Scatterometer, Altimeter (& SAR)

Active Techniques for Wind and Wave Observations: Scatterometer, Altimeter (& SAR)

Giovanna De Chiara & Saleh Abdalla ECMWF

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 1 Outline Scatterometer Winds

 The importance of scatterometer wind observations  Physical mechanism and wind inversion  Data usage at ECMWF and their impact  How we can improve the use and impact  Concluding remarks

Altimeter Wind and Waves  Introduction  Verification and Monitoring  Wave Data Assimilation  Assessment of Model Performance (inc. Model Effective Resolution)  Error Estimation  Long Term Studies  Concluding Remarks

Synthetic Aperture Radar (SAR)

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 2 Why is Scatterometer important? The scatterometer measures the ocean surface winds (ocean wind vector).

Ocean surface winds: . affect the full range of ocean movement (from individual surface waves to complete current systems)

. modulate air-sea exchanges of heat, momentum, gases, and particulates

Wind observations below 850 hPa FSO values relative quantities (in %)

Wide daily coverage of ocean surface winds Ex: 1 day of ASCAT-A data

[Horanyi et al, 2013]

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 3 What is a Scatterometer?

 A Scatterometer is an active microwave instrument (radar) . Day and night acquisition . Not affected by clouds

 The return signal, backscatter (σ0 sigma-nought), which is sensitive to: . Surface wind (ocean)

. Soil moisture (land) [ASCAT-A from http://www.eumetsat.int] . Ice age (ice)

Scatterometer was originally designed to measure ocean winds:

 Measurements sensitive to the ocean-surface roughness due to capillary gravity waves generated by local wind conditions (surface stress)

 Observations from different look angles: wind direction

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 4 Dependency of the backscatter on... Wind speed

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 5 Dependency of the backscatter on... Wind direction Backscatter response depends on the relative angle between the pulse and capillary wave direction (wind direction)

Mid Fore Beam

Mid Beam Wind direction wrt Mid Beam

Fore Aft Beam

C-band SCAT geometry

Wind direction wrt Mid Beam

Aft

Wind direction wrt Mid Beam

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 6 How can we relate backscatter to wind speed and direction?

 The relationship is determined empirically . Ideally collocate with surface stress observations . In practice collocation between buoy winds and 10m model winds

휎0 = 퐺푀퐹(푈10푁, 휙, 휃, 푝, 휆)

U10N: equivalent neutral wind speed : wind direction w.r.t. beam pointing : incidence angle p : radar beam polarization : microwave wavelength

 Geophysical model functions (GMF) families . C-band: CMOD . Ku-band: NSCAT, QSCAT

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 7 How can we relate backscatter to wind speed and direction?

For the C-band observations, σ0 triplets lies on a double conical surface

σ0 triplets with the same wind speed

0°

CMOD5 z_mid 180°

270°

90°

[Stoffelen]

 Wind inversion: search for minimum distances between the σ0 triplets and all the solutions on the GMF surface.  Noise in the observations can change the position wrt the cone: wind ambiguities

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 8 C- band scatterometers

Used on European platforms (1991 onwards):  SCAT on ERS-1, ERS-2 by ESA  ASCAT on Metop-A, Metop-B by EUMETSAT

. f~5.3 GHz (λ~5.7 cm) . VV polarization . Three antennae

Pros and cons:  Hardly affected by rain  High quality wind direction (especially ASCAT)  Two nearly opposite wind solutions  Rather narrow swath: . ERS-1/2: 500km . ASCAT-A/B: 2x500km

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 9 Ku-band scatterometers Used on US and Japanese platforms, later also India, China  NSCAT, QuikSCAT, SeaWinds by NASA (and Japan)  Oceansat by ISRO  Haiyang-2A by China

. f~13 GHz (λ ~ 2.2 cm) . VV and HH polarization: . Two rotating pencil-beams (4 look angles)

Pros and cons:  Up to four wind solutions (rank-1 most often correct one)  Broad swath (1,800km)  Affected by rain OSCAT daily coverage  Problems regarding wind direction: . azimuth diversity not good in centre of swath . outer 200km only sensed by one beam.

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 10 Operational usage of Scatterometer winds at ECMWF

95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14

ERS-1

ERS-2 ERS-2 (Regional Mission Scenario)

QuikSCAT

METOP-A ASCAT

METOP-B ASCAT

Oceansat-2 (OSCAT)

C-Band Ku Band

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 11 ASCAT-A & ASCAT-B assimilation strategy ASCAT (25km) from EUMETSAT

 Wind inversion is performed in-house: . Sigma nought bias correction . Inversion using CMOD5.N . Wind speed bias correction  Quality control, thinning: . Screening: sea ice check based on SST and sea ice data . Thinning: 100 km . Threshold: 35 m/s  In the 4D-Var 2 solutions provided: best one chosen dynamically during the minimization  Assimilated as 10m equivalent neutral winds ASCAT-A vs ECMWF FG Wind Speed  Observation error: 1.5 m/s

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 12 OCEANSAT-2 assimilation strategy

OCEANSAT-2 (50km)

 Use of Level-2 wind products from OSI-SAF (KNMI)  Wind speed bias correction (wind-speed dependent)  Quality control: . Screening: sea ice check on SST and sea ice model . Rain flag . No thinning; weight in the assimilation 0.25 . Threshold: 25 m/s  Assimilated as 10m equivalent neutral winds  Observation error: 2 m/s OSCAT vs ECMWF FG Wind Speed

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 13 Impact of scatterometer winds Contribution to the reduction of the 24h Forecast Error (total dry energy norm) [Cardinali, 2009, Q.J.R.Met.Soc. 135]

Global statistics with respect to the total observing network Dec 2012 / Feb 2013

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 14 Impact of scatterometer winds …on Tropical Cyclone FC

 For each storm the min SLP have been detected from the ECMWF model fields  SLP have been compared to observation values (from NHC and JMA)

Statistics based only on cases where ASCAT-A, ASCAT-B and OSCAT passes were available Dec 2012/ Feb 2013

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 15 Impact of scatterometer winds …on the ocean parameters

Temperature difference Scatt – NoScatt November 2013

Verified against conventional temperature observations Tropical Atlantic Tropical Indian Tropical East Pacific

[P. Laloyaux] ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 16 Is the model able to propagate the information?

Single Observation Experiment (1 ASCAT-A)

ML PL(hPa)

137 ~ 1013 114 ~ 850 96 ~ 500 79 ~ 250 60 ~ 100

analysis increments (U) analysis increments (V)

50 50 Model Levels Model 100 100

S N S N 137 ECMWF Seminar 2014 - Active techniques for wind and wave observations137 Slide 17 How can we improve usage and impact?

 Include dependency from other geophysical quantities such as ocean currents . Stress is related to relative wind . The observation operator should act on relative wind . Accurate ocean current input are needed

 Improve QC mostly for extreme events . Because of thinning and QC the strongest winds can be rejected

 Test on the observation error, thinning and use of high resolution products

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 18 Concluding remarks

Scatterometer observations widely used in NWP  Ocean wind vectors Typhoon Haiyan 20131107  Positive impact on analysis and the forecast ASCAT-A ASCAT-B OSCAT  Global scale and extreme events  Available continuously from 1991 onwards

ECMWF has a long experience with scatterometry  Assimilation since 1995 . ERS1/2, QuikSCAT, ASCAT-A/B, Oceansat-2 . Future missions: ASCAT-C, Oceansat-3,…  GMF development  Monitoring, validation, assimilation, re-calibration

On-going efforts to improve usage and impact  Improve QC for tropical cyclones (Huber norm)  Adapt observation errors, thinning, super-obbing  Include dependency from other geophysical quantities

Use in the Reanalysis  ERS1/2 and QuikSCAT in ERA-Interim  ASCAT-A reprocessed products will be used in ERA5

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 19 Altimeter Wind & Waves

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 20 Outline Scatterometer Winds

 The importance of scatterometer wind observations  Scatterometer principle  Data usage at ECMWF and their impact  How we can improve the use and impact  Concluding remarks

Synthetic Aperture Radar (SAR)

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 21 Introduction

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 22 Radar Altimeters ENVISAT

● Radar altimeter is a nadir looking instrument.

● Specular reflection. Radar Altimeter-2 ● Electromagnetic wave bands used in altimeters: Primary: Ku-band (~ 2.5 cm) – ERS-1/2, Envisat, Jason-1/2/3, Sentinel-3. Ka-band (~ 0.8 cm) – SARAL/AltiKa (only example). Secondary: C-band (~ 5.5 cm) – Jason-1/2/3, Topex, Sentinel-3. S-band (~ 9.0 cm) – Envisat.

● Main parameters measured by an altimeter: Sentinel-3 1. Sea Surface Height (ocean) 2. Significant wave height 3. Wind speed

4. Ice/land/lakes characteristics,… Radar Altimeter (SRAL)

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 23 How Radar Altimeter Works:

a t m o s p h e r e radar signal Height=∆t/2  c

ocean surface

surface emitted signal returned signal illuminated area flat surface rough surface

Power of

illumination power

time

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 24 Information extracted from a radar echo reflected from ocean surface (after averaging ~100 waveforms)

slope of leading edge  significant wave height

waveform amplitude of the signal

 wind speed power

epoch at mid-height time  sea surface height

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 25 Impact of the atmosphere on Altimeter signal: ● Delay  sea surface height – Water vapour impact: ~ 10’s cm. – Dry air impact: ~ 2.0 m.

● Attenuation  wind speed

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 26 Typical Daily Coverage of:

Envisat/SARAL

Jason-1/2

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 27 Verification & Monitoring of Altimeter Surface Wind Speed

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 28 Altimeter surface wind speed:

waveform amplitude of returned signal

 wind speed power

emitted signal backscatter time ● backscatter is related to water surface mean square slope (mss). ● mss can be related to wind speed. ● Stronger wind  higher mss  smaller backscatter.

● Errors are mainly due to algorithm assumptions, waveform retracking (algorithm), unaccounted-for attenuation & backscatter. ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 29 Relation between wind speed and altimeter backscatter

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 30 100000 25 10000 Comparison between 5000 1000 500 20 100 ENVISAT NRT wind speed 50 10 5

and ECMWF model AN (top) 15 1 Wind Speed (m/s) STATISTICS 10 ENTRIES 1128826 and in-situ measurements MEAN ECMWF 7.7799 MEAN ENVISAT 8.0723 BIAS (ENVISAT - ECMWF) 0.2924 ENVISAT ENVISAT STANDARD DEVIATION 1.1181 5 SCATTER INDEX 0.1437 (bottom) for all data; CORRELATION 0.9461 SYMMETRIC SLOPE 1.0339 REGR. COEFFICIENT 0.9595 0 REGR. CONSTANT 0.6076 0 5 10 15 20 25 ECMWF Wind Speed (m/s)

1 Jan. 2011 – 31 Dec. 2011 50000 25 1000 500 300 160°W 120°W 80°W 40°W 0° 40°E 80°E 120°E 160°E 100 20 50 60°N 60°N 30 15 40°N 40°N 5 15 1 20°N 20°N

Wind Speed (m/s) STATISTICS 0° 0° ENTRIES 14597 10 MEAN BUOY 8 .4282 20°S 20°S MEAN ENVISAT 8 .3144 BIAS (ENVISAT - BUOY) - 0 .1138

40°S 40°S ENVISAT STANDARD DEVIATION 1 .4560 5 SCATTER INDEX 0 .1728 CORRELATION 0 .9008 60°S 60°S SYMMETRIC SLOPE 0 .9884 REGR. COEFFICIENT 0 .9019

160°W 120°W 80°W 40°W 0° 40°E 80°E 120°E 160°E REGR. CONSTANT 0 .7128 0 0 5 10 15 20 25 Typical locations of in-situ measurements Buoy Wind Speed (m/s) ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 31 Comparison between Jason- 2 NRT wind speed and ECMWF model AN (top) and in-situ measurements (bottom) for all data;

1 May 2013 – 30 April 2014

160°W 120°W 80°W 40°W 0° 40°E 80°E 120°E 160°E

60°N 60°N

40°N 40°N

20°N 20°N

0° 0°

20°S 20°S

40°S 40°S

60°S 60°S

160°W 120°W 80°W 40°W 0° 40°E 80°E 120°E 160°E Typical locations of in-situ measurements

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 32 Verification & Monitoring of Altimeter Significant Wave Height

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 33 Altimeter Significant Wave Height (SWH) slope of leading edge  SWH

waveform power

time

 SWH is the mean height of highest 1/3 of the surface ocean waves.  Higher SWH  smaller slope of waveform leading edge.

 Errors are mainly due to waveform retracking (algorithm) and instrument characterisation. ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 34 Global comparison between Altimeter and ECMWF wave model (WAM) first-guess SWH values (From 02 February 2010 to 01 February 2011)

14 100000 10000 5000 12 1000 500 100 10 50 Envisat Jason-2 Jason-1 10 5 8 1 STATISTICS 6 ENTRIES 1 125908 142 50 55 138 29 9 7 MEAN WAM 2 . 6014 2 . 7 073 2 . 6 939 MEAN ENVISAT 2 . 5851 2 . 7 041 2 . 8 078 4 BIAS (ENVISAT - WAM) - 0 . 0163 - 0 . 0 032 0 . 1 140 STANDARD DEVIATION 0 . 2733 0 . 2 826 0 . 3 232

ENVISAT Wave Heights (m) Heights Wave ENVISAT SCATTER INDEX 0 . 1051 0 . 1 044 0 . 1 200 2 CORRELATION 0 . 9786 0 . 9 791 0 . 9 738 SYMMETRIC SLOPE 1 . 0026 0 . 9 983 1 . 0 397 REGR. COEFFICIENT 1 . 0163 0 . 9 753 1 . 0 025 0 REGR. CONSTANT - 0 . 0587 0 . 1 072 0 2 4 6 8 10 12 14 0 . 0 637 WAM Wave Heights (m)

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 35 Comparison between Cryosat-2 NRT SWH and ECMWF model FG (top) and in-situ measurements (bottom) for all data;

1 April 2013 – 17 June 2014

160°W 120°W 80°W 40°W 0° 40°E 80°E 120°E 160°E

60°N 60°N

40°N 40°N

20°N 20°N

0° 0°

20°S 20°S

40°S 40°S

60°S 60°S

160°W 120°W 80°W 40°W 0° 40°E 80°E 120°E 160°E Typical locations of in-situ measurements

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 36 Comparison between SARAL (Ka) NRT SWH and ECMWF model FG (top) and in-situ measurements (bottom) for all data; 1 May 2013 – 30 April 2014

160°W 120°W 80°W 40°W 0° 40°E 80°E 120°E 160°E

60°N 60°N

40°N 40°N

20°N 20°N

0° 0°

20°S 20°S

40°S 40°S

60°S 60°S

160°W 120°W 80°W 40°W 0° 40°E 80°E 120°E 160°E Typical locations of in-situ measurements

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 37 Assimilation of Altimeter Significant Wave Height

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 38 Data Assimilation Method

● Assimilation Method for Altimeter data: – Data are subjected to a quality control process (inc. super-obbing). – Bias correction is applied. – Simple optimum interpolation (OI) scheme on SWH. – The SWH analysis increments  wave spectrum adjustments... (several assumptions)

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 39 Operational Assimilation of SWH

NRT Altimeter SWH operational assimilation at ECMWF: - ERS-1 URA, 15 Aug. 1993 – 1 May 1996; - ERS-2 URA, 1 May 1996 – 23 Jun. 2003; - ENVISAT RA-2 FDMAR, 22 Oct. 2003 – 7 Apr. 2012; - Jason-1 OSDR, 1 Feb. 2006 – 1 Apr. 2010; - Jason-2 OGDR, 10 Mar. 2009 – on-going; - Cryosat-2 FDM, Coming model change (~Q4, 2014); - SARAL OGDR, Coming model change (~Q4, 2014).

Jason-1 Jason-1 Jason-2 Jason-2 ERS-1 ERS-2 Envisat Jason-2 Jason-2 Envisat Envisat Envisat Envisat

15 Aug. 1 May 22 Oct. 1 Feb. 10 Mar. 8 Jun. 1 Apr. 7 Apr. 1993 1996 2003 2006 2009 2009 2010 2012

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 40 Mean impact of assimilating Cryosat-2 SWH (with BC) on SWH analysis [CS & J2] - [J2 only]

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 41 Impact of assimilating altimeter data on SWH random error as verified against Extra-tropical in-situ data, Feb. – April 2014

160°W 120°W 80°W 40°W 0° 40°E 80°E 120°E 160°E

60°N 60°N

40°N 40°N

20°N 20°N

0° 0°

20°S 20°S

40°S 40°S

60°S 60°S

160°W 120°W 80°W 40°W 0° 40°E 80°E 120°E 160°E Typical locations of in-situ measurements

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 42 Impact of assimilating altimeter data on SWH random error as verified against Tropical in-situ data, Feb. – April 2014

160°W 120°W 80°W 40°W 0° 40°E 80°E 120°E 160°E

60°N 60°N

40°N 40°N

20°N 20°N

0° 0°

20°S 20°S

40°S 40°S

60°S 60°S

160°W 120°W 80°W 40°W 0° 40°E 80°E 120°E 160°E Typical locations of in-situ measurements

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 43 Impact of assimilating SARAL data on SWH error as verified against Cryosat -2 SWH, Feb. – March 2014

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 44 Mean Impact of using SARAL (with BC) SWH on Geopotential anomaly correlation (bars @ 95% level)

Northern Hemisphere Southern Hemisphere

1000 hPa

500 hPa

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 45 Assessment of model changes & Monitoring of model performance

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 46 Change Assessment: Change of SDD between ENVISAT RA-2 and ECMWF Model Wind Speed

1.4

)

IPF 5.02IPF

Model C32R2 Model C35R2 Model 1.3

1.2

Std. Dev. of Difference (m s (m Difference of Dev. Std. 1.1

10 U Alt. algorithm change Model change Model change

1.0

2011

2005

2010 2012

2004 2007 2008 2006 2009

- -

- - - - -

Jan

Jan Jan

Jan Jan Jan Jan Jan

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 47 Performance Monitoring: Change of SDD between Envisat RA-2 and ECMWF Operational SWH

0.40

Change of alt.

algorithm6.02L04 IPF 0.35

0.30

0.25 SWH Std. Dev. of Difference Difference of Std. SWHDev. (m)

0.20

2011

2005

2010 2012

2004 2006 2009 2007 2008

- -

- - - - -

Jan

Jan Jan

Jan Jan Jan Jan Jan

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 48 Effective Model Resolution

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 49

Surface wind speed spectra

50 km 50 km 20

500 km 500 km 200 km 100

1000 km 1000 5000 km 5000 km 2000

50 km50 km20

100 km100 500km 200km

2000km 1000km 5000km | | | | | | | | | | | | | | | | -2.5 6 k 10 1024 (~7,100 km) -5/3 k 6 -2.5 10 k -2.5 k sampling at 7 km 685 seq. 5 1 year 10 Envisat 5

)

3 3

4 10 ~400km 4

10 km = ~125 ResolutionEffective -5/3 k

Spectral Density (m 3 10 3 Envisat RA-2

Spectral Density Spectral Density (m 10 ECMWF Model

2 10 k -5/3 2 Envisat RA-2 10 Env. RA-2, 685sp, 1024x7km T1279, 592sp, 2011, 512x16km

-6 -5 -4 -6 -5 -4 10 10 10 10 10 10 -1 wavenumber (m ) wavenumber (m-1) ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 50 Effective Model Resolution

700

20 km 20

50 km 50

100 km 100

200 km 200

500 km 500

1000 km 1000

10 km 4 x D

125 km 125 ~ 500

)

- s

3 400 50% 50% Resolution~65

4 10 ResolutionEffective 300

T511 3 T799

Spectral Density Spectral Density (m 10 200 T639 Shortest Shortest Resolved Scale (km) T2047 2 100 T1279 10 Envisat RA-2 Model (T1279) T3999

-6 -5 -4 10 10 10 0 wavenumber (m-1) 0 10 20 30 40 50 60 70 80 Grid Resolution, D , (km)

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 51 Error estimation

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 52 Random Error Estimation of Wind Speed & SWH using triple collocation technique

4 1.2

Model FC Model FC Buoy Buoy

Jason-1 1.0 Jason-1

) 1

- Jason-2 Jason-2

3 ENVISAT ENVISAT m.s 0.8

2 0.6

0.4

m Wind Speed Error ( Error Speed Wind m 1

Sig. Wave Height Error (m) ErrorHeight Wave Sig. 10 0.2

0 0.0 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 Forecast Days Forecast Days

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 53 Long Term Studies

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 54 Trend of “zonal” winds between 1992 and 2010 according to:

Scatterometer

Altimeter

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 55 Mean “zonal” winds in Tropical Pacific basin according to:

Scatterometer

Altimeter

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 56 Concluding Remarks

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 57 Model – Satellite Data: 2-Way Benefit

● Model wind and wave data are used for: - Monitoring quality of satellite data. - Assessing changes in processing of satellite data. - Detecting anomalies in the data. - Cal/Val of new instruments/products.

● Satellite wind and wave data are used for: - Data assimilation. - Monitoring of model performance (inc. model resolution). - Assessment of model changes. - Use in reanalyses (assimilation and validation).

● Error estimation.

● Long term assessments & climate studies.

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 58 Synthetic Aperture Radar (SAR)

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 59 Outline Scatterometer Winds

 The importance of scatterometer wind observations  Scatterometer principle  Data usage at ECMWF and their impact  How we can improve the use and impact  Concluding remarks

Synthetic Aperture Radar (SAR)

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 60 Synthetic Aperture Concept

● A radar system with a “synthetic” aperture simulates a “virtually” long antenna in order to increase azimuth resolution without increasing the actual antenna. ● The green target remains within the radar beam for the distance travelled by the satellite. The length of the synthesized antenna is equivalent to this distance. Synthetic Aperture Radar allows for a

resolution of ~30 meters. Azimuth (flight) direction

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 61 Synthetic Aperture Radar (SAR) ● SAR is a side-pointing radar that provides 2D spectra of ocean surface waves (wavelength and direction of wave systems). ● Measures distances in the “slant range”  distortions. ● Water motion introduces further (nonlinear) distortions. ● 5 km x 6 (or 10) km images  SAR image spectra. ● SAR inversion: ocean wave https://earth.esa.int/ spectrum is retrieved from SAR spectrum using nonlinear mapping.

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 62 ENVISAT Advanced Synthetic Aperture Radar (ASAR)

https://earth.esa.int/ ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 63 SAR Wave Mode Image & SAR Image Cross- Spectrum (Level 1b)

https://earth.esa.int/

● Almost full Resolution in the range direction (30 m ~ 1000 m). ● Limited resolution in the azimuth direction (>~200 m).

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 64 Global comparison between ASAR and WAM model (2011)

Inverted L1b L2 (full spectrum) (within azim. cut-off)

9 12 50000 100000 1000 10000 500 8 10’s 5000 300 1000 10 100 7 500 50 100 50 30 6 8 15 10 5 5 1 5 OK 1 6 STATISTICS STATISTICS 4 ENTRIES 776756 ENTRIES 453973 MEAN WAM 2. 6720 MEAN WAM 1. 1044 3 100’s 4 MEAN ASAR 2. 5195 MEAN WVW 0. 8514 BIAS (ENVISAT - WAM) - 0. 1525 BIAS (ENVISAT - WAM) - 0. 2530 STANDARD DEVIATION 0. 3690 2 STANDARD DEVIATION 0. 3903

WVW SwellWVW Wave Height (m) SCATTER INDEX 0. 3534 ASAR Sig.ASAR Wave Height (m) 2 SCATTER INDEX 0. 1381 CORRELATION 0. 7884 CORRELATION 0. 9591 1 SYMMETRIC SLOPE 0. 9565 SYMMETRIC SLOPE 0. 7303

REGR. COEFFICIENT 0. 9722 REGR. COEFFICIENT 0. 4499 0 0 0 2 4 6 8 10 12 REGR. CONSTANT - 0. 0781 0 1 2 3 4 5 6 7 8 9 REGR. CONSTANT 0. 3545 WAM Sig. Wave Height (m) WAM Swell Wave Height (m)

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 65 Assimilation of SAR Wave Data - Method

●Assimilation Method: – L1b SAR spectra are inverted to ocean wave spectra. – Ocean spectra are partitioned into wave systems which then paired with the corresponding systems in the model. – Simple OI scheme on integrated parameters of the systems. – Each partition is adjusted based on its analysis increment.

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 66 Assimilation of Wave Data ● NRT Altimeter (Ku) SWH operational assimilation at ECMWF: - ERS-1 URA, 15 August 1993 – 1 May 1996; - ERS-2 URA, 1 May 1996 – 23 June 2003; - ENVISAT RA-2 FDMAR, 22 October 2003 – 7 April 2012; - Jason-1 OSDR, 1 February 2006 – 1 April 2010; - Jason-2 OGDR, 10 March 2009 – on-going

● NRT SAR spectra operational assimilation at ECMWF: - ERS-2 SAR UWA, 13 January 2003 – 31 January 2006; - ENVISAT ASAR WVS, 1 February 2006 – 21 October 2010. 13 Jan. 1 Feb. 21 Oct. 2003 2006 2010 SAR ERS-2 SAR ENVISAT ASAR Jason-1 Jason-1 Jason-2 Jason-2 Alt. ERS-1 ERS-2 Envisat Jason-2 Jason-2 Envisat Envisat Envisat Envisat

15 Aug. 1 May 22 Oct. 10 Mar. 8 Jun. 1 Apr. 7 Apr. 1993 1996 2003 2009 2009 2010 2012 ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 67 Impact of assimilating WM data on SWH bias and SDD in the Tropics

Verified against Envisat and Jason-1 altimeter SWH data.

L2 (QC) L1b No data -0.08

-0.10

Alt. (m) - -0.12

-0.14

-0.16 Bias = Mod. Bias 0 1 2 3 4 5 0.30

0.25

0.20

0.15 01 Aug. – 04 Sep. 2008 St. Dev. of Dev. St. Error (m) 0.10 0 1 2 3 4 5 FORECAST DAYS (0 = AN)

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 68 SWH Error reduction Due to Assimilating WM (& RA-2 SWH) Products Verified against in-situ measurements in Tropics

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 69 Conclusions (SAR)

• Fast delivery ENVISAT ASAR Wave Mode products: - L1b  inverted in-house  operationally assimilated. - L2  already inverted  difficulties in assimilation.

● Assimilation of ASAR WM L1b product leads to positive impact on the model forecasts (up to 4% error reduction and last for 2-5 days).

● Assimilation of ASAR WM L2 leads to rather limited impact (<2% error reduction).

● Similar work will be carried out for Sentinel-1.

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 70 Definition of Difference Measures

N ● Systematic error (Bias) is: 1 Bias  N (xi Ti )  x  T i1

● Root mean square difference N 1 2 (RMSE): RMSE  N (xi Ti ) i1

● Standard deviation of the N SDD  N 1 (x T  Bias)2 difference (SDD):  i i i1

N 1 2 2 SDD  N (xi Ti )  Bias i1

● Scatter index (SI): SI  SDD /T

● x = altimeter data set, T = reference data set (e.g. in-situ), N = total number of collocations

ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 71