2004. Indian Journal of Science and Technology