Sources of Surface Wind Fields for Climate Studies

• From Surface Measurements– Ships– Buoys

• From Models– GCM (with K-theory PBLs)– UW Similarity Model (with OLE)

• From Satellites– Scatterometers – SAR, Altimeter, SSMI,

WindSat….– Lidar?

R.A. Brown,, 2003

Sources of Tropospheric Wind Fields for Climate

Studies

• From Surface Measurements– Radiosondes– Radar

• From Models– GCM

• From Satellites– Lidar

R.A. Brown, 1997, 2001

Ways to improve Wind accuracy for Climate Studies Problem Remedy Date ?

Accuracy Better surface obs. (2010) Better satellite Model functions (2003) Improve Surface Wind Error in GCMs

Increase Resolution to meters in the PBL 2025 Better PBL parameterizations Analytic Similarity Theory 2003 LES & CFD Numerical Models 2010 Sparsity More buoys (2010) More Satellite Data 1991 - Satellite Data Sparsity SSM/I (1988) + WindSat (2003-) QuikScat(1996 ) + SeaWinds 2003 Lidar Troposphere Winds 2009

R.A. Brown 2003

SeaSat 1978 ERS -1 1991-95

ERS-2 1995-2001NSCAT 1996-97

QuickScat 1999 -

SeaWinds 2002

ASCAT 2004

R. A. Brown 2003

Better GCM Progs (initialization)(even better than Atlas says)

Better Storms Definition(not quite as good as R. A. Brown says)Evolution of

Fronts & Cyclones

Better Hurricane Initialization, forecasts

Revelations 1

– Storms are:

• Often misplaced

• Stronger (deeper Pressures)

• More frequent

than found in GCMs and climatology records.

Hence tandem data will improve all of these

R. A. Brown 2003

Revelations 2 There still exist large regions of High

Winds (1000 km2/storm) that nobody knows of……

These do not appear in:• GCM analyses• Buoy data• Climate data• Satellite data (some)They will appear twice as often in

tandem dataR. A. Brown 2000-2003

• Buoy winds are not good surface truth for U > 7 m/s

• GCM PBL models still have wrong physics

• There is no usable o when there is a modest rain rate

• The oV saturates (due to white water) @ U10 ~ 35 m/s (but oH may

respond to 65 m/s)

• The PMF/scat pressure data has better resolution than GCMs.

• The tandem scatterometers are very valuable for research into synoptic and smaller scales

• The WindSat will attempt to replace scats --- chances are 50-50 at best

CONCLUSIONS

R.A. Brown 2003

3

Revelations 3

Fronts:Defined as lines of different sea

state (roughness variation)

From Scat data appear:

• Ubiquitous

• Long (1000s-km)

• Persistent (week)

• Unstable, cyclogenetic

R. A. Brown 1/2000

What is a Front?

Synopticians disagree. Defined by: Temperature difference Air mass difference (history)

From our perspective, defined by: (sea-state) Surface Wind difference

What we know: There’s a difference in small-scale sea state along a line

The (long, slow, tortuous) Path to a Global PBL Model

It must include all turbulence effects, from the surface to geostrophic (gradient) wind balance

• The Ekman solution with constant K or variable K (1904) , was not observed

• The nonlinear equilibrium Ekman solution with organized large eddies (OLE) (1970) is observed

• A mixed-layer model for the tropics is patched to the OLE similarity model global PBL model (2003)

R.A. Brown 2003

• The winds can be inferred from any measurement of a dependent variable in the balance equations or from a good numerical model (2003)

• Learning (and teaching) the dynamics is difficult. The sensors are expensive. Crude correlations with inexpensively measured passive variables (Temperature, clouds, aerosols..) are relatively easy.

• However, these winds are much less accurate, greatly averaged and lack the resolution of active satellite wind sensors.

• The active sensor winds yield good improvement in GCM at coarse resolution, smaller improvements at greater resolutions

• The greatest value of increased resolution (SAR, Lidar) and coverage (tandem) is in science applications

• The USA & NASA, probably lack the funds to do things right.

Summary: Status of remote sensing of Winds

R. A. Brown 2003

Wind vectors

PBL turbulence spectrum

RollsAerosol statistics

Inversion height

Surface characteristicsR. A. Brown 2002

0

1000

5000

Height in meters

10

Scatterometer Measurements

Scatt correlation

Scatt-PBL Inference

Lidar Msmt

Lidar inference

Lidar Measurements

Surface Stress, u*

Ocean surface

Geostrophic

Flow

U10Surface Layer

Ekman Layer with OLE

Thermal Wind

Nonlinear OLE

Advection,centrifugal termsNon steady-state

U10(u*) effects

Stratification

Variable Surface Roughness

VG(u*) effects

R.A. Brown PORSEC 2000

Gradient Wind

1-3 km

0 – 100 m

High Winds Study -Motivation

• High Marine Surface Winds do not appear in: – Buoy data– Climate records– General Circulation Models– Satellite sensor algorithms (most)

• Higher winds imply higher heat fluxes in climatology; revised ocean mixed layer models.

• High Marine Surface Winds do appear in:– Ocean Meteorology Ship reports– Dedicated Airplane PBL Flights– A PBL model that includes OLE

R.A. Brown, 1997, 2000,2003

Surface ‘Truth’ Limits Ship winds: Sparse and inaccurate (except Met. Ships).Buoy winds: Sparse; tilt; a point average; variable height; miss high winds and low wind directions.GCM winds: Bad physics in PBL Models; Too low high winds, too high low winds. Resolution coarse (getting better).Satellite winds: Lack good calibration data. Coverage coarse (getting better).

Practical Aspects of a Wind Model function

7/00 RAB

Practical Aspects of a Geostrophic Wind Model Function (Pressures)

Surface ‘Truth’ Limits

• Buoy and ship pressures: Accurate in low and high wind regimes; sparse

• GCM: Good verification; compatible scale

11-99, RAB

The Measurement of very high winds

• Some Met. Ship records have sustained winds ~ 45 m/s. About 2-3 events/year

• Some airplane measurements ~ 35 m/s @ 50’; 50 m/s @ 500’; 70 m/s @ 5000’.

• Rare buoys ~ 45 m/s.

• Rare towers ~ 45 m/s.

• Pressure gradients VG ~ 60 m/s U10 ~ 40 m/s

R.A. Brown 2001