NSF BriefingNSF Briefing

28 February 200328 February 2003

Rit CarboneRit Carbone

Issues and Opportunities

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

Impediments?Impediments?

Initial Condition Uncertainty

Triggering of Deep Convection Non-linear thunderstorm dynamicsCloud microphysics, surface physicsChaotic multi-storm evolution

Must we understand all this to make Must we understand all this to make headway in climate science? headway in climate science? Probably Not

Rainfall “episodes” span substantial distances over North America on a dailydaily basis in mid-summer.

Sequences of convective systems often result from a coherent regeneration of organized convection.

Carbone et al. 2002

Radar <Rainrate>

The “Vector Component“ of the Diurnal CycleThe “Vector Component“ of the Diurnal Cycle

LongitudeH

our U

TC

July 1997

July 1997

Fraction of Time with Precipitation Echo

0

12

6

18

110

105

100

95 90 85 80

0

12

6

18

  

 

How bad is it?How bad is it?

Wrong timesWrong times

Wrong placesWrong places

Wrong phase Wrong phase speedsspeeds

Davis et al. 2003Davis et al. 2003

ETA WRFETA WRF

Fraction of Time with Precipitation Echo1996-2002 (Jun-Aug)1996-2002 (Jun-Aug)

The Forest and the TreesThe Forest and the TreesStatistically, precipitation episodes appear to possess an intrinsic predictabilityintrinsic predictability far greater than the chaotic behavior of storms would suggest.

This is particularly significant in the context This is particularly significant in the context of probabilistic forecast systems from intra-of probabilistic forecast systems from intra-seasonal through inter-annual ranges of seasonal through inter-annual ranges of variability.variability.

…but we need a quick look at a few trees in an unexplored part of the forest.

Objectives Specific to Tier IObjectives Specific to Tier Ibetter understanding and more realistic simulations:   

Diurnal Cycle of RainfallDiurnal Cycle of Rainfallwhen, where, why, how much, far-field effects

Forcing/Triggering/MaintenanceForcing/Triggering/MaintenanceE-waves, surges, breezes, blocking, density

Path to Adequate RepresentationPath to Adequate Representationvia CRMs toward parameterization in AGCMs

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.

Mountains, Jets, Breezes, BlockingMountains, Jets, Breezes, Blocking

(Fuller and Stensrud 2000; Brenner 1974)

Mesoscale ? Synoptic Scale?Mesoscale ? Synoptic Scale?

Gulf Moisture SurgesTrop. E. Waves/Mid-lat interaction

A significant forecast problem. A significant forecast problem. Moisture source? Moisture source? Mid-latitude synoptic Mid-latitude synoptic influence?influence?

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

Easterly WavesEasterly Waves

Composited Convective Vertical Profile vs. Area CoverageComposited Convective Vertical Profile vs. Area Coverage

30 dBZ Rel. Frequency/ Phase 20 dBZ Area Coverage/Phase

R N T S R

%

R N T S R

LOG10

Vert. Struct.

Area covg.

Rain Gauges

Radars ( )

ss

MM

SSss

II

II

II

II

II

NSF FacilitiesNSF Facilities

• Quantitative Core Monsoon Radar

• Backbone of Linkage to U.S.

• Critical Elements of Budget Array

PresentationsPresentations

Rutledge Rutledge observing clouds/storms

Johnson Johnson forcing and budgets

Moncrieff Moncrieff simulation, parameterization

PresentationsPresentations

Rutledge Rutledge observing clouds/storms

Johnson Johnson forcing and budgets

Moncrieff Moncrieff simulation, parameterization

Observing StormsObserving Storms

Steve Rutledge

NCAR S-POL (portable)• Polarimetric, Doppler• S-band, 10.7 cm• Zh, Vr, Zdr, Kdp,Ldr

S. Nesbitt, U. of Utah (CSU)S. Nesbitt, U. of Utah (CSU)

Locations of features in each 4 hour time bin + MCSs . PFs WI

CSUCSU

TRMM

Objectives for which S-pol is required…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 Much of this work is model-validation oriented/motivated oriented/motivated CSUCSU

Hydrometeor IdentificationHydrometeor Identification

From Polarimetric DataFrom Polarimetric Data

CSUCSU

Retrieve Retrieve mixing ratio mixing ratio estimatesestimates

from from polarimetric polarimetric

datadata

Provides insights into precipitation processes

and data for comparison to numerical

modelsCSUCSU

S-POL Radar Rainfall Estimation 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.

CSUCSU

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

= Potential ALDF site

= Current NALDN site

300 km

Lightning Over Complex Topography in the TropicsLightning 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:OBJECTIVES:—Dynamical and microphysical/precipitation structureDynamical and microphysical/precipitation structure related to lightning characteristics.

—Diurnal cycleDiurnal cycle of tropical convection/lightning over complex topography

—Intra-seasonalIntra-seasonal changes in convective regime, precipitation characteristics and bursts/breaks in monsoon convection reflected in lightning data—Inter-annualInter-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 characteristicsunderlying 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.