Statistical Analysis of Massive Data Streams: Proceedings of a Workshop (2004)

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GLOBAL AND REGIONAL SURFACE WIND FIELD INFERENCES FROM SPACE-BORNE SCATTEROMETER DATA 57 circulation models will be discussed. References Berliner, L.M., R.F.Milliff and C.K.Wikle, 2002: “Bayesian hierarchical modelling of air-sea interaction” , J. Geophys. Res., Oceans, in press. Chin, T.M., R.F.Milliff, and W.G.Large, 1998: “Basin-scale, high-wavenumber sea surface wind fields from multi-resolution analysis of scatterometer data”, J. Atmos. Ocean. Tech., 15, 741–763. Milliff, R.F., M.H.Freilich, W.T.Liu, R.Atlas and W.G.Large, 2001: “Global ocean surface vector wind observations from space”, in Observing the Oceans in the 21st Century, C.J.Koblinsky and N.R.Smith (Eds.), GODAE Project Office, Bureau of Meteorology, Melbourne, 102–119. Milliff, R.F., W.G.Large, J.Morzel, G.Danabasoglu and T.M.Chin, 1999: “Ocean general circulation model sensitivity to forcing from scatterometer winds”, J. Geophys. Res., Oceans, 104, 11337–11358. Royle, J.A., L.M.Berliner, C.K.Wikle and R.F.Milliff, 1998: “A hierarchical spatial model for constructing wind fields from scatterometer data in the Labrador Sea.” in Case Studies in Bayesian Statistics IV, C.Gatsonis, R.E.Kass, B.Carlin, A.Cariquiry, A.Gelman, I.Verdinelli, and M.West (Eds.), Springer-Verlag, 367–381. Wikle, C.K., R.F.Milliff, D.Nychka and L.M.Berliner 2001: “Spatiotemporal hierarchical Bayesian modeling: Tropical ocean surface winds”, J. Amer. Stat. Assoc., 96(454), 382–397. Figure Captions Table 1. Past, present, and planned missions to retrieve global surface vector wind fields from space (from Milliff et al., 2001). The table compares surface vector wind accuracies with respect to in-situ buoy observations. Launch dates for SeaWinds on ADEOS-2 and Windsat on Coriolis have slipped to 14 and 15 December 2002, respectively. Figure 1. Three panel depiction of the statistical blending method for surface winds from scatterometer and weather-center analyses. Panel (a) depicts the wind stress curl for the weather-center analyses on 24 January 2000 at 1800 UTC. Wind stress curl from QSCAT swaths within a 12-hour window 

GLOBAL AND REGIONAL SURFACE WIND FIELD INFERENCES FROM SPACE-BORNE SCATTEROMETER DATA 58 centered on this time are superposed on the weather-center field in panel (b). Panel (c) depicts the wind stress curl for the blended field. Derivative fields such as wind stress curl are particularly sensitive to unrealistic boundaries in the blended winds. Figure 2. A Bayesian Hierarchical Model is used to infer surface vector wind fields in the tropical Indian and western Pacific Oceans, given surface winds from QSCAT and the NCEP forecast model. Five realizations from the posterior distribution for (left) zonal wind and (right) surface divergence are shown for the entire domain on 30 January 2001 at 1800 UTC. The two panels in the first row are zonal wind and divergence from the first realization. Subsequent rows are zonal wind differences and divergence differences with respect to the first realization. The differences are for realizations 10, 20, 30, and 40 from a 50 member ensemble of realizations saved from the Gibbs sampler. Figure 3. Summary plots for the Air-Sea interaction Bayesian hierarchical model (from Berliner et al., 2002). The basin average ocean kinetic energy distributions as functions of time are compared with a single trace (solid) from a “truth” simulation described in the text. The posterior mean vs. time (dashed) is indicated in panel (a) for the full air- sea BHM, and in panel (b) for an air-sea BHM from which all pseudo-altimeter data have been excluded. Panels (c-f) compare BHM probability density function estimates at days 1, 3, 5, and 7. 

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GLOBAL AND REGIONAL SURFACE WIND FIELD INFERENCES FROM SPACE-BORNE SCATTEROMETER DATA 62 Mission Measurement Swath (km) Resolution Accuracy(wrt URL(http://) approach daily cov. (km) buoys) ERS-1/2 AMI C-BAND 500/41% 50 (~70) 1.4–1.7 m/s earth.esa.int 4/91–1/01 SCATT. rms spd 20º rms dir ~2 m/s random comp. ASCAT/ C-BAND 2×550/68% 25 50 Better than ERS esa.int/esa/progs/ METOP SCATT. www.METOP.html NSCAT 9/96– Ku-BAND 2×600/75% (12.5) 25 50 1.3 m/s (1–22 winds.jpl.nasa.gov/ 6/97 SCATT. (fan m/s) spd 17º missions/nscat beam) (dir) 1.3 random comp. SeaWinds/ Ku-BAND 1600/92% 12.5 25 1.0 m/s (3–20 winds.jpl.nasa.gov/ QuickSCAT SCATT. (dual (1400) m/s) spd 25º missions/quickscat 7/99–present conical scan) (dir) 0.7 random comp. SeaWinds/ Ku-BAND 1600/92% (12.5) 25 Better than winds.jpl.nasa.gov/ ADEOS-2 2/02 SCATT. (w/u- (1400) QuickSCAT missions/seawinds wave Rad.) WINDSAT/ DUAL-LOOK 1100/~70% 25 ±2 m/s or 20% www.ipo.noaa.gov/ CORIOLIS POL. RAD. spd windsat.html 3/02 ±20°?? CMIS/ SINGLE-LOOK 1700/>92% 20 ±2 m/s or 20% www.ipo.noaa.gov/ NPOESS 2010? PO. RAD. spd cmis.html ±20°?? (5–25 m/s)