Earth Observations: • Aquarius Mission Microwave Radiometer and Scatterometer Data

1. 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 Program Mississippi 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: $463,271 Duration: 18 months *Department of Marine Science The University of Southern Mississippi #Naval Research Laboratory Stennis Space Center

2. Concept: Rapid Prototyping of Accurate SSS Retrievals 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

3. 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.

4. *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.

5. 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).

6. Roughness Correction Models Wave Spectra & Model Evaluation

7. Sea Surface Roughness Components • Ambient Roughness: swell from distant storms • Wind Wave Roughness: wind-generated short waves • Breaking Roughness: breakers, whitecaps, foam Slight Sea Short Wind Waves Rough Sea

8. Surface Wave Height Frequency Spectrum Observed During Cold Front Passage in Gulf of Mexico Wind- Waves T=2s Swell T=7s Swell tilts the short waves, changing their slope. l=1 m 1 cm Short Waves Reference: Pan, et al., (2005) JGR C, v110, C02020

9. er (q) = cos(f) (1-Rr(q,f)) df Reflection Coef. , Rr(q,f) Rr(q,f)=Er2 / Ei2 (Determine using numerical experiments) Roughness Changes Emissivity and Hence Brightness Temperature, Causing Errors in Salinity Retrievals that Assume Sea is Flat. • = Flat Sea Emissivity • Brightness Temp, Tb = e Ts er=Rough Sea Emissivity Tb = (e + er) Ts Scattered Reflected Incident Incident q q q f E=Er+Ei E=Er+Ei Flat Sea (Ts, S) Rough Sea e(q,Ts,S)=1-Rf(q,Ts,S) Reflection Coef., Rf(q,Ts,S) Rf(q,Ts,S)=Er2/Ei2 (Klein & Swift) S = Salinity, Ts = Temperature

10. Roughness Correction Models considered for Satellite Mission Processing: Empirical models (Simple and efficient, but have limited applicability range): • 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 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)]. A High Accuracy Reference E-M Interaction Model: 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 Reference: Reul, et al., (2005) IGARSS '05. Proc. 2005 IEEE v3, 2195 – 2198

11. 14 14 14 14 12 12 12 12 10 10 10 10 10 10 10 10 8 8 8 8 8 8 8 8 6 6 6 6 6 6 6 6 4 4 4 4 4 4 4 4 2 2 2 2 0 0 0 0 2 2 2 2 0 0 0 0 -2 -2 -2 -2 Predicted Brightness Temps from Two-scale Model (TSM) and SSA/SPM for Given Roughness Spectra versus Wind Direction at Wind Speed, Ws=15 m/s Differ by Up To ~ 2K (4 psu S)! Optimal for SSS sensing Optimal for SSR sensing TSM V-Pol TSM H-Pol Tb [k] Tb [k] Azimuth (deg) Azimuth (deg) Incidence Angle (deg) Incidence Angle (deg) L-band f=1.4 GHz SPM/SSA V-Pol SPM/SSA H-Pol Tb [k] Tb [k] Azimuth (deg) Azimuth (deg) Incidence Angle (deg) Incidence Angle (deg) Wave Age W-1=0.5 References: TSM (Yueh, 1997), SSA/SPM (Reul, 2007)

12. SSA/SPM Tb Predictions Based on Different Wind- Wave Spectra Differ Significantly (dTb~1 K, dS=2 psu) B(k) Kudryavtsev Curvature Spectrum U=10 m/s kL kC U=5 m/s B(k)=S(k)k3 l=1 m 1 cm Elfouhaily K (rad s-1) V-Pol Tb (K) Tb (K) H-Pol Tb (K) 1K 1K U (m/s) U (m/s) Parameters: Inc. Angle 37 deg., Ts=298 K, Salinity=35 psu, Wave age=0.84 Compare 2 psu roughness correction error with observed SSS difference across Gulf Stream ~ 4 psu (Wilson et al., 1999) References: Elfouhaily, et al., 1997; Kudryavtsev, et al. 2003

13. Rigorous EM Scattering Finite Difference Time Domain (FDTD) Model Reference Model Development (Early coarse resolution version)

14. Er Rf=|Er|2/|Ei|2 Ei Wave vector Virtual Surface Reflected Wave front q Incident Wave front f E=Er+Ei Procedure to Determine Radar Cross Section (RCS) and Hence Emissivity Using FDTD Reference Model and Monte Carlo simulation An incident plane wave (Ei) is generated at the Virtual Surface and 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).

15. Grid Cell # 0->100 Grid Cell # 0->100 Grid Cell # 0->100 Grid Cell # 0->100 Configuration for Simulating Reflection from Smooth and Rough Seas Level slightly rough surface Level flat surface Point Source Air Air 0.4 m Flat Sea Slight Sea Sloping flat surface Level very rough surface Air Air Sloping Sea Rough Sea

16. FDTD Simulation of C-band Energy [dB] for Surface Backscatter |E|2 db, E = Ei + Er |E|2 db Level flat surface Level slightly rough surface Shadow Zone Mean Sea Surface Grid Cell # 0->100 l=0.05 m 0.4 m Sloping flat surface Level very rough surface Grid Cell # 0->100 Grid Cell # 0->100 Grid Cell # 0->100

17. Field Campaigns (VIRGO & COSSAR)

18. STARRS Visible- Bands IR-Band L-Band C-Band STARRS Piper Navajo STARRS Sampling Scheme Ocean (Ts, Tb, Oc) Flight Direction NEDT(1s)= 0.50 K dS=1 psu C, IR & Vis-Bands (SeaWiFS Chs.) L-Band Scan Pixel Altitude (km) 6.0 ~1 2.6 0.6 ~0.1 0.26 Incidence Angles: +/- 7,22,37 (deg) The STARRS Sampling Scheme STARRS Airborne Microwave Radiometer System NRL’s Salinity, Temperature, and Roughness Remote Scanner (STARRS)

19. Virgo Optical Images Crossing Gulf Stream On 12 Dec., 2006: Swell, White Caps and Foam Also Influence SSS Retrievals White Caps & Foam Inshore 14:10 15:30 Swell ~100m Offshore NIKON D1X Digital Camera 15:49 16:10

20. Virgo SST and SSS Crossing Gulf Stream On 12 Dec., 2006: Effect of Empirical Wind-Induced Roughness Corrections NOAA Met. Buoys: NOAA Met. Buoys: CBBV2 CBBV2 CHLV2 CHLV2 44014 44014 STARRS SST STARRS SSS Chesa- peake Bay #(cf 1.1 psu For Hs=0.7 m) Cape Hatteras Gulf Stream Terra MODIS SST *Based on WISE wind model (# Wave model correction is smaller by a factor of two!)

21. Color, Surface Salinity and Roughness (COSSAR) Wind and Wave Data and STARRS flights 10-15 May 07 Buoy NDBC 42007 NDBC 42040 STARRS flights over Mississippi Outfall U (m/s) q (deg) Mississippi Outfall Hs (m) f (deg) NOAA NDBC 42007

22. 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.

23. STARRS Salinity, Morning flight 10 May 2007 Salinity Range 0-40 psu all figures

24. STARRS Salinity, Afternoon flight 10 May 2007

25. Shipboard underway Salinity, STARRS Salinity, Afternoon flight 10 May 2007

26. Ship (green) and aircraft (blue) Salinity Afternoon Flight, 10 May 2007

27. Salinity Regression vs STARRS Salinity Morning flight, 10 May 2007, outbound leg Sal=c+a(ch5/ch2)+b(ch5/ch6)

28. Regression salinity, morning flight, 10 May 2007

29. Regression salinity, afternoon flight, 10 May 2007

30. VJ Maisonet: Student Project on Optics and Salinity

31. Overview Introduction Equipment Study Site Algorithm used Results Summary Current/Future work

32. 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))

33. Colored dissolved organic matter 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. • 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

34. Equipment STARRS Piper Navajo IR-Band C-Band L-Band OCR-507

35. STARRS/OCR Sampling Pattern STARRS Sampling Rates: STARRS ~2.0 s OCR ~.17 s OCR:STARRS ~ 11:1 Ocean (Ts, Tb) Flight Direction NEDT(1s): 0.50 K  dS=1 psu C, IR & Vis-Band SeaWiFS Chs. L-Band Incidence Angles: +/- 7,22,37 (deg) Scan 6 km Pixel ~1km Alt. 2600 m

36. Area of Study

37. Sampling Flights

38. 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

39. Results R2 Value= 0.76 n= 5220

40. Results cont. R2 Value= 0.90 N=1100

41. 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

42. Results cont. R2 Value= 0.88

43. 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  

44. 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

45. 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

46. 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.

47. Sea Surface Roughness Impacts on Microwave Sea Surface Salinity Measurements (SRIMS) ESA SMOS Derek Burrage, David Wang, Joel Wesson (SSC) and Paul Hwang (DC) NASA Aquarius 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. 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. 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)

48. Conference Papers 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. Reports Howden, 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)

49. Refereed Papers by Team Members (Arising from related projects) 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).

50. 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.