The Satellite – PBL Model Connection The microwave scatterometers, radiometers, SARs and altimeters have now provided nearly three decades of inferred surface winds over the oceans. R. A. Brown 2005 EGU In many cases these products are revolutionary, changing the way we view the world.

SeaSat was launched in 1978 as an oceanography satellite * Primary mission was to determine ocean motion; geostrophic and surface layer. Seasat had a scatterometer to measure surface small- scale roughness, inferring surface stress. * The scatterometer data have been extensively studied and compared to in situ measurements — they comprise a ‘surface truth’ base of surface winds. * Seasat also had a SAR to determine sea state, an altimeter to determine ocean height and a radiometer to determine attenuation of the signals.

1980 – 2005: Using surface roughness as a lower boundary condition on the PBL, considerable information about the atmosphere and the PBL has been inferred. The symbiotic relation between surface backscatter data and the PBL model has been beneficial to both. The PBL model has established superior ‘surface truth’ winds (or pressures) for the satellite model functions. Satellite data have proven that the nonlinear PBL solution with OLE is observed most of the time.

Includes a rotating frame of reference R. A. Brown 2003 U. Concepci Ó n

Turbulence and PBL analyses and The Red Queen After attending the session this day, I was reminded of my feelings about the progress that I have witnessed over 35 years of attending sessions directed at Turbulence and PBL Modeling. I employed the term used in Evolution Theory, the Red Queen concept, which came from biology that seems appropriate here: All progress is relative and the more things change, the more they stay the same. This is from the chess piece that Alice meets in “Through the Looking-Glass”, who perpetually runs without getting very far because the landscape moves with her. Progress toward what? Perhaps the end goal is simply to play with turbulence. That has certainly been accomplished for a few decades. Or perhaps, as a speaker before me said, the goal is to develop a PBL model that can be incorporated into weather and climate models (to do something “operational”; read “productive”). I have pursued the latter course, altho I accept it being called quixotic (trying to ‘save the world’). The former course could be called panzaic (trying to assure the next paycheck). The injection of remote sensing data into the mixture is a Red Queen challenge (the environment has just gotten more complicated, with a large supply of new and different data). But it has also been a boon to the analytic, similarity models. This is the essence of my talk.

U t +(KU z ) z +f V- P y /  = 0 V t +(KV z ) z -f U+ P x /  =A(u 2 w 2 ) V t +V  V+f(k×V)+  /  +A(z) = 0 or Small-scale eddy momentum flux Large-scale eddy = OLE momentum flux Boundary Layer Equations R. A. Brown 2003 U. Concepci Ó n

fV + K U zz - p z /  = 0 fU - K V zz + p z /  = 0 The solution, U (f, K,  p ) was found by Ekman in State of The analytic solution for a PBL fV + K U zz - p z /  = 0 fU - K V zz + p z /  = A(v 2 w 2 ) Solution, U (f, K,  p ) found in OLE are part of solution for 80% of observed conditions (near-neutral to convective). Unfortunately, this was almost never observed. The complete nonlinear solution for OLE exists, including 8 th order instability solution, variable roughness, stratification and baroclinicity, Being integrated into MM5, NCEP (2005) Unfortunately, this scale was difficult to observe. R. A. Brown 2005 EGU

18° (neutral stratification) Surface wind Geostrophic Wind direction Geostrophic Wind Mid- PBL V G =  P / (  f ) V =  P / (  f) - F viscous 25° (Stable strat.) 5° (Unstable strat.) -10° to 40° (Thermal Wind Effect) R. A. Brown 2003 U. Concepci Ó n The nonlinear Ekman layer mean wind

The Dinosaur Cartoon (next slide) This Gary Larson cartoon struck me for some reason, probably because it is very close to how I feel these days. The dinosaur lecturer says: The picture’s pretty bleak, Gentlemen…..the world’s climates are changing, the mammals are taking over, and we have a brain about the size of a walnut. I would simply substitute a couple of words to describe my feeling as an analytic PBL modeler: The picture’s pretty bleak, Gentlemen…..the world’s climates are changing, the numerical modellers are taking over, and we have a brain about the size of a coconut. The dinosaurs successfully morphed into birds, and I successfully morphed into a satellite remote sensing specialist.

R. A. Brown 2003 U. Concepci Ó n

Or, the mathematical solution for the PBL flow that includes coherent structures (Organized Large Eddies or OLE) is correct R. A. Brown 2005 EGU

Status of organized large eddies (OLE) verification Airplane campaigns in cold air outbreaks ( ). Ground based Lidar detects OLE (1996 -); Lidar from Aircraft PBL flights (1999 -). Satellite derived surface pressures (1997) using nonlinear PBL model are accurate. Satellite SAR data of ocean surface shows evidence of ubiquitous OLE (1978; 1986; 1997-). R. A. Brown 2005 EGU

R.Foster Dec99 RADARSAT (copyright) Weak cold air outbreak, rolls imprint. LongEZ flight track. Note SST increase – rolls to cells; internal waves. R. A. Brown 2003 U. Concepci Ó n

Analysis of RADARSAT’s C-band SAR for PBL Roll Signature X 51 km SAR images in N. Pacific RollsNo Rolls Indecipherable %34%22% Of 1616 good images, 56% Rolls, 44% No Rolls From Levy, 2003; R. A. Brown 2005 EGU

Analysis of frequency of SAR PBL Roll Signature by RADARSAT Of a random sampling of X 100 km SAR images of the North Pacific (1997 – 1999) Minimum Occurrence of Roll Vortices by Month % Jan Feb Mar Apr May --- Sep Oct Nov Dec no 30 no data data Gad Levy, R.A. Brown 2001

Conclusions SAR stats show Roll signatures % of the time. This is sufficient evidence that Rolls are present, not necessary. * Since the nonlinear (Roll-containing) PBL solution yields significantly different wind profiles and fluxes, it must be considered for wind, weather and climate models. R. A. Brown 2005 EGU

R. A. Brown 2004 EGU

The solution for the PBL boundary layer (Brown, 1974, Brown and Liu, 1982), may be written U/V G = e i  - e –z [e -iz + ie iz ]sin  + U 2 where V G is the geostrophic wind vector, the angle between U 10 and V G is  [u*,  H T, (T a – T s,) PBL ] and the effect of the organized large eddies (OLE) in the PBL is represented by U 2 (u*, T a – T s,  H T) U/V G =  {  (u*), U 2 (u*), u*, z o (u*), V T (  H T),  (T a – T s ), } Or U/V G =  [u*, V T (  H T),  (T a – T s ),, k, a ] =  {u*,  H T, T a – T s }, for = 0.15, k= 0.4 and a = 1 V G =  (u*,  H T, T a – T s )   n (  P, , f) Hence  P =  n [ u*(k, a, ),  H T, T a – T s, , f ]  f n (  o ) This may be written: In particular, R. A. Brown 2003 U. Concepci Ó n

SLP from Surface Winds UW PBL similarity model joins two layers: Use “inverse” PBL model to estimate from satellite. Get non-divergent field U G N. Use Least-Square optimization to find best fit SLP to swaths There is extensive verification from ERS-1/2, NSCAT, QuikSCAT (U G N ) G R. A. Brown 2005 EGU

Dashed: ECMWF R. A. Brown 2005 EGU

ECMWF analysis QuikScat analysis Surface Pressures J. Patoux & R. A. Brown

Raw scatterometer winds UW Pressure field smoothed JPL Project Local GCM nudge smoothed = Dirth (with ECMWF fields) (JPL) R. A. Brown 2005 EGU

The nonlinear solution applied to satellite surface winds yields accurate surface pressure fields. These data show: * Agreement between satellite and ECMWF pressure fields indicate that both Scatterometer winds and the nonlinear PBL model (V G /U 10 ) are accurate within  2 m/s. * A 3-month, zonally averaged offset angle of 19° suggests the mean PBL state is near neutral (the angle predicted by the nonlinear PBL model). * Swath deviation angle observations infer thermal wind and stratification. * Higher winds are obtained from pressure gradients and used as surface truth (rather than from GCM or buoy winds). * V G (pressure gradients) rather than U 10 could be used to initialize GCMs R. A. Brown 2005 EGU

R. A. Brown, J. Patoux R. A. Brown 2005 EGU

These data allow study of the development of fronts in general and frontal waves in particular: QuikScat 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 (predictively?). See Jerome Patoux presentation

1.The nonlinear PBL solution prevails. 2.Global winds are non-homogeneous at the surface over 1-5 km. High velocity winds are advected to the surface in lines. 3.The average wind profile is DIFFERENT from the Ekman solution --- nonlinear winds are 10-50% different, depending on stratification and thermal wind. (likewise ocean PBL) 4. The PBL contains ADVECTING flow not amenable to diffusion modeling. Numerical models cannot portray correct physics of mean flow without extreme increase in resolution. 5. The correct PBL model allows excellent daily global satellite surface pressure analyses from space. R. A. Brown 2005 EGU

Programs and Fields available on Questions to rabrown, neal or Direct PBL model: PBL_LIB. (’75 -’00) An analytic solution for the PBL flow with rolls, U(z) = f(  P,  T o,  T a, ) The Inverse PBL model: Takes U 10 field and calculates surface pressure field  P (U 10,  T o,  T a, ) ( ) 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) (2004) Surface stress fields from PBL_LIB corrected for stratification effects along all swaths (2005) R. A. Brown 2005 EGU