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Serendipity the faculty or phenomenon of finding valuable or agreeable things not sought for

Serendipity the faculty or phenomenon of finding valuable or agreeable things not sought for. R. A. Brown 2006. Mission statement for the scatterometer ’78

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Serendipity the faculty or phenomenon of finding valuable or agreeable things not sought for

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  1. Serendipity the faculty or phenomenon of finding valuable or agreeable things not sought for R. A. Brown 2006

  2. Mission statement for the scatterometer ’78 “We have an instrument that can measure surface stress (or winds near the surface) -- the most important forcing of the oceanic mixed layer.“ Or (might have been…) We have an instrument that provides backscatter from a 25-km patch of ocean proportional to waves in the 1-6 cm. R. A. Brown 2006

  3. 1 9 7 8 Scatterometer Product from Space Surface WIND vectors R. A. Brown 2006

  4. 2 0 0 5 Frontogenesis info Near real time pressure maps for forecasts Scatterometer Products from Space Ocean Fronts WIND vectors Atmospheric Fronts Nonlinear model proof Daily Global Marine Surface Pressure Fields Land Vegetation Pack Ice location, concentration, thickness Storms: location, Strength Antarctic ice flow movement Mean PBL temperature Mean PBL stratification Surface stress vector R. A. Brown 2006

  5. Mission statement for a lidar: “We have an instrument that can measure winds in the troposphere -- the most important dependent variable in the equations of motion --- essential to good weather & climate modelling“ Or We have an instrument that provides doppler signal return from atmospheric aerosols between the satellite and surface. R. A. Brown 2006

  6. Lidar PBL possibilities GCM updates Wind vectors Air-surface Fluxes PBL turbulence spectrum Rolls PBL dynamics and air-surface fluxes Aerosol statistics Inversion height Surface characteristics R. A. Brown 2006

  7. Hazards of taking measurements in the Rolls e.g. a dropsonde profile Hodograph from convergentzone Hodograph from center zone 1-km The OLE winds Station A 3 U 2 - 5 km 2 The Mean Wind Z/ 1 Station B V Mean Flow Hodograph RABrown 2004

  8. Principle of The Red Queen • Named after the chess piece in Alice in Wonderland, --- she moves faster & faster, in more complicated ways, yet Nothing Significant Changes. • Used in Evolution and Biology, mainly to describe the predator-prey relationship. • I note today that the principle seems to apply to modelling the turbulent PBL? (fantastically complicated turbulence models yet no better weather & climate models) R. A. Brown 2005 EMS

  9. Surface Pressures from Space R. A. Brown 2005 AGU

  10. R. A. Brown 2005 AGU R. A. Brown 2004 EGU

  11. Dashed: ECMWF Solid: UW-Quikscat The UW PBL Model is now global and operational R. A. Brown 2005 AGU

  12. Pressure Fields used in NCEP Forecast Analyses R. A. Brown 2005 AGU

  13. a b 996 991 999 996 OPC Sfc Analysis and IR Satellite Image 10 Jan 2005 0600 UTC GFS Sfc Analysis 10 Jan 2005 0600 UTC c d 984 982 UWPBL 10 Jan 2005 0600 UTC QuikSCAT 10 Jan 2005 0709 UTC

  14. Some Conclusions R. A. Brown 2005 AGU

  15. Surface pressures as surface ‘truth’ yield high wind predictions. This suggests that the global climatology surface wind record is too low by 10 – 20%. Brown, R.A., & Lixin Zeng, 2001: Comparison of Planetary Boundary Layer Model Winds with Dropwindsonde Observations in Tropical Cyclones, J. Applied Meteor., 40, 10, 1718-1723; Foster & Brown, 1994, On Large-scale PBL Modelling: Surface Wind and Latent Heat Flux Comparisons, The Global Atmos.-Ocean System, 2, 199-219. R. A. Brown 2005 AGU

  16. The dynamics of the typical PBL revealed in remote sensing data indicate that K-theory in the PBL models is physically incorrect. This will mean revision of all GCM PBL models as resolution increases. Brown, R.A., 2001:On Satellite Scatterometer Model functions, J. Geophys. Res., Atmospheres, 105, n23, 29,195-29,205; Patoux, J. and R.A. Brown, 2001: Spectral Analysis of QuikSCAT Surface Winds and Two-Dimensional Turbulence, J. Geophys. Res., 106, D20, 23,995-24,005; Patoux, J. and R.A. Brown, 2002: A Gradient Wind Correction for Surface Pressure Fields Retrieved from Scatterometer Winds, Jn. Applied Meteor., Vol. 41, No. 2, pp 133-143; R.A. Brown & P. Mourad, 1990: A Model for K-Theory in a Multi-Scale Large Eddy Environment, AMS Preprint of Symposium on Turbulence and Diffusion, Riso, Denmark.On the Use of Exchange Coefficients and Organized Large Scale Eddies in Modeling Turbulent Flows. Bound. Layer Meteor., 20, 111-116, 1981. R. A. Brown 2005 AGU

  17. There is evidence from the satellite data that the secondary flow characteristics of the nonlinear PBL solution (with Rolls or Coherent Structures) are present more often than not over the world’s oceans. An understanding of this solution contributes to the basic understanding of PBL modelling, data analysis and air-sea fluxes. Refs: Brown, R.A., 2002: Scaling Effects in Remote Sensing Applications and the Case of Organized Large Eddies, Canadian Jn. Remote Sensing, 28, 340-345; Levy G., 2001, Boundary Layer RollStatistics from SAR. Geophysical Research Letters. 28(10),1993-1995. R. A. Brown 2005 EMS

  18. These data allow us to build a climatology of primary and secondary cyclones (in particular their kinematics as revealed by scatterometer winds): e.g. test the hypotheses that explosive frontal storm development may have predictors (e.g. upper level vorticity or surface vorticity anomolies) and the possibility that the strength of storms and fronts is increasing due to global warming. References:Patoux, J. and R.A. Brown, 2002: Spectral Analysis of QuikSCAT Surface Winds and Two-Dimensional Turbulence, J. Geophys. Res., 106, D20, 23,995-24,005; Brown, R.A., 1998: Global High Wind Deficiency in Modeling,Chapterin Remote sensing of the Pacific Ocean by Satellites, p69-77, Southwood Press Pty Limited, Marrickville Australia, pp. 454. R. A. Brown 2005 EMS

  19. For Lidar, there is an Opportunity: • There exist no satellite determined winds in the PBL (there is a scatterometer, but it is due to expire; there’s a radiometer, but it is limited) • There are very few in situe direct measurements of winds in the PBL • Sonde and buoy point wind measurements incur large errors due to turbulence & OLE • The successful parameterization of fluxes (air-surface) requires good boundary layer winds • Climate Analyses are being made on extremely poor data • There are no USA wind satellites planned to launch R. A. Brown 1/2006

  20. Programs and Fields available onhttp://pbl.atmos.washington.eduQuestionsto rabrown, ralph orjerome @atmos.washington.edu • Direct PBL model: PBL_LIB. (’75 -’05) An analytic solution for the PBL flow with rolls, U(z) = f( P, To , Ta , ) • The Inverse PBL model: Takes U10 field and calculates surface pressure field P (U10 , To , Ta , ) (1986 - 2005) • Pressure fields directly from the PMF: P (o) along all swaths (exclude 0 -  5° lat.?) (2001) (dropped in favor of I-PBL) • Global swath pressure fields for QuikScat swaths (with global I-PBL model) (2005) • Surface stress fields from PBL_LIB corrected for stratification effects along all swaths (2006) R. A. Brown 2006

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