Group 3 Karen Simpson Paul Fomenky Roman Sizov Sameh Ebeid

Automated Cyclone Discovery and Tracking using Knowledge Sharing in Multiple Heterogeneous Satellite Data

Group 3Karen SimpsonPaul FomenkyRoman SizovSameh Ebeid

AuthorsShen-Shyang Ho

Ashit TalukderJet Propulsion Laboratory

California Institute of Technology

Assignment 102/22/2010

2/22/2010Automated Cyclone Discovery

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Outline

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

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

Polar cyclone Polar low Extra-tropical Sub-tropical Tropical Mesoscale


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Introduction

Extra-tropical

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

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

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

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


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– QuikSCAT wind satellite data– Merged precipitation data using TRMM and other

satellites


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Introduction


Challenges pertaining to mining data from orbiting satellites– Each orbiting satellite cannot monitor a region

continuously and the measurements are instantaneous


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Introduction




Can minimize their effects by using data from multiple satellite


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Introduction




– Different satellites provide different measurements– Different satellites sensors acquire measurements

at different spatial and temporal resolution


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Introduction




– Different satellites provide different measurements– Different satellites sensors acquire measurements

at different spatial and temporal resolution

These problems make mining heterogeneous data

from multiple orbiting satellites extremely

challenging and remains as a now primarily an

unsolved problem


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Introduction


Challenges related to the problem of detection and tracking of cyclones– 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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Previous work

Previous work

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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Data description

QuikSCAT Wind Data

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

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

Outline


Detection and Tracking Approach Experimental Results and Conclusions


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Issues and Challenges

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

Non-Continuous Region Monitoring – Problem

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

Satellite systems consist of two types– 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

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

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

QuikSCAT showing only a small part of event of interest.

Hurricane Dean – Aug 17th 2007, 0900


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print


Next QuikSCAT swath shows a bit more.



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print


Another QuikSCAT swath shows much more, but missing eye of storm.



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print


QuikSCAT swath from previous day showed more!

Hurricane Dean – Aug 16hth 2007, 2156


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Event Occlusion – Solution

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

Different aspects influence the temporal resolution of measurements:– 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

Spatial resolution depends on – 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

In addition to inter satellite differences, there are some intra satellite tempo-spatial differences.– TRMM Level 3 data has lower temporal resolution

than levels 1 and 2.


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Varying Temporal and Spatial Resolution – Solution

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

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

Random “non-event” days were monitored and fed to system as examples of non event.


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Heterogeneous Remote Satellite-Based Detection and Tracking Approach


QuikSCAT Feature Selection

Ensemble Classifier for Cyclone Detection

Knowledge Sharing between TRMM and

QuikSCAT data for Cyclone Tracking


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QuikSCAT Feature Selection

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)

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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Table 1. The fields of interest from Level 2B data structure

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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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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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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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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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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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 non-frontal synoptic-scale” system, with “organized deep convection and a closed surface wind circulation about a well-defined center”


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To discriminate between cyclone and non-cyclone 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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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

ERB is used to quantify the relative strength of the dominant wind direction (DOWD) within B


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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


The relative wind vorticity (RWV) at location (i,j) is calculated by the formula:

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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ωz or ζ: vertical component of relative vorticity


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Ensemble Classifier for Cyclone Detection

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)

The ensemble classifier is built to identify cyclones in QuikSCAT images

The TRMM precipitation data are not used in the ensemble because– 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

Ensemble Classifier for Cyclone Detection (cont`d)

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 Figure 5. Ensemble Classifier (Cyclone Discovery Module)


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Knowledge Sharing between TRMM and QuikSCAT data for Cyclone Tracking

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

Knowledge Sharing between TRMM and QuikSCAT data for Cyclone Tracking (cont`d)

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

Figure 6. Knowledge sharing between TRMM and QuikSCAT data for Cyclone Tracking


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Knowledge Sharing between TRMM and QuikSCAT data for Cyclone Tracking (cont`d)

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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Training Set and Test Data

Training Set– 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)

Test Set– 54 cyclone events in North Atlantic Ocean in 2006– 1822 non-cyclone events


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Experimental Results

Classification Performance

Step 1: Determine thresholds for DOWD (Dominant Wind Direction) and RWV (Dominant Wind Vorticity) features from test set results


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0.59

0.80

0.38


Performance of DOWD classifier

Positive

Performance of RWV Classifier


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1.51

0.85 0.80

0.89



Step 2: Analyze performance of different classifier ensembles


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Classification Performance

Different Classifier Ensembles

•RWV •DOWD •SVM ensemble•CDM•SVM + RWV ensemble•SVM + DOWD ensemble•CIS (Ho and Talukder, 2008)



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ROC Curve

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•RWV is a more robust feature than DOWD in discriminating cyclone and non-cyclone events


(Receiver Operating Characteristics)

Automated Cyclone Discovery and tracking

Classifier Performance


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Step 2: Analyze performance of classifiers Step 3: Use CDM to track an isolated hurricane

event (Hurricane Isabel, 2003) using QuikSCAT and TRMM data


Tracking Hurricane Isabel


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Experimental ResultsExperimental Results


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Conclusions

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


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Questions?


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Documents

Group 3 Karen Simpson Paul Fomenky Roman Sizov Sameh Ebeid