radiative coupling in the oceans using modis-aqua ocean radiance data watson gregg, lars nerger...
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Radiative Coupling in the Oceansusing MODIS-Aqua Ocean Radiance Data
Watson Gregg, Lars Nerger Cecile Rousseaux
NASA/GMAO
Assimilate MODIS-Aqua Water-Leaving Radiances to:
1) Improve understanding of the propagation of light in the surface layer Primary ProductionHeat transfer -- Changes in ocean density structure and circulation
2) Invert the radiance assimilation into new information on model phytoplankton groups, CDOM, and other internal model variables.
Chlorophyll, Phytoplankton GroupsPrimary ProductionNutrientsDOC, DIC, pCO2
Spectral Irradiance/Radiance
Outputs:
RadiativeModel
(OASIM)
CirculationModel
Winds SSTShortwave Radiation
Layer DepthsIOP, Eu
Ed
Es
Sea Ice
NASA Ocean Biogeochemical Model (NOBM)
Winds, ozone, humidity, pressure, precip. water, clouds, aerosols
Dust (Fe)
Advection-diffusion
Temperature, Layer DepthsBiogeochemicalProcesses Model
Ed
Es
Ozone
Molecules, aerosols
OxygenWater vaporCO2
air
sea
Ed
Es
(1 - )
EdEs
(1 - )EdEsEu
Ed, Es Ed, Es
LwN
Total Surface Irradiance (direct+diffuse; spectrally integrated; clear/cloudy): bias=1.6 W m-2 (0.8%)RMS=20.1 W m-2 (11%)r=0.89 (P<0.05)
OASIM
Surface Clear Sky Spectral Irradiance (PAR wavelengths):RMS=6.6%Integrated PAR:RMS=5.1%
mW
cm
-2 u
m-1 s
r-1
Modeling Water-Leaving Radiances (with assimilated chlorophyll) Tropical
Rivers(CDOM)
Cocco-lithophores
450 475 500 600 625 650 675
a(λ), bb(λ)
525 550 575 700350 375 400 425
Chlorophyll components:diatomschlorophytescyanobacteriacoccolithophores
CDOM
water
aw(λ), bbw(λ)
aCDOM(λ)
ap(λ), bbp(λ)
OASIM Upwelling Irradiance(Forward Model)
detritus
ad(λ),bbd(λ)
443 488412 550 667
MODIS-Aqua Upwelling Radiance(Inverse Model)
Objective 2: Invert the radiance assimilation into new information on model phytoplankton groups, CDOM, and other internal model variables.
Consistent Ocean Chlorophyll from NPP/VIIRS NPP Science Team for Climate Data Records
Watson W. Gregg and Nancy W. Casey
Investigate the ability of an established approach to improve the consistency of VIIRS ocean color data.
Utilizes completely processed and gridded ocean color data (Level-3 Environmental Data Records):
1) applies in situ data for a posteriori correction, and then 2) applies data assimilation to correct sampling problems.
In situ Data
NODCNASA AMT
ESRIDEmpirical Satellite
Radiance-In situ Data
Assimilated Data
ESRID-Assimilated VIIRS Ocean Color Data
Derive statistical relationships
to reduce bias
Global couplednumerical model
Assimilate to reduce sampling biases
Standard processingSatellite Data
VIIRS EDRs
ESRIDReduces bias in satellite observations,and sensor-to-sensor differences.Including those due to:
Radiometric calibrationBand locations and widthsBand misregistrationOut-of-band effects/crosstalk (mostly)AlgorithmsOrbit
Gregg, et al., 2009, Remote Sensing of Environment
Satellite-Weighted Error: Global Ocean
-5%
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%P
erce
nt
Err
or
SeaWiFS 28.0% 40.6%
ESRID 0% w/h 0.7% 37.3%
ESRID 50% w/h -0.3% 38.9%
1 2
(0.130) (0.298)
(0.020)
(0.028)
(0.291)
(0.291)N=2922
N=5994
N=5920
Bias Uncertainty
w/h = withholding of in situ datalog bias and uncertainty in parentheses
ESRIDEmpirical Satellite Radiance-In situ Data Algorithm
0.150
0.155
0.160
0.165
0.170
0.175
0.180
0.185
0.190
0.195
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Ch
loro
ph
yll (
mg
m-3
)
SeaWiFS-standard
MODIS-standardSeaWiFS-ESRID
MODIS-ESRID
Mean Difference MODIS-SeaWiFS:
Standard -12.2% ESRID -0.6%
Global Annual Median Chlorophyll circa 2007 Processing
ESRID improves consistency between disparate data setsUnifies the representation of ocean biology from ships and satellitesGregg and Casey, Geophysical Research Letters, 2010
Aerosol optical thickness limit = 0.4 globally
= 0.3North/Equatorial Indian
= 0.3North Central/South Atlantic
Data eliminated Equatorial Atlantic
Two-Step Process to Remove Sampling Differences:1) Remove high aerosol regions and tropical river regions (Equatorial Atlantic)