Radar Meteorology: Overview and applica5ons in Africa. Paul A. Kucera (NCAR/RAL) African Weather and Climate Colloquium 26 July 2011

Transcription

1 Radar Meteorology: Overview and applica5ons in Africa Paul A. Kucera (NCAR/RAL) African Weather and Climate Colloquium 26 July 2011

2 Outline Review of radar basics Radar sampling considera5ons Radar applica5ons in West Africa Summary

3 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 7/27/11 3

4 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 7/27/11 4

5 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) 7/27/11 5

6 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 7/27/11 6

7 History First Echoes 7/27/11 7

8 Modern Radar Display Squall line approaching Bamako, Mali

9 Block Diagram of a Radar reflector antenna waveguide duplexer transmitter receiver modulator master clock signal processor/ computer display 7/27/11 9

10 Polariza5on The direc5on of the electric field defines the direc5on of polariza5on Possible (or major) direc5ons: horizontal, ver5cal or even diagonal circular ellip5cal 7/27/11 10

11 Radar Frequency, Wavelength, Designa5on Band Designa5on Frequency Wavelength HF 3-30 MHz m VHF MHz 10-1 m UHF MHz m L 1-2 GHz 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 cm (3 cm) K u GHz cm K GHz cm K a GHz cm mm GHz mm 7/27/11 11

12 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. 7/27/11 12

13 Distributed Targets Example Cloud Droplets Con5nental clouds have on order of 200 cloud droplets/cm 3 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 cloud droplets simultaneously There will be fewer raindrops, but s5ll 10 9 to raindrops in typical sample volume 7/27/11 13

14 Radar Equa5on for Distributed Targets 7/27/11 14

15 Radar Equa5on for Distributed Targets The general radar equa5on for distributed targets is given by: where p r is the received power, p t 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 7/27/11 15

16 What about the Sum of All ScaXers? Generally, we will not know the value of Σσ i 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, πr 2 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 7/27/11 16

17 Rayleigh Assump5on Lord Rayleigh (1870 s) showed that: where σ i is the backscaxering cross- sec5onal area of the i th sphere, λ is the radar wavelength, and K is the magnitude of the complex number of the scaxering and absorp5on characteris5cs of the medium: 7/27/11 17

18 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 Most radars assume the targets are all water and use K 2 = 0.93 for all reflec5vity calcula5ons 7/27/11 18

19 Rayleigh ScaXering in the Radar Equa5on If we subs5tute the expression for Rayleigh scaxering into our radar equa5on, we get: 7/27/11 19

20 Radar Equa5on Radar reflec5vity factor as: where the summa5on is carried out over a unit volume Finally, subs5tute this into our radar equa5on: 7/27/11 20

21 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: 7/27/11 21

22 Radar Reflec5vity The radar reflec5vity, z, is computed using radar observa5ons (rearranging the radar equa5on): 7/27/11 22

23 Radar Sampling

24 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

25 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

26 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

27 Radar Sampling and Range Resolu5on 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 =Storm Cell Azimuth Radar Range

28 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) (K) (Ku) (X) (C) (S) (L) cm at cm at 1 Attenuation

29 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

30 Radar Sampling with Height The sampling coverage for eleva5on angles Note: Overshoot storm w/ height and bright band contamina5on (mel5ng hydrometeors) Bright Band

31 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

32 ged pewsged pews)bd( sol rewop)ged( htumiza Beam Blockage Example - Guam Par5al beam blockage can be hard to detect unless data are evaluated over a long period (~5 years) 0.5 POD of Echo 1.0

33 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

34 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

35 AXenua5on Two-Way Attenuation (db) Atmospheric Attenuation 3 cm (X-band) 5 cm (C-band) 10 cm (S-band) Rain Attenuation non-attenuated attenuated Distance (km)

36 noitaived dradnats eno /+ saib yliad egareva,thgieh mk2 XYBK,ECAF LATSYRC)Bd( saib ytivitcelferyad nailuj noitaived dradnats eno /+ saib yliad egareva,thgieh mk2 BLMK,ECAF LATSYRC)Bd( saib ytivitcelferyad nailuj noitaived dradnats eno /+ saib yliad egareva,thgieh mk2 WBTK,ECAF LATSYRC)Bd( saib ytivitcelferyad nailuj noitaived dradnats eno /+ saib yliad egareva,thgieh mk2 LOPN,ECAF LATSYRC)Bd( saib ytivitcelferyad nailuj 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

37 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)

38 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.)

39 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) 40

41 Example Radar Applica5ons in West Africa Study of MCS characteris5cs during NAMMA Evalua5on of convec5on observed in Mali

42 Radar Network in West Africa Current Radars Burkina Faso Ouagadougou Bobo Dioulasso Mali Bamako Mop5 Manantali Senegal Linquere 42

43 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

44 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?

45 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

46 Example NPOL Observa5ons Event 6: Aug 2006:

47 Example NPOL Observa5ons Event 11: 11 Sep 2006:

48 Example NPOL Observa5ons Event 12: Sep 2006:

49 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? 50

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/ I 8/31/ I 9/1 9/2/ II 9/7 9/8/ I 9/11/ II 9/13 9/14/ II 9/22/ II 9/28/

52 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) 53

54 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

55 Radar Derived Hydrometeor Iden5fica5on from NPOL

56 Use of Radar to Characterize Storm Proper5es in Mali (Bamako Radar) 56

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 57

58 Radar Storm AXributes ( Rainy Seasons) 58

59 Example of Convec5ve Systems Observed in Mali Large variability in convec5on was observed, ranging from large organized squall lines to small isolated cells

60 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

61 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 Start of Study

62 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

63 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 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

64 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)

65 Thank You Dakar at Sunset