Cloud Microphysical Properties, Processes, And Rainfall Estimation Opportunities Daniel Rosenfeld

1. Cloud Microphysical Properties, Processes, And Rainfall Estimation Opportunities Daniel Rosenfeld Institute of Earth Sciences The Hebrew University of Jerusalem Carlton W. Ulbrich Department of Physics and Astronomy Clemson University Clemson, SC, USA Presented in the weather radar conference, Orlando, 2002 Rosenfeld D. and C. W. Ulbrich, 2003: Cloud microphysical properties, processes, and rainfall estimation opportunities. Chapter 10 of " Radar and Atmospheric Science: A Collection of Essays in Honor of David Atlas". Edited by Roger M. Wakimoto and Ramesh Srivastava. Meteorological Monographs52, 237-258, AMS.

2. In the beginning (1947) Marshall and Palmer created the MP Drop Size Distribution And MP Z-R relationship Z = aRb; a=200, b=1.6

3. And we said that the ZR was good, and created more…

4. and more… and more…

5. Early classification of Z-R by rainfall types Fujiwara, 1965: Thunderstorms Z = ARb A Rain Showers Continuous Rain b

6. However, lack of physically based classification created a tower of Babel of Z-R relations The building of the tower of Babel by Pieter Bruegel, 1563 Oil on oak panel, Kunsthistorisches Museum Wien, Vienna

7. Method of using the Gamma distribution with the Z-R law parameters. Any two integral rainfall parameters P and Q; Eliminating D0 yields; Inverting gives; With P=Z and Q=R in Z=ARb, can findand N0from A and b. These can then be used to find coefficients and exponents in theoretical D0-R, NT-R, W-R relations.

8. Z-R relations used in this work Source No. Z-Rs Description Foote (1966) 2 Mountain thunderstorm in AZ, Filter paper meas. Sims (1964) 1 Thundershowers. Illinois. Joss and Waldvogel (1970) 1 Thunderstorm. 25 days total, Locarno, disdrometer) Petrocchi and Banis (1980) 1 Thunderstorm, Norman, OK (disdrometer) Sauvageot (1994) 1 Tropical squall line. Congo Ulbrich et al. (1999) 1 Ave. of 7 thunderstorms in Arecibo, PR.(disdrometer) Tokay et al. (1995) 2 Tropical. Disdrometer data. Darwin, Australia. Maki et al. (2001) 6 15 squall lines. Disdrometer, Darwin, Australia. Tokay and Short (1996) 2 TOGA COARE data. Kapingamarangi atoll, disdrometer Stout and Mueller (1968) 3 Trade wind showers; Marshall Islands Jorgensen and Willis (1982) 2 Hurricane Frederic, 9/12/79 Ulbrich and Atlas (2001) 2 Tropical storms. PMM analysis, TOGA COARE 2DP data. Fujiwara and Yanase (1968) 3 Filter paper meas. Orographic, slopes of Mt. Fuji. Atlas et al. (1999) 2 TOGA COARE, Kapingamarangi atoll, disdrometer. Blanchard (1953) 1 Orographic rain, windward slopes on Mauna Loa. Only those relations used which involve direct measurements of drop size spectra and for which the environmental conditions are well known. No combinations of instruments (radar-raingages or radar-disdrometer, etc.) were considered.

9. Rain water content, W [g m-3] as a function of rain drop median mass diameter D0 [cm] and drop concentration NT [m-3] for R=10 and R=30 mm h-1, For all Z-R’s

10. Processes determining the Rain Drop Size Distribution Equilibrium DSD: Z = 600 R; D0 = 1.75 mm Hu and Srivastava (1995)

11. Impact of Cloud DSD on the evolution of Rain DSD • Maritime Cloud DSD • Cloud drop coalescenceDrizzle • Drizzle coalescence Raindrops • More coalescence larger raindrops • breakup and equilibrium DSD • Approaching D0e from below • Continental Cloud DSD • Cloud drop accretiongraupel •  haillarge raindrops • breakup  • equilibrium DSD • Approaching D0e from above

12. Equilibrium Maritime Continental Trends of D0 for convective maritime and continental clouds. Rain water content, W [g m-3] as a function of rain drop median mass diameter D0 [cm] and drop concentration NT [m-3] for R=30 mm h-1, For all Z-R’s.

13. The classification scheme of convective clouds into microphysical zones according to the shape of the temperature – effective radius relations Note that in extremely continental clouds re at cloud base is very small, the coalescence zone vanishes, mixed phase zone starts at T<-15oC, and the glaciation can occur at the most extreme situation at the height of homogeneous freezing temperature of –39oC. In contrast, maritime clouds start with large re at their base, crossing the precipitation threshold of 14 mm short distance above the base. The deep rainout zone is indicative of fully developed warm rain processes in the maritime clouds. The large droplets freeze at relatively high temperatures, resulting in a shallow mixed phase zone and a glaciation temperature reached near –10oC Rosenfeld and Lensky, 1998

14. Why is Continental - Maritime classification so fundamental? Annual average lightning density [flashes km-2] Lightning prevail mostly over land, whereas rainfall is similar over land and ocean, indicates fundamental differences between continental and maritime rainfall.

15. Disdrometer measured DSD of continental and maritime rainfall, as micophysically classified by VIRS overpass. The DSD is averaged for the rainfall during +- 18 hours of the overpass time. The disdrometers are in Florida (Teflun B),Amazon (LBA),India (Madras) and Kwajalein. Application of TRMM Z-R shows a near unity bias in maritime clouds, but overestimates by a factor of 2 rainfall from continental clouds. Rosenfeld and Tokay, 2002

16. a & b in Ze = 10^a R^b Ze-R relations in Gadanki SW NE SW NE Rain Obs in Asia for PR Algorithm Tuning Study items: DSD, BB model, BB & RR comp. with PR. One of the Major Findings:DSD properties: Significant seasonal dependence in India: Large D0 in SW monsoon,low D0 in NE. Not much seasonal dependence in Singapore. By T. Kozu Kozu

17. Z-R relations for rainfall from maritime and continental convective clouds. The rain intensities for 40 and 50 dBZ are plotted in the figure. Note the systematic increase of R for a given Z for the transition from continental to maritime clouds. 1. Swiss Locarno thunderstorms, continental (Joss and Waldvogel , 1970) 830 1.50 2. Arizona mountain thunderstorms (Foote 1966) 646 1.46 3. North Dakota, September. 429 1.59 4. Illinois thunderstorms, continental (Sims, 1964) 446 1.43 5. Oklahoma thunderstorms, moderate continental (Petrocchi and Banis, 1980) 316 1.36 6. Congo Squall line. Tropical continental (Sauvageot, 1994) 425 1.29 7. PurtoRico thunderstorms. Coastal, moderate maritime (Ulbrich et al., 1999). 261 1.43 8. Darwin Squalls. Coastal, tropical maritime (Maki et al., 2001) 232 1.38 9. Darwin Convective DSD. Coastal, tropical maritime (Tokay et al., 1995) 175 1.37 10. COARE Convective DSD. Equatorial maritime (Tokay and Short, 1996). 139 1.43 11. Marshall Trade wind cumulus. Warm rain maritime (Stout and Mueller, 1968) 126 1.47 12. Marshall Showers. Equatorial maritime. (Stout and Mueller, 1968) 146 1.42 E. Equilibrium DSD. 600 1.00

18. The variation of D0 with the liquid water content (W) and total drop concentration (NT) for R=10 and 30 mm hr-1 of convective rainfall in maritime and continental regimes. Note that D0 decreases from continental to maritime clouds.

19. ConvectiveStratiform classification In Maritime Convective clouds : More Coalescence Rainout D0 < D0e  Smaller R(Z) Less evaporation  Smaller D0  Smaller R(Z) In Stratiform clouds : Aggregation of ice  Larger D0  Larger R(Z) More evaporation  Larger D0  Larger R(Z) .

20. Tropical Convective – Stratiform received much attention because of different vertical profiles of latent heating, to which the global circulation is quite sensitive. Convective R(Z)almost double than Stratiform R(Z). [Tokay and Short (1995, 1996); Atlas et al. (2000); Ulbrich and Atlas (2001) ] C/S classification already applied to TRMM Z-R

21. Z-R relations for rainfall from tropical convective and stratiform clouds. The rain intensities for 30 and 40 dBZ are plotted in the figure. Note the systematic decrease of R for a given Z for the transition from convective to stratiform clouds. 1. Darwin Convective DSD. Coastal, tropical maritime (Tokay et al., 1995) 175 1.37 a. Darwin Stratiform DSD. Coastal, tropical maritime (Tokay et al., 1995) 335 1.37 2. Marshall Showers. Equatorial maritime. (Stout and Mueller, 1968) 146 1.42 b. Marshall continuous. Equatorial maritime. (Stout and Mueller, 1968) 226 1.46 3. COARE Convective DSD. Equatorial maritime (Tokay and Short, 1996). 139 1.43 c. COARE Stratiform DSD. Equatorial maritime (Tokay and Short, 1996). 367 1.30 4. COARE Convective aloft, by updraft (Ulbrich and Atlas, 2001) 120 1.43 d. COARE Stratiform aloft, by updraft (Ulbrich and Atlas, 2001) 203 1.46 5. Hurricane eye wall. (Jorgensen and Willis, 1982) 287 1.27 e. Hurricane rain bands. (Jorgensen and Willis, 1982) 301 1.38 E. Equilibrium DSD. 600 1.00

22. The variation of D0 with the liquid water content (W) and total drop concentration (NT) for R=10 and R=30 mm hr-1 of convective rainfall in maritime and continental regimes. Note that D0 increases from convective to stratiform clouds. Note that Hurricane has near equilibrium D0

23. In orographic clouds with active coalescence: Early fall to the mountain slope of small hydrometeors  Highly immature DSD D0 << D0e  Much Smaller R(Z) .

24. Z-R relations for rainfall from maritime orographic clouds. The rain intensities for 30 and 40 dBZ are plotted in the figure. Note the systematic increase of R for a given Z for greater orographic uplifting. 1. Mount Fuji, at height of 1300 m. (Fujiwara and Yanase, 1968) 240 1.48 2. Mount Fuji, at height of 2100 m. (Fujiwara and Yanase, 1968) 88 1.28 3. Mount Fuji, at height of 3400 m. (Fujiwara and Yanase, 1968) 48 1.11 a. Marshall Trade wind cumulus, 0 m. (Stout and Mueller, 1968) 126 1.47 b. Hawaii, Mauna Loa, 800 m. (Blanchard., 1953) 31 1.71 E. Equilibrium DSD. 600 1.00

25. The variation of D0 with the liquid water content (W) and total drop concentration (NT) for R=10 and R=30 mm hr-1 of orographic rainfall in maritime clouds. Note that D0 decreases substantially with the orographic uplifting.

26. Summary: All Z-R classifications combined The variation of D0 with the liquid water content (W) and total drop concentration (NT) for R=10 and 30 mm hr-1 of convective rainfall in maritime and continental regimes.

27. Summary A review of Z-R relations based on cloud physics RDSD forming processes revealed that Z-R behave systematically, Producing larger R for the same Z when going from: Continental  Maritime (X4) Maritime  Orographic (X4) Stratiform  Convective Maritime (X2) Opportunities Classification criteria can be detected by: Satellite (cloud drop effective radius for continentality); Radar 3-D structure; Dynamics of orographic lifting. Potential for dynamic Z-R for space and ground based radars, accounting for systematic biases by factors of 2 to 4.