1. Hyperspectral Wind Retrievals Dave Santek Chris Velden CIMSS Madison, Wisconsin 5th Workshop on Hyperspectral Science 8 June 2005

2. Winds algorithm heritage Proof of concept
Overview
Winds algorithm heritage
Proof of concept
Simulated GIFTS data
NAST-I observations
AIRS data
Radiances
Moisture retrievals
Future work

3. Winds Heritage
It began around 1970 when the first geostationary satellites were launched.
Manual targeting.
Manual quality control.
Window channel height assignment.

4. Current Winds Algorithm

5. Winds Heritage
The geostationary method is based on cloud and water vapor feature tracking.
Global coverage.
Hourly time resolution.
Single level, at and above clouds.
Used in many operation global models.
Height assignment issues.

6. One Day of Arctic Orbits with Winds
MODIS Band mm

7. Determining AMVs from Hyperspectral Soundings
Goal: Derive clear-sky, altitude-resolved “water vapor winds”
Use simulated GIFTS data, NAST-I data, and AIRS retrievals
Same basic automated code for determining AMVs
Input to the algorithm is constant-level moisture analyses derived from hyperspectral sounding information.
In clear-sky regions, vertical profiles of moisture can be derived from multiple water vapor sensing channels
In this approach, time sequences (30-min analyses) of retrieved water vapor fields (such as constant-pressure mixing ratio analyses) become the ‘imagery’ for tracking winds.

8. Determining AMVs from Hyperspectral Soundings
Since the moisture fields will already be analyzed to constant pressure surfaces by the retrieval, the heights of tracked moisture gradients (water-vapor wind vectors) are pre-determined (altitude-resolved). Therefore, height assignment errors that contemporary AMVs suffer from should be minimized, and improved water vapor winds should result.
The hyperspectral information allows analyses of moisture at multiple vertical levels in cloud-free areas, which can then be used to attempt to create vertical profiles of wind.
The measurement concept for altitude-resolved water vapor winds from hyperspectral observations should provide the needed vertical resolution to derive profiles of wind velocity necessary to realize the full potential of satellite wind measurements.

9. 500 mb Q MM5 "Truth" 2600 targets Noiseless Retrievals 2478 targets
Noise Filtered Retrievals targets
Noisy Retrievals targets
500 mb Q

10. 500 mb winds MM5 "Truth" 429 vectors Noiseless Retrievals 314 vectors
Noise Filtered Retrievals vectors
Noisy Retrievals vectors
500 mb winds

11. Simulated GIFTS winds (left) versus GOES operational winds (right)
GIFTS - IHOP simulation 1830z 12 June 02  
GOES-8 winds 1655z 12 June 02 
Simulated GIFTS winds (left) versus GOES operational winds (right)

12. NAST-I: 2 km pixel resolution; 40 km ground swath

14. MODIS vs. AIRS coverage

15. AIRS Radiance Winds
The MODIS features are tracked at a 2 km resolution. Since AIRS has a subpoint resolution of 13.5 km, we chose 16 km resolution for tracking. This is a 64 times reduction in area.
Cloud height determination is problematic due to the narrow spectral response of the AIRS channels is not included in our height assignment algorithm.
The QI step is reducing the number of vectors. This may be related to data resolution since a one-pixel error, 16 km, is nearly 3 m/s.

16. MODIS vs. AIRS Radiance Winds
A test was performed to track AIRS radiance features from 2 channels for one day, 7 April The channels chosen were close to the MODIS bands used for the real-time polar winds processing: 6.7 and 11 mm.

17. AIRS Retrieval Loop
Specific humidity SFOV AIRS retrievals Remapped composites at 16 km resolution

18. Target size 5x5 pixels. Search box size 14x14. 400 hPa
AIRS Retrieval Targets and Raw Winds
Target size 5x5 pixels. Search box size 14x hPa

19. Summary & Future Work
Successful determination of multi-level winds from retrieved moisture fields derived from model and satellite data.
Future work:
Use cloud-cleared retrievals from AIRS [polar regions]
Apply to GOES sounder retrievals [mid- and low-latitude regions].
HES retrievals for an ATReC case to get boundary layer winds.