water vapor and cloud detection validation for aqua using raman lidars and aeri david whiteman,...

Water Vapor and Cloud Detection Water Vapor and Cloud Detection Validation for Aqua Using Raman Validation for Aqua Using Raman

Lidars and AERILidars and AERIDavid Whiteman, Belay Demoz

NASA/Goddard Space Flight Center

Zhien Wang, Chris Barnet, Wallace McMillan, Raymond Hoff, Igor Veselovskii, Felicita Russo

University of Maryland, Baltimore County/JCET

Gary J. JedlovecNASA/Marshall Space Flight Center, GHCC

Daniel H. DeSloverUniversity of Wisconsin/CIMSS

And thanks to

John Braun, Chris Rocken, Teresa VanHove, Larry Miloshevich, June Wang – UCAR

Barry Lesht – ANL

Rich Ferrare – NASA/LaRC

OutlineOutline

Overview of work performed to date NWS radiosonde conversion plans Raman Lidar introduction AFWEX water vapor measurements

– 4 water vapor lidars, Vaisala radiosonde, SW Chilled mirror, in-situ sensors, GPS, MWR

Raman Lidar calibration Lidar/retrieval comparisons

– “To tune or not to tune”– Clear and cloudy water vapor and temperature profiles versus 5 retrieval

schemes

Activity to Date and OutlookActivity to Date and Outlook Sept – Nov, 2002 at GSFC

– 26 nighttime clear and cloudy overpasses Scanning Raman Lidar water vapor, sonde T & P, GPS PW

– Study various clear and cloudy retrievals of T & q Jan, 2003 and UMBC

– 15 measurement sessions Scanning Raman Lidar, BBAERI, ALEX, ELF, sonde, GPS 15 Lidar/BBAERI measurements

– 7 AIRS overpasses– Others for study of cirrus particle size retrievals

• AERI vs lidar

Dual sonde launches at GSFC Jan, 2004 deployment to UMBC planned MWR and AERI at GSFC to be running soon (Tsay, Code 913)

– GPS vs MWR

NWS Radiosonde ConversionNWS Radiosonde Conversion NWS announced that Sippican and Intermet will be

supplying radiosonde packages for operational launches over next 3 years– Vaisala and Sippican used formerly– Previous comparisons have found significant differences

Calibration Response at cold temperatures

The new sondes will be phased in and transfer functions between the previous sondes (Vaisala or Sippican) implemented– Smooth through glitches

Dual sonde (Intermet, Sippican) launches coming soon

Raman LidarRaman Lidar

Laser transmitter (UV better)– excites Raman scattering in

atmospheric species. Telescope receiver

– wavelength selection optics separate the wavelengths

Time gated data acquisition gives range information

Measurements– Water vapor mixing ratio– Aerosol backscatter, extinction,

depolarization– Liquid water mixing ratio– Many other things

0

= 351, 355 O2

N2

H O2

W avelength (nm )

Log

In

ten

sity

R ayleigh , M ie and R am an S ignals

Raman Lidar for water vapor measurementsRaman Lidar for water vapor measurements Advantages

– Based on scattering physics-> direct measurement of mixing ratio– Purely vertical profiles– No time lag, sensor contamination issues– Stable calibration has been demonstrated– Temperature sensitivity of Raman scattering well known, is a

small effect (<5%), and can be simulated (accepted Appl. Opt.)

Disadvantages– UT measurements only at night– Raman cross section not well known

Absolute calibration not difficult but will have uncertainties > 10%

),()(

)()( RL

H

H

zP

zPzTkzw

Scanning Raman Lidar (SRL), Sippican Radiosonde Scanning Raman Lidar (SRL), Sippican Radiosonde (T+P), SuomiNet GPS(T+P), SuomiNet GPS

GSFC site• North-largely forested, some open fields• South-suburban sprawl• Patuxent River, Chesapeake Bay, Potomac River

ARM FIRE Water Vapor Experiment (2000)ARM FIRE Water Vapor Experiment (2000)

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8Dec 5, 2000, 0330 UT, AFWEX, ARM CART

SRL, 0330-0400 UT CARL, 0330-0400 UT 0329 UT Vaisala LASE, 0330-0400 UT (<0.05 deg) MPI DIAL, 0329-0339 UT

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Water vapor mixing ratio (g/kg)1E-3 0.01 0.1 1 100

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Water vapor mixing ratio (g/kg)

Goal: characterize UT water vapor technologies (4 WV lidar, sondes, passive, in-situ)Location: ARM CART SGP site northern OK

AFWEX Dec. 5, 2000 06:38 UT

Water Vapor Mixing Ratio (g/kg)

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0123456789

101112

0.01 0.10 1.00

LASECARLSRLVaisalaChilled MirrorSippican (VIZ)

Example Water Vapor Profile Comparison

• CART Raman (CARL) and Scanning Raman (SRL) Lidar profiles correspond to 10 minute averages• LASE profile corresponds to a 2 minute average centered over the SGP site.

Courtesy of Richard Ferrare – NASA/LaRC

% Difference (CART Raman Lidar - LASE)

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<= 1(1,2](2,3](3,4](4,5](5,6](6,7](7,8](8,9]

(9,10](10,11](11,12]

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InitialWith overlap

correction

CART Raman Lidar

% Difference (GSFC Raman Lidar - LASE)

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GSFC Raman Lidar

% Difference (Vaisala sonde - LASE)

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(9,10](10,11](11,12]

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Vaisala (corrected for calib.& temp. dep. calib. corr.)Vaisala (corrected for above + time lag)

Vaisala (uncorrected)Vaisala (scaled to MWR)

wetter than LASEdrier than LASE

Vaisala RS80H

% Difference (sensor - LASE)

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(9,10](10,11](11,12]

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DC-8 In situDLHcryogenic

hygrometer

chilled mirror radiosonde

Sippican radiosonde

Summary of LASE Comparisons from AFWEX


Vaisala RS-80H and RS-90 Time LagVaisala RS-80H and RS-90 Time Lag

Courtesy Larry Milosevich - UCAR

TPW ComparisonsTPW ComparisonsRS-80H and RS-90 vs MWR (SGP)RS-80H and RS-90 vs MWR (SGP)

RS-80H: ~2% dryer than MWR, ~5% standard deviationRS-90 : ~1% dryer than MWR, ~5% standard deviation

Courtesy Barry Lesht, ANL

Assessment of Upper Troposphere Water Vapor Measurements using LASE

• LASE and Raman lidars in excellent average agreement (within ~2-3%)• Corrections to Vaisala RS-80H radiosondes reduced sonde dry bias from 10% to less than 5%• Chilled Mirror sondes about 8-10% drier than LASE and Raman lidars• DC-8 diode laser hygrometer slightly (~3% drier) than LASE and Raman lidars• DC-8 cryogenic frost point hygrometer 10-20% drier than LASE and Raman lidars


Raman water vapor lidar calibrationRaman water vapor lidar calibration

Comparison with respect to radiosonde profile– Normal radiosondes show variability and dry bias – Research radiosondes are promising

Need large enough sample to study variability of calibration

Comparison based on total column water – MWR scaling being performed at the SGP site– GPS provides a stable reference as well

large volume average implies numerous comparisons required to assess calibration constant

– Important to get the bottom part of the lidar profile correct ~5% of TPW can be in the bottom 250 meters SRL uses vertical and scanned lidar measurements along with 2 ground

sensors for composite profile

SuomiNet GPS vs ARM CF MWR (WVIOP3)SuomiNet GPS vs ARM CF MWR (WVIOP3)

Plot courtesy John Braun, Chris Rocken, Teresa Van Hove, UCAR GPS Research Group

SuomiNet GPS vs FSL GPS (WVIOP3)SuomiNet GPS vs FSL GPS (WVIOP3)

Plot courtesy John Braun, Chris Rocken, Teresa Van Hove, UCAR GPS Research Group

SRL Calibration constant determined from SRL Calibration constant determined from GPS and SippicanGPS and Sippican

260 280 300 320 340 360

0.005

0.01

0.015 GPS x 309.374, 27.8469

275 300 325 350 375 400

0.002

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0.006

0.008

0.01

0.012

0.014

VIZ x 338.188, 31.7734

SuomiNet GPS (PW) Sippican radiosonde(profile comparison ~1-2 km)

Sippican profile calibration ~6% moister, PW calibration ~15% moister

Chris Barnet’s retrieval “tweaks”Chris Barnet’s retrieval “tweaks”

Tune: T, E(n,m)=0

NoTune: T=0, E(n,m)=0

Covar: T=0, E(n,n)=(Obs-Calc(n))2

Covar_New: T=0, E(n,m) =(Obs-Calc(n)*Obs-Calc(m))

B50: T=0, E(n,n)=1/4 of Covar

TT EENN

TuneCalcObs

Retrieval Retrieval Tweaks..Tweaks..

• Two versions of “NoTune” for Sept 13 “clear” case

• 2/3/03• 2/21/03

More “clear” examples “NoTune” vs “NoTune2”More “clear” examples “NoTune” vs “NoTune2”

More “clear” examples “NoTune” vs “Notune2” - IIMore “clear” examples “NoTune” vs “Notune2” - II

Some “Clear” Water Vapor ExamplesSome “Clear” Water Vapor Examples

Some “Cloudy” Water Vapor ExamplesSome “Cloudy” Water Vapor Examples

(a)

(b)

The September 14 CaseThe September 14 Case

Cirrus cloud scattering ratio Quick look plot - uncalibrated

A sharp increase in water vapor beneath cirrus cloud decks is common and can persist

SRL vs Aqua water vapor retrieval SRL vs Aqua water vapor retrieval (mean of 17)(mean of 17)

ret dry ret wet

SRL vs Aqua water vapor retrieval SRL vs Aqua water vapor retrieval (9 clear cases)(9 clear cases)

ret dry ret wet

SRL vs Aqua water vapor retrieval SRL vs Aqua water vapor retrieval (8 cloudy cases)(8 cloudy cases)

ret dry ret wet

Sippican vs Aqua Temperature Retrieval Sippican vs Aqua Temperature Retrieval (17 Cases)(17 Cases)

ret warm ret cold

Sippican vs Aqua Temperature Retrieval Sippican vs Aqua Temperature Retrieval (9 clear cases)(9 clear cases)

ret warm ret cold

Sippican vs Aqua Temperature Retrieval Sippican vs Aqua Temperature Retrieval (8 cloudy cases)(8 cloudy cases)

ret warm ret cold

(a)

(b)

Summary of PW comparisonsSummary of PW comparisons Total precipitable water comparisons Ret/SRL

– All: 0.97 – 1.02, Clear: 0.91 – 0.95, Cloudy: 1.01 – 1.07 All retrievals show the following general trends

– increase in moisture below 800-900 mb More rapid for cloudy cases than clear

– clear conditions ~5 - 15% dry bias with respect to SRL above 900 mb

– cloudy conditions generally good agreement (+2% to - 7%) with SRL above 900 mb wet bias (25 – 30%) between 270-460 mb due to missed structure below cloud layers

“Tune” and “Covar_New”– performed similarly in water vapor comparisons

Similar results can be obtained from damping as with tuning “No_Tune”

– shows significant oscillations in clear profiles (+/- 30 – 50%), large positive bias near the surface in cloudy profiles (40-50%)

“Covar” – showed the least positive bias (~25%) in the UT (270 – 460 mb)– Generally dryer than “Tune” by 1-7%

Summary of Temperature ComparisonsSummary of Temperature Comparisons Overall agreement of all retrievals within +/- 1K above 900

mb– NoTune2 up to 2K cold ~950 mb

All retrievals show temperatures decreasing between 850 – 950 mb (~1K), then abrupt increase below 950 mb (~3K)

Under both clear and cloudy conditions, warm bias between 250 – 600 mb of ~0.3 – 1.0K

Significant difference between clear and cloudy retrievals between 600 – 900 mb

– Clear retrievals ~0.5 – 1.0K colder “Tune” and “Covar_New”

– similar performance with the smallest overall biases “NoTune”

– Showed the largest deviations under most conditions “Covar”

– Showed largest warm bias in UT (~0.7 – 1.1 K)

TPW and Temperature comparison results-IITPW and Temperature comparison results-II TPW: (retrieval/SRL)

– 1) entire profile 2) UT (460 – 270 mb), 3) above 1st km, 4) within 1st km, Temp: mean (Sonde – Retrieval) for same layers

Retrieval PW/SRL(986 – 51, 460-270, 904–51, 986–900 mb)

Sonde-Retrieval (K) (986 – 51, 460-270, 904–51, 986–900 mb)

All

(Above 986, UT, Above 1 km,

1st km)

Clear Cloudy All Clear Cloudy

Tune 0.99, 1.16,

0.94, 1.06

0.94, 0.90, 0.88, 1.04

1.03, 1.32, 0.98, 1.08

-0.1, -0.2,

-0.1, +0.0

0.1, -0.3, + 0.1, +0.2

-0.2, -0.2, -0.2, -0.1

NoTune 0.97, 1.17,

0.89, 1.10

0.91, 0.95, 0.84, 1.03

1.02, 1.29, 0.93, 1.15

-0.1, -0.6,

-0.1, +1.3

0.1,-0.8,

+ 0.0, +1.2

-0.1, -0.5, -0.2, +1.4

Covar 0.98, 1.10,

0.94,1.04

0.91, 0.84, 0.87, 1.00

1.04, 1.25, 1.00, 1.07

-0.2, -0.9,

-0.2 +0.0

0.0, -1.1,

-0.1, +0.3

-0.3, -0.7, -0.4, -0.1

CovarNew 0.97, 1.14,

0.92, 1.04

0.91, 0.89, 0.86, 1.01

1.01, 1.29, 0.97, 1.07

-0.1, -0.2,

-0.1 +0.3

+0.1, -0.4, +0.1, +0.4

-0.2, -0.2, -0.2, +0.2

B50 1.02, 1.15,

0.97, 1.10

0.95, 0.95, 0.90, 1.03

1.07, 1.28, 1.02, 1.14

-0.2, -0.9

-0.2, +0.3

-0.1, -1.2,

-0.1, +0.3

-0.3, -0.6, -0.3, +0.2

October 2, 2002 October 2, 2002 a “clear” case with a sharp increasea “clear” case with a sharp increase

Use of MODIS to look for clouds and PW variabilityUse of MODIS to look for clouds and PW variability

Summary and Future WorkSummary and Future Work

Continue to work interactively in algorithm research– Clear and cloudy cases– Focus on UT and 900-1000 mb bias

Assess AIRS cloud products– Lidar, MODIS

Expand use of MODIS to study scene variability UMBC deployments

– Analyze data from 2003 – Deploy in 2004

GSFC-based measurements – Dual (or more?) sonde launches– Compare GPS and MWR at GSFC– Further lidar measurements as needed

Revised Work Plan – July 2001Revised Work Plan – July 2001

L+3 to L+5– Perform validation measurements from mid-Atlantic

region during Aqua overpasses Raman Lidar water vapor/aerosols, radiosonde, GPS 26 successful measurements out of 40 possible

– Nighttime, angles > 40 degrees

Years 2 and 3– Deploy SRL to UMBC

correlated measurements of SRL, BBAERI, ALEX, radiosonde, GPS

Validation under presence of clouds Study particle size retrievals from ground based sensors

Temperature sensitivity of water vapor Raman scatteringTemperature sensitivity of water vapor Raman scattering(accepted by Appl. Opt.)(accepted by Appl. Opt.)

Simulated Raman water vapor spectrum (Avila et. al., 1999)

Simulated temperature correction curves for SRL and CARL water vapor

measurements

AFWEX (2000) Examples - IAFWEX (2000) Examples - I

0.0 0.5 1.0 1.5 2.0 2.5 3.00

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SRL, 0230-0300 UT CARL, 0230-0300 UT 0230 UT Vaisala LASE, 0230-0300 UT (<0.05 deg) 0214 UT Chilled mirror (WFF)

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SRL, 0230-0300 UT CARL, 0230-0300 UT 0230 UT Vaisala LASE, 0230-0300 UT (<0.05 deg) 0214 UT Chilled mirror (WFF)

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Vaisala RS-80 H vs RS-90Vaisala RS-80 H vs RS-90

Courtesy June Wang - NCAR

Vaisala RS-80H and RS-90 – RH CorrectionsVaisala RS-80H and RS-90 – RH Corrections

Courtesy Larry Milosevich - UCAR

Estimate of the residual dry bias in RS-90s is 5-8% RH.

Sippican PW versus Sippican PW versus SuomiNet GPSSuomiNet GPS

Both slope of line and offset indicate Sippican moister– Sippican mean is 15%

moister than GPS

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SRL-MPI # poins used

SRL-MPI, 12/3,5,8,9,10, AFWEX

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Water vapor mixing ratio difference (%)

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CARL-Vaisala, AFWEX, 12/3,5,7,8,9,10

CARL-Vaisala # points used


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SRL-CARL, 12/3, 5, 7-10, AFWEX

SRL-CARL # of points


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SRL-LASE, 12/5, 7, 9, 10, AFWEX

SRL-LASE # of points

Water vapor mixing ratio difference (%)A

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SRL-Vaisala, 12/3, 5, 7-10, AFWEX

SRL-Vaisala # of points


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SRL-Chilled Mirror, 12/5, 7-10, AFWEX

SRL-Chilled Mirror # of points


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SRL, 0623-0652 UT CARL, 0630-0700 UT 0623 UT Vaisala LASE, 0630-0700 (<0.05 deg) 0610 UT Chilled Mirror (WFF)

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September 13September 13

September 20September 20

November 3November 3

SA02 vs GOES (GSFC)SA02 vs GOES (GSFC)

SA02 vs MODIS – Near IR (GSFC)SA02 vs MODIS – Near IR (GSFC)

SA02 vs CIMEL (GSFC)SA02 vs CIMEL (GSFC)

CIMEL vs MWR at SGPCIMEL vs MWR at SGP

ARM SGP (March 2000-Sept 2001) MODIS clear-sky subset

MWR PWV (cm)

Cim

el P

WV

(cm

)

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Slope = 1.07Intercept = -0.029 cmN = 44R = 0.996rms diff= 0.156 cm (9%)bias diff (cimel-mwr)=0.089 cm (5%)

Good

Bad

“UT bad, mid OK”“mid trop bad”“bad”“mid trop a little bad”

% Difference (Vaisala sonde - LASE)

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<= 1

(1,2]

(2,3]

(3,4]

(4,5]

(5,6]

(6,7]

(7,8]

(8,9]

(9,10]

(10,11]

(11,12]

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Vaisala (corrected for calib.& temp. dep. calib. corr.)Vaisala (corrected for above + time lag)

Vaisala (uncorrected)Vaisala (scaled to MWR)

wetter than LASEdrier than LASE

LASE vs. Vaisala RS-80H Radiosondes• Uncorrected Vaisala sondes showed ~8-10% dry bias in upper troposphere• Scaling Vaisala sondes to match MWR PWV did not reduce dry bias in upper troposphere• Vaisala corrections for calibration and temp. dependent calibration reduced bias by 4-5%• Additional correction (Miloshevich) for time lag of Vaisala sondes further reduced bias by 2-3% Total of corrections to Vaisala radiosonde measurements reduced dry bias to less than 5%

Courtesy of Rich Ferrare – NASA/LaRC

LASE vs. DC-8 In situ sensors, Chilled Mirror and Sippican sondes In upper troposphere…

• Chilled Mirror sondes about 8-10% drier than LASE and Raman lidars

• DC-8 diode laser hygrometer slightly (~3% drier) than LASE and Raman lidars

• DC-8 cryogenic frost point hygrometer 10-20% drier than LASE and Raman lidars

% Difference (sensor - LASE)

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(km

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<= 1(1,2](2,3](3,4](4,5](5,6](6,7](7,8](8,9]

(9,10](10,11](11,12]

-150 -100 -50 0 50 100 150

DC-8 In situDLHcryogenic

hygrometer

chilled mirror radiosonde

Sippican radiosonde


LASE vs. CART Raman Lidar (CARL)

• Initial (uncorrected) CARL profiles were 5-7% wetter than LASE in upper trop.• Correction for altitude dependence of CARL overlap reduced CARL values in upper trop. by ~4% LASE and CARL measurements generally within 5% on average for all altitudes

% Difference (CART Raman Lidar - LASE)

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(9,10](10,11](11,12]

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InitialWith overlap

correction

CART Raman Lidar


LASE vs. Scanning Raman Lidar (SRL)

• SRL profiles were ~3% wetter than LASE in upper trop.• SRL slightly (~3-4%) drier than LASE below 5 km LASE and SRL measurements generally within 5% on average for all altitudes

% Difference (GSFC Raman Lidar - LASE)

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GSFC Raman Lidar


AFWEX (2000) Examples - IIIAFWEX (2000) Examples - III

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AFWEX(2000) AFWEX(2000) Examples – IVExamples – IV

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SRL, 0228-0257 UT CARL, 0230-0300 UT 0228 UT Vaisala MPI DIAL, 0226-0236 UT

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AFWEX – Profile SummariesAFWEX – Profile Summaries

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SRL-CARL, 12/3, 5, 7-10, AFWEX

SRL-CARL # of points


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SRL-Vaisala, 12/3, 5, 7-10, AFWEX

SRL-Vaisala # of points


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1) SRL/CARL agreement within +/- 5% from 1-10 km (no low channel in SRL). SRL tends toward being moister than CARL above 10km and below 2 km.

2) SRL/LASE shows moist bias of LASE in lowest 2 km, dry bias above 6 km.3) SRL/Vaisala show increasing dry bias of sonde above 5 km

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SRL-LASE, 12/5, 7, 9, 10, AFWEX

SRL-LASE # of points


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SRL vs Sippican Mean ComparisonSRL vs Sippican Mean Comparison

SRL vs Sippican (Sept. 5)SRL vs Sippican (Sept. 5)

SRL vs Sippican (Sept 6)SRL vs Sippican (Sept 6)

SRL vs Sippican (Sept 7)SRL vs Sippican (Sept 7)

water vapor and cloud detection validation for aqua using raman lidars and aeri david whiteman,...

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