1ASIC-3 Workshop16-18 March 2006

Climate Quality Observations from Satellite LidarDave Winker, NASA LaRC, Hampton, VA

28 April ‘06

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Initial Thoughts

Propose that lidar can provide climate-quality measurements with extremely good stability and sufficient accuracy

Two ways to look at lidar and climate data records (CDRs):

CDRs can be constructed from lidar (cloud, aerosol) measurements

Lidar measurements can be used to intercompare with CDRs derived from other instruments/systems or assess underlying assumptions of retrievals

Lidar allows direct measurements of some parameters

Pre-launch characterization, but not calibration per se

‘self-calibrating’ in a sense: avoids traditional calibration concerns

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Primary Space Lidars for Earth, So Far

LITE (STS-64)

1994

GLAS (ICESat)

2002-?

CALIOP (CALIPSO)

2006-9

1064/532/355 nm 1064 nm: altimetry 532 nm: profiling

1064/532 nm532 par and perp

57o inclination 94o inclination180-day precession

98o inclinationSun-synchronous

Focus is on cloud and aerosol measurements

Won’t discuss DIAL, Doppler, etc.

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Climate Dataset Requirements: Clouds

To directly observe climate change, stable and accurate cloud measurements are necessary

The2002 Satellite Calibration Workshop defined cloud measurement requirements for climate datasets (NISTIR 7047; Ohring, et al., BAMS, 2005):

Accuracy Stability

Cloud cover 1% 0.3%

Cloud height 150 m 30 m

Optical depth 10% 2%

Ice/water phase N/A N/A

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CMIP – year 70 mean of a 1% per year increase in CO2 minus the control (fixed CO2)

The model-to-model spread is a measure of ‘uncertainty’ and it is thought to be largely governed by uncertain climate ‘feedbacks’ that all involve cloud processes.

The response of low-cloud cover to CO2 doubling

One ‘IPCC’ climate model

Another ‘IPCC’ climate model

Change in low cloud amount w/ 2X CO2

ForT ~ 0.2 K per decade,

cloud cover) ~ 0.2 - 0.4%/decade

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705 km, sun-synchronous orbit

Three co-aligned instruments:

CALIOP: polarization lidar– 532 nm || and 1064 nm

– 2 x 110 mJ @ 20 Hz

– 1-meter telescope

– 0 – 40 km altitude, 30 - 60 m

IIR: Imaging IR radiometer

WFC: Wide-Field Camera

CALIPSO

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CALIOP

CALIPSO Payload

Laser Nd: YAG, 2x110 mJ Wavelength 532 nm, 1064 nm Repetition rate 20.25 Hz Receiver telescope 1.0 m diameter Polarization 532 and Footprint/FOV 100 m / 130 rad Vertical resolution 30 - 60 m Horizontal resolution 333 m Lin. dynamic range 22 bits

Lidar Transmitter/boresight system

1-meter receiver telescope

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Cloud Detection/Height

’air (from model)

’total (measured)

Adaptive threshold:

Cloud and aerosol layers identified by contrast with molecular background

Sea surface establishes reference for height measurements

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‘Typical’ Scene

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CALIOP Layer Detection Sensitivity

0

5

10

15

20

25

1E-3 0.01 0.1 1

single-shot

1 km

20 km day night

Layer optical depth (532 nm)

Alti

tud

e (

km)

(assuming 300 m layer and Sc = 20)

0

5

10

15

20

25

1E-4 1E-3 0.01 0.1 1 10

Stratus

Sv Ci

Cirrus

LITE aerosol

single-shot

1 km20 km

Backscatter Cross-section (532 nm, /km/sr)

Alti

tud

e (

km)

day night

Clouds with ≥ 0.01 could be climactically significant, should be monitored

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Lidar vs. 95 GHz Radar for Cloud Detection

Radar-lidar composite from CRYSTAL-FACE (26 July) blue: lidar-only, yellow: radar-only, green: both

Lidar is sensitive to the thinnest clouds, easily detects water clouds Cloud-profiling radar (CPR) is insensitive to small droplets and low concentrations of ice crystals

Lidar + CPR: best approach for cloud base:

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2- Improves Cloud-Aerosol Discrimination

Separation of cloud and aerosol using ’ = ’1064/’532

To a large degree, cloud and aerosol can be separated by scattering strength, but there are two problem areas:

• There is a region of overlap in scatter strength where 2- measurements are necessary.

• Attenuation by upper layers will cause lower cloud layers to be classified as aerosol.

’

Integrated scatter

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Detection of Multiple-layers

Vertical distribution of highest cloud-top vs. all detected cloud tops (Tropics)

0

5

10

15

20

0 5000 10000 15000 20000 25000 30000

Cloud PDF, 20N - 0N

# events

altit

ude

(km

)

FirstTop20_0 Layers20_0

LITE:

45% of cloudy returns reached surface

75% reach top of boundary layer (2 km)

80% mean cloud cover

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Accuracy is driven by spatial sampling

Example: cloud cover

Nadir-pointing lidar can achieve required accuracies at seasonal-zonal scales

Cross-track measurements would enable monitoring at regional scales

RMS uncertainty in cloud cover for nadir-only observations

Fowler et al., 2000:

4o x 5o, monthly

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Cloud Ice/Water Phase

The threshold temperature dividing mixed-phase and ice clouds is not well known Ice/water partitioning is an important modulator of the climate sensitivity in climate models

Direct, vertically resolved observations of ice/water phase are needed to address this issue

(Fowler and Randall, 1996: J. Clim. 9, 561)

Back-scatter

Depol

Lidar can directly sense particle sphericity

- Backscatter from liquid droplets retains the incident polarization

- lidar depolarization = P/P||

- Backscatter from ice crystals is depolarized (typically 20-40%)

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Cloud optical depth

Radiative forcing is most sensitive to changes in thin clouds ( < 1), and clouds with optical depths as small as 0.01 need to be monitored

Lidar provides direct measurement of optical depth of ‘thin’ cirrus– Optical depth from layer transmittance (for < ~3)

– Corrections for multiple scattering may be required

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HSRL: Direct measurement of AOD

Climate requirement for aerosol optical depth (AOD):

- Accuracy: 0.01 (~ 7%)

- Stability: 0.005 (~ 3%)

HSRL has molecular and Mie channels, providing direct measurement of aerosol attenuation

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Instrument Requirements for Clouds

The required measurements can be obtained from current (CALIPSO), planned (EarthCare) satellite lidars and suitably-designed follow-ons:

Vertical resolution: 30-150 meters

Two-wavelengths (for cloud/aerosol discrimination)

Depolarization (for ice/water phase)

Exact wavelengths and instrument sensitivity need not be the same

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Current and Under Development

CALIOP (CALIPSO) ALADIN (ADM/Aeolus) ATLID (EarthCare)

1064 nm 532 nm 532 depolarization

355 nm HSRL X

No depolarization X

1064 nm 355 nm HSRLdepolarization

z = 30/60 m z = 1 km X z = ?

w/ passive sensors ---- w/ passive sensors

98o inclination, 16-day Sun-synch, 1:30 PM

97o inclination, 7-day rptSun-synchronous

Sun Sync polar orbit10:30 AM

2006-2009 2008 - ? 2012 - ?

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Mission Considerations

Sun-synchronous polar orbit preferred– Good latitude coverage

– Provides sampling at constant local time of day

Precessing orbit– Biases the diurnal cycle into the record

– Rapidly precessing orbits have low inclination angles: limited global coverage

– Slowly precessing orbits> Precession through 24 hours in 1 year: preserves interannual variability but prevents

monitoring changes on shorter timescales

Prefer to fly with other instruments – Provide acquisition of simultaneous, coincident CDRs

– Allow intercomparison/cross-calibration

Due to inherently high stability of lidar measurements, overlap between instrument data records may not be required

– Needs to be tested

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Issues/Opportunities

How to relate lidar cloud cover to passive cloud cover– High lidar sensitivity gives higher cloud fraction than passive

How to relate lidar cloud top to passive cloud ‘top’– Lidar profiles cloud to optical depth of 3-5 (the portion which interacts radiatively in

the thermal IR)

Expand definition of ‘cloud height’ to include multilayer cloud height

Can we stitch together data records collected at different equator crossing times?

Poor spatial sampling of nadir-viewing lidar– Restricts climate-accuracy monitoring to large space-time scales

– Scanning or multi-beam lidar provides improved statistics

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Notional Multi-beam Concept

“Simple” backscatter lidar in formation with NPOESS or other platform

Accuracy improved by adding multi-beam lidar with cross-track coverage

For cloud monitoring, want independent samples– Widely spaced

measurements are more efficient