WindSat — Space Borne
Remote Sensing of Ocean
Surface Winds
Peter Gaiser, Mike Bettenhausen,
Zorana Jelenak, Elizbeth Twarog, and Paul Chang
Naval Research Laboratory, Washington DC
NOAA/NESDIS, Camp Springs, MD
08 February 2005
Miami, FL
WindSat - Mission Description
Overview:
•
Demonstrate Ocean Surface Wind Speed and
Direction Measurement Capability with
Polarimetric Microwave Radiometry
•
Launched 06 January 2003 on STP’s Coriolis
Satellite Bus Into a Sun-Synchronous Orbit
(830 km; 98.7 deg; 1759 LTAN)
•
3 Year Design Life; Current plan calls for
continued operation throughout useful life of
Coriolis/WindSat
•
Wind Vector Remains High Priority EDR for
the Navy
•
Risk Reduction for NPOESS CMIS
•
WindSat Brightness Temperature and
Environmental Data Now Available to Science
and User Community
WindSat_FEB05.2
Miami, Florida
Polarimetric Radiometry
• Ocean Surface Emission and Scattering Vary With
Wind Vector
37 GHz, Wind Speed = 9 m/s
- Wind Direction Dependence Arises From
Anisotropic Distribution and Orientation of Wind
Driven Waves
Polarimetric Radiometry Measures Stokes Vector
- Polarization Properties of Emitted/scattered
Radiation
- Contains Directional Information
DTu, K
•
- Wind Direction Signal Is Two Orders of Magnitude
Smaller Than Background Signal
- Two Means of Measuring
-
Correlation of Primary Polarizations
-
Direct Measure of 45, LHC, RHC Polarizations
*
*
 I   Eh Eh  E v E v
Q   E E *  E E *
h
h
v
v
Is     
*
U   2 Re E v Eh
  
*
V   2 Im E v Eh
WindSat_FEB05.3
  Tv  Th 
 

T

T
v
h


 T45  T45 
 

  Tlc  Trc 
Available from “Dual Polarization” Systems
(SSM/I, SSMIS)
New Capability Available from “Polarimetric” Systems
(WindSat)
Miami, Florida
WindSat Payload Configuration
GPS Antenna
Main Reflector
Reflector
Support
Structure
Warm Load
Canister Top Deck
and Electronics
(Rotating)
Launch Locks
(4 Places)
WindSat_FEB05.4
Bearing and
Power Transfer
Assembly
(BAPTA)
Height
10.5 ft.
Width
8.25 ft.
Weight
675 lbs.
Power
295 Watts
Spin Rate (Nom) 31.6 rpm
22 Channels
RF
5 Frequencies
Cold Load
Feed Bench
Feed Array
Stationary Deck
Spacecraft
Interface
Miami, Florida
WindSat Description
Freq, GHz
Channels
BW, MHz
msec
NEDT (1)
EIA, deg
IFOV, km
6.8
v, h
125
5.00
0.48
53.5
40x60
10.7
v, h, ±45, lc, rc
300
3.50
0.37
49.9
25x38
18.7
v, h, ±45, lc, rc
750
2.00
0.39
55.3
16x27
23.8
v, h
500
1.48
0.55
53.0
12x20
37.0
v, h, ±45, lc, rc
2000
1.00
0.45
53.0
8x13
(1) NEDT for IFOV, WindSat at 25°C, Warmload=281 K
•
Uses 11 Feeds Horns and a 6-foot Spinning Offset Parabolic Reflector
•
Calibration Is Performed Once Per Scan As Feeds Pass Below Stationary
Targets
•
Design Minimizes “New Technologies” and Uses Heritage On-board
Calibration — Must Be Able to Separate Phenomenology and Sensor
Behavior
WindSat_FEB05.5
Miami, Florida
WindSat_FEB05.6
WindSat Flight Build
WindSat in TVAC Chamber
Coriolis Satellite at Launch Site
WindSat Feed Horn Array
Miami, Florida
Calibration/Validation
• WindSat operating as designed
NEDT Performance
37L
37P
37V
23V
18L
18P
18V
10L
- Thermal gradients on warmload;
Seasonal and orbit location dependent
10P
- RFI and lunar interference in cold sky
data calibration (corrected)
Requirement
10V
– Two significant performance anomalies
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
6V
– Geolocation accuracy is much better than
5 km
NEDT [K]
– System noise is low; receivers are stable
Pre-Launch
Measurement Max
Post-launch
Measurement Typical
Channel
• Continue WindSat sensor calibration
– Incorporate feedback from wind vector
validation
– Warm load anomaly mitigation
– Tuning antenna pattern correction
• Ground Processing Upgrades
– Incorporate more automated screening for
anomalous conditions
- RFI, Rain, Sea Ice flags
– Faraday rotation improvements
WindSat_FEB05.7
Miami, Florida
WindSat_FEB05.8
WindSat Imagery 37 GHz Imagery
Miami, Florida
WindSat Wind Direction Sensitivity
18.7GHZ 3rd Stokes directional dependence wspd= [5,15]m/s and wv=[5,45] mm^2
WindSat_FEB05.9
Miami, Florida
WindSat Wind Direction Sensitivity
18.7GHZ 4th Stokes directional dependence wspd= [5,15]m/s and wv=[5,45]mm^2
WindSat_FEB05.10
Miami, Florida
WindSat_FEB05.11
WindSat View of Hurricane Isabel
- Wind direction signature is clearly evident in WindSat data
Miami, Florida
Wind Retrievals
•
Physically-based algorithm using nonlinear optimization (NRL)
– Uses physical forward model
– Solves for all EDRs simultaneously
•
Empirical regression technique (NRL)
– Two-stage regression for wind vector components
– Maximum likelihood estimator (MLE) for final wind direction
•
Maximum Likelihood Estimator (NOAA)
– Uses empirical forward model for wind vector retrievals
– Regression retrievals for other EDRs
– Solves for each EDR separately
•
Other retrieved EDRs are columnar water vapor, cloud liquid water and SST
WindSat_FEB05.12
Miami, Florida
Physically-based Retrieval Algorithm
Outline
•
•
•
Uses a parameterized forward model
Simultaneous retrieval of 5 EDR's: TS , W, V, L, and wind direction (R);
Retrieval technique is Optimal Estimation:
– C.D. Rodgers, Rev. Geophys. & Space Phys., 14, Nov. 1976;
•
Two-stage retrieval followed by a median filter for ambiguity selection:
– Stage 1: solve for TS , W, V, L using the V-pol and H-pol channels only to
obtain a priori values for stage 2;
– Stage 2: solve for all 5 EDR's using all available channels.
WindSat_FEB05.13
- Currently the 6.8 GHz H-pol and the 37 GHz 4th Stokes are not used
Miami, Florida
WindSat_FEB05.14
Parameterization of the Model Function
TBV , H  Tup   eTS  r(Tdown  Tc )
TB 3, 4  eTS  (Tdown  Tc )
where
TB : Brightness temperature incident at the antenna
e,r : sea surface emissivity and reflectivity
TS : sea surface temperature
TC : cosmic temperature  2.7 K
 : correction to Tdown
 : atmospheric transmissivity
Tup, Tdown : upwelling and downwelling temperatures
Miami, Florida
Atmospheric Parameterizations,
Part 1
•
One-layer (isotropic) atmosphere
•  = exp[-sec θ (A
O
+ AV + AL)]
– AO vertically integrated oxygen absorption
– AV vertically integrated water vapor absorption
– AL vertically integrated cloud liquid water absorption
•
Parameterize up- and down-welling atmospheric brightness temperatures
in terms of effective temperatures
– Tup = Teff, up (1 –)
– Tdown = Teff, down (1 – )
WindSat_FEB05.15
Miami, Florida
Atmospheric Parameterizations,
Part 2
Linear least squares fit at each frequency to results from our
radiative transfer model
WindSat_FEB05.16
Teff ,down  bD 0  bD1  bD 2V  bD 3V
2
3
Teff ,up  Teff ,down  bU 0  bU 1V
AO  bO 0  bO1V  bO 2V
2
AV  bV 0  bV 1V  bV 2V
2
AL  bL 0 (1  bL1V ) L
V is vertical columnar water vapor
L is vertical columnar cloud liquid water
Miami, Florida
Sea Emissivity Calculation
•
NRL two-scale model is used to generate a 3D lookup table (EIA, Ts , wind
speed) for the emissivity
•
Correction term for contribution from non-specular reflected downwelling
radiation, Ω (Wang et al, next talk)
•
Empirical corrections are made to account for foam and modeling errors:
– Measured emissivity is calculated
- measured brightness temperature (SDR)
- cross-track biases applied
- atmospheric contribution is removed
– Isotropic terms only for V and H polarizations
emeasured  emod el  c0  c1W  c 2TS  c 3TS
2
– Harmonics for 3rd and 4th Stokes

WindSat_FEB05.17
- correction is applied as a scaling factor, emeasured / emodel
emeasured  emodel  c0  c1 sin( )  c2 sin( 2)
Miami, Florida
Quality Control
– Retrievals are performed for all SDR's except
- In the aft scan
- Surface type other than ocean (no coast or near coast)
- TBs out of physical bounds for no or light rain
- EIAs out of expected range (>0.5o from nominal)
– The following are flagged for the condition and also as“low confidence
retrieval” in the EDR QC flag
- Lakes or inland seas (geographic mask)
- May contain rain (rain flagging based on cloud retrieval, about 6%
flagged)
- 10 GHz RFI (geographic mask)
- Ice flag
- Likely land contamination (geographic mask)
- Beam averaging threshold
– Calibration likely influenced by thermal gradients in warm load
WindSat_FEB05.18
Miami, Florida
Two-Stage Approach to Retrievals
•
Stage 1: Get a-priori data for stage 2:
– A-priori data are constants
– A-priori error covariance matrix terms set high
– Only uses isotropic terms of the V-pol and H-pol channels
•
Stage 2: Final Retrieval:
– A-priori data obtained from stage 1 retrieval;
– Four a-priori wind directions for four retrievals:
- R = Regression + 0o, 90o, 180o, 270o
- Yields four solutions (“ambiguities”) for the entire state vector
- Regression is the wind direction from the second stage regression
– A-priori covariance matrix terms set lower;
•
Measurement error covariance matrix determined from model function –
measurement differences
WindSat_FEB05.19
Miami, Florida
Wind Direction Ambiguity Removal
•
Circular Vector Median Filter
– Based on S.J. Shaffer, et al., TGRS, 29, 1991
– Minimize cost function
•
7x7 box size (h = 3)
– Central pixel is included in cost function
•
Cost Function weighting (wmn)
– Wind speed
– Low confidence conditions: ice, RFI, land contamination, etc.
•
Nudging (optional)
– Uses spatially interpolated NCEP GDAS 1o x 1o analysis closest in time
– Near-real-time system uses spatially interpolated NOGAPS .5o x .5o
analysis
– Initialize median filter with first or second rank wind vector closest to
GDAS wind vector
WindSat_FEB05.20
Miami, Florida
Retrieval Comparison & Error Analysis
•
Uses collocated GDAS, SSM/I, TMI and Science QuikSCAT data:
– 6 months (Sept ‘03 – Feb ‘04);
– Alternating 2 days model function development, 1 day testing:
- Filtered out GDAS, SSMI & QuikSCAT ice and non-ocean;
- Filtered out GDAS, SSMI & QuikSCAT rain only for model
development;
– 25 km spatial collocation window;
– 1 hour for GDAS and QuikSCAT, 35 minutes for SSM/I;
•
Spatially interpolated NCEP GDAS 1o x 1o analyses for SST;
•
SSM/I and TMI retrievals for V and L from Remote Sensing Systems;
•
Science QuikSCAT product for wind vectors:
– Filtered out matchups where one or more of the expected beam
combinations was missing.
WindSat_FEB05.21
Miami, Florida
WindSat_FEB05.22
Retrieval Performance
Comparison to Separate Matchup Datasets (“1-way”)
GDAS 1 hour for SST
QuikSCAT 1 hour for wind vectors (W, R)
SSM/I, TMI 35 min. for water vapor and cloud water
Excludes Low Confidence Retrievals
EDR
SST (K)
Bias
-0.14
Std.Dev.
RMS
0.99
1.00
W First Rank (m/s) 0.06
0.91
0.91
W Selected (m/s)
0.05
0.89
0.90
Water Vapor (mm)
0.51
1.07
1.18
Cloud Water (mm)
0.005
0.034
0.035
Miami, Florida
Wind Direction Performance (1-Way)
Matchup with Science QuikSCAT
No Low Confidence Retrievals
W
FR
MF
MF NG
CL
2-4
94
82
51
25
4-6
80
64
37
22
6-8
54
40
22
15
8-10
32
23
15
10
10-12
22
17
13
9
12-14
19
15
12
9
14-16
17
13
11
8
16-18
16
12
11
8
FR = First Rank
MF= Median Filtered
NG= Nudged with closest GDAS analysis
CL = Closest Ambiguity to true wind dir.
WindSat_FEB05.23
Miami, Florida
WindSat_FEB05.24
Wind Speed Histogram (old)
• Wind speed histogram
problem near 10 m/s wind
speed.
• Caused by transition in the
form of the sea emissivity
correction for the forward
model
Miami, Florida
WindSat_FEB05.25
Wind Speed Histogram
• Wind speed histogram
corrected using a smooth
transition between high and
low wind speeds.
Miami, Florida
WindSat_FEB05.26
Wind Direction Histograms
Miami, Florida
WindSat_FEB05.27
WindSat Wind Vector Retrieval
Miami, Florida
WindSat_FEB05.28
WindSat Wind Vector Retrieval
Miami, Florida
Ongoing Work
•
Improving performance of wind vector retrievals
– Wind speeds below 7 m/s
– Improving forward model performance across all conditions
– Incorporate lessons learned from ocean wind science community and
other data users
– Improve ambiguity removal techniques
•
Higher spatial resolution
– Train and test retrieval algorithms at higher spatial resolutions (smaller
footprint but higher noise)
•
Demonstrate improvement with two-look retrieval technique
WindSat_FEB05.29
Miami, Florida
WindSat_FEB05.30
WindSat Data Availability
•
Data Release
Data products available for the six-month period from
September 2003 – February 2004 (July’04 Processing)
- Wind vector, SST, Columnar water vapor and cloud
liquid water
– Data products accessible via the NASA/JPL Physical
Ocean Data Active Archive Center (PO.DAAC)
– Data set is being reprocessed with latest ground
processing algorithms
- NRL Optimal Estimation EDRs
- NOAA/NESDIS EDRs
- WindSat SDRs (Brightness Temperatures)
– New Data to be Uploaded in March
– Additional Months to Follow Immediately
http://podaac.jpl.nasa.gov/windsat
Miami, Florida
WindSat Status and Summary
•
WindSat Successfully Launched on
Coriolis on 06 January 2003
•
WindSat Operation Initiated on 24
January 2003
•
All Radiometers and Subsystems
Are Performing As Expected
•
WindSat Version 0 Retrievals
Demonstrate the Capability to
Retrieve the Ocean Surface Wind
Vector With Polarimetric Microwave
Radiometry
•
Retrieval Performance and
Calibration Continue to Improve
WindSat_FEB05.31
Miami, Florida
WindSat_FEB05.32
Backup
Miami, Florida
WindSat Mission Objectives
Parameter
System
Wind Speed
Accuracy
Predicted
Performance
< 2 m/s
Goal
Range
CMIS
CMIS
Predicted
Goal
IORD Performance
IORD
± 2 m/s or 20%
o
Wind Direction 3-5 m/s <25 o ±20° (3-25 m/s)
WindSat_FEB05.33
5-25 m/s <20
Spatial Resolution
3 – 25 m/s
25 km
CMIS
IORD
20 km
0 - 360°
25 km
20 km
Goal
1. To Demonstrate the Viability of Retrieving Ocean Surface Wind
Vectors from Space Borne Polarimetric Microwave Radiometry
2. Show Potential to Measure the Additional Environmental Data
Types: Sea Surface Temperature, Integrated Atmospheric Water
Vapor, Cloud Liquid Water, Rain Rate, Sea Ice, Snow Cover, Etc.
3. Transition of Polarimetric Microwave Radiometer Science and
Technology for Use in the Development and Production of the
NPOESS Conical Microwave Imagery and Sounder (CMIS)
Miami, Florida
WindSat_FEB05.34
Scan Angle and Wind Direction
Dependence
Miami, Florida
WindSat_FEB05.35
Wind Direction Sensitivity
Miami, Florida
WindSat_FEB05.36
Earth Projected Beams
• Multiple Feeds Results in 11 Sets of Dual-Polarized Antenna Beams
• Beams within Frequency Bands Have Same EIA
• Data Co-Located Within Bands by Time Shifting Data; Co-Location Across Bands
Requires Interpolation
Miami, Florida
WindSat and NPOESS Risk
Reduction
•
NPOESS Plans to Fulfill the Ocean Wind Speed and Directions Requirements
Using Polarimetric Microwave Radiometry - Conically-scanned Microwave Imager
and Sounder (CMIS)
•
WindSat Provides Risk Reduction to NPOESS and CMIS Is Several Ways
– Space Borne Demonstration of Capability of Polarimetric Microwave
Radiometry to Measure the Ocean Surface Wind Direction
– Real Polarimetric Radiometer Data From Space for Model Function and
Retrieval Algorithm Development
– Windsat Lessons Learned
- Hardware Development and Testing (Antenna Characterization, Receiver
Design and Testing)
- Calibration and Data Processing (Warm Load Target Design, On-orbit
Anomalies, RFI Detection and Mitigation)
- Post-Launch Calibration/Validation Techniques
– Coriolis/Windsat Mission Uses NPOESS Ground Segment for Data Downlink
and Distribution
WindSat_FEB05.37
Miami, Florida
Validation
•
•
•
Scatterometer data for wind speed and direction
Buoy matchups for wind speed and direction
Higher resolution NCEP GDAS analyses wind vectors, SST, water vapor and
cloud liquid
– Simulation of brightness temperatures with full forward model
simulation
– Training and testing of forward models and empirical regressions
•
SSMI, SSMIS
– Continue using matchups for wind speed, water vapor and cloud liquid
water
•
Will also use AMSR, TMI, radiosondes, other NWP models as appropriate
WindSat_FEB05.38
Miami, Florida
Ongoing and Future Work
•
Other Ocean EDRs
– Wind vector algorithms require retrieval of sea surface temperature
(SST) and columnar water vapor and cloud liquid water
- Current retrievals of these EDRs only done in support of wind vector
retrievals
- Additional work will enable these to be quality products on their own
– Current algorithms determine the presence of rain; We are confident
that rain rate can be extracted from WindSat data over the ocean
– However, these products have not been rigorously validated; evaluated
to be good enough to support wind vector retrievals
•
Capability of WindSat and polarimetric radiometry in general has not been
exploited over land
WindSat_FEB05.39
Miami, Florida
Continuing Cal/Val Tasks
• Continue WindSat sensor calibration
– Incorporate feedback from wind vector validation
– Warm load anomaly mitigation
– Upgrade antenna pattern correction
• GDPS Upgrades
– Incorporate more automated screening for anomalous conditions
- RFI, Rain, Sea Ice flags
- Faraday rotation improvements
– Upgrade EDRP with latest retrieval algorithm
- Need background fields to initialize median filter
• Ongoing Maintenance Tasks
– Data Processing and Distribution
– GDPS Maintenance
– Cal/Val Monitoring and Performance Tracking
WindSat_FEB05.40
Miami, Florida
Wind Retrievals
•
Developed multiple retrievals
– Primary operational algorithm is physically based and uses optimal
estimation (OE)
- Uses physical forward model
- Solves for all EDRs simultaneously
– Empirical regression technique
- Stage 1 solves for everything but wind direction
- Stage 2 solves for wind direction based on Stage 1 results
– Maximum Likelihood Estimator (MLE)
- Uses empirical forward model
- Solves for each EDR separately
•
Also retrieving other supporting parameters such as columnar water vapor,
cloud liquid water and SST
WindSat_FEB05.41
Miami, Florida