cln qa/qc efforts ccny – (barry gross) umbc- (ray hoff) hampton u. (pat mccormick) uprm- (hamed...
TRANSCRIPT
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
• Indirect (Downstream) tests for retrieval accuracy
• Potential QA/QC efforts for CLN
Testing multi-wavelength lidar signals to the molecular reference
0 2 4 6 8 10 12
10-2
100
102
Altitude (km)
Lida
r si
gnal
s (m
V)
CCNY-lidar, 20060329: 4:24pm (30 min. ave)
Lidar-355molecule
0 2 4 6 8 10 12
10-2
100
102
Altitude (km)
Lid
ar
sig
na
ls (
mV
)
CCNY-lidar, 20060329, 4:24pm (30 min. ave)
Lidar-532molecule
0 2 4 6 8 10
10-2
100
102
Altitude (km)
Lida
r si
gnal
s (m
V)
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
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
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
Range
Blue=exact FernaldGreen=iterative approximations
N=2
N=5
N=10
N=20
Validation (2)Consistency Check Comparison of theoretical and
Measurement Signal
RP
RP
eqnLidar
meas
532nm
355 nmErrors < .3%
Long term stability and evaluation of Lidar System Ratio
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
Cross-Testing of Retrieval Algorithmson same Data
10-6
10-5
10-4
10-3
10-2
0
1
2
3
4
5
6
7
8
a [km-1
sr]
Altit
ude
(km
)
Aerosol Backscatter Coefficient
355
5321064
CCNY Processing UPRM Processing
Extra slides
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.