Development of Reflectivity (Z) And Rainfall Intensity (R) Equation for Short Term Rainfall...

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Universiti Malaysia Perlis - Universiti Sains Malaysia

Development of Reflectivity (Z) And Rainfall Intensity (R) Equation for Short Term Rainfall

Forecasting For Northern Region Of Peninsular Malaysia

Mahyun Ab Wahab (PWD-0163)

Supervisor : Prof Hj Ismail AbustanCo – Supervisor : Assoc Prof Dr Rozi Abdullah

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Presentation Structure

• Research Stage• Introduction• Problem Statement• Objectives of research• Data Collection• Scope of Works• Result • Discussion

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Research Stage

Literature Review

Develop New Power Law Equation Between Reflectivity And Rainfall Intensity - DONE!

Short Term Rainfall Forecasting - On Going

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Introduction

• Weather forecast is one of the most important things to consider before making a decision such as planning a holiday, festival or gathering

• Rain gauge and weather radar is a tool to measure rainfall depth.

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Cont..

• But, weather radar cannot measure the rainfall depth directly as oppose to rain gauge.

• Therefore, Power Law Equation between reflectivity (Z) and rainfall intensity (R), known as Z-R relationship (Z=ARb), is commonly used to assess the rainfall depth using radar

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Problem Statement

• Short term forecasting (that is, up to 12 hours ahead) in the tropic has long been recognized as one of the most difficult prediction problems in meteorology (Matthew P. Van Horne et. al, 2003)

• The demand for rainfall forecasts with high spatial and temporal resolution has increased recently (Van Horne, M. P et. al, 2003)

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Cont..

• Application of radar-rainfall forecasting is not new, but in Malaysia application of radar-rainfall is still at the infant stage (Ramli. S et. al, 2011).

• In Malaysia, weather radar plays an important role in meteorological applications especially in aviation safety and flood warnings through monitoring of rainfall intensity (Adam, M. K. M. & Moten, 2012)

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Cont..

• Marshall and Palmer (1948) had developed Power Law Equation Between Reflectivity (Z) And Rainfall Intensity (R) (known as Z-R

relationship) (Z=200R1.6).

• However, it is still being used by many countries such as Thailand, Australia, Libya and Malaysia (Ramli. S et. al, (2011), Adam, M. K. M. & Moten, (2012), Mapiam, P. P. & Sriwongsitanon, N.

2008, Seed, A. et. al (2002), Ali, K. S. & Said, M. H (2009)

• Therefore, this study has a tendency to recheck the classical work

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Cont..

Number of disasters in ASEAN from 2001-2009:1. Flood – 213 (13% of world total)2. Storm – 132 (13%)3. Earthquake – 42 (15%)4. Landslide – 42 (24%)5. Epidemic – 36 (6%)6. Volcanic eruption – 15 (26%)7. Drought – 12 (7%)8. Wildfire – 7 (5%)

Source: http://www.emdat.be

Affect 584 million or nearly 1/10 of world

population

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Objectives of research• To justify the used of Marshall-Palmer Equation in Malaysia

•To develop new Z-R relationship from Northern Region data (Perlis rain gauge & Alor Star Radar

•To forecast short term rainfall using the propose Z-R relationship for Kangar, Perlis.

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Data Collections

Data Collectio

Radar reflectivity data

Rain gaug

e data

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Radar reflectivity data

REFLECTIVITY DATA

Composite Plan Position Indicator, CompPPI

(make from 2 to 4 PPI scan)

Volumetric data (contain 15 PPI

scans at 0.5, 1.2, 1.9, 2.7, 3.5, 4.7, 6.0, 7.5, 9.2, 11.0, 13.0, 16.0, 20.0,

25.0 and 32.0 degree elevation)

• Malaysian Meteorological Department (MMD) responsible to collect reflectivity data.

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Cont..

• Station number for Alor Star Radar is 267 and located at 6.183°N, 100.408°E, with 4 meter altitude and using S-band radar.

• S-band radar has longer wave length compare with C-band or X-band radar, which means attenuation, is not a problem for the S-band radar. S-band radar has a maximum horizontal coverage of 480km [Adam, M. K. M. & Moten, (2012)].

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Cont..

• In this study, the reflectivity data used is CompPPI data which are derived at 10 minutes interval using radar beam with three different angles (0.5°, 0.8°, and 1.1°).

• Reflectivity data collected from Alor Star Radar was chosen for an investigation of the Z-R relationship since the study area is in Perlis and close to the radar location.

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Cont..

Figure 1a: Location of seven radars in Peninsular Malaysia; and Figure 1b: Alor Star Radar scans ranges [Adam, M. K. M. & Moten, (2012)].

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Radar operating principle

Working principle of Doppler weather radar (Source: www.hko.gov.hk, 2013)

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Cont..

• Reflectivity data were captured every 2km by 10 range bin and there is no data for the first 4 km. Data collected every 10 minutes

• Reflectivity data is in ASCII format and it contains the reflectivity values measured in decibels (dBZ).

• The 16 level encoding systems employ a deviation system to compress the data

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Read the radar data

Step 1

• Get the radar data ASCII format

Step 2

• Using the reference table to get the video level

Step 3

• Using the rain table to convert the video level to signal strength and rain rate

** (Currently MMD using Marshall-Palmer Equation)

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Example

Example : Level video = 2

Signal Strength = 23 dBZ

dBZ = 10 log10 (Z)

23 = 10 log10 (Z)Z = 199.53 mm6/mm3

Z = 200R1.6

R = 1.6 √(199.53/200)R = 1 mm/hour

A4v2XJ = 0 0 0 0 0 1 2 2 2 2 5 9

A 4 v 2 X J

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Rain gauge data

• Rain gauge data is provided by Department of Irrigation and Drainage Malaysia (DID).

• Rainfall data were derived according to time interval of radar which is in 10 minutes interval.

• The available data collected at 14 rainfall stations were used for the calibration of the Z-R relationship.

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Cont..

Fig. 2: Rainfall station in Perlis

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Scope of workS

fall F

fall I

ysia Phase 1 : How to develop

new Z-R relationship for Northern Region of

Peninsular Malaysia ?

Phase 2 : How to forecast rainfall using new Z-R

relationship for Northern Region of Peninsular

Malaysia ?

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5W1H in developint the new Z-R relationship

• What ? - To determine the best value of parameters A and b, calibration and validation process were done.

• How ? - According to Mapiam, P. P. & Sriwongsitanon, N. 2008, Z-R relationship is normally derived using raindrop size distribution (DSD) or optimization (regression techniques) method.

• Since the Disdrometer (a device that measures the size distribution of raindrops) in Malaysia was not available, the optimization method is then applied

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Methodology : Phase 1*(Mapiam, p. P. & Sriwongsitanon, n. 2008) (Seed, a., et. al. 2002)

• Step 1 - Parameter A and b were fixed at 200 and 1.6 respectively. Z=200R1.6 were used to convert reflectivity

• Step 2 - Estimated radar rain rate was then accumulated into daily radar rainfall in millimetre (mm). Same goes to gauge rainfall.

• Mean gauge rainfall and mean radar rainfall of each day were estimated using these equations

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Cont..

Where,– Gj is the mean gauge rainfall on day j;

– gij is gauge rainfall at station i and on day j; and

– N is the total rain gauge numbers. Rj is the mean radar rainfall on day j;

rij is radar rainfall accumulation computed using the Z=200R1.6, for day j

N is the total rain gauges.

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Cont..

• From the literature, initial parameter A can be determined from the following equation.

• Where ;– A1 is the new multiplicative term A in Z-R relationship,

– A0 is the initial parameter A,

– m is the gradient of the regression line between the predicted and the observed rainfall obtained from the standard Z-R relationship (Z=200R1.6), and

– b is the exponent in the Z-R relationship.

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Statistical measures• Four statistical measures recommended by Mapiam,

P. P. & Sriwongsitanon, n. (2008) , Seed, A., et. al. (2002) ] were using to compared estimated mean radar rainfall and mean gauge rainfall as stated below

Mean error (ME),

Mean absolute error (MAE),

Root mean square error (RMSE),

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RESULTS :Scatter plot of mean radar rainfall based on the relationship Z=200R1.6 and mean gauge rainfall

0 5 10 15 20 25 30 35 40 45 500

50f(x) = NaN x + NaNR² = 0

Radar Rainfall (mm)

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Time series plot of mean gauge rainfall and radar rainfall using Marshall-Palmer Equation

Z=200R1.6

10.7.2006

11.7.2006

30.7.2006

7.9.2006

12.9.2006

16.9.2006

18.9.2006

24.9.2006

25.9.2006

29.9.2006

1.10.2006

2.10.2006

21.10.2006

17.8.2007

22.3.2007

2.5.2007

22.7.2007

17.10.20070

Marshall-Palmer Equation Z=200R1.6 Mean Gauge Rainfall

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Cont..

• From the equation y = 2.4765x + 4.8745• The m value is equal to 2.4765 which is

the value of the slope gained from previous Figure

• From the calculation done, the initial parameter A is equal to 46.9.≈ 50

Ao = 200m = 2.4765

b = 1.6

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Scatter plot of mean radar rainfall based on the relationship Z=40R1.6 and mean gauge rainfall for

calibrated event

0 5 10 15 20 25 30 35 40 45 500

Mean Radar Rainfall (mm)

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Time series plot of mean gauge rainfall and radar rainfall using the relationship Z=40R1.6

10.7.2006

11.7.2006

30.7.2006

7.9.2006

12.9.2006

16.9.2006

18.9.2006

24.9.2006

25.9.2006

29.9.2006

1.10.2006

2.10.2006

21.10.2006

17.8.2007

22.3.2007

2.5.2007

22.7.2007

17.10.20070

Mean Radar Rainfall Z=40R1.6Mean Gauge Rainfall (G')

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Comparisons Of The Statistical Measures Gained

From The Different Z-R Relationships

Statistical Measures Z = 200R1.6 Z = 40R1.6 Z = 50R1.6 Z = 30R1.6

ME (mm) 15.48 2.69 5.48 -1.515

MAE (mm) 15.48 3.75 5.73 5.425

RMSE (mm) 17.43 7.06 8.51 7.24

BIAS (mm) 0.54 0.89 0.77 1.063

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VALIDATION : Time series plot of mean gauge rainfall and radar rainfall using the relationship Z =

40R1.6 comparing with Marshall-Palmer equation (Z=200R1.6)

10.9.2006 26.9.2006 19.10.2006 29.4.2007 2.5.2007 6.7.2007 30.9.2007 18.11.200719.11.200720.11.20070

Z=40R1.6 Mean Gauge Rainfall (G') Marshall Palmer Equation Z=200R1.6

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Statistical Result For Validation Process Using

Z = 40R1.6

Statistical Measures Z = 40R1.6

ME (mm) 2.65MAE (mm) 3.29RMSE (mm) 3.81

BIAS (mm) 0.85

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Scatter plot of mean radar rainfall and mean gauge rainfall based in the relationship Z=40R1.6 and

Z=200R1.6

0 5 10 15 20 25 30 35 400

R² = 0.94041018614725

R² = 0 Z = 40R1.6 Linear (Z = 40R1.6) Line 1:1Z = 200 R1.6 Linear (Z = 200 R1.6)

Radar Rainfall (mm)

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• Marshall-Palmer Equation could not be used in Malaysia especially in high intensity rainfall

• So, the development of new Z-R relationship for better rainfall estimation is essential

Conclusion

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Forecast rainfall using new Z-R relationship

• What ? – Short term rainfall forecasting using radar data

• How ? – Linear extrapolation of the centroids of feature of rainfall cells or cross correlation techniques applied to radar rainfall fields

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References1. ADAM, M. K. M. & MOTEN, S. Rainfall Estimation from Radar Data.

Research Publication No. 6/2012, Malaysian Meteorological Department (MMD), Ministry of Science, Technology and Innovation (MOSTI)

2. ALI, K. S. & SAID, M. H. Determination of Radar ZR Relationship For Libya Tripoli City. Proceedings of the World Congress on Engineering, 2009.

3. MAPIAM, P. P. & SRIWONGSITANON, N. 2008. Climatological ZR relationship for radar rainfall estimation in the upper Ping river basin. ScienceAsia J, 34, 215-222.

4. MARSHALL, J. S. & PALMER, W. M. K. 1948. The distribution of raindrops with size. Journal of Meteorology, 5, 165-166.

5. MATTHEW P. VAN HORNE, MIT, CAMBRIDGE, MA; AND E. R. VIVONI, D. ENTEKHABI, R. N. HOFFMAN, AND C. GRASSOTTI Short-term radar nowcasting for hydrologic applications over the Arkansas-Red River basin 17TH Conference on Hydrology, 2003.

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6. RAMLI, S., BAKAR, S. H. A. & TAHIR, W. Radar hydrology: New Z/R relationships for Klang River Basin, Malaysia based on rainfall classification. Humanities, Science and Engineerin (CHUSER), 2011 IEEE Colloquium on IEEE, 537-541.

7. SEED, A., SIRIWARDENA, L., SUN, X., JORDAN, P. & ELLIOTT, J. 2002. On the calibration of Australian weather radars, CRC for Catchment Hydrology

8. SUZANA, R. & WARDAH, T. Radar Hydrology: New Z/R Relationships for Klang River Basin, Malaysia. International Conference on Environment Science and Engineering IPCBEE vol.8 (2011) © (2011) IACSIT Press, Singapore

9. VAN HORNE, M. P., VIVONI, E., ENTEKHABI, D., HOFFMAN, R. & GRASSOTTI, C. Quantitative flood forecasts based on short-term radar nowcasting. 17TH Conference on Hydrology, 2003.

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Example of Radar Data supply by MMD

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Reference tables

Code A B C D E F G H I J K L M N O P

Level 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

DeviationFirst Range Bin

-3 -2 -1 0 1 2 3

Second Range Bin

-3 ! [ a b c ] @-2 / d e f g h \-1 i j k < l m n0 o p - . + q r1 s t u > v w x2 ( y S T U V )3 $ { W X Y } &

Table 3.2: The sixteen characters that define absolute level

Table 3.3: Forty nine characters that define the deviation encoding

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Rain tableVideo level Sig. Strength (dBZ)

(dBZ = 10 log (Z))

Assumed Rain Rate (mm/hr)

(Z=200R1.6)15 64.0 364.614 61.0 236.813 58.0 153.812 55.0 99.911 52.0 64.810 49.0 42.19 46.0 27.38 43.0 17.87 40.0 11.56 37.0 7.55 34.0 4.94 31.0 3.23 28.0 2.12 23.0 1.01 11.8 0.2

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