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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). Outline. “Raw” signal tests. Matchups against Rayleigh Linearity tests with ND filters Member processing algorithms Efforts to test algorithms for consistency - PowerPoint PPT Presentation

Text of CLN QA/QC efforts

  • CLN QA/QC effortsCCNY (Barry Gross)UMBC- (Ray Hoff)Hampton U. (Pat McCormick)UPRM- (Hamed Parsiani)

  • OutlineRaw signal tests. Matchups against Rayleigh Linearity tests with ND filtersMember processing algorithms Efforts to test algorithms for consistencyIndirect (Downstream) tests for retrieval accuracyPotential QA/QC efforts for CLN

  • Testing multi-wavelength lidar signals to the molecular referenceLidar System Calibration Regression at 10-11 kmRepresentative matching of lidar profiles with Molecular profiles

  • Good linearity!NDF-1 (OD=1.6) at 12:56 pmNDF-2 (OD=1.0) at 12:59 pm Lidar signal profilesLidar signal ratioLidar signal linearity: signal profiles and their ratios

  • CCNY ProcessingStandard 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 method1064 channel uses system constant based on cirrus cloud calibration

  • CCNY Lidar Algorithm and Cross-Testing EffortsDifferent algorithms tested against each other.Intercompare iterative and Fernald solutionsConsistency check Compare Measured Signal with Retrieved Signal after optical property retrieval1064 channel system constant evaluation over long time periodsIndirect 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 RangeBlue=exact FernaldGreen=iterative approximationsN=2N=5N=10N=20

  • Validation (2)Consistency Check Comparison of theoretical and Measurement Signal 532nm355 nmErrors < .3%

  • Long term stability and evaluation of Lidar System Ratio

  • Indirect Check of Optical Property retrieval using Cloud Optical DepthRaman COD retrieval based on successful derivation of cloud extinction and integratingMie COD based on S. Young regression method and uses aerosol backscatter corrections above and below cloudClear sky for aerosol backscatter correction to COD

  • Cross-Testing of Retrieval Algorithmson same DataCCNY ProcessingUPRM Processing

  • Extra slides

  • Test of lidar signal linearity at 355-nmTime 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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