Linking the Radiative Energy Budget and Remote Sensing of Complex

Cloud and Aerosol FieldsS. Song, K. S. Schmidt, P. Pilewskie (University of Colorado)

Data from: M. D. King, S. E. Platnick, J. Redemann, C. Brock, B. Anderson, R. Ferrare, J. Hair3D radiative transfer model: Hiro Iwabuchi (Tohoku University, Japan)

SSFR support (NASA Ames): Warren Gore, Tony Trias Super-computer “Janus” (NSF MRI) at the University of Colorado

+50%

-40%

SEAC4RS STM, 0845, 4/29/2015 2

• Question: How accurate are imagery-derived surface radiative fluxes (irradiances) below complex cloud-aerosol fields?

• Objective: Develop corrections for such (satellite) products, based on lessons learned from SEAC4RS, TC4, and 3D radiative transfer

Introduction

SEAC4RS STM, 0845, 4/29/2015 3

Can we measure 3D cloud effects?net horizontal photon transport

affects remote sensing and energy budget

energy budgetSchmidt et al., 2010Han et al., 2014Song et al., 2015

remote sensingPlatnick, 2001Marshak et al., 2008

molecular scattering & aerosol scattering+absorption

introduce λ perturbation

F

F I

F

F

In the visible, A≈0

R+T=1-(A+H) 1D 3D

SEAC4RS STM, 0845, 4/29/2015 4

Can we measure 3D cloud effects?net horizontal photon transport

affects remote sensing and energy budgetF

F I

F

F

In the visible, A≈0

magnitude & slope

magnitude [%]

slop

e [%

/nm

]

Photon loss

Photon gain

Photon lossPhoton gain

SEAC4RS STM, 0845, 4/29/2015 5

Can we measure 3D cloud effects?net horizontal photon transport

affects remote sensing and energy budgetF

F I

F

F

In the visible, A≈0

magnitude [%]

slop

e [%

/100

nm

]

08/16/2003

processed:2007/08/062013/08/162013/08/232013/09/022013/09/13

SEAC4RS STM, 0845, 4/29/2015 6

Can we measure 3D cloud effects?net horizontal photon transport

affects remote sensing and energy budgetF

F I

F

F

In the visible, A≈0

magnitude [%]

slop

e [%

/100

nm

]

08/23/2003

without aerosols

processed:2007/08/062013/08/162013/08/232013/09/022013/09/13

SEAC4RS STM, 0845, 4/29/2015 7

τtrue (unknown)

Δτ1 (unknown)

3D RT “nature”

τeMAS (known)

1D retrieval

Δτ2 (known)

τmodel

(known)

1D retrieval

Model 3D RT modeled radiances (synthetic observations)

modeled irrradiances

radiances (measured by eMAS)

irradiances (measured by SSFR)

Radiance-Irradiance approach I

F

F

Radiance effect (Δτ2T)Use eMAS-retrieved cloud distribution and 3D vs. 1D radiance calculations to estimate this effect for various cases.

“cloud 1” “cloud 2”

eMAS

Irradiance effect (H=0 vs. H≠0)Use eMAS-retrieved cloud distribution + aerosol properties (4STAR+LARGE) to calculate irradiance below clouds with 1D and 3D and compare with DC-8 SSFR measurements.

SSFReMAS

8/16/13 – stacked legs

Example – 8/16/13 stacks

1) >≈50% bias (smaller when averaging over large domains)2) Direction of bias depends on cloud spatial context!

“cloud 1” (~30 x 20 km) “cloud 2” (~30 x 30 km)

3D effects on remote sensing

3D effects on irradiance

Example – 8/16/13 stacks

In general, the remote sensing bias (ΔτΔT) is much smaller than irradiance bias, the relative magnitude depends on cloud morphology, sun sensor geometry, surface albedo etc.

Photon “loss”Photon “gain”

Added offset between 3D and 1D for better readability

3D

1D

Spectral multi-pixel signature

eMAS RGB2013/08/23

Radiance Irradiance

Radiance Irradiance

Spectral multi-pixel signature

2013/08/16

Photon lossPhoton gain

Radiance Irradiance

Summary / Future Work

±50%

• Bias of 50% in (satellite-)imagery-derived irradiance common for most SEAC4RS and TC4 cases; they survive spatial aggregation (but get smaller – Song et al., 2015)

• Cloud spatial inhomogeneity manifests itself in spectral perturbations in irradiance and radiance – studied spectro-spatial correlations for SEAC4RS / TC4 cloud morphology + LES

• Aerosols cause additional perturbations, but we can also extract additional information from shadow+sun-lit pixels combined (multi-pixel retrieval)

• Parameterize correlations between spectral perturbations in radiance and irradiance for different cloud types, then use those to retrieve H from radiance spectral perturbations

• Retrieve first-order 3D correction factors for 3D biases in imagery-derived flux products