Transcript Automated Cyclone Discovery.FINAL

Automated Cyclone Discovery and
Tracking using Knowledge Sharing in
Multiple Heterogeneous Satellite Data
Authors
Shen-Shyang Ho
Ashit Talukder
Jet Propulsion Laboratory
California Institute of Technology
Group 3
Karen Simpson
Paul Fomenky
Roman Sizov
Sameh Ebeid
Assignment 1
02/22/2010
Outline
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Introduction
Previous Work
Data Description
Issues and Challenges
Heterogeneous Remote Satellite-Based
Detection and Tracking Approach
Experimental Results
Lessons Learned and Conclusions
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Introduction
What is Cyclone
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An area of closed,
circular fluid motion
rotating in the same
direction as the Earth
Low pressure areas,
their center is the
lowest atmospheric
pressure in the region
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Introduction
Surface-based Types
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Polar cyclone
Polar low
Extra-tropical
Sub-tropical
Tropical
Mesoscale
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Introduction
Extra-tropical
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Synoptic scale low pressure weather system
that has neither tropical nor polar
characteristics
Often described as depressions or lows by
weather forecasters
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Introduction
Tropical
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Storm characterized by a low pressure
center and numerous thunderstorms that
produce strong winds and flooding rain
Referred to by other names such as
hurricane, typhoon, tropical storm
Develop over large bodies of warm water,
and lose strength if they move over land
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Introduction
Tropical
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An average 86 tropical cyclones of tropical
storm intensity form annually worldwide, 47
reaching hurricane/typhoon strength, and 20
becoming intense tropical cyclones
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Introduction
Cyclone detection and tracking
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The tropical prediction center / National
Hurricane Center (TPC/NHC) use conventional
surface and upper-air observations and
reconnaissance aircraft report
In recent years, some studies have used
satellite images that are manually retrieved
and analyzed to improve the accuracy of
cyclone tracking
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Introduction
Cyclone detection and tracking
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A new automated global cyclone discovery
and tracking approach on a truly global basis
using near real-time (NRT) and historical
sensor data from multiple satellite
This implementation employs two types of
satellite sensor measurements
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QuikSCAT wind satellite data
Merged precipitation data using TRMM and other
satellites
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Introduction
Cyclone detection and tracking
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Challenges pertaining to mining data from
orbiting satellites
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Each orbiting satellite cannot monitor a region
continuously and the measurements are
instantaneous
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Introduction
Cyclone detection and tracking
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Challenges pertaining to mining data from
orbiting satellitesCan minimize their effects by
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using data from multiple satellite
Each orbiting satellite cannot monitor a region
continuously and the measurements are
instantaneous
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Introduction
Cyclone detection and tracking
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Challenges pertaining to mining data from
orbiting satellites
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Each orbiting satellite cannot monitor a region
continuously and the measurements are
instantaneous
Different satellites provide different measurements
Different satellites sensors acquire measurements
at different spatial and temporal resolution
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Introduction
Cyclone detection and tracking
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Challenges pertaining
to mining
These problems
makedata from
orbiting satellites
mining heterogeneous data
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from multiple
orbiting
Each orbiting satellite
cannot
monitor a region
satellites
extremely are
continuously and
the measurements
challenging and remains
instantaneous
as a provide
now primarily
an measurements
Different satellites
different
unsolved problem
Different satellites sensors acquire measurements
at different spatial and temporal resolution
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Introduction
Cyclone detection and tracking
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Challenges related to the problem of detection
and tracking of cyclones
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Cyclone events are dynamic in nature
There is lack of annotated negative (non-cyclone)
examples by experts
A single satellite sensor may miss a cyclone event
due to a pre-defined orbiting trajectory
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Outline
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Introduction
Previous Work
Data Description
Issues and Challenges
Heterogeneous Remote Satellite-Based
Detection and Tracking Approach
Experimental Results
Lessons Learned and Conclusions
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Previous work
Previous work
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No solution currently exists that uses
heterogeneous sensor measurement to
automatically detect and track cyclones
The current solutions involve human
interference and decision
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Outline
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Introduction
Previous Work
Data Description
Issues and Challenges
Heterogeneous Remote Satellite-Based
Detection and Tracking Approach
Experimental Results
Lessons Learned and Conclusions
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Data description
QuikSCAT Wind Data
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The QuikSCAT (Quick Scatterometer) mission
provide important high quality ocean wind data
set
Recent research showed QuikSCAT data is
useful for early detection of tropical cyclones
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Data description
Precipitation Data from TRMM satellite
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The Tropical Rainfall Measurement Mission
(TRMM) is a joint mission between NASA and
JAXA designed to monitor and study tropical
rainfall
The (Level) 3b-42 TRMM data product used in
this paper is produced using a combined
instrument rain calibration algorithm
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Outline
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Introduction
Previous Work
Data Description
Issues and Challenges
Heterogeneous Remote Satellite-Based
Detection and Tracking Approach
Experimental Results and Conclusions
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Issues and Challenges
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Main issues and challenges
Non-Continuous Region Monitoring
Event Occlusion
Varying Temporal and Spatial Resolution
Lack of Annotated Negative Examples
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Main issues and challenges
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Satellite measurements are instantaneous;
hence, satellites cannot measure sustained
winds. Remember, a leading characteristic of
cyclones is sustained winds
TRMM 3B42 data is known to underestimate
rainfall, which might lead to false negatives
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Non-Continuous Region
Monitoring – Problem
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Geostationary Operational Environmental Satellites
(GOES) monitor specific area at all times, helping
identify “sustained” winds etc. Unfortunately, most
countries do not have these.
Because QuikSCAT and TRMM are motile, this
monitoring is “lost.” This results in “invisible” swaths.
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Non-Continuous Region
Monitoring – Problem Evidence
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Non-Continuous Region Monitoring –
Operational Weather Satellite System
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Satellite systems consist of two types
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Geostationary Operational Environmental
Satellites are static and throw light on current and
short term weather trends.
Orbiting satellites like QuikSCAT and TRMM help
with longer term forecasting.
http://noaasis.noaa.gov/NOAASIS/ml/genlsatl.html
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Non-Continuous Region
Monitoring – Solution
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Usage of multiple satellites produces a
higher temporal density hence helping
alleviate the problem.
A group of complementary satellites can
make this problem almost insignificant.
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Event Occlusion - Problem
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Satellite swath can partially (or worst case,
totally) miss events of interest.
Though in continuous orbit, event can be
gone by time satellite comes back.
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Event Occlusion –
Problem Evidence 1
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QuikSCAT showing
only a small part of
event of interest.
Hurricane Dean –
Aug 17th 2007, 0900
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Event Occlusion –
Problem Evidence 2
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Next QuikSCAT
swath shows a bit
more.
Hurricane Dean –
Aug 17th 2007, 1041
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Event Occlusion –
Problem Evidence 3
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Another QuikSCAT
swath shows much
more, but missing
eye of storm.
Hurricane Dean –
Aug 17th 2007, 2310
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Event Occlusion –
Problem Evidence 4
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QuikSCAT swath
from previous day
showed more!
Hurricane Dean –
Aug 16hth 2007,
2156
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Event Occlusion – Solution
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Clearly, multiple orbits of the same satellite
can produce more information on the event
being examined.
Also, as in continuity monitoring issue,
numerous satellites working together are less
likely to miss important events.
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Varying Temporal and Spatial
Resolution – Problem
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Different aspects influence the temporal
resolution of measurements:
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Satellite orbit time (QuikSCAT 101 minutes,
TRMM 92.5mins)
Swath width of measuring instrument (SeaWinds
on QuikSCAT 1800km; PR, TMI and VIRS on
TRMM 247km, 878km, 873km respectively)
Geographic coverage (QuikSCAT – global;
TRMM – 50N to 50S)
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Varying Temporal and Spatial
Resolution – Problem Cont’d
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Spatial resolution depends on
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Sensor instruments (PR, TMI and VIRS on TRMM
5.1km, 5.0km, 2.4km respectively)
Satellite orbital altitude ((TRMM Pre-boost
(350km) (TMI): 4.4km to 5.1km (Post-boost (403
km))
Processing algorithm (operational QuikSCAT data
has spatial resolutions of 12.5km and 25km )
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Varying Temporal and Spatial
Resolution – Problem Cont’d 2
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In addition to inter satellite differences, there
are some intra satellite tempo-spatial
differences.
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TRMM Level 3 data has lower temporal resolution
than levels 1 and 2.
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Varying Temporal and Spatial
Resolution – Solution
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On TRMM, mine areas QuikSCAT showed
events of interest on.
Also, because of different swath sizes,
latitudes and longitudes were used to identify
locations.
Temporal tracking done on TRMM as
temporal resolution higher than in
QuikSCAT.
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Lack of Annotated Negative
examples - Problem
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Scientists have not clearly shown what a
“non-event” is despite the large archives of
events.
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Lack of Annotated Negative
examples - Solution
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Random “non-event” days were monitored
and fed to system as examples of non event.
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Introduction
Previous Work
Data Description
Issues and Challenges
Heterogeneous Remote Satellite-Based
Detection and Tracking Approach
Experimental Results and Conclusions
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Heterogeneous Remote Satellite-Based
Detection and Tracking Approach
Heterogeneous Remote Satellite-Based
Detection and Tracking Approach
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QuikSCAT Feature Selection
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Ensemble Classifier for Cyclone Detection
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Knowledge Sharing between TRMM and
QuikSCAT data for Cyclone Tracking
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QuikSCAT Feature Selection
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Features that characterize and identify a
cyclone are selected from QuikSCAT satellite
data
The QuikSCAT Level 2B data that consist of
ocean wind vector information are utilized
The Level 2B data are grouped by rows of
wind vector cells (WVC) which are squares of
dimension 25 km or 12.5 km
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QuikSCAT Feature Selection (cont`d)
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1624 WVC rows at 25 km or 3248WVC rows
at 12.5 are required to cover the earth
circumference
Out of 25 fields in the data structure for the
Level 2B data we are interested only in
latitude, longitude, wind speed(WS) and wind
direction (WD)
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QuikSCAT Feature Selection (cont`d)
Table 1. The fields of interest from Level 2B data structure
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Field
Unit
Minimum
Maximum
WVC latitude
Deg
-90.00
90.00
WVC longitude
Deg E
0.00
359.99
Selected speed
m/s
0.00
50.00
Selected direction
Deg from
North
0.00
359.99
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QuikSCAT Feature Selection (cont`d)
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The Level 2B data needs to be interpolated
on a uniformly gridded surface due to the
non-uniformity in the measurements taken by
the QuikSCAT satellite on a spherical surface
The nearest neighbor rule is used for this
pre-processing procedure for both wind
speed (WS) and wind direction (WD)
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QuikSCAT Feature Selection (cont`d)
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Histograms are constructed to estimate
probability density of the wind speed (WS)
and wind direction (WD) within a predefined
bounding box extracted from a QuikSCAT
image
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QuikSCAT Feature Selection (cont`d)
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WS(i,j),WD(i,j) – wind speed and wind
direction at location (i,j)
DSR(i,j) – the direction to speed ratio at (i,j)
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QuikSCAT Feature Selection (cont`d)
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When there is a strong wind with wind
circulation, the DSR at a WVC will be small
DSR histogram will have a skewed
distribution towards the smaller value
When there is weak or no wind with no
circulation, DSR histogram does not have the
skewed characteristics
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QuikSCAT Feature Selection (cont`d)
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When a region contains a cyclone, the WS
histogram shows a density estimate skewed
towards the larger values and WD histogram
shows a “near uniform” distribution
A cyclone is defined as a “warm-core nonfrontal synoptic-scale” system, with “organized
deep convection and a closed surface wind
circulation about a well-defined center”
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QuikSCAT Feature Selection (cont`d)
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To discriminate between cyclone and noncyclone events based on the circulation
property two additional features are used:
(1) a measure of relative strength of the
dominant wind direction (DOWD)
(2) the relative wind vorticity (RWV)
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QuikSCAT Feature Selection (cont`d)
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u(i,j) and v(i,j) are the u-v components of the wind direction
WD(i,j) at location (i,j) with
1≤i ≤m and 1≤j≤n
The (mn)-by-2 matrices M are constructed as follows:
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If λ1 and λ2 are the eigenvalues of matrix M
such that λ1 < λ2, then the eigenvalue ratio of a
bounding box B of dimension m by n is
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ERB is used to quantify the relative strength of
the dominant wind direction (DOWD) within B
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QuikSCAT Feature Selection (cont`d)
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If there is a circulation (i.e. a cyclone in B), ERB
will be near to 1
If the wind is unidirectional (no storm or
cyclone in B), λ2 will be much greater than λ1,
and as a result ERB is much larger
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QuikSCAT Feature Selection (cont`d)
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The relative wind vorticity (RWV) at location
(i,j) is calculated by the formula:
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where u and v are the two wind vector
components in the west-east and south-north
directions, and d is the spatial distance between
two adjacent QuikSCAT measurements in a
uniformly gridded data
ωz or ζ: vertical component
of relative vorticity
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Ensemble Classifier for Cyclone Detection
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Ensemble methods are learning algorithms
that make predictions on observations based
on a majority or weighted vote from a set of
classifiers or predictors
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Ensemble Classifier for Cyclone Detection (cont`d)
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The ensemble classifier is built to identify
cyclones in QuikSCAT images
The TRMM precipitation data are not used in
the ensemble because
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It has a weak discriminating power; heavy rainfall
does not imply existence of cyclone
It is very unlikely that one has QuikSCAT and
TRMM data concurrently
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Ensemble Classifier for Cyclone Detection (cont`d)
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Regions in a QuikSCAT image likely to
contain a cyclone are localized based on
wind speed
Regions that have areas less than some
threshold are removed
Five classifiers based on features
extracted from the QuikSCAT training
data are constructed to identify the
cyclones
Two classifiers are thresholding classifier
based on the DOWD and RWV features,
and the other three are support vector
machine (SVM) that use histogram
features for WS, WD and DSR
The classification decision is based on
majority vote among the five classifiers
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Figure 5. Ensemble Classifier (Cyclone Discovery Module)
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Knowledge Sharing between TRMM and
QuikSCAT data for Cyclone Tracking
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The multi-sensor knowledge-sharing solution is based on the
strength of each remote sensor type
– QuikSCAT has excellent information for cyclone detection
but lack sufficient temporal resolution (each pass-through is
repeated only every 12 hours)
– TRMM has excellent temporal resolution of 3 hours, but
lacks good discriminative ability for accurate cyclone
detection
– Therefore, QuikSCAT data are used for cyclone detection,
and TRMM data for tracking based on knowledge obtained
from the ensemble classifier using QuikSCAT features
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Knowledge Sharing between TRMM and
QuikSCAT data for Cyclone Tracking (cont`d)
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QuikSCAT data are retrieved, and are input
into the cylone discovery module to locate
or identify possible cyclones
The cyclone location is used to predict the
likely regions to contain a cyclone at the
next incoming data stream retrieved using
a linear Kalman filter predictor, which is
important because TRMM precipitation data
are not a definitive indicator of cyclones
A cyclone localized by applying a threshold
to the TRMM precipitation rate
measurement (T6 = 0)
After a cyclone is located the Kalman filter
measurement update or correction is
applied to obtain an estimate of the new
state vector or the predicted location of the
cyclone in the next TRMM (or QuikSCAT)
observation cycle
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Figure 6. Knowledge sharing between TRMM and QuikSCAT data
for Cyclone Tracking
Automated Cyclone Discovery
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Heterogeneous Remote Satellite-Based
Detection and Tracking Approach
Knowledge Sharing between TRMM and
QuikSCAT data for Cyclone Tracking (cont`d)
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A cyclone is a dynamic event and its size evolves rapidly over
time, and therefore modeling and predicting only the cyclone
center in space over time would be grossly inadequate
Thus, the maximum and the minimum latitude/longitude of the
bounding box spanned by the cyclone is used based on the
hypothesis that the cyclone evolves linearly in space over time
The estimated bounding box was expanded (or contracted) based
on the estimated Kalman error covariance to define a search
region for the cyclone in the TRMM image
This modeling significantly improves the quality of knowledge
sharing between heterogeneous satellites compare to the model
that uses only the center coordinates of the cyclone
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Outline
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Introduction
Previous Work
Data Description
Issues and Challenges
Heterogeneous Remote Satellite-Based
Detection and Tracking Approach
Experimental Results and Conclusions
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Experimental Results
Training Set and Test Data
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Training Set
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Test Set
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191 QuikSCAT images of cyclones occurring in
North Atlantic Ocean in 2003
1833 negative examples (unlabeled examples
from four days in 2003 that no tropical cyclone)
54 cyclone events in North Atlantic Ocean in 2006
1822 non-cyclone events
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Experimental Results
Classification Performance
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Step 1: Determine thresholds for DOWD
(Dominant Wind Direction) and RWV (Dominant
Wind Vorticity) features from test set results
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Experimental Results
Performance of DOWD classifier
0.80
Positive
0.59
0.38
1.958
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Experimental Results
Performance of RWV Classifier
0.89
0.85
0.80
1.51
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Experimental Results
Classification Performance
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Step 1: Determine thresholds for DOWD
(Dominant Wind Direction) and RWV (Dominant
Wind Vorticity) features from test set results
Step 2: Analyze performance of different
classifier ensembles
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Experimental Results
Different Classifier Ensembles
•RWV
•DOWD
•SVM ensemble
•CDM
•SVM + RWV
ensemble
•SVM + DOWD
ensemble
•CIS (Ho and
Talukder, 2008)
Experimental Results
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Experimental Results
ROC
Curve
(Receiver Operating Characteristics)
•RWV is a more
robust feature
than DOWD in
discriminating
cyclone and
non-cyclone
events
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Experimental Results
Classifier Performance
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Step 1: Determine thresholds for DOWD
(Dominant Wind Direction) and RWV (Dominant
Wind Vorticity) features from test set results
Step 2: Analyze performance of classifiers
Step 3: Use CDM to track an isolated hurricane
event (Hurricane Isabel, 2003) using QuikSCAT
and TRMM data
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Experimental Results
Tracking Hurricane Isabel
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Experimental Results
Tracking Hurricane Isabel
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Experimental Results
Tracking Hurricane Isabel
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Experimental Results
Tracking Hurricane Isabel
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Experimental Results
Tracking Hurricane Isabel
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Experimental Results
Experimental Results
Tracking Hurricane Isabel
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Experimental Results
Tracking Hurricane Isabel
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Tracking Hurricane Isabel
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Experimental Results
Tracking Hurricane Isabel
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Experimental Results
Tracking Hurricane Isabel
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Experimental Results
Tracking Hurricane Isabel
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Experimental Results
Tracking Hurricane Isabel
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Experimental Results
Tracking Hurricane Isabel
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Experimental Results
Tracking Hurricane Isabel
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Automated Cyclone Discovery
and tracking
Experimental Results
Tracking Hurricane Isabel
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Automated Cyclone Discovery
and tracking
Experimental Results
Tracking Hurricane Isabel
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2/23/2010
Automated Cyclone Discovery
and tracking
Conclusions



87
Conventional methods that utilize human
resources cannot handle massive, unlabeled
high-dimensional heterogeneous data
This method provides an efficient solution to
track cyclonic events which combines
information from multiple satellites
The threshold values depend on the desired
accuracy, as well as the desired rate of true
positives and true negatives Automated Cyclone Discovery
2/23/2010
and tracking
Questions?
88
2/23/2010
Automated Cyclone Discovery
and tracking