radar%meteorology:%overview% and%applicaons%in%africa · 2011-07-28 · outline% •...

Radar Meteorology: Overview and applica5ons in Africa

Paul A. Kucera (NCAR/RAL)

African Weather and Climate Colloquium

26 July 2011

Outline

•  Review of radar basics •  Radar sampling considera5ons

•  Radar applica5ons in West Africa

•  Summary

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Useful Radar Resources

•  Polarimetric Doppler Weather Radar, Bringi and Chandrasekar

•  Radar for Meteorologists, Rinehart

•  Radar Meteorology, Sauvageot

•  Doppler Radar and Weather Observa<ons, Doviak and Zrnić

•  Radar in Meteorology, David Atlas, Ed.

•  Radar Observa<on of the Atmosphere, BaXan

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Uses of Radar

•  Weather (Severe storm detec5on, rainfall es5ma5on, flash flood detec5on, etc.)

•  Research (Storm structure, storm velocity, hydrometeor type, tracking of insects and birds, …)

•  Agriculture (Water resources, crop growth, land usage, etc.)

•  Avia5on (Tracking, weather detec5on, etc) •  Military •  Shipping

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Research

•  Uses of various types of radars –  Reflec5vity-‐only radars for storm morphology (lifecycle of storms)

–  Doppler radars for kinema5c studies (storm mo5on) –  Polariza5on diversity radars for advanced studies (hydrometeor type, precipita5on es5ma5on)

–  Hail detec5on •  conven5onal reflec5vity & structure •  dual-‐wavelength •  dual-‐polariza5on

– Mesocyclone detec5on (Doppler)

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History of Radar

•  First detec5on of precipita5on echo was recorded almost simultaneously in the UK and the United States: –  21 February 1941: Rain showers were tracked with a 10 cm radar to a range of 7 miles off the English coast

–  07 February 1941: Radia5on Laboratories in MassachuseXs recorded echoes over the Boston airport

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History – First Echoes

Modern Radar Display

•  Squall line approaching Bamako, Mali

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Block Diagram of a Radar

receiver transmitter

modulator master clock

antenna

display

duplexer

waveguide

reflector

signal processor/ computer

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Polariza5on

•  The direc5on of the electric field defines the direc5on of polariza5on

•  Possible (or major) direc5ons: – horizontal, ver5cal or even diagonal – circular – ellip5cal

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Radar Frequency, Wavelength, Designa5on

Band Designa5on Frequency Wavelength HF 3-‐30 MHz 100-‐10 m VHF 30-‐300 MHz 10-‐1 m UHF 300-‐1000 MHz 1-‐0.3 m L 1-‐2 GHz 30-‐15 cm (20 cm) S 2-‐4 GHz 15-‐8 cm (10 cm) C 4-‐8 GHz 8-‐4 cm (5 cm) X 8-‐12 GHz 4-‐2.5 cm (3 cm) Ku 12-‐18 GHz 2.5-‐1.7 cm K 18-‐27 GHz 1.7-‐1.2 cm Ka 27-‐40 GHz 1.2-‐0.75 cm mm 40-‐300 GHz 7.5-‐1 mm

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Two Classes of Radar Targets

•  Point targets: – Small compared to the radar sample volume

– Birds, aircral, single insects, buildings, towers, single raindrops, etc.

•  Distributed targets: – Completely or nearly fill the sample volume – Hydrometeors: raindrops, snow, cloud droplets, etc.

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Distributed Targets – Example Cloud Droplets

•  Con5nental clouds have on order of 200 cloud droplets/cm3 – For 1° beamwidth radar at range of 57 km, beam will be 1 km in diameter

–  If radar uses 1 µs pulse length, the radar will illuminate effec5ve volume of 150 m length

•  So, radar sample volume will illuminate more than 2•1016 cloud droplets simultaneously

•  There will be fewer raindrops, but s5ll 109 to 1012 raindrops in typical sample volume

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Radar Equa5on for Distributed Targets

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Radar Equa5on for Distributed Targets

•  The general radar equa5on for distributed targets is given by:

where pr is the received power, pt is power transmiXed, g is the gain of antenna, λ is the wavelength, θ and φ are the horizontal and ver5cal beam widths, h is the pulse length (cτ), σ is cross sec5onal area of the hydrometeors, r is the range from the radar

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What about the Sum of All ScaXers?

•  Generally, we will not know the value of Σσi •  For spheres which are small compared to the radar wavelength, Rayleigh approxima5on applies

•  For spheres that are large compared to the wavelength, targets will be in the op5cal region, πR2

•  Between these is the Mie or resonant region •  Most of the )me sca-ers are assumed in the Rayleigh region, good for S-‐Band, can be poor for C-‐ or X-‐Band for hail or large raindrops, respec)vely

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Rayleigh Assump5on

•  Lord Rayleigh (1870’s) showed that:

where σi is the backscaXering cross-‐sec5onal area of the ith sphere, λ is the radar wavelength, and |K| is the magnitude of the complex number of the scaXering and absorp5on characteris5cs of the medium:

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Value of |K|2

•  |K|2 depends upon the material, temperature, and wavelength

•  The temperature and wavelength dependencies are not very large (see BaXan, 1973 or Doviak and Zrnic’, 1993 for details) and are olen ignored

Material |K|2 water 0.93 ice 0.197

•  Most radars assume the targets are all water and use |K|2 = 0.93 for all reflec5vity calcula5ons

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Rayleigh ScaXering in the Radar Equa5on

•  If we subs5tute the expression for Rayleigh scaXering into our radar equa5on, we get:

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Radar Equa5on

•  Radar reflec5vity factor as:

where the summa5on is carried out over a unit volume

•  Finally, subs5tute this into our radar equa5on:

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Simplifying the Radar Equa5on

•  All the constants can be combined to give the radar constant (which is unique for every radar) and the final form of the radar equa5on for single polariza5on for related to reflec5vity:

Radar Reflec5vity

•  The radar reflec5vity, z, is computed using radar observa5ons (rearranging the radar equa5on):

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Radar Sampling

Radar Sampling

•  Radar provides a wealth of informa5on about storm characteris5cs (intensity, ver5cal structure, rainfall es5mates, etc) on fine spa5al (~km) temporal (~minutes) scales

•  However, radar has many limita5ons due to sampling characteris5cs as shown in the figure to the right

Radar Scan Strategy •  Typically, a radar is capable of scanning in the ver5cal (above

the horizon) –  Useful for determining the loca5on and height of storms –  The different angles above the horizon are typically called eleva5on

angles, 5lts, sweep angles –  Usually, a radar will scan have between 1 and 25 eleva5on angles

ranging from 0° to 60° above the horizon

Radar Scan Strategy •  A radar will usually scan over all azimuth angles (360°) at one

eleva5on angle. This is called a sweep •  A radar will con5nue to scan 360° azimuth for all eleva5on

angles. The combina5on of scanning over all azimuths and eleva5on angles is called a volume scan

•  It is called a volume scan because it samples a volume of space surrounding the radar

•  A volume scan usually takes 5 – 10 min to complete before the cycle is repeated

•  Radar data are sampled in polar coordinates (range, azimuth, and eleva5on)

•  Radar resolu5on is a func5on of range •  Typically, a radar pixel has azimuth

resolu5on of 1° and range resolu5on of 1 km (at 60 km, the radar pixel is ~1 km x 1 km)

•  Radar provides high resolu5on at close ranges, but low resolu5on at far ranges

•  Par5al beam filling is a issue at far ranges, results in weaker intensi5es

Radar Sampling and Range Resolu5on

Radar

Azimuth

Range

=Storm Cell

Sample Volume •  Weather radar wavelengths tend to be in the range of 3 cm (Mobile Research) to 10 cm (WSR-‐88D) –  Antenna size increases in size for fixed beam width (e.g., 1°)

–  Beam widths are olen larger with larger wavelength to reduce cost and increase mobility

–  Tradeoff: Signal aXenuates significantly at shorter wavelengths

Antenna diameters for a 1° beam width as a func5on of wavelength: Wavelength(cm) Diameter (m) Diameter (l) 1 (Ka) 0.73 2.4 1.5 (K) 1.09 3.6 2 (Ku) 1.46 4.8 3 (X) 2.18 7.2 5 (C) 3.93 12.9 10 (S) 7.28 23.9 25 (L) 18.19 59.7

5 cm at 0.5°

10 cm at 1°

Attenuation

Radar Sampling with Range

•  A radar will sample higher in the atmosphere as a func5on of increasing range from the radar –  Curvature of the Earth –  Refrac5on of the atmosphere

(Bending of the beam that is a func5on of T, RH, and P varia5ons

•  Radar provides good sampling of ver5cal structure near the radar but tends to overshoot storms with range

Radar Sampling with Height •  The sampling coverage for eleva5on

angles 0.5 – 7.1°

•  Note: Overshoot storm w/ height and bright band contamina5on (mel5ng hydrometeors)

Bright Band

Beam Blockage Example -‐ Guam

•  Complete or par5al beam blockage can be an issue in complex terrain

•  Example: Guam terrain data overlaid with the 0.5° eleva5on sweep

Beam Blockage Example -‐ Guam •  Par5al beam blockage can be hard to detect

unless data are evaluated over a long period (~5 years)

240681012141618%

05010015020025030035020181614121086420Azimuth (deg)Power Loss (dB)Sweep 0 0.5 degSweep 1 1.5 deg

0.5°

1.0°

POD of Echo

Ground CluXer

•  Ground cluXer and non-‐meteorological echo olen exists in radar data, especially in regions with complex terrain

•  The cluXer could be considered weather echo unless it is quality controlled properly

•  The can lead to false radar retrievals (rainfall, storm loca5on, etc.)

Clutter Clutter

Weather

Weather

Ver5cal Profile of Reflec5vity

•  The ver5cal structure of storms have large varia5ons depending on storm characteris5cs (e.g., stra5form (Fig. a, b) or convec5ve precipita5on (Fig. c, d))

•  “Bright Band” signature is olen observed in stra5form rainfall, which is a reflec5vity enhancement due to mel5ng hydrometeors

•  Causes significant es5ma5on errors

Bright Band

Bright Band

AXenua5on

0 100 200 300 400 500

6

5

4

3

2

1

0

Distance (km)

Two-

Way

Atte

nuat

ion

(dB

) 3 cm (X-band)

5 cm (C-band) 10 cm (S-band)

non-attenuated

attenuated

Atmospheric Attenuation Rain Attenuation

Radar Calibra5on •  Radar calibra5on errors can range 1-‐10 dB •  Note: Comparison of five radar in South

Florida ranged ~5 dB compared to the TRMM Precipita5on Radar

18819019219419619820020220420615105051015Julian DayReflectivity Bias (dB)CRYSTALFACE, KAMX 2km Height, Average Daily Bias +/ One Standard Deviation

18819019219419619820020220420615105051015Julian DayReflectivity Bias (dB)CRYSTALFACE, KBYX 2km Height, Average Daily Bias +/ One Standard Deviation

18819019219419619820020220420615105051015Julian DayReflectivity Bias (dB)CRYSTALFACE, KMLB 2km Height, Average Daily Bias +/ One Standard Deviation

19019520020521015105051015Julian DayReflectivity Bias (dB)CRYSTALFACE, KTBW 2km Height, Average Daily Bias +/ One Standard Deviation

19219419619820020220420620821015105051015Julian DayReflectivity Bias (dB)CRYSTALFACE, NPOL 2km Height, Average Daily Bias +/ One Standard Deviation

Sampling Considera5ons

•  Difference in sample loca5on: Radar is usually scanning above the surface where we want to know what is happening at the surface (e.g., surface rainfall)

•  For strong wind shear at low-‐levels, radar observed precipita5on may propagate considerably before reaching the ground

•  Precipita5on from high based clouds may evaporate (virga) considerably or completely before reaching the ground

•  Par5al or total beam blockage will reduce the amount of energy received to the radar (underes5mate storm intensity)

Summary

•  Radar provides a wealth of informa5on about storm characteris5cs at high spa5al (~km) and temporal (minutes) resolu5ons

•  Radar data are very complex and have a variety of limita5ons: –  Radar calibra5on, radar characteris5cs, range resolu5on, range-‐height dependence, aXenua5on, sample volume, cluXer, beam blockage, bright band contamina5on, etc.

•  These issues need to be considered when using radar data for various studies (e.g., rainfall es5ma5on, kinema5c studies, NWP verifica5on, etc.)

Advanced Uses of Radar

•  Polariza5on Diversity – Makes use of polarized electromagne5c informa5on

–  If a radar has more than one polariza5on, it is called a dual-‐polarized or polarimetric radar

40

What Addi5onal Informa5on can be Gain by using Polarimetric Radar?

•  We can learn more informa5on or beXer informa5on about: – Shape of the hydrometeors – Phase (liquid, frozen, mixed) – BeXer es5mates of rainfall – Hydrometeor size distribu5ons – Type of par5cles: hail, snow, graupel, raindrops, etc.

– Ground cluXer or non-‐meteorological targets (birds, insects, aircral)

Example Radar Applica5ons in West Africa

•  Study of MCS characteris5cs during NAMMA •  Evalua5on of convec5on observed in Mali

Radar Network in West Africa

•  Current Radars –  Burkina Faso

•  Ouagadougou •  Bobo Dioulasso

– Mali •  Bamako

•  Mop5

•  Manantali

–  Senegal •  Linquere

42

NAMMA

•  NAMMA was a NASA supported component of the larger African Monsoon Mul5disciplinary Analyses (AMMA) project

•  The intensive observing period (IOP) was conducted between 15 August 2006 and 30 September 2006

•  NAMMA focused on sampling mesoscale convec5ve systems (MCSs) and tropical cyclone forma5on in Western Senegal and the Cape Verde Islands –  Radar, aircral, flux towers, soundings, precipita5on networks

Specific Research Ques5ons

•  What is the spa5al/temporal variability of MCS as they transi5on off the West African Coast?

•  What are the characteris5cs of MCSs associated with tropical cyclone versus non-‐tropical cyclone systems?

•  Is there a change in the storm aXributes of MCSs systems as they transi5on off of West Africa?

NPOL Observations •  NPOL is a S-Band (10-cm)

polarimetric weather radar operated by NASA

•  NPOL was operational between 21 August – 30 September 30 and was located 40 km SE of Dakar

•  NPOL observational goals: –  Characterize the intensity, vertical

structure, and areal extent of mesoscale convective systems (MCSs)

–  Track the lifecycle of MCSs as the transition from land to ocean

Example NPOL Observa5ons

•  Event 6: 30-‐31 Aug 2006:


•  Event 11: 11 Sep 2006:


•  Event 12: 13-‐14 Sep 2006:

Characterizing the Differences between MCSs associated with

Tropical Cyclone and Non-‐Tropical Cyclone systems

50

Mo5va5on •  There are s5ll many unknowns leading to tropical cyclone (TC) genesis

•  This study examined the characteris5cs Mesoscale Convec5ve Systems (MCSs) – Are there dis5nct characteris5cs for TC and non-‐TC forming MCSs as they transi5on off of West Africa?

51

Methodology •  NPOL MCS cases → 19 cases observed only 8 analyzed

→ Table of case, type (I: tropical cyclone), date, and 5me

Case Number System Type Date Time (UTC) 5 I 8/29/06 11 – 14 6 I 8/31/06 06 – 13 7 I 9/1 – 9/2/06 22 – 04

10 II 9/7 – 9/8/06 16 – 10 11 I 9/11/06 08 – 15 12 II 9/13 – 9/14/06 18 – 08 15 II 9/22/06 12 – 18 18 II 9/28/06 00 – 06

Example Cases

TC Non-TC

53

MCSs Results

•  NPOL analysis of MCS –  Tropical Cyclone Cases more occurrences of intense convec5on at mid-‐levels (4-‐5 km)

•  60% of the convec5ve radar grids had reflec5vity values: 40 dBZ < Z < 50 dBZ at heights > 4 km

–  Non-‐Tropical Cyclone Cases largest percentage of the most intense convec5on occurred at lower heights (3-‐4 km)

•  75% of these are > 45 dBZ –  Tropical Cyclone MCSs have larger maximum reflec5vity at higher heights (e.g., convec5on tends to be more intense)

•  These results were put into the context of the associated large scale environment (not shown)

Storm Proper5es during Land-‐Ocean Transi5on

•  Storms transi5on structurally as they moved over the cooler ocean

•  Storms tended to weaken as they moved over the ocean –  Reduced intensity –  Lighter rainfall –  Shallower convec5ve region

54

Radar Derived Hydrometeor Iden5fica5on from NPOL

56

Use of Radar to Characterize Storm Proper5es in Mali

(Bamako Radar)

57

Project Objec5ves

•  The project was conducted to determine if clouds were amenable to cloud seeding for rainfall enhancement

•  The project provided an opportunity to document the convec5ve variability of storms observed in West Africa for three seasons

Radar Storm AXributes (2006 – 2008 Rainy Seasons)

58

Example of Convec5ve Systems Observed in Mali

•  Large variability in convec5on was observed, ranging from large organized squall lines to small isolated cells

Diurnal Cycle of Cell Development

•  Three year radar study examining precipita5on systems in West Africa

•  There is a very dis5nct alernoon maximum (at 1500 LT) for all three years

•  Large year to year variability

•  Secondary maximum in the early morning hours 60

Radar Climatology Summary

•  Storms occurred almost every day for both seasons

•  Large day, seasonal, and yearly variability was observed

•  The maximum number of cells in 2008 were about half on average compared to 2006 and 2007

61

2006

2007

2008

Start of Study

Convec5ve Storm Cell AXribute Distribu5ons

•  There is a large year-‐to-‐year variability in convec5ve cell aXributes

•  The 2008 season observed the most intense storms (max dBZ)

•  2006 storms had the largest variability and max cell heights

•  2008 storms tended to propagate faster in more uniform direc5on

•  2008 storms had a larger spread in storm dura5on 62

Radar Analysis Summary •  Convec5on was observed almost every day during the rainy season, but with large day to day variability

•  A large year-‐to-‐year variability in convec5ve proper5es for the 2006-‐2008 rainy seasons was observed

•  There is a large diurnal cycle observed in the number of storms –  The peak occurs in mid-‐alernoon

–  A secondary maximum occurs during the night

•  The storm aXributes need to be put into the context of the large scale environment 63

Summary •  Radar provides a wealth of informa5on about storm

characteris5cs (storm structure, rainfall es5ma5on, par5cle type, lifecycle, etc.)

•  However, radar has limita5ons (radar sampling, aXenua5on, etc.) that must be accounted for in any radar study

•  Radar is a useful tool for developing applica5ons such as flash flood predic5on, water resource management (agriculture, water supplies), energy, health, avia5on, etc.

•  A network of radars (e.g., in West Africa) would provide a wealth of informa5on to further our understanding of the rela5onship of convec5on and larger scale forcing (e.g., AEWs)

Thank You

Dakar at Sunset

radar%meteorology:%overview% and%applicaons%in%africa · 2011-07-28 · outline% •...

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