In the spring of 2010, the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) project will be releasing version 3 of its level 2 data products. In this paper we describe several changes to the algorithms and code that yield substantial improvements in CALIPSO's retrieval of aerosol optical depths (AOD). Among these are a retooled cloud-clearing procedure and a new approach to determining the base altitudes of aerosol layers in the planetary boundary layer (PBL). The results derived from these modifications are illustrated using case studies prepared using a late beta version of the level 2 version 3 processing code

Avery, Melody A.

Getzewich, Brian J.

Hu, Yongxiang

Kuehn, Ralph E.

Liu, Zhaoyan

Omar, A.

Powell, Kathleen A.

Rogers, Raymond R.

Tackett, Jason L.

Trepte, Charles R.

Vaughan, Mark A.

Winker, David M.

Young, Stuart A.

NASA Technical Reports Server

  
STRATEGIES FOR IMPROVED CALIPSO AEROSOL OPTICAL DEPTH ESTIMATES 
M. Vaughan1, R. Kuehn2, J. Tackett2, R. Rogers1, Z. Liu3, A. Omar1, B. Getzewich2, 
K. Powell1, Y. Hu1, S. Young4, M. Avery1, D. Winker1, and C. Trepte1 
1 NASA Langley Research Center, Hampton, VA, USA:  mark.a.vaughan@nasa.gov; raymond.r.rogers@nasa.gov; 
ali.h.omar@nasa.gov; kathleen.a.powell@nasa.gov; yongxiang.hu-1@nasa.gov; melody.a.avery@nasa.gov; 
david.m.winker@nasa.gov; charles.r.trepte@nasa.gov 
2 Science Systems and Applications, Inc., Hampton, VA, USA:  ralph.e.kuehn@nasa.gov; jason.l.tackett@nasa.gov; 
brian.j.getzewich@nasa.gov 
3 National Institute of Aerospace, Hampton, VA, USA: zhaoyan.liu@nasa.gov 
4 CSIRO Marine & Atmospheric Research, Aspendale, VIC, Australia: stuart.young@csiro.au 
 
ABSTRACT 
In the spring of 2010, the Cloud-Aerosol Lidar and 
Infrared Pathfinder Satellite Observation (CALIPSO) 
project will be releasing version 3 of its level 2 data 
products.  In this paper we describe several changes to 
the algorithms and code that yield substantial 
improvements in CALIPSO’s retrieval of aerosol 
optical depths (AOD).  Among these are a retooled 
cloud-clearing procedure and a new approach to 
determining the base altitudes of aerosol layers in the 
planetary boundary layer (PBL).  The results derived 
from these modifications are illustrated using case 
studies prepared using a late beta version of the level 2 
version 3 processing code. 
1. INTRODUCTION 
One of the primary objectives of the Cloud-Aerosol 
Lidar and Infrared Pathfinder Satellite Observation 
(CALIPSO) mission is to help reduce uncertainties in 
climate model predictions by acquiring global measure-
ments of the vertical distribution of aerosols [1].  To 
accomplish this goal, CALIPSO employs a fully auto-
mated data analysis system to detect cloud and aerosol 
layers in the backscattered signal [2]; determine which 
of these layers are clouds and which are aerosols [3]; 
estimate aerosol lidar ratio, Sa, via an empirical 
determination of aerosol type [4]; and finally, based on 
the combined results of these preliminary analyses, 
retrieve profiles of aerosol extinction coefficients, 
which are then integrated to derive estimates of aerosol 
optical depths (AOD) at 532 nm and 1064 nm [5].  At 
present, the consensus of various validation studies is 
that the CALIPSO data analyses are performing well 
and that the results produced are reliable [e.g., 6–9].  
Still, there are times when this is not so, and the 
CALIPSO retrieval scheme can be savagely battered by 
an indifferent Mother Nature, who pays no attention 
whatsoever to the carefully constructed models of the 
atmosphere upon which the CALIPSO data analyses 
depend, and instead intermingles clouds and aerosols 
arbitrarily, according to her own whims.  In this paper 
we describe some (but certainly not all) of the latest 
innovations incorporated into the CALIPSO software to 
help us more successfully grapple with these hard 
cases.  With respect to improving AOD accuracies, 
these enhancements fall into three main categories: (i) 
more accurate clearing of small-scale clouds embedded 
in aerosols in the planetary boundary layer (PBL); (ii) 
significant refinements of the probability distribution 
functions (PDFs) used to distinguish between aerosols 
and clouds; and (iii) implementation of a new procedure 
for layer base determination for PBL aerosols.  An in-
depth assessment of items (i) and (iii) will be given in 
the sections that follow.  The PDF improvements men-
tioned in item (ii) are described in detail in a companion 
paper published in these same proceedings [10], and 
hence will be addressed only briefly here. 
2. CLOUD-CLEARING CORRECTIONS 
Since its inception, the CALIPSO layer detection algor-
ithm has included a procedure devoted specifically to 
detecting small scale clouds in the PBL.  The primary 
motive for including this special, high-resolution cloud 
search is to remove the high-intensity backscatter data 
identified as cloud, and thus permit extended spatial 
averaging of the surrounding data, which in the PBL is 
presumed to contain aerosols.  Based on prelaunch soft-
ware testing using synthetic data sets, the initial imple-
mentation of this cloud-clearing procedure was 
expected to work as designed.  However, shortly after 
version 2 of the CALIPSO data products was released, 
data users began commenting on the unexpectedly 
frequent occurrence of weakly scattering cumulus 
clouds being reported in the 5-km layer products.  An 
investigation by the algorithm and software develop-
ment teams found that while the high-resolution cloud 
detection was being performed correctly, and that the 
layer boundaries being reported in the CALIPSO 1/3-
km and 1-km cloud layer products were accurate.  
However, the procedure subsequently used for cloud-
clearing and reaveraging the surrounding data was 
incomplete.  The layer detection algorithm operates on 
attenuated scattering ratios, and for this realization of 
the data the cloud clearing was being done properly, as 
evidenced by the correctness of the results at 1/3-km 
and 1-km.  The cloud-aerosol discrimination (CAD) 
algorithm, on the other hand, uses the attenuated 
backscatter coefficients, and, because these data were 
https://ntrs.nasa.gov/search.jsp?R=20100026019 2019-08-30T10:25:15+00:00Z
  
not being used in the layer detection routine, they were 
not being cloud cleared.  Both the cause and the cure 
for this malady are illustrated in Figure 1.  The upper 
panel of this figure shows the 532 nm attenuated back-
scatter coefficients acquired on August 4 2007 during a 
daytime overpass along the eastern seaboard of North 
America.  Occasional small scale clouds (e.g., cumulus 
humilis and/or cumulus mediocris) are seen to be 
embedded in an extensive layer of urban pollution that 
rises to a maximum altitude of approximately 4.5-km.  
 
 
 
Figure 1: (a) CALIPSO 532 nm attenuated backscatter coeffi-
cients acquired during a daytime overpass along the eastern 
seaboard of the United States, with all data averaged to 60-m 
vertically and 5-km horizontally; (b) vertical feature mask, 
showing the locations of all clouds (pale blue), aerosols 
(orange), and surface data (green) reported in the version 2.01 
CALIPSO level 2 data products; regions where no features 
were detected are rendered in dark blue (clear sky), pale gray 
(subsurface), and black (signal totally attenuated); (c) vertical 
feature mask for the version 3 CALIOP data products. 
Figure 1(b) shows the layer detection and CAD results 
reported in the version 2.01 data products for this scene.  
Given the low SNR of the CALIPSO daytime data, 
layer top detection seems to be adequate (though 
certainly not perfect).  Layer base determination and 
layer classification are, on the other hand, clearly sub-
optimal.  The reason for the poor CAD performance can 
be traced directly to the software defect described 
earlier.  As seen in Figure 1(c), which displays the re-
sults from the version 3 processing, aerosol layers mis-
classified as cloud in the version 2.01 analysis were all 
cloud-contaminated to some degree.  However, as 
demonstrated by the layer at ~2.5-km and ~36.0° N, the 
mere presence of small scale clouds is not sufficient to 
skew the classification scheme.  What is required is 
that, in the mean, the total backscatter contribution from 
embedded clouds overwhelms the backscatter contri-
bution of the surrounding aerosols.  In these cases, the 
bulk optical properties of the feature detected at the 
coarser resolution more closely resemble clouds than 
aerosols, and hence the CAD algorithm recognizes 
them as clouds. 
3. PBL AEROSOL BASE ALTITUDES 
As pointed out above, the layer base determination in 
Figure 1(b) is also clearly suboptimal.  When examin-
ing an aerosol layer that, by inspection of Figure 1(a), is 
clearly in contact with the Earth’s surface, the 
CALIPSO layer detection algorithm instead reports the 
lowest base altitudes as being up to 2-km above the 
surface.  To some extent, this sort of behavior was anti-
cipated prior to launch.  Based on the authors’ prior ex-
perience with data from the Lidar In-Space Technology 
Experiment, erroneous estimates of base altitude were 
known to occur most often in highly absorbing, 
optically thick aerosol layers measured during daylight 
over bright surfaces; i.e., exactly the circumstances 
encountered in this example.  As illustrated in Figure 2, 
the rapid attenuation of the signal within the layer, 
combined with relatively weak backscattering (high 
lidar ratio) and very high noise levels can cause the 
signal to fall below the detection limit before the full 
extent of the layer is measured. 
   
Figure 2: profile data averaged to a horizontal resolution of 
20-km, taken from Figure 1(a) at ~41.3° N (red arrow); as 
seen in Figure 1(b), an aerosol layer is detected between ~2.5-
km and ~1.9-km, where the backscatter coefficients rise 
continuously above the detection threshold. 
To minimize the occurrence of premature base deter-
mination, a very simple new procedure has been incor-
porated into version 3 of the CALIPSO level 2 analy-
ses.  As was done in all previous versions, following 
execution of the layer detection algorithm, all features 
within a scene are assigned an initial classification by 
the CAD algorithm.  (Note that, in multi-layer scenes, 
A 
B 
C 
Final Base Altitude 
(Figure 1C) 
Initial Base Altitude 
(Figure 1B) 
  
this initial classification can later change, as a result of 
attenuation corrections applied to the data within lower 
layers to compensate for the extinction incurred in 
overlying layers.  The final classification of any layer 
occurs immediately before an extinction solution is 
attempted.)  In the version 2 processing, the next and 
final step in the analysis was to launch the extinction 
and optical properties retrievals.  In the version 3 
processing, an intermediate step is inserted: the initial 
classification is now followed by an assessment of the 
lowest layers within each column.  If the lowest layer is 
an aerosol and  
(a) the layer is not opaque (i.e., the surface was 
detected below); 
(b) the initial layer base is within some minimum 
distance of the surface (2.5-km for the version 3 
processing); and  
(c) the 532 nm integrated attenuated backscatter in the 
region between the initial base altitude and the top 
of the surface echo is positive 
then the estimate of the layer base altitude is revised 
downward to a point three range bins (~90 m) above the 
top of the surface spike.  The extinction and optical pro-
perties retrievals are conducted immediately after the 
conclusion of the base readjustment procedure.  An ex-
ample of the algorithm’s performance on a single pro-
file is given in Figure 2, where the arrows indicate the 
approximate altitudes of the initial and final altitudes of 
the aerosol layer shown in Figure 1(a). 
4. RESULTS 
The primary motivation for incorporating the aerosol 
base extender algorithm into the version 3 processing is 
to improve the quality of the CALIPSO AOD retrievals.  
This is because the CALIPSO data products only report 
extinction profiles – and hence, only calculate optical 
depths – in those regions where features are detected.  
Especially during daytime operations, the SNR 
achieved by the current signal averaging levels, which 
extends to a maximum of 80-km horizontally, is 
inadequate for deriving reliable extinction profiles in 
the so-called “clear air” regions where no layers are 
detected.  Thus the AODs reported by CALIPSO 
depend critically on an accurate determination of layer 
boundaries, combined with the ability to accurately 
differentiate between clouds and aerosol and to 
correctly recognize different aerosol species. 
To quantify the improvements that the aerosol base 
extension procedure makes in the CALIPSO optical 
depth retrievals, we conducted two types of studies.  
The first of these was a straightforward exercise in data 
product validation.  Since acquiring its first lidar mea-
surements in June, 2006, the CALIPSO validation 
program has relied heavily on coincident measurements 
made by the airborne high spectral resolution lidar 
(HSRL) developed by researchers at NASA’s Langley 
Research Center (LaRC) [11].  HSRL makes separate 
measurements of total and molecular backscatter, and 
thus provides direct measurements of AOD and lidar 
ratio without any requirement for assumptions about the 
nature of the aerosol being observed.  The story is 
different for CALIPSO: with very few exceptions, 
CALIPSO cannot make direct measurements of aerosol 
optical depth.  Instead, estimates of optical depth are 
derived using a model-based lidar ratio selected on the 
basis of measured spatial and optical properties of each 
layer [4].  HSRL underflights of CALIPSO thus 
provide the high quality validation measurements that 
are essential for assessing the performance of the 
CALIPSO retrieval algorithms. 
 
 
Figure 3: (a) comparisons of CALIPSO version 2 and version 
3 AOD retrievals with coincident measurements made by the 
NASA Langley’s airborne HSRL system; when no aerosol is 
detected within a column, the AOD is zero; (b) as in (a), but 
for lidar ratios.  All measurements/estimates in both panels 
are for data acquired at 532 nm.  To facilitate direct com-
parisons, the CALIPSO version 3 data are averaged to the 
same 40-km horizontal resolution that was used in the version 
2 data products. 
For the August 4 2007 data shown in Figure 1, the 
LaRC HSRL acquired a full complement of validation 
measurements along the CALIPSO track.  Figure 3 
compares the HSRL measurements of AOD (upper 
panel) and lidar ratio (lower panel) with the estimated 
quantities reported in CALIPSO’s version 2 and version 
3 data products.  The effects of the base extension 
procedure are readily seen in Figure 3(a), where the 
AOD from CALIPSO’s version 3 products is uniformly 
and substantially larger and closer to the HSRL 
measurement than in the previous version.  That this 
effect is due largely, if not entirely to the extra vertical 
A 
B 
  
extent included in the extinction retrieval is, paradoxi-
cally, reinforced by the disparity in lidar ratio estimates 
between CALIPSO versions 2 and 3.  In the optically 
dense parts of the aerosol layer, the lidar ratio estimates 
in version 3 are lower than their version 2 counterparts 
by as much as 10 sr.  This lowering of lidar ratios is due 
to increased depolarization in the aerosol closest to the 
surface.  This change in bulk layer properties is subse-
quently reflected in a change in aerosol type assign-
ment.  Layers that were identified as smoke or pollution 
(Sa = 70 sr) in version 2 are reclassified as pollution 
mixed with dust (Sa = 55 sr) in the version 3 analysis. 
 
Figure 4: changes in aerosol optical depth resulting from 
application of the base extender algorithm.  Those samples 
enclosed by the dashed orange line represent layers whose 
aerosol type changed as a result of the base extension, and 
thus the ‘extended’ extinction solution was derived using a 
lower lidar ratio than the ‘not extended’ solution. 
That aerosol type identification might change as a result 
of the base extension algorithm is not unexpected, as 
new and different data are now being included in the 
layer-integrated properties used by the aerosol typing 
scheme.  However, a change in type identification 
usually results in a change to the lidar ratio assigned to 
a layer, and if the reduction in the lidar ratio is large, 
the AOD estimate for the extended layer may actually 
be lower than it would have been for the original, 
unextended layer.  This might happen, for example, if a 
layer initially classified as smoke (Sa = 70 sr) were 
reclassified as marine (Sa = 20 sr).  To assess the 
occurrence frequency of this phenomenon, we analyzed 
two full days of data (2007-01-01 and 2007-08-27) first 
with the base extender enabled and then with it 
disabled.  A comparison of the two sets of results is 
shown in Figure 4.  For this data, layers were extended 
an average of 0.54-km.  As expected, when there was 
no change in aerosol type, the median optical depth of 
the extended layers rose by ~22%.  Similarly, those 
occasions when the AOD of the extended layer was 
smaller than that of the original layer were heavily 
dominated by cases where the initial lidar ratio was 
drastically reduced by the reclassification process. 
Addition of the base extender procedure is expected to 
have a significant positive impact on the AOD 
estimates reported in version 3 of the CALIPSO data 
products.  In a more comprehensive test that used four 
full months of data, the initial base altitude was 
extended for 8.7% of all aerosol layers detected. 
REFERENCES 
[1] Winker, D. M., et al., 2009: Overview of the 
CALIPSO Mission and CALIOP Data Processing Al-
gorithms, J. Atmos. Oceanic Technol., 26, 2310–2323. 
[2] Vaughan, M. A., et al., 2009: Fully Automated 
Detection of Cloud and Aerosol Layers in the 
CALIPSO Lidar Measurements, J. Atmos. Oceanic 
Technol., 26, 2034–2050. 
[3] Liu, Z., et al., 2009: The CALIPSO Lidar Cloud 
and Aerosol Discrimination: Version 2 Algorithm and 
Initial Assessment of Performance, J. Atmos. Oceanic 
Technol., 26, 1198–1213. 
[4] Omar, A., et al., 2009: The CALIPSO Automated 
Aerosol Classification and Lidar Ratio Selection Algor-
ithm, J. Atmos. Oceanic Technol., 26, 1994–2014. 
[5] Young, S. A. and M. A. Vaughan, 2009: The 
retrieval of profiles of particulate extinction from Cloud 
Aerosol Lidar Infrared Pathfinder Satellite Obser-
vations (CALIPSO) data: Algorithm description, J. 
Atmos. Oceanic Technol., 26, 1105–1119. 
[6] McGill, M. J. et al., 2007: Airborne validation of 
spatial properties measured by the CALIPSO lidar, J. 
Geophys. Res., 112, D20201. 
[7] Mielonen, T. et al., 2009: Comparison of CALIOP 
level 2 aerosol subtypes to aerosol types derived from 
AERONET inversion data, Geophys. Res. Lett., 36, 
L18804. 
[8] Mioche, G. et al., 2009: Validation of the CALIP-
SO/CALIOP Extinction Coefficients from In Situ 
Observations in Mid-latitude Cirrus Clouds during 
CIRCLE-2 Experiment, J. Geophys. Res., in press. 
[9] Mona, L. et al, 2009: One year of CNR-IMAA 
multi-wavelength Raman lidar measurements in coinci-
dence with CALIPSO overpasses: Level 1 products 
comparison, Atmos. Chem. Phys., 9, 7213–7228. 
[10] Liu, Z. et al., 2010: The CALIPSO Cloud and 
Aerosol Discrimination: Version 3 Algorithm and Test 
Results, current proceedings 
[11] Hair, J. et al., 2008: Airborne High Spectral 
Resolution Lidar for profiling aerosol optical 
properties, Appl. Opt., 47, 6734–6752. 


Strategies for Improved CALIPSO Aerosol Optical Depth Estimates

Strategies for Improved CALIPSO Aerosol Optical Depth Estimates

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