What Can the Atmospheric Emitted Radiance Interferometer (AERI) Tell Us About Clouds?
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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
11
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
12
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
19
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