NSF Briefing 28 February 2003 Rit Carbone Issues and Opportunities

1. NSF Briefing 28 February 2003 Rit Carbone Issues and Opportunities

2. Numerical predictions of rainfall from continental convection exhibit low skill at all ranges with all prediction models over all non-polar continents.Why is this so?…….especially when major episodes of rainfall often exhibit: - strong topographical forcings, - a regular diurnal cycle,- temporal and spatial coherence This is aproblem much bigger than NAME...and It won’t be solved without adequate representation of organized convection in global models.

3. Impediments?Initial Condition UncertaintyTriggering of Deep Convection Non-linear thunderstorm dynamicsCloud microphysics, surface physicsChaotic multi-storm evolutionMust we understand all this to make headway in climate science? Probably Not

4. Rainfall “episodes” span substantial distances over North America on a daily basis in mid-summer.Sequences of convective systems often result from a coherent regeneration of organized convection.Carbone et al. 2002 The “Vector Component“ of the Diurnal Cycle Radar <Rainrate>

5. 0 0 6 6 12 12 18 18 Fraction of Time with Precipitation Echo July 1997 Hour UTC Longitude

6. How bad is it? Wrong times Wrong places Wrong phase speeds Davis et al. 2003 ETA WRF

8. Fraction of Time with Precipitation Echo1996-2002 (Jun-Aug)

9. The Forest and the TreesStatistically, precipitation episodes appear to possess an intrinsic predictability far greater than the chaotic behavior of storms would suggest. This is particularly significant in the context of probabilistic forecast systems from intra-seasonal through inter-annual ranges of variability.…but we need a quick look at a few trees in an unexplored part of the forest.

10. Objectives Specific to Tier Ibetter understanding and more realistic simulations:Diurnal Cycle of Rainfallwhen, where, why, how much, far-fieldeffects Forcing/Triggering/MaintenanceE-waves, surges, breezes, blocking, density Path to Adequate Representation via CRMs toward parameterization in AGCMs

11. Mountains, Jets, Breezes, Blocking There are 2 important low-level jets that transport significant moisture to the continent and that play an important role in the diurnal cycle of precipitation.

12. Mesoscale ? Synoptic Scale? Gulf Moisture Surges Trop. E. Waves/Mid-lat interaction A significant forecast problem. Moisture source? Mid-latitude synoptic influence? (Fuller and Stensrud 2000; Brenner 1974)

14. R/V Ronald H. Brown During EPIC 2001 • Instruments • Radar (Scanning C-band Doppler; Vertically pointing Ka-band Doppler) • Rawinsonde • 915 MHz wind profiler • DIAL/Mini-MOPA LIDAR • Multi-spectral radiometers • Air-sea flux system • Meteorological observation (T,RH, P), aerosol concentrations, rain gauges and ceilometer • Oceanographic measurements including SST, CTD and ADCP

15. LOG10 % S S R R R R T T N N Easterly Waves Composited Convective Vertical Profile vs. Area Coverage  30 dBZ Rel. Frequency/ Phase  20 dBZ Area Coverage/Phase Vert. Struct. Area covg.

16. s Rain Gauges Radars ( )

17. I I M I I S s • NSF Facilities • Quantitative Core Monsoon Radar • Backbone of Linkage to U.S. • Critical Elements of Budget Array I

18. Presentations Rutledge observing clouds/stormsJohnson forcing and budgetsMoncrieff simulation, parameterization

19. Presentations Rutledge observing clouds/stormsJohnson forcing and budgetsMoncrieff simulation, parameterization

20. Observing Storms Steve Rutledge • NCAR S-POL (portable) • Polarimetric, Doppler • S-band, 10.7 cm • Zh, Vr, Zdr, Kdp,Ldr

21. TRMM S. Nesbitt, U. of Utah (CSU) Locations of features in each 4 hour time bin + MCSs . PFs WI CSU

22. Objectives for which S-pol is required… • Describe daily evolution of convective rainfall • Identify, quantify organized convection regimes • Diagnose kinematic and microphysical properties • Estimate rainfall to close heat/moisture budgets • “Tune” SMN and RB radars for rainfall estimation • Properties/processes associated with variability • Much of this work is model-validation oriented/motivated CSU

23. Hydrometeor Identification From Polarimetric Data CSU

24. Retrieve mixing ratio estimates from polarimetric data Provides insights into precipitation processes and data for comparison to numerical models CSU

25. Method BIAS STANDARD ERROR S-POL Optimal -4.8% 14.4% S-POL Median -10.7% 17.9% S-POL Closest -11.1% 20.6% S-POL Radar Rainfall Estimation relative to rain gauges, February 1999 S-pol provides accurate estimates of accumulated rainfall. These estimates will be used to “train” Mexican radars to produce better rainfall estimates. CSU

26. Lightning Observations During NAME Walt Petersen1*, Rich Blakeslee2*, Steve Goodman2, Hugh Christian2, Phil Krider3, Steve Rutledge4, and Bob Maddox3 1UAH-NSSTC/ESSC; 2 NASA-MSFC/NSSTC; 3UA; 4CSU

27. 300 km = Potential ALDF site = Current NALDN site

28. Lightning Over Complex Topography in the Tropics An ideal laboratory for the study of lightning and precipitation processes (e.g., Watson et al., 1994; Boccippio et al., 2000; Petersen and Rutledge, 2001; Christian et al., 2003) OBJECTIVES: Dynamical and microphysical/precipitation structure related to lightning characteristics. Diurnal cycle of tropical convection/lightning over complex topography Intra-seasonal changes in convective regime, precipitation characteristics and bursts/breaks in monsoon convection reflected in lightning data Inter-annual monsoon variability- impact on lightning and convection Preferred locations/timing of lightning/convection/rainfall in NAME domain as a function of underlying land surface characteristics. Lightning data will be a valuable tool in the remote sensing of tropical convection/rainfall over complex terrain of the SMO- where gaps exist in current proposed NAME observational network Learn from NAME….Apply to tropical mountainous regions globally.