2014 First International Conference on Systems Informatics, Modelling and Simulation
A Short Term Tornado Prediction Model Using Satellite Imagery
Viswanath Kambhampaty
Vardhaman College of Engineering
Hyderabad, India
E-mail: [email protected]
Rohith Gali
Abstract— Environmental disaster is something that inflicts
adverse effects on biodiversity, economy and human health. In
order to reduce such adverse effects proper methodologies are
to be brought into action to forecast them before in hand. This
paper presents an alternative in predicting a tornado in
advance by taking into account the results obtained from
processing of satellite images contrary to the earlier methods
where radar Images were analyzed. By using the satellite
Images the noise levels can be reduced and higher accuracy of
the results can be obtained compared to radar Images. Taking
into consideration the observed values, the results obtained can
be compared and a more accurate prediction can be made that
would help us reduce huge losses in terms of both life and
property. Till date none of the methods put forward were up to
the mark and provided inaccurate predictions, and the
drawbacks of which are eliminated in this paper providing
with more accurate predictions. During the data mining
process, obstacles like noise and attenuations deteriorate the
quality of image hence denoising by k-mean clustering should
be done after which coif wavelet transformations are applied
on the image in order to obtain parameters such as square root
balance sparsity norm threshold value which are used to find
the wavelength ranges to formulate accurate predictions of
tornadoes. The proposed technique capitulate an average
accuracy of 92% in the prediction of a tornado.
TABLE I. FUJITA SCALE
F Scale
F0. 40-72 mph
F1. 73-112 mph
F2. 113-157 mph
F3. 158-207 mph
F4. 208-260 mph
F5. 261-318 mph
Damage
Light
Moderate
Considerable
Severe
Devastating
Incredible
EF Scale (U.S.)
EF0. 65-85 mph
EF1. 86-110 mph
EF2. 111-135 mph
EF3. 136-165 mph
EF4. 166-200 mph
EF5. Over 200 mph
Each year, around 1000 tornadoes pass over the US, but
only about 2% of them reach either F4 or F5 mark on the
Fujita scale. Majority of the tornadoes are not sturdy
enough, occurring in rural areas and doing little or no
damage. Some tornadoes, however, are much disastrous,
and can cut their way through major urban localities or
through an entire small town. These usually strike without
prior intimation and the devastation can be remembered for
years. No accurate tornado prediction method has yet been
invented in order to reduce the devastating effect in terms of
both life and property. Previous approaches in the prediction
of a tornado involved processing of radar based data in
order to detect the occurrence of one. But this approach
gave vague and inaccurate results since radar images
consisted of high noise levels and attenuations which
reduced the efficiency of the mechanism used over there.
The methodology employed here involves clustering i.e,
denoising and processing of satellite images and then
finding the threshold and standard deviation values which
are further used to locate the region of visible wavelengths
in which tornado occurs. In future, using this mechanism,
tornado can be predicted by matching the wavelengths of
the then satellite images with the values calculated
previously in this paper.
The rest of the paper is organized as follows: Section II
gives a glance to all the recent research carried on for the
prediction of Tornado. Section III depicts the formation of
tornado.
Section
IV
describes
experimentation
methodology. Section V illustrates the experimental results
and finally Section VI gives a conclusion.
INTRODUCTION
A tornado is an aggressively gyrating column of air that
is in contact with both the surface of a cumulonimbus cloud
and the earth or, in exceptional cases, the base of the
cumulonimbus cloud. It is often referred to as a twister. The
word tornado is derived from the Spanish word tronada,
which means thunderstorm, which was taken from the Latin
term tonare, meaning to thunder. The combination of
tronada and tornar gave rise to the word tornado which
means to turn.
Generally, a tornado occurs when wind shear, which can
produce a horizontal vorticity, integrates with
thunderstorms. The horizontal vorticity is turned vertical by
a thunderstorm and sets the storm rotating, transforming it
into a supercell. A mesocyclone is the strong rotating
updraft of storm. If the storm strengthens rapidly enough, a
downdraft may distort around the bottom portion of the
mesocyclone, tightening and escalating the rotation and
bringing it in contact with the ground to form a tornado.
The factors affecting the formation of a tornado include
humidity, temperature, and unsteadiness in the wind flow,
and the presence of a storm, all of which contribute to the
978-0-7695-5198-2/14 $31.00 © 2014 IEEE
DOI 10.1109/SIMS.2014.29
Vardhaman College of Engineering
Hyderabad, India
E-mail: [email protected]
formation of the thunderstorms needed to produce
tornadoes. A final aspect is wind shear, which gives these
storms the aptitude to produce tornadoes.
The EF (Enhanced Fujita) Scale given in Table I is the
criterion to measure tornadoes based on wind damage.
Keywords - Tornado; Coif wavelet; Image processing; k-means
clustering; Satellite imagery.
I.
Narasimha Prasad
Vardhaman College of Engineering
Hyderabad, India
E-mail: [email protected]
II.
RECENT RESEARCH
Research is the systematic observation of phenomena
for the purpose of learning new facts or testing the
105
application of theories to known facts. The prime purposes
of research are documentation, innovation, analysis, or the
development and research of methods and systems for the
progression of human intellect. Advent to research depends
on philosophical theory of knowledge, which varies
noticeably both within and between humanities and
sciences.
The main intention behind literature survey is to show
what you have read and understood about the hold of other
researchers who have studied the issue/problem that you are
analyzing and include that in your thesis. It can be done by
simple summarization, evaluating and complementing or
various ways that show that one has done the research and
which led to new and better conclusions.
Thomas A. Jones et al. [1] determined what percentage
of Doppler radar–detected vortices leads to tornadoes and
what vortex features are most useful in differentiating
vortices that are not and are coupled with a tornado. Caren
Marzban et al. [2] analyzed data produced by the national
severe storms laboratory’s tornado detection algorithm
which was a result of several methods in order to conclude
which storm-scale vortex features based on doppler radar is
the best predictors of tornadoes. V Lakshmanan, Indra
Adrianto et al. [3] used the least-squares method in order to
estimate shear, morphological image processing to establish
gradients, fuzzy logic to simulate compact measures of
tornado possibility and a classification neural network to
generate the final spatio-temporal probability field. C. A.
Doswell [4] emphasizes on the super cell identification
criteria and also the difference between tornado bearing
cloud and non tornadic clouds. Marzban, Caren, Gregory J.
Stumpf [5] used circulation database for training the neural
network which was truthed in order to determine which
circulation s were associated with reports of actual tornado
events at the ground. According to them if a circulation is
detected within 20 minutes prior to the ground report of
severe weather or a tornado or 5 minutes after a report, that
particular circulation was classified as a prediction of a
tornado. Hongkai Wang et al. [6] proposed and evaluated a
method of identification of tornadoes automatically in
doppler radar images by detecting the hook echoes. To
evaluate the hook echo detection algorithm, hook echoes
were detected in several radar datasets by using the
algorithm and compared with those proposed by the experts.
In their experiment on radar images, they obtained an
average probability of detection of tornado equal to 61.5%.
Davies, J. M [7] used a database of sounding s derived from
the rapid update cycle model to examine thermodynamic
features of F1 and higher intensity tornado events associated
with small storm relative helicity and/or high lifting
condensation level heights. Dean, A.R., and R.S. Schneider
[8] in their study examined the frequency of important
severe weather conditions and the relationship of these
conditions to the performance of convective watches issued
by NOAA storm prediction center. They analysed
parameters such as potential energy, wind shear, etc. This
helped in improving forecast performance. Ostby, Frederick
P [9] discusses and differentiates the methods that were used
to predict tornadoes in the late 70's and 90's. Mitchell, E. De
Wayne et al. [10] along with NSSL developed and tested a
tornado detection algorithm that has been designed to
recognize the locally intense vortices connected with
tornadoes using the WSR-88d base velocity data.
Colquhoun, J. R. [11] proposed a decision tree approach to
forecast tornadoes which uses only meteorological
parameters that are essential requirements for this
phenomenon to develop. Hamill, Thomas M and Andrew T.
Church [12] proposed a model that specifies the conditional
probability that a major tornado will occur given that a
thunderstorm occurs and given that certain RUC-2 CAPE
and helicity values are forecasted. Trapp, Robert J. et al
[13] collected a large dataset that were obtained using WSR88D mesocyclone detection algorithm to estimate the
percentage of tornadic mesocyclones. Vasiloff, Steven V.
[14] gave an alternate way where federal aviation
administration’s terminal doppler weather radar can be used
instead of the weather surveillance radar-1988 doppler
(WSR-88D) for tornado detection in order to yield better
results. Donaldson Ralph J and Paul R. Desrochers [15]
showed that an improvement in the reliability and timeliness
of tornado forecasting can be achieved through quantitative
measurement by doppler radar of selected mesocyclone
features such as excess rotational kinetic energy. Roger
Edwards et al. [16] brought into light two loopholes in the
present forecasting mechanisms they are 1) occasionally
inaccurate synoptic-scale guidance by operational numerical
weather prediction models, especially with regard to several
critical parameters 2) model and observational difficulties
regarding convective initiation and evolution. A. E. Mercer
et al. [17] studied a sample of 50 tornado outbreaks and 50
primarily non tornadic outbreaks were simulated using the
WRF to determine if they are able to distinguish outbreak
type using synoptic-scale input.
III.
FORMATION OF TORNADO
Tornadoes are usually the intense outcome of a very large
thunderstorm called a supercell. During the storm warm air
and cold air amalgamate. The warm air rises and the cold air
drops. The warm air in due course twists into a spiral and
forms a funnel cloud. Firstly, just before the thunderstorm
builds up, a change in wind course and a rise in wind speed,
at an increasing altitude, create an unseen horizontal
gyrating effect in the lower atmosphere. Secondly, rising air
within the thunderstorm’s updraft tilts the gyrating air to
vertical from horizontal. Next, within a vast majority of the
storm, an area of gyration, 2-6 miles wide is contained. The
robust, most vicious tornadoes form within this area of
gyration. Subsequently, a lower cloud base in the center of
the storm becomes a gyrating wall cloud. This area is mostly
rain-free. Finally, within a few minutes, a tornado builds up
and starts to inflict its annihilation.
In the initial stages, as the mesocyclone advances towards
the ground, a noticeable condensation funnel materializes
and descends from the bottom of the storm. As the funnel
106
descends, the rear flank downdraft (RFD) also comes in
contact with the ground. This creates a gust of air front that
can afflict damage, a reasonable distance from the tornado.
The funnel cloud transforms into a tornado within minutes
of the RFD approaching the ground. If the initial supercell
thunderstorm is powerful enough it can give birth to
numerous tornadoes. A new mesocyclone and a new tornado
can be formed from the dissipation of one mesocyclone as
shown in Figure 1.
Later on, the tornado has an adequate supply of warm,
moist in-flowing air to boost it, so it grows to maturity. This
can take up to an hour. This is the most critical phase of the
tornado and can be more than 1.6 km wide. The RFD turns
into an area of cool surface winds and begins to envelop
around the tornado, isolating the inflow of warm air,
successfully choking the tornado.
As the RFD cuts off the tornadoes air supply, the vortex
begins to deteriorate. This dissolving stage only lasts a few
minutes then the tornado weakens. The tornado is still
potent of inflicting damage. The storm is shrinking, but the
speed of winds can increase.
information retrieved may go haywire from original value.
Hence, the image must be denoised.
Every image consists of various textures such as asphalt,
barren lands, clouds, concrete, forests, grass and water
bodies out of which not every texture is needed. These
textures are to be segregated in order to get the desired
texture for further analysis.
Clustering is a task of grouping data into several
segments whose members have a common resemblance.
These segregations are based on wavelength as resemblance
factor. The pixel values of various textures are used for this
segregation which is shown in Table II.
Figure 1. Formation of Tornado
IV.
EXPERMINTATION METHODOLOGY
The primary purpose of this processing of satellite
imagery is to identify the wavelength for prediction of
tornado. The entire course of action employed is shown in
the Figure 2. The pre-requirements for this methodology are
satellite imagery of tornadoes with noise or without noise.
When the satellite image containing noises such as
illumination variations, occlusions, and scale variations,
deformation of objects and so on, is analyzed, the
Figure 2. Real Time Detection of Tornado
107
TABLE II. PIXEL VALUES OF VARIOUS TEXTURES
Textures
Water body
Forest
Grass
Asphalt
Barren lands
Concrete
Clouds
Pixel values
0-20
21-33
34-81
82-140
141-199
200-224
225-255
Clusters are obtained based on the pixel values. The
satellite image is thus segmented into various clusters and
the region of interest which is cloud texture is extracted
whose pixel value lies in the range of 225 to 255.
K-means clustering is the mechanism deployed in this
paper. It segregates the image such that the textures in a
cluster have approximately same pixel values when
compared with other clusters formed. In order to anticipate a
fore-coming tornado a higher range k-means algorithm is
adopted to cluster the image into six segments using
Euclidean distance metric to avoid local minima.
The clustering mechanism is applied for the image shown
in Figure 3 and the resultant clusters produced are shown in
Figure 4. MATLAB was used for the processing of images
with image processing tool box, which provide image
segmentation algorithms, tools and a complete data analysis,
visualization and algorithm development platform.
Wavelet transformation is applied on the cloud cluster
obtained above. The coif wavelet transform is chosen
because the Coiflets assure minimum signal disruption and
provide a suitable technique. Coiflets are discrete wavelets
designed by Ingrid Daubechies, to have scaling functions
with vanishing moments. The Coifman wavelet (Coiflet) has
enhanced steadiness and better regularity than the
Daubechies wavelet, because it has more vanishing
moments for the scaling function. The Coiflet has been
established to be a popular and efficient basis in the signal
and image processing.
Further, the coif wavelet transformation converts the
RGB image into gray scale image and eventually de noises
the image.
Figure 4. Images segmented as separate objects. (a) Cluster 1 (b) Cluster 2
(c) Cluster 3 (d) Cluster 4 (e) Cluster 5 (f) Cluster 6.
To foretell the occurrence of a tornado an estimation of
certain range of values from the clustered image should be
made. Since the cloud image taken falls into visible infrared
range, the wavelength will be in the visible spectrum range
that would lie between 400 nm - 700 nm.
The value of square root balance-sparsity norm threshold
is calculated for those denoised, clustered cloud images.
From the Table IV, it can be observed that, whenever a
tornado occurs in the denoised, clustered image in frequency
domain square root balance-sparsity norm threshold value
ranges between 13 and 14. Soft threshold was chosen to
find the value for square root balance-sparsity norm,
because this procedure does not cause non-continuants at
ck = ± . The notations used in this paper are given in
Table III.
TABLE III. SYMBOLS AND NOTATIONS USED FOR WAVELENGTH FACTOR
ESTIMATION
Symbol
c s k cki 2
σ
n
ck k
d
d
Figure 3. Original image
108
Notation
Soft threshold value
Wavelength of ith Pixel
Square root balance-sparsity norm threshold
Variance
Standard deviation
Number of pixels
Mean wavelength
Fixed threshold value
Correction factor
Wavelength factor
TABLE IV. VALUES FOR VARIANCE, SQUARE ROOT BALANCE-SPARSITY NORM THRESHOLD, WAVELENGTH FACTOR AND THE WAVELENGTH
Image No.
Square root balance
sparsity norm
threshold ( )
Standard
deviation
(σ)
Wavelength
factor
13.29
13.99
13.80
13.94
13.85
13.93
13.63
13.89
13.05
13.99
13.02
13.87
13.03
13.07
13.97
2.84
2.75
2.68
2.89
2.70
2.78
2.54
2.74
2.75
2.68
2.76
2.74
2.72
2.67
2.60
43.29
44.66
45.82
42.54
45.55
44.23
48.29
44.87
44.69
45.91
44.53
44.85
45.23
46.08
47.25
Image 1
Image 2
Image 3
Image 4
Image 5
Image 6
Image 7
Image 8
Image 9
Image 10
Image 11
Image 12
Image 13
Image 14
Image 15
The values of variance, square root balance-sparsity
norm threshold, wavelength factor and the wavelengths are
calculated using Equations 1, 2, 3 & 4 are shown in Table 4.
(1)
For the determination of threshold values, equation (2) is
used.
2 2 log n n
(2)
The equation (3) is used to calculate the variance.
2
ck ck 2
i
n 1
(3)
Fixed threshold value is calculated by using equation (4).
255 d 2 2 log n k dn
d
n 1 V.
Wavelength
(λ) = * d
Observed result
575.35
624.79
632.37
593.06
630.90
616.07
658.16
623.26
583.23
642.27
579.78
622.14
589.40
602.28
660.07
Tornado
Tornado
Tornado
Tornado
Tornado
Tornado
Tornado
Tornado
Tornado
Tornado
Tornado
Tornado
Tornado
Tornado
Tornado
d
mainly affect the formation of tornado are acknowledged
and studied. Images contain primary lower attributes such as
texture, pixel values and intensity. In this study, pixel values
are considered as the main factor that is to be extracted from
the satellite images. Using pixel values, wavelength is
calculated and this wavelength is used for the detection of
tornado.
The actual analysis was carried out on a sample of 579
images and out of those 579 images the results of 50 images
have been tabulated in table 5. Out of 50 images tabulated
in Table IV and Table V, 38 of them were tornadic and rest
of the 12 were non-tornadic images. Of these 38 tornadic
images 35 were experimentally proved to be true and the
remaining 3 images contradicted the observed results, and
out of the 12 non-tornadic images analyzed, 11 turned out to
be in accordance with the observed results and 1 image
negated with the observed result.
This analysis yileded an accuracy of 92.1% for the
tornadic images and an accuracy of 91.6% for the nontornadic images. The mean accuracy for both these results
turn out to be 92%. Since the above results and research is
more accurate than the previous methodology, it is more
convincing and satisfactory.
Consider image 18 which is of a tornado. The results
found experimentally shows a threshold value of 13.15 and
wavelength equal to 631.76 nm. Since it is in the range of
wavelength and threshold values established earlier, it is
experimentally proved to be a tornado. Taking into account
image 40 which is a non-tornadic image, the experimental
results showed a threshold value of 13.18 and wavelength
equal to 617.06, which falls under the established range
turned out to be contradicting the observed result.
The soft threshold expression is shown in equation (1).
sign c k c k , c k c s k 0,
c k d d (4)
EXPERIMENTAL RESULTS
Tornado is one of the biggest and most brutal form of a
natural disaster, forcasting which can prevent lot of damage.
Many researches have put forward many theories which
have turned to be inaccurate due to the lack of proper
analysis and study of various parameters that involve in the
formation of a tornado. Mining of data from satellite images
is even more challenging and cumbersome task since it
involves processing of not one but hundreds of satellite
images in order to extract the required results.
The satellite images are obtained from Indian
meteorological department and are used to analyze in order
to identify the tornado. The extraction of factors which
109
TABLE V. IDENTIFICATION OF TORNADO
Image No.
Image 16
Image 17
Image 18
Image 19
Image 20
Image 21
Image 22
Image 23
Image 24
Image 25
Image 26
Image 27
Image 28
Image 29
Image 30
Image 31
Image 32
Image 33
Image 34
Image 35
Image 36
Image 37
Image 38
Image 39
Image 40
Image 41
Image 42
Image 43
Image 44
Image 45
Image 46
Image 47
Image 48
Image 49
Image 50
VI.
Threshold Wavelength Experimental Observed
( )
(λ)
results
result
13.01
616.13
Tornado
Tornado
13.06
595.80
Tornado
Tornado
13.15
631.76
Tornado
Tornado
13.99
672.64
Tornado
Tornado
13.85
651.90
Tornado
Tornado
13.73
671.95
Tornado
Tornado
13.93
659.95
Tornado
Tornado
13.91
645.10
Tornado
Tornado
14.11
642.27
Non Tornado Tornado
13.45
604.40
Tornado
Tornado
13.62
619.05
Tornado
Tornado
13.9
660.57
Tornado
Tornado
13.65
637.12
Tornado
Tornado
13.89
636.49
Tornado
Tornado
14.2
587.27
Non Tornado Tornado
13.88
618.53
Tornado
Tornado
13.17
648.68
Tornado
Tornado
12.56
607.21
Non Tornado Tornado
13.11
646.76
Tornado
Tornado
13.61
632.85
Tornado
Tornado
13.76
611.85
Tornado
Tornado
13.79
673.02
Tornado
Tornado
13.96
628.92
Tornado
Tornado
16.51
663.19
Non Tornado Non Tornado
13.18
617.06
Tornado
Non Tornado
12.06
646.26
Non Tornado Non Tornado
12.2
668.92
Non Tornado Non Tornado
12.27
652.69
Non Tornado Non Tornado
12.36
608.78
Non Tornado Non Tornado
12.65
637.35
Non Tornado Non Tornado
12.16
620.54
Non Tornado Non Tornado
12.68
647.08
Non Tornado Non Tornado
11.97
613.13
Non Tornado Non Tornado
12.53
611.28
Non Tornado Non Tornado
12.42
602.81
Non Tornado Non Tornado
CONCULSION
Identification
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
FALSE
TRUE
TRUE
TRUE
TRUE
TRUE
FALSE
TRUE
TRUE
FALSE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
FALSE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
REFERENCES
This paper proposes an alternate and more accurate
method of predicting a tornado by choosing Square root
balance- sparsity norm threshold and spectral characteristics
of cloud in the visible and infra-red spectrum to differentiate
between tornadic and non-tornadic clouds from the satellite
images. The methodology employed here surpasses previous
approaches where radar images were processed. Therefore
more accurate prediction of tornadoes can be made by
exploiting k-mean clustering algorithm and coif wavelet
transformations. The risk of getting deviating and inaccurate
results due to presence of noise in radar images can be
eliminated by utilization of satellite images for forecasting
of tornado.
Extra parameters such as cloud dimensions, velocity,
stratospheric temperatures and surface temperature that
effect the precipitation of the cloud can be taken into
considereration which will be stressed on in our future work.
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