Shortwave ADM Types |
Longwave ADM Types |
SW/LW Neural Network ADM Types
* CF = cloud fraction
tau = cloud optical depth
Su W., J. Corbett, Z. A. Eitzen, and L. Liang, 2015: Next-generation angular distribution models for top-of- atmosphere radiative flux calculation from the CERES instruments: Methodology, Atmospheric Measurement Techniques, 8(2), 611-632. doi: 10.5194/amt-8-611-2015.
Su W., J. Corbett, Z. A. Eitzen, and L. Liang, 2015: Next-generation angular distribution models for top-of- atmosphere radiative flux calculation from the CERES instruments: Validation, Atmospheric Measurement Techniques, 8:3297-3313. doi: 10.5194/amt-8-3297-2015.
Corbett, J. and W. Su, 2015: Accounting for the effects of sastrugi in the CERES clear-sky Antarctic shortwave angular distribution models, Atmospheric Measurement Techniques, 8(1), 375-404. doi: 10.5194/amtd-8-375-2015.
Note: Window ADM Types same as Longwave
SW ADM Types |
LW ADM Types |
Cloud Classification Parameter |
Neural Network Based ADMs |
Snow & Ice ADMs |
SSF Snow Identification Parameters
The CERES/Terra SSF dataset includes about 5% of CERES footprints with missing imager information or
insufficient MODIS data for a reliable scene ID. The frequency of occurrence of these fields-of-view depends
on imager viewing geometry, geographic location, and on certain cloud conditions, and can reach up to 50% of
data locally or for a specific scene type (see validation plots below). In order to avoid any systematic bias
in radiative budget data, it is very important to provide accurate TOA flux values for such footprints. This
requires ADMs that can be used with the CERES measurements alone.
Details on building of training sets, ANN training algorithm and results on reproducing CERES ADMs are available in:
General ANN structure 5IN-7TS-11TS-1L (SW case)
For our purpose we use partially connected feed-forward error back-propagation
multi-layer neural neworks. Generalized Delta rule with variable learning rate and a constant momentum
is used as the ANN training algorithm.
Generally, ANN algorithm can be divided into three phases:
Training sets for Edition-2 CERES/Terra ANN-based ADMs are built on one year (2001) of
RAPS and along-track data, both day-time and night-time. The data stratified in the chosen
ANN input variables independently, and for every bin the mean ADM value and STD of distribution
are calculated using CERES footprints with reliable imager information. The goal is to make ANN
reproduce the ADM values over entire training set with minimal error. To avoid noisy configurations,
each training set has an upper limit for STD of ADM value within configuration (bin). On the figure
below it is illustrated for the SW ocean and dark desert scene types:
Standard deviation of the training set ADM versus glint angle. Every entry on the plot represents
a configuration in the training set. The color scale is number of training configuratrions. Noise
cut is shown with a horizontal black line. As it is expected, the noise is cut off uniformly for
the land scene types, and mostly at small glint angle for the ocean.
The configurations with STD(ADM) above the limit are exluded from ANN training.
As result, they are not considered for flux retrieval. All training sets for
all scene types and channels (SW, LW and WN), exept the ocean scene type, consisted
from 10,000 to 15,000 training configurations. The ocean (SW, LW, WN) training set
consisted of about 25,000 training configurations. The off-limit configurations range
from 2% to 5% in case of land scene types, and about 10% in case of ocean scene types
(in sun-glint region the original Terra/CERES ADMs become noisy).
Training was performed with 10,000 iterations for all ANN scene types and channels
(SW, LW and WN). The number of iterations is a compromise between achieving the
lowest possible error and computation time. Figure below illustrates training
process (right) and the result at the end of training (left) for shorwave Bright
Desert ANN scene type. The ANN training errors are defined as mean (bias) and STD of
the difference between the training and ANN ADM values over entire set.
Shortwave Bright Desert scene type ANN training. RIGHT: Decrease of the Error Index with
training iterations. LEFT: ANN value of ADM versus training set ADM value. The mean and STD
values of the difference between two over a training set are considered as ANN training errors.
The ANN scene types are defined by using IGBP geomap (see the table above): evergreen forests (EF),
decidous forects (DF), shrubs (WS), dark desert (DD), bright desert (BD), water bodies (WB), grass (GR),
crops and urban areas (CC), snow-ice land (SN), sea-ice (SI).
When training is successfully finished, ANN provides a very complex 5-dimentional
transfer function. Here are examples of 2-dimentional projections in VZA and RAZ for
Shortwave ANN dark desert
and ocean scene
types (fixed variables: LWR = 90 W/m2sr, SWR = 100 W/m2sr, SZA = 10 and 80 deg.)
The first two weeks of February 2001 CERES/Terra RAPS and along-track data are used
for ANN-based ADMs validation. The ANN-derived flux is compared with CERES/Terra
Edition-2A empirical ADMs flux.
Cloud-mask snow/ice percent coverage (SSF-69):
This parameter is PSF-weighted percent of snow/ice within the CERES FOV
(see CERES SSF Collection Guide, p. 51).
Surface type index (SSF-25):
This is a list of the 8 most prominent surface types within the CERES FOV, which are ordered on area coverage
with the largest being first
(see CERES SSF Collection Guide, p. 25).
Snow/Ice percent coverage clear-sky overhead-sun vis albedo (SSF-30):
Downloads: SW ADM Types | LW Day ADM Types | LW Night ADM Types | CERES/TRMM Shortwave ADMs (tar.Z file)
Shortwave ADMs |
Longwave Day |
Longwave Night |
Window Day |