1. Linking the Radiative Energy Budget and Remote Sensing of Complex Cloud and Aerosol Fields S. 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. Hair 3D 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%

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 SEAC 4 RS, TC 4, and 3D radiative transfer SEAC4RS STM, 0845, 4/29/20152 Introduction

3. SEAC4RS STM, 0845, 4/29/20153 Can we measure 3D cloud effects? net horizontal photon transport affects remote sensing and energy budget energy budget Schmidt et al., 2010 Han et al., 2014 Song et al., 2015 remote sensing Platnick, 2001 Marshak 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

4. SEAC4RS STM, 0845, 4/29/20154 Can we measure 3D cloud effects? net horizontal photon transport affects remote sensing and energy budget F F I F F In the visible, A≈0 magnitude & slope magnitude [%] slope [%/nm] Photon loss Photon gain Photon lossPhoton gain

5. SEAC4RS STM, 0845, 4/29/20155 Can we measure 3D cloud effects? net horizontal photon transport affects remote sensing and energy budget F F I F F In the visible, A≈0 magnitude [%] slope [%/100 nm] 08/16/2003 processed: 2007/08/06 2013/08/16 2013/08/23 2013/09/02 2013/09/13

6. SEAC4RS STM, 0845, 4/29/20156 Can we measure 3D cloud effects? net horizontal photon transport affects remote sensing and energy budget F F I F F In the visible, A≈0 magnitude [%] slope [%/100 nm] 08/23/2003 without aerosols processed: 2007/08/06 2013/08/16 2013/08/23 2013/09/02 2013/09/13

7. SEAC4RS STM, 0845, 4/29/20157 τ true (unknown) Δτ 1 (unknown) 3D RT “nature” τ eMAS (known) 1D retrieval Δτ 2 (known) τ model (known) 1D retrieval Model 3D RT modeled radiances (synthetic observations) modeled irrradiances radiances (measured by eMAS) radiances (measured by eMAS) irradiances (measured by SSFR) irradiances (measured by SSFR) Radiance-Irradiance approach I F F

8. Radiance effect (Δτ 2  T) 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. SSFR eMAS 8/16/13 – stacked legs Example – 8/16/13 stacks

9. 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 (Δτ  ΔT) is much smaller than irradiance bias, the relative magnitude depends on cloud morphology, sun sensor geometry, surface albedo etc.

10. Photon “loss”Photon “gain” Added offset between 3D and 1D for better readability 3D 1D Spectral multi-pixel signature eMAS RGB 2013/08/23 RadianceIrradiance

11. RadianceIrradiance Spectral multi-pixel signature 2013/08/16 Photon loss Photon gain

12. RadianceIrradiance Summary / Future Work ±50% Bias of 50% in (satellite-)imagery-derived irradiance common for most SEAC 4 RS and TC 4 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 SEAC 4 RS / TC 4 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