Simulating the wind and sea-surface roughness effects on Aquarius Sea Surface Salinity Retrievals: Evaluating alternative models to

correct for the effects of the rough sea surface on L-band radiometer emission and scattering

NASA Applied Sciences ProgramMississippi Research Consortium

Prototype Solutions from Next Generation NASA Earth Observing and Predictive Capabilities

Investigators: S. Howden* (P.I.), D. Burrage#, J. Wesson#, D. Ko,D. Wang#

Funding Requested: $463,271Duration: 18 months

*Department of Marine ScienceThe University of Southern Mississippi

#Naval Research LaboratoryStennis Space Center

Earth Observations:

• Aquarius Mission Microwave Radiometer and Scatterometer Data• NASA (Quick SCAT), Jason-1 (Wind Speed and Wave Height)

Predictions and Measurements:

• Coastal Sea State (NDBC, STARRS C-band radiometer)• Coastal SSS, SST, and SSR (Roughness Model Simulations and STARRS L-band rad.)

Decision Support:

• Optimizing the accuracy of Aquarius SSS retrievals • Selecting operational Roughness Correction models• Monitoring the quality and accuracy of Aquarius SSS retrievals over time

Benefits:

• Improved knowledge of SSS retrieval and SSR influence• Accurate remotely sensed SSS for ocean circulation models and assimilation • Expanded knowledge of salinity at the air-sea boundary for constraining hydrological cycle• Improved Navy, NOAA, and NASA ocean circulation models• Better information for near shore fisheries• Improved hydrographic, conductivity and sound speed information for Navy operations• Enhanced information on deep ocean and coastal salinity and temperature fronts

Concept: Rapid Prototyping of Accurate SSS Retrievals

Project Status at 31 May, 2009

• First written report presented July, 2007.

• Last Review presentation 4 April, 2008.*

• Extension to original NRL/USM CRADA granted June, 2008.

• Roughness modeling aspects still progressing, but with reduced emphasis on Aquarius simulation.

• New results presented on both roughness and optics aspects at engineering and science meetings.

• Project has spawned a successful bid to NRL for base funding to continue the roughness work as well as two ROSES proposals.

*Decisions arising from 4 April, 2008 review

• NASA Program seeking more emphasis on immediate returns and demonstrating new applications with utility for real world problems.

• Agreement to place new emphasis on optical/SSS results.

• Possible extension discussed to allow for late initial funding transfers(6-month delay in establishing NRL/USM CRADA).

• Administrative difficulties of funding NASA for simulation work led to decision to perform additional roughness field work instead.

Recent accomplishments

• Analysis and reporting of results from STARRS surveys flown off Virginia during Dec 2006.

• Analysis and reporting of field campaign in the Gulf of Mexico in May 2007 (microwave and optical SSS retrievals compared).

• Two papers presented at IEEE Transactions on Geoscience and Remote Sensing (on roughness and optical aspects).

• Virgilio Maisonet investigated optical aspects and presented an award-winning paper on optical CDOM/SSS.

• Preliminary assessment of available roughness models (follow on NRL base-funded project approved).

Roughness Correction Models

Wave Spectra

&

Model Evaluation

1.1. Ambient Roughness: Ambient Roughness: swell from distant stormsswell from distant storms 2.2. Wind Wave Roughness: Wind Wave Roughness: wind-generated short wind-generated short

waveswaves3.3. Breaking Roughness: Breaking Roughness: breakers, whitecaps, breakers, whitecaps,

foamfoam

Rough SeaRough Sea

Slight SeaSlight Sea

Short Wind WavesShort Wind Waves

Sea Surface Roughness ComponentsSea Surface Roughness Components

Reference: Pan, et al., (2005) JGR C, v110, C02020

Surface Wave Height Frequency Spectrum ObservedSurface Wave Height Frequency Spectrum ObservedDuring Cold Front Passage in Gulf of MexicoDuring Cold Front Passage in Gulf of Mexico

SwellT=7s

Wind-WavesT=2s

=1 m 1 cm

Short Waves

Swell tilts the short waves,changing their slope.

r () = cos() (1-Rr(,)) d

Reflection Coef. , Rr(,)

Rr(,)=Er2 / Ei

2

(Determine

using numerical experiments)

Roughness Changes Emissivity and Hence Brightness Temperature,Roughness Changes Emissivity and Hence Brightness Temperature,Causing Errors in Salinity Retrievals that Assume Sea is Flat. Causing Errors in Salinity Retrievals that Assume Sea is Flat.

(,Ts,S)=1-Rf(,Ts,S)

Reflection Coef., Rf(,Ts,S)

Rf(,Ts,S) =Er2/Ei

2 (Klein & Swift)

S = Salinity, Ts = Temperature

= Flat Sea Emissivity

Brightness Temp, Tb = Ts

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Incident

Sca

ttere

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r=Rough Sea Emissivity

Tb = ( + r) Ts

Roughness Correction Models consideredRoughness Correction Models consideredfor Satellite Mission Processing:for Satellite Mission Processing:

Rigorous E-M scattering models (High accuracy, but computationally intensive):Taflove and Hagness (2005) Finite Difference Time Domain Method (FDTD) Method – Accurate and Adaptable, Rigorous Solution of Maxwell’s Equations

Asymptotic models (Questionable accuracy, but efficient for operational use):Yueh (1997) Two-scale model - Divides wave spectrum into long and short wave parts.

Employs a Gaussian input spectrum.

SSA/SPM Voronovich (1994) - Works well only for certain types of wave spectrum Employs optional spectra, such as Kudryavtsev et al.

[Combines Small Slope Approximation (SSA) of Voronovich (1985) and Johnson (1999) with Small Perturbation Method (SPM) of Rice (1951)].

Empirical models (Simple and efficient, but have limited applicability range):

A. Camps, et al., (2003) WISE model of Tb for specified Wind and/or Wave Height.

Gabarro, et al., (2003) Retrieves SSS, Wind Speed and Water Temperature simultaneously from Multi-angle L-Band measurements (Multi-parameter retrieval).

Neural Network Model to be trained on SMOS data after launch

Reference: Reul, et al., (2005) IGARSS '05. Proc. 2005 IEEE v3, 2195 – 2198

A High Accuracy Reference E-M Interaction Model:A High Accuracy Reference E-M Interaction Model:

Predicted Brightness Temps from Two-scale Model (TSM) andPredicted Brightness Temps from Two-scale Model (TSM) andSSA/SPM for Given Roughness Spectra versus Wind DirectionSSA/SPM for Given Roughness Spectra versus Wind Directionat Wind Speed, Ws=15 m/s Differ by Up To ~ 2K (4 psu S)!at Wind Speed, Ws=15 m/s Differ by Up To ~ 2K (4 psu S)!

References: TSM (Yueh, 1997), SSA/SPM (Reul, 2007)

TSM V-Pol TSM H-Pol

SPM/SSA V-Pol SPM/SSA H-Pol

Tb [k]

Azimuth (d

eg)Incidence Angle (deg)

Azimuth (d

eg)Incidence Angle (deg)Azimuth (d

eg)Incidence Angle (deg)

-2

2

0

46

8

1012

14

-2

2

0

46

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1012

14

-2

2

0

46

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1012

14

-2

2

0

46

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1012

14

Wave Age -1=0.5

0

4

1086

2

0

4

1086

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4

1086

2

0

4

1086

2

L-bandf=1.4 GHz

Azimuth (d

eg)Incidence Angle (deg)

Tb [k]

Tb [k]

Tb [k]

Optimal for SSS sensing Optimal for SSR sensing

U (m/s) U (m/s)

Parameters: Inc. Angle 37 deg., Ts=298 K, Salinity=35 psu, Wave age=0.84Parameters: Inc. Angle 37 deg., Ts=298 K, Salinity=35 psu, Wave age=0.84

References: Elfouhaily, et al., 1997; Kudryavtsev, et al. 2003

1K1K

Tb (K)

Compare 2 psu roughness correction error with observedSSS difference across Gulf Stream ~ 4 psu (Wilson et al., 1999)

B(k)=S(k)k3

kL kC

Kudryavtsev

Elfouhaily

U=5 m/s

U=10 m/s

=1 m 1 cm

CurvatureSpectrum

V-Pol H-PolTb (K)Tb (K)

SSA/SPM Tb Predictions Based on Different Wind-SSA/SPM Tb Predictions Based on Different Wind-Wave Spectra Differ Significantly (Wave Spectra Differ Significantly (Tb~1 K, Tb~1 K, S=2 psu)S=2 psu)

K (rad s-1)

B(k)

Rigorous EM ScatteringFinite Difference Time Domain

(FDTD) Model

Reference ModelDevelopment

(Early coarse resolution version)

Procedure to Determine Radar Cross Section (RCS) and Hence EmissivityProcedure to Determine Radar Cross Section (RCS) and Hence EmissivityUsing FDTD Reference Model and Monte Carlo simulationUsing FDTD Reference Model and Monte Carlo simulation

An incident plane wave (Ei) is generated at the Virtual Surfaceand is reflected off the rough sea surface.

This surface is one realization of a roughness spectrum.

The reflected wave (Er) is detected above the virtual surface (the incident wave is absent there).

The Reflectance or RCS are determined from Rr=|Er|2 / |Ei|2

Repeat for multiple incidence angles and roughness spectrum realizations (i.e., using Monte Carlo Simulation).

Results are averaged to estimate Rough Emissivity (Integral of 1-Rr).

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E=Er+Ei

Virtual SurfaceVirtual Surface

Incident Wave front

Wav

e ve

ctorReflected W

ave front

Rf=|Er|2/|Ei|2

Ei

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Configuration for Simulating Reflection from Smooth and Rough SeasConfiguration for Simulating Reflection from Smooth and Rough Seas

Sloping flat surface

Level slightly rough surfaceLevel flat surface

Level very rough surface

Air

Flat Sea

Sloping Sea

Slight Sea

Rough Sea

Air

Air

Air

Grid Cell # 0->100

Grid

Cel

l # 0

->10

0G

rid C

ell #

0->

100

Grid Cell # 0->100

0.4 m

PointSource

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-100

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0

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FDTD Simulation of C-band Energy [dB] for Surface BackscatterFDTD Simulation of C-band Energy [dB] for Surface Backscatter

Level slightly rough surfaceLevel flat surface

Sloping flat surface Level very rough surface

Grid Cell # 0->100

Grid

Cel

l # 0

->10

0G

rid C

ell #

0->

100

Grid Cell # 0->100

0.4 m

=0.05 m

ShadowZone Mean Sea

Surface

|E|2 db|E|2 db, E = Ei + Er

Field Campaigns(VIRGO & COSSAR)

Scan Pixel Altitude (km) 6.0 ~1 2.6 0.6 ~0.1 0.26

FlightDirection

Ocean (Ts, Tb, Oc)

Piper Navajo

IR-Band

L-Band

C-Band

STARRS

STARRS

Incidence Angles: +/- 7,22,37 (deg)

NEDT(1s)=0.50 K

dS=1 psu

Visible- Bands

L-Band

C, IR &Vis-Bands(SeaWiFS Chs.)

STARRS Sampling SchemeSTARRS Sampling Scheme

NRL’s Salinity, Temperature, and Roughness Remote Scanner (STARRS)

STARRS Airborne Microwave Radiometer System

The STARRS Sampling Scheme

15:30

15:49 16:10

14:10

NIKON D1X Digital Camera

Swell~100m

White Caps& Foam

Virgo Optical Images Crossing Gulf Stream On 12 Dec., 2006: Virgo Optical Images Crossing Gulf Stream On 12 Dec., 2006: Swell, White Caps and Foam Also Influence SSS RetrievalsSwell, White Caps and Foam Also Influence SSS Retrievals

Inshore

Offshore

*Virgo 12 Dec 06 Estimated Roughness Corrections (Inc. Angle 7 deg)

2.50

5.805.20

0.531.22 1.101.06

2.45 2.20

0.00

1.00

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CBBV2 CHLV2 44014

Wind Spd m/s Delta Tb (K) Delta S (psu)

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La

t [d

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Map of Salt [psu] for flight on 12-Dec-2006 from 11:52:45 to 15:01:57UTC in file C12dec06a

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44014

CHLV2

CBBV2

Virgo SST and SSS Crossing Gulf Stream On 12 Dec., 2006: Virgo SST and SSS Crossing Gulf Stream On 12 Dec., 2006: Effect of Empirical Wind-Induced Roughness CorrectionsEffect of Empirical Wind-Induced Roughness Corrections

-76.5 -76 -75.5 -75 -74.5 -74 -73.5 -7336

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La

t [d

eg

]

Map of SST [C] for flight on 12-Dec-2006 from 11:52:45 to 15:01:57UTC in file C12dec06a

15 20 25

STARRS SST

44014

CHLV2

CBBV2

Chesa-peake Bay

CapeHatteras

Gulf Stre

am

Terra MODIS SST

STARRS SSS

*Based on WISE wind model (# Wave modelcorrection is smaller by a factor of two!)

#(cf 1.1 psuFor Hs=0.7 m)

NOAA Met. Buoys:NOAA Met. Buoys:

STARRS flights over Mississippi Outfall

Color, Surface Salinity and Roughness (COSSAR) Color, Surface Salinity and Roughness (COSSAR) WindWind

and Wave Data and STARRS flights 10-15 May 07and Wave Data and STARRS flights 10-15 May 07

NDBC 42040

Buoy NDBC 42007

Mississippi Outfall

U (m/s)

(deg)

Hs (m)

(deg)

NOAA NDBC 42007

Observations and Analysis

• R/V Pelican survey from Atchafalaya Bay to deep ocean salinity (8-10 May 2007).

• Two aircraft surveys 10 May 2007 with STARRS and Satlantic (SeaWifs Airborne Simulator) instruments.

• Confirm STARRS Salinity matches shipboard.

• Show that Optical measurements detect fronts and can be used in Salinity regression.

• Compare regression Salinity with STARRS Salinity over flight survey region.

-92.6 -92.4 -92.2 -92 -91.8 -91.6 -91.4 -91.2

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t [d

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STARRS Salinity, Morning flight10 May 2007

Salinity Range 0-40 psu all figures

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STARRS Salinity, Afternoon flight10 May 2007

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Shipboard underway Salinity,STARRS Salinity, Afternoon flight

10 May 2007

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Distance (km) from Outbound Endpoint

Sa

linit

y (

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u)

Ship (green) and aircraft (blue) Salinity

Afternoon Flight, 10 May 2007

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Re

gre

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su

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Salinity Regression vs STARRS SalinityMorning flight, 10 May 2007, outbound leg

Sal=c+a(ch5/ch2)+b(ch5/ch6)

-92.6 -92.4 -92.2 -92 -91.8 -91.6 -91.4 -91.2

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Regression salinity, morning flight, 10 May 2007

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Regression salinity, afternoon flight, 10 May 2007

VJ Maisonet: Student Project onOptics and Salinity

Overview• Introduction

• Equipment

• Study Site

• Algorithm used

• Results

• Summary

• Current/Future work

Ocean Color Remote Sensing• Light from the Sun

(irradiance ,Ed(λ)) penetrates , reacts with the water with a portion of the light energy being reflected back out (water leaving radiance ,Lu(λ))

Colored dissolved organic matter• Colored dissolved organic

matter (CDOM) is the optically measurable component of the dissolved organic matter in water.

• Naturally occurring substance– When plant tissue

decomposes either in the soil or in a body of water the organic matter is broken down by microbes

• The color of water will range through green, yellow-green, and brown as CDOM increases

The right side of the figure is the Remote sensing reflectance (Rrs(λ, 0-) = Lu(λ, 0-) / Ed(λ, 0-)) of CDOM, where Lu is water leaving radiance and Ed is downwelling irradiance.

Equipment

Piper Navajo IR-Band

L-Band

C-Band

STARRS

OCR-507

Scan6 km

FlightDirection

Ocean (Ts, Tb)

Pixel ~1km

STARRS

IncidenceAngles:+/- 7,22,37 (deg)

NEDT(1s):0.50 K dS=1 psu

Alt. 2600 m

L-Band

C, IR &Vis-BandSeaWiFS Chs.

STARRS/OCR Sampling Pattern

Sampling Rates:STARRS ~2.0 sOCR ~.17 sOCR:STARRS ~ 11:1

Area of Study

Sampling Flights

Algorithm

• For ease of computation an empirical algorithm for CDOM from D’Sa et al. 2006 was used.– Their study was conducted in the same region and time of year– Their study was preformed with similar optical equipment

• Below is the algorithm they developed:– Acdom (412) = 0.227 x (Rrs510/Rrs555)-2.022

Results

R2 Value= 0.76n= 5220

Results cont.

R2 Value= 0.90N=1100

Results cont.• Using the regression analysis from the morning

flight combined with the CDOM algorithm to create the following:

• Salinity= 0.227 (Rrs 510/Rrs 555)-2.022 – 0.34 -.0082• This Salinity model was then applied to the

afternoon flight for verification

Results cont.

R2 Value= 0.88

Summary

• This study resulted in a Ocean Color-Salinity model that can measure with ~88% accuracy the Sea-Surface Salinity of the Louisiana shelf

• These results come with a few caveats:– This study is a seasonal model not a annual model– This model is only effective in the near Coastal zone

• This model assumes :– Photo-degradation is low in the near coastal waters– That CDOM is behaving conservatively  

Current/Future Work• In late 2009 early 2010 NASA will

deploy Aquarius– L-Band Radiometer– 100 km Resolution

• Currently we are in the process of applying the Ocean Color-Salinity Algorithm to SeaWiFS & Modis A for a broader view of the coastal zone

• Our next step is to develop a ‘smart’ algorithm to interpolate between the CDOM-Salinity and the Aquarius-Salinity

– In hopes to fill in the gaps left by the satellite to assemble a ‘whole’ picture

Acknowledgments

Funding Agency: NASA/Mississippi Research Consortium

Project Contract Number: NNS06AA98B

Title: Simulating the Wind and Sea-Surface Roughness Effect on Aquarius Sea Surface Salinity Retrievals: Evaluating Alternative Models to Correct for the Effects of The Rough Sea Surface on L-band Radiometer Emission and Scattering

New Developments, Spinoffs, Publications

• Better understanding of how E-M radiation interacts with the rough sea surface – leading to NRL New Start.

• New techniques for comparing and selecting wind/wave spectra and roughness models for more accurate microwave open ocean remote sensing of SSS – NRL New Start.

• New parameters and algorithms for retrieving SSS from optical remote sensing data in Gulf of Mexico coastal seas.

• Advanced preparations for accurate retrieval of SSS from SMOS and Aquarius satellite-borne L-band radiometers.

Derek Burrage, David Wang, Joel Wesson Derek Burrage, David Wang, Joel Wesson (SSC) and Paul Hwang (DC)(SSC) and Paul Hwang (DC)

Goal: Advance understanding of physical processes governing sea surface roughness (SSR) and its interaction with electromagnetic (E-M) radiation, to enhance salinity remote sensing using L-band radiometers.

NASANASAAquariusAquarius

ESAESA SMOSSMOS

Sea Surface Roughness Impacts Sea Surface Roughness Impacts on Microwave Sea Surface on Microwave Sea Surface

Salinity Measurements (SRIMS)Salinity Measurements (SRIMS)

Hypothesis: Small-scale roughness components generated by diverse physical processes including wind, swell, breaking waves and foam dominate microwave sea surface emission and scattering, and thus sea surface salinity (SSS) retrieval accuracy.

Payoff: More accurate global sea surface salinities for input to navy ocean circulation models and data assimilation systems.

NRL New Start 6.1 (FY 2010-12)

Burrage, D., J. Wesson, D. Wang, and S. Howden (2007). Airborne Passive Microwave Measurements of Sea Surface Salinity, Temperature and Roughness, and Implications for Satellite Salinity Retrieval. IEEE Geoscience and Remote Sensing Society (IGARSS) 2007, Barcelona, Spain, 23-27 July, (Poster Paper).

Burrage, D., J. C. Wesson, D. W. Wang, S. D. Howden, and N. Reul (2008). Sea Surface Roughness Influence on Salinities Observed with an Airborne L-Band Microwave Radiometer: Model Inter-Comparisons, Validation and Implications for Satellite Salinity Retrieval. IEEE Geoscience and Remote Sensing Society (IGARSS), Boston, MA.(Poster Paper), July 7-11.

Wesson, J., D. Burrage, C. Osburn, V.J. Maisonet, S. Howden, and X. Chen (2008). Aircraft and In Situ Salinity and Ocean Color Measurements and Comparisons in the Gulf of Mexico. IGARSS, Boston, MA (GRSS 2008 IGARSS IEEE Int’l Vol 4, pp.383-386).

Maisonet, V. J., J. Wesson, C. Osburn, D. Burrage and S. Howden (2009) Using Ocean Color to Measure Coastal Sea-Surface Salinity of the Louisiana Shelf. Virgilio (Oral presentation) Mississippi Academy of Sciences (MAS) annual meeting, Feb. 26-27, 2009, Olive Branch, MS. (Published abstract: Journal of the Mississippi Academy of Sciences, 54, 1, 83-84., Outstanding Oral Presentation Award in Division of Marine and Atmospheric Science.

Conference Papers

ReportsHowden, S., D. Burrage, J. Wesson and D. Ko. Simulating Wnd and Sea-Surface Roughness Effects on Aquarius Retrievals. (First progress Report submitted to NASA Applied Sciences Program and Mississippi Research Consortium, July 2007)

Burrage, D. M, J. Wesson and J. Miller (2008), Deriving Sea Surface Salinity and Density Variations from Microwave Radiometer Measurements: Application to Coastal River Plumes using STARRS, Transactions on Geoscience and Remote Sensing, SMOS Special Issue, 46, 3, 765-785.

Burrage D. M., J. Wesson, M. A. Goodberlet and J. L. Miller (2008). Optimizing performance of a microwave salinity mapper: STARRS L-band radiometer enhancements, J. Atm. & Oc. Tech. 25, 776-793.

Burrage, D., J. Wesson, C. Martínez, T. Perez, O. Moller, Jr. and A.Piola (2008). Patos Lagoon outflow within the Rio de la Plata plume using an Airborne Salinity Mapper: Observing an embedded plume, Cont. Shelf Res. PLATA project special issue, 28, 1625-1638.

Gabarro, C., J. Font, J. Miller, A. Camps, J. Wesson, D. Burrage and A. Piola (2008) Use of empirical sea surface emissivity models to determine sea surface salinity from an airborne L-band radiometer, Scientia Marina, June, 72, 2, 329-336.

Jerry L. Miller, David W. Wang, Paul A. Hwang and Derek M. Burrage (2007) Small-scale Rogue Waves in the Ocean (In Revision).

Refereed Papers by Team Members(Arising from related projects)

Final Steps

• Execute roughness field campaign off Chesapeake Bay (Virgo II) in late 2009, if possible coinciding with SMOS over flights (Piggyback with NRL 6.1 project).

• Continue development of Rigorous Reference model and L-band Scatterometer Simulation (NRL 6.1 Project).

• Complete roughness model evaluation and selection process.

• Finalize papers on roughness and optical SSS retrieval.

• Compile and submit final report.