Transcript What Can the Atmospheric Emitted Radiance Interferometer (AERI) Tell Us About Clouds?

What Can the Atmospheric Emitted
Radiance Interferometer (AERI)
Tell Us About Clouds?
Dave Turner
SSEC / University of Wisconsin - Madison
CIMSS Seminar
9 Nov 2005
1
Motivation to Study Clouds with Low LWP
• Solar and infrared radiation is most sensitive to changes in cloud
optical depth at low optical depths
• ISCCP results show mean LWP for low level clouds is 51 g m-2
(Rossow and Schiffer 1999)
• Over 50% of the warm liquid water clouds at the SGP site have LWP
< 100 g m-2 (Marchand et al. 2003)
• Over 80% of all liquid-bearing clouds observed during SHEBA have
LWP < 100 g m-2 (Shupe and Intrieri 2004)
• Over 90% of the non-precipitating liquid clouds over Nauru have
LWP < 100 g m-2 (McFarlane and Evans 2004)
• Uncertainty in the LWP observed by 2-channel microwave
radiometers (23 & 31 GHz) is (at least) 20-30 g m-2 (i.e., errors of
20% to over 100%)
• Implications for
– Earth energy balance
– Understanding cloud processes
– Aerosol indirect effect
2
Radiative Flux Sensitivity
Atmos
Tropical
MLW
Eff. Radius
6 µm: Solid
12 µm: Dashed
3
LWP Frequency Distribution for
Warm (> 5°C) Clouds at SGP
Temperature from sonde
Mode:
40 g/m2
Median:
93 g/m2
% below 100 g/m2: 53%
Jan – Dec 1997
Temperature from IRT
Mode:
85 g/m2
Median:
142 g/m2
% below 100 g/m2: 33%
From Marchand et al.,
JGR, vol 108, 20034
Uncertainty in the ARM MWR’s
Statistical Retrieval of LWP
• PDF of LWP using
the statistical retrieval
during clear sky
periods from 1996 –
2001.
• Clear sky periods
were classified as
such if the MMCR and
lidar did not detect any
cloud for a 3-hr period
• Doesn’t include any
systematic biases!
From Marchand et al., JGR, 2003
5
Understanding the Accuracy of the
MWR’s LWP: Different Methods,
Different Results
• 1 Set of MWR Tb observations
• 4 different submissions
– 3 different retrieval methods
– 4 different absorption models
Spread of
40 g/m2
6
Atmospheric Emitted Radiance
Interferometer (AERI)
• Automated instrument measuring downwelling IR
radiation from 3.3-19 µm at 0.5 cm-1 resolution
• Uses two well characterized blackbodies to achieve
accuracy better than 1% of the ambient radiance
• Data used in a
wide variety of
research
• Instrument details
in Knuteson et al.
JAM 2004
• Typically collects
3-min avg every 8
min
7
Location of the ARM AERIs
Pt. Reyes (Mobile Facility)
8
Clear Sky Spectra
25 µm
15 µm
10 µm
7.1 µm
9
Cloudy Sky Spectra (1)
Liquid water cloud at 1.0 km
US Standard Atmosphere
10
Cloudy Sky Spectra (2)
Liquid water cloud at 1.0 km
US Standard Atmosphere
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Atmospheric Model
Assumes a single layer infinitesimally thin cloud
R 

pc
ps
d
B(T ( p))
d ln p 
d ln p
 ppcs  c B(Tc ) 
0
(1   c  rc )  B(T ( p))
pc
d
d ln p 
d ln p
ps


d
pc
rc   B(Ts ) s  ps   B(T ( p))
d ln p 
pc
d ln p


ps
pc
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AERI Retrieval Method (MIXCRA)
• Infrared radiance is very sensitive to cloud with the
condensed water path is small (less than ~50 g/m2)
• Many groups have used the 8-13 µm band to retrieve cloud
properties
• Algorithm developed to retrieve microphysical properties of
mixed-phase clouds in Arctic
– Uses observations in the 8-13 µm and 17-24 µm bands
– Dual-phase retrieval only applicable for low PWV conditions (PWV
< ~1 cm)
– Use of optimal estimation allows algorithm to retrieve single-phase
cloud properties in higher PWV conditions
– Method published in Turner, JAM 2005
• Liquid-only method extended to use observations in the 3-5
µm band during daytime
– Allows the retrieval of a “cloud fraction” term
– Increases the range of the total cloud optical depth that can be
retrieved
– Improves accuracy of the effective radius retrieval
– Method published in Turner and Holz, GRSL 2005
13
Retrieval Uncertainties
• Uncertainties in observations and parameters, as well as
the sensitivity of the forward model, result in
uncertainties in retrieved cloud properties
• Often sample/case specific
• Bayesian and Optimal-Estimation techniques (and
others) are excellent approaches, but can be difficult to
set up problem and identify correlated errors
• A couple of case studies does not replace a more robust,
point-by-point, uncertainty estimation analysis!
14
Uncertainties Example
Retrieving Optical Depth from IR Radiation
• Uncertainty in PWV has a variable impact on the cloud
emissivity (i.e., cloud optical depth)
• Impact is a function of cloud temperature
15
A Word About Optimal Estimation
•
•
•
•
Technique is an old one, with long history
Excellent book by Rodgers (2000)
Many good examples exist in literature
Assumes problem is linear and uncertainties are Gaussian
A priori
State vector
Jacobian
A priori’s Covariance
Observation
Forward model
“Obs” Covariance
• However, the accuracies of the uncertainty in X is directly
related to ability to properly define the covariance matrix of
the observations Sε, which is a non-trivial exercise
• Key advantage is that uncertainties in the retrieved state
vector X are automatically generated by method !
16
Calculating the Observation
Covariance Matrix Sε
• Observed variable is downwelling radiance
• Sources of uncertainty:
–
–
–
–
–
Clear sky radiance (primarily driven by PWV)
Cloud temperature
Instrument noise
Sky variance during sky dwell
Cloud single scattering properties (habit and size distribution)
• Instrument noise is only source that is assumed to be
uncorrelated across the spectrum
• Off-diagonal elements of Sm are critical, but often ignored!
• Determined in my application by using the chain-rule
• Currently not capturing the uncertainty in the cloud
scattering properties in my retrievals
17
Multiple Solutions In Thermal IR?
• More than one answer possible using only thermal
infrared (shown by Moncet and Clough JGR 1997)
• Including 3-5 μm radiance during the daytime results in
unique solution
• However, must invoke a radiative “cloud fraction” term…
14.3
5.0 μm
3.84
7.7 μm
AERI
AERIObs:
Obs:66Nov
Nov2003
2003at
at20.258
20.258UTC
UTC
LBLDIS
LBLDISCalc:
Calc:Tau
Tau==6.6,
6.6,reff
reff==1.5
1.5μm,
μm,Fc
Fc==82%
82%
LBLDIS
LBLDISCalc:
Calc:Tau
Tau==3.2,
3.2,reff
reff==2.5
2.5μm,
μm,Fc
Fc==100%
100%
AERI Obs: 6 Nov 2003 at 20.258 UTC
LBLDIS Calc: Taug = 6.6, reff = 1.5 μm, Fc = 82%
NOTE: LWP the same in both cases!
LBLDIS Calc: Taug = 6.6, reff = 1.5 μm, Fc = 82
Turner and Holz, GRSL 2005
18
Multiple Solutions Example
TSI Cloud Images
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18:00
18:30
TSI Cloud
Images
6 Nov 2003
19:00
19:30
20:00
20
Evaluating
MIXCRA
6 Nov 2003
Turner and Holz, GRSL 2005
• AERI sensitive to LWPs
approaching 70 g/m2
(depends on PWV)
• If only thermal band is
used, then unable to
retrieve optical depths
above 6 so remaining
mass was put into
larger droplets
• Using 8-13 and 3-5 μm
observations gave
much better agreement
with Min algorithm
• Retrieved Fc ~ 1
21
Cumulus Field on 20 Apr 2003 over SGP
22
“Cloud Fraction” on 20 Apr
• AERI samples sky
for 3-min every 8
with ~2° FOV
• Fc retrieved using
both 8-13 and 3-5
μm AERI obs
• Compared with 10
Hz IRT (2.5° FOV)
and 10° zenith
FOV from TSI
• Good correlation
with both high-res
IRT and TSI obs
Turner and Holz, GRSL 2005
23
Cumulus LWP comparisons on 20 Apr
• Challenging to compare instruments with different FOVs and
different sampling in broken clouds like cumulus
• Nonetheless, averaged MWR data correlated well with LWP
from MIXCRA (0.673); non-surprising bias seen
• 3-min sky averages every 8-min are inadequate for Cu studies
24
CLOWD
[Clouds with Low Optical (Liquid) Depth]
• New working group in ARM
• Objectives:
– Characterize current retrievals of LWP and re from different
approaches for LWP < 100 g m-2 for different atmospheric
conditions and cloud types
– Develop a robust retrieval algorithm using standard ARM to
provide accurate LWP and re for all conditions
• First step: Organize an intercomparison of published
algorithms for a finite set of case study days
• Cases include warm stratus, Cu, mid-level mixed-phase,
and overlapping clouds
• Article being written now, will submit to BAMS in Dec ‘05
25
Some Low LWP Retrievals
• MWR retrievals (Clough et al. 2005, Liljegren et al. 2001,
Lin et al. 2001, ARM statistical method)
– Invert brightness temps at 23.8 and 31.4 GHz to get PWV and
LWP
– 4 different submissions, which use different retrieval methods
and absorption models
• MFRSR (Min and Harrison 1996)
– Diffuse transmittance at 415 nm yields 
– Using LWP from MWR, can retrieve re
• MIXCRA (Turner 2005, Turner and Holz 2005)
– Infrared radiance inverted using optimal estimation technique to
yield  and re
• Microbase (Miller et al. 2003)
– Used Liao and Sassen (1994) to relate radar reflectivity to LWC
and estimate re
– Constrained the LWC to agree with the MWR’s LWP
• VISST (Minnis et al. 1995)
– Uses GOES radiance obs at 0.65, 3.9, 11, and 12 μm
– 10 km diameter footprint
26
Overcast Stratiform Case 3/14/2000
20:30
21:00
21:30
27
Retrieved Results
and Closure for
3/14/00
28
Comparing MIXCRA and MWR LWP
retrievals for marine stratiform clouds
• ARM deployed its mobile facility to Pt. Reyes CA from
April – September 2005
• Marine stratiform clouds with low LWP were present very
frequently
• Excellent opportunity to compare the MWR’s retrieved
LWP with that retrieved by MIXCRA…
• Note: Clear sky biases have been observed in the
MWR’s retrieved LWP. Thus, ARM is pursuing a
hypothesis that the clear sky bias can be subtracted,
yielding improved LWPs in cloudy conditions
29
Pt. Reyes Example: 1 July 2005
30
LWP Comparison (MIXCRA and MWR)
MWRRET
MIXCRA-L
MWR Retrievals performed before Tb offsets removed
NSA Site during M-PACE (Oct 2004)
31
MWR Retrievals performed before Tb offsets removed
LWP Comparison (MIXCRA and MWR)
32
Motivation to Study High Clouds
• Upper tropospheric ice clouds cover 40% of the
globe on average at any given time (Liou 1986,
Wylie et al. 1994)
• Occur in extensive sheets covering a large area
• Ice clouds tend to have smaller optical depths,
reflect less incoming solar, and absorb more
infrared radiation than water clouds (i.e. stratus)
• High altitude tropical cirrus can play an
important role in stratospheric/tropospheric
exchange
• Accurate cloud properties are crucial to
– Improving and evaluating GCMs
– Understanding the radiative feedback of high clouds
on climate
33
Sensitivity to Ice Habit
• Most ice cloud remote sensing methods are required to
make some assumption on the habit (or effective density)
of the ice crystals
• This assumption dictates the single scattering properties
of the ice crystals
• Lots of work in the last decade deriving scattering
properties (models) of ice crystals of different habits (bullet
rosettes, hexagonal columns, plates, aggregates, droxtals,
etc.) using geometric optics, FDTD, T-Matrix, anomalous
diffraction approximation, etc.
• Do these models accurately represent the scattering
properties of real ice crystals with that shape?
• Are these models consistent across the entire
electromagnetic spectrum?
• Do these models capture the dynamic range of ice crystal
scattering in the atmosphere?
• How should the vertical variability in habit be treated in
passive retrievals? Or in active retrievals, for that matter?
34
Habit Case Study: NSA 17 Oct 2004
Liquid water
CPI Observations at ~21:00 UT indicated particles were mostly bullets…35
Habit Case Study: NSA 17 Oct 2004
80
Radar – Lidar
Method
(Donovan /
McFarlane)
Wang and Sassen Radar-Lidar
DM-Bullet Rosette
DM-Complex Polycrystal
PY-Bullet Rosette
PY-Column
PY-Aggregate
0
80
AERI
Method
(Turner)
0
• Habit scattering properties from P. Yang and D. Mitchell
• Different sensitivities between IR and radar-lidar techniques!
36
Vertical Profile of Microphysics
• Passive retrievals are extremely limited in ability to
retrieve vertical profiles of re, IWC, etc.
• Comparing active vs. passive methods, need to consider
weighting functions
NSA 17 Oct 2004 at 15:00 UTC
Total optical depth: ~0.8
Radar-lidar re
Radar-lidar extinction coef
Both
methods
assumed
bullet
rosettes
MIXCRA re
37
Sensitivity to Phase in the Infrared
38
Example of a mixed-phase retrieval
Turner, JAM 2005
39
Ice re
Liquid re
Optical
Depth
SHEBA Results: Statistics
Turner, JAM 2005
Liquid only
Mixed-phase
Ice only
40
SHEBA Results
Effective Radius in Liquid-only Clouds
• It is well known that aerosols from mid-latitudes are
advected into the Arctic in the springtime
• Are we looking at the 1st indirect effect of aerosols?
Unfortunately, there were no routine aerosol
observations during SHEBA…
Turner, JAM 2005
41
The Need to
‘Rapid-Sample’
• Initial AERI temporal
resolution: 3 min sky
avg every 8 min
• Selected for clear sky
RT studies and
thermodynamic profiling
• Inadequate to capture
changes in cloud
properties
42
Another RS Example: Cumulus
Rapid sample (16 s avg every 20 s, with occasional 20 s gaps)
Nominal sample (3 min avg every 8 min)
43
AERI Noise Filter Algorithm
• By reducing the averaging time of the radiance
observations, more sky spectra can be collected although
the random error increases proportionally
• Desire to reduce the uncorrelated random error in these
‘rapid-sample’ observations
• Decompose the matrix of AERI radiance obs using
principal component analysis (PCA)
• PCs associated with small eigenvalues are typically
associated with uncorrelated random error; therefore,
reconstruction of the data using a subset of PCs with
largest eigenvalues will reduce the random error
• Objective method is used to identify the number of PCs to
use in the reconstruction
• Algorithm has been extensively tested on over 6 years of
data (2 years from each of the three ARM sites)
• Paper detailing the method and results has been
submitted to JTECH in Aug 2005
44
Noise Filter VAP “Teaser”
• Number of PCs needed for adequate reconstruction is a
function of:
– Instrument
– Location
– Season
– Instrument sampling rate
45
IR Sensitivity to Aerosols
46
“Retrieving” Relative Number of Giant vs.
Accumulation Mode Aerosol
• Assume an effective radius and chemical composition
• Use MIXCRA to retrieve optical depth for different ratios
of number of giant mode to number of accumulation
mode aerosol
• Compare results with MFRSR
• Infer the “correct” ratio of giant vs accumulation mode
aerosol
47
Another Remote Sensor to the Rescue!
48
Can We Say Anything About the
Aerosol Composition?
49
Summary
• Optically thin clouds (both water, ice, and mixed-phase)
occur frequently in nature
• AERI radiances can be inverted to retrieve cloud water
path, effective radius, optical depth (and if PWV is low
enough, phase)
• AERI-retrieved cloud properties are being used to
investigate:
–
–
–
–
–
Biases & sensitivity of MWR retrievals of LWP
Properties of cumulus and marine stratus
Properties of mixed-phase clouds (Arctic and mid-lat)
Consistency of ice single scattering property models
Important input to large ARM effort to compute broadband
heating rate profiles to use in SCM & CRM evaluation
• Information about the coarse mode aerosols, including
their composition
50