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ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 1
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 2
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)
Outline
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 3
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
[Horanyi et al, 2013]
Ex: 1 day of ASCAT-A data
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 4
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)
Ice age (ice)
What is a Scatterometer?
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
[ASCAT-A from http://www.eumetsat.int]
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 5
Dependency of the backscatter on... Wind speed
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 6
Dependency of the backscatter on... Wind directionBackscatter response depends on the relative angle between the pulse and capillary wave
direction (wind direction)
Fore Beam
Mid Beam
Aft Beam
Wind direction wrt Mid Beam
Wind direction wrt Mid Beam
Wind direction wrt Mid Beam
Mid
Fore
Aft
C-band SCAT geometry
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 7
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
Geophysical model functions (GMF) families
C-band: CMOD
Ku-band: NSCAT, QSCAT
U10N: equivalent neutral wind speed
: wind direction w.r.t. beam pointing
: incidence angle
p : radar beam polarization
: microwave wavelength
𝜎0 = 𝐺𝑀𝐹(𝑈10𝑁 , 𝜙, 𝜃, 𝑝, 𝜆)
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 8
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
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
0°
90°
180°
270°
CMOD5
z_
mid
[Stoffelen]
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 9
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 10
Ku-band scatterometersUsed on US and Japanese platforms, later also India, China
NSCAT, QuikSCAT, SeaWinds by NASA (and Japan)
Oceansat by ISRO
Haiyang-2A by China
Pros and cons:
Up to four wind solutions (rank-1 most often correct one)
Broad swath (1,800km)
Affected by rain
Problems regarding wind direction:
azimuth diversity not good in centre of swath
outer 200km only sensed by one beam.
f~13 GHz (λ ~ 2.2 cm)
VV and HH polarization:
Two rotating pencil-beams (4 look angles)
OSCAT daily coverage
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 11
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 12
ASCAT (25km) from EUMETSAT
Wind inversion is performed in-house: Sigma nought bias correction Inversion using CMOD5.N Wind speed bias correction
ASCAT-A & ASCAT-B assimilation strategy
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 Observation error: 1.5 m/s
ASCAT-A vs ECMWF FG Wind Speed
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 13
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 14
Impact of scatterometer windsContribution 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 15
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
Impact of scatterometer winds…on Tropical Cyclone FC
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 16
Verified against conventional temperature observations
[P. Laloyaux]
Tropical Atlantic Tropical Indian Tropical East Pacific
Temperature difference Scatt – NoScatt
November 2013
Impact of scatterometer winds…on the ocean parameters
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 17
analysis increments (U)
Mo
de
l L
eve
ls
137
100
50
ML PL(hPa)
137 ~ 1013
114 ~ 850
96 ~ 500
79 ~ 250
60 ~ 100
Single Observation Experiment (1 ASCAT-A)
137
100
50
N S NS
Is the model able to propagate the information?
analysis increments (V)
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 18
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 19
Concluding remarks
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
Typhoon Haiyan 20131107
ASCAT-A ASCAT-B OSCAT
Scatterometer observations widely used in NWP Ocean wind vectors Positive impact on analysis and the forecast Global scale and extreme events Available continuously from 1991 onwards
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 20
Altimeter Wind & Waves
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 21
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
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)
Outline
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 22
Introduction
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 23
Radar Altimeters
● Radar altimeter is a nadir looking instrument.
● Specular reflection.
● 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:
1. Sea Surface Height (ocean)
2. Significant wave height
3. Wind speed
4. Ice/land/lakes characteristics,…
ENVISAT
Radar Altimeter-2
Sentinel-3
Radar Altimeter (SRAL)
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 24
How Radar Altimeter Works:
surface
Height=∆t/2 c
emitted signal returned signal
a t m o s p h e r e
time
ocean surface
illuminated
area
Power of
illumination
radar signal
time
po
we
r
flat surfacerough surface
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 25
Information extracted from a radar echo reflected from ocean surface (after averaging ~100 waveforms)
slope of leading edge
significant wave height
amplitude of
the signal
wind speed
epoch at mid-height
sea surface heighttime
po
we
r
waveform
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 26
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 27
Typical Daily Coverage of:
Envisat/SARAL
Jason-1/2
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 28
Verification & Monitoringof Altimeter
Surface Wind Speed
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 29
Altimeter surface wind speed:
● 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.
amplitude of
returned signal
wind speed
time
po
wer
waveform
emitted signal backscatter
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 30
Relation between wind speed and altimeter backscatter
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 31
Comparison between
ENVISAT NRT wind speed
and ECMWF model AN (top)
and in-situ measurements
(bottom) for all data;
1 Jan. 2011 – 31 Dec. 2011
60°S 60°S
40°S 40°S
20°S 20°S
0° 0°
20°N 20°N
40°N 40°N
60°N 60°N
160°W
160°W
120°W
120°W
80°W
80°W
40°W
40°W
0°
0°
40°E
40°E
80°E
80°E
120°E
120°E
160°E
160°E
Typical locations of in-situ measurements
0 5 10 15 20 25
ECMWF Wind Speed (m/s)
0
5
10
15
20
25
EN
VIS
AT
W
ind
Sp
ee
d
(m/s
)
SYMMETRIC SLOPE
CORRELATION
SCATTER INDEX
STANDARD DEVIATION
BIAS (ENVISAT - ECMWF)
MEAN ENVISAT
MEAN ECMWF
ENTRIES
STATISTICS
REGR. CONSTANT
REGR. COEFFICIENT
1.0339
0.9461
0.1437
1.1181
0.2924
8.0723
7.7799
1128826
0.6076
0.9595
1
5
10
50
100
500
1000
5000
10000
100000
0 5 10 15 20 25
Buoy Wind Speed (m/s)
0
5
10
15
20
25
EN
VIS
AT
W
ind
Sp
ee
d
(m/s
)
SYMMETRIC SLOPE
CORRELATION
SCATTER INDEX
STANDARD DEVIATION
BIAS (ENVISAT - BUOY)
MEAN ENVISAT
MEAN BUOY
ENTRIES
STATISTICS
REGR. CONSTANT
REGR. COEFFICIENT
0 .9884
0 .9008
0 .1728
1 .4560
- 0 .1138
8 .3144
8 .4282
14597
0 .7128
0 .9019
1515
3050100300500100050000
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 32
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
60°S 60°S
40°S 40°S
20°S 20°S
0° 0°
20°N 20°N
40°N 40°N
60°N 60°N
160°W
160°W
120°W
120°W
80°W
80°W
40°W
40°W
0°
0°
40°E
40°E
80°E
80°E
120°E
120°E
160°E
160°E
Typical locations of in-situ measurements
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 33
Verification & Monitoringof Altimeter
Significant Wave Height
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 34
Altimeter Significant Wave Height (SWH)
time
po
we
rwaveform
slope of leading edge
SWH
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 35
Global comparison between Altimeter and ECMWF wave model (WAM) first-guess SWH values (From 02 February 2010 to 01 February 2011)
Jason-1Jason-2Envisat
0 2 4 6 8 10 12 14
WAM Wave Heights (m)
0
2
4
6
8
10
12
14
EN
VIS
AT
Wa
ve
He
igh
ts
(m
)
SYMMETRIC SLOPE
CORRELATION
SCATTER INDEX
STANDARD DEVIATION
BIAS (ENVISAT - WAM)
MEAN ENVISAT
MEAN WAM
ENTRIES
STATISTICS
REGR. CONSTANT
REGR. COEFFICIENT
1 . 0026
0 . 9786
0 . 1051
0 . 2733
- 0 . 0163
2 . 5851
2 . 6014
1 125908
- 0 . 0587
1 . 0163
1
5
10
50
100
500
1000
5000
10000
100000
0 . 9 983
0 . 9 791
0 . 1 044
0 . 2 826
- 0 . 0 032
2 . 7 041
2 . 7 073
142 50 55
0 . 0 637
0 . 9 753
1 . 0 397
0 . 9 738
0 . 1 200
0 . 3 232
0 . 1 140
2 . 8 078
2 . 6 939
138 29 9 7
0 . 1 072
1 . 0 025
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 36
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
60°S 60°S
40°S 40°S
20°S 20°S
0° 0°
20°N 20°N
40°N 40°N
60°N 60°N
160°W
160°W
120°W
120°W
80°W
80°W
40°W
40°W
0°
0°
40°E
40°E
80°E
80°E
120°E
120°E
160°E
160°E
Typical locations of in-situ measurements
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 37
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
60°S 60°S
40°S 40°S
20°S 20°S
0° 0°
20°N 20°N
40°N 40°N
60°N 60°N
160°W
160°W
120°W
120°W
80°W
80°W
40°W
40°W
0°
0°
40°E
40°E
80°E
80°E
120°E
120°E
160°E
160°E
Typical locations of in-situ measurements
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 38
Assimilation of Altimeter Significant Wave Height
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 39
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 40
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).
10 Mar.
2009
Jason-1
Envisat
Jason-2
EnvisatEnvisat
8 Jun.
2009
Jason-1
Jason-2
Envisat
1 Apr.
2010
Jason-2
Envisat
7 Apr.
2012
Jason-2
22 Oct.
2003
ERS-2
1 May
1996
ERS-1
15 Aug.
19931 Feb.
2006
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 41
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 42
Impact of assimilating altimeter data on SWH random error as verified against Extra-tropical in-situ data, Feb. – April 2014
4
60°S 60°S
40°S 40°S
20°S 20°S
0° 0°
20°N 20°N
40°N 40°N
60°N 60°N
160°W
160°W
120°W
120°W
80°W
80°W
40°W
40°W
0°
0°
40°E
40°E
80°E
80°E
120°E
120°E
160°E
160°E
Typical locations of in-situ measurements
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 43
Impact of assimilating altimeter data on SWH random error as verified against Tropical in-situ data, Feb. – April 2014
60°S 60°S
40°S 40°S
20°S 20°S
0° 0°
20°N 20°N
40°N 40°N
60°N 60°N
160°W
160°W
120°W
120°W
80°W
80°W
40°W
40°W
0°
0°
40°E
40°E
80°E
80°E
120°E
120°E
160°E
160°E
Typical locations of in-situ measurements
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 44
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 45
Mean Impact of using SARAL (with BC) SWH on Geopotentialanomaly correlation (bars @ 95% level)
Northern Hemisphere Southern Hemisphere
1000 hPa
500 hPa
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 46
Assessment of model changes&
Monitoring of model performance
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 47
Change Assessment: Change of SDD between ENVISAT RA-2 and ECMWF Model Wind Speed
IPF
5.0
2
Mod
el C
32R
2
Mod
el C
35R
2
Jan-
2004
Jan-
2005
Jan-
2006
Jan-
2007
Jan-
2008
Jan-
2009
Jan-
2010
Jan-
2011
Jan-
2012
1.0
1.1
1.2
1.3
1.4
U10
Std
. Dev
. of
Dif
fere
nce
(m
s-1
)
Model changeAlt. algorithm
change Model change
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 48
Performance Monitoring: Change of SDD between Envisat RA-2 and ECMWF Operational SWH
IPF
6.0
2L04
Jan
-20
04
Jan
-20
05
Jan
-20
06
Jan
-20
07
Jan
-20
08
Jan
-20
09
Jan
-20
10
Jan
-20
11
Jan
-20
12
0.20
0.25
0.30
0.35
0.40
SW
H S
td. D
ev.
of
Dif
fere
nce
(m
)
Change
of alt.
algorithm
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 49
Effective Model Resolution
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 50
| | | | | | | |
50
00
km
20
00
km
10
00
km
50
0 k
m
20
0 k
m
10
0 k
m
50
km
20
km
10-6
10-5
10-4
wavenumber (m-1)
102
103
104
105
106
Spectr
al D
ensity
(m
3s
-2)
1024 (~7,100 km)
k-5/3
k-2.5
sampling at 7 km 685 seq. 1 year Envisat
k-2.5
k-5/3
~400km
Envisat RA-2
| | | | | | | |
5000 k
m
2000 k
m
10
00
km
500 k
m
200 k
m
100 k
m
50 k
m
20 k
m
10-6
10-5
10-4
wavenumber (m-1)
102
103
104
105
106
Sp
ectr
al
Den
sit
y
(m3
s-2
)
Env. RA-2, 685sp, 1024x7km
T1279, 592sp, 2011, 512x16km
k -5/3
k -2.5
Eff
ecti
ve R
eso
luti
on
= ~
125 k
m
Envisat RA-2
ECMWF Model
Surface wind speed spectra
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 51
T255
T511
T639T799
T1279T2047
T3999
0 10 20 30 40 50 60 70 80Grid Resolution, D , (km)
0
100
200
300
400
500
600
700
Sho
rtest
Resolv
ed S
ca
le
(km
)
Full Resolution
50% Reduction8 x D4 x D
| | | | | | | |
5000 k
m
2000 k
m
10
00
km
500 k
m
200 k
m
100 k
m
50 k
m
20 k
m
10-6
10-5
10-4
wavenumber (m-1)
102
103
104
105
106
Sp
ec
tra
l D
en
sit
y
(m3
s-2
)
Envisat RA-2
Model (T1279)
Eff
ecti
ve R
eso
luti
on
~125 k
m
50%
Reso
luti
on
~65 k
m
Effective Model Resolution
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 52
Error estimation
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 53
0 1 2 3 4 5 6 7 8 9 10Forecast Days
0
1
2
3
4
10
-m W
ind
Sp
eed
Err
or
(m.s
-1)
Model FC
Buoy
Jason-1
Jason-2ENVISAT
0 1 2 3 4 5 6 7 8 9 10
Forecast Days
0.0
0.2
0.4
0.6
0.8
1.0
1.2
Sig
. W
av
e H
eig
ht E
rro
r
(m
)
Model FCBuoy
Jason-1Jason-2ENVISAT
Random Error Estimation of Wind Speed & SWH using triple collocation technique
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 54
Long Term Studies
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 55
Trend of “zonal” winds between 1992 and 2010 according to:
Scatterometer
&
Altimeter
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 56
Mean “zonal” winds in Tropical Pacific basin according to:
Scatterometer
&
Altimeter
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 57
Concluding Remarks
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 58
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 59
Synthetic Aperture Radar (SAR)
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 60
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
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)
Outline
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 61
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 62
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 spectrum is retrieved from SAR spectrum using nonlinear mapping.
https://earth.esa.int/
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 63
ENVISAT Advanced Synthetic Aperture Radar (ASAR)
https://earth.esa.int/
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 64
SAR Wave Mode Image & SAR Image Cross-Spectrum (Level 1b)
● Almost full Resolution in the range direction (30 m ~ 1000 m).
● Limited resolution in the azimuth direction (>~200 m).
https://earth.esa.int/
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 65
Global comparison between ASAR and WAM model (2011)
0 2 4 6 8 10 12
WAM Sig. Wave Height (m)
0
2
4
6
8
10
12
AS
AR
Sig
. W
ave
He
igh
t
(m)
SYMMETRIC SLOPE
CORRELATION
SCATTER INDEX
STANDARD DEVIATION
BIAS (ENVISAT - WAM)
MEAN ASAR
MEAN WAM
ENTRIES
STATISTICS
REGR. CONSTANT
REGR. COEFFICIENT
0. 9565
0. 9591
0. 1381
0. 3690
- 0. 1525
2. 5195
2. 6720
776756
- 0. 0781
0. 9722
15
153050
100300500100050000
0 1 2 3 4 5 6 7 8 9
WAM Swell Wave Height (m)
0
1
2
3
4
5
6
7
8
9
WV
W S
we
ll W
ave
He
igh
t
(
m)
SYMMETRIC SLOPE
CORRELATION
SCATTER INDEX
STANDARD DEVIATION
BIAS (ENVISAT - WAM)
MEAN WVW
MEAN WAM
ENTRIES
STATISTICS
REGR. CONSTANT
REGR. COEFFICIENT
0. 7303
0. 7884
0. 3534
0. 3903
- 0. 2530
0. 8514
1. 1044
453973
0. 3545
0. 4499
1
5
10
50
100
500
1000
5000
10000
100000
10’s
100’s
OK
Inverted L1b
(full spectrum)
L2
(within azim. cut-off)
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 66
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 67
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.
10 Mar.
2009
Jason-1
Envisat
Jason-2
EnvisatEnvisat
8 Jun.
2009
Jason-1
Jason-2
Envisat
1 Apr.
2010
Jason-2
Envisat
7 Apr.
2012
Jason-2
22 Oct.
2003
ERS-2
1 May
1996
ERS-1
15 Aug.
1993
13 Jan.
2003ERS-2 SAR ENVISAT ASAR
1 Feb.
2006
21 Oct.
2010
Alt.
SAR
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 68
Impact of assimilating WM data on SWH bias and SDD in the Tropics
0 1 2 3 4 5
-0.16
-0.14
-0.12
-0.10
-0.08
Bia
s =
Mo
d.-
Alt
. (
m)
L2 (QC) L1b No data
0.10
0.15
0.20
0.25
0.30
St.
Dev.
of
Err
or
(m
)
0 1 2 3 4 5FORECAST DAYS (0 = AN)
01 Aug. – 04 Sep. 2008
Verified against Envisat and Jason-1 altimeter SWH data.
ECMWF Seminar 2014 - Active techniques for wind and wave observations Slide 69
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 70
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 71
Definition of Difference Measures
● Systematic error (Bias) is:
● Root mean square difference (RMSE):
● Standard deviation of the difference (SDD):
● Scatter index (SI):
● x = altimeter data set, T = reference data set (e.g. in-situ),N = total number of collocations
TxTxNN
i
ii
1
1 )(Bias
N
i
ii TxN1
21 )(RMSE
N
i
ii TxN1
21 )Bias(SDD
N
i
ii TxN1
221 Bias)(SDD
T/SDDSI
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