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)

description

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

Transcript of CLN QA/QC efforts

Page 1: CLN QA/QC efforts

CLN QA/QC efforts

CCNY – (Barry Gross)UMBC- (Ray Hoff)

Hampton U. (Pat McCormick)UPRM- (Hamed Parsiani)

Page 2: CLN QA/QC efforts

Outline• “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

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Testing multi-wavelength lidar signals to the molecular reference

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Altitude (km)

Lida

r si

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s (m

V)

CCNY-lidar, 20060329: 4:24pm (30 min. ave)

Lidar-355molecule

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CCNY-lidar, 20060329, 4:24pm (30 min. ave)

Lidar-532molecule

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Lidara-1064molecule

CCNY-lidar, 20060329, 4:24pm (30 min. ave)

Lidar System Calibration Regression at 10-11 km

Representative matching of lidar profiles with Molecular profiles

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Good linearity!

NDF-1 (OD=1.6) at 12:56 pmNDF-2 (OD=1.0) at 12:59 pm

Lidar signal profiles

Lidar signal ratio

Lidar signal linearity: signal profiles and their ratios

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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

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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.

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Validation (1)Fernald vs Iterative

Range

Blue=exact FernaldGreen=iterative approximations

N=2

N=5

N=10

N=20

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Validation (2)Consistency Check Comparison of theoretical and

Measurement Signal

RP

RP

eqnLidar

meas

532nm

355 nmErrors < .3%

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Long term stability and evaluation of Lidar System Ratio

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Indirect Check of Optical Property retrieval using Cloud Optical Depth

• 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

Clear sky for aerosol backscatter correction to COD

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Cross-Testing of Retrieval Algorithmson same Data

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Aerosol Backscatter Coefficient

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CCNY Processing UPRM Processing

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Extra slides

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Test of lidar signal linearity at 355-nm

1. Time and date: 1256PM--1259PM, April 21, 2006 2. 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.