MODIS OMI CALIOP Goal: To use A-Train aerosol obs to constrain aerosol radiative properties to calculate F aerosol (z) Myhre, Science, July 10, 2009: 1) 1)Observation-based methods too large 2) 2)Models show great divergence in regional and vertical distribution of DARF. 3) 3)“remaining uncertainty (in DARF) is probably related to the vertical profiles of the aerosols and their location in relation to clouds”.
Direct aerosol radiative effects based on combined A-Train
observations Jens Redemann, Y. Shinozuka, J. Livingston, M.
Vaughan, P. Russell, M.Kacenelenbogen, O. Torres, L. Remer BAERI
NASA Langley - NASA Ames SRI NASA Goddard Outline Goal: To devise a
new, methodology to derive direct aerosol radiative effects - F
aerosol (z) based on CALIOP, OMI and MODIS Motivation, data sets
and role of field observations Methodology for combining CALIOP,
OMI and MODIS data Checking consistency of input data Proof of
concept for 4-month data set Jan., Apr., Jul., Oct Impact of input
data sets Comparisons to AERONET and CERES flux products
Conclusions MODIS OMI CALIOP Goal: To use A-Train aerosol obs to
constrain aerosol radiative properties to calculate F aerosol (z)
Myhre, Science, July 10, 2009: 1) 1)Observation-based methods too
large 2) 2)Models show great divergence in regional and vertical
distribution of DARF. 3) 3)remaining uncertainty (in DARF) is
probably related to the vertical profiles of the aerosols and their
location in relation to clouds. Target: F aerosol (z) + F aerosol
(z) Constraints/Input: - MODIS AOD (7/2 ) + AOD - OMI AAOD (388 nm)
+ AAOD - CALIPSO ext (532, 1064 nm) + ext - CALIPSO back (532, 1064
nm) + back Goal: To use A-Train aerosol obs to constrain aerosol
radiative properties to calculate F aerosol (z) Retrieval: ext (,
z) + ext ssa (, z) + ssa g (, z) + g MODIS aerosol models: 7 fine
and 3 coarse mode distribution models define size and refractive
indices of bi-modal log-normal size distribution 100 combinations
Free parameters: N fine, N coarse Issues to consider - Differences
in data quality land/ocean - - Impact of model assumptions - -
Spatial variability - - Aerosols above & near clouds Rtx code
Methodology: Role of field observations Use suborbital observations
to: 1) 1) Guide choices in aerosol models 2) 2)Test retrievals of
aerosol radiative properties 3) 3)Test calculated radiative fluxes
4) 4)Study spatial variability = uncertainty involved in
extrapolating to data- sparse regions (e.g., above clouds) See
poster A43A-0127 Kacenelenbogen et al. Re 4): Shinozuka and
Redemann, ACP, 2011 Solution space: expansion from over-ocean MODIS
models ARCTAS data are corrected after Virkkula [2010]. Role of
suborbital observations: 1) Test realism of aerosol models 7 fine +
3 coarse modes SSA and EAELidar Ratio and EAE : retrieved
parameters : observables : uncertainties in obs. : weighting
factors Current choices in retrieval method: 1) 1)Metric / error /
cost function 2) 2)4 Observables x i = AOD 550nm (0.035%) AOD 1240
nm (0.035%) - MODIS AAOD 388 nm ( %) - OMI 532 (0.1Mm -1 sr %), -
CALIOP 3) 3)Minimize X and select the top 3% of solutions that meet
for all i Example of successful retrieval from actual collocated
MODIS, OMI, CALIOP (V3) data: Oct. 23, 2007 Consistency issues: AOD
comparisons (CALIOP V3) Eight months of data: January, April, July
and October 2007 and 2009 Use CALIOP 5/40km-avg. (V3/V2) aerosol
extinction profiles, and 5km aerosol and cloud layer products Find
all instantaneously collocated, MODIS MYD04_L2 (10x10km) aerosol
retrievals traversed by 5km/40km CALIPSO track Judicious use of
data quality flags Break down geographically zonal mean AOD See
Redemann et al., ACPD for details See Kacenelenbogen et al., ACP
2011 for potential explanations for CALIOP-MODIS differences
Latitudinal distribution of AOD differences between MODIS and
CALIOP V3 MODIS-CALIOP AOD Latitude Redemann et al., ACPD Main
findings, ocean: 1. 1.bias differences of (with CALIOP
