1. PORSEC 2010 Taiwan Tutorial Surface Wind Fields from Satellite Radar and Radiometer Measurements Abderrahim Bentamy Laboratoire d’Océanographie Spatiale IFREMER Brest France

2. PORSEC 2010 Taiwan Tutorial Acknowledgement  Denis Croizé-Fillon (IFREMER)‏  Pierre Queffeulou (IFREMER)‏  Marcos Portabella (UTM – CSIC)‏  CERSAT  NASA / JPL  SAF OSI / KNMI  ESA  CNES

4. The important Air-Sea fluxes (Taylor et al,2004)

5. The important of Air-Sea fluxes

7.  Several Human activities and applications request high quality of surface fluxes at global and regional scales : –Climate variability –Ocean and Weather forecasting –Ship routing –Oil production –Fisheries –Food production –Extreme event detection and impact Estimation of surface parameters from satellite data

8.  Wind Stress Surface Winds Air Humidity Air and Surface Temperatures  Latent Heat Flux Surface Winds Air Humidity Surface Humidity  Sensible Heat Flux Surface Winds Air Temperature Sea Surface Temperature Wind speed and direction (or components)‏ Need of Accurate Surface Winds

9. Ocean Wind Vector Requirements (SoW, ESA, 2010) No Available Satellite Instrument Meets All Requirements

10. PORSEC 2010 Taiwan Tutorial Surface Wind Measurements Credit NOAA Credit Météo-France ALADIN BLENDED QuikSCAT

11. PORSEC 2010 Taiwan Tutorial  Aims –To learn about the basic methods used to estimate surface winds from scatterometers and radiometers –To appraise global ocean wind datasets from satellites –To understand how to confront in situ, numerical model, and remotely sensed flux data in the context of scientific studies and operational applications  Objective : Understanding what satellite radars and radiometers actually measure, and how the surface parameters derived from remotely sensed measurements are useful. Lecture Purpose

12. PORSEC 2010 Taiwan Tutorial  Methods of retrieving surface wind speeds and directions from satellite measurements  Calibration / Validation  Accuracy of surface Wind retrievals  Enhancement of Spatial and temporal resolution  Applications Outline of Lecture

13. PORSEC 2010 Taiwan Tutorial  Scatterometers Surface Wind Vector (Wind Speed and Direction)‏  Radiometers Surface Wind Speed Surface Wind Vector  Altimeters Surface Wind Speed  SAR Surface Wind Vector (Wind Speed and Direction)‏ Satellite Instruments

14. PORSEC 2010 Taiwan Tutorial ERS-1/2 ADEOS-1 (NSCAT)‏ QuikScat (SeaWinds)‏ ADEOS-2 (SeaWinds)‏ METOP-A (ASCAT)‏ Scatterometers OceanSat-2

15. PORSEC 2010 Taiwan Tutorial Specifications  Polar Orbits –Sun-Synchronous –Altitude of 800km –Two observations / day  Microwave Measurements –Most ocean regions are covered with clouds 75% of time! –Microwaves “see” through clouds and atmosphere at wavelengths of 1-5cm. –Microwaves sensitive to sea surface roughness

16. Scatterometer measurement : Examples ERS-1/2 Polarization : VV Swath : 500km WVC Resolution : 50 km Coverage : 41% Period : 1991 - 2001 NSCAT Polarization : V; H Swath : 2x600km WVC Resolution : 50 km (25km) Coverage : 78% Period : 1996 – 1997 QuikSCAT Polarization : V; H Swath : 1800km WVC Resolution : 25 km (12.5km) Coverage : 92% Period : 1999 - 2009 ASCAT Polarization : V Swath : 550 km WVC Resolution : 50km / 25 km Coverage : 84% Period : 2006 - Present

17. PORSEC 2010 Taiwan Tutorial  Wind creates small waves on the ocean surface (capillary waves) which in the absence of wind will continue to propagate.  If wind continues, waves will grow in size and increase in wavelength and height to become ultra-gravity waves and eventually gravity waves.  A water surface affected by wind will have a spectrum of surface waves, e.g, multiple wavelengths and heights  Microwave EM energy has been shown through wave tank experiment to constructively interfere or resonate with surface capillary and ultra- gravity waves.  This phenomenon is known as Bragg Scattering Scatterometer Principle

18. Scatterometer measurements  The main scatterometer measurements are the backscatter coefficients calculated as a ratio between the emitted power P e and the received one P r : : the wavelength, G the antenna gain, A the radar footprint, R the distance between the sensor and the reached target.  Scatterometers are active microwave sensors: they send out a signal and measure how much of that signal returns after interacting with the target. Microwaves are Bragg scattered by short water waves; the fraction of energy returned to the satellite (backscatter) is a function of wind speed and wind direction.

19. PORSEC 2010 Taiwan Tutorial Backscatter coefficient Behaviors  ° as a function of Wind Speed and Incidence Angle  0 increases with wind speed. The increasing gradient is higher for surface winds less than 12m/s than for higher wind conditions.

20. PORSEC 2010 Taiwan Tutorial Backscatter coefficient Behaviors  ° as a function of Wind Direction and Speed Due to electromagnetic interactions  0 are different whether the measurement is made upwind (  =0°), downwind (  = 180°), and crosswind (  =90° or 270°)‏

21. PORSEC 2010 Taiwan Tutorial GMF : Scatterometer Geophysical Relationships  : Wind direction wrt azimuth  : Incidence angle U : Wind Speed P: Polarization Fc : Frequency GMF Determination Calibration / Validation

22. PORSEC 2010 Taiwan Tutorial  Buoy Networks NDBC (NOAA)‏ MFUK (MFUK)‏ TAO (PMEL/NOAA)‏ PIRATA (INPE/IRD/PMEL)‏ RAMA (PMEL)‏  Multi-Satellite  In-Situ  COADS  Experiments (Fastex; KNORR; EPIC; PACS N/S; FETCH; POMME; EQUALANTE;EGEE(AMA))‏ Calibration and Validation Issues: Collocation Procedures

23. PORSEC 2010 Taiwan Tutorial Moored Buoys

24. PORSEC 2010 Taiwan Tutorial  The collocation consists in grouping measurements close in space and time from various sensors (or other data sources like numerical model outputs). Two measurements are said to be close if they are below a given distance and time difference. These collocation criteria are set according to each sensor geometry as well as each satellite orbital parameters; For each collocated measurement, a selection of parameters from each source data product (associated to a sensor) is provided. Collocation Procedure

25. PORSEC 2010 Taiwan Tutorial GMF : Scatterometer Geophysical Relationships Buoy Wind Speed Range 8m/s

26. PORSEC 2010 Taiwan Tutorial GMF : Scatterometer Geophysical Relationships Buoy Wind Speed Range 3m/s

27. PORSEC 2010 Taiwan Tutorial GMF : Scatterometer Geophysical Relationships Buoy Wind Speed Range 12m/s

28. PORSEC 2010 Taiwan Tutorial Behaviours of fore-beam (top), mid- beam (middle), and aft-beam (bottom) A0, A1, and A2 as a function of incidence angle for three wind speed ranges (3m/s (blue), 8m/s (red), and 12 m/s (black)). A0 = (  u +  d + 2  c )/4 A1 = (  u -  d )/2 A2 = (  u +  d - 2  c )/4  0 u = A 0 + A 1 + A 2 ;  0 d = A 0 -A 1 +A 2 ;  0 c = A 0 -A 2 Scatterometer Geophysical Relationships (Bentamy et al, 2008)‏

29. PORSEC 2010 Taiwan Tutorial Assumptions   0 =  0 P +   0 P states for « truth » backscatter coefficient.  is the error measurement  is assumed Gaussian with zero mean and variance  .   0 P is related to GMF through :  0 P =  0 mod +  mod  0 mod is backscatter coefficient value estimated from GMF  mod is the model error assumed Gaussian with  mod variance. For given wind speed and direction over WVC, the difference between measured and simulated backscatter coefficients is calculated:  =  0 -  0 mod Assuming that instrumental and model errors are independent,  is Gaussian with zero mean and variance   =   +   mod Scatterometer Surface Wind Vector Retrievals (1)‏

30. PORSEC 2010 Taiwan Tutorial Therefore the probability density function of  constrained by  0 : P(  /  0 ) = P(  /{U,  }) = (8)‏ Let is consider N the number of  0 over WVC (3 in ERS case), and the corresponding  are independent. The conditional probability is provided by: P(  1 …  N /{U,  }) = (9)‏ The maximum likelihood estimator (MLE) criterion implies that the solution {U,  } is the local minimum of P. In general, over each WVC the wind speed and direction solutions are determined as a maximum of the following function : J(U,  ) = (10)‏ J is related to P through logarithm transform. The algorithm proposes up 4 solutions, called ambiguities. The most probable vector is indicated as the selected wind vector for the specific WVC. This selection is mainly based on the MLE and quality control (QC) Scatterometer Surface Wind Vector Retrievals (2)‏

31. PORSEC 2010 Taiwan Tutorial Scatterometer Surface Wind Vector Retrievals (3)‏ Up 4 Solutions

32. PORSEC 2010 Taiwan Tutorial  If scatterometer observes a particular cross section  0(  ) at an azimuthal angle  relative to the wind, all points on the curve are possible wind vectors that yield the observed cross section. If the oceanic area is observed from three different directions,  -45°, ,  +45° (ERS case) as shown in the example, 2 or 4 possible wind vectors satisfy the observations, because scatter is only weakly anisotropic SATSAT Wind 8m/s 120° Scatterometer Surface Wind Vector Retrievals : Ambiguity issue

33. PORSEC 2010 Taiwan Tutorial QuikSCAT Swath Wind Data

34. The Abdu Salam Internal Center for Theoretical Physics. Trieste Italy. February 2009 Examples of the Scatterometer Retrieved Surface Wind Vectors.

35. PORSEC 2010 Taiwan Tutorial Accuracy issue : Statistical parameters Statistical moments : Linear moments : Regression parameters : Wind direction : Test Hypothesis : Mean, variance, correlation coefficient, and distribution

36. PORSEC 2010 Taiwan Tutorial  Comparison of the wind speeds (left panel) and directions (right panel)observed by ERS-1 (top), ERS-2 (middle), and QuikScat (bottom) scatterometers with 10-m buoy winds moored in the Atlantic ocean (first column), the Pacific ocean (second column), and in the Tropical oceans (third column). Tropical Scatterometer Wind Accuracy

37. PORSEC 2010 Taiwan Tutorial Special Sensor Microwave / IMAGER (SSM/I) Principle

38. PORSEC 2010 Taiwan Tutorial SSM/I Measurements  Main SSM/I measurement : –Definition : Brightness Temperature is a measure of the intensity of radiation thermally emitted by an object, given in units of temperature because there is a correlation between the intensity of the radiation emitted and physical temperature of the radiating body which is given by the Stefan-Boltzmann law. T A = etT s + (1-t)T’ +(1-t)(1-e)tT’ + (1-e)t²(T ext - T sol ) (Stewart, 1985)‏ Ts = Surface temperature e, (1-e) : emissivity and reflectivity T : transmissivity T’ : the vertical average of the tropospheric temperature profile From Seelye Martin, (2004)‏

39. PORSEC 2010 Taiwan Tutorial SSM/I Surface and Atmospheric Parameter Retrievals (1)‏  Atmospheric water vapor content  Atmospheric water liquid content (cloud)‏  Wind speed on ocean surfaces  Ground humidity  Rain rates  Snow surfaces detection and water content analysis  Sea-ice detection and concentration sea-ice characterization

40. PORSEC 2010 Taiwan Tutorial SSM/I Surface Wind Speed Retrievals (2)‏  Statistical models are used to estimate the geophysical parameters from Brightness temperatures  Wind Speed : U = 1.0969T B19V – 0.4555T B22V – 1.76T B37V + 0.786T B37H + 147.9 (Goodberlet et al, 1989)‏ U = f(T B ) + f(WV) (Bentamy et al, 1999)‏ Schlussel, 1997

41. PORSEC 2010 Taiwan Tutorial Examples of SSM/I Surface Wind Observations 1 St January 2004 3am – 9am SSM/I F13 SSM/I F14 SSM/I F15 QuikSCAT

42. PORSEC 2010 Taiwan Tutorial Validation of SSM/I Wind Retrievals Ussmi = f(T B, WVC)‏ Uers = f(  0 )‏

43. PORSEC 2010 Taiwan Tutorial Part 1 : Summary  The remotely sensed winds provide valuable and unique source of the main surface parameter at global and regional scales  They compare well with in situ data in various geographical areas  Some improvements are needed : Wind conditions Rain detection Sea State Parameterization Coastal Resolutions …

44. PORSEC 2010 Taiwan Tutorial Part 2  Remotely Sensed Use : –Regional and global ocean model forcing; Process analysis; Meteorology; Operational costal and global oceanography, …  Calculation of Surface Wind Analysis Using Satellite Observations  Estimation of Surface Parameters at Regular Space/Time Resolution  Enhancement of Spatial and Temporal Resolutions

45. Higher Level Wind Processing  Level 3: spatio-temporally consistent wind product from a single wind source  Level 4: spatio-temporally consistent wind product from combined wind sources

46. PORSEC 2010 Taiwan Tutorial L3 Product Daily Wind Fields 27 th – 29 th August 2005

47. 2nd EPS/METOP 20 - 22 May 2009 Barcelona Spain only valid (  0, U, u, v)‏ wind selection sampling Daily Gridded Wind Field Estimation Scheme Gridding Additional data computation Data selection Masks (land, ice…)‏ stress SCAT data Geographic grids Neighbours search X i x y t X 0 (x 0, y 0, t 0 )‏ Variogram Objective analysis WinterSummer

48. 2nd EPS/METOP 20 - 22 May 2009 Barcelona Spain Derived quantities computation Quality control wind divergence stress curl Quality AssessmentValidation graphs Geographic grids Gridded Wind Field Estimation Geographic grids

49. PORSEC 2010 Taiwan Tutorial Accuracy Issue : Difference Sources In-situ / satellite Differences  Raw data  Calibration / Validation Procedures  Spatial and Temporal Resolutions  Estimation of basic variables : Winds, Humidity, Sea Surface and Air Temperatures  Analysis Methods  Flux Algorithms  …

50. PORSEC 2010 Taiwan Tutorial Error related to the Objective Method Satellite Sampling Scheme  Use of simulated satellite data from buoy measurements or from ECMWF analysis  Temporal Sampling Impact :  : Time - Averaged surface parameter from Hourly Buoy Data  : Time - Averaged surface parameter from Hourly Buoy Data close to satellite passes  Rms of -

51. PORSEC 2010 Taiwan Tutorial Time Series of weekly buoy and Satellite wind data North-West Atlantic Buoy ERS-1 ERS-2 QuikSCAT

52. PORSEC 2010 Taiwan Tutorial Vector correlation between Scatterometer and ECMWF wind fields

53. PORSEC 2010 Taiwan Tutorial 53 High Wind Field Spatial and Temporal Resolution QuikSCAT SSMI AMSR-E TMI Jason METOP Objectives  Estimation of high spatial and temporal resolution of surface wind fields (wind vector and wind stress) using ECMWF Numerical Weather analysis outputs with high remotely sensed surface parameters.  Production in near real-time merged wind fields (6-hourly, 0.25° x 0.25) ‏  Assess the quality of derived blended wind fields at near shore and offshore areas. MERSEA and MyOcean Projects Operational ECMWF Analysis

54. PORSEC 2010 Taiwan Tutorial 54 Objective Method  Objective Method : External Drift  Wind Observations (U) are from NRT Scatterometer and SSM/I External Data (S) are from ECMWF analysis.  Assumption : E(U(X,t)) = a + b*S(X,t)  The space and time correlation is parameterized by

55. PORSEC 2010 Taiwan Tutorial AMS Conference 20 - 24 August 2007 Portland 55 Blended Surface Wind Method  Method : Objective OI ( Bentamy et al, 2007 )‏ Results : 6-hourly global wind vector and wind stress 0.25°×0.25° May 4 th 2008. 00h:00 May 4 th 2008. 06h:00

56. PORSEC 2010 Taiwan Tutorial 56 Accurcay of Blended Wind Fields  Assessment of the Objective Method – Comparisons between 6-hourly ECMWF and Simulated Satellite Wind Fields – Impact Of the External Drift (ECMWF)‏  Accuracy of 6-hourly NRT Surface Wind Vectors – Buoy Comparisons  Global and regional validation – ECMWF and Blended Wind Fields Comparisons – Spatial and Temporal Patterns

57. PORSEC 2010 Taiwan Tutorial Assessment of the Objective Method 57 Simulated Satellite Observations ≡ Interpolated (in space and time) ECMWF data Comparison between ECMWF and Simulated Satellite 6-hourly Wind Fields.Period : January 2006

58. PORSEC 2010 Taiwan Tutorial 58 Accuracy of Blended Wind Fields : Comparisons to Buoy 6- hourly Wind Estimates 190 moored buoys are used

59. PORSEC 2010 Taiwan Tutorial AMS Conference 20 - 24 August 2007 Portland 59 Buoy and Blended Zonal Component Correlations 00H:0006H:00 12H:00 18H:00 QuikSCAT(daily)Blended (Chao et al, 2004)‏ Correlation0.560.94 RMS4.01.75

60. PORSEC 2010 Taiwan Tutorial 60 Evaluation Versus QuikSCAT (off-line) Wind Observations January 2005 QuikSCAT – Blended BiasRms QuikSCAT – ECMWF BiasRms W U V

61. PORSEC 2010 Taiwan Tutorial Regional Evaluations: Southern Oceans

62. PORSEC 2010 Taiwan Tutorial 62 Spectral analysis

63. PORSEC 2010 Taiwan Tutorial AMS Conference 20 - 24 August 2007 Portland 63 Wind Curl Features Blended QuikSCAT ECMWF

64. PORSEC 2010 Taiwan Tutorial Summary / Conclusion  Global High Resolution Wind Fields are Estimated from MultiPlatform Satellite Observations  The resulting fields compare well with in-situ and numerical model analysis estimates.  Impact of satellite winds and LHF in a numerical simulation  Data available at ( http://cersat.ifremer.fr/)‏

65. PORSEC 2010 Taiwan Tutorial Future  Ongoing compilation, evaluation and intercomparisons of existing satellite estimates  Characterization of uncertainties of data products and development of metada for these products  Further improvement of model and parameterization used in satellite processing  Development of strategy methods for merging and combining satellite/satellite/in-situ and and/or satellite/NWP flux estimates

66. PORSEC 2010 Taiwan Tutorial THANKS

67. PORSEC 2010 Taiwan Tutorial Example of Blended Surface Wind Fields Results : 6-hourly global wind vector and wind stress / 0.25°×0.25°

68. PORSEC 2010 Taiwan Tutorial L4 Products MultiSatellite Use Enhancement of Surface Wind Field Issues 10-Jan-2005 06:4710-Jan-2005 05:06 10-Jan-2005 06:0010-Jan-2005 12:00