CloudSat and CALIPSO cloud and aerosol information is convolved with CERES and MODIS cloud and radiation data to produce a merged 3-dimensional cloud and radiation dataset

Chen, Yan

Gibson, Sharon

Kato, Seiji

Minnis, Patrick

Stephens, Graeme

Sun-Mack, Sunny

Trepte, Charles a.

Wielicki, Bruce

NASA Technical Reports Server

Characterizing the Radiation Fields in the 
Atmosphere Using a Cloud-Aerosol-Radiation 
Product From Integrated CERES, MODIS, 
CALIPSO and CloudSat Data 
 
 
Patrick Minnis, Bruce Wielicki, Charles A. Trepte 
NASA Langley Research Center 
Hampton, VA USA 
p.minnis@nasa.gov 
Sunny Sun-Mack, Yan Chen, Sharon Gibson  
SSAI 
Hampton, VA USA 
Seiji Kato 
National Institute of Aerospace 
Hampton, VA USA 
Madison, WI USA 
Graeme Stephens 
Department of Atmospheric Science 
Colorado State University 
Ft. Collins, CO USA 
 
 
 
Abstract—CloudSat and CALIPSO cloud and aerosol 
information is convolved with CERES and MODIS cloud and 
radiation data to produce a merged 3-dimensional cloud and 
radiation dataset.  
Keywords-component; clouds; radiation; CALIPSO; CloudSat 
I.  INTRODUCTION 
Understanding the relationships between atmospheric 
components and the radiation fields within and external to the 
atmosphere is critical for constraining weather and climate 
models and, possibly, for improving the same through 
assimilation of those fields. Our current understanding of those 
relationships and the means to estimate those radiation fields 
on a global basis are limited to a 2 or 2.5 dimensional 
characterization. Current passive remote sensing techniques 
using multispectral radiances measured by imagers, such as the 
MODerate-resolution Imaging Spectroradiometer (MODIS) on 
the Aqua satellite, can generally retrieve the bulk optical 
properties of a cloud system and provide an estimate of the 
cloud top and a cruder assessment of the cloud base for single-
layered clouds. In scenes free of clouds, it is possible to 
retrieve the bulk optical properties of the aerosol column from 
the same imagers. Broadband radiometers, such as those 
developed for the Clouds and the Earth’s Radiant Energy 
System (CERES) [1], can be used to determine the total 
outgoing longwave and shortwave radiative fluxes, thereby, 
defining the amount of radiative energy available for heating 
the atmosphere and surface, or for evaporating water. Various 
algorithms are now available for using that same information to 
estimate the radiation budget at the surface, so that the amount 
of radiant energy deposited in the atmosphere can also be 
estimated. 
By combining the passive retrievals from MODIS [2] with 
the broadband radiative fluxes, the CERES project has made 
substantial advances in characterizing the relationships 
between atmospheric components and the radiation budget. 
Yet, many questions and uncertainties remain. Clouds often 
occur in overlapping layers and, therefore, many of the derived 
cloud properties are often very inaccurate. For example, an ice 
cloud overlapping a water cloud is generally interpreted as an 
ice cloud and the resulting optical depths and water paths can 
be extremely biased. Aerosols can only be retrieved in clear 
skies and there is no knowledge of their vertical distribution. 
Thus, it is not possible to accurately place clouds and aerosols 
in the same layers and determine the indirect effect of the 
aerosols without many assumptions. Furthermore, the vertical 
impact of the radiative heating by the aerosols cannot be 
reliably characterized without knowledge of the aerosol 
altitudes. Overall, the vertical heating rate profile is very 
difficult to determine with any accuracy from passive satellite 
measurements because of these and other shortcomings. The 
A-Train, which includes Aqua, provides new resources for 
unscrambling the vertical profiles of clouds and aerosols while 
simultaneously measuring broadband fluxes. 
Pulling the various A-Train datasets together to determine 
the vertical structure of the cloud and aerosol fields and their 
impact on the atmospheric radiation budget is a challenge due 
to the different fields of view and nature of the several datasets. 
This paper describes the integration of the passive MODIS and 
CERES measurements with the vertical profile information 
Sponsored by NASA Science Program 
https://ntrs.nasa.gov/search.jsp?R=20070030249 2019-08-30T01:31:55+00:00Z
derived from the cloud radar on CloudSat [3] and from the 
lidar on the Cloud-Aerosol Lidar and Infrared Pathfinder 
Observation (CALIPSO) Satellite [4]. Methods to distribute the 
information from the small CALIPSO and CloudSat footprints 
into the larger MODIS and CERES fields of view are 
discussed. Initial comparisons of the passive and active fields 
are presented along with initial estimates of the heating rates. 
The combined datasets should provide an unprecedented 
characterization of the atmospheric heating rates as well as a 
better understanding of the impacts of clouds and aerosols on 
the radiation budget. 
II. DATA AND MATCHING 
A. CERES 
The CERES-MODIS (CM) cloud analysis algorithms use 
the 1-km 0.65, 1.38, 1.64 (2.13), 3.8, 6.7, 8.5, 10.8, and 12.0 
µm channels. The CERES scanners measure broadband 
shortwave, total, and infrared window (8-12 µm) radiances 
with a nadir footprint of ~20 km. The CERES instrument scans 
from limb to limb, while the MODIS scans out to a 55° scan 
angle that translates to a viewing zenith angle (VZA) of ~70°. 
Thus, CERES data are analyzed out VZA = 70°. Each MODIS 
pixel is analyzed to retrieve cloud properties as described in [2] 
and the references therein. The results are then convolved into 
the CERES footprint to associate a given cloud-surface scene 
with a broadband radiance measurement that is then inverted 
with a scene-based anisotropic directional model to determine 
the top-of-atmosphere irradiance.  
B. CALIPSO and CloudSat 
CALIPSO and CloudSat are off the Aqua nadir by several 
degrees. The CERES field of view (fov) having a center point 
closest to the CALIPSO ground track is matched to a segment 
of the CALIPSO data stream. The CALIPSO lidar (CALIOP) 
takes a vertical profile every 333 m along track and has a 70-m 
fov. It is aligned with the 125-m resolution CALIPSO visible 
imager and every 3 or 4 CALIOP profiles are matched with the 
nearest 1-km MODIS pixel. An idealized cloud and aerosol 
field is also indicated in Figure 1 with a clear portion, scattered 
cumuli between 1.5 and 3 km, broken cirrus between 11 and 12 
km, and a uniform aerosol layer centered at 2 km. The pattern 
of lidar shots, shown as the dark green stripes at the bottom of 
Figure 1a, indicate that the lidar returns are not contiguous but 
are spaced by either 125 or 250 m. The cloud profiling radar  
(CPR) footprints are 1.4-km wide across track and 2.5 km 
along track with a spacing of 1.4 km between profiles and will 
therefore be more contiguous than the higher resolution 
CALIOP data. They have a vertical resolution of 240 m, while 
the lidar has a 30-m resolution. Figure 2 shows the matched 
footprints in plan view. All 1-km MODIS pixels will be used 
instead of the nominal 2-km sampling indicated in the figure.  
The discretized lidar sampling in Figure 1 requires 
interpolation to fill in the gaps. Interpolation is used whenever 
there is a gap no greater than the space in the lidar sampling to 
interpret the cloud boundaries. A schematic of the CPR 
sampling in Figure 1c shows that it misses most of the cirrus 
clouds but picks up the cloud extinction down to cloud base 
that is not seen by the lidar. The boundaries of the entire cloud  
 
Figure 1.  Schematic of a vertical profile aerosol and cloud layers for a) 
reference truth b) lidar sub-sampling with a 70m fov profile every 333m along 
the ground track, and c) radar with ~1 km sampling along track. 
 
 
 
Figure 2.  Schematic CERES FOV (circle) overlaid on sampled 1-km 
MODIS pixels (large squares, CPR track and IIR pixels (continuous gray), 
and CALIPSO lidar footprints (blue squares). 
field are estimated by merging the interpolated lidar and CPR 
cloud boundaries. Differences in the vertical resolutions 
between the radar and lidar are reconciled by using the lidar 
cloud top and base altitudes for layers that are completely 
penetrated by the lidar beam and by using cloud top altitude 
only for optically thick clouds. The radar provides information 
about cloud base height for optically thick clouds. 
III. MERGED DATA PRODUCTS 
The goal of this effort is produce several products that can 
lead to effective understanding of cloud fields giving rise to the 
observed radiation, which can then be used to constrain 
radiative transfer calculations of surface and atmospheric 
heating rates based on the cloud properties.  
The first step after matching the CALIOP and CPR data 
with the MODIS pixels is to determine how close the retrieved 
cloud properties are to those determined from the active 
sensors. The cloud top and base heights of the CERES-MODIS 
(CM) retrievals are then adjusted to match the active sensor 
results and the radiative properties recomputed as constrained 
by the MODIS data. These adjusted parameters for pixels along 
the CALIPSO track (gray area in Figure 2) will then be 
convolved into the CERES footprint without the other pixels 
off the track that fall within the CERES fov. A second dataset 
will convolve the unadjusted pixels along the CALIPSO track 
into the CERES fov. These two convolved datasets constitute 
track-sampled CERES single scanner footprints (SSF). The 
vertical profile information from the CALIOP-CPR data will 
also be used to adjust the CM cloud parameters off-track to 
provide a more realistic cloud field within the entire CERES 
footprint. These off-track pixel parameters will be convolved 
along with their along-track cousins to constitute a complete 
enhanced CERES SSF. The standard CERES SSF convolves 
the parameters from all of the pixels within the CERES fov 
using the standard CM retrievals. All convolutions weight the 
cloud properties for each MODIS pixel by the CERES point-
spread function at that particular location relative to the 
CERES fov center. The result of these analysis and 
enhancement processes yields four datasets: standard and 
enhanced track-sampled SSF and standard and enhanced 
complete SSFs. From these results, the cloud fields will be 
used to compute the surface, atmospheric, and top-of-
atmosphere radiative fluxes and divergences. The calculations 
are constrained by the CERES observations. The differences 
between the standard and enhanced products will be useful for 
determining the errors in the atmospheric heating rates. 
Differences in the fluxes between the enhanced track-sampled 
and complete SSFs yield the uncertainties in basing the 
assessment on the narrow fov’s of the active instruments.  
IV. RESULTS 
As of this writing, the CM pixels have been merged with 
the early releases of the CloudSat and CALIPSO retrievals. 
Figure 3 shows an example of the matched data for a track over 
the southeastern Indian Ocean during 6 August 2006. The RGB 
image at the top of the figure shows the cloud fields as 
observed by MODIS. The white area at left is located near 
Antarctica and is in darkness. The track proceeds northward to 
the right (latitude and longitude are shown at bottom of the 
figure). Generally CALIPSO yields slightly higher cloud tops 
(light blue areas) than CloudSat and picks up some boundary-
layer clouds not observed by the radar. It also detects a 
boundary-layer aerosol layer (gold areas) between the clouds. It 
does not detect the depth of the deep storm cloud systems 
picked up by CloudSat (light green areas). The two instruments 
yield similar cloudy volumes (white regions) just below cloud 
top in many instances. The CM retrievals show water phase in 
red and ice phase in dark blue. The low-cloud heights from CM 
are typically too low, but the scattered clouds appear to be 
detected properly. In the deep storm systems, the CM retrieval 
determines the cloud phase as ice and cloud top is located at 
some point between the radar-only boundary and the cloud top.
 
 
Figure 3.  Example of matched CALIPSO, CloudSat, and CM cloud retirevals along the CALISPO ground track, 6 August 2006. Pseudoclor RGB image from 
MODIS data shown at top. Glround track for the image shown on globe at upper right. Each colored dot denotes a CM-retrieved cloud-top height. 
A discontinuity in the cloud heights is seen near the terminator. 
This ”twilight” area causes more difficulty for the CM cloud 
mask than in full daylight or darkness. In some of the multi-
layered cloud cases, the CM result falls between the layers, 
while it corresponds to the highest cloud in other instances, 
probably reflecting the optical thickness of the higher cloud. 
Although the differences seen in this example are not 
uncommon, agreement between the CM and active sensor 
cloud heights are much better in many other marine areas. 
V. CONCLUDING REMARKS 
The next steps in the processing will use the information 
like that visualized in Figure 3 and adjust the CM retrievals so 
that the cloud boundaries and phase determinations are correct. 
That information will be used to improve the CM retrieval 
algorithms and to perform the radiative transfer calculations to 
produce heating profiles for the CERES footprints when the 
extinction profiles and higher level products become available 
form the CALISPO and CloudSat programs. 
 
c 
ACKNOWLEDGMENT 
The authors are indebted to the members of the CALIPSO 
and CloudSat teams for providing the latest datasets for 
processing. 
REFERENCES 
 
[1] Wielicki, B. A., et al., “Clouds and the Earth's Radiant Energy System 
(CERES): Algorithm overview,” IEEE Trans. Geosci. Remote Sens., 
vol. 36, pp. 1127-1141, 1998. 
[2] Minnis, P., et al., “A multi-year data set of cloud properties derived for 
CERES from Aqua, Terra, and TRMM,” Proc. 2006 IEEE Intl. Geosci. 
and Remote Sens. Symp., Denver, CO, 31 July - 4 Aug., CD-ROM, 
02_50P03, 2006. 
[3] Stephens, G. L., et al., “The CloudSat mission and the A-Train,” Bull. 
Amer. Meteor. Soc., vol. 83, pp. 1771-1790, 2002. 
[4] Winker, D. M., J. Pelon and M. P. McCormick, “The CALIPSO 
mission: Spaceborne lidar for observation of aerosols and clouds,” Proc. 
SPIE Asia-Pacific Symposium on Remote Sensing of the Atmosphere, 
Environment and Space, Hangzhou, China, 23-27 October, 4893-01, 
2002. 
 
 


Characterizing the Radiation Fields in the Atmosphere Using a Cloud-Aerosol-Radiation Product from Integrated CERES, MODIS, CALIPSO and CloudSat Data

Characterizing the Radiation Fields in the Atmosphere Using a Cloud-Aerosol-Radiation Product from Integrated CERES, MODIS, CALIPSO and CloudSat Data

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