1 ASIC-3 Workshop 16-18 March 2006 Climate Quality Observations from Satellite Lidar Dave Winker,...
-
Upload
leonard-singleton -
Category
Documents
-
view
219 -
download
1
Transcript of 1 ASIC-3 Workshop 16-18 March 2006 Climate Quality Observations from Satellite Lidar Dave Winker,...
1ASIC-3 Workshop16-18 March 2006
Climate Quality Observations from Satellite LidarDave Winker, NASA LaRC, Hampton, VA
28 April ‘06
2
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
3
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.
4
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
5
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
6
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
7
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
8
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
9
‘Typical’ Scene
10
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
11
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:
12
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
13
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
14
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
15
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%)
16
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
17
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
18
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
19
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 - ?
20
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
21
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
22
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