PORSEC 2010 Taiwan Tutorial Surface Wind Fields from Satellite Radar and Radiometer Measurements...

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PORSEC 2010 Taiwan Tutorial

Surface Wind Fields from Satellite Radar and Radiometer Measurements

Abderrahim Bentamy

Laboratoire d’Océanographie Spatiale

IFREMER Brest

France

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Acknowledgement

Denis Croizé-Fillon (IFREMER) Pierre Queffeulou (IFREMER) Marcos Portabella (UTM – CSIC)

CERSAT NASA / JPL SAF OSI / KNMI ESA CNES

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

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The important of Air-Sea fluxes

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The important of Air-Sea fluxes

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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

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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

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Ocean Wind Vector Requirements (SoW, ESA, 2010)

No Available Satellite Instrument Meets All Requirements

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Surface Wind Measurements

Credit NOAA

Credit Météo-France

ALADIN

BLENDED

QuikSCAT

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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

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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

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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

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ERS-1/2ADEOS-1 (NSCAT)

QuikScat (SeaWinds)

ADEOS-2(SeaWinds)

METOP-A (ASCAT)

Scatterometers

OceanSat-2

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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

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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

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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

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The main scatterometer measurements are the backscatter coefficients calculated as a ratio between the emitted power Pe

and the received one Pr :

: the wavelength, G the antenna gain, A the radar footprint, R the distance between the sensor and the reached target.

e

r

APGPR

22

430 )4(

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.

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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.

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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°)

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),,,(;2coscos 2100 fcPUAAAAA JJ GMF :

Scatterometer Geophysical Relationships

: Wind direction wrt azimuth: Incidence angleU : Wind SpeedP: PolarizationFc : Frequency

GMF Determination

Calibration / Validation

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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

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Moored Buoys

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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

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Buoy Wind Speed Range 8m/s

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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 + 2c)/4

A1 = ( u - d)/2

A2 = ( u + d - 2c)/4

),,,(;2coscos 2100 fcPUAAAAA JJ

0u = A0 + A1 + A2; 0

d = A0-A1+A2; 0c = A0-A2

Scatterometer Geophysical Relationships(Bentamy et al, 2008)

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Assumptions 0 = 0

P +

0P states for « truth » backscatter coefficient.

is the error measurement

is assumed Gaussian with zero mean and variance . 0

P is related to GMF through :

0P = 0

mod + mod

0mod 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 - 0mod

Assuming that instrumental and model errors are independent, is Gaussian with zero mean and variance = + mod

Scatterometer Surface Wind Vector Retrievals (1)

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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)

N

ii

N

i1

2i

1i

)Δ

2δ

Δexp(

Δ2π

1

)ln()),((

1

2mod

00

i

N

i

ii

i

U


)2δ

(expδ2π1

Δ

2

Δ

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Up 4 Solutions

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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

SAT

Wind 8m/s

120°

Scatterometer Surface Wind Vector Retrievals : Ambiguity issue

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QuikSCAT Swath Wind Data

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The Abdu Salam Internal Center for Theoretical Physics. Trieste Italy. February 2009

Examples of the Scatterometer Retrieved Surface Wind Vectors.

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Accuracy issue : Statistical parameters

22

4

2/32

32

)))(((

))((;

)))(((

))((;))(();(

XEXE

XEXEK

XEXE

XEXEXEXEXEX XXX

dFFPFxn n )()(1

0

*1

•Statistical moments :

))())((((;)2

)42)(()((;; 2/1

2/12

1 YEYXEXESxySSSSS

SS

SrbXaY xyyyxxyyxx

P

yyxx

yxSSS

•Linear moments :

•Regression parameters :

•Wind direction :

))()(((;))(cos())((sin(1);1457.01)((sin

))cos(

)sin((tan

211

22121

1122231

1

TrDD

Dsdb

DsDbD

D

•Test Hypothesis : Mean, variance, correlation coefficient, and distribution

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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

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Special Sensor Microwave / IMAGER (SSM/I) Principle

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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.

TA = etTs + (1-t)T’ +(1-t)(1-e)tT’ + (1-e)t²(Text - Tsol) (Stewart, 1985)Ts = Surface temperaturee, (1-e) : emissivity and reflectivityT : transmissivity T’ : the vertical average of the tropospheric temperature profile

From Seelye Martin, (2004)

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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

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SSM/I Surface Wind Speed Retrievals (2)

Statistical models are used to estimate the geophysical parameters from Brightness temperatures

Wind Speed : U = 1.0969TB19V – 0.4555TB22V – 1.76TB37V + 0.786TB37H + 147.9 (Goodberlet et al, 1989)

U = f(TB) + f(WV) (Bentamy et al, 1999)

Schlussel, 1997

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Examples of SSM/I Surface Wind Observations

1St January 2004 3am – 9am

SSM/I F13 SSM/I F14 SSM/I F15

QuikSCAT

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Validation of SSM/I Wind Retrievals

Ussmi = f(TB, WVC)

Uers = f(0)

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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

• …

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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

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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

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L3 ProductDaily Wind Fields 27th – 29th August 2005

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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 Xi

x

y

t

X0 (x0, y0, t0)

Variogram

Objective analysis

WinterWinter SummerSummer

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2nd EPS/METOP 20 - 22 May 2009 Barcelona Spain

Derived quantities

computation

Quality control

• wind divergence• stress curl

Quality

Assessment

Validation graphs

Geographic grids

Gridded Wind Field Estimation

Geographic grids

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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 …

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Error related to the Objective Method Satellite Sampling Scheme

Use of simulated satellite data from buoy measurements or from ECMWF analysisTemporal Sampling Impact :

<X> : Time - Averaged surface parameter from Hourly Buoy Data <X’> : Time - Averaged surface parameter from Hourly Buoy Data close to satellite passes Rms of <X> - <X’>

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Time Series of weekly buoy and Satellite wind dataNorth-West Atlantic

Buoy

ERS-1

ERS-2

QuikSCAT

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Vector correlation between Scatterometer and ECMWF wind fields

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PORSEC 2010 Taiwan Tutorial 53

High Wind Field Spatial and Temporal Resolution

QuikSCAT

SSMI

SSMI

SSMIAMSR-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

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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)

n

i

n

i

j

n

i

i

SiiS

i

jSjji

1

1

1

21

)0()(

1

02)()0,(),(

))exp(1(),(b

tchath p

The space and time correlation is

parameterized by

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PORSEC 2010 Taiwan TutorialAMS Conference 20 - 24 August 2007 Portland55

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 4th 2008. 00h:00

May 4th 2008. 06h:00

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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

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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

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Accuracy of Blended Wind Fields : Comparisons to Buoy 6-hourly Wind Estimates

190 moored buoys are used

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Buoy and Blended Zonal Component Correlations

00H:00 06H:00

12H:00 18H:00

QuikSCAT(daily) Blended(Chao et al, 2004)

Correlation 0.56 0.94

RMS 4.0 1.75

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Evaluation Versus QuikSCAT (off-line) Wind ObservationsJanuary 2005

QuikSCAT – Blended

Bias Rms

QuikSCAT – ECMWF

Bias Rms

W

U

V

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Regional Evaluations: Southern Oceans

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Spectral analysis

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Wind Curl Features

Blended

BlendedQuikSCAT

QuikSCAT ECMWF

ECMWF

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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/)

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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

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THANKS

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Example of Blended Surface Wind Fields

Results : 6-hourly global wind vector and wind stress / 0.25°×0.25°

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L4 Products MultiSatellite UseEnhancement of Surface Wind Field

Issues

10-Jan-2005 06:47 10-Jan-2005 05:06 10-Jan-2005 06:00 10-Jan-2005 12:00

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PORSEC 2010 Taiwan Tutorial Surface Wind Fields from Satellite Radar and Radiometer Measurements...

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