ID.10532 Atmospheric Dynamics from LIDAR - earth.esa. · PDF filesea surface wind vectors ......

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Transcript of ID.10532 Atmospheric Dynamics from LIDAR - earth.esa. · PDF filesea surface wind vectors ......

  • ID.10532 Atmospheric Dynamics from LIDAR

    Gluing Method for Combining Lidar Signals Detected in Analog-to-Digital and Photon Counting Modes

    Bing-Yi Liu*, Chang-Zhong Feng, Jin-Tao Liu, Song-Hua Wu, Zhi-Shen Liu Ocean Remote Sensing Institute, Ocean University of China

  • Project Objectives We will provide an independent and credible measurement of radial wind speed, wind profile, 3D wind vector, aerosol backscatter coefficient, aerosol extinction coefficient, extinction-to-backscatter ratio (limited in the atmospheric boundary layer and troposphere), sea surface wind vectors using the direct-detect Doppler wind lidar / HSRL (High Spectral Resolution Lidar) with an iodine filter during ADM-Aeolus overpass ground-based lidar site (N 36.04, E 120.20). In addition to the lidar observations, wind profiles and other relevant meteorological data from radiosonde could also be provided. These results can be compared with the data products of ADM-Aeolus, and we will analyze the comparison results and present assessment reports to ESA.

  • Background

    For a ground-based lidar system, the backscattered light usually has large dynamic range.

    Photon-counting mode is used to measure weak signal from high altitude with high sensitivity.

    Analog-to-Digital mode is used to measure strong signal at low altitude with good linearity.

  • 4

    Gluing Photon Counting and Analog-to-Digital data

    0 1 2 3 4 5 6 7 8 9 1010

    -2

    10-1

    100

    101

    102

    103

    Height (km)

    Phot

    on C

    ount

    s

    Measurement ADReference ADMeasurement PCReference PCMeasurement GluedReference Glued

  • 5

    Gluing principle

    PC data

    AD data

    Glue Glued Data

    The gluing coefficient is calculated from the analog and photon counting signals in a linear range.

    2

    1( ( ) ( log( ) )) min

    n

    i ii

    PC z a Ana z b=

    + =

  • Previous data gluing method

    Analog-to-digital data and photon counting data of atmospheric return signal are merged.

    The gluing coefficient is calculated from the signals in the linear area.

    Linear area 2

    1( ( ) ( log( ) )) min

    n

    i ii

    PC z a Ana z b=

    + =

    Problem: Retrieved wind speed is highly sensitive to the merging coefficient.

    Standard gluing method used by Licel

  • Doppler wind lidar based on iodine filter

  • Wind Error caused by data gluing

    (201132281552)

    Large wind error this height

  • 9

    2 22

    0 0[ ( ) ]

    m m

    i i ii i

    E E S x y= =

    = =

    22 2 2 2( )S x a x b x c= + +

    Scatterplot of PC data and AD data

    2 21

    0 0[ ( ) ]

    m m

    i i ii i

    E E S x y= =

    = =

    1 1 1( )S x a x b= +

    Non-linear relationship is obtained from a binomial regression

    Methodology

    Data Density

    Linear relationship is obtained from a linear regression

  • 10

    Determine the linear region of AD and PC data

    The linear region is determined from both the standard deviation of the binned data and the relative standard deviation.

    Fig. 3. Analysis of PC and AD data showing the amount of data pairs, mean, standard deviation and relative deviation in corresponding bin of photon counts from top to bottom.

    0 50 100 150 200 250 300 350 400 450 500100102104106

    Am

    ount

    0 50 100 150 200 250 300 350 400 450 5000123 x 10

    5

    Mea

    n

    0 50 100 150 200 250 300 350 400 450 5000

    2000400060008000

    SD

    0 50 100 150 200 250 300 350 400 450 5000123

    Photon counts (a.u.)

    RSD

    Methodology

    The derivative of the binomial regression curve at 0 should be equal to the slope of linear regression.

  • 11

    Original AD SAD Original PC SPC

    Scatterplot

    Binomial Regression S2

    Linear Regression S1

    SAD (in PC unit) SPC (corrected)

    Weighting Function

    Glued Signal

    Linear fiting Curve fiting

    Flow chart of gluing PC data and AD data

    (1 )Glued PC ADS W S WS = +

    min

    minmin max

    max min

    max

    0,( )

    , ( )

    1,( )

    PC

    PCPC

    PC

    S CS CW C S CC C

    S C

    = <

  • 12

    Wavelength 532 nm

    Reputation rates 100 Hz

    Pulse energy 0.12J

    Pulse duration 10ns

    Linewidth 100MHz

    Spectrum purity > 99.9%

    Analog-to-Digital converters 400MHz 14bit

    Photon counters 200MHz

    A shipborne Doppler lidar system developed by Ocean University of China (OUC) is used to acquire data for testing the proposed gluing method.

    Results

  • 0 200 400 600

    1

    2

    3

    4

    5

    6

    7

    Photon counts (a.u.)

    Hei

    ght (

    km)

    GluedADPC

    Hei

    ght (

    km)

    00

    0 200 400 6000.6

    0.7

    0.8

    0.9

    1

    Photon counts (a.u.)

    Hei

    ght (

    km)

    Hi

    ht (k

    )

    8 10 12 14 164

    4.1

    4.2

    4.3

    4.4

    4.5

    Photon counts (a.u.)

    Hei

    ght (

    km)

    0 50 100 1505

    5.5

    6

    6.5

    7

    Photon counts (a.u.)

    Hei

    ght (

    km)

    100 150 2001.3

    1.35

    1.4

    1.45

    1.5

    1.55

    1.6

    Photon counts (a.u.)

    Hei

    ght (

    km)

    6

  • Deviation of AD and PC data compared to the glued data

  • Conclusions

    Gluing method causes wind error. Accuracy of gluing is influenced by:

    data source for regression linear region to calculate the slope of linear regression average method (weighted or not) cloud layer

    Lidar signals are analyzed using the optimized gluing method and show good results.

  • Thank you for your attention!

    Slide Number 1Project ObjectivesBackgroundGluing Photon Counting and Analog-to-Digital dataSlide Number 5Slide Number 6Doppler wind lidar based on iodine filterWind Error caused by data gluingSlide Number 9Slide Number 10Slide Number 11Slide Number 12Slide Number 13Slide Number 14ConclusionsSlide Number 16