Development of Correction Factors to Derive a Global Radiation Budget From Triana Data

1
Development of Correction Factors to Derive a
Global Radiation Budget From Triana Data D. R.
Doelling, J. Huang, J. D. Kenyon Analytical
Services Materials Inc. Hampton, VA P. Minnis,
NASA Langley Research Center, Hampton, VA F. P.
J. Valero Atmospheric Research Laboratory,
Scripps Institute of Oceanography, La Jolla,
CA Corresponding author email d.r.doelling_at_larc.na
sa.gov
TRIANA GLOBAL CORRECTION FACTOR RESULTS
Introduction One of Triana goals is to monitor
the earths energy budget by monitoring both the
reflected Shortwave (SW 0.2-4µm) and emitted
Longwave (LW 4-100µm) radiation - 3 active cavity
radiometers to measure radiance, Total
(0.2-100µm), SW (0.2-4µm), NIR (0.7-4µm) - Each
radiometer measurements represents an entire
earth view Triana has a Lagrange-1 (L1) orbit,
designed to continually monitor the sunlit side
of the earth - Eliminates the need for regional
SW temporal averaging to estimate monthly
fluxes - Radiometer measurements taken every 10
minutes - The L1 orbit is 1.5 million km from the
earth and orbits the sun the same rate as the
earth - Triana will orbit about L1 and varies
from 4 to 15 from the Earth-sun line. Triana
radiances must be converted into fluxes and the
global flux must take into account radiation not
viewed by Triana - In the SW a small sliver of
the illuminated earth disc is not seen by
Triana - Triana earth views are entirely in the
backsatter direction, with scattering angles from
165 to 176 - Triana does not measure night time
LW fluxes Approach Simulate a hourly Broadband
spatially gridded global earth radiation budget
dataset Compute the Triana-view radiance, by
summing regional radiances derived from the
gridded fluxes using SW bidirectional and LW
limb darkening models Derive global correction
factors - SW bidirectional factor Triana-view
SW flux / Triana SW radiance - Missing light SW
(Albedo) factor total earth SW flux /
Triana-view SW flux - LW limb darkening factor
Triana-view LW flux / Triana LW radiance - LW
night time (OLR) correction factor total earth
LW flux / Triana-view LW flux Can the global
correction factors be predicted? - Use fourier
analysis to retrieve the diurnal, seasonal,
inter-annual and orbit about L1 cycles - Estimate
the global earth radiation budget errors Triana
Global Correction Factor Results The greatest
SW bidirectional factor occurs in direct
backscatter or 0from L1 The greatest missing
light SW factor occurs when highly reflective
polar regions are observed at 15N and 15S of
L1, the missing light usually increases the
albedo The greatest seasonal correction occurs
at the L1 position closest to the unlit pole,
which has the greater ice cover The LW
night-time factor has a greater diurnal cycle
than seasonal cycle, it is dependent on diurnally
varying hot desert surface temperatures and high
cloud coverage The LW night-time factor is
usually less than 1 since most daytime scenes
emit more radiation during the day than at
night, especially clear-sky land LW Triana Global
Correction Prediction Model Results First the
4 year simulated Earth fluxes have their
inter-annual trend removed, so that the corrected
Triana fluxes have no inherent inter-annual
biases, based on a second degree polynomial fit
Fourier analysis reveals a strong diurnal and
seasonal variations Diurnal amplitude depends
on season Prediction model contains a long-term
trend, seasonal and seasonaly modulated diurnal
cycle Derive Prediction model coefficients
using 1985-1987 data and compare to 1988
simulated data - Preliminary results indicate
prediction can resolve most of the LW correction
factor variation - longer time series needed to
perfect fourier analyses Future Efforts Cloud
information from the Triana EPIC visible imager
shows promise in predicting the LW night-time
factor Use CERES instead of ERBE bidirectional
models, random errors in the bidirectional have a
small effect on the global correction factors
especially after an extended temporal averaging
Evaluate Prediction models for the other global
factors Improve and extend the Simulated data
set to remove ERBE and ISCCP sampling biases, ie
differing satellite combinations, which may show
up in the prediction model coefficients Simulat
ed Earth Radiation Budget (ERB) Dataset
Midlatitudes (60S-60N) Obtain ERBE-ERBS
instantaneous 2.5 by 2.5 latitude by longitude
gridded fluxes during 1985-1988 - ERBS samples a
given location only twice a day - ERBS cycles
through all local hours in 36 days - ERBS
coverage is from 70S to 70N Obtain ISCCP 3
hourly geostationary (GEO) visible and IR gridded
radiances Derive narrowband to broadband
monthly relationships for ocean, land and snow
for each geostationary satellite using
coincident GEO narrowband and ERBS broadband
fluxes - Takes into account calibration drifts
and season vegetation cycles - SW relationship is
a function of visible albedo (ERBE bidirectional
model), square of and a solar zenith angle
term - LW relationship is a function of IR flux,
square of and the column wieghted relative
humidity Apply the monthly relationships to
compute GEO derived broadband fluxes Normalize
the GEO derived broadband fluxes with coincident
ERBS fluxes to remove regional biases Use ERBE
temporal interpolation to estimate fluxes between
two ISCCP measurements Validate the hourly
fluxes by comparing with NOAA-9 or NOAA-10 ERBE
fluxes - determine improvement from using ERBE
time interpolated fluxes only Poles (90S-60S
60N-90N) Use NOAA-9 ERBE instantaneous
fluxes during 1985-1986 and NOAA-10 fluxes during
1987-1988 Both are polar orbitors and sample
the poles up to 14 times a day Local equator
crossing times are 1420 and 730 for NOAA-9 and
NOAA-10 respectively Use ERBE temporal
interpolation to estimate fluxes between NOAA
measurements Simulated (ERB) Results
Seasonally (wavelength dependent) varying surface
features and differences in satellite imager
filters are apparent in the narrowband to
broadband coefficients GEO derived broadband
fluxes improve time interpolation compared with
ERBE-only interpolation especially the longer
the time span between ERBE measurements - In the
clear-sky the LW the diurnal cycle is preserved -
In the SW improvements in cloudy scenes are
evident Normalization removes the biases in
the GEO derived flux product Need to take out
regional biases in the clear-sky land albedo, not
enough data to perform narrowband to broad band
relationships over each surface type Future
efforts Perform ERBE time interpolation using
4 cloud scene types instead of relying on the
clear and overcast models only
Triana-view albedo for March 21, 1986 at 15 east
from L1
March 1986 mean shortwave parameters as a
function of L1 offset position
1986 Seasonal monthly mean missing light
correction factor as a function of L1 offset
position
Fourier analysis of the monthly hourly LW
night-time correction factor for 15 North of L1
Predicted and simulated 1988 monthly LW
night-time correction factors
seasonal
diurnal
March 1986 mean shortwave parameters as a
function of L1 offset position
1986 Seasonal monthly mean night-time LW
correction factor as a function of L1 offset
position
Scatter plot of LW night-time correction factor
as a function of cloud correction factor
diurnal
Where cloud correction factor equals the global
cloud amount divided by Triana-view cloud amount
seasonal
Regional instantaneous simulated flux rms errors
when compared with NOAA for Jan. 1986
SIMULATED EARTH RADIATION BUDGET RESULTS
ERBE t-s
GEO only
Geo normalized
1986 Seasonal variation of the narrowband to
broadband coefficients for each GEO satellite
Monthly mean fluxes for Jan 1986
GMS-03 SW and LW narrowband to broadband
relationships for January and August of 1986
SW
Red line for sza10 Green line for sza45 Blue
line for sza75
SW
LW
SW ocean
SW-clear
LW
Red line for sza10 Green line for sza45 Blue
line for sza75
Instantaneous simulated flux rms errors as
function of hours from closest ERBS measurement
when compared with NOAA for Jan. 1986
SW land
LW-clear
SW
Monthly mean LW diurnal range for Jan 1986
ERBE t-s
GEO only
Geo normalized
Red line for colrh10 Green line for
colrh50 Blue line for colrh90
SW-clear
LW
LW
LW
LW-clear
LW-clear