Application of appropriate spatial averaging techniques is crucial to correct evaluation of ocean color radiometric data, due to the common log-normal or mixed log-normal distribution of these data. Averaging method is particularly crucial for data acquired in coastal regions. The effect of averaging method was markedly demonstrated for a precipitation-driven event on the U.S. Northeast coast in October-November 2005, which resulted in export of high concentrations of riverine colored dissolved organic matter (CDOM) to New York and New Jersey coastal waters over a period of several days. Use of the arithmetic mean averaging method created an inaccurate representation of the magnitude of this event in SeaWiFS global mapped chl a data, causing it to be visualized as a very large chl a anomaly. The apparent chl a anomaly was enhanced by the known incomplete discrimination of CDOM and phytoplankton chlorophyll in SeaWiFS data; other data sources enable an improved characterization. Analysis using the geometric mean averaging method did not indicate this event to be statistically anomalous. Our results predicate the necessity of providing the geometric mean averaging method for ocean color radiometric data in the Goddard Earth Sciences DISC Interactive Online Visualization ANd aNalysis Infrastructure (Giovanni)

Acker, James G.

Leptoukh, Gregory G.

Schollaert Uz, Stephanie

Shen, Suhung

NASA Technical Reports Server

James G. Acker1
Stephanie Schollaert Uz2
Suhung Shen3
Gregory G. Leptoukh4
1Goddard Earth Sciences Data and Information Services Center / Wyle IS LLC
2Earth System Science Interdisciplinary Center, University of Maryland
3Goddard Earth Sciences Data and Information Services Center / George Mason University
4Goddard Earth Sciences Data and Information Services Center / NASA
ABSTRACT
Application of appropriate spatial averaging techniques is crucial to
correct evaluation of ocean color radiometric data, due to the 
common log-normal or mixed log-normal distribution of these data. 
Averaging method is particularly crucial for data acquired in coastal 
regions. The effect of averaging method was markedly demonstrated
for a precipitation-driven event on the U.S. Northeast coast in October-
November 2005, which resulted in export of high concentrations of 
riverine colored dissolved organic matter (CDOM) to New York and 
New Jersey coastal waters over a period of several days.  Use of the 
arithmetic mean averaging method created an inaccurate representation 
of the magnitude of this event in SeaWiFS global mapped chl a data, 
causing it to be visualized as a very large chl a anomaly. The apparent chl
a anomaly was enhanced by the known incomplete discrimination of 
CDOM and phytoplankton chlorophyll in SeaWiFS data;  other data sources 
enable an improved characterization.  Analysis using the geometric mean 
averaging method did not indicate this event to be statistically anomalous. 
Our results predicate the necessity of providing the geometric mean 
averaging method for ocean color radiometric data in the Goddard Earth 
Sciences DISC Interactive Online Visualization ANd aNalysis Infrastructure 
(Giovanni).
Introduction
The widespread availability of mapped remote-sensing data products 
which have been binned and averaged spatially and temporally (Level 3 
data) makes this type of data useful for many different varieties of Earth 
Science research.   It is recognized, however, that the methodology of 
binning and averaging can introduce biasing in the presentation and 
interpretation of these data.   The output result of the averaging method 
is directly related to the statistical distribution of the data.  
While many types of Earth Science data are normally distributed, 
ocean color radiometric data, particularly the chlorophyll a (chl a) data 
product, usually have a log-normal or mixed log-normal distribution.  This 
characteristic of the data is most commonly observed in coastal regions 
which exhibit wide ranges in chl a values.
In this presentation, we examine how the arithmetic mean averaging 
method, as well as the radiometric characteristics of the Sea-viewing 
Wide Field-of-view Sensor (SeaWiFS) data products  ̶ overestimated the 
magnitude of an apparent chl a anomaly caused by a unique 
precipitation-driven event on the U.S. East Coast in November 2005.  
October-November 2005 Precipitation Event
In October 2005, anomalously high precipitation occurred in the U.S. 
Northeast.   The elevated precipitation events were unusual for autumn, and 
several Northeast states set all-time records for October precipitation.   (Figure 
1)
To better characterize this event, Tropical Rainfall Measuring Mission (TRMM) 
data in Giovanni was utilized to show the monthly precipitation pattern and the 
total precipitation amount (Figure 2).   We also selected stream gauge stations 
from the United States Geological Survey  “Real-Time Water Data  for the Nation” 
Web site (http://waterdata.usgs.gov/nwis/rt) to examine the streamflow
response to the October precipitation (Figure 3).    An ocean monitoring site off 
of Sandy Hook, New Jersey (NOAA) also provided conductivity data which 
demonstrated the effect of the increased flow of the Hudson River on New York 
and New Jersey coastal waters (Figure 4). 
Figure 1:  National Climatic Data
Center ranking of precipitation
amounts for October 2005.
a
b
Figure 2:  (a) TRMM daily accumulated rainfall 
(mm)  for  October 2005 over southern New 
England, eastern New York and Pennsylvania, 
and New Jersey.  (b)  Time-series of TRMM 
daily accumulated rainfall for this region in 
October 2005.
Figure 3:  
USGS 
streamflow
(cfs) for 
selected 
gauge 
stations in 
New York 
and New 
Jersey, 
October-
December 
2005.  
Figure 4:   Conductivity from the NOAA 
National Ocean Service buoy off of Sandy 
Hook, New Jersey, October-December 
2005, showing the effect of  increased 
flow in the Hudson River caused by the 
elevated rainfall in October 2005.
EFFECTS OF THE EVENT:   Coastal Observations by SeaWiFS and MODIS
Data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and the Moderate  Resolution Imaging Spectro-
radiometer (MODIS) on the Aqua satellite was examined to determine the approximate length of time that 
northeastern coastal waters were influenced by the elevated rainfall event n October 2005.   This examination 
indicated that the period of time with the strongest  influence occurred from October 30 – November 6, 2005.   
Imagery prior to October 30 was sparse, likely due to cloud cover associated with the elevated rainfall.   An image 
acquired on  November 20 indicated that there may have been a waning influence through that date.
MODIS-Aqua chl a browse
image, October 30, 2005.
SeaWiFS chl a browse
image, November 3, 2005. 
MODIS-Aqua chl a browse
image, November 6, 2005.
Figure 5: SeaWiFS chl a anomaly plot generated by 
Giovanni  for the study region, November 2005, 
showing the high positive chl a anomaly along the  
coast of New Jersey and  western Long Island. The 
anomaly palette range was extended to values of 
-2.5 (purple) to +2.5 (red) for this plot.
Figure 6:   SeaWiFS chl a anomaly time-series 
generated by Giovanni for the study region, 
September 1997 through  December 2008.  The 
extremely high chl a anomaly value for the study 
region in November 2005 is clearly visible.
Comparison of chl a data products in Giovanni
Giovanni currently provides three different chl a data products:   MODIS–Aqua chl a, SeaWiFS chl a,  and 
Garver-Siegel Maritorena (GSM) merged (MODIS+SeaWiFS) chl a.  The GSM merged chl a data product is 
less sensitive to interference from CDOM than either the MODIS-Aqua or SeaWiFS chl a products.   (Note 
that the most recent SeaWiFS reprocessing has improved the discrimination of CDOM and chl a;  a 
comparison will be shown subsequently.)    The three data products are shown side-by-side in Figure 8 for 
the study area in November 2005.   The SeaWiFS data set is SeaWiFS.R5.2.
Figure 8.   Comparison of SeaWiFS chl a (left), MODIS-Aqua chl a (center) and GSM chl a (right). 
It was initially thought that the high SeaWiFS chl a
values might be due to a late-autumn phytoplankton 
bloom, caused by the delivery of nutrients to the 
coastal zone by the increased river flows.  It is clear 
from this comparison, however, that SeaWiFS chl a is 
higher than MODIS-Aqua chl a, and significantly 
higher than GSM chl a.  Due to the known  
incomplete discrimination of chl a and CDOM in 
SeaWiFS data, the GSM acdm data product (absorption 
coefficient due to detrital and dissolved organic 
matter) was examined.   This data product, shown in 
Figure 9, indicated a significant contribution  to the 
absorption signal from dissolved detrital and organic 
matter. 
Figure 9.  GSM acdm data product for 
the study area, November 2005. 
Analyzing the influence of averaging method
Utilizing the Garver-Siegel-Maritorena (GSM) chl a and acdm data products, we sought to determine 
if the elevated coastal data values in November 2005 were anomalous.  This analysis was performed 
independent of Giovanni.  Geometric mean chl a was calculated by taking the median value of each 
scene for each time step to create a time series;  the seasonal mean was calculated by averaging each 
month  for the whole time series to create a 12-month climatology; subtracting the climatology from 
the time series yielded the chl a anomaly. Bio-optical data such as chl a have a lognormal distribution 
at a variety of spatial and temporal scales (Campbell, 1995) and therefore must be averaged 
geometrically, by taking the average of the lognormal distribution of the data or by taking the median 
value.
The time series and anomaly time series were calculated for GSM chl a and GSM acdm, and are 
shown in Figure 7.
a b
c d
Figure 9.    GSM chl a and acdm time series, with accompanying anomaly time series.
(a) GSM chl a.   (b) GSM chl a anomaly.  (c) GSM acdm.  (d) GSM acdm anomaly.  
Dotted line in (a) and (c) is the climatology that was subtracted to create the 
anomaly time series.
Discussion
The GSM chl a and acdm time series do not show significantly elevated values 
of  either data product, nor a noteworthy anomaly for either data product, in 
November 2005.    We attribute this distinct difference from the SeaWiFS data in 
Giovanni to two factors;  one, the incomplete discrimination of phytoplankton 
chlorophyll and  CDOM  in SeaWiFS data;  and two, more importantly, the 
difference in results from the use of the arithmetic mean and the geometric 
mean averaging method.  The lognormal distribution of chl a data, combined 
with the sensitivity of the arithmetic mean to outliers, caused the SeaWiFS data 
anomaly calculation in Giovanni to produce an inaccurately high monthly anomaly 
for November 2005.  This anomaly was likely enhanced by the limited amount of 
data available for the month, causing the elevated SeaWiFS chl a values 
occurring early in the month, due to the export of riverine CDOM, to dominate 
the monthly statistics. 
Because the current arithmetic mean averaging method in Giovanni was  
inherited from GrADS, and because Giovanni serves many different types of 
remote-sensing data, the need for the geometric mean calculation for ocean 
color data has not been a high priority.   Our results indicate that in order to 
provide the most accurate presentation of chl a data in Giovanni, it is necessary 
to add the geometric mean calculation for ocean color data.   This improvement 
will allow more representative results and improved comparisons with new data 
products that will be added to Giovanni by upcoming projects.
Comparison of SeaWiFS.R5.2 and SeaWiFS.R2009 data products
Following the SeaWiFS data reprocessing in December 2009, the reprocessed SeaWiFS data 
products (R2009) were added to Ocean Color Radiometry in Giovanni.   The new climatology 
has not yet been added, so anomalies cannot be calculated for the R2009 data products.  It is 
possible,  however to directly compare the R2009 SeaWiFS data to the previous version (R5.2) 
of the data  products.   
According to the OBPG, the refinements to the data substantially improve agreement in 
chlorophyll retrievals relative to ground truth measurements in turbid and highly productive 
regions.  Figure 10 shows a difference plot between the R5.2 and the R2009 chl a data for the 
study area in November 2005, calculated as (R5.2 – R2009).   Thus, positive values indicate 
where the R5.2 chl a exceeds R2009 chl a.   The predominance of positive values indicates 
that R2009 chl a was reduced,  further supporting that the high chl a values in the SeaWiFS
R5.2 data product off the U.S. northeast  coast in November 2005 were primarily due to 
increased export of riverine CDOM and detritus by the  elevated river flow.  
Figure 10.   Difference plot of SeaWiFS. 
R5.2 chl a and SeaWiFS.R2009 chl a for
November 2005, showing that R5.2
chl a is significantly greater than R2009
chl a near the coast.   This plot 
demonstrates that the reprocessing 
reduced the influence of CDOM on 
chl a retrievals in turbid waters.
Reference: Campbell, J. W. (1995), The lognormal distribution as a model for 
bio-optical  variability in the sea.  Journal of Geophysical Research, 100(C7), 
13,237–13,254.
Presented at the American Geophysical Union 2010 Ocean Sciences Meeting, Portland, Oregon, February 22-26
n
i
iavg X
n
X
1
1x
m
geo eX
Calculation of mean values and climatological anomalies
Xa ( i ) = X (i ) - Xm ( i )
where: 
• i denotes month (1, 2, ... , 12)
• Xa(i) represents the anomaly data for month i
• X(i) is the original data for month i
• Xm(i) is the monthly climatology data for month i
Mean calculation methods
Given a selected area with n data points X1, X2, … 
Xn,  Xi denotes the Level 3 mapped data points 
within a selected area.    
The arithmetic mean is calculated by:
The geometric mean is calculated by
where 
The SeaWiFS chl a anomaly plot generated for the study region (Figure 5) indicated a large chl a 
anomaly in November 2005. The time-series of SeaWiFS chl a anomalies (Figure 6) showed that 
this anomaly was unprecedented over the length of the mission.   The time-series values were 
calculated by arithmetic mean.
Giovanni provides an anomaly analysis capability utilizing climatologies generated by the data 
provider – in this  case, the ten-year SeaWiFS chl a climatology is provided by the Ocean Biology 
Processing Group (OBPG) at Goddard Space Flight Center (GSFC).   The anomaly is calculated for 
the designated time period, and can be averaged over  several months for periods up to one year.   
The anomaly is calculated by 
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Influence of Averaging Method on the Evaluation of a Coastal Ocean Color Event on the U.S. Northeast Coast

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