R. A. Brown 2005 Miami Active Radars

From Neil Tyson’s address/campaign On the Future of NASA Univ. Wash. Jan 20, 2005 “LEO (low earth orbits) are old hat and boring. NASA must do new stuff – space” President’s commission --- “Vision” (thing) Winners: Space Exploration Planetary Science Astrobiology Astrophysics Astronomy Losers: Beyond Einstein missions Earth Science

R. A. Brown 2004 EGU

Winds --- see elsewhere

Toward a Surface Pressure Model Function A Scatterometer doesn’t measure Winds. It measures Capillaries & Short Gravity Waves, related to z o., u* Fortunately, there exists a relation U 10 /u* = F (z, z o, stratification…). –Established over land, assumed over the ocean. –Verified in the 26 years since Seasat. There’s an easy extrapolation to: Fortunately, there exists a relation U G /u* = F (z, z o, stratification,, ……). –Established in UW PBL_LIB. is a constant, a similarity parameter. –Verified in the last 10 years of satellite data R. A. Brown 2004 EGU 2005 Miami

Try a direct correlation with pressure Since V G =  P / (  f ) Use ECMWF/NCEP surface pressures analyses get  P and V G ; substitute V G for U 10 in the Model Function i.e. We’re using the model surface pressures as truth rather than the PBL analysis of U 10 Results: V G correlates with  o as well as U 10 * Better alias selection with scatterometer data alone * High winds appear * Low winds, directions appear * Stratification, Thermal Wind Effects Prospects: R. A. Brown EGU 2004

Example of  o vs look angle for U 10 = 20m/s; Incidence = 45  Example of  o vs look angle for V G = 27m/s; Incidence = 45  R. A. Brown 2004 EGU

Producing smooth wind fields R. A. Brown 2003 U. Concepci Ó n Usually from rainDirection information is poor

Raw scatterometer winds UW Pressure field smoothed JPL Project Local GCM nudge smoothed = Dirth (JPL) R. A. Brown 2003 PORSEC, U. Concepci Ó n

QuikSCAT pressure gradients are comparable to ECMWF in general –QuickSCAT implies stronger gradients in frontal zones. Comparison with ECMWF similar in both hemispheres Good correlation overall We’ve grown accustomed to this level of quality in QS-SLP QuikSCAT Foster, 2005

Dashed: ECMWF R. A. Brown 2005 Swath QuikScat surface pressure fields available at washington.edu

ECMWF analysis QuikScat analysis Surface Pressures J. Patoux & R. A. Brown 2003 PORSEC, U. Concepci Ó n; 2005 Miami

 Agreement between satellite and ECMWF pressure fields indicate that both Scat winds and the nonlinear PBL model (V G /U 10 ) are accurate within  2 m/s. Results from Satellite Scatterometer surface pressure analyses :  3-month, zonally averaged offset angle of 19° suggests the mean PBL state is near neutral. This is the nonlinear PBL predicted angle (18°).  Swath deviation angle observations show thermal wind and stratification effects, implying temperatures.  V G rather than U 10 could be used to initialize GCMs  Predicted higher winds from pressure gradients (than from GCM or buoys) agree with OLE effect, observations. R. A. Brown 2004 EGU SLP gradients (e.g. from buoys) provide surface truth for V G, hence U 10.

R. A. Brown 2004 EGU

Storms & Fronts Analyses

In the second case, the system is decaying but a secondary low is developing behind the remnants of the cold front. Note also the correspondence between convergence and clouds. Fronts: Location; Analysis; Frontogenesis; Prognosticators R. A. Brown, J. Patoux003

Analysis of QuikScat derived fronts and pressure fields suggests there are correlations between frontal characteristics, upper level conditions (PV) and subsequent development of explosive storms development Patoux, J., PhD thesis Univ. of Washington, MWR in press

R. A. Brown 2004 EGU

Southern Hemisphere Pressures ECMWF & NSCAT Comparison Surface Pressure Fields of 102 Storms surveyed for 1996: * 5% missed entirely (vs 20% in 1990) * 70% misplaced average an 280 km * 25% good matches (-3 mb ave. diff.) R. A. Brown 2003 PORSEC, U. Concepci Ó n; 2005 Miami

Revelations from scatterometers R. A. Brown 2004 EGU; 2005 Miami Great global surface pressure fields are available daily. The winds are higher; the low pressures are lower & more frequent; heat fluxes are greater; and surface stress is much greater than climatology states. Data on storms and fronts is exceptional. (Patoux, J., G.J. Hakim and R.A. Brown, 2004: Diagnosis of frontal instabilities over the Southern Ocean, Monthly Weather Review, in press) Storms frequency, strength and statistics are different Fronts (defined as wind convergence zones) are ubiquitous, persistent and provide new data

The global marine wind and pressure data from scatterometers (the 19 deg turning) and SARs (OLE surface imprints) indicate that the nonlinear OLE (Rolls) are present 50 – 70% of the time. Hence the nonlinear PLB model prevails (the Ekman solution does not exist). While this is a nonlinear finite perturbation, it can have large effects on measurements 10-km or less and in the mean. Air-Sea fluxes are non-homogeneous, take place in advective plumes, and interact with the inversion. New PBL models are needed to get good heat and momentum fluxes for ocean and climate modeling. K-theory (diffusion modelling) is physically incorrect for modeling these fluxes. Winds are higher than climate and ocean modelers thought.

In addition to better initialization of GCMs; Global marine surface winds and pressures are best available Storms and fronts analyses are revolutionary. –Provides surface truth for storms –Provides statistics for storms –Possibility of predictors of storms genesis. Winds and fluxes are different than climatology records. Climate and ocean dynamics modelers take note.

The Contributions of Scatterometry The microwave scatterometers have now provided over two decades of inferred surface winds over the oceans. These data have been extensively studied and compared to in situ measurements so that they comprise a ‘surface truth’ base comparable to other sources of winds. In fact, in many cases these products are revolutionary, changing the way we view the world. Examples are: Mainline products The surface winds are exceptional in resolution and coverage. The nonlinear solution applied to satellite surface winds provides sufficient accuracy to determine surface pressure fields from satellite data alone. We can uniquely offer a continuous record of QS-derived surface pressure fields: these pressure fields extend through the Tropics - a region that is poorly characterized by numerical weather forecast models - and contain fine details that are absent from numerical model analyses The pressure fields can be used as a low pass filter to aid ambiguity selection and provide smooth wind fields from scatterometer data alone.

From the Scatterometers (2) Storms and Weather Revelations The scatterometer data allow study of the development of fronts in general and frontal waves in particular: QS reveals mesoscale features that are not captured by numerical models or other satellite-borne instruments, in particular the surface signature of frontal instabilities that sometimes develop into secondary cyclones. (Patoux, J., G.J. Hakim and R.A. Brown, Diagnosis of frontal instabilities over the Southern Ocean, Monthly Weather Review, in press). These data allow us to build a climatology of primary and secondary cyclones (in particular their kinematics as revealed by scatterometer winds), to test the hypothesis that explosive frontal storm development may have surface predictors (e.g. surface PV anomaly coincident with upper-level vorticity) and to investigate the possibility that the strength of storms and fronts is increasing due to global warming Capturing storm and frontal dynamics require at least 25-km resolution. New revelations are stimulating and surely forthcoming from QuikScat data. (Brown, R.A., Comments on the synergism between the analytic PBL model and remote sensing data, Bound.-Layer Meteor., in press)

From the scatterometers (3) Basic Revelations The numerical global models of the 90s were inadequate in representing Southern Hemisphere and tropical weather systems. In 1991, they missed 20% of the So. Hemisphere storms. After the scatterometer revelations, the numerical models improved resolution and incorporated satellite data so they now (2004) miss only 5% of the storms (tho miss-locating 70% by an average 250km). QuikScat data forms the basis of this evaluation. The data indicate that the global climatology surface wind record is too low by 10 – 20%. Basic Science There is evidence from these data that the secondary flow characteristics of the nonlinear PBL solution (Rolls or Coherent Structures) are present more often than not over the world’s oceans. This contributes to basic understanding of air-sea fluxes. The revealed dynamics of the typical PBL indicate that K-theory models are physically incorrect. This will mean revision of all GCM PBL models. QuikScat data will help convince them. The conclusions from these observations are important yet often ignored by the modeling community. The continued accumulation of data from QuikScat is essential to wake up the community.

SLP from Surface Winds UW PBL similarity model Use “inverse” PBL model to estimate from satellite Use Least-Square optimization to find best fit SLP to swaths Extensive verification from ERS-1/2, NSCAT, QuikSCAT (U G N ) R. A. Brown 2005

Using SLP to Assess Direction Winds derived from SLP are optimal smooth winds Arbitrary threshold of 35 o from Model U 10 used to distinguish potentially wrong ambiguity choice Look for an ambiguity with closer direction to Model winds in these cases R. A. Brown 2005

Station B km Taking measurements in the Rolls Station A 1-km RABrown 2004 U VMean Flow Hodograph Z/  The Mean Wind The OLE winds Hodograph from center zone Hodograph from convergent zone

Station B km Taking measurements in the Rolls Station A 1-km RABrown 2004 U VMean Flow Hodograph Z/  The Mean Wind The OLE winds Hodograph from center zone Hodograph from convergent zone

The gradient wind correction is described in Patoux and Brown (2002) and uses the simple balance of forces in natural coordinates shown in the figure The Gradient Wind Correction R. A. Brown 2003 U. ConcepciÓn

R. A. Brown 2004 EGU Geostrophic balance Gradient balance

On the right, the gradient wind correction has been included. The obtained pressure field is very similar to the uncorrected one, except for the center of the anticyclone, where the radius of curvature is smaller, and the effect of the correction bigger. The pressure gradients are weaker and the central area of the high is flatter, which seems in better agreement with ECMWF. R. A. Brown 2003 U. ConcepciÓn

Best surface winds, pressures available Much Better Storms Depiction Shows Evolution of Fronts & Cyclones Better Hurricane PBLs, GCM Initializations, forecasts Proof of Rolls (OLE) Ubiquity (PBL model) Higher Winds (heat fluxes) for Climate models R. A. Brown 2005

* Great global surface marine winds are available daily. Revelations from scatterometers * Great global surface pressure fields are available daily. * Ship or Buoy winds are not good surface truth in general. * GCM PBL models have the wrong physics. * The  oV saturates (due to white U 10 ~ 35 m/s, but the  oH does not saturate even at U 10 ~ 65 m/s. * The winds are higher, the low pressures are lower & more frequent, heat fluxes are greater and stress much greater than climatology states. Climate modelers take note. * Scatterometer derived pressure fields can be used to de-alias winds and correct (smooth)  o single or small area anomalies (rain or nadir/edge ambiguities). R. A. Brown 2004 EGU; 2005 Miami * Data on storms and fronts is revolutionary. (Patoux, J., G.J. Hakim and R.A. Brown, 2004: Diagnosis of frontal instabilities over the Southern Ocean, Monthly Weather Review, in press)

1.The winds are non-homogeneous at the surface over a km horizontal distance. Upper high velocity wind is advected to the surface in lines. OLE must be taken into account in surface truth measurements (In the average and point values). (Brown, Canadian Jn. Remote Sensing, 28, , 2002) R. A. Brown The average wind profile is different from the Ekman solution – and from a profile 100m away – the nonlinear wind solution (and hence fluxes of momentum, heat, CO 2 etc.) is 10-50% different, depending on stratification and thermal wind. In a satellite’s 25-km footprint there will be 10-OLE so the periodic effect will be the average. Not true at 6-km or less --- the SAR resolution. (Brown & Foster, The Global Atmos.-Ocean System, 2, , 1994; , 1994; , 1994.) 3. The PBL contains advecting flow not amenable to diffusion modeling. Numerical models cannot portray correct physics of mean flow without extreme increase in resolution.

Dashed: ECMWF