Lightning Observations During NAME

Walt Petersen1*, Rich Blakeslee2*, Steve Goodman2, Phil Krider3, Steve Rutledge4, and Bob Maddox3

1UAH-NSSTC/ESSC; 2 NASA-MSFC/NSSTC; 3UA; 4CSU

*Contacts:

walt.petersen@msfc.nasa.gov

richard.blakeslee@msfc.nasa.gov

GAP FILLING CONTRIBUTIONS TO NAME

• Hydrometeorology

• Lightning observations can help to fill existing observational network gaps in NAME, providing continuous long-term climatological/hydrological observations of convection and rainfall.

—Established connections between lightning, cloud physics and improvement of QPE (e.g., rainfall, convective structure, precipitation microphysics, latent heating)

• Climate

• Multi-year sampling of convective processes and precipitation in the NAME Tier-1 domain via installation of Cloud-to-Ground (CG) lightning network

– provide an enhancement/complement to observation network, including satellites

—Continuous, wide-area detection [O(105 km2)]

—500-1000 m location accuracy; 70-90 % Detection efficiency

DIRECT APPLICATIONS TO NAME SCIENCE

• Lightning is forced by, and varies with, outbreaks of convective activity over a variety of temporal/spatial scales (storm to climate)

— Storm-scale convective structure

— Intraseasonal changes in convective regime and bursts/breaks in SW monsoon convection

— Diurnal cycle of convection

— Interannual variability of convection

• Lightning location is a strong function of topography in SW monsoon region.

— Indicate preferred locations/timing of convection/convective rainfall in NAME domain as a function of underlying land surface characteristics.

— Valuable/useful over complex terrain of the SMO where gaps exist in current observational network

Hail/Graupel

Rain

Snow/Ice

+

+

+ = Positive Charge = Negative Charge

THE SCIENTIFIC BASIS

Well-established physical links between lightning, cloud dynamics, and precipitation microphysics Latent heating

-40oC

-10oC

LIS FD vs. Fraction dBZ > 30 (GC) above 7 km (98-00 TRMM)

y = 22.795x + 0.0454

R2 = 0.8056

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0 0.05 0.1 0.15 0.2Fraction

Flashes/km

2/mo

2-3 km Rainrate vs. Frac. dBZ > 30 (GC) above 7 km(98-00 TRMM)

y = 17.574x + 3.2978

R2 = 0.7985

2

2.5

3

3.5

4

4.5

5

5.5

6

6.5

7

0 0.05 0.1 0.15 0.2

Fraction

mm hr-1

Mean 2-3 km Rainrate vs. LIS Flash Density (98-00 TRMM)

y = 0.6043x + 3.4839

R2 = 0.6089

y = 0.6043x + 3.4839

R2 = 0.6089

2

2.5

3

3.5

4

4.5

5

5.5

6

6.5

7

0 1 2 3 4 5

Flashes/km2/mo

mm hr

-1

Mixed Phase - Lightning Mixed Phase - Rainrate

Lightning - Rainrate

Climatologically from TRMM LIS/PR………….

Petersen et al., 2001

Warm season statistics for 20 10o x 10o boxes across the Tropics.

= East anomaly* regime* defined by 700 mb u-wind

• Intraseasonal variability apparent

• East (west) anomaly=more (less) lightning.

TRMM-LBA

•But, similar daily mean rain rates?

•True reflection of varying cloud physics and vertical structure as a function of intraseasonal regime.

NASA-MSFC Brazilian Lightning Detection Network deployed in the Amazon since 1/99

Petersen et al., 2002

TRMM-LBA: Polarimetric ComparisonEasterly Regime

(Frequent Lightning)

• 990126 ZDR-LDR signature suggests hail production via drop freezing

105

LDR ZDR

Westerly Regime(Reduced Lightning)

Cifelli et al., 2002

RECENT APPLICATIONS RELATED TO QPE

4) Direct estimation of bulk rain-yields (rain mass/flash count) ranging from storm to climate scales (identification of climatological convective regimes)

5) Continuous and instantaneous measurement of rainfall, periodically calibrated by external radar or passive microwave (PM) measurements

6) Constraint on convective structure identification (e.g., convective/stratiform partitioning) leading to blended IR/Lightning or IR/PM/Lightning satellite rainfall estimation algorithms (e.g., Goodman et al,. 1988; Grecu et al., 2000)

7) Assimilation of lightning data into regional forecast models to improve QPE/QPF (operational NWP/NIMROD, Golding, 1997, 2000; research- MM5, Alexander et al., 1999).

• Use items 1-3 as needed to tune/nudge rain rates

• Constrain integrated latent heating (also adjust profile shape)

• Assimilate nudged heating profile

BEST WHEN ICE PROCESSES MAKE A SIGNIFICANT CONTRIBUTION TO RAINWATER BUDGET!

20+ dBZ

35+ dBZ

43+ dBZ

49+ dBZ

52+ dBZ

• 50-75 % of Rainfall associated with lightning-producing storms over SMO

Tier-1

Courtesy D. Cecil UAH/NSSTC

• Max precipitation feature reflectivity at ~-30oC. Ice processes are plentiful.

The presence of robust ice processes near the SMO is NOT an Issue!

•One of the most electrically active areas in the world

• 28-33 dBZ @ -30oC; 0.7-2.2 Flashes/min

— Comprise 3.3% of sample but 50-75% of the rainfall!!

Courtesy D. Cecil UAH/NSSTC

0% 25% 50% 75% 100%

Courtesy D. Cecil, UAH/NSSTC

0% 25% 50% 75% 100%

Potential NAME ALDF network geometry

= Potential ALDF site

= Current NALDN site • 5-station Advanced Lightning Direction Finder (ALDF) network

• TOA/DF technique,

300 km

Plans

•Pending

1) Endorsement of this idea by NAME SWG

2) Identification of Univ./Govt. collaborators from Mexico

3) Identification of suitable sites (EM noise reasonable, require security, internet access for real-time data processing)

•Pursue funding via NSF Hydrology and Physical Meteorology programs

• NASA-MSFC will supply antenna/computer hardware (cost sharing = $235 K)

• Vaisala-GAI (current NALDN operators) may acquire network if NALDN expanded all the way into Mexico- this would ensure long term sampling over all of the NAME Tiers.

NAME offers Atmos. Elec./Hydrology/Meteorology/Climatology communities a

UNIQUE INTERDISCIPLINARY OPPORTUNITY