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Surface Vector Winds Climate Data Records M.H. Freilich CIOSS/COAS 29 August 2006

Surface Vector Winds Climate Data Records M.H. Freilich CIOSS/COAS 29 August 2006. CMIS/NPOESS-C1. Surface Vector Wind Missions. 97. 98. 99. 00. 01. 02. 03. 04. 05. 06. 07. 08. 09. 10. 11. 12. 13. 14. 15. 895 km, ~50 km res. X. 1700 km. WINDSAT.

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Surface Vector Winds Climate Data Records M.H. Freilich CIOSS/COAS 29 August 2006

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  1. Surface Vector Winds Climate Data Records M.H. Freilich CIOSS/COAS 29 August 2006

  2. CMIS/NPOESS-C1 Surface Vector Wind Missions 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 895 km, ~50 km res. X 1700 km WINDSAT 500 km, 50 (25) km resolution 2 x 550 km w/ 768-km nadir gap AMI/ERS-2 ASCAT/METOP – 3-satellite series 1400 km, 25 km res., Ku-band 1 beam SeaWINDS/ADEOS-II HY-2,-2B – 2 satellite series 1600 km, 12.5 km resolution Seawinds/QuikSCAT OceanSat 2 (5 year) In orbit Approved Planned/Pending Approval While CMIS has been cancelled, a CMIS-successor is planned beginning with C-2 in 2016. ??? Modified from Stan Wilson

  3. Surface Vector Winds: Objectives & Issues • Need to construct (satellite?) ocean SVW data set(s) • Global, Multi-decadal • Well-characterized • Synthesis of all missions • Must accommodate: • Refinement/improvement of individual mission data • Accuracy mix • Resolution mix • Sampling • Role of CIOSS/CIs • Role of assimilative AGCMs

  4. Sampling Schlax, Chelton, Freilich, 2001: JTech

  5. Product Refinement

  6. 12.5 km Hi-Res “MGDR-slice” Winds • Near-real-time product • 12.5 km backscatter measurements from QSCAT slices • “Composite2” processing to yield 4 so per retrieval • Standard MLE wind retrieval algorithm • Erroneous wind variability (noise) • Poor far-swath performance • Systematic spikes in wind speed histograms (corrected, 7/06) • In the 21st century, why must NOAA provide degraded products? • Degraded real-time products may limit the role of operational • centers for assimilative synthesis

  7. Windsat & QSCAT (dir. edit) vs. NDBC -- Rain-Free 50 km distance threshold, Non-Raining • Speed bias statistics nearly identical (0.75 m/s along-wind random component error • for QuikSCAT and B1, 1.0 m/s along-wind rce for NESDIS_0) • Windsat dir. std. dev. larger than QSCAT for wind speeds 2-9 m/s (all 3 WS data sets) • (~ 64% of all global speeds are between 2 and 9 m/s, ~90% between 2 and 15 m/s) • Windsat dir. std. dev. generally less than QSCAT for wind speeds > ~15 m/s • B1, B2 dir. std. devs. best for speeds 12-17 m/s Freilich and Vanhoff, IEEE TGARS, 2006

  8. CIOSS Role in SVW CDR • CIOSS/COAS is probably the center of expertise for • surface vector winds processing, validation, analysis • Strong connections to (NASA) flight projects • Complete in-house scatterometer processing systems • Model Function development • Validation expertise • Buoys • NWP • Satellite-satellite • ERS-1/2, NSCAT, QSCAT/SWS • WindSat validation • Strong connections to geophysical analyses • Preserves focus on key performance characteristics • (e.g., sampling, derivative quantities) • Single synthesized wind data set (still haven’t done) • Rapid feedback

  9. Assimilative Model Role in SVW CDR • AGCM models with proper assimilation may be the optimal • approach for synthesizing multiple SVW data sets • Can incorporate appropriate dynamical information • Accommodates differing sampling, resolution, accuracy • “Re-analysis” mode • This will be a different way of doing business • Requires focus on surface winds, fluxes • Issues of input format (dealt with in previous reanalyses?) • Proper specification and accommodation of error characteristics • Should/can this be done at JCSDA, NWS, GSFC/GEOS, or • another center?

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