1. Radar Detection of Shallow Weather and Orographic Phenomena Paul Joe MSC Basic Radar 2010 20100404

2. 1. This module briefly explores “radar meteorology” issues of low level weather detection in a generic way. 2. Radar meteorology in complex terrain Module Objective

3. Outline Some “back of envelope calculations” of key elements –Typical reflectivities of rain, drizzle, fog, snow (detection issue) –Beam height (detection issue) –Beam width (quantitative and detection issues) –Sensitivity (detection issue) Meteorology –Drizzle –Lake Effect Snow –Orographic Precipitation

4. Low Level Phenomena Detectable by Radar Meteorological Targets –Precipitation (Rain, Snow, Hail, Drizzle) –Lake Breezes, Convergence Lines, Gust fronts, cold pools –Index of Refraction/Humidity –Turbulence (Bragg scattering) Ground Clutter (not discussed here) –Building, Mountains, Forests Hard Targets (not discussed here) –Wind turbines, Cars, ships, airplanes, space debris Biological Targets (not discussed here) –Insects, birds, bats Electro-magnetic Targets (not discussed here) –Other radars, RLANs, Sun, second trip echoes Other –Forest fires –Sea Clutter Romanian Gust Front

5. General Comments – Low Scanning Wide variety of phenomena and intensity of targets –Turbulence (too weak) to Mountains (very intense) –From very weak to very strong (-30 dBZ to 95 dBZ) Different Doppler signatures –Some have 0 velocity –Some have aliased velocity (> Nyquist) Advanced uses of weather radar –VDRAS – variational doppler radar assimilation system –Refractivity retrieval – use of ground clutter echoes Quantitative Precipitation Estimation –Need low level scanning –Accurate at ranges < 80-100km Commonality –Limited range! –Low echo strength (generally), Low height of weather, radar sensitivity is an issue

6. Detection vs Measurement Glossary Detection – can see Measurement – can quantitatively measure

7. Drizzle Some Radar Examples

8. Drizzle reported in surface observations but no radar echoes. Drizzle in surface observations BUT NO/Little RADAR DATA Germany Example 1 Lang, DWD

9. Drizzle (mm/h) and very few echoes Germany Example 2 Lang, DWD

10. Drizzle in Finland! Saltikoff, FMI 1.Why was drizzle observed in Finland but not Germany? 2.Why is the drizzle observed only around the radar? 3.Why is the reflectivity pattern stronger near the radar and decreases away from the radar? 4.Why is there a range limit to see drizzle?

11. Minimum Detectable Signal Concept

12. Minimum Detectable Signal The detection threshold (as a function of range). Range [km] Reflectivity [dBZ] Probability Distribution of Reflectivity with Range (not important for this discussion). Function of Wx. Minimum Detectable Signal (constant power) P = C Z r 2 The Radar Equation MDS can expressed as a noise temperature or a power measurement but for meteorologist it more useful to express as reflectivity at a particular range. Typically, -1 dBZ at 50 km.

13. Some Radar Considerations P = C Z r 2 P = power, C = radar constant, r = range Z = N D 6 [Z] = mm 6 /m -3 dBZ = 10 log Z Reflectivity Factor - Linear

14. Radar Equation and MDS P min = C Z min (r) r 2 The Radar measures “P” – power received The Radar Equation converts P to Z for a given range (r) –Radar Equation accounts for expanding beam with range (1 /r 2 ) Sensitivity (or MDS) is a certain power level –Just above the noise (hsssssss) level –In terms of P (power), it is a constant –In terms of Z (reflectivity), it is a function of range (1 /r 2 ) A limitation for long range detection of weak echoes is the radar sensitivity! –If the reflectivity of the target is below MDS then the radar does not detect it! artificial MDS –Beware of artificial MDS! The display of the radar data may be thresholded! Some data may not be displayed! Range Power Range Reflectivy

15. Homework Question Echo Power Size [microns or mm] Average Intensity [mm/h, cm/h] Or Liquid Water Content [g/m 3 ] Number of particles in a cubic meter Calculate Z [mm 6 /m 3 ] Leave this empty if you wish. Calculate dBZ Leave this empty if you wish. Fog 0.01mm.1 g/m^31x10^80.0001-40 Drizzle 0.1 mm.1 g/m^31x10^50.1-10 Rain 1 mm1 mm/h100 20

16. A Drizzle Calculation Radius of a drizzle drop ~= 100 microns Rainrate of drizzle ~= 1 mm/h Fall speed ~= 1 cm/s Therefore, Number of drops ~= 28,000 m^-3 Reflectivity ~= -5 dBZ Can your radar see drizzle? How far can you see drizzle from the radar?

17. So, how far can you see drizzle (-5dBZ)? Or anything else? P = C Z r 2 Minimum Detectable Signal (power) ~ 25km -5dBZ

18. Can you see drizzle – part 2? The Artificial MDS Situation 7dBZ Data in this shaded area is thresholded (not displayed)! ~ 25km Typical Drizzle reflectivity

19. Reflectivity vs Range for Constant Power (1/r 2 ) Where does your radar fit on this diagram? Typical Radars

20. Survey Question about your Radars? How well do you know your radars? What is the minimum value that you have seen on your radar and at what range? dBZRainrate20 km50 km100 km150 km 20 dBZ0.5 mm/h 15 dBZ0.3 10 dBZ0.16 5 dBZ0.08 0 dBZ0.03 Put a check in as many boxes as you want! Are you limited by an artificial MDS?

21. Beam Propagation Re-visited

22. Beamheight Considerations

23. Overshoot Key Concept! 0.5 o Beam totally overshoots the weather beyond this range! No detection at all! Shallow Weather The weather is detected but the beam is not filled beyond this range, so reflectivities are quantitatively underestimated from this range and beyond Note: the lower the beam the longer the range for detection ability! Non-uniform beamfilling

24. Drizzle Drizzle is due to warm rain process. Slow growth which results in small drops (0.1 mm, 1 mm/h) Note: Colour scales are different! dBZ ZDR Saltikoff, FMI Drizzle is round! 1 km

25. Survey: How well do you know your radars? What is the lowest elevation angle of your radars? Minimum Elevation AnglePlease put a check mark in this column -0.5 -0.3 0.0 0.3 0.5

26. How low is your weather? Minimum Height [km]How Far in Range [km]? Fog Drizzle Convective Rain Stratiform Rain Freezing Level Snow (system) Snow (local, lake effect, etc)

27. Summary: Drizzle in Finland! Saltikoff, FMI 1.Why was drizzle observed in Finland but not Germany? Thresholded! 2.Why is the drizzle observed only around the radar? Sensitivity 3.Why is the reflectivity pattern stronger near the radar and decreases away from the radar? Beamfilling 4.Why is there a range limit to see drizzle? ~80-100km, function of sensitivity, beamfilling, depth of the drizzle!

28. 5-6°C Drizzle,, Unusual widespread drizzle from cloud echoes aloft. At surface only few echoes above 1dBZ. Note: change in threshold for DWD, see more drizzle! Hamburg Germany Example 3 Lang, DWD

29. Major Factors for Detection Radar Sensitivity –Target Reflectivity/Radar MDS combination Overshoot –Lowest Angle of Radar/Height of weather / Earth Curvature combination Beam filling (quantitative) –Weather is too shallow or too low –Beam is very broad Thresholding –Artificial MDS = Minimum Displayed Signal*

30. FOG Can the radar see fog?

31. Fog Special Cloud/Fog Radar (35 GHz or Ka Band) Fog has drop sizes from 10 to 30 microns, so very low reflectivities. An operational radar has a sensitivity as -8 dBZ at 50 km. What is the controlling factor of detecting fog for this radar? - Sensitivity? or elevation angle? Or Artificial MDS (color table?) Drop Size Distributions dBZ 10 km Non-operational

32. Snow

33. Beamheight Again Quantitative measurements (Advanced Material)

34. Partial Beam Filling Range bins that are partially beamfilled, decreasing reflectivity with range! 0.5 degree

35. Question: What do you think the reflectivity will look as a function of range? 0.5 o Shallow Weather Non-uniform beamfilling dBZ Range

36. Vertical Profile of Snow Function of Range 1. Snow originates aloft but grows as it falls. 2. The same vertical profile as observed by radar at increasing range due to beam filling, beam broadening (smoothing) and Earth curvature (can’t see lowest levels)!

37. Quantitative Impact of Beamfilling Michelson, SMHI Note the fall off of values with range. This is NOT attenuation to which this is commonly attributed. It is a beam filling effect!

38. Impact of Beamwidth / Beamfilling 30 day Accumulation Example of the impact of beamwidth or beamfilling on quantitative precipitation estimation. One radar is 0.65 o and the rest are 1.1 o beamwidth radars. Smaller beamwidth means less beamfilling problems with range and farther quantitative reflectivity information. 0.65 o (no blue) Patrick, EC 1.0 o (blue)

39. Applying the Correction aka Vertical Profile Correction aka Range Correction Koistinen, FMI

40. Orographic

41. Mountain Top Radars Germann, MCH

42. Freezing Level and Mountain Sited Radars Time-Height Temperature Freezing Level from Radiosondes March 2008 Payerne July 2008 Payerne Most of the time, the radar sees snow!

43. Valley Radar Whistler Mtn Squamish Pemberton Winter Olympic Park Blackcomb H99

44. Distance Range to Terrain VVO Azimuth North East South West North Whistler Squamish Callaghan Elevation Angle Snow Callaghan Whistler Squamish

45. Whistler Doppler Weather Radar

46. Another View VVO Dave Murray Downhill Start

47. What is this? Would you see it on a mountain top radar?

48. Blocked flow (downslope winds) means Intense precipitation is on the slope and not on mountain peak Doppler velocity: Blue means air is moving to the left or downslope Precipitation: the intense precipitation is on the slope.

49. How many low level jets do you see? Do you see convergence?

50. Remember RABT = Red Away Blue Toward (except in Switzerland)

51. Why is there a hole in the data? Would you see this on a mountain top radar?

52. Summary Shallow Weather –Focus on drizzle as an example to explain detectability and measurability –Observability is a function of the radar too (MDS, beamheight, beamwidth) –A few simple but key calculations to explain (not calibration, not attenuation) –A little insight into “radar meteorology” A few case examples –Drizzle, snow, lake effect snow, orographic

53. Examples of Shallow Weather

54. Lake Effect Snow shallow but lots of weather 1/8SM +SN +BLSN Patrick, EC

55. Morphology of Snowbands Thermal Convergence - Single Band Development

56. Morphology of Snowbands Frictional Convergence - Single Band Development

57. Ocean/Lake Effect Snow Conceptual Model Niziol, NWS/COMET

58. Fetch Length of time cold air is over warm water. Note that small variations in wind direction can result in significant changes in fetch. On Lake Erie for instance, a 230 degree wind has a 130-km fetch, while a 250 degree wind results in a 360-km fetch!

59. Morphology of Snow Bands Horizontal Roll Multiple Banding Single Band Land Breeze Banding MesoLow Multi-Lake Banding The direction of the wind will produce significantly different results from lake to lake depending on the shape and orientation of a body of water.

60. Ocean/Lake Effect Snow Multiple Bands Single Band Frictional Convergence Lake Ontario Lake Erie Lake Huron Georgian Bay Lake Michigan Lake Superior

62. Morphology of Snow Bands Multiple Band - Horizontal Roll Convection Counter-rotating vortices in the boundary layer. Major axes aligned with the mean boundary layer wind shear vector. Wavelength (updraft to updraft) is about three times the height of the Boundary Layer.

63. Lake effect snow wind from northwest

64. Velocity Structure dBZ Vr Low speeds in the middle of the band indicating low horizontal speeds or convergence

65. Separated Bands dBZ Vr

66. Thermal Convergence

67. The MesoLow Light Winds

68. Lake Breeze and Convective Weather

69. Morning Mid Afternoon “Pure” LB example Enhance convergence Lake Breeze Boundaries Lake Huron Lake Ontario Lake St Clair Lake Erie

70. ++++ = Pure lake breeze Moderate SW Flow Lake Breezes

71. Spring (15 Mar - 15 Jun) Tornado Touchdown Points … overlaid with boundaries from 31 July 1994... … tornadoes are suppressed in regions where Southwest winds are onshore... … and enhanced in regions where lake breeze boundaries often form. Forecasters use knowledge of lake breeze positions in their severe weather forecast for weak tornadoes

73. Extremely Shallow Case 3.5 1.5 -0.1

74. Orographic Precipitation Rain shadow

75. Two Conceptual Models of Orographic Precipitation Medina and Houze, 2003

76. Stable Case: Blocked Flow from South North South ALPS Precipitation on plains, Italy Flow is blocked, flow is towards the south MAP IOP-8 Medina and Houze, 2003

77. Radial Velocity - Blocked Flow Medina and Houze, 2003

78. Unstable Case: Up and Over Precipitation is on the first range No blocked flow. Flow is from the south Medina and Houze, 2003

79. Radial Velocity – Up and Over Medina and Houze, 2003

80. FOEHN, no rain H3km The Alps Lee Side Suppression

81. Erzgebirge Ore mountains, grey Doppler No rain 1h accumulation No rain Accumulations

82. Wind Drives Precipitation Germann, MCH