25th International Laser Radar Conference

DETAILED DESCRIPTION OF DATA PROCESSING SYSTEM
FOR LIDAR NETWORK IN EAST ASIA
Atsushi SHIMIZU, Nobuo SUGIMOTO, Ichiro MATSUINaional Institute for Environmental Studies, 16-2 Onogawa, Tsukuba 305-8506 Japan, E-mail:shimizua@nies.go.jp
ABSTRACT
More than 20 Mie-scattering lidars are operated continuously in East Asian region including Japan, Korea,China, and Mongolia. Vertical distribution of aerosols
and clouds detected by these lidars are utilized in widerways. All observed data are transferred to National Institute for Environmental Studies (NIES), and processed inorder to distinguish cloud and aerosols, to eliminate signal from rain/snow, and to obtain two components (dust/ spherical) extinction coefficients. Here the methodology of data processing is precisely described including
various threshold values for reference of further network
development in the world.
1.

INTRODUCTION

Mie-scattering lidar is a simple but useful system toevaluate air quality in the troposphere. In East Asian
region long range transport of Asian dust (Kosa) andincreasing anthropogenic aerosols in accordance witheconomic growth are two major issues in environmental science of the atmosphere. National Institute forEnvironmental Studies (NIES) and collaborating institutes/organizations/universities are operating Mie-lidars
in this region continuously to understand distribution andtransportation of airborne particles[1]. Although the network has a decadal history [2] and the observation results
have been widely applied [3; 4] , precise description ofcurrent data processing method is not reported because it
is modified gradually but frequently for better analysis. In
this paper the most recent parameters etc. are described,and the newest applications are also introduced.
2.
2.1.

DATA PROCESSING PROCEDURES
Low level data preparation

All lidars are designed and controlled uniformly byNIES. Specifications are depicted in Table 1. Each lidar
makes an observational data file every 15 minutes. Oncea hour, 4 files are transported to NIES lidar data serverusing SSH connection via internet. After all profiles arrive at NIES, data processing program (written in IDL)
is executed. As some statistical calculation is neededin the following procedures, data during last 5 days (total 480 profiles) are loaded from harddisk of the server.
At first background light is subtracted using the tail ofraw data. The data is stored up to 24 km in raw data
file, and the averaged value of signal intensity in uppermost 600 m is subtracted from data in all other ranges.
Then range squared value is multiplied at each height
range. For 532nm (VIS) perpendicular and parallel components are summed to obtain total intensity and divided

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Table 1: Hardware specifications of NIES network lidars

Laser
Repetition
PowerDirection
Diameter of telescope
Detectors
Range resolution
A-D converter
Signal accumulation
Operation

Nd:YAG (532nm, 1064nm)
10Hz
50 mJ
Zenith (fixed)
20 cm
PMT × 2(532 nm ",⊥)APD (1064nm)
6m
12bits3000 shots (5 min.)4 profiles / hour(regardless of weather)to obtain volume depolarization ratio. Ratio of these twochannels are compensated with reference observation obtained with seat polarizer which is inserted before cubic
polarizer to equalize both components, or rotated PMT
unit. Geometrical overlap correction factor is determinedwith an assumption of homogeneous mixing in clear sky
daytime. Though the shape of this function is depending on the site, it becomes unity below 600 m altitude.
However the accuracy of retrieved data in the lower atmosphere is unknown, and the final results below 120 m
altitude are not published in the further analysis. Attenuated backscatter coefficient (ABC) is calculated using
profiles of intensity and system constants which were inferred at previous data production. It is assumed that allsystem constants do not change strongly in short time. Finally the five successive range bins are summed to obtainbetter signal-to-noise ratio (SNR). So the height resolution hereafter is 30m
2.2. Classification of scatteres and estimation of optical parameters
Clouds, falling raindrops/snow/fog, aerosols are classified, and optical parameters of aerosols are calculated in
the following manner.
1. Cloud base and apparent cloud top is determinedusing 1064 nm (IR) profile. Vertical gradient of
ABC of IR is examined from the surface with upward direction. If the gradient becomes greater than
4 × 10 8 /sr/m, the range is assumed as cloud baseheight and apparent cloud top is selected in higher
altitude wherein the ABC is equal to that of corresponding cloud base. The maximum of ABC between cloud base and apparent cloud top must ex-25th International Laser Radar Conference
ceed 5 × 10 6 /sr/m, otherwise the height is notmarked as cloud base. Thresholds are determined
empirically using obtained data in East Asian region
in last 10 years.
2. Rain detection. Raining are detected in several
ways. Strong rain causes strong backscatter / extinction near the surface due to its sprays at theground. If both large ABC at lowest height andsmall ABC in upper layers are detected, the wholeheight ranges are marked as rain. Surface fog is
also detected in similar way. Threshold is that themaximum ABC below 150 m exceeds 20 times ofABC at 600 m. Medium/weak rain is examinedfrom just below clouds with downward direction. As
far as color ratio between IR and VIS backscatter(ABCIR /ABCV IS ) exceeds unity the layer is classified as rain. This method is efficient to detect falling
droplet which evaporates before reaching surface.
3. Inversion of lidar equation. Fernald’s method [5] isemployed to solve the lidar equation for VIS channel. Although the upper boundary should be selected in the stratosphere, lidars in the network iscompact and SNR in the stratosphere is not goodenough to retrieve optical parameters of aerosols.
Historically we set the upper boundary at 6 km because the air quality of lower troposphere including
planetary boundary layer is most interesting targetof our group. When the cloud appears below 6 km,
the boundary is set just below cloud base. Sometimes dense surface aerosol concentration reduces
SNR at 6km. In such cases the boundary is lowered.
We employ air density profiles in The COSPAR
International Reference Atmosphere (CIRA-86) for
Rayleigh component. Lidar ratio is fixed at 50 sr because the initial target of the network was Asian dust
whose lidar ratio is distributed 40 ∼ 60 sr in the literature [6]. The boundary condition of aerosol freeat 6 km is not proper when floating dust layers exist. In that case negative aerosol extinction appears
in some height below 6 km. If it occurs, non-zeroaerosol is attributed at 6 km and again Fernald’s calculation is applied. Once extinction coefficient isobtained, particle depolarization is calculated. Finally dust extinction and spherical particle extinction are calculated based on the external mixing of
two kind of particles[7]. Depolarization for dust is
assumed as 35%, and that of spherical particles isassumed as 2%. These values are also determinedstatistically[2]. In the final dataset, height profilesof extinction coefficient is provided up to cloud baseor maximum height determined by SNR. If clouds
are detected, cloud base/top heights are indicated,and the region above cloud and rain conditions aremarked as non observable(missing data). An example of final result is shown in Figure 1.
4. Estimation of system constants. Using inverted extinction coefficient in clear conditions, we infer the912

Figure 1: An example of final results of routine data reduction system. Dust extinction (top) and spherical particle extinction (bottom) at Tsukuba, Japan in February
2010. Black indicates cloud (between cloud base and apparent cloud top), and gray indicates areas where no data
is obtained from lidar observation. This figure is made bypost processing using one month data.

spontaneous system constant which is used in thenext analysis. In the assumption that the extinction
below 1.2 km is not so severe, range corrected signal intensity and total (Rayleigh plus Mie) backscatter coefficient between 600 m and 1200 m are compared. The ratio between them are the system constant. Data below 600 m is excluded due to the uncertainty of overlap correction in that range. Next,
IR channel is compensated using backscatter fromcloud. Color ratio of scattering from cloud between
450 m - 1950m are controlled to be unity. Here itis again assumed that the wavelength dependency ofextinction in lower atmosphere is not strong.
All parameters required in the whole process are listed inTable 2.
Table 2: Required parameters in data production

Parameter
Geometrical form factor
Ratio of two PMT gains
System constant for VIS
Ratio of system constants
between IR and VISFrequency
every maintenance work
every maintenance work
every analysis
every analysis

3. APPLICATIONSDATAPROCESSEDOFLIDAR

3.1. Data assimilation in chemical transport model
It is widely accepted that the data assimilation is quickly
developing method to understand the nature of atmospheric processes. Dust extinction coefficient is usefulfor 4DVAR method in the chemical transport model [3]25th International Laser Radar Conference
because the adjoint model must be run and the inverted
data is required in this method. Model results with lidar
data assimilation exhibited that the surface concentrationof dust was improved at the independent monitoring station. Attenuated backscatter is used directly in ensemble
Kalman filter [8] if it is well calibrated.
3.2.

Information for publicAsian dust strongly attracts the social concerns because astrong dust event causes many side effects, for example
preventing airport operations, strengthening hay fever,and staining washing / cars. Every hour the lidar dust
extinction is provided to public from WWW server inthe Ministry of Environment, Japan. Averaged dust extinction near the surface (below 1 km) is displayed but
is converted to dust mass concentration using a mass extinction conversion factor of 1/km = 1mg/m3 . Of cource
the conversion introduces a big uncertainly, but the massconcentration is more friendly than extinction for a certain kind of people. Also, both vertical profiles of dust
/ spherical extinction is displayed on Environmental GIS
pages at NIES.
3.3.

Epidemiology

To clarify the influences of Asian dust to human health,epidemiology is now conducted in Japan. For example,
daily reports of number of death in certain areas are analyzed with the several indeces of Asian dust, namelyofficial weather report by Japan Meteorological Agency,
weight of suspended particulate matters (SPM) measured
by the Ministry of Environment, and dust extinction coefficient derived by network lidars. Results are not fixed
yet, but using lidar data is the most objective method because it provides numerical time series of dust concentration itself.
4.

CONCLUDING REMARKS

Detailed method of automated lidar data reduction
system in NIES is described.
Sometimes heavy
Asian dust and anthropogenic pollutants spread invast area in East Asia. Routine analysis of Miescattering lidar data provides a variety of information which is utilized in both of scientific communities and social public. For atmospheric scientists,hourly updated time-height sections are provided at
http://www-lidar.nies.go.jp. One of possibility of improvement in the data reduction procedure isutilizing IR data for aerosol specification. If the color ratio of extinction is well determined, it will provide some
information about size distribution of aerosols or opticalcharacteristics of particles. To accomplish it, simultaneous observation of lidar and particle sampling is insufficient.
ACKNOWLEDGMENTS
This work is partly supported by the Global Environment
Research Fund, Ministry of Environment, Japan (S-71 and C-091) and Grant-in-Aid for Scientific Research

913(20244078) from the Japanese Ministry of Education,Culture, Sports, Science, and Technology. Authors appreciate all scientists who help the lidar operation in all
observatories.
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