# Presentation: 559119

## WindSat — Space Borne Remote Sensing of Ocean Surface Winds 

**Karen St.Germain**
**Presenting on behalf of the WindSat Cal/Val Team**
**Polar Max, 21 October 2004**

## 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 Mission Supports Calibration, Validation and Operational Users
Wind Direction remains high priority requirement of Oceanographer of the Navy
Risk Reduction for NPOESS CMIS

**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 Mission Supports Calibration, Validation and Operational Users**
**Wind Direction remains high priority requirement of Oceanographer of the Navy**
**Risk Reduction for NPOESS CMIS**

_***Organizations***__***:***_
**OPNAV ****	****– N6 (Sponsor)****	****– N096 (Rqmts)**
**NPOESS IPO (Sponsor)**
**Space Test Program (Coriolis Sponsor)**
**SPAWAR PMW155 (Executing Agent) **
**Naval Research Laboratory (Payload and Science)**
**Spectrum-Astro, Inc. (Spacecraft)**

## Coriolis Bus continues to operate well and meet performance requirements
Experienced four safe-holds
Recovered smoothly from each 
Solar Mass Ejection Imager (SMEI)
AFRL payload on Coriolis
All-sky camera experiment that images coronal mass ejections (CME) propagating from the Sun through the solar wind
Through Mar ’04 SMEI has detected ~100 CMEs. SMEI has also shown that CMEs can be tracked as they propagate through interplanetary space between the sun and earth.

**Coriolis Bus continues to operate well and meet performance requirements**
**Experienced four safe-holds**
**Recovered smoothly from each **
**Solar Mass Ejection Imager (SMEI)**
**AFRL payload on Coriolis**
**All-sky camera experiment that images coronal mass ejections (CME) propagating from the Sun through the solar wind**
**Through Mar ’04 SMEI has detected ~100 CMEs. SMEI has also shown that CMEs can be tracked as they propagate through interplanetary space between the sun and earth.**

## Cal/Val is proceeding well and WindSat operating as designed
System noise is low, instrument is stable
Have uncovered two significant performance anomalies
RFI and lunar interference in cold sky calibration (corrected)
Thermal gradients on warmload depending on season and location in orbit (mitigation development underway)
Problem results from reflected sunlight heating the surface of the warm load
Have held multiple telecons with CMIS team to discuss source and severity of problem
Continue to see RFI
Predominantly 6 GHz over land in populated areas
Also see 10 GHz near Europe over water (reflected 10 radiation from European satellite television(ID’ed courtesy of Wentz))
Effect is worst in descending passes because of reflection geometry

**Cal/Val is proceeding well and WindSat operating as designed**
**System noise is low, instrument is stable**
**Have uncovered two significant performance anomalies**
**RFI and lunar interference in cold sky calibration (corrected)**
**Thermal gradients on warmload depending on season and location in orbit (mitigation development underway)**
**Problem results from reflected sunlight heating the surface of the warm load**
**Have held multiple telecons with CMIS team to discuss source and severity of problem**
**Continue to see RFI**
**Predominantly 6 GHz over land in populated areas**
**Also see 10 GHz near Europe over water (reflected 10 radiation from European satellite television(ID’ed courtesy of Wentz))**
**Effect is worst in descending passes because of reflection geometry**

## Cal/Val has been a group effort involving many organizations
Led by NRL Remote Sensing Division
NOAA/NESDIS/Office of Research and Applications (Chang)
Significant contributions from NRL-MRY, JPL, Univ. Central Florida, Univ.  Michigan, Ohio State, Univ. Massachusetts, Texas A&M, UW/APL, UC Santa Cruz
IPO has provided critical support to extended cal/val and science team

**Cal/Val has been a group effort involving many organizations**
**Led by NRL Remote Sensing Division**
**NOAA/NESDIS/Office of Research and Applications (Chang)**
**Significant contributions from NRL-MRY, JPL, Univ. Central Florida, Univ.  Michigan, Ohio State, Univ. Massachusetts, Texas A&M, UW/APL, UC Santa Cruz**
**IPO has provided critical support to extended cal/val and science team**

## 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

Each retrieval producing quality wind vectors
Differences in retrieval statistics are subtle

As of mid-July NOAA MLE producing best retrievals; These constitute the first released wind vectors
Development continues on all 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**
**Each retrieval producing quality wind vectors**
**Differences in retrieval statistics are subtle**
**As of mid-July NOAA MLE producing best retrievals; These constitute the first released wind vectors**
**Development continues on all retrievals**

## WindSat and QuikSCAT Wind Fields Rev 03409

- WindSat

- QuikSCAT

## WindSat and QuikSCAT Wind Fields rev 04255

- WindSat

- QuikSCAT

## WindSat View of Isabelrev 03618

- Wind Speed accuracy suffers in center of storm, but WindSat appears to be getting reasonable directions.
- Requires further validation.

## Full resolution image of Hurricane Isabel wind direction retried by the ORA MLE Algorithm

**Full resolution image of Hurricane Isabel wind direction retried by the ORA MLE Algorithm**
**Color coding represents retrieved Wind Speed**

## Full resolution image of Hurricane Isabel wind direction retried by the ORA MLE Algorithm
Color coding represents retrieved Cloud Liquid Water
High values indicate regions of rain
These regions are observable because of the WindSat low frequency (10 GHz) fully polarimetric channels.

**Full resolution image of Hurricane Isabel wind direction retried by the ORA MLE Algorithm**
**Color coding represents retrieved Cloud Liquid Water**
**High values indicate regions of rain**
**These regions are observable because of the WindSat low frequency (10 GHz) fully polarimetric channels.**

## 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**
**Continue using matchups for wind speed, water vapor and cloud liquid water**
**Also using AMSR, TMI, radiosondes, other NWP models as appropriate**

## Improving performance of wind vector retrievals
Wind speeds below 7 m/s
Improving forward model performance across all conditions
Empirical and Physical Forward Models
Better incorporate the effects of foam
Atmospheric attenuation and reflected downwelling radiation
Improve rain flag and autonomous flagging of RFI
Incorporate lessons learned from feedback from ocean wind science community and other data users
Improve ambiguity removal techniques
May be able to improve ambiguity selection by incorporating results from different retrieval algorithms, which have different ambiguity structures
Add physical realism criteria to selection process
Higher spatial resolution
Train and test retrieval algorithms at higher spatial resolutions (smaller footprint but higher noise)
Demonstrate improvement with two-look retrieval technique

**Improving performance of wind vector retrievals**
**Wind speeds below 7 m/s**
**Improving forward model performance across all conditions**
**Empirical and Physical Forward Models**
**Better incorporate the effects of foam**
**Atmospheric attenuation and reflected downwelling radiation**
**Improve rain flag and autonomous flagging of RFI**
**Incorporate lessons learned from feedback from ocean wind science community and other data users**
**Improve ambiguity removal techniques**
**May be able to improve ambiguity selection by incorporating results from different retrieval algorithms, which have different ambiguity structures**
**Add physical realism criteria to selection process**
**Higher spatial resolution**
**Train and test retrieval algorithms at higher spatial resolutions (smaller footprint but higher noise)**
**Demonstrate improvement with two-look retrieval technique**

## 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

**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**

## 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

**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 Data Availability

_**Data Release**_
**Data products are available for the six-month**** ****period from September 2003 – February 2004**
**Wind vector, SST, Columnar water vapor and cloud liquid water**
**Data products are accessible via the NASA/JPL Physical**** ****Ocean Data Active Archive Center (PO.DAAC)**
** ****Available since 30 July 2004**
**In addition to retrieved environmental data records (EDRs), WindSat SDRs (calibrated brightness temperatures) are also available**
**OSTP chartered Intercomparison Working Group was the first to get data**

## WindSat Data Portal

- http://podaac.jpl.nasa.gov/windsat

## Conclusion

**WindSat is working well**
**Instrument is stable**
**Biases are small and manageable**
**WindSat data exhibits strong wind direction sensitivity**
**Have demonstrated capability of polarimetric radiometry to measure wind direction**
**Version 0 results show that polarimetric microwave radiometry can measure ocean surface wind vector**
**Additional work needed to improve accuracy, especially in low winds**
**Improve robustness of retrievals**

## Polarimetric Radiometry

******T****4****, K**

******T****u****, K**

**NRL Model; JPL Data**

**37 GHz, Wind Speed = 9 m/s**

- We are taking the difference of two large numbers

**Microwave emission varies with wind driven surface roughness**
**Surface roughness is anisotropic thus creating a directional dependence **
**SSM/I has been providing wind speed operationally for over 15 years**
**SSM/I data and aircraft radiometer experiments in 1990s revealed a wind direction signal**
**SSM/I lacks necessary channels to solve for wind direction**
**WindSat adds capability by measuring the Stokes vector, which characterizes the emission polarization**
**Stokes vector data contains necessary information to solve for wind speed and direction **

## WindSat Payload Configuration 

- 22 Channels
- 5 Frequencies

- RF

- 31.6 rpm

- Spin Rate (Nom)

- 295 Watts

- Power

- 675 lbs.

- Weight

- 8.25 ft.

- Width

- 10.5 ft.

- Height

**WindSat Payload Configuration **

**Reflector Support Structure**

**Warm Load**

**Canister Top Deck and Electronics (Rotating)**

**Bearing and Power Transfer Assembly (BAPTA)**

**Launch Locks**
**(4 Places)**

**Spacecraft Interface**

**Stationary Deck**

**Feed Bench**

**Feed Array**

**Cold Load**

**Main Reflector**

**GPS Antenna**

## Wind Direction Sensitivity

- - Sinusoidal behavior in initial test data in excellent agreement with expectations

## WindSat View of Hurricane Isabel

**WindSat View of Hurricane Isabel**

**Approximate**
**Wind Direction**

**Approximate Look Angle**

- - Wind direction signature is clearly evident in WindSat data

## Isabel – Rev 03562

## WindSat Requirements

*** Driven by Antenna Size**

-  The WindSat system also has the potential to provide secondary products:
-  Column Integrated Cloud Liquid Water
-  Column Integrated Precipitable Water
-  Sea Surface Temperature
-  Rain Flag
-  Sea Ice and Land Parameters

| Parameter | Accuracy |  | Range |  | Spatial Resolution |  |
| --- | --- | --- | --- | --- | --- | --- |
| System | WindSat Goal | CMIS IORD | WindSat Goal | CMIS IORD | WindSat Goal | CMIS IORD |
| Wind Speed | ± 2 m/s or 20%
 |  | 3 – 25 m/s |  | 25 km | 20 km |
| Wind Direction | ±20° (5-25 m/s)
±25° (3-5 m/s)
 |  | 0 - 360°
 |  | 25 km | 20 km |

## SMEI (Solar Mass Ejection Imager)

- Description** **
**All-sky camera experiment that images coronal mass ejections (CME) propagating from the Sun through the solar wind. Detection of potentially damaging earth-bound CMEs will help protect space assets and maintain stable communications. All-sky images will also greatly aid astronomers and astrophysicists in understanding solar processes and detecting astronomical phenomena.**
**SMEI consists of three cameras, each with a field of view of roughly 60****o****, that are oriented on the Coriolis spacecraft in such a way that, together, they image a strip of sky that is roughly 180****o**** wide. As the spacecraft moves through its Sun-synchronous polar orbit, the SMEI cameras sweep out nearly the entire sky in a pushbroom fashion.**
**SMEI was designed and constructed by a team of scientists and engineers primarily from AFRL, UCSD, and the University of Birmingham, UK.**

**SMEI Cameras/Baffles & Processing Unit**

**SMEI Cameras Fields of View**

- Status
**Jan ’03-Dec ’03: Completed instrument calibration, data processing, and technique development for CME tracking and storm prediction**
**Jan ’04-present: Currently validating forecast techniques via post hoc and real-time tests. Validation and testing to continue through Dec ’04.**
**Through Mar ’04 SMEI has detected ~100 CMEs. SMEI has also shown that CMEs can be tracked as they propagate through interplanetary space between the sun and earth.**
**Future: Jan ’04-End of Mission: SMEI contributes to operational forecasting.**