Transcript Development of a Lightning NOx Algorithm for WRF-CHEM

Development
of a Lightning NOx
Algorithm for WRF-Chem
Amanda Hopkins Hansen
Department of Meteorology
Florida State University
[email protected]
Henry E. Fuelberg
Florida State University
Kenneth E. Pickering
NASA Goddard Space Flight Center
O2
N2
N2
N2
N2
O2
N2
N2
O2
N2
N2
N2
O2
Air is ~78%N2 and ~21% O2
N2 + O2  NO + NO
Ozone Formation in the Troposphere
N2 + O2  NO + NO
Air is ~78%N2 and ~21% O2
NO + O3  NO2 + O2
NO2 + hν NO + O
hn = 290nm < l < 410nm
O + O2 + M  O3 + M
•These reactions set up the photostationary state.
•O3 depends on the rates of formation and photolysis of NO2
•In order for ozone to increase, NO2 formation
must happen without destroying O3
Oxidation of methane
Oxidation of carbon monoxide
Rationale - why is LNOx important
1. Lightning is an important source of NOx in the
relatively clean upper troposphere
December, January, and February
Pressure (hPa)
June, July, and August
Zonal Mean Lightning NOx production (10-2kg/s)
From the NASA GISS GCM
Rationale - why is LNOx important
2. LNOx indirectly affects our local air quality and
global climate
Has a strong influence on Ozone (O3) and hydroxyl
radical (OH) concentration
NOx [NO+NO2]:
•is a primary pollutant found in photochemical smog
•is a precursor for tropospheric ozone formation
TROPOSPHERIC OZONE:
•is the third most important greenhouse
gas
•impacts the Earth’s radiation budget and
can cause changes in atmospheric
circulation patterns.
•is toxic to humans, plants and animals.
Photo courtesy of University of California at Berkley
Global NOx Budget
3. LNOx is difficult to realistically model, but is
Important in the global budget of NOx
aircraft
2%
biomass
burn
15%
soils
15%
fossil fuels
52%
lightning
16%
Figures from Global Emissions Inventory Activity data sets
Note: lightning figures are calculated by model internally
LNOx algorithm for WRF-Chem with
Parameterized Convection
what are the necessary ingredients?
1)Flash rate parameterization
2) Average NO production per flash
3) Method of specifying the vertical distribution
of LNOx emissions
Schemes often used for Flash Rate Production
1. Cloud Top Height [Price and Rind, 1992; Michalon et al., 1999]
2. Convective Precipitation Rate [Meijer et al., 2001; Allen and Pickering, 2002]
Separate parameterizations are needed to simulate land and oceanic lightning
3. Upward Cloud Mass Flux/Updraft Velocity [Grewe et al., 2001; Allen and
Pickering, 2002]
This scheme cannot be developed based on observations. Model
output must be used. Therefore, the relationship between these variables
and flashrate will be model-dependent.
None of these schemes yield global flash distributions that compare well
with satellite flash observations. What is needed is a more
microphysically-based scheme. Possibilities include the schemes by
Deierling et al. (2005) and Futyan and Del Genio (2007).
Flux Hypothesis
Deierling et al., 2005
They propose that lightning frequency, f, is proportional to the product of
the precipitation rate, p, and the mass upflux of ice crystals, Fi.
f  JpFi
The charging mechanism used in the this study involves rebounding
collisions between heavy graupel pellets and the lighter ice crystals
(Reynolds et al., 1957; Takahashi, 1978; Jayaratne et al., 1983;
Baker et al, 1987). This process creates a vertical electric dipole by
gravitational separation of oppositely charged graupel and ice crystals.
This scheme would work very well on the cloud-scale
We need a Regional scale relationship!!!
Convective Radar Storm Height
Futyan and Del Genio, 2007
Cloud top height may be several kilometers higher than the
height of significant radar signal
fifth order power law for radar
top height (above surface)
second order for radar top height
above 0 degree isotherm.
WSR-88 Doppler Radar and the Lightning Detection and
Ranging (LDAR) data
Futyan and Del Genio (2007) used TRMM data for their research:
we will use Doppler radar and LDAR data to form a relationship
between flash rate and radar storm height.
3-D Lightning Mapping locations to be incorporated
Kennedy Space Center, Florida** - LDAR/Vaisala
Huntsville, Alabama – NM Tech
Dallas, Texas – LDAR/Vaisala
Washington D.C. – NM Tech
**We will start with KSC
WSR-88 Doppler Radar and the Lightning Detection and
Ranging (LDAR ) data from Kennedy Space Center (KSC)
Doppler Radar
Melbourne, Florida
KSC LDAR. Sensor 0 is the central LDAR receiver.
(After Poehler and Lennon 1979 and Vollmer 2002)
WSR-88 Doppler Radar and the Lightning Detection and
Ranging (LDAR ) data from Kennedy Space Center (KSC)
The location of the Melbourne, Florida National Weather Service Office WSR-88D
radar in relation to the Kennedy Space Center. Range rings are provided at 10 km
intervals. (After Nelson 2002).
WDSS: Warning Decision Support System
This will provide a way to visualize lightning flashes with radar reflectivity
The image depicts individual Lightning Detection and Ranging (LDAR) sparks as
measured by the Kennedy Space Center LDAR network, the cloud-to ground
strikes measured by the National Lightning Detection Network (NLDN), and quality
controlled reflectivity data from the Melbourne NWS radar. From the LDAR sparks
we can calculate flash rate.
WDSS: Warning Decision Support System
Cross section view of cell 49 in previous slide
LNOx algorithm for WRF-Chem
what are the necessary ingredients?
Lightning Flash Rate Parameterization
•Flash rate (F) will be calculated based on the following relationship:
•F=AHn
•Relationship between Radar storm height above the freezing level
and LDAR data is needed to determine A and n above.
•Radar Reflectivity is calculated within WRFChem using hydrometeors
from microphysical scheme (WSM6) coupled with the Kain-Fritsch
cumulus parameterization.
•Convective storm height (H) is found using the 20dbz contour and the
freezing level is obtained from the WRF temperature field.
LNOx algorithm for WRF-Chem
what are the necessary ingredients?
LNOx parameterization
•Production rate of NO from both IC and CG lightning:
-500 moles per flash (Ott et al.,in progress)
•Vertical Distribution of NO:
Pickering et al., 1998: -Wind fields from Goddard Cumulus
Ensemble (GCE) model were used to redistribute LNOx throughout
the duration of the storm. Profiles were constructed for mid-latitude
continental, tropical continental, and tropical marine regimes
based on profiles computed for individual storms in each regime
Verification
Simulations for Summer 2004 INTEX-NA period
over eastern US and comparisons with aircraft data
DC-8 flight track showing
the path through the
Huntsville, AL storms
We have the same data for
Kennedy Space Center
Plot courtesy of Mike Porter
Summary
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Flash rate parameterization being developed for
WRF-Chem using observed radar and 3-D lightning
mapping array data.
WRF-Chem will first be tested with the Futyan and
Del Genio (2007) relationship and then with the
newly developed scheme.
Existing NO production per flash and vertical
distribution information will be used.
Testing of WRF-Chem with lightning will be
conducted using aircraft NOx observations from the
ICARTT (NASA and NOAA data) experiment from
Summer 2004.