K-3, 7th International Symposium on Tropospheric Profiling, June 11-17 2006, Boulder, Colorado
Characteristics and Performance of the Operational Wind Profiler Network of the
Japan Meteorological Agency
Masahito Ishihara
Observations Department, Japan Meteorological Agency
Chiyoda, Tokyo 100-8122, Japan
E-Mail:[email protected]
Abstract
Japan Meteorological Agency (JMA) started the operation of a wind profiler network named as the
Wind profiler Network and Data acquisition System (WINDAS) in April 2001.
WINDAS is a
network consisting of thirty-one 1.3GHz-band wind profilers with dense spatial resolution of 130 km
on the average over the main islands of Japan, being operated with high data accuracy under strict
data quality control and high data availability from reliable system operation.
Height coverages of
wind measurement are 6-7 km in summer, 3-4 km in winter and 5.3 km on the average through a
year.
The main purpose of the operation WINDAS is to provide upper-air wind data to the
numerical weather prediction (NWP) of JMA, particularly to the hydrostatic (till 2004) and
non-hydrostatic (from 2004) mesocale numerical model (MSM).
The WINDAS data are
assimilated into MSM using a full forecast-analysis system with 4-dimensional variational method.
From statistical analyses and some case studies, it was conformed that WINDAS data has contributed
to improve accuracy of MSM for mesoscale weather systems, particularly for heavy rainfall events.
Being put on GTS, the wind data are distributed to the world in real-time.
Although a problem of
data contamination from migrating birds had occurred in the first year of the operation of WINDAS,
it was practically solved by developing a removal algorithm.
1.
Introduction
Atmospheric radars were originally developed in 1970s for research of the mesosphere and
stratosphere (Woodman and Guillen, 1974), and were widely applied to research of winds in the
troposphere referred to as wind profilers during 1980s. Usefulness of a wind profiler network in
meteorological services was firstly demonstrated in the U.S. in early 1990s as the NOAA Profiler
Network (NPN)
(Weber et al., 1990 ; NOAA, 1994 ; Stanley et al., 2004).
NPN has been
operated using thirty-five 400MHz-band wind profilers located mainly in the central U.S. as shown
by at the web site http://www.profiler.noaa.gov/jsp/aboutProfilerData.jsp.
The European wind
profiler network has also been operated using 50MHz-band atmospheric radars, 400MHz-band wind
profilers and one GHz-band wind profilers since mid 1990s as the CWINDE project in COST–74
(Action 74 in the European co-operation in the field of scientific and technical research) and as
EUPROF in the COST-76 project (Nash et al., 2000 : Oakley et al, 2000 ; Dibbern, et al., 2001) as
described
at
the
web
site
http://www.met-office.gov.uk/research/interproj/cwinde/profiler/index.html.
In Japan, extremely severe rainstorms frequently occur associated with typhoons, the
Baiu-front and midlatitude cyclones, causing serious damages to human activities due to flash floods
or landslips.
Actually during 2004, the number of records of one-hour rainfall amount more than 50
mm (80 mm) amounted to be 453 (29) in the mesoscale surface network of JMA, which consists of
1,300 automated weather stations (AMeDAS).
One of the most important missions of JMA for
weather disaster prevention is to increase ability of monitoring and forecasting severe rainstorms.
The improvement of forecasting severe rainstorms primarily depends on enhancement of NWP.
JMA thus developed the mesoscale numerical model (MSM) with horizontal grid size of 10 km and
started its experimental operation in 1998, in order to predict mesoscale severe rainfall systems being
organized on horizontal scale from several 10 km to several 100 km (meso-beta scale).
The
existing upper-air observation stations using radiosondes in Japan are located at the intervals of 300
to 350 km, and are insufficient for monitoring mesoscale weather systems.
In order to operate
MSM most effectively, a newly developed upper-air observation network with horizontal space
being comparable to the horizontal scale of targeting weather systems was required for providing
initial values to MSM.
JMA made observation system experiments (OSEs) using MSM with data from wind
profilers, Doppler radars and commercial aircrafts, and then verified that wind profilers should be
introduced to the operation of NWP with the highest priority in order to increase forecast accuracy of
MSM for severe rainstorms (JMA Numerical Prediction Division, 2000).
On the basis of the
assessment from the OSE and the experience of wind profiler operation since 1889 at the
Meteorological Research Institute of JMA (Kobayashi et al., 1999), JMA decided to establish an
operational wind profiler network in order to increase capability of upper-wind observations over
Japan (Ishihara and Goda, 2000).
Twenty-five wind profilers had firstly started to be in operation
in April 2001 and six more wind profilers were added to the network during March to June 2003.
The network is referred to as Wind profiler Network and Data acquisition System (WINDAS).
In
this paper, we present characteristics and performance of WINDAS and its data quality control, as
well as impacts of its wind data on NWP for heavy rainstorms.
2. System configuration and characteristics of WINDAS
2-1 Selection of radio frequency
One of the parameters to evaluate of performance of wind profilers is height coverage
(height range) of wind measurement. Height coverage of wind measurement strongly depends on
radio frequency being used in wind profilers (Rottger and Larsen, 1990 ; Dibbern et al., 2001).
VHF atmosphere radars operated at frequencies around 50 MHz have high capability of wind
measurement from the troposphere to the stratosphere and even to the mesosphere (Fukao et. al,
1985), but they need relatively high expenses and large site for their construction of facility.
wind profilers using frequencies
UHF
around 400 MHz have capability to measure winds in the whole
troposphere, and then are most suitable for operational upper-air measurement in weather services.
Actually, NPN of NOAA consists of 404 MHz wind profilers (NOAA, 1994).
However, use of
frequencies of the 400-MHz band for wind profiling is strictly limited in Japan, particularly in areas
nearby medical facilities, because the frequency bands are also used in medical equipments.
Wind profilers operated at frequencies from 915 MHz to 1.3 GHz (1GHz-band wind
profilers) started to be used in research activities in early 1990s. Height coverage of 1GHz-band
wind profilers had been initially limited to the top of the boundary layer, usually up to 2 to 3 km in
height (Ecklund et al., 1990 ; Carter et al., 1995 ; Hashiguchi et al., 1995 ; Ohono, 1995) , but was
improved to be beyond the top of the boundary layer in late 1990s (Winston, 2000 ; Hashiguchi,
2004).
Water vapor producing rain clouds is concentrated below the middle level of the
troposphere, approximately below 5 km in height.
Hence, we thought that it is a necessary
condition to observe airflows in the layer from the surface to around 5 km in height in order to
monitor behavior of rainfall systems. Considering the cost performance (cost of a 400MHz-band
wind profiler is estimated to be twice or third higher than that of a 1.3GHz-band wind profiler) as
well as the restriction of radio frequency allocation in Japan described above, JMA selected
1.3GHz-band wind profilers for the network, rather than 50MHz-band atmospheric radars or
400MHz-band wind profilers. WINDAS is the first operational wind profiler network consisting of
1.3GHz-band wind profilers.
2.2 Network of the wind profilers
As illustrated in Fig. 1, JMA selected the site locations of thirty-one wind profilers, giving
the high priority on the middle and western Japan, where severe rainstorms occur almost every year
due to typhoons, the Baiu-front and midlatitude depressions. The distances from each wind profiler
site to its neighboring wind profiler site are ranged from 67 to 262 km, and 130 km on the average
over the four main islands of Japan.
Particularly at the south and west coasts of the main islands of
Japan, the wind profilers are distributed with higher spatial resolution so as to correspond to the
horizontal scale of meso-beta weather systems.
The spatial resolution of upper-air wind
observation in Japan has been extensively improved with the start of operation of WINDAS.
2.3 System configuration
Figure 2a and 2b shows photographs of one of the wind profilers and the Control Center of
WINDAS.
Fig.3 is the system configuration of WINDAS as illustrated in a block diagram.
The
wind profilers used in WINDAS were selected in 2000 through an international tender of
procurement.
The main unit of the wind profilers was designed with the technologies developed at
the Radio Science Center for Space and Atmosphere (currently the Research Institute for Sustainable
Humanosphere) of the Kyoto University (Hashiguchi et al., 2004) in collaboration with the
Mitsubishi Electric Corporation (Wakayama, 2000).
Table 1 summarizes the main characteristics of WINDAS.
The wind profilers of
WINDAS have the transmitting peak power as large as 2 kW and the antenna gain of 33dBi by use of
4 m x 4 m active phased array antenna consisting of 96 sub-antennas (Fig.2d). The phase-coded
pulse compression technique with the 8-bits binary code developed by Spano and Ghebrebrhan
(1996) is used to preserve the height resolution at the first range gate (usually at 400 m) of wind
measurements.
More detailed description of the structure and mechanism of the wind profiler is
presented by Hashiguchi et al. (2004). A clutter fence with 2 m in height surrounds the array
antenna in order to prevent ground clutter contamination.
Loss of radio wave power in case of
snow accumulation of 10 cm depth over the antenna is estimated to be one dB.
As shown in Fig.2b,
semi-globe radar domes made from FRP are installed over the array antennas at nine wind profilers
which are located in the heavy snowfall areas, in order to prevent from heavy snow accumulation
over the antenna. All the wind profilers of WINDAS are in remote operation from the Control
Center located in Tokyo as shown in Fig.2c.
2.4 Signal/data processing and data quality control
Figure 4 illustrates the schematic chart of signal processing and data processing including
data quality controls in WINDAS.
Data of WINDAS are used in real-time for operational weather
services, and then they have to be kept in high quality as much as possible.
One of the prominent
features of WINDAS is the strict quality control at the stages of the signal and data processing.
After the FFT processing in the signal processor, removal of ground clutter is made by eliminating
spectral components around zero-velocity in the Doppler velocity spectrum.
migration-bird echo described in Section 2.5 is then followed.
Removal of
In the data processor, vertical
profiles of spectral moments on the five beams are estimated at every one minute.
One of the
specific procedures to WINDAS is its estimation scheme of Doppler spectral moments using the
Gaussian-function fitting method (Hashiguchi et al., 1995).
The scheme has an advantage that
spectral moments are available in case of low signal to noise ratio. After the Doppler spectrum
width check, homogeneity among Doppler velocities on four oblique beams is examined.
The
homogeneity is distorted in some cases, particularly at edge of severe convections, where some
beams among the four beams are penetrated into precipitation clouds and the other beams stay in
clear sky. 10-minute averages of three components of wind (u, v, w) over each wind profiler site
are finally calculated from Doppler velocities using the four-beam method (Adachi et al., 2005).
The 10-minute averaged data of wind with data quality flag are sent to the Control Center
every hour by using the JMA telecommunication lines or public telephone lines.
The quadratic
surface check for u and v components (Sakota, 1997) is made in the computer of the Control Center.
The check is based on continuity of u or v components on a time-height domain and has shown high
performance to eliminate erroneous data automatically.
One to two percents of the total amount of
obtained data has been judged as erroneous data in the sequence of the data quality controls.
The
quality-assured data are then sent to the JMA central computer (Numerical Analysis and Prediction
System : NAPS) by 20 minutes after each hour, and are used as initial values of NWPs.
Being
coded into BUFR (Binary Universal Form for the Representation of Meteorological Data), the wind
profiler data have been put onto the Global Telecommunication System (GTS) for global exchange
on an operational basis since April 2002.
2.5 Migrating-bird echo removal
After the operation of WINDAS was started, it was found that significant error of wind
measurements sometime occurred due to migrating birds. The erroneous data due to migrating
birds appeared mostly in nights of spring and autumn under fair weather condition as reported in the
profiler networks of the U.S. and of Europe (Wilczak et al., 1995 ; Engerbart and Gorsdorf, 1997).
As indicated in Fig.5, the amount of data contaminated by migrating birds in WINDAS shows
distinct seasonal change with peaks of spring and autumn.
The directions of migration of birds are
mainly northeastward in spring and southwestward in autumn, being aligned along the main islands
of Japan.
In the mostly contaminated cases from migrating birds, deviation of false “winds" of
WINDAS from true winds observed with radiosondes are up to 90 degree in direction and to 10 ms-1
in speed. In October 2001, when birds actively migrated over about half numbers of the profiler
sites, 12% of the total amount of data obtained from WINDAS were contaminated by migrating birds.
The percentage of the contamination was much lager than what we had presumed before the start of
the operation of WINDAS. Doppler spectrum width of migrating-bird echoes is, in general, larger
than that of atmospheric echoes (Wilczak et al., 1995). Using this characteristic of migrating-bird
echoes, wind data with relatively large Doppler spectrum width under clear air condition were judged
to be bird-contaminated data and were removed, during the period from December 2001 to March
2003.
A more sophisticated algorithm of migrating-bird echo removal was developed in March
2003 (Kobayashi et al., 2005) and was installed into the signal processors of the wind profilers.
The algorithm is based on the fact that intensity of time series of migrating-bird echoes are generally
greater than that of atmospheric echoes and that migrating-bird echoes are fluctuated more rapidly
than precipitation echoes.
In WINDAS, incoherent integration is made using twenty-eight Doppler
spectra obtained during 0.4 second as shown in Fig.4.
A set of twenty-eight Doppler spectra
prepared for the incoherent integration are examined.
When spectrum densities of the spectra set
shows low time-fluctuation, it is judged that the spectra come from atmosphere or precipitation, and
then all the spectra will be used in the following incoherent integration. On the other hand, when
time-fluctuation of spectrum densities in a spectral set is large, it is thought that atmospheric spectra
and migrating-bird spectra are mixed in the spectra set.
Since spectra with relatively large spectrum
density are originating from migrating birds, they are abandoned.
Then, the incoherent integration
is made using the remained spectra, which are originated from atmosphere. This algorithm has
been successfully worked and true winds are retrieved from nearly half of bird-contaminated data.
Wilczak et al. (1995) and Pekour and Coulter (1999) examined shapes of a spectrum before
incoherent integration and eliminated the spectral peaks which are not fitted to the Gaussian
distributions as migrating-bird echoes.
Their method is excellent to find migrating-bird echoes but
is thought to use relatively large calculation time in the signal processing.
The algorithm developed
in WINDAS is relatively simple and shows practical effectiveness for removal of migrating-bird
echoes in operational use.
3. Performance of WINDAS
3.1 Height coverage of wind measurement
The wind profilers of WINDAS have four operation modes with height resolutions of 100,
200, 300 and 600 m, corresponding to four pulse lengths.
We here define “height coverage of wind
measurements” as the maximum height up to where wind measurements are available.
In general,
the longer pulse length is used, the better height coverage is obtained due to higher transmitting
power in spite of less height resolution.
Correlation between height coverage and height resolution
were actually examined in the period from May to June 2001 prior to the full operation of WINDAS.
The mean height coverage in 100, 200, 300 m height resolution modes during the period were 3.0 km,
5.4 km and 6.4 km, respectively. Hence, we selected the height resolution of 300 m as the default
operation mode, because the mode has enough height coverage and enough height resolution to be
used for initial values of the latest NWP models operated in JMA.
Signal intensity of 1.3 GHz-band wind profilers highly depends on weather conditions and
shows pronounced temporal variation.
Figure 6 shows the monthly means of height coverage of
wind measurement at 25 wind profilers during the period from April 2003 to March 2004.
The
height coverage attains 6 to 7 km in summer and decreased to 3 to 4 km in winter, being 5.3 km for
the annual mean.
It is thought that this seasonal variation of the height coverage mainly results
from change of water vapor amount contained in the lower troposphere. At the stage of the initial
planning of WINDAS we made a goal that the annual mean of height coverage in wind
measurements should reach 5 km, and the goal has been accomplished.
3.2 Data accuracy and system reliability
The wind measurement accuracy of WINDAS was evaluated by comparisons with winds
forecasted by the NWP model in June and July 2001 (Tada, 2001).
As shown in Fig. 7, biases and
root mean square errors (RMSEs) of wind speed deviation of profiler winds from model winds are
the same as those of radiosonde winds from model winds. There is no significant difference
between profiler winds and radiosonde winds in the biases and RMSEs.
This means that the
accuracy in the wind measurement with WINDAS is comparable to that from radiosonde
observations.
In order to evaluate the reliability of the operation of WINDAS, the percentage of data
received in real-time at the Control Center were examined.
The monthly means of data availability
over all the wind profiler have been more than 98.8% since May 2001.
This indicates that stable
operation of the system has been accomplished in WINDAS.
4. Data applications
4.1 Impact on NWP models
The upper-air wind data from WINDAS have been used as an initial value in all the NWP
models being operated in JMA since June 2001. The WINDAS data particularly contribute to the
initial value for the hydrostatic mesocale model (MSM) during 2001 to 2004 and to the
non-hydrostatic MSM (Saito et al., 2006) from 2004 with 10 km horizontal resolution (the horizontal
resolution has been farther improved to 5 km from March 2006). The data assimilation in MSM is
made using a full forecast-analysis system with 4-dimensional variational (4D-VAR) method
(Ishikawa, 2001 ; Koizumi, 2002).
Forecasts using MSM has been made eight times a day since
March 2006. Hourly wind data from WINDAS during six hours before the initial times are used in
the data assimilation. The amount of upper-air data being provided to MSM was significantly
increased at the start of the operation of WINDAS, particularly in the lower troposphere. 4D-VAR
makes the best use of the ability of wind profilers that provide temporally and spatially high-density
data of upper-air winds.
The impact of the WINDAS data on the numerical forecasts by MSM for rainfalls was
statistically examined using threat score (Tada, 2001). The threat score of 3 to 6 hour forecast of
MSM were examined in the period from 27 June to 26 July 2001. The threat scores for rainfalls
smaller than 10mm/h were not clearly improved by using wind data obtained from WINDAS.
On
the other hand, the threat score for rainfalls greater than 30mm/h in case of using only conventional
upper-air observation data was 0.02, and the score was improved to 0.05 by adding wind data
obtained from WINDAS.
This verified that WINDAS has given positive impacts to forecasting
heavy rainfalls by providing wind data in the lower troposphere.
An observation system experiment was performed with MSM using WINDAS data and the
4D-VAR method for a severe rainstorm occurred in the western Japan in June 2001 (Ishikawa,
2001). As shown in Fig.8a, the location of the rainstorm being forecasted using only conventional
upper-air observation data is shifted approximately 60 km to the north of the true location of the
rainstorm.
On the other hand, the location of the rainstorm forecasted with wind profiler data well
agrees to the true location as shown in Fig.8b. This is because that the northerly component of
winds around the storm which were observed with WINDAS and were assimilated to MSM using
4D-VAR method (Fig.8b) was actually smaller than that being forecasted from only radiosonde
data (Fig.8a).
Namely, the advection effect on the storm by surrounding winds being simulated
without WINDAS data was overestimated, while the assimilation of WINDAS data into MSM
using the 4D-VAR method correctly represented the advection effect. This experiment practically
indicated that data of WINDAS improved the accuracy of the NWP models of JMA.
4.2 Hourly wind analyses
JMA newly developed weather products named as “Hourly wind analyses" corresponding to
the start of operation of WINDAS.
The product are wind analysis maps over Japan, which is made
by correcting the first guess of wind generated on 10 km-horizontal grids in MSM by using wind
data from WINDAS, commercial aircrafts and Doppler radars at the airports as ground truth. The
products are powerful tools to illustrate three-dimensional kinematic fields in mesoscale weather
systems, and are mainly used for nowcasting of significant weather events in forecast services and in
aviation weather services.
4.3 Example of data analysis
An example showing advantage of wind profiler data for analysis of kinematic aspects of a
tropical storm will be presented here.
The 16th typhoon occurred in 2004 (Chaba) crossed over
Japan from the southwest to the northeast from 29 to 31 August 2004 as shown in Fig.9a, changing
its structure from a tropical cyclone into an extratropical cyclone.
Wind data obtained from the
wind profilers of WINDAS located near the track of the typhoon inner core depicted tilting of the
typhoon vortex due to the influence from the midlatitude westerly flow. Figure10b-d shows the
time-height cross sections of winds from the wind profilers located at 31.7°, 32.8° and 34.9° N,
respectively. The typhoon vortex at 31.7° was vertical through the surface to the upper troposphere,
but the vortex at 32.8° was started to tilt below the 4 km level and the tilting was recognized more
clearly at 34.9°N.
This tilting of the typhoon core was thought to be resulted from the increase of
environmental vertical shear around the typhoon as it intruded into the midlatitude westerly flow
(Jones et al., 2003).
This is a typical example showing usefulness of the wind profiler network for
analyzing kinematical structure of mesoscale weather systems.
5. Conclusions
JMA started the operation of the wind profiler network “WINDAS” in April 2001.
WINDAS consists of thirty-one 1.3GHz-band wind profilers and is characterized with dense spatial
resolution of 130 km on the average over the main islands of Japan, high data accuracy due to strict
data quality control and high data availability. Height coverage of wind measurement is 6-7 km in
summer, 3-4 km in winter and 5.3 km on the average through a year.
The wind data from
WINDAS has significantly contributed to improve accuracy of the numerical weather prediction of
JMA mainly for mesoscale weather systems.
world in real-time.
Being put on GTS, the wind data are distributed in the
Although a problem of data contamination from migrating birds had occurred in
the first year of its operation of WINDAS, it was practically solved by developing a removal
algorithm.
Extending uses of the data, such as physical retrieval or data assimilation of vertical
profile of water vapor amount from wind profiler signal, will be next issues in the operation of
WINDAS.
Acknowledgments
The authors would like to thank Drs. Shoichiro Fukao and Hiroyuki Hashiguchi of the
Research Institute for humanosphere of Kyoto University for their sustainable help to construct
WINDAS.
We also thank Ms. Margot Ackley of the NOAA Profiler Network (NPN) of the
Forecast Systems Laboratory of NOAA for providing us with useful information on operation of
wind profilers.
The project of the European wind profiler network operated under COST-74 and
COST-76 gave us many instructive suggestions.
The impact experiment with MSM described in
Section 4 was made by the Forecast Prediction division of JMA
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Table 1.
Main characteristics of WINDAS.
Frequency
Antenna
Peak Power
Beam width
Beam configuration
Pulse length
PRF
Side robe level
Data processing height
vertical resolution
Basic data
Distributed data
1357.5 MHz
coaxial colinear arrays with gain of 33dB
and size of 4m x 4m
1.8 kW
4 degree
5 beams
0.67, 1.33, 2.00*, 4.00 x 10-6sec.
5, 10*, 15, 20 kHz
-40 dB or –60 dB at elevation angles of 0-10 degree
400 m to 9.1 km*
291 m*
Doppler moments every 1 minute
u,v,w-components of wind, S/N ratio
and data quality flag every 10 minutes
* Default values in the operational mode
Figure 1.
The site locations of the wind profilers
of WINDAS and the radiosonde
stations of JMA.
Figure 2.
Photographs of WINDAS. Standard type of
wind profiler (a), wind profiler with a radome
(b), the Control Center at the headquarters of
JMA (c), and a coaxial and colinear Array
antenna in a radome (d).
Figure 3.
Block diagram of the wind profilers and the Control Center of WINDAS.
Figure 4. Block diagram of signal processing and data processing including data quality control in
WINDAS.
Figure 5.
Ten-day average of occurrence rate of migration-bird echoes in WINDAS during 2001.
Figure 6.
Figure
Monthly
6. Monthly
means of
means
height
of coverage
height coverage
of windofmeasurements
wind measurements
at the wind
at theprofilers
wind profilers
of
of
WINDAS
WINDAS
during April
during2003
April
to 2003
March
to 2004.
March 2004.
Figure 7.
Vertical profiles of bias and RMSE of deviation for u-components of wind between
the wind profilers and the mesosocale model of JMA (MSM), and those between
the radiosondes and MSM during June to July 2001.
Figure 8.
Results of an impact experiment of wind profiler data on the mesoscale model of JMA
(MSM). (a)rainfall amount for 3 hours following the start of a MSM forecast at
12UTC on 19 June 2001 using the 4D-VAR assimilation scheme with radiosonde data
but without wind profiler data, (b) using the 4D-VAR scheme with both wind profiler
data and radiosonde data, and (c) rainfall amount estimated with radars and calibrated
by the rain gauges of the automatic weather station network AMeDAS during 12 to
15UTC. (d) and (e) indicate the forecasted winds at 850hPa at 15UTC corresponding
to (a) and (b), respectively.
Figure 9. The track of the 16th Typhoon in 2004 (Chaba) (a), and time-height cross section of winds
obtained from wind profilers at (b) Ichiki (31.7°N, 130.3°E) , (c) Kumamoto (32.8°N,
130.7°E) and (d) Hamada (34.9°N, 132.1°N).