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 a problem much bigger than NAME...and It won’t be solved without adequate representation of organized convection in global models.

3. Impediments. Initial Condition Uncertainty
Impediments? Initial Condition Uncertainty Triggering of Deep Convection Non-linear thunderstorm dynamics Cloud microphysics, surface physics Chaotic multi-storm evolution Must we understand all this to make headway in climate science? Probably Not

4. The “Vector Component“ of the Diurnal Cycle
Radar <Rainrate>
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

5. July 1997 Fraction of Time with Precipitation Echo Hour UTC Longitude12 6 18
July 1997
Hour UTC 12 6 18
Clear 2nd day signal
110
105
100 95 90 85 80
Longitude

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

8. Fraction of Time with Precipitation Echo 1996-2002 (Jun-Aug)

9. The Forest and the Trees Statistically, 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 I better understanding and more realistic simulations:   Diurnal Cycle of Rainfall when, where, why, how much, far-field effects Forcing/Triggering/Maintenance E-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. Trop. E. Waves/Mid-lat interaction
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. Easterly Waves
Composited Convective Vertical Profile vs. Area Coverage
 30 dBZ Rel. Frequency/ Phase
 20 dBZ Area Coverage/Phase R N T S % R N T S
LOG10
Vert. Struct.
Area covg.

16. s
Rain Gauges
Radars ( )

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

18. Presentations. Rutledge observing clouds/storms
Presentations Rutledge observing clouds/storms Johnson forcing and budgets Moncrieff simulation, parameterization

19. Presentations. Rutledge observing clouds/storms
Presentations Rutledge observing clouds/storms Johnson forcing and budgets Moncrieff simulation, parameterization

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

21. S. Nesbitt, U. of Utah (CSU)
TRMM
Locations of features in each 4 hour time bin + MCSs PFs WI
S. Nesbitt, U. of Utah (CSU)
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. S-POL Radar Rainfall Estimation relative to rain gauges, February 1999
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 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. = Potential ALDF site
= Current NALDN site
300 km

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.