WORLD METEOROLOGICAL ORGANIZATION
CBS-CIMO Remote Sensing/Doc. 6 (2)
_______________________________
(17.XI.2009)
Joint Meeting of
______
CBS Expert Team on Surface-based
Remotely-Sensed Observations
(First Session)
ITEM:
6
and
CIMO Expert Team on Remote Sensing
Upper-air Technology and Techniques
Original:
ENGLISH ONLY
(Second Session)
Geneva, Switzerland, 23-27 November 2009
DEVELOP GUIDANCE AND METHODOLOGY FOR
SURFACE BASED REMOTE SENSING MONITORING
Report from China
(Submitted by China)
Summary and Purpose of Document
The document presents a brief overview of the methods used for
surface-based remote sensor monitoring in China.
ACTION PROPOSED
The meeting will be invited to develop the methodology and standard guidance
material that can be used for the monitoring of surface-based remote sensor systems.
_________________
CBS-CIMO Remote Sensing/Doc. 6 (2), p. 2
REPORT ON METHODS USED TO DOPPLER RADAR NETWORK FOR CHINA
1．Operational radar system
Since 1998, The China Meteorological Administration (CMA) has being constructed the
158 China New Weather Radar—CINRAD (Doppler radar). The deployment of the network has
provided meteorologists critical information toward the issuance of warnings for heavy
rainfall, typhoon and severe storms. The CINRAD include S band and C band Doppler radars.
Most of S band radar located in southeastern China and designed to observe and warn the
heavy rainfall and typhoon. C band radar located in northern China for regional heavy rainfall
and severe storms. The S band radar system measures reflectivity within the 460km coverage,
and radial velocity and spectrum with 230km coverage. The C band radars have coverage of
150km.
CMA has also plan to build additional 58 Doppler radars.
2．Operational Model
The radar system perform a volume scan with 14 or 9 elevation angles once every 5-6
min （Fig.1 and 2）, which are similar with WSR-88D. For S band radar (SA) , the radar gate
width for reflectivity and velocity observation are 1km and 250km, respectively, and beam
width is 1.0°, for C band radar, the radar gate width is 0.15 km and beam width is 0.75°.
VCP11
Number of scans 14
Beam width 0.95
VCP21
Number of scans 9 Beam with 0.95
20
20
Height above radar(km)
Height above radar(km)
16
12
8
4
0
16
12
8
4
0
0
50
100
150
200
250
300
Horizontal range(km)
0
50
100
150
200
250
300
Horizontal range(km)
（a）
（b）
Fig.1 The volume scan used in CINRAD
The radar raw data and some products, such as composite reflectivity (CR), Echo top, are
transferred to the National Meteorological Information Centre of CMA in Beijing. In some
local meteorological centers region, for example, Beijing and Shanghai, can get the raw radar
data in time for important weather service.
The national radar product mosaic and regional raw data mosaic are used in CMA.
CBS-CIMO Remote Sensing/Doc. 6 (2), p. 3
3．The method for CHRAD
（1） Radar data quality control
The fuzzy logical based algorithm is used to detect the anomalous propagation ground
clutter (AP). The reflectivity, radial velocity and spectrum width in polar coordinate are input
into the “feature generator” for calculation of the features. The features used in the software
includes: texture of the reflectivity (TdBZ), the vertical difference of the reflectivity (GDBZ)
between two elevation angles, the median radial velocity (MVE) and spectrum width (MSW),
the standard deviation of radial velocity (SDEV), Percent of all of possible differences, that
exceed the minimum difference threshold (SPIN) and mean sign of reflectivity change along
range (SIGN). All of memberships are given a equal weight when calculating the interest field
of AP clutter, and 0.5 is the threshold to detect the AP clutter.
This AP detection algorithm was used in serve for Beijing 2008 Olyphic game and
operational system - Severe Weather Warning and Analysis system (SWAN).
The other noise from radio interpolation was also detected in radar data QC.
（2） Regional Reflectivity mosaic
The transformation of raw radar data from a spherical coordinate to a Cartesian grid
provides a more direct approach of combining multiple radars onto a common grid. The 3D
mosaic grid can benefit forecasters, meteorologists, and researchers with a wide variety of
products and displays, including flexible horizontal or vertical cross sections in addition to
regional rainfall maps. High-resolution reflectivity analyses can also serve as an important
source in data assimilations for convective-scale numerical weather modeling over large
domains and for merging conventional datasets with the radar data. Regional Reflectivity 3-D
mosaic was used in Beijing, Shanghai and other local meteorological centers. The 3-D mosaic
includes QC of radar data, interpolation of reflectivity, mosaic and production generation.
Four interpolation approaches were investigated to remap raw radar reflectivity fields onto
a 3D Cartesian grid with high resolution:
1) Nearest-neighbor mapping
The nearest neighbor mapping (NNM) uses the value of the closest radar bin to grid cell,
where distance is evaluated using the location of the centers of the radar bins.
CBS-CIMO Remote Sensing/Doc. 6 (2), p. 4
2) Nearest neighbor on range-azimuth planes combined with a linear interpolation in vertical
direction，NVI）
3）Linear interpolation in vertical direction plus a horizontal interpolation，VHI）
Fig. 2 shows the VHI approach. For the grid cell ( r , a, e) , the four reflectivity
observations are f a (r , a, e2 ) 、 f a (r , a, e1 ) 、 f a (r1 , a, e2 ) 、 f a (r2 , a, e1 ) ,the analysis formula
for the vertical and horizontal interpolation (VHI) scheme is
f a ( r , a , e) 
we1 f a (r , a, e1 )  we 2 f a (r , a, e2 )  wr1 f a (r1 , a, e2 )  wr 2 f a (r2 , a, e1 )
we1  we 2  wr1  wr 2
（3.1）
Here wr1 、 wr 2 are nterpolation weights given to the he reflectivity observations
wr1  (r2  r ) /( r2  r1 )
（3.2）
wr 2  (r  r1 ) /( r2  r1 )
（3.3）
(r,a,e 2 )
(r,a,e)
(r1 ,a,e 2 )
(r2 ,a,e 1 )
(r,a,e 1 )
Fig. 2 The schedule for NVI
4) Dual linear interpolation
As shown in Fig. 3 f1o (r1 , a1 , e1 ) 、 f 2o (r2 , a1 , e1 ) 、 f 3o (r1 , a 2 , e1 ) 、 f 4o (r2 , a2 , e1 ) 、
f 5o (r1 , a1 , e2 ) 、 f 6o (r2 , a1 , e2 ) 、 f 7o (r1 , a 2 , e2 ) 、 f 8o (r2 , a 2 , e2 )
( r , a, e) are 8 reflectivity
observations near the grid cell f a (r , a, e) , the analysis values is :
f a ( r, a, e)  we1 [( wr1 f1o  wr 2 f 2o ) wa1  ( wr1 f 3o  wr 2 f 4o ) wa 2 ] 
we 2 [( w f  wr 2 f ) wa1  ( w f  w f ) wa 2 ]
o
r1 5
o
6
o
r1 7
(3.4)
o
r2 8
wa1  (a2  a) /( a2  a1 )
（3.5）
wa 2  (a  a1 ) /( a2  a1 )
（3.6）
o
f6
o
f5
o
f8
o
f7
o
o
f1
Fig. 3
f2
o
f3
o
f4
Dual linear interpolation
CBS-CIMO Remote Sensing/Doc. 6 (2), p. 5
In addition to remapping single radar reflectivity fields, one must consider a multiple radar observation
where more than one radar collects reflectivity data for the same point in space. The mosaic approach is
to calculate a final reflectivity value for grid cells oversampled by radar bins. Three
approaches of combining multiple-radar reflectivity fields were investigated. They are
maximum reflectivity, Nearest-neighbor reflectivity and distance weighted means.
1) Nearest-neighbor mosaic approach
In this approach, the analysis value from the closest radar is assigned to the grid cell. The
nearest-neighbor method does not impose any smoothing when creating a mosaic from
multiple radars. However, discontinuities may appear at the equidistant lines between radars
in the mosaicked field.
2） Maximum reflectivity
This mosaicking method simply uses the maximum reflectivity value among the multiple
observations that cover the same grid cell. This method does not involve smoothing and it
retains the highest reflectivity intensities in the data fields.
3) Distance weighted means
The mosaic method considered is a weighted mean, whereby the weight is based on the
distance between an individual grid cell and the radar location. Two weighting functions were
tested; both monotonically decrease with range, that is exponential weighting function and
Cressman weight function are used in this study.
This distance weighted means take
advantage of multiple observations while handling those observations that do not agree with
each other. The shape of an exponential weighting function is easily adjustable to achieve a
rapid decrease with range while retaining a positive weight value. Thus, an exponentially
decaying function is especially useful for radar analysis and is employed in the algorithm. The
two functions are expressed as following:
 r2 
w  e x p  2 
 R 
（3.7）
Here r is the distance of the observation from its respective radar and R is an adaptable
length scale (i.e., 100 km).

 R2  r 2
w
0

 R
2
 r2

rR
rR
（3.8）
CBS-CIMO Remote Sensing/Doc. 6 (2), p. 6
Where R is effective radius (i.e. 300 km), and r is the distance of the observation from its
respective radar.
The shape of an exponential weighting function is easily adjustable to achieve a rapid
decrease with range while retaining a positive weight value. Thus, an exponentially decaying
function is especially useful for radar analysis and is employed in the algorithm.
(3) Qualitative Precipitation estimation (QPE)
The QPE algorithm in this study is based on CINRAD and raingauge. It composed of five
main scientific processing components and one external support functions. The five scientific
subalgorithms are identified as follows:
1) Rain gauge data and CINRAD data acquisition and QC
The CAPPI of 3D mosaic reflectivity or hybrid scan data are chosen to calculate the
precipitation. This radar data QC first detect AP echo and other non meteorological echo, and
then removes reflectivity data that are abnormally large in magnitude but small in area, if a
grid has a reflectivity that exceeds this threshold, it is replaced with either an average of
surrounding values.
2) Rain rate conversion from reflectivity
Z–R power law relationship is sued to initial precipitation under the assumption of Z–R
power law relationship.
3) Gauge–radar adjustment
A temporally fixed Z–R relationship will not be appropriate for all rainfall events. Because it is not
currently feasible to manually adjust the Z–R parameters in real time due to the lack of proven, robust, and
objective criteria valid over a broad range of rainfall types, it is useful to make automated adjustments to
the rainfall estimates by comparing them with real-time rain gauge data on an hourly time step. Real-time
rain gauge data can be used to adjust the radar rainfall estimates in the algorithm. Average calibration,
Kalman filter calibration, optimal interpolation calibration and their combination are used in QPE.
（3） Nowcasting systems
Severe Weather Warning and Analysis system (SWAN) will be used in watching and warning severe
weather. It composed of following functions: Radar data QC, Public product generation, Regional
3-D mosaic, Nowcasting with radar echo extrapolation. AP and other no meteorological echo, noise
are processed in this function. The algorithm includes Thunderstorm Identification, Tracking, Analysis
CBS-CIMO Remote Sensing/Doc. 6 (2), p. 7
and Nowcasting (TITAN), Storm Cell Identification and Tracking (SCIT) and Tracking Radar Echo by
Correlation (TREC).
4．Development of advantage radar technology
(1) Dual polarization radar used in field experiment
The C band and X band dual polarization radar systems have been developed and used in
field experiments. Most of dual polarization radar systems are movable and work in
polarimetric base of simultaneous transmission of H and V wave and simultaneous reception
using a pair of receivers (SHV). There radar can distinguish precipitation phase and improve
the QPE skills.
(2) Cloud radar
The firs ground based cloud radar (8mm wavelength) with Doppler and polarization
capabilities for cloud observation was developed in 2007 and used in cloud observation in
China for weather modification, heavy rainfall observation and so on (Fig. 4.1). The radar operates at
a frequency of 34.7GHz with vertical pointing,
plan position indicator (PPI) and range
height indicator (RHI) scan models. The traveling-wave tube (TWT) transmitter operates with
a peak power of 600W at a pulse repetition frequency of 2500 and 5000Hz. Radar system
parameters are listed in Table 1. The cloud radar measurements include reflectivity (Z),
Doppler velocity (V), velocity spectrum width (W) and the de-polarization ratio (LDR) .
(a)
CBS-CIMO Remote Sensing/Doc. 6 (2), p. 8
(b)
(c)
Fig. 4.1 Radar hardware (a) simple description of radar structure (b) transmitter and receiver channels
CBS-CIMO Remote Sensing/Doc. 6 (2), p. 9
Table1. Characteristics of the Ka-band (35GHz) cloud radar
Antenna
Diameter
1.3m
Receiver
Mode
Transmit H,
receive
H and V
Gain
50dB
Sensitivity
-98.4dBm
Beamwidth
0.44°
Noise coefficient
5.6dB
First main sidelobe
<-30dB
Dynamic range
70.0dB
Cross
>33dB
polarization
Data processing system
isolation
Transmitter
Beam number
500
Frequency
Ka band
Beam length
30m or 60 m
Peak power
600W
Base parameters
Z、Vr、SW、LDR
Plus length
0.3、1.5、20、40
Processing method
FFT、PPP
μs
Plus repetition frequency
2500or5000Hz
FFT
accumulation 128、256、512
number
Polarization state
Horizontal
Compress
performance
side lobe<30dB
CBS-CIMO Remote Sensing/Doc. 6 (2), p. 10
(3) The S band Phase array weather also was studied in China.
The S band and X band weather phase array are been developed to observe the storms and
hail.
5. Problems in operational usage of CINRAD
(1) The coverage of CINRAD
The CINRAD could not cover the precipitation system in the mountain, desert and
depopulated zone in northwestern China and other areas. In this area, the raingauge are also
rare.
(2) Mosaic with C and S band radar
The reflectivity bias for S band and C band radar is obvious. It make difficult in mosaic
with S and C band.