1. Mesoscale wind divergence profiles in convecting regions Brian Mapes, Jialin Lin, Paquita Zuidema Brian Mapes, Jialin Lin, Paquita Zuidema data thanks to: CSU Radar met group, UW mesoscale group, TRMM ground validation office Outline 1.Motivations and methods 2.Results in some easy and hard cases 3.Statistical results 4.A well-measured oddity: JASMINE May 22 5.Summary and future plans

2. Motivations Heating in tropical convective clouds drives larger-scale circulations (LSCs) of many scales Heating in tropical convective clouds drives larger-scale circulations (LSCs) of many scales Heating profiles are important to LSC structure, including feedbacks to convection Heating profiles are important to LSC structure, including feedbacks to convection Divergence profiles (inflow/outflow) are closely linked to heating; and affect layer cloudiness Divergence profiles (inflow/outflow) are closely linked to heating; and affect layer cloudiness Clear-air (unheated) component of divergence is smaller, but especially important to feedbacks Clear-air (unheated) component of divergence is smaller, but especially important to feedbacks Divergent flow is hard to measure accurately, so better observations may lead to new discovery Divergent flow is hard to measure accurately, so better observations may lead to new discovery

3. Continuing hope: better obs. glimpses of a unique aspect of deep moist atmospheric convection: it’s embedded in a stratified environment, where gravity waves disperse by vertical wavelength

4. Wind divergence: interpretation = ∂/∂p( Q/  ) Mapes and Houze 1995 JAS Rearranging, For Q >> d h T/dt (tropical convection scaling), w/the

5. Wind divergence : measurement      V dA A =   perim V normal dP A The divergence theorem for an area A with perimeter P: Area averaged divergence = Defining the perimeter-mean velocity V normal,  V normal x P/A For a circular area, the overbar is an azimuth mean: = [V radial ] x 2  R/  R 2 = [V radial ] x 2/R

6. Velocity-Azimuth Display (VAD) At fixed horizontal range (radius) R and altitude, consider radial velocity Vr vs. azimuth angle At fixed horizontal range (radius) R and altitude, consider radial velocity Vr vs. azimuth angle Vr azimuth (deg from N) 0 360 0 wd ws Mean wind = sine wave Area-avg divergence  = [Vr] *2/R (NOTE: no uniformity assumption within circle) [Vr] wave-2 is deformation; real flows may have jets, etc. etc.

7. Background: Doppler radar Precipitation radar Precipitation radar –Radio pulses bounce off hydrometeors –Z is 6th moment of the DSD, log scale dBZ –For applications, rainrate R ~some power law in Z Doppler (coherent) radar Doppler (coherent) radar –Pulse pairs sent; Doppler spectrum P(  received –A mean  gives along-beam velocity:  V r =  t +/- n  t =2nV nyq –For applications, remove fallspeed V t (Z)cos(zenith)

8. Cylindrical data binning for VAD  r = 8 km (12 bins, 0-96 km)  az = 15° (24 bins)  z = 500 m (36 bins) -->  p = 50 hPa  t = 1 hour  dBZ bins (for Z-R)  m/s bins (only need n adjust )  m/s bins

9. Further pool data, e.g. height to pressure: simply sum the histogram arrays. (same for range or time pooling)

10. VAD plot for each hour, layer, range (pool) [Vr] <0: convergence -2m/s *2/(40km) = -1e-4 /s wind: 7 m/s from 240 deg unfolding guide for absolute Vr (after histogram compactification) From GOF, or ind. ws,wd guess sampling error bar used for rel. weighting in least-square 3-param fit: [Vr], ws, wd Repeat, repeat, repeat…

11. research radar deployments TEPPS: July 1997 TEPPS: July 1997 JASMINE: May 1999 JASMINE: May 1999 EPIC: September 2001 EPIC: September 2001 several 10s of days = many 100s of hours each several 10s of days = many 100s of hours each

12. EPIC 2001: time- pressure (Vr data everywhere, but sometimes just noise) IvoSparse 48h

13. An example hourly product convection developing near radar

14. 48 glorious hours: Sept 23-24 RHB-C

15. All points on all curves at all times are from independent data: Continuity in range, time, altitude encouraging 10x sonde div; decent mass balance ------>

16. ice sat dQ/dp >0 R=44km div dQ/dp >0 ‘onion’ RH soundings, sfc rain, mm cloud radar, VAD div: Paquita Zuidema, ETL RH profiles ascending convection tops mass flux (-omega)

17. Hard case: lots of Doppler folding Tropical Storm Ivo Sept. 10, 2001 convergence

18. TS Ivo, Sept. 10, 2001 cyclone moves westward, N of ship 13Z 15Z 17 18 19 21Z u>0 v<0 u>0 v>0 wind convergence 10 -4 s -1 x 7h = 2.5, ~10-folding of ~100km scale vorticity

19. Vr folding problem at 925mb looks OK clutter in S-W azimuths: Vr ~0. Spike hists dominate fit ~OK N E S W N

20. Mesoscale wind divergence profiles in convecting regions Brian Mapes, Jialin Lin, Paquita Zuidema Brian Mapes, Jialin Lin, Paquita Zuidema data thanks to: CSU Radar met group, UW mesoscale group, TRMM ground validation office Outline 1.Motivations and methods 2.Results in some easy and hard cases 3.Statistical results (EPIC, cloud model) 4.A well-measured outlier: JASMINE May 22 5.Summary and future plans

21. EPIC simple-mean divergence small circles have fallspeed overcorrection error (steep beam tilt) noisy - sea clutter ice liq smaller circles’ fluctuations have larger amplitude; convergent times are undersampled --> divergent bias Mapes and Houze 1995 COARE airborne (mid-troposphere) Doppler radar over W. Pac. mean s.d. (dQ/dp)

22. To isolate latent heat associated signal, regress div on R(Z) all data rain >0.5 standard error overshoot cooling?

23. Regress div(p) to surface rainfall: reduces scale dependence, biases scale-dependent fluctuation intensities cancel out scale-dependent fluctuation intensities cancel out can pool or average ranges for more robustness can pool or average ranges for more robustness Badness of crude ice/liq Vt(Z) assumption is ~uncorrelated w/ surface rainrate, since that mainly varies with coverage, not Z. Uses data when available. Undersampled times w/upper convergence & R~0 don’t affect slope div per rain (1e-6 s-1 per mm/h)

24. Moisture convergence per unit rainfall: should be ~1 Gross variations of magnitude in rain- convergence regression slopes between experiments e.g. COARE-MIT Z-R rainrate is about half the linearly regressed moisture convergence, 5 dbZ too low? (Radar has always been low outlier of rain estimates - Weller et al. 2004) R(Z) too small R(Z) too great re- calibrated re-calibrated

25. all data rain >0.5 sigma Why is rain weakly associated w/ low-level convergence? (hourly, simultaneous, ~100km scale) Convective vs. stratiform Convective vs. stratiform by horiz. echo texture (thanks S. Nesbitt, CSU) div>0 ° div<0

26. Multiple linear regression teases apart their pure signatures Multiple linear regression teases apart their pure signatures C,S rainrates are correlated but have independent variability too. C,S rainrates are correlated but have independent variability too. C autocorrelation C-S cross correlation hours convergence ~525mb

27. Con-strat evolution in total precip lag regression Rapid rise of convergence to middle levels at time of precip max Rapid rise of convergence to middle levels at time of precip max Trimodal cloud tops? (divergence) rising with time in advance of precipitation Trimodal cloud tops? (divergence) rising with time in advance of precipitation precipitable water from microwave radiometer (ETL) 0 1 mm per mm/h

28. 3D cloud model Marat Khairoutdinov KWAJEX case Divergence of cloudy updraft mass flux Divergence of cloudy updraft mass flux Trimodal cloud tops, PW rises then falls Trimodal cloud tops, PW rises then falls

29. 3D cloud model’s KWAJEX forcing Sonde-array div vs. model rainrate Sonde-array div vs. model rainrate Triple divergence levels, sounding PW rises then falls Triple divergence levels, sounding PW rises then falls Note 55h time axis! Note 55h time axis!

30. internal variations in 3D cloud model divergence and rain in 64x64 km subset of domain (1/16 of area) divergence and rain in 64x64 km subset of domain (1/16 of area) (about VAD size) (about VAD size) specific humidity in same area specific humidity in same area

31. Tracing back to cases day 270 (fold)

32. Similar picture from other radar deployments (convergence rises up over several hours as convective clouds turn stratiform) note JASMINE oddity

33. Convective, stratiform,…? Are these archetypal components of convective rainfall rooted in physical processes…? detrainment entrainment ice physics water physics …or in dynamical modes? (strictly, spectral bands) m=1/2 m=1 m=3/2 One clue: Does this exist? (…and is there a physical process behind it)?

34. (A: Yes, and Maybe) A nice touch: interleaved tilts, for buttery-smooth vert. res. when pooled over an hour (2+ volumes)

35. Mesoscale wind divergence profiles in convecting regions Brian Mapes, Jialin Lin, Paquita Zuidema Brian Mapes, Jialin Lin, Paquita Zuidema data thanks to: CSU Radar met group, UW mesoscale group, TRMM ground validation office Outline 1.Motivations and methods 2.Results in some easy and hard cases 3.Statistical results 4.A well-measured outlier: JASMINE May 22 5.Summary and future plans

36. equator 40N JASMINE Bay of Bengal May 1999 Eastern Ghats Dave Lawrence

37. Meteosat-5 Infrared Imagery Mean diurnal cycle of 210K low level wind figs by Paquita Zuidema

38. Meteosat-5 Infrared Imagery Mean diurnal cycle of 210K prevailing wind figs by Paquita Zuidema

39. JASMINE May 1999, spanning monsoon onset in the Bay of Bengal

40. JASMINE squall (wave?) May 22, 1999 (figs from U. of Washington web pages on JASMINE) ~15 m/s Webster et al. 2003, Zuidema 2003

41. JASMINE div(p) 1604 Z 17-18Z mass flux

42. JASMINE wavy div(p): Double-decker convection? from UW JASMINE web pages

43. JASMINE div(p) 20-21Z bad

44. JASMINE May22 time-height section x - old/ folded mm cloud radar data and overlay presentation by Paquita Zuidema, ETL note very low drying Is it dynamical wave# 3/2? Or is it moist (dry air) processes?

45. Recalling the EPIC case… dry intrusion: a physical process, not simply related to a dynamical mode… but note deeper layer of descent drying in this case, onion RH min ~3km (725 mb) JASMINE case IS significantly unusual….

46. SUMMARY & CONCLUSIONS: Sensibly consistent w/ surface rain & zenith cloud radar obs. (full EPIC and JASMINE overlay sections at http://www.etl.noaa.gov/~pzuidema) We see good ol’ convective & stratiform profiles (ho-hum), but also Anvils snowing into dry layers, moistening and cooling their tops (cooling flattens the dry layer by diabatic divergence dQ/dp>0) Steady convergence below 700mb in TS Ivo, to ~10-fold  in 7h m=3/2 divergence profile in JASMINE propagating squall/wave: double-decker convection, and deviations from that pillar of tropical meso-meteorology, middle level convergence in stratiform rain Wavy profiles of div regressed on shallow convective rainfall - are high and low tropospheric clouds/heating dynamically coupled on the mesoscale? If so, is random overlap a good assumption? Hourly VAD gives simple, automatic, good divergence measurements

47. Mapes and Houze 1992 airborne Doppler, EMEX Better top! Whole troposphere shorter in EPIC than in Australian monsoon More complete life cycle: MH92 obs were in mature MCSs and rarely saw fresh development 1km res mean sd (subjectively categorized samples)

48. EPIC MLR for 4 categories of precipitation: shallow, deep, very deep convection; stratiform ~~Vertical waviness?~~ ~~~~ convective rain from 0-5km top cells stratiform 5-10km tops >10km tops (like MH92 “intermediary”?)

49. convective rain from 0-5km top cells Corresponding omega EPIC MLR for 4 categories of precipitation: shallow, deep, very deep convection; stratiform 5-10km tops >10km tops stratiform

50. Difficulty of measuring divergence: sounding arrays Erroneous barotropic part of sonde-array divergence is comparable to signals sought in baroclinic component… low bar for our work!

51. Hard case: a sparse time N E S W N (sea clutter?)