Download CONTINUOS THUNDERSTORM MONITORING: RETRIEVAL OF

CONTINUOS THUNDERSTORM MONITORING: RETRIEVAL OF
PRECIPITATION PARAMETERS FROM LIGHTNING OBSERVATION
Carlos Augusto Morales
University of Connecticut, Dept. of Civil and Enrionmenal Engeneering, Storrs, CT 06269,
USA
Emmanouil N. Anagnostou
University of Connecticut, Dept. of Civil and Enrionmenal Engeneering, Storrs, CT 06269,
USA
James A. Weinman
GSFC/NASA, Microwave Sensors Branch, Greenbelt, MD20771, USA
Abstract:
A detail analysis of the relationship between rainfall parameters measured by the Tropical
Rainfall Measuring Missing (TRMM) and lightning measurements will be presented. Our
ultimate goal is to develop a methodology to retrieve ice content and the associated mean
vertical profile of radar reflectivity using combined measurements of lightning, and satellite
infrared and passive microwave brightness temperatures. The lightning information is derived
from sferics measurements. Sferics is the radio noise emitted by a lightning flash. At very low
frequencies ground receivers can detect electrical discharges at distances beyond 3,000 km.
During 1997-1998, NASA in conjunction with Resolution Display Inc. installed such sensors
and collected continuous sferics measurements over the North and South America. This study
will be carried out based on coincident sferics and TRMM data from the above period. An
error analysis will be performed to quantify uncertainties associated with the proposed method.
1. Introduction:
Information on the spatial and temporal variability of precipitation is of fundamental
importance to applications ranging from hydrologic engineering to climate change research.
Rainfall measurements can improve water and energy budgets, as well the numerical weather
models’ prediction accuracy. Furthermore, policy and decision makers on various aspects of
socio-economic activities would need to know the precipitation distribution along large regions
to better plan their actions. It is known that two thirds of the Earth’s precipitation falls over the
tropics and subtropics. These regions are primarily covered by ocean, tropical rain forests and
undeveloped areas where direct precipitation measurements are infrequent and confined at few
locations. Subsequently, remote sensing observations are the only available tool to monitor
precipitation over these regions. However, remote sensors have limitations, and the challenge
has been in developing methodologies to yield reliable quantitative rainfall estimates at suitable
spatial and temporal scales.
The latest advances in remote sensing technology and computer engineering have lead
to improvements in global precipitation monitoring. Despite these advances, the global rainfall
estimates are continually constrained at scales of the order of 1x1 to 2.5x2.5-degree grid boxes
with time scales ranging from daily to monthly. These constraints are associated with sampling
and physical limitations of the sensor measurements. New instruments and technologies are
been investigated with purpose to supplement some of these deficiencies. Systems to detect
3723
lightning occurrence over very large areas (several millions of square miles) is a technology
that offers valuable indirect rainfall information, which in conjunction with other remote
sensing data could be used to improve the rainfall estimation at smaller spatial scales (< 25×25km2) and higher temporal frequencies (< 1hour).
Lightning, an electrical phenomenon in the atmosphere, is produced by the collision of
ice particles embedded in intense cloud updrafts. These electrical discharges can be measured
continuously at large distances by radio receivers (Lee, 1986). Workman and Reynold (1949)
related flash rates to convective rain fluxes, and suggested that the frequency of lightning may
be a measure of convective activity. Goodman (1990) has developed a relationship between
lightning frequency and rainfall intensity for systems in Florida. Similarly, Buechler et al.
(1994) have demonstrated a linear relationship between rainfall and lightning activity for
Florida thunderstorms. Petersen and Rutledge (1996) in a more generalized study computed
ratios of rainfall yelds to cloud-to-ground lightning flash frequency for different parts of the
world.
A more physical approach for retrieving rainfall rate from lightning would be to relate
it to the ice content of the precipitating cloud. Mohr et al (1996) have found that flash rates are
inversely correlated to the satellite microwave (85 GHz) brightness temperature observations,
which signal depression is associated with the presence of graupel and ice. Illingworth and
Lees (1991), who studied the relation between radar reflectivity and flash rates, suggest that
lightning is associated with convective clouds containing wet hail. Cheze and Sauvageot
(1997) also found a very tight relationship between lightning and average rainfall for
convective systems in France using as reference weather radar rainfall data. Finally, Toracinta
et al. (1996) have shown that the majority of negative cloud to ground (CG) lightning flashes
are located near high-reflectivity convective cores in the mixed-phase region.
The ultimate goal of this paper is to present a multi-remote sensor algorithm for
continuously (in time) retrieving precipitation parameters over the North and South America.
Specifically, the objectives are:
I.
Investigate the potential of thunderstorm monitoring from combination of
lightning, IR, and MW sensors;
II.
Develop statistical relationships between lightning and rain parameters;
III.
Develop a multi-stage rain retrieval scheme, which would combine lightning, IR
and microwave observations;
IV.
Evaluate the error statistics of the scheme’s precipitation parameter retrievals.
2. Methodology
2.1 Microwave brightness temperature
In the microwave wavelengths the radiation penetrates the clouds. Precipitating
particles (water droplets and ice particles) interact strongly with the microwave radiation.
Depending on the frequency, some of these particles emit, absorb, or scatter the radiation.
Spencer et al (1989) have shown calculations of scattering and absorption by rain particles for
three frequencies (19.35, 37 and 85.5 Ghz). They concluded that ice particles do not absorb
microwave radiation, but only scatters; liquid water droplets mainly absorb; and finally
scattering and absorption both increase with frequency and rainfall intensity. The disadvantage
of using these sensors for rainfall retrieval arises from the fact that they have low field of view
resolution (15-50 km) and poor sampling frequency (~twice a day). Over land rainfall
estimation algorithms are primarily dependent on the scattering channels (85 Ghz), while over
ocean more reliable multi-frequency techniques are used.
3724
The ice particles are present in intense and developed clouds and have a strong signal
at the higher frequencies in microwave radiation. Grody (1991) has developed a scattering
index for identifying varying scattering surfaces. This index uses a combination of the
brightness temperature at 19, 22, 37 and 85 Ghz vertically polarized signal. Such methodology
can be applied to define regions with ice particles. The knowledge of ice content can improve
the computation of latent heat release responsible for phase changes in the cloud system.
Information on latent heat release can be used to initialize numerical weather prediction
models.
In seeking a procedure to relate ice content with electrical discharges, lightning
measurements from NLDN and brightness temperature from TRMM-TMI were coupled for a
set of five events during December 1997 and January 1998. The mean number of flashes per
1x1 degree boxes conditional to the TRMM brightness temperature is shown in figure 1, in a
manner similar to what was shown by Mohr et al.(1996). It is apparent that the number of
flashes per area is inversely proportional to the brightness temperature.
Figure 1. Mean number of flashes per 1x1 degree categorized by the brightness temperature at
85 Ghz.
We propose to develop climatological (based on several months of data) relationships
between lightning and 85 Ghz brightness temperature. Subsequently, one may use existing
parametrizations of microwave to ice content. Such parametrization may be derived from
physical retrievals based on radiative transfer model simulations and satellite observations. In
this study the variational method of Grecu and Anagnostou (2000), which combines efficiently
satellite radar and radiometer observations based on an optimization method, will be used to
derive the necessary parametrizations. Figure 2 presents an example of the method’s retrieved
vertical integrated ice content versus the corresponding observed 85 Ghz brightness
temperatures for several storm cases over land.
3725
Figure 2. Vertical integrated ice content derived from Grecu and Anagnostou (2000) variational
method versus corresponding 85 Ghz microwave brightness temperature observations.
2.2 Radar Reflectivity Factor
Radar rainfall rate, R(mmh-1), is commonly retrieved by a known Z-R relationship. The
Z-R relationship is evaluated based on ground measurements such as rain gauges and/or
disdrometers (drop size distribution counters). Battan (1973) has compiled several Z-R
relationships for different types of precipitation systems around the world. In the absence of
validation measurements, the Marshal and Palmer (1948) exponential drop size distribution is
applied. Therefore, the radar rainfall estimation suffers from a host of uncertainties.
Nevertheless, weather radars are the only instruments capable of continuously monitoring
precipitation at high spatio-temporal scales and over large areas 400-km by 400-km. It is
expected that with a proper rain gauge calibration radar rainfall algorithm may provide reliable
rainfall field estimates (Anagnostou and Krajewski, 1998).
Statistical analyses that combines the TRMM precipitation radar and lightning
measurements over larger regions over the globe, can lead to better understanding of the
different types of precipitation systems. These results can be applied in conjunction with other
remote sensed measurements to improve the rainfall estimation. Example of such statistical
analysis is evaluation of the probability density functions of reflectivity versus height for
varying lightning density categories. Figure 3 presents the vertical frequency distribution of
reflectivity factor on the presence of lightning strikes from NLDN for the storms used in figure
1. The mean vertical profile is plotted as a thick black line. It is noteworthy that the mean
vertical profile of radar reflectivity resembles the profiles of convective storm types. In
addition, high values are observed near the surface, which means high precipitation, and a
second peak near the freezing level.
3726
Figure 3. Vertical frequency distribution of TRMM-PR reflectivity values conditional to
lightning occurrence. The line represents the mean vertical radar reflectivity profile.
2.3 Development of a Multi-Remote Sensor Rain Parameters retrival: Combination of
lightning, IR, MW and PR measurements.
The algorithm proposed herein applies the lightning location error correction scheme
and the statistical relationships developed in the analysis described in the previous section.
These relationships would use the continuous measurements of lightning and infra-red GOES
images to retrieve instantaneous convective and stratiform rainfall fields at 0.5x0.5 degree grid
areas. The area resolution is selected to minimize uncertainty associated with lightning location
errors (10-50 km depending on the range). The temporal scale of agreegation is 30 minutes.
This scale corresponds to the time interval of available GOES8 images. The above
relationships and multi-sensor algorithm are assessed based on coincident TRMM-Sferics
measurements for the period December, 1997 to January, 1998. The proposed multicomponent algorithm is summarized below.
A cloud classification, which combines lightning and infra-red brightness temperature
observations, is used to determine the type of precipitation system: electric-active (convective)
and clouds with no lightning (stratiform). An IR temperature threshold to delineate rainy
versus non-rainy areas will be obtained by matching microwave-based rain estimates (GPROF
algorithm) with IR observations for both types of systems.
Databases of matched probability density functions (pdf) between lighting frequency
and surface rainfall rate (derived from GPROF), radar reflectivity factor, and ice content, will
be evaluated for the two precipitation types, as a function of geographic location, time,
land/ocean background, and cloud’s infra-red brightness temperature.
The rainfall rate pdf is retrieved from the assumption that the Goddard Profiling
alogorithm (GPROF), Kummerow and Giglio (1994a, 1994b), represents reliably the
precipitation fields. The ice content pdf will be retrieved from relationship obtained from
Grecu and Anagnostou (2000) variational method applied to coincident PR/TMI and lightning
3727
data. Finally the radar reflectivity factor will be obtained by statistical analyses of the vertical
profiles of TRMM-PR in the presence of lightning.
It is expected that the derived climatological relationships would have significant
uncertainty if applied to cloud system with meteorological characteristics different to the ones
studied. Subsequently, an assimilation scheme is proposed, where whenever a new TRMM
overpass is over an area of interest brightness temperature and radar reflectivity data from
TRMM are augmented with lightning and infra-red temperature observations and new pdfs are
compared with the current database to establish the relationships that better represent the type
of systems observed.
2.4 Rain Parameters retrieval errors
The month of February 1998 will be used for algorithm assessment. The rain parameter
products will be evaluated at the instantaneous, hourly, and daily time scales. Independent data
sets will be used for these comparisons. A statistical analysis of the errors will produce the
expected mean errors and variances for the rain parameters estimates.
Rainfall estimates will be compared with rain gauges and satellite estimates from GPI
and GPROF. The rain gauges are located in Florida over the coverage area of Melbourne
weather radar. Hourly to daily comparisons will be performed. Instantaneous comparisons
over the entire month of February will be carried for GPI and GPROF in the region of 130W60W and 50N-10S.
The radar reflectivity factor errors will be estimated from the precipitation radar of
TRMM and the Melbourne (FL) weather surveillance radar (WSR-88D). Constant altitude plan
position indicator (CAPPI) reflectivity values above the freezing level will be used for the
comparisons.
Ice content retrievals will be tested against the retrievals obtained from Grecu and
Anagnostou (2000) variational method for all the TRMM overpasses over the period selected
for comparisons against GPROF.
3. Conclusion
The proposed paper outlined a methodology for rain parameter retrieval from
continuous measurements of lightning combined with infra-red GOES8 images and TRMM
precipitation radar and microwave brightness temperature. It is strengthened by the fact that
lightning is related to the convective regions of precipitation where ice is present. The optimal
configuration of the technique is based on statistical (empirical) relationships between matched
remote sensing measurements. The scheme involves a lightning location error correction
scheme, which is incorporated to minimize uncertainties associated with locating correctly the
convective cores. The proposed multi-component remote sensing rainfall estimation algorithm
is an attractive tool for the continuous monitoring of precipitation over remote and un-gauged
regions of earth.
4. References
Anagnostou, E.N., and W.F. Krajewski, 1998: Calibration of the WSR-88D Precipitation
Processing Subsystem. Weather and Forecasting, 13, 396-406.
Baker, M.B., H.J. Christian, J. Latham, 1995: A computational study of the relationships linking
lightning frequency and other thundercloud parameters, Q. J. R. Meteorol. Soc., 121, pp. 15251548.
3728
Battan, L.J., 1973: Radar Observation of the Atmosphere. pp. 324, The University of Chicaco
Press.
Buechler, D., H.J. Chirstian, S.J. Goodman, 1994: Rainfall estimation using lightning data.
Seventh Conf. Satell. Meteor. and Ocean., Amer. Meteorol. Soc., June 6-10, pp. 171-174.
Cheze, J.L. and H. Sauvaguot, 1997: Area average rainfall and lightning activity., J. Geophys.
Res., 102(D2), pp. 1707-1715.
Goodman , S.J., 1990: Predicting thunderstorms evolution using ground-based lightning
detection networks. NASA Tech. Memo. TM-103521.
Grecu, M. and E.N. Anagnostou, 2000: Variational based retrieval of hydrometeor profiles
from satellite active and passive microwave observations, Submitted to J. Geophys. Res.Atmospheres.
Grody, N.C, Classification of snow cover and precipitation using the special sensor microwave
imager, J. Geophys. Res., Vol 96, D4, 7423-7435.
Holle, R.L., A.I. Watson, R.E. Lopez, D.R. MacGorman, and R. Ortiz, 1994: The life cycle of
lightning and severe weather in a 3-4 June 1985 PRE-STORM mesoscale convective system.
Mon. Wea. Rev. 122, 1798-1808.
Illingworth, A.J. and M.I. Lees, 1991: Comparison of lightning location data and polarization
radar observations of clouds. 1991 Int. Aerospace and Ground Conf. On Lightning and Static
Electricity, April 16-19, J.F. Kennedy Space Center, FL, NASA Conf. Pub. 3106, pp. 85-1 - 8510.
Petersen, W.A. and S.A. Rutledge, 1996: Characteristic differences in cloud-to-ground
lightning flash densities and rain yields for different climate regions. Int. Conf. On Atmos.
Elec., June 10-14, Osaka, Japan.
Petersen, W.A. and S.A. Rutledge, 1998: On the relationships between cloud-to-ground
lightning and convective rainfall, J. Geophys. Res. Atmos., 103,D12, 14025-14040.
Rutledge, S.A. and W.A. Petersen, 1994: Vertical radar reflectivity structure and cloud-toground lightning in the stratiform region of MCS - Further evidence for in-situ charging in the
the stratiform region, Mon. Weather Rev. 122, 8, 1760-1776.
Keighton, S.J., H.B. Bluestein, and D.R. MacGorman, 1991: The evolution of a severe
mesoscale convective system: Cloud-to-ground lightning location and storm structure, Mon.
Wea. Rev., 119, 1533-1556.
Kummerow, C. and L. Giglio, 1994a: A passive microwave technique for estimating rainfall
and vertical structure information from space, Part I: Algorithm description. J. Appl. Meteor.,
33, pp. 3-18.
Marshall, J.S. and W.M.K. Palmer, 1948: The distribution of raindrops with size, J. Meteor.
5,165-166.
Mohr, K.I., E.R. Toracinta, E.J. Zipser, R.E. Orville, 1996: A comparison of WSR-88D
reflectivities, SSM/I brightness temperatures, and lightning for mesoscale convective systems in
Texas. Part II: SSM/I brightness temperature and lightning. J. Appl. Met., 35, pp. 919-931.
3729
Reap, R.R., and D.R. MacGorman, 1989: Cloud-to-ground lightning: Climatological
characteristics and relationships to model fields, radar observations, and severe local storms.
Mon. Wea. Rev., 117, 518-535.
Rutledge, S. A., and D.R. MacGorman, 1988: Cloud-to-ground lightning activity in the 10-11
June 1985 mesoscale convective system observed during the Oklahoma-Kansas PRE-STORM
project. Mon. Wea. Rev., 116, 1393-1408.
Spencer, R.W., H.M. Goodman, and R.E. Hood, 1989: Precipitation retrieval over land and
ocean with the SSMI/I: Identification and characteristics of the scattering signal. J. Atmos.
Ocean. Tech., 6, 254-273.
Toracinta, E.R., K.I. Mohr, E.J. Zipser, R.E. Orville, 1996: A comparison of WSR-88D
reflectivities, SSM/I brightness temperatures, and lightning for mesoscale convective systems in
Texas. Part I: radar reflectivity and lightning. J. Appl. Met., 35, pp. 902-918.
Workman, E.J. and S.E. Reynold, 1949: Electrical activity related to thunderstorm growth. Bull.
Amer. Meteor. Soc., 30, pp. 142-145.
3730