1. Studying Meteorological Applications using Research and Technology - Advanced Seminar Session

2. Studying Meteorological Applications using Research and Technology - Advanced Seminar Session SMART-ASS

3. Weather - Advanced Training Technology, Hazards, Education and Forecasting

4. WAT THE F

5. Reviewing Algorithms, Definitions, And Resources

6. “RADAR” for short Kevin Kloesel kkloesel@gcn.ou.edu

7. Radar Equation

8. BEAM ME UP!!

13. You have access to more than one radar for a reason!

14. Radar “sees” precipitation Spotters observe cloud structures

15. Outline for this afternoon: Precipitation Structure* (The Radar stuff: Kevin & Jim) Cloud Structure (The Storm Spotter Stuff: NWS) * And some non-precipitating too!

16. Cloud Structure? or Precip Structure? VIPIR Double low level locks Shear markers MOAR Edge Advantage WARN Double/Super/Mega/ Doppler Doppler (insert large numbers and letters here) Graphics courtesy of lots of friends with tv sets!

17. Two sessions for a reason: Why radar? Why spotter? Attempt to identify precursors to hazards, and then the hazards themselves. Attempt to identify precursors to hazards, or the hazards themselves.

18. Two sessions for a reason: Radar? Spotter? What can radar provide that spotters have trouble with? Seeing through areas of precipitation. What can spotters provide that radar has trouble with? Cloud Structure, particularly in precip- free areas

27. Base versus Derived Base is raw, Derived has human intervention

29. Photo by Putnam Reiter 1: 3:30 pm, 4 W Thomas 2: 3:30 pm, 4 NNE Custer City 3: No time, WNW from 3 WSW Thomas 4: 3:25-3:30 pm, WNW from Thomas 5: No time, N of Custer City 6: 3:30 pm, 4 NW Thomas 7: No time, ENE from 2 NE Custer City 8: about 3:35 pm, N of Custer City 9: No time, brief N of Custer City 10: No time, N of Custer City 11: No time, N/NW from 2 NE Custer City 12: Before 3:36 pm, 5 WSW Thomas Graphic, reports courtesy of Doug Speheger, NWS

30. We have LOTS of work to do! Meteorologists: Need to improve algorithms to help identify reflectivity and velocity pattern ID Spotters: Need to improve cloud description techniques and accurate reporting

31. Radar Strategies for Pro-Active Spotter Deployment BEFORE: Look for boundaries (thin lines)! DURING: Look for the highest reflectivity regions in both base and composite reflectivity data. Look for tight base reflectivity gradients on the lowest elevation scan – usually on south or southeast side of storm (inflow side) Look for familiar patterns (bows, hooks, lines, BWERS, etc.)

34. High reflectivity values Tight Reflectivity gradients

35. THE DOPPLER DILEMMA Pulse rates can be set to acquire highly accurate velocity data, OR highly accurate reflectivity data. Although both are sensed simultaneously, there is a trade-off!

36. WSR-88D Radial Velocity Radar site Reds = outbound motions Greens = inbound motions Brighter colors = higher speeds Cannot “see” motions perpendicular to beam

37. Radial Velocity Signatures CYCLONIC ROTATION ANTICYCLONIC ROTATION DIVERGENCE CONVERGENCE

38. Base Velocity vs Storm Relative Velocity During some events, it may be beneficial to look at BOTH products Use ___________ for estimating the speed of wind gusts near the surface for straight line winds Use ___________ for identifying circulation features or convergence/divergence in or near thunderstorms Base Velocity Storm Relative Velocity

41. 1 2 3 4 5

42. 8000’ 3400 Feet AGL 12,000’21,000’

43. 3400 Feet AGL8000’ 12,000’ 21,000’

44. 3800 Feet AGL9000’ 23,000’12,000’

45. 3800 Feet AGL9000’ 12,000’ 23,000’

46. For this grid box, the composite reflectivity would display……. Composite Reflectivity 50 25 50 30

47. Courtesy NWS

48. Radar Algorithms Computer programs to assist in storm analysis Hail size estimation, mesocyclone/ tornado detection, rainfall estimation Based on 3-D precipitation structure, empirical and mathematical correlations AND NOT CLOUD STRUCTURE! ALGORITHMS ARE TOOLS, NOT GOSPEL!

49. Dangers of “Pathcasting” Exact arrival times used by television (and some NWS) forecasters Radar does not allow the level of precision implied by detailed forecasts Be Careful!!!

51. There are quite a few sources for error inherent in the process of estimating precipitation using radar. * Hail Contamination - Radar-based precipitation measurements are based on the relationship between "reflectivity" of raindrops and the rainfall rate. Wet hail stones within a storm reflect much more energy back to the radar than an equivalent amount of all-liquid precipitation, which results in overestimation. * Beam Blockage - Mountains, forests, towers, etc., very near the radar can block the radar beams from adequately sampling the atmosphere. This can result in large areas of underestimated rainfall.

53. * Anomalous Propagation (AP) - Under certain atmospheric conditions, the radar beams actually bend back toward the ground, and reflect off of buildings, hills, etc. This "ground clutter" may appear as “radar indicated” rain where none fell. * Non-Precipitation Echoes - Radar beams occasionally reflect off of items in the air that are not producing rain at the surface. Examples of this include birds, bats, virga (rain that evaporates before it reaches the surface) and chaff (reflective materials used by the military to confuse/counter enemy radar, and tested occasionally on domestic military bases).

55. Always “CHECK” your radar data against other data sources (e.g. other radars, other tilts, satellite imagery, etc.)

56. * Bright Banding - Bright banding occurs due to the reflectivity gradients associated with snow/ice that is melting as it is falling. When the snow is melting, a film of water forms on the outside of the snowflake. These water coated snowflakes show up on radar as high reflectivity bands, resulting in an overestimate of rain.

58. * Range Degradation - As the radar beams go farther out, they sample higher parts of the storm. Storms with low cloud tops are frequently under-sampled when they are farther away from the radar. This is particularly common with winter weather events. * Improper Z-R Relationships - Convective storms, tropical storms, and winter storms all require different reflectivity-to-rainfall (Z-R) relationships. An incorrectly set Z-R relationship can seriously impact the rainfall estimates.

59. “….street corner!!” Radar is NOT able to tell us EXACTLY where severe weather is occurring! (or headed!) October 9, 2001 Courtesy Rick Smith, Doug Speheger NWS, OUN

63. IMPORTANT POINT GO TO THE SOURCE!!! The National Weather Service! The have highly trained meteorologists! They SEE the radar data before we do! They see the derived (algorithm) data before we do! They have more VOLUMETRIC radar data than we have. They KNOW the RADAR limitations. PRECIP STRUCTURE vs CLOUD STRUCTURE! This is why THE NWS NEEDS YOU. YOU ARE THEIR “RADAR” IN THE FIELD..WITH UNIQUE CAPABILITIES! PRO-ACTIVE and TRAINED SPOTTERS improve the warning process and help save lives.

64. “LET THY ALGORITHMS GO!!!!” “THOU SHALT NOT BASE DECISIONS ON ALGORITHM DATA”

65. RELIANCE on ALGORITHMS, MAGNIFIES STUPIDITY, ENHANCES SCREWUPS

66. DON’T BE “RAMSES”!

67. I’ll Take Questions while Jim sets up!!