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Short-term Forecasting
Introduction:
How might continuous lightning measurements be used to improve
short-term (0-24 hr) weather forecasting? This question is addressed
under two different forecast strategies. At shorter time scales
(0-6 hr) the lightning data can be integrated with other intensive
observations and models to determine the initiation and evolution
of convective storms and severe weather hazards, while the assimilation
of lightning data into regional to national scale numerical weather
prediction models, especially in specifying the initial convective
activity in data sparse regions, may potentially improve weather
forecast accuracy out to 24-36 hours.
Short Term Forecasts Of Severe Weather
Figure 1 shows the co-evolving relationship between total lightning
rates, the precipitation and ice phase development, and updraft
velocity during the life-cycle of an airmass thunderstorm in
Alabama. The physics of charge separation and lightning channel
breakdown are sufficiently well understood that 3-D cloud models
have matured to include explicit microphysical charging through
particle interactions and electric field breakdown. Explicit
microphysics in these models yields large scale relationships
consistent with both observations and theoretical prediction,
e.g., the connection between total flash rate and total ice mass
(itself a direct product of storm updrafts). Similar relationships
between lightning and precipitation ice are found when spaceborne
Lightning Imaging Sensor (LIS) data are compared with 85 GHz
and 13 GHz Precipitation Radar measurements [Petersen and Rutledge,
2001; Goodman and Cecil, 2002].
The close coupling between lightning activity and storm updrafts
and ice content implies that increases in lightning activity
should be observed prior to severe weather, as many events such
as damaging winds, tornadoes and hail are direct by-products
of extreme updrafts and ice production aloft.
Lightning jumps associated
with a variety of severe weather events were observed in Florida
by Williams et al. [1999] and in Alabama by McCaul et al., [2002]
(Figure 2). In addition to increases in total lightning rate,
MacGorman et al. [1991] have hypothesized that stronger updrafts
will loft the main storm electric dipole to higher levels in
a storm, thus favoring in-cloud (IC) over cloud-to-ground (CG)
discharges. This hypothesis is supported by evidence from electric
field balloon soundings. Consistent with this hypothesis, the
dominant component of the severe weather lightning "jump"
described above is often found to be from IC lightning [MacGorman
et al., 1991; Goodman et al., 1988]. In the most severe storms,
the ratio of IC to CG lightning can be much greater than its
mean values of ~3:1 [animations].
While prediction, modeling, and observation find close correspondence
of lightning flash rates with convective properties, a degree
of scatter and dependence upon local convective regimes, is common
[Petersen and Rutledge, 1998; 2001]. It is thus important to
establish the forecast model physics deficiencies, resolution
limitations, or initialization data inadequacies that can be
addressed by the additional information content provided by lightning.
Assimilation Of Lightning Data Into Forecast Models
Alexander et al. [1999] demonstrated improved forecasts of surface pressure
and precipitation through continuous assimilation of lightning
data (from the National Lightning Detection Network) into models
of the March 1993 southern U.S. Superstorm (Figure 3). The high
temporal resolution of the lightning data (which were correlated
with instantaneous estimates of rainrate to adjust model latent
heating) was critically important for the model to correctly
forecast the large scale development of the extratropical cyclone,
including key parameters such as precipitation and minimum pressure.
Notably, comparable improvements over control runs were not achieved
upon less frequent assimilation of satellite infrared or passive
microwave estimated rainfall rates. Another success was achieved
by Chang et al. [2001] through the assimilation of continuous
low frequency VLF measurements of lightning, again calibrated
by intermittent satellite estimates of rainrate (through a probability
matching technique). Rogers et al. [2000] produced an improved
24-h forecast of the rainfall pattern for a summertime mesoscale
heavy rain event. Only the presence of deep convection (as might
be indicated by lightning) at a model grid point triggered the
convective parameterization scheme, on or off. Such an approach
has the advantage that the convective precipitation rate and
heating profiles generated by the parameterization are compatible
with the local (model) environment. The effectiveness of the
technique is enhanced in weakly forced environments, common in
the summertime, where convective initiation and organization
are governed by previous convective activity and the resulting
temperature and moisture discontinuities (i.e., boundaries).
Such methods, however, must continuously assimilate the convective
parameter (lightning is this case); otherwise the model eliminates
the imposed disturbance through convective adjustment. These
lightning data assimilation strategies all rely on the relationship
(correlation) between convective rainfall and lightning flash
rate [Cheze and Sauvageot, 1997], and constant lightning detection
efficiency within the forecast domain. Errors will be amplified
if the relationship is non-constant (i.e., rainfall-lightning
relationship varies with storm type or life-cycle, or the ratio
of cloud flashes to ground flashes varies).
Future Prospects
1. Nowcasting and the Severe Weather Hazard
The first step in the roadmap for algorithm and display product
development is identification of candidate precursor signatures,
or inputs. Potentially useful signatures exist in some known
environments (e.g., increasing flash rates and dominance of in-cloud
lightning implies a hazardous storm). The repeatability (or variability)
of such signatures in different environments must be assessed
as part of a larger scale evaluation, to refine the data products
and displays provided to forecasters. Information on false positive
and false negative rates will be gained from limited regional
ground studies such as those underway in Florida [Williams et
al., 1999] and Alabama [Goodman et al., 2003]. Total lightning
data from short-range VHF lightning mapping networks, full-resolution
NEXRAD radar, other data and model output should be used to characterize
potentially severe storms and their environment.
Assessment of the utility of total lightning data for short-term severe
weather forecasting can be performed using a forecaster decision-support
system. Lightning flash rate, flash density, flash polarity,
and trending of candidate pre-event signatures (e.g., lightning
jumps concomitant with outflow boundaries interacting with storms)
can be provided to forecasters through their primary data integration
tool, the Advanced Weather Information Processing System (AWIPS),
which is located in every NWS Weather Forecast Office (WFO) in
the U.S. The decision making process involves assessment of the
near storm environment, candidate signatures, and the forecasters'
own knowledge. Useful ways to display data (e.g., flash rate
time tendency) and interpret data (e.g., growth/decay/intensification
of updrafts) should be provided to forecasters and skill with
and without lightning data utilization should be objectively
assessed once a sufficiently large sample size is achieved. Forecaster
feedback can then be used to guide selection of appropriate inputs
for statistical (e.g., neural network, hierarchical clustering)
analysis of the event database. A Warning Event Simulator (WES)
already in each WFO allows a forecaster to replay storm events
and assess his/her decision-making skill.
The Aviation Weather Center (AWC) and other members of the aviation
weather research product development teams in the U.S. are tasked
with developing 0-6 hr CONUS and oceanic convective weather hazard
forecast products to improve the safety and efficiency of the
international aviation system. AWC has Significant Meteorology
(SIGMET) advisory responsibility under international treaties
for convective weather hazardous to aircraft over the oceans
and land areas extending to the middle of the oceans. For remote
offshore regions, proposed lightning from Geostationary Earth
Orbit (GEO) combined with other satellite and surface lightning
measurements could be the primary means by which aviation hazards
related to deep convection (severe turbulence, severe icing and
hail) would be identified and spatially resolved in a timely
manner. Lightning data combined with other satellite microwave
and GEO imaging (GOES infrared) and sounding data would also
produce more detailed convective cloud classifications and diagnostics.
Statistical/dynamical expert systems (e.g., NCAR's Autonowcaster,
one of nine nowcasting systems evaluated at the Sydney 2000 Olympic
Games, Keenan et al., 2000) are now used in the U.S., Canada,
the U.K., and Australia for 0-2 hr thunderstorm forecasts.
2. Numerical Weather Prediction
Assessment of the benefits of lightning data for numerical forecast
improvement follows a well-structured process. First, physical
or statistical relationships between observed lightning flash
rates and observed convective properties must be established.
Ample theoretical, empirical and model evidence is available
that such relationships exist, but operationally useful knowledge
of variance, or of dependence on local environmental conditions
and convective regime, is less established. Second, techniques
to incorporate lightning-derived convective properties into numerical
models (i.e., data assimilation techniques) must be established.
Again, a variety of approaches have already been explored. Third,
lightning data must be assimilated into a variety of models using
several candidate techniques, and compared against control runs
to both subjectively and quantitatively assess performance. This
process should emphasize models in use or slated for use by operational
forecasters and researchers alike, such as the Weather, Research
and Forecast model [WRF, http://www.wrf-model.org/].
Establishment of a collaboration infrastructure or test bed, shared data
formats and a shared quantitative assessment strategy is also critical
for the evaluation. Scientists at New Mexico Tech, the National
Space Science and Technology Center in Huntsville, Alabama and
the National Severe Storms Laboratory in Norman, Oklahoma intend
to archive total lightning data from their respective Lightning
Mapping Array systems in a common format data structure. The
results of ongoing VHF mapping network studies will provide continued
high-detail case study information such as that shown in Fig.
4. A more comprehensive approach is utilization of a 3-D numerical
cloud model with explicit microphysics and electrification. This
model allows 'laboratory' testing of lightning/convective parameter
relationships (or, more generally, lightning/storm property relationships
such as latent heating or convergence), examination of justifying
physical theories for the relationship between large scale storm
electrical energetics, kinematics and microphysics, and examination
of a variety of storm environments and morphologies. The results
of this modeling will provide direct guidance and physical justification
for later data assimilation strategies. Finally, combined multi-satellite
infrared and microwave rainfall (and hence latent heating) estimates
can be augmented with lightning data (Morales and Anagnostou,
2003). Lightning observations have the potential to identify
convective core locations within IR cloud shields to improve
the delineation of convective / stratiform rainfall. Identification,
design, and evaluation of candidate techniques are on-going at
the NASA Short-term Prediction Research and Transition (SPoRT)
Center, a data assimilation test bed established by NASA which
is collocated with the NSSTC and the NWS WFO in Huntsville. Approaches
include use of lightning data as a static constraint at forecast
initialization time, and continuous use of lightning data to
dynamically prescribe cloud quantities throughout the assimilation
period. Physical approaches include use of lightning as a deterministic
trigger for the cloud parameterization scheme, and use of lightning
to quantitatively nudge model fields, including dynamical (updraft/downdraft
profile and intensity), thermodynamical (latent heating), microphysical
(precipitation efficiency) and/or environmental (boundary layer
heat and moisture) properties.
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