LALINET

UNIVERSIDAD DE CONCEPCIÓN, CENTRO DE ÓPTICA Y FOTÓNICA
Programa de Financiamiento Basal para Centros Científicos y Tecnológicos de Excelencia -CONICYT
March, 10 to 13, 2014

Program Workshop Lidar Inversion March 2014 - PDF

Program Workshop Lidar Inversion March 2014 - HTML

REPORT of activities during this workshop. <- DOWNLOAD IT!

Participants

• Antonieta Silva, CEFOP - Chile
• Daniel Nisperuza, UNAL - Colombia
• Fabio Lopes, IPEN - Brasil
• Henrique Barbosa, USP - Brasil
• Pablo Ristori, CEILAP - Argentina

Data files

Our friends from Earlinet have provided datasets for us to try our algorithms.

• Profiles used for the first Earlinet paper on the elastic retrievals (Bockmann et al, App. Opt. 2004)
  • Signals for 355, 532 and 1064 nm and input P, T download
  • P and T with better format download
    • pressure = hPa
    • temperature e dew point = degC
    • lidar ratio = sr
    • altitude = m
  • Noise and background added to the input signal as Signal -> Poissrnd(Signal + BG). Columns are: alt, 355, 532 and 1064. This is wrong because the original signal if very small at the end and the Poisson() will result in single values.
    • Bg=10^6-- download holger-bg1e6.txt
    • Bg=10^4-- download holger-bg1e4.txt
    • Bg=10^2-- download holger-bg1e2.txt
    • Bg=10^0-- download holger-bg1e0.txt
  • Noise and background added to the input signal as Signal -> Poissrnd(1000*(Signal + BG)). Columns are: alt, 355, 532 and 1064.
    • Bg=10^8-- download holger-poisson-S1k-bg1e8.txt
    • Bg=10^7-- download holger-poisson-S1k-bg1e7.txt
    • Bg=10^6-- download holger-poisson-S1k-bg1e6.txt
    • Bg=10^5-- download holger-poisson-S1k-bg1e5.txt
    • Bg=10^4-- download holger-poisson-S1k-bg1e4.txt
    • Bg=10^3-- download holger-poisson-S1k-bg1e3.txt
    • Bg=10^2-- download holger-poisson-S1k-bg1e2.txt
    • Bg=10^1-- download holger-poisson-S1k-bg1e1.txt
    • Bg=10^0-- download holger-poisson-S1k-bg1e0.txt
  • Expected output 355nm, 532nm and 1064nm
• Profiles used for the raman Earlinet paper (Pappalardo et al, 2004)
  • Signals for 355, 387, 532, 608, 1064 download input and output

Pablo Ristori (Ceilap - Argetina) also provided a simulated dataset. This is more complicated as it includes clouds and aerosols. Input is only available for 355nm at the moment. Two versions are available:

• Strong cloud - download input
  • Input is: 1- altitude and 2 - signal at 355nm
  • Cloud at 6km LR=28 and beta ~= 280 Mm^-1 sr^-1 (Cloud optical depth = 1.0)
  • BL Aerosols up to 1.5km, LR=28, beta = 5 Mm^-1 sr^-1
  • Residual aerosols, LR=28, beta = 0.5 Mm^-1 sr^-1
• Weak cloud - download input and the output
  • Input is: 1- altitude and 2 - signal at 355nm
  • Cloud at 6km LR=28 and beta ~= 57 Mm^-1 sr^-1 (Cloud optical depth = 0.2)
  • BL Aerosols up to 1.5km, LR=28, beta = 5 Mm^-1 sr^-1
  • No residual aerosols
  • Alternative input with extra noise and background
    • Bg=10^6-- download ristori-bg1e6.txt
    • Bg=10^4-- download ristori-bg1e4.txt
    • Bg=10^2-- download ristori-bg1e2.txt
    • Bg=10^0-- download ristori-bg1e0.txt

Data format

The output of your algorithms should produce simple ascii files without header and columns separated by TAB with the following columns:

1. height = m
2. backscatter = 1/Mm / sr
3. extinction = 1/Mm
4. lidar ratio = sr
5. molecular backscatter = 1/Mm / sr (step 3)
6. molecular extinction = 1/Mm (step 3)
7. synthetic molecular signal = 1/m / sr /m2 (step 3)

References

• Böckmann, C., Wandinger, U., Ansmann, A., Bösenberg, J., Amiridis, V., Boselli, A., … Wiegner, M. (2004). Aerosol Lidar Intercomparison in the Framework of the EARLINET Project. 2. Aerosol Backscatter Algorithms. Applied Optics, 43(4), 977. doi:10.1364/AO.43.000977
• Pappalardo, G., Amodeo, A., Pandolfi, M., Wandinger, U., Ansmann, A., Bösenberg, J., … Wang, X. (2004). Aerosol Lidar Intercomparison in the Framework of the EARLINET Project. 3. Raman Lidar Algorithm for Aerosol Extinction, Backscatter, and Lidar Ratio. Applied Optics, 43(28), 5370. doi:10.1364/AO.43.005370