Analysis of Satellite Observations to Estimate Production of Nitrogen Oxides from Lightning
Lightning Observations During NAME
description
Transcript of Lightning Observations During NAME
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:
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