Cln qa qc efforts
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CLN QA/QC efforts. CCNY – (Barry Gross) UMBC- (Ray Hoff) Hampton U. (Pat McCormick) UPRM- (Hamed Parsiani). Outline. “Raw” signal tests. Matchups against Rayleigh Linearity tests with ND filters Member processing algorithms Efforts to test algorithms for consistency

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CLN QA/QC efforts

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CLN QA/QC efforts

CCNY – (Barry Gross)

UMBC- (Ray Hoff)

Hampton U. (Pat McCormick)

UPRM- (Hamed Parsiani)


  • “Raw” signal tests.

    • Matchups against Rayleigh

    • Linearity tests with ND filters

  • Member processing algorithms

  • Efforts to test algorithms for consistency

  • Indirect (Downstream) tests for retrieval accuracy

  • Potential QA/QC efforts for CLN

Testing multi-wavelength lidar signals to the molecular reference

Representative matching

of lidar profiles with

Molecular profiles

Lidar System Calibration

Regression at 10-11 km

Lidar signal linearity: signal profiles and their ratios

NDF-1 (OD=1.6) at 12:56 pm

NDF-2 (OD=1.0) at 12:59 pm

Lidar signal profiles

Good linearity!

Lidar signal ratio

CCNY Processing

  • Standard processing for 355 and 532 channels using Fernald Back-Integration method with S ratio pinned by AERONET AOD closure

  • Far end Scattering Ratio Condition (1.01 at 355nm, 1.06 at 532 nm)

  • Zmax determined by “minimum signal” method

  • 1064 channel uses system constant based on cirrus cloud calibration

CCNY Lidar Algorithm and Cross-Testing Efforts

  • Different algorithms tested against each other.

    • Intercompare iterative and Fernald solutions

  • Consistency check

    • Compare Measured Signal with Retrieved Signal after optical property retrieval

  • 1064 channel system constant evaluation over long time periods

  • Indirect assessment of standard Mie and Raman optical properties using thin Cloud Optical Depth retrievals.

  • Some preliminary cross-matchups with UPRM.

Validation (1)Fernald vs Iterative

Blue=exact Fernald

Green=iterative approximations






Validation (2)Consistency Check Comparison of theoretical and Measurement Signal


355 nm

Errors < .3%

Long term stability and evaluation of Lidar System Ratio

Raman COD retrieval based on successful derivation of cloud extinction and integrating

Mie COD based on S. Young regression method and uses aerosol backscatter corrections above and below cloud

Indirect Check of Optical Property retrieval using Cloud Optical Depth

Clear sky for aerosol backscatter correction to COD

Cross-Testing of Retrieval Algorithmson same Data

CCNY Processing

UPRM Processing

Extra slides

Test of lidar signal linearity at 355-nm

  • Time and date: 1256PM--1259PM, April 21, 2006

  • Method:

  •  Insert the different Neutral density Filters (NDF) in front of

  • interference filter and PMT.

  • Background level is calculated from the average of

  • last 5-km lidar raw data. Mean and standard deviation are given.

  •  Signal ratios are calculated with the different NDFs.

  • Their ratios should be the constant if both two signals are in the

  • linear ranges.

  •  All data are the 2-min average lidar signal profiles.

  • please note: ignore the variability of atmosphere and laser power.

  • 3. For the NDF, higher optical density (OD) values correspond to the LOWER transmittances.

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