Global cyclone detection and tracking GLYDER for climate variability studies: A multisensor data pro

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Global cyclone detection and tracking (GLYDER)
for climate variability studies A multisensor
data processing approachNASA AISR Program
Ashit Talukder, Andrew Bingham, Timothy
Liu Email Ashit.Talukder_at_jpl.nasa.gov 818 354
1000 Jet Propulsion Laboratory Oct. 04, 2006
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Outline

• Science and Technical Objectives
• Customers/end-users
• Tech transfer integration possibilities
• Explore mission alignment
• Technical Approach
• Preliminary Results (!!)
• Plans for FY07 (Yr 1)

Hurricane Georges, Sept 24, 1998
Hurricane Floyd, Sept 14, 1999
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GLYDER Primary Science Motivation

• Earths climate system exhibits intrinsic
  variability
• Tropical extra-tropical cyclones important
  components of Earth climate system
• Key manifestations of the oceanic air-sea
  interaction
• Contribute to regional heat exchanges, which
  affects ocean atmosphere dynamics
• Complex pattern in the variability in number and
  intensity of global cyclone events.
• Some regions (sub tropical northeast Pacific)
  experiencing increase in cyclonic frequency over
  last several decades
• Moderate-strong tropical cyclones have decreased
  in number intensity since the 1980s (attributed
  to more frequent occurrences of El Niño)
• Intergovernmental Panel on Climate Change (IPCC)
  has clearly identified the need to quantify the
  variability in global cyclones and in particular
  characterize changes in cyclone tracks
• GLYDER will provide better understanding for the
  reasons behind and effects of global climatic
  variations via autonomous cyclone detection and
  tracking

Hurricane Floyd, Sept 14, 1999
Hurricane Georges, Sept 24, 1998
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GLYDER Primary Science Objectives

• Software and IT Products to better characterize
  global cyclone variability
• Tools that empower JPL/NASA climate scientists to
  study and quantify the spatial and temporal
  variability of cyclones and their tracks
• Proof of concept algorithms and software tools
  for each technology component exists and has been
  proven in other applications
• Integrate observations from multiple remote (and
  in-situ) sensors robustly and automatically
• Enable yet un-achievable functionality to fuse
  multiple disparate spatio-temporal measurements
  for variety of earth observation needs
• Extend to other maritime and terrestrial event
  detection and tracking
• NASAs data providers to tag metadata with
  information pertaining to cyclones and enable
  content-based searching
• Automatically feed information to current NASA
  RD projects , including GHRSST (GODAE High
  Resolution Sea Surface Temperature), Earth
  Science Datacasting and the Physical Oceanography
  DAAC

Hurricane Dennis, August 28, 1999
Hurricane Mitch, Oct 26, 1998
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Primary Technical Objectives

• Co-register multiple remote (and in-situ)
  spatio-temporal sensor observations using
  multiscale techniques
• Pattern recognition techniques to detect cyclones
  from multiple sensors autonomously
• Develop core technologies to coordinate multiple
  spatially distributed in-situ and remote sensors
• Track cyclones over time
• Visualize tracks of cyclones globally
• Build tools to integrate cyclone event detection
  and tracking results into science (PODAAC)
  software
• Initiate mechanisms to tag metadata onto remote
  sensing data distributed to science community

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GLYDER Customers and End Users

• Ocean/Climate Researchers need GLYDER to mine
  the vast data sets and extract cyclone
  information
• Ocean weather data providers will use the
  technology to automatically generate data
  products and enable content-based searching
• Ocean/Climate Researchers and Ocean/weather data
  providers will use GLYDER for visualization of
  global cyclone tracks
• Application/operational scientists could
  potentially use the technology for real-time
  detection and tracking
• Longer term end-user after proven off-line
  operation

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Current State of Art

• Estimates of cyclone variability currently
  derived from analyses of surface level pressure
  (SLP) fields
• Model output fields from NCEP/NCAR Reanalysis
  Project based on in-situ inputs
• Analyses assimilate observational and in-situ
  data into a physical model to produce atmospheric
  fields
• Data span more than 50 years
• Measured every 6 hours on a 2.52.5 (275 km)
  global grid
• Accuracy of analyses is severely limited over the
  oceans,
• Lack of assimilated pressure and radiosonde
  observations
• Mean cyclone size varies from 120 440 km
  Liu99
• Resolve up to a maximum of only 50 of the global
  cyclones.
• Satellite remote sensing provides global coverage
  and greater spatial resolution, potentially
  allowing detection of most/all global cyclones

Map of daily pressure observations used by ECMWF
Reanalysis forecast (6-29-02). Under sampling
in the worlds oceans especially in the Southern
Hemisphere is evident, as shown in circles.
Boxes indicate regions of interest for GLYDER
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Remote Sensors for Cyclone Detection

• Individually remote sensing datasets have
  limited detection ability
• Poor temporal or spatial resolution
• Loss of data due to environmental effects.
• GOES visible cloud formation
• AVHRR cloud-free surface temperature or top of
  the atmosphere temperature
• QuikSCAT surface wind speed and direction
• AMSR-E surface temperature

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GLYDER Primary Challenges

• Data extraction for training and validation is
  non-trivial
• Multiple datasets generated by different data
  providers
• Different data formats and file naming
  conventions
• Large data volumes
• Initial efforts to extract relevant data from
  multiple sensors for training and performance
  verification will be manually intensive and
  laborious
• Co-registration of multiple sensors non-trivial
• Cyclone detection
• Cyclone tracking from multiple sensors

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Multisensor Co-Registration

• Traditional interpolation and extrapolation
  algorithms insufficient
• Spline, NN, Bilinear etc. not suited for
  non-stationary data
• Initial manual mining of data tedious and
  laborious
• Discrete wavelet transform (DWTs) for
  interpolation of each spatiotemporal
  non-stationary sensory dataset
• Characterize variety of smoothness function
  spaces
• Estimate all wavelet coefficients that describe a
  signal, given a subset of the sampled data that
  is noisy
• parameter a in Sobolev spaces relates the rate of
  decay of the DWT coefficients across scale
  (frequency) low value for a denotes highly
  discontinuous signals.
• Problem definition
• wavelet coefficients for an M-sampled data with a
  dyadic tree of size log2(M) should be estimated
  based on N lt M observations.
• A linear set of N equations relates the N
  observations to the scale and wavelet
  coefficients (for a given Sobolev smoothness a).
  Since MltN, this results in an under-determined
  equation, which can be solved using a
  pseudo-inverse solution.
• Segment signal into regions with varying
  discontinuities
• Each region has different a value
• Presence of discontinuities using quadratic
  Bspline wavelet coefficient magnitudes

Spline interpolation fails
Segmentation of signal into 2 regions, with high
and low discontinuities using the B-spline
wavelet coefficients
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Multisensor Co-Registration

• Segment Sampled 2D data
• Using the Bspline wavelet coefficients, the 2D
  data can be segmented into a library of regions
  with different smoothness.
• Spatial Interpolation Extend formulation of 1D
  wavelet interpolation to a 2D spatial grid
• For every spatial data sample, estimate the
  wavelet interpolated data for a 15x15Km grid
  using techniques similar to the 1D case.
• The Sobolev a value is different for each
  segmented region helps in better interpolation
  of non-stationary signals.
• Temporal Interpolation
• Obtain 2D spatially interpolated data grid for a
  given sensor at a given time
• Compute 1D temporal wavelet at every grid point
  interpolate sensor value in time at every spatial
  grid location.

Wavelet interpolation results (bottom row) using
proposed Sobolev smoothing constraint on 1D
signal using only (b) 256 of 1024 samples and
(c) 32 of 1024 samples.
(a) Correct signal (b) 256 of 1024
samples and reconstruction (c) 32 of 1024
samples
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Multisensor Cyclone Detection

• Pattern recognition fusion technique to
  accurately detect cyclones
• Should generalize well from limited training sets
• Nonlinear classifier for better recognition from
  noisy data
• Maximum discriminant feature classification
• Yields more generalized decision surfaces for
  high dimensional data sets (images, multispectral
  data) therefore less affected by the curse of
  dimensionality
• Improved cyclone detection from spatial image
  datasets since it implicitly guarantees
  translation-invariance in the design phase.
• Proven better performance than PCA, NNs (and SVMs
  in certain cases)

(a) 3 bands of a multispectral image data of M1
tank and (b) MDF fusion results with higher
signal-to-noise fusion vs. (c) Linear PCA fusion
results
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Cyclone Tracking
Tracking Using LK Features (DARPA RV2020
Program)

• Improve and adapt image feature-based tracking
  techniques to multidimensional data
• Prior work by JPL team on real-time tracking of
  non-rigid objects for robotics
• Dense optical flow (movie)
• Lucas-Kanade feature tracking
• Determine corner (Forstner) features
  automatically
• Track corner features using multiscale approach
• Used to determine visual odometry (camera motion)
  combining stereo with temporal feature tracking
• Segment camera motion from moving objects
• Track moving objects from moving camera over time
• Scale space David Lowe features
• Slower to derive, more robust

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Preliminary Cyclone Tracking Results

• QuikSCAT - polar orbiting satellite with an 1800
  km wide measurement swath on the earth's surface.
  
• SeaWinds instrument on QuikSCAT satellite
• Specialized radar measures near-surface wind
  speed direction at 25km resolution.
• Launched on 19 June 1999
• Measuring winds over 90 of the ocean daily since
  19 July 1999.
• Temporal resolution twice per day coverage over
  a given region
• QuikScat wind speed 0.1o resolution, 135-190o
  longitude, 5-60o latitude
• Goal Track Hurrican Ioke from remote wind speed
  (single sensor) data

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Preliminary Cyclone Tracking Results

• Wind speed at 0.1o resolution, 135-190o
  longitude, 5-60o latitude
• Goal Tracking of Hurrican Ioke from remote wind
  speed (single sensor) data

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Preliminary Tracking Results
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Preliminary Tracking Results
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Schedule (FY07)

• Extract sample data subsets for previously
  identified cyclones (Dec 2006)
• Use for training and validation of detection and
  tracking algorithms
• Develop tracking algorithms to track cyclones
  over time from single sensor observations (April
  2007)
• Extend concepts for multidimensional, multisensor
  tracking
• Algorithm development for co-registration of
  multisensor data at different spatial and
  temporal resolutions (Sept. 2007)
• Interact frequently with ocean data providers and
  scientists to prepare for tech. transfer in Years
  1, 2, 3
• Data providers are interested in GLYDER
  technology for metadata tagging
• Ocean research scientists need the technology for
  climate research
• Application scientists can use the technology
  for detecting and tracking cyclones in real-time)

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Personnel/Workforce

• Project commences Oct 1, 2006
• Technology Development
• Dr. A. Talukder (PI) - Ashit.Talukder_at_jpl.nasa.go
  v
• Dr. Anand Panangadan (Post-doc)
• NASA NPP Post-Doctoral Scholar
• Science Validation and Tech Transfer
• Dr. Andrew Bingham JPL
• Dr. Timothy Liu JPL