1. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science 1 Quantifying the effect of wind drift on radar-derived surface rainfall estimations Steve Lack NATR 410: Seminar

2. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science2 Motivation  Flooding causes more deaths than both tornadoes and lightning strikes  30 yr national average for flooding deaths is 127 as opposed to 65 and 73 for the others  Worldwide there are many who live in areas susceptible to flash flooding  Property damage can be intense ($20 billion for Mississippi River flood of 1993)

3. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science3 Motivation (cont.)  Flood warnings need to be issued on time so that proper measures can be taken  Flood warnings rely on accurate observations, rain gauge networks do satisfactorily, but new radar technology with increased temporal and spatial resolution is the present and future  Precipitation estimates to be useful in hydrological models need to be within 10-20% accurate (Collier 1985)…radars currently cannot meet this criteria without corrections

4. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science4 Outline  Errors in Radar Measurements  Data Sets  Methodology- Program Structure Assumptions  Results from convective and stratiform cases  Conclusions  Future Work

5. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science5 Errors in Radar Measurements  Most errors exist aloft and are not carried to the surface  Error in radar returns aloft eventually must be carried down to the surface in some way, correcting aloft does not do justice for the spatial accuracy needs of hydrological models

6. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science6 Calibration Errors  Like any other instrument, radars are sensitive to calibration errors 1) Variability in transmitter power 2) Poorly known antenna and other component characteristics  All lead to sources of error that are generally ignored or corrected by gauge measurements, or solar calibration

7. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science7 Attenuation  Attenuation is simply the reduction is power that is caused when electromagnetic radiation passes through a medium of any density and material  Gas attenuation is easily corrected for  Cloud and precipitation non-uniform…large errors (S-band less error than X-band) (radome wetting)

8. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science8 Bright Band and Mixed Precip  The bright band is a result of enhancement of reflectivity resulting from frozen precipitation falling through a melting layer, thus yielding an artificial high reflectivity in this region aloft  Hail also causes problems in estimating precipitation amounts…overestimation (hail spikes and flares)

9. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science9 Bright Band Example  Taken from KOUN radar during JPOLE

10. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science10 Misc Errors  Beam blockage due to mountainous area  Anomalous propagation caused by radar beam ducting and intercepting surface targets  Beam filling, overestimation on storm tops and edges, especially at higher elevations, wider beams

11. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science11 Reflectivity to Rainfall rates  Marshall and Palmer (1948) first empirical relationships relating reflectivity to rainfall rates (Z-R relationships) RELATIONSHIPOptimum for:Also recommended for: Marshall-Palmer (Z=200R 1.6 )General stratiform precipitation East-Cool Stratiform (Z=130R 2.0 )Winter stratiform precipitation - east of continental divideOrographic rain - East West-Cool Stratiform (Z=75R 2.0 )Winter stratiform precipitation - west of continental divideOrographic rain - West WSR-88D Convective (Z=300R 1.4 )Summer deep convectionOther non-tropical convection Rosenfeld Tropical (Z=250R 1.2 )Tropical convective systems

12. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science12 New Relationships  Increased accuracy in measuring drop size distributions, and new radar technology, dual-polarization radars…enhanced relationships  R  c(h) 1.06 Z eh 0.3 K DP 0.50 Z dr -0.84  Reduces error to 20-40% range  Still not acceptable for hydro apps (10-20%)

13. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science13 Spatial Accuracy?  All of the previous correction schemes plus the Z-R relationships and relationships based on advanced radars attempt to quantify rainfall aloft  The problem is getting accurate results to the surface…evaporation, collision/coalescence, and wind-drift come into play

14. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science14 Wind Drift Considerations  Gunn and Marshall (1955): parabolic trajectory of raindrops in a constant wind shear environment, lateral advection along the ground could be quite large from the original location of the droplet aloft.  Atlas and Plank (1953): drop sorting, same size drops come from two different spots aloft

15. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science15 Data Used  Sydney 2000 World Weather Research Programme’s (WWRP) Forecast Demonstration Project (FDP) (Keenan et al., 2003)  CPOL radar 40km west of Sydney  45 x 45 km Cartesian grid of reflectivity, horizontal, and vertical velocities  Dates used: Convective- 11-03-00, Stratiform 11-18-00 (3 hour time frame)

16. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science16 Horizontal Velocities  Radar assimilation into an adjoint model (Sun and Crook 1995)  Adjoint model is based off of sensitivity…data is inserted where needed  Near real-time output from model

17. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science17 Adjoint Example

18. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science18 Cartesian Horizontal Velocities Sample U Component Sample V Component

19. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science19 Vertical Velocities  M.W. Sleigh (2002): derived w wind field from the u and v components using the continuity equation, matched to reflectivity fields

20. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science20 Program Background  MATLAB: matrix based program language, graphical display  Input Cartesian grids of reflectivity (CAPPIs in this case) and velocity  Generates images and movies of corrected rainfall rate, reflectivity, and accumulation errors

21. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science21 Methodology  2 schemes used –Bulk Advection –Drop Sorting  Experimental Resolutions –Native: 1.5km CAPPI height/ 2.5km horizontal resolution –Change in horizontal resolution: 1.5km and 0.5km run –Change in CAPPI to 0.75km

22. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science22 Bulk Advection Scheme  From reflectivity, solve for fall speed, which is a function of approx. mean drop diameter (Lacy, 1977)  Fall speed, calculations of time of fall to sfc, multiply by u,v components (parabolic trajectory avg) to get drift  Sum up contribution from pixels to new reflectivity grid

23. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science23 Bulk Advection Scheme (cont.)

24. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science24 Bulk Advection Scheme (cont.) (Convective bulk advection) 590.1451839.145000 5731.565000 00000 00000 00000 (Stratiform bulk advection) 565.93061812.999000 5808.071000 00000 00000 00000 Starting with 40 dBZ (10000 mm 6 m 3 ) in upper left, using u,v wind 6 ms -1

25. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science25 Bulk Advection Scheme (cont.)  Summary of scheme… –This only uses a mean drop size diameter –Stratiform fall speed slower, more spread than convective –Z-R relationships based on ROC criteria (Z=200R 1.6 and Z=300R 1.4 ) –Dimensions of the wind drift grid are conserved “bulk-advection” of grid

26. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science26 Drop Sorting Scheme  More complex (computational time increases), than simplified BA scheme  Divided drop distributions into 25 size bins depending on strat/conv storm type  Each bin has unique fall speed given by its size  Drop numbers in different bins calculated by adjusted M-P relationships

27. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science27 Drop Sorting Scheme (cont.) Adjusted M-P relationships for drop sort Integrate (1) to yield (5) and substitute, (6) solves for reflectivity contribution from bin (every 0.3mm from 0-7.5mm (conv) every 0.2mm from 0-5mm (strat)

28. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science28 Drop Sorting Scheme (cont.)  Summary of scheme… –Uses 25 drop size bins –Uses adjusted M-P relationships and different fall speed calculations than BA scheme –Spread of reflectivity from one cell greater extent than bulk advection scheme –Original grid square spreads out more than its original dimension –Longer Computational Time

29. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science29 Topography Considerations  5 min res of NSW, smoothed out to match the res of the native reflectivity and wind grids (2.5km x 2.5km)  High terrain…less wind drift effect evident over western part of image  Take CAPPI height- terrain

30. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science30 Convective Case  November 3 rd, 2000 (3hr, 10 minute intervals)  Severe Convective Case, dBZ>53 are reduced to 53 dBZ and converted to rainfall rate, hail contamination  Series of supercellular storms moving through the region of interest  First images are uncorrected, followed by some corrected rainfall totals, and accumulation error images

31. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science31 Original Reflectivity/Rainfall Loop

32. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science32 Original vs. Bulk Advection Rainfall Totals at Native Resolution

33. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science33 Original vs. Drop Sort at Native Resolution

34. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science34 Accumulation Errors for BA and DS at Native Resolution

35. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science35 Accumulation Error (BA) Movie at Native Resolution

36. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science36 Accumulation Error for BA and DS at CAPPI height of 1500m and Horizontal Resolution of 500m

37. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science37 Selected Error Statistics (Conv)  Conv BA scheme at z=1.5km and r=2.5km –Max Error ~17mm after 3hr accumulation –Max R is ~69mm while Max Corr R is ~65mm (divergence)  Conv BA scheme at z=1.5km and r=0.5km –Max Error ~47mm after 3hr accumulation –Max Corr R is ~70mm (slight convergence in grid)

38. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science38 Selected Error Statistics (Conv)  Conv DS scheme at z=1.5km and r=2.5km –Max Error ~16mm after 3hr accumulation –Max R is ~69mm while Max Corr R is ~64mm (divergence)  Conv DS scheme at z=1.5km and r=0.5km –Max Error ~52mm after 3hr accumulation –Max Corr R is ~72mm (convergence in grid)

39. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science39 Stratiform Case  November 18 th, 2000 (3hr, 10 min interval)  Typical stratiform case with a mass of light showers hanging around the region  Rainfall totals are really low, making the errors less between the original and corrections  Follows similar order to the convective slides

40. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science40 Original Reflectivity/Rainfall Loop

41. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science41 Original vs. Bulk Advection Rainfall Totals at Native Resolution

42. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science42 Original vs. Drop Sort at Native Resolution

43. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science43 Accumulation Errors for BA and DS at Native Resolution

44. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science44 Accumulation Error (BA) Movie at Native Resolution

45. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science45 Accumulation Error for BA and DS at CAPPI height of 1500m and Horizontal Resolution of 500m

46. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science46 Selected Error Statistics (Strat)  Strat BA scheme at z=1.5km and r=2.5km –Max Error ~3mm after 3hr accumulation –Max R is ~22mm while Max Corr R is ~21mm (divergence)  Strat BA scheme at z=1.5km and r=0.5km –Max Error ~9mm after 3hr accumulation –Max Corr R is ~21mm (divergence)

47. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science47 Selected Error Statistics (Strat)  Strat DS scheme at z=1.5km and r=2.5km –Max Error ~2mm after 3hr accumulation –Max R is ~22mm while Max Corr R is ~21mm (divergence)  Strat DS scheme at z=1.5km and r=0.5km –Max Error ~8mm after 3hr accumulation –Max Corr R is ~21mm (divergence)

48. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science48 Including Topography to Convective Case

49. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science49 Conclusions  Increasing the horizontal resolution increases the error (significant when dealing with sub-watershed (urban-hydrology) scales)  Errors are reduced when incorporating terrain into wind-drift or using lower beam elevations or CAPPIs (introduce clutter, beam blockage)  Schemes implicitly handle convergence and divergence  Both schemes give similar results however drop sort has longer computational time  Radar is a give and take device in many ways

50. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science50 Future Work  Wind-drift is only one portion of gaining spatial accuracy for surface rainfall estimation…need to account for evaporation of drops and changes in DSDs…develop enhanced scheme  Incorporate more vertical levels of wind field data to get a more accurate vertical profile instead of assuming parabolic trajectory  More case studies (local)  Apply results in a simple hydrological model to examine changes in streamflow based on corrections

51. 1 March 2004 University of Missouri-Columbia Dept. of Soils, Environmental, and Atmospheric Science51 Acknowledgements  Alan Seed from the BoM  Andrew Crook from NCAR