Aircraft and Ground-based Measurements
of Spectral Solar Radiation
K. Sebastian Schmidt, P. Pilewskie, O. Coddington, B. Kindel
University of Colorado, Laboratory for Atmospheric and Space Physics, Boulder, CO
Focus: Cloud-aerosol spectral radiative effects
SORCE Science Meeting, Annapolis, September 19, 2012
Aircraft and Ground-based Measurements
of Spectral Solar Radiation
…fill in the gaps in understanding the radiative effects of clouds and aerosols
(no satellite instrument looks at Earth like SORCE at the Sun!)
…offer a different perspective of clouds and aerosols
(“within” and “below”)
Aircraft and Ground-based Measurements
of Spectral Solar Radiation
…fill in the gaps in understanding the radiative effects of clouds and aerosols
(no satellite instrument looks at Earth like SORCE at the Sun!)
…offer a different perspective of clouds and aerosols
(“within” and “below”)
Summary:
• Spectral cloud-aerosol property retrieval methods are augmenting current techniques
• Examine issues in cloud-aerosol ‘radiative budget’ by zooming in (out) spectrally
• Caution: 3D effects mess with the spectrum – does information content analysis help?
Why spectral observations of clouds?
…Because they carry information…
(1) …about the adequacy of model assumptions (e.g., inhomogeneites; crystal shape;
thermodynamic phase; aerosols; ...) through spectral consistency
(1) ...that can be exploited with ‘cloud spectroscopy’
• McBride et al., ACP [2011] use slopes of normalized transmittance
to retrieve optical thickness and effective radius from radiance
Caution
• Kokhanovsky et al. [2011]: derive vertical profile of effective radius
King and Vaughan [2012]: assess uncertainty requirements (1-2%)
• Ehrlich et al., ACP [2009]: retrieve properties of mixed-phase clouds
• Spectral approaches now under development for aerosol-cloud scenes
• Thermodynamic phase and ice crystal habit information
3D
(2) ...about the radiance  irradiance conversion (deriving forcing terms from space)
• CERES-derived ADMs are broadband, but 3D effects introduces spectral biases
• Combined aerosol-cloud forcing cannot be determined with current remote
sensing approach
• Satellite-derived surface forcing problematic under ‘messy’ cloud conditions
Why spectral observations of clouds?
…Because they carry information…
or instrument issues
(1) …about the adequacy of model assumptions (e.g., inhomogeneites; crystal shape;
thermodynamic phase; aerosols; ...) through spectral consistency
{R860 nm ; R1600 nm}
{ ; reff }
{h ;  }
gas profiles
{R350 nm – R3800 nm}
Kindel et al., JGR, 2010
Why spectral observations of clouds?
…Because they carry information…
(1)
the adequacy of model
assumptions (e.g., inhomogeneites; crystal shape;

(F…about
net (top) – Fnet (bottom)) / F (top) * 100%
Regularly
observed:
thermodynamic phase; aerosols; ...) through spectral
consistency
slope is only
reproduced by 3D RTM
Non-zero apparent absorption
(1) ...that can be exploited with ‘cloud spectroscopy’with spectral slope.
• McBride et al., ACP [2011] use slopes of normalized transmittance
Mechanism:
net horizontal
to retrieve optical thickness and effective radius
from radiance
Caution
photon transport and molecular
• Kokhanovsky et al. [2011]: derive vertical profile
of
effective
radius
scattering
King and Vaughan [2012]: assess uncertainty requirements (1-2%)
Can
be reproducedclouds
by 3D
• Ehrlich et al., ACP [2009]: retrieve properties
ofonly
mixed-phase
RTM (Schmidt et al., 2010).
• Spectral approaches now under development
for aerosol-cloud scenes
• Thermodynamic phase and ice crystal habit information
3D
(2) ...about the radiance  irradiance conversion (deriving forcing terms from space)
• CERES-derived ADMs are broadband, but 3D effects introduces spectral biases
• Combined aerosol-cloud forcing cannot be determined with current remote
sensing approach
• Satellite-derived surface forcing problematic under ‘messy’ cloud conditions
Schmidt et al., JGR, 2010; Kindel et al., JGR, 2010; Schmidt and Pilewskie, Light Scattering Reviews, 2011
Why spectral observations of clouds?
…Because they carry information…
(1) …about the adequacy of model assumptions (e.g., inhomogeneites; crystal shape;
thermodynamic phase; aerosols; ...) through spectral consistency
(1) ...that can be exploited with ‘cloud spectroscopy’ ƒ (cld)+ƒ (aer)
λ
λ
• McBride et al., ACP [2011] use slopes of normalized transmittance
≠
to retrieve optical thickness and effective radius from
radiance
ƒλ (cld+aer)
Caution
• Kokhanovsky et al. [2011]: derive vertical profile of effective radius
King and Vaughan [2012]: assess uncertainty
requirements
(1-2%)
Schmidt
et al. (2009)
• Ehrlich et al., ACP [2009]: retrieve properties of mixed-phase clouds
• Spectral approaches now under development for aerosol-cloud scenes
• Thermodynamic phase and ice crystal habit information
3D
(2) ...about the radiance  irradiance conversion (deriving forcing terms from space)
• CERES-derived ADMs are broadband, but 3D effects introduces spectral biases
• Combined aerosol-cloud forcing cannot be determined with current remote
sensing approach
• Satellite-derived surface forcing problematic under ‘messy’ cloud conditions
Schmidt et al., 2009; Schmidt and Pilewskie, Light Scattering Reviews, 2011
Why spectral observations of clouds?
…Because they carry information…
(1) …about the adequacy of model assumptions (e.g., inhomogeneites; crystal shape;
Caution
(2) ...that can be exploited with ‘cloud spectroscopy’
• McBride et al., ACP [2011] use slopes of normalized transmittance
to retrieve optical thickness and effective radius from radiance
• Kokhanovsky et al. [2011]: derive vertical profile of effective radius
King and Vaughan [2012]: assess uncertainty requirements (1-2%)
• Ehrlich et al., ACP [2009]: retrieve properties of mixed-phase clouds
• Spectral approaches now under development for aerosol-cloud scenes
• Thermodynamic phase and ice crystal habit information
3D
(3) ...about the radiance  irradiance conversion (deriving forcing terms from space)
• CERES-derived ADMs are broadband, but 3D effects introduces spectral biases
• Combined aerosol-cloud forcing cannot be determined with current remote
sensing approach
• Satellite-derived surface forcing problematic under ‘messy’ cloud conditions
McBride et al., JGR, 2012
Why spectral observations of clouds?
R
T
Why spectral observations of clouds/aerosols?
R
T
T
R
Why spectral observations of clouds/aerosols?
Realistic water clouds (LES, G. Feingold)
cloud top height
variability within box?
reflectance
Caution
Y [km]
3D
spectrum from 1D calculations
for cloud optical thickness and
effective radius retrieved at two
MODIS wavelengths
cloud edge effect?
spectrum from 3D
calculations,0.5 km box (0.1 km
resolution)
effective radius
variability within box
retrieval wavelengths
X [km]
MCARaTS (Monte Carlo Atmospheric Radiative
Transfer Simulator), Iwabuchi 2006
wavelength [nm]
reflectance difference (residual)
Spectral consistency / spectral residual
zero at retrieval
wavelengths
spectrum from 3D calculations
Caution
–
3D
zero at all
wavelengths
would be
expected for a
homogeneous
cloud
spectrum from 1D calculations
for cloud optical thickness and
effective radius retrieved at two
MODIS wavelengths
Spectral 3D effects may be complex!
wavelength [nm]
Multiple mechanisms possible; Rayleigh bluing
near clouds (Marshak/Wen) is one of them.
Spectral Residual (albedo difference)
Alebdo
The spectral residual carries information
1) Observe reflectance (albedo) spectrum from ice cloud (droxtals)
2) Retrieve optical thickness and effective radius, (wrongly) assuming liquid water
3) Based on retrieval, calculate full spectrum and subtract from observation (1)
4) Non-zero residual at non-retrieval wavelengths indicates phase was wrong!
retrieval wavelengths
retrieval wavelengths
wavelength [nm]
wavelength [nm]
Spectral Residual (albedo difference ice cloud – water cloud)
The spectral residual carries information
1) Observe reflectance (albedo) spectrum from ice cloud (droxtals)
2) Retrieve optical thickness and effective radius, (wrongly) assuming liquid water
3) Based on retrieval, calculate full spectrum and subtract from observation (1)
4) Non-zero residual at non-retrieval wavelengths indicates phase was wrong!
retrieval wavelengths
wavelength [nm]
The spectral residual carries information
Residual
Spectral
[%][%]
residual
Spectral
1)1)Observe
Observereflectance
reflectance(albedo)
(albedo)spectrum
spectrumfrom
fromice
icecloud
cloud(droxtals)
(droxtals)
2)2)Retrieve
Retrieveoptical
opticalthickness
thicknessand
andeffective
effectiveradius,
radius,(wrongly)
(wrongly)assuming
assumingcolumns
liquid water
3)3)Based
Basedon
onretrieval,
retrieval,calculate
calculatefull
fullspectrum
spectrumand
andsubtract
subtractfrom
fromobservation
observation(1)
(1)
4)4)Non-zero
Non-zeroresidual
residualatatnon-retrieval
non-retrievalwavelengths
wavelengthsindicates
indicateshabit
phase
was
waswrong.
wrong!
retrieval wavelengths
If we did not need to worry about spectral 3D effects, we could use subtle
differences in the residual spectrum to detect, e.g., thermodynamic phase
or even verify whether the correct habit was used in forward calculations.
wavelength [nm]
Information Content
 Use Bayes’ theorem to sequentially update state vector x (set of cloud parameters
we are interested in) with information from spectral channels [Coddington et al., 2012].
 Gain in information from prior to posterior state vector is seen as narrowing of the
probability density function of cloud-aerosol parameters x, and the entropy S with
respect to x is calculated at each step:
S(x)   p(x)log p(x)
2
 The gain in information at each
H  S prior  S posterior
consecutive step is measured as

Question: To what extent can 3D effects cause spectral contrasts that lead to retrieval
biases?

Answer: Implement known 3D effects as additional
model error.
probability ice
Probability liquid
Assume ice cloud with τ=10 and effective radius 20 micron.
Use a measurement uncertainty of 3%, model error 2%.
wavelength [nm]
2.25 μm channel
Aircraft and Ground-based Measurements
of Spectral Solar Radiation
…fill in the gaps in understanding the radiative effects of clouds and aerosols
(no satellite instrument looks at Earth like SORCE at the Sun!)
…offer a different perspective of clouds and aerosols
(“within” and “below”)
Summary:
• Spectral cloud-aerosol property retrieval methods are augmenting current techniques
• Examine issues in cloud-aerosol ‘radiative budget’ by zooming in (out) spectrally
• Caution: 3D effects mess with the spectrum – does information content analysis help?