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An Operational Weather Radar-Based Calibration of Z-R Relationship over Central Region of Thailand

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Weather radar is an important tool that integrates meteorology and hydrology. It plays an important part in the hydrology and it is also associated with the quantitative use of radar measurements that find the relationship between radar reflectivity (Z) and rain intensity (R) such as Z-R relationships. To obtain better rainfall estimation for daily rainmaking purposes over the central region of Thailand, the calibration of the Z-R relationship were estimated using the series of Z-R pair data which derived from weather radar and rain gauge network of DRRAA and TMD. The a and b parameters of Z-R relationship will be calibrated by minimizing the Root Mean Square Error (RMSE) and linear regression method between radar and corresponding rain gauge rainfall with the constant of b parameter of 1.5. The 98 convective rainfall events during rainy season in May to September 2013 over the middle part of Thailand are chosen then these selected rainfall events were divided into two groups. The first group of 35 events with rain intensity from DRRAA rain gauges was used to Z-R calibration and derived parameters. The second group of 63 events with rainfalls from MET were considered as validated data. The Z-R relationships were derived using RMSE as Z = 144R^1.5 and a regression method as Z = 206R^1.5. These rainfall events were used to test an accuracy of the proposed radar rainfall estimation based on different Z-R relationship including Z-R relationships obtained from the calibration. It was evident that using of proposed Z-R relationship in rainy season over central Thailand; Z=144R^1.5, gave the smallest RMSE compared to using Z=206R^1.5, Z = 200R^1.6 and Z = 300R^1.4.
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92
International Journal of Engineering Issues
Vol. 2016, no. 2, pp. 92-100
ISSN: 2458-651X
Copyright © Infinity Sciences
An Operational Weather Radar-Based
Calibration of Z-R Relationship over Central
Region of Thailand
Pakdee Chantraket
1
, Chanti Detyothin
1
, Sukit Pankaew
1
, Sukrit Kirtsaeng
2
1
Department of Royal Rain-making and Agricultural Aviation (DRRAA), Bangkok 10900,Thailand
2
Meteorological Development Bureau, Thai Meteorological Department, Bangkok 10260, Thailand
E-mail:pakdee2@hotmail.com
Abstract- Weather radar is an important tool that integrates meteorology and hydrology. It plays an important part
in the hydrology and it is also associated with the quantitative use of radar measurements that find the relationship
between radar reflectivity (Z) and rain intensity (R) such as Z-R relationships. To obtain better rainfall estimation
for daily rainmaking purposes over the central region of Thailand, the calibration of the Z-R relationship were
estimated using the series of Z-R pair data which derived from weather radar and rain gauge network of DRRAA
and TMD. The a and b parameters of Z-R relationship will be calibrated by minimizing the Root Mean Square Error
(RMSE) and linear regression method between radar and corresponding rain gauge rainfall with the constant of b
parameter of 1.5.The 98 convective rainfall events during rainy season in May to September 2013 over the middle
part of Thailand are chosen then these selected rainfall events were divided into two groups. The first group of 35
events with rain intensity from DRRAA rain gauges was used to Z-R calibration and derived parameters. The
second group of 63 events with rainfalls from MET were considered as validated data. The Z-R relationships were
derived using RMSE as Z=144R
1.5
and a regression method as Z=206R
1.5
. These rainfall events were used to test an
accuracy of the proposed radar rainfall estimation based on different Z-R relationship including Z-R relationships
obtained from the calibration. It was evident that using of proposed Z-R relationship in rainy season over central
Thailand; Z=144R
1.5
, gave the smallest RMSE compared to using Z=206R
1.5
, Z=200R
1.6
and Z=300R
1.4
.
Keywords: Radar rainfall estimation; Z-R relationship; Weather radar; Central of Thailand; Rainmaking.
I. INTRODUCTION
Weather radar has been utilized as substitute tools for more than 50 years to improve rainfall measurement in
hydrology. The integration of meteorology and hydrology is one of the solutions that will bring a more effective
management of water resources[1,2]. Weather radar is also tool that combines meteorology and hydrology, the
meteorological information measured by radar are used for hydrological analysis as referred to Uijlenhoet, Joss &
Waldvogel, and Peng et alexplained that the advantage of using radar for precipitation measurement is the coverage
of a large area in real-time, and radars also experience difficulty in achieving an accurate estimation for hydrological
applications [3-5]. In addition, the advantages of weather radar are the ability to detect cloud and precipitation
structures [6,7]and also the process of the rainfall system itself by providing real-time regional information and with
the existence of long radar data sets, these data could be also applied for climatologically applications as well.
P. Chantraket et al. / International Journal of Engineering Issues
93
Applications of weather radar in Thailand are still limited mostly for meteorology and monitoring the weather
routines. Not much work has been done in the field of hydrological. Finding rainfall intensity is one of the essential
applications for weather radar in the process of hydrology. Correspondingly, DRRAA has also concerned to use the
weather radar to evaluate rainfall from rainmaking activities in Thailand. This is done to estimate the amount of
water falling into the destined watershed and rainmaking’s benefit area as well.
For the purpose in radar rainfall estimation, the relationship between radar reflectivity and rainfall rate which is
developed for rainfall measurement by using the Z-R relationship. The Z-R relationship is highly dependent on the
precipitation types such as convective, stratiform or mixed types [8]. Event type is one of the major influences of Z-
R relationship that must be studied accordingly. Moreover, the location of areas and seasonal also plays an important
factor in applying Z-R relationships to radar rainfall measurements [9,10]. Most weather radars in Thailand are not
calibrated for the Z-R relationship. As the results, the developed Z-R relationships are needed in Thailand providing
a more systematic and comprehensive approach to achieve in water management also additionally to implement in
rainmaking evaluation purposes.
Owning to the middle part of Thailand is an important area of rice production and agriculture but it is almost in
transition from water richness to water scarcity because of the increasing demands on this limited resource as well as
there is no universal Z-R relationship that can be applied to all cases of rainfall events. Therefore, the focus of this
paper will be on the calibration of Z-R relationships during rainy season which were tuned to fit the rain gauge
measurements that turn into inaccuracies over the central region of Thailand.
II. METHODOLOGY
2.1 Framework of Methodology
Framework for the methodology of all processes and brief detail for this study including the calibration of Z-R
relationship and its application in quantitative precipitation estimation of daily rainmaking over central region in
Thailand as presented in the Figure 1.
Figure 1. Framework of study methodology
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94
2.2 Data collection
2.2.1 Central Region of Thailand
The central region of Thailand traditionally the center of rice production and farming covers an area of
important basin such as Chao Phraya River basin, Pasak and Tha Chin, approximately 67,398 km
2
[11]. This region
is mountainous with agriculturally productive valleys found in the upper region. The lower region contains alluvial
plains that are highly productive for agriculture. The river system drains from north to south. Monsoon weather
dominates, with a rainy season lasting from May to mid-October and supplementary rain from occasional westward
storm depressions originating in the Pacific. Temperatures range from 15°C in December to 40°C in April except in
high altitude locations. The whole basin can be classified as a tropical rainforest with high biodiversity. The lower
part has extensive irrigation networks and hence intensive rice paddy cultivation. The study about estimation and
prediction of rainfall are absolutely necessary for an effective water management in agriculture area [12-17].The
geography of central region is shown in Figure 2.
2.2.2 Weather radar data
The volume scan data are collected from weather radar of Takhli station, Nakhonsawan Province, during wet
season in May to September 2013. This radar station is operated by Department of Royal Rain-making and
Agricultural Aviation (DRRAA). It is located at 15.2493°N and 100.34°E, central part of Thailand as illustrated in
Figure 2. The radar characteristics are given in Table 1. The 6 minute interval of volume scan data with 15 elevation
angles is used. These volume data are one of the radar collections that record the reflectivity and their properties of
the precipitation in the sky. The radar reflectivity measurements received from the radar are in a raw volume format
of Gematronik weather radar systems. The Meteorological Data Volume (MDV) format for gridded data are
converted by using TITAN (Thunderstorm Identification Tracking Analysis and Nowcasting) [18] applications and
these data are then converted to Cartesian coordinates at a specified height 1.5 km above mean sea level (MSL) for
the next step. These raw reflectivity values are assessed to see if they are within realistic thresholds. In these data,
there are in Cartesian coordination at girded resolution at 750 m which vary from 0.75 to 240 km are given. The
maximum range for radar measurement is 240 km.
The quality control of reflectivity was performed as ground clutter and noisy signal were removed from the
measured reflectivity data. The reflectivity overlapped to the other adjacent radars should be checked and it reveals
almost similar in both reflectivity shape and dBZ. Hence, there is only reliable radar data were used in the analysis.
Reflectivity data that were greater than 53 dBZ were limited to mitigate contamination from hail [19]. Additionally,
the reflectivities that are less than 15 dBZ were excluded from the analysis in order to avoid the effect of noise in the
measured radar reflectivity.
Table 1 Radar Characteristics of DRRAA at Takhli station, Nakhonsawan Province
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95
2.2.3 Rainfall data
Hourly rainfall data from 49 automatic rain gauge stations located mostly in Chaopraya river basin is also
collected from DRRAA. The location of study areas and these automatic rain gauges is illustrated in Figure 2. The
quality control of rain gauge rainfall was performed by comparing rainfall data of the considered rain gauge with
rainfall data of the nearby stations. Rainfall data of the considered rain gauge should be consistent with rainfall data
of the adjacent stations. All of the automatic rain gauges are of tipping bucket type with 0.245 mm accuracy. The
hourly rainfall intensity of each rain gauge station from the selected rainfall event will be used to calibration using
average hourly reflectivity values in the gridded radar data. Additionally, the rain gauge data from Thai
Meteorological Department (TMD) are also use for verify the Z-R calibration including 178 stations around 160 km
of Takhli radar station.
Figure 2. The location of study areas within the radar radius 240 km (blue circle lines) from 15.2493°N and
100.34°E and the network of automatic rain gauges of DRRAA using for calibration (grey circles) and MET using
for verification (white triangles).
2.2.4 Radar rainfall estimation
For the purpose of conventional practice in radar rainfall estimation, the relationship between radar reflectivity
(Z) and rainfall rate (R) which is developed for rainfall measurement by using basic power empirical equation as
explained in the equation (1) is provided to convert the reflectivity into the rainfall intensity.
 = 
(1)
Where ‘a’ and ‘b’ are the relationship parameters, Z is the reflectivity data in mm
6
.m
-3
, and R is the rainfall rate
in mm.h
-1
.
P. Chantraket et al. / International Journal of Engineering Issues
96
Since most of the rainfall stations of DRRAA in the Chaopraya river basin are non-automatic, the archived
rainfall data were used to calibrate the Z-R relationship. The method for a pair of Z-R data is set as an average hourly
reflectivity and rainfall intensity couple. When both dataset are in the same temporal interval (hourly), the rainfall
intensity values corresponding to the values of average hourly reflectivity at the synchronized time is then used for
the Z-R relationship analysis.
Rainfall events during rainy season on May 2013 to September 2013 will be selected and events are considered
from observed convective rainfall. Total 98 rainfall events are chosen then these selected rainfall events were divided
into two groups including Z-R calibrated and validated data. These rainfall events were used to obtain the
appropriated Z-R relation for central Thailand as well as to test an accuracy of the proposed radar rainfall estimation
based on different Z-R relationship as Z=200R
1.6
[20] and the existing Z-R relationship for convective rainfall that
has been used for the Takhli radar Z=300R
1.4
[21].
III. RESULT ABD DISCUSSION
Relationships between radar reflectivity (mm6.m-3) and rainfall intensity (mm.h-1) were estimated using the
series of Z-R pair data. The ‘a’ and ‘b’ parameters of Z-R relationship will be calibrated by minimizing the Root
Mean Square Error (RMSE) [22] and linear regression method between radar and corresponding rain gauge rainfall.
The constant of ‘b’ parameter of 1.5 was used as suggestion by Doelling et al, Steiner & Smith and Hagen & Yuter
[23-25] according to the results from the study of Seed et al [26] which showed that variation of ‘b’ parameter did
not affect the RMSE between radar and rain gauge rainfall much. The ‘a’ parameter was derived by minimizing
RMSE between radar and corresponding rain gauge rainfall. The calibration was performed in an hourly timestep.
The criteria of accumulated rainfall R 0.254 mm were used to analysis. Radar reflectivities were selected at
the lowest specific height at 1.5 km MSL and ranged reflectivity as 15 dBZ to 53 dBZ. All values of Z-R pairs of
selected rainfall events were considered to establishing the radar general equation.
Total 98 rainfall events are chosen then these selected rainfall events were divided into two groups. The first
group of 35 events with 538 Z-R pairs from DRRAA rain gauges was used to Z-R calibration and derived
parameters. The second group of 63 events with 1,423 Z-R pairs from TMD were considered as validated data. The
Z-R relationships were derived using RMSE as Z=144R
1.5
and a regression method as Z=206R
1.5
. The results of the
Z-R analysis were presented as Figure 3.
(a)
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97
(b)
Figure 3. Scatter plot between measured and estimated rainfall rate for Z=144R
1.5
and Z=206R
1.5
, both in linear (left
side) with the 1:1 line and logarithmic (right side) scale for all graphs the threshold Z min=15dBZ and Z max=53
dBZ is adopted (a) calibration events and (b) verification events.
These rainfall events were used to test an accuracy of the proposed radar rainfall estimation based on different
Z-R relationship including Z-R relationships obtained from the calibration, Z=200R
1.6
and Z=300R
1.4
were compared
with the corresponding rain gauge data in order to select the most suitable Z-R relationship in rainy season over
central Thailand. It was evident that using of proposed Z-R relationship; Z=144R
1.5
, gave the smallest RMSE
compared to using Z=206R
1.5
, Z=200R
1.6
and Z=300R
1.4
for all cases.
Table 2 RMSEs and Bias obtained by using difference Z-R relationships in rainy season
IV. CONCLUSION
The Z-R relationships have been derived accuracy form statistical measurements to rainfall events during wet
season in central region of Thailand by varies ‘a’ parameter and fixed ‘b’ parameter to 1.5. The 1,961 of Z-R pairs
from 98 events of convective rainfall during rainy season in 2013 are derived then these selected rainfall events were
divided into two groups. The first group of 35 events with precipitation intensity from DRRAA rain gauge network
was used to Z-R calibration and derived parameters. The second group of 63 events from TMD were considered as
validated data. The relationship between the point rainfall from rain gauge network and the radar rainfall at specific
height of 1.5 km in central regions were calibrated by minimizing RMSE. The appropriated Z-R relationship for this
region is Z=144R
1.5
, so far as the central basin is concerned. The result show significant improvement with the
developed Z-R relationship in producing better rainfall estimation and the errors are also minimized. These results
will provide to assess for evaluating of rainfall estimation in the rainmaking’s benefit area of rainmaking activities. It
also made to provide improvement of hydro-meteorological relations that are pertinent to hydrological applications
in the central region as well as further research conducted to the other parts of Thailand.
ACKNOWLEDGMENTS
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98
The authors gratefully acknowledge the Department of Royal Rainmaking and Agricultural Aviation (DRRAA),
Bangkok Thailand for funding support through DRRAA research project 2013: radar rainfall estimation in central
Thailand. We also appreciate the Royal-Rainmaking Atmospheric Observation Group for much helpfulness and
providing the radar data sets and weather observation data used in this study.The use of the rainfall observation data
from the Bureau of Meteorological Development, TMD are thankfully acknowledged.
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