Case Study of CDOM to Salinity Relationships in Mississippi River Outflow

EAS 4480

V.J. Maisonet

4/25/12

Introduction• Real-time distribution of salinity around the world’s

coasts are yet not accessible.

• Large regions of the ocean do not have sufficient data to estimate a mean salinity accurately.

• Ocean salinity is important measure in the determination of ocean circulation

• As a result forecast skill is limited leading to large errors in the surface fluxes

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Introduction• The relationship between CDOM and salinity may

vary spatially between regions and watersheds.

• As well as temporally dues to seasonal variation in rainfall, biological inputs, soil runoff and vegetation.

• It has been demonstrated that a well defined CDOM/SSS relationship is evident in both the MR & AR.

• This relationship has been shown to maintain itself during both high and low flow seasons.

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CDOM Algorithms Tested

• Using a algorithm from D’Sa et. al., 2005, CDOM absorbance from the optical ratio Rrs510/Rrs555 using in water samples:

– Log(acdom (412)) = -0.643-2.022(Log [Rrs(510)/ Rrs(555)]) (1)

• Using a power law equation derived from satellite and field measurements, D’Sa et. al., 2006 created an empirical algorithm for the absorbance of CDOM:

– Acdom (412) = 0.227[Rrs(510)/ Rrs(555)]-2.022 (2)

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Goal• Our goal is to determine a relationship between

CDOM and SSS in the coastal zone and test this correlation to attempt to fill the gaps left by the satellites to create a ‘whole picture’ of the sea surface salinity.

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Sampled Data

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Tasks

• CDOM algorithms (Eq. 1 & 2) were applied to the data set using MATLAB.

• A linear regression of the resulting CDOM values with salinity data, with supporting statistics determined which of the algorithms was better suited for the data

• The linear regression equations from the best fit(s) were used to create a Salinity Model.

• Using the contourf function in MATLAB, the salinity model was used to create a salinity map of the Louisiana shelf.

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Algorithm comparison

• For the 2 datasets visual comparisons between the 2 algorithms were made.

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Algorithm comparison

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Ignore #3

Algorithm comparison

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Ignore #3

Algorithm comparison• For each flight in May the CDOM/SSS

relationship broke down at ~28 PSU

• The two algorithms were run through a linear regression in MATLAB also Chi square statistic (chisq), uncertainties (dp), and the mean squared error (sigy) where produced.

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Linear Regression

Flight Alg p(1):p(2) dp chisq sigy

10May07a Alg1 -49.78 0.25 4071.2 3.45

    41.13 0.13    

  Alg2 -115.28 0.58 4071.2 3.45

    41.13 0.13    

11May07a Alg1 -44.20 0.37 20064 4.98

    35.90 0.19    

  Alg2 -102.37 0.85 20064 4.98

    35.90 0.19    

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Salinity Model Development• Empirical salinity models for the optical data

were made using the slopes and intercepts of the linear regressions from the 3 May flights

– based on: Salinity= p(1)*[CDOM Alg#] + p(2).

• Salinity1= -49.78*[CDOM Alg#] + 41.13

• Salinity2= -44.20*[CDOM Alg#] + 35.90

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Flight Alg p(1):p(2) dp chisq sigy

10May07a Alg1 -49.78 0.25 4071.2 3.45

    41.13 0.13    

  Alg2 -115.28 0.58 4071.2 3.45

    41.13 0.13    

11May07a Alg1 -44.20 0.37 20064 4.98

    35.90 0.19    

  Alg2 -102.37 0.85 20064 4.98    35.90 0.19    

Salinity Model Development• To determine the best model, the 2 algorithms

were run for each flight and applying a 5km low pass filter.

• The Standard Deviations, Root Mean Squared Error was used to determine which Salinity Model best predicted salinity in all flights

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Salinity Model Development

10May07a Mean Diff. St. Dev RMS10a alg 1.16 12.67 2.7511a alg 1.74 11.26 2.88

11May07a Mean Diff. St. Dev RMS10a alg 2.38 5.76 5.4511a alg 0.1 5.12 4.92

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Salinity Model

• The Salinity models are too close to choose which it best

• Solution?

– Combine all three flightsFlight Alg p(1):p(2) dp chisq sigy

Combined Alg1 -46.8707 0.1894 4.06E+04 4.7652    40.5713 0.0947   

Salinity_combine= -46.48*[CDOM Alg#] + 42.94

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Salinity Model

• The combine salinity model from May was applied to a SeaWiFS monthly composite of May 2007

• It was necessary to shift to using CDOM Algorithm 2 for the CDOM input.

– CDOM Algorithm 2 should be utilized in this case since it was created with atmospherically corrected satellite data.

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Salinity Model

Salinity

Salinity Combine Model

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Summary-Taking the highest error as worst case this salinity model has an error +/- ~4 PS

-This error is quite large and will inhibit the ability to accurately define features seen in satellite salinity maps

-The CDOM/SSS relationship broke down in area of salinities > 28 PSU

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