Boundary layer observations with radar wind profilers and other ground-based remote sensors

Wayne M. Angevine

CIRES, University of Colorado,

and

NOAA ESRL

Outline Wind profiler

• Principles of operation• Quantities measured• Time & height resolution• Uncertainties

Other ground-based remote sensors• Lidars• Sodars

Applications• Air quality• Weather forecasting / modeling

Science examples• Assimilation into mesoscale models• Morning transition• Entrainment• Afternoon transition• Coastal flows• Diurnal / slope flows• Thermal structure (statistics by lidar)

What’s a profiler? Properly “radar wind profiler” Sensitive Doppler radar Vertical beam and 2-4 beams at

15-20° off vertical Low power, long dwell time, and

low cost compared to weather radars

Return signal is Bragg scattered from refractivity variations in clear air• Any hydrometeors or insects

may contribute or even dominate

Range depends on frequency• BL profilers are at UHF

(typically ~1 GHz) Radio acoustic sounding

(RASS) attachment for temperature profiling

Doppler Beam Swinging vs Spaced AntennaDoppler Beam Swinging vs Spaced Antenna

Doppler shift along 3 or 5 beam directions to measure winds

10 – 30 minute wind measurement

Traces backscattered signal motions over 3 or 4 receivers

1 – 10 minute wind measurement Source: Bill

Brown NCAR

Performance of a typical BL profiler

Wind measurement time 10 – 60 min Height resolution 60 – 200 m Minimum range 120 m Max range: at least to BL top Wind component precision ~1 m/s

• May be better but no way to prove it

Careful QA required:• Low signal• Birds• Hydrometeors

What data does a profiler produce?

Winds• from radial Doppler velocities

Reflectivity• in clear air: product of humidity gradient and turbulence

intensity• in precipitation: dominated by hydrometeor scattering• insects: 10 microbugs = typical clear air reflectivity

Spectral width• a measure of velocity distribution in the sample volume• in clear air: turbulence intensity (qualitative)• in precipitation: information about size distribution and/or

turbulence

Example of a Example of a

Precipitating Cloud Precipitating Cloud System Passing System Passing over a Profiler over a Profiler during TEFLUN Bduring TEFLUN B

Horizontal Axis:Time – 6 hours

Vertical Axis:Altitude – 11 km

Data are collected:Every minute30 second dwell100 meter vertical resolution

(actual-105m)

Courtesy of Ken Gage

Other ground-based remote sensors

Lidar and Sodar use principles similar to radar Many types of lidars exist Lidars provide:

• very fine resolution• fast sampling• measurements of water vapor, ozone, particulate characteristics

(some types) Lidar disadvantages:

• cost (capital and operational)• limited by cloud

Sodar advantages:• low cost• low minimum range

Sodar disadvantages:• noise pollution• impacted by ambient noise (including wind and rain)• low maximum range

Applications

Weather analysis & forecasting Air quality (non-weather analysis and forecasting) Process studies

Current Profiler Displays on AWIPSCurrent Profiler Displays on AWIPS

Isobaric Map of Hourly Data

Time-Height Section of Hourly Data

Perspective Wind Profile Display

Source: Steve Koch, FSL

Assimilating profiler data into a mesoscale model for process studies

How often does a sea breeze occur in the simulation AND measurement?

Definition: Northerly component >1 m/s between 0600 and 1200 UTC and southerly >1 m/s after 1200 UTC

Assimilating 1 profiler with FDDA WRF at 5 km grid for Houston FDDA or FDDA+1hSST run closer

to measurement at all 7 sites (at least a little)

Results not sensitive to threshold Red is FDDA runBlue has FDDA, 1-h SST, and reduced soil moistureGreen has reduced soil moisture only

Coastal winds

Pease is on the mainland

Appledore is on an island ~10 km offshore

Coastline oriented northwest-southeast locally

Low-level jet stronger offshore early

Sea breeze in afternoon

Sunrise Noon Sunset Sunrise

Residual LayerResidual Layer

Stable (nocturnal) Layer

2000

1500

1000

500

0

Inversion

Hei

gh

t (m

ete

rs)

Adapted from Introduction to Boundary Layer Meteorology -R.B. Stull, 1988

Convective Mixed Layer

Stable (nocturnal) Layer

Atmospheric Boundary LayerDiurnal Variation

How does a profiler see the

ABL?

Reflectivity is roughly the product of humidity gradient and turbulence intensity

Coastal BL with sea breeze

Pease day 215 2002

Marine BL

Appledore day 181 2002

Overcast and rain

Pease day 196 2002

Spatial variation of BL height

Urban dome or urban heat island measured by profilers in urban core and in surrounding rural areas

Implications for pollutant concentration and transport

Lidar time-heightcross-sections of w with the same time scale comparing aday with light wind(top: U = 2.2 m/s)with moderate wind (bottom: U = 7.2 m/s).Courtesy of Don Lenschow

Lidar time-height cross-sections of w with the same aspect ratio (AR ≈ 7.8) comparing a day with light winds (top: U = 2.2 m/s) with moderate wind (bottom: U = 7.2 m/s). Courtesy of Don Lenschow

Time-height cross-section of w for 16 August 2996 with U = 2.2 m/s

and AR ≈ 1.0 Courtesy of Don Lenschow

Morning and evening transitions and BL top entrainment

Truly stationary BLs are unusual Transitions are critical for air quality and dispersion

applications Temporal transitions may cast light on spatial (e.g.

coastal) transitions Entrainment is poorly characterized Profiler (and lidar) data provide a BL-top perspective

to supplement more traditional in-situ surface or tower viewpoints

Morning transition

Establishes initial conditions for ABL growth Prognostics require initialization Models must be calibrated and validated Profiler observations provide estimate of end of

transition (onset of daytime convective ABL) Data from two sites

• Tower observations from Cabauw provide detailed insights• Long profiler and surface flux dataset from Flatland (Illinois)

Timing of transition events(composite median)

Entrainment

Definition: Incorporation of air from the free troposphere into the turbulent (convective) ABL

A change of condition (laminar to turbulent) but not necessarily of position

One of the two largest terms in the ABL heat and moisture budgets

Poorly understood and crudely parameterized Difficult to measure

Entrainment from heat budget

Entrainment flux =

– heat storage + surface flux + radiative heating – advection

Entrainment ratio = – entrainment flux / surface flux

Measurements during Flatland (Illinois) experiments ABL depth from profiler reflectivity (3 profilers) Temperature change from RASS (BL average) Surface flux from 3 Flux-PAM stations (NCAR) Radiative heating from radiation model + aerosol measurements Advection from Eta model

Heat budget results (mean of all good hours)

zi

Radiative heating

Entrainment flux

Advection?

Surface flux

-0.050.01 K m s-1

0.100.004 K m s-1

0.030.002 K m s-1

0.0010.005 K m s-1

Fra

ctio

n of

tota

l hea

ting

rate

Partitioning

Variability of partitioning

Afternoon transition

Transition between fully-developed daytime convective ABL and nocturnal ABL

How does turbulence vary with time and height in the afternoon? • Sudden collapse or a gradual decline?• When does transition start?

Timing and shape of transition are critical to initiation of inertial oscillation / low-level jet, nighttime transport, distribution of pollutants, etc.

Unforced transition – all budget terms are important, few simplifications are possible

Measurements from Flatland profiler• Simple homogeneous terrain

Profiler reflectivity and spectral width patterns for a “typical” day

Doppler spectral width

When does transition start?

Three different definitions based near daytime max. ABL height

All definitions show transition starting well before sunset

sunset

Final thoughts

Ground-based remote sensors provide continous data in a column or volume• a valuable complement to sparse aircraft measurements

Can be (and usually should be) deployed in groups Wind profilers are good for much more than just wind Output must be used carefully – beware of “black

boxes”

References (1)

Angevine, W.M., A.B. White, and S.K. Avery, 1994: Boundary layer depth and entrainment zone characterization with a boundary layer profiler. Boundary Layer Meteor., 68, 375-385.

Angevine, W.M., and J.I. MacPherson, 1995: Comparison of wind profiler and aircraft wind measurements at Chebogue Point, Nova Scotia. J. Atmos. Oceanic Technol., 12, 421-426.

Carter, D.A., K.S. Gage, W.L. Ecklund, W.M. Angevine, P.E. Johnston, A.C. Riddle, J. Wilson, and C.R. Williams, 1995: Developments in UHF lower tropospheric wind profiling at NOAA's Aeronomy Laboratory. Radio Sci., 30, 977-1001.

Riddle, A.C., W.M. Angevine, W.L. Ecklund, E.R. Miller, D.B. Parsons, D.A. Carter, and K.S. Gage, 1996: In situ and remotely sensed horizontal winds and temperature intercomparisons obtained using Integrated Sounding Systems during TOGA COARE. Contributions to Atmospheric Physics, 69, 49-62.

Angevine, W.M., 1997: Errors in mean vertical velocities measured by boundary layer wind profilers. J. Atmos. Oceanic. Technol., 14, 565-569.

Angevine, W.M., P.S. Bakwin, and K.J. Davis, 1998: Wind profiler and RASS measurements compared with measurements from a 450 m tall tower. J. Atmos. Oceanic. Technol., 15, 818-825.

Grimsdell, A.W., and W.M. Angevine, 1998: Convective boundary layer height measured with wind profilers and compared to cloud base. J. Atmos. Oceanic Technol., 15, 1332-1339.

Angevine, W.M., 1999: Entrainment results including advection and case studies from the Flatland boundary layer experiments. J. Geophys. Res., 104, 30947-30963.

References (2)

Cohn, S.A., and W.M. Angevine, 2000: Boundary layer height and entrainment zone thickness measured by lidars and wind profiling radars. J. Appl. Meteorol., 39, 1233-1247.

Angevine, W.M., and K. Mitchell, 2001: Evaluation of the NCEP mesoscale Eta model convective boundary layer for air quality applications. Mon. Wea. Rev., 129, 2761-2775.

Angevine, W.M., H. Klein Baltink, and F.C. Bosveld, 2001: Observations of the morning transition of the convective boundary layer. Boundary-Layer Meteorol., 101, 209-227.

Grimsdell, A.W., and W.M. Angevine, 2002: Observations of the afternoon transition of the convective boundary layer. J. Appl. Meteorol., 41, 3-11.

Angevine, W.M., C.J. Senff, and E.R. Westwater, 2002: Boundary Layers/Observational techniques -- Remote. Encyclopedia of Atmospheric Sciences, J.R. Holton, J. Pyle, and J.A. Curry, Eds., Academic Press, 271-279.

Angevine, W.M., A.B. White, C.J. Senff, M. Trainer, and R.M. Banta, 2003: Urban-rural contrasts in mixing height and cloudiness over Nashville in 1999. J. Geophys. Res., 108(D3), doi:10.1029/2001JD001061.

Nielsen-Gammon, J.W., R.T. McNider, W.M. Angevine, A.B. White, and K. Knupp, 2007: Mesoscale model performance with assimilation of wind profiler data: Sensitivity to assimilation parameters and network configuration. J. Geophys. Res., 112, D09121, doi:10.1029/2006JD007633.

Angevine, W.M., 2008: Transitional, entraining, cloudy, and coastal boundary layers. Acta Geophysica, 56, 2-20.

Acknowledgements

Ken Gage, Don Lenschow for slides