1. DETERMINATION OF WIND VECTORS BY TRACKING FEATURES ON SEQUENTIAL MOISTURE ANALYSES DERIVED FROM HYPERSPECTRAL IR SATELLITE SOUNDINGS Christopher Velden CIMSS, U of Wisconsin Contributors: Allen Huang, Paul Menzel, Hank Revercomb, William Smith CIMSS NESDIS CIMSS LaRC, NASA

3. CIMSS Automated Geostationary Satellite Winds Algorithm 20+ years of development and experience Operational demonstration and transition Fully automated, robust and state-of-the-art

4. Current Applications GOES, GMS and Meteosat VIS, IR and WV channels Operational users: NOAA/NESDIS, NWS, and the US Air Force and Navy Recent field experiment support using rapid scan technologies: GWINDEX/PACJET, THORPEX

6. Winds Algorithm

7. The “path” to Hyperspectral Sounding VAS (geo experimental) GOES Sounder (geo operational) GIFTS (geo experimental) (12) (18) (~1600) HES (geo operational) time (# of spectral bands) VTPR, HIRS (leo operational) CrIS (leo operational) IASI (leo operational) AIRS (leo operational) HIS (airborne experimental) IRIS (leo experimental) (~2400) (~3600) IMG (leo experimental) NAST-I (airborne experimental)

8. Future GOES The future GOES will address all four key remote sensing areas: * spatial resolution – what picture element size is required to identify feature of interest and to capture its spatial variability; * spectral coverage and resolution – what part of EM spectrum at each spatial element should be measured, and with what spectral resolution, to analyze an atmospheric or surface parameter; * temporal resolution – how often does feature of interest need to be observed; and * radiometric resolution – what signal to noise is required and how accurate does an observation need to be.

9. Moisture Weighting Functions Pressure (hPa) Advanced Sounder (3074) GOES (18) 1000 100 UW/CIMSS High spectral resolution advanced sounders will have more and sharper weighting functions compared to current GOES sounder. Retrievals will have better vertical resolution.

10. High-spectral resolution Data Information - Spectral Information -> Vertical Resolution Current - GOES ~2-3 Pieces8-9 Pieces GIFTS GOES Vert-Res.: 6-8 Km GIFTS Vert-Res.: 2-4 Km Water Vapor

11. Advanced sounder has more and sharper weighting functions UW/CIMSS Water vapor contributions are function of spectral region being observed Moisture Weighting Functions Pressure Weighting Function Amplitude Wavenumber (cm-1)

12. GOES-R HES temporal (15 min), spectral (0.5 cm-1), spatial (4-10 km), & radiometric (0.1 K) capabilities will: * Depict water vapor as never before by identifying small scale features of moisture vertically and horizontally in the troposphere * Allow tracking of tropospheric motions much better by discriminating more levels of motion and assigning heights more accurately

14. Hyperspectral Data - Experimental Tracking Methods Single channels Superchannels Altitude-determined fields from moisture retrievals

15. Hyperspectral Winds Development Path Simulated data (GIFTS) Real data (Case Studies)

16. MM5 "Truth" 2600 targetsNoiseless Retrievals 2478 targets Noise Filtered Retrievals 2580 targetsNoisy Retrievals 2604 targets 500 mb Q

17. MM5 "Truth" 429 vectorsNoiseless Retrievals 314 vectors Noise Filtered Retrievals 326 vectorsNoisy Retrievals 262 vectors 500 mb winds

18. VisAD display of simulated GIFTS winds

19. Hyperspectral Winds Development Path Simulated data (GIFTS) Real data (Case Studies)

25. Achievement: Proof of Concept Using Simulated GIFTS Data The GIFTS measurement concept for altitude-resolved "water vapor winds" from hyperspectral measurements should provide the needed vertical resolution to derive profiles of wind velocity necessary to realize the full potential of satellite measurements. An algorithm to derive clear-sky, altitude-resolved water vapor winds has been developed and evaluated using simulated GIFTS data. The method utilizes the same basic automated cloud-tracking code developed at CIMSS, however, the input to the algorithm is in the form of constant-level moisture analyses derived from hyperspectral sounding information. In clear-sky regions, vertical profiles of moisture can be derived from multiple simulated GIFTS water vapor sensing channels (it has already been extensively demonstrated through GIFTS aircraft experiments how well these moisture features can be depicted). 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. 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. Therefore, height assignment errors that contemporary geo-based winds suffer from should be minimized, and improved water vapor winds should result. Furthermore, 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.

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

27. SUMMARY CIMSS is developing new approaches to passive wind tracing that will be possible from hyperspectral sounders to be flown on future geosynchronous satellites. These new concepts have been demonstrated by first examining simulated hyperspectral data sets, and also on one case of real data from airborne observations provided by the NASTI instrument. The results from the NASTI case show good agreement with a Doppler wind lidar also flown on the aircraft. This new approach to retrieve winds from satellites in cloud-free areas could become a standard in regions where geosynchronous satellite hyperspectral observations are available. Our current work is focusing on refining the algorithms using hyperspectral data collected from aircraft platforms (Thorpex-Hawaii).

28. DETERMINATION OF WIND VECTORS BY TRACKING FEATURES ON SEQUENTIAL MOISTURE ANALYSES DERIVED FROM HYPERSPECTRAL IR SATELLITE SOUNDINGS Christopher Velden, Gail Dengel, Russ Dengel, Allen Hung-Lung Huang, David Stettner, Hank Revercomb, Robert Knuteson CIMSS/SSEC/Univ. of Wisconsin, Madison, WI and William Smith Sr NASA Langley, Virginia P2.2 4 MM5 "Truth" 2600 targetsNoiseless Retrievals 2478 targets Noise Filtered Retrievals 2580 targets Noisy Retrievals 2604 targets 500 mb MM5 "Truth" 629 vectorsNoiseless Retrievals 314 vectors Noise Filtered Retrievals 326 vectorsNoisy Retrievals 262 vectors 500 mb Algorithm Heritage Goals for New Sensors Plots of the targets (left grouping) and wind vectors (right grouping) from tracking 3 sequential 500mb moisture analyses derived from: MM5 Q field only (upper/left), MM5 with simulated GIFTS and no introduced noise (upper/right), MM5 with simulated GIFTS and “expected” (specification-level) noise (lower/left), and MM5 with simulated GIFTS and amplified noise (lower/right). Proof of Concept Using Simulated GIFTS Data The GIFTS measurement concept for altitude-resolved "water vapor winds" from hyperspectral measurements should provide the needed vertical resolution to derive profiles of wind velocity necessary to realize the full potential of satellite measurements. An algorithm to derive clear-sky, altitude-resolved water vapor winds is being developed and evaluated using simulated GIFTS data. The method utilizes the same basic automated cloud-tracking code developed at CIMSS, however, the input to the algorithm is in the form of constant-level moisture analyses derived from hyperspectral sounding information. In clear-sky regions, vertical profiles of moisture can be derived from multiple simulated GIFTS water vapor sensing channels (it has already been extensively demonstrated through GIFTS aircraft experiments how well these moisture features can be depicted). 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. 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) will be pre-determined. Therefore, height assignment errors that contemporary geo-based winds suffer from should be minimized, and improved water vapor winds should result. Furthermore, 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. VisAD display of the simulated GIFTS winds illustrates the data density and vertical distribution. Proof of Concept Using NAST-I Observations A racetrack aircraft flight pattern was flown over the region shown above. This provided multiple overpasses of the same region, and allowed sequential moisture fields to be derived for winds production. Tracer winds produced by CIMSS algorithms from multi-level NASTI moisture analyses. 3 sequential constant-altitude moisture analyses from NASTI retrievals are used to identify traceable WV gradients for determining wind vectors The wind vectors derived from the NASTI fields are compared to coincident wind LIDAR measurements and show good qualitative agreement. The contemporary algorithm utilized to derive winds from GOES at NESDIS has been evolving since 1980, and now provides routine wind products for national and international users. This algorithm is fully automated and includes an elaborate quality control step as part of post-processing. CIMSS has played a major role in the development and advancement of this winds algorithm, which uses the approach of tracking features in clouds and water vapor gradients from selected channels on geostationary satellites. The current/traditional method employs sequential images of clouds and moisture derived from radiance fields to track the motion of selected targets (e.g., well-defined clouds, moisture gradients). This method has proven successful and effective in deriving wind fields, and numerous studies have indicated the usefulness of the satellite-derived wind data on weather analysis and forecasting. CIMSS is developing new approaches to passive wind tracing that will be possible from hyperspectral sounders to be flown on future geosynchronous satellites. These new concepts have been demonstrated by first examining simulated hyperspectral data sets, and also on one case of real data from airborne observations provided by the NASTI instrument. The results from the NASTI case show good agreement with a doppler wind lidar also flown on the aircraft. This new approach to retrieve winds from satellites in cloud-free areas could become a standard in regions where geosynchronous satellite hyperspectral observations are available. Our future work will concentrate on improving the target identification and tracking schemes. Summary Contact Info Chris Velden: chrisv@ssec.wisc.edu