The Detection and Tracking of
Satellite Image Features
Associated with Extreme
Physical Events for Sensor Web
Targeting Observing
AIST-QRS-06-0026
ESTO Interim Review
Principle Investigator:
Co-Investigator:
Co-Investigator :
Co-Investigator :
Co-Investigator:
John F Moses, GSFC Code 586 Data Systems
Liping Di, George Mason University CSISS
Wayne Feltz, U of Wisconsin SSEC/CIMSS
Jason Brunner, U of Wisconsin SSEC/CIMSS
Robert Rabin, National Severe Storms Laboratory
The Detection and Tracking of Satellite Image Features Associated
with Extreme Physical Events for Sensor Web Targeting Observing
PI: John Moses, GSFC
Objective
This project will demonstrate a capability to detect,
track and rank radiance structures in satellite image
data that are associated with extreme physical
events. Our objective is to define key elements of a
generalized technology capable of populating crossdiscipline target ranking models for Sensor Web
application. Secondly, a single interface is desired for
event detection and tracking algorithms to access
data from multiple, diverse sensors and models. The
third objective is to enable discovery of new physical
event detection models by implementing the
capability to measure and rank geometric
characteristics and show application in detection of
the onset of convection.
Approach
• Our technical approach involves developing a capability
to evaluate prototype components for image event
detection that can support the needs of complex
multi-discipline physical models.
• The prototype detection and tracking algorithms and
techniques will serve as a basis for comparative
analysis of detailed implementation approaches for
the virtual sensor platform and event data models.
Co-I’s/Partners
Liping Di / GMU
Wayne Feltz, Jason Brunner / U. of WI
Robert Rabin / NOAA
A prototype algorithm detected and tracked over a thousand objects
in the September 18, 2003 GOES IR image with Hurricane Isabel.
The detected objects appear as an overlay of orange dots; if
tracked, objects appear as green lines.
Key Milestones
• Modify WCS server to work with GOES data prototype
detection and tracking algorithms
03/2007
• Install and configure virtual sensor platform
06/2007
• Develop automated algorithms for overshooting tops and
Enhanced-V, and methods to assess convective initiation
detection skills
09/2007
• Finish assessment of physical data models and methods on
GEO and LEO case study datasets
12/2007
TRLin = 2
Dec 2006
2
Detector Project
Objectives and Goals
•
•
Principle area of research: Data model for detection of structured radiance
events in remote sensor images
Basis: case histories and best practices for discovering satellite image
‘features’ associated with extreme physical events
– Using principles for discovery of physical structures and their relations to extreme
phenomena
•
Data Model Extensions
– Incorporate geostationary and low earth orbit data structures utilizing WCS and
WFS platforms
– Examine implications of alternate data models for detection
• Cross-Correlation methods
• Contour Integral methods
– Extend to radiance field structures by instrument
•
Proof of Concept Demonstrations
– Feature selection & ranking methods
• Enhanced-V
• Convective Initiation
– Tracking and Winds for forecasts
– Storm event reports for determining missed events and false alarm rates
3
Detector Project Formulation
•
•
•
•
•
Web Service Studies (GMU)
Science Case Studies (UofWisc)
Cross-Correlation methods (UofWisc)
Contour Integral methods (GSFC)
Tracking (NSSL)
4
Technical Status: Prototype
5
Web Service Architecture
6
Web Processing Service (WPS)
WPS is a standard interface that can offer any sort of GIS functionality to
clients across a network – getCapabilities, describeProcess, and execute.
WPS support web-based geo-processes; Geo-processes become
interoperable through Web Services;
The data required by the WPS can be delivered across a network, or
available at the server.
WPS-CLIENT
Communication over the web using HTTP
Web Processing Service
GetCapabilities
DescribeProcess
WPS
Algorithms Repository
…
Tracking
Detection
Execute
Data Handler Repository
…
…
GML Data Handler
7
WPS USE Example
Here is a request to execute new-added “DetectContour” and its process
result.
HTTP://localhost:8080/wps?service=“WPS”&version=“0.4.0”&request=“Ex
ecute”&store=“true”&Identifier=“DetectContour”&DataInputs=contourURL,
http://localhost:8080/wps/grid.txt
Output:
Input:grid.txt
CloudSlice (Top)
8
NetCDF ingest added to WCS
9
GMU Web Coverage Service
 WCS is a standard interface that supports the networked
access to multi-dimensional and multi-temporal
geospatial data
 WCS provides intact geospatial data products encoded
in HDFEOS, NITF, GeoTIFF, and netCDF (soon) to meet
the requirements of client-side rendering, multi-source
integration and analysis, and inputs to scientific models
and other clients beyond simple viewers.
 WCS Operations include:
GetCapabilities
DescribeCoverage
GetCoverage
 Current repository: serving 1199 products and 24GB
data for all four test cases acquired from U of Wisc.
10
Standard Process to Access Data through WCS
•
•
•
WCS 1.1.0 server : http://data.laits.gmu.edu/cgi-bin/wcs110
Use example: http://data.laits.gmu.edu/pli/reqnote.htm
One route to access data –
–
getCapabilities (find coverage)->describeCoverage( describe coverage)->getCoverage(get coverage data)
http://data.laits.gmu.edu/cgibin/wcs110?service=WCS&request=getCap
abilities&version=1.1.0
http://data.laits.gmu.edu/cgibin/wcs110?service=WCS&request=DescribeCoverage&version=1.1.0&identifier=
NETCDF:"/Data/G12IR04D20011009221100.nc":Band4_TEMP
http://data.laits.gmu.edu/cgibin/wcs110?service=WCS&version=1.1.0&request=
GetCoverage&identifier=NETCDF:"/Data/G12IR04D
20011009221100.nc":Band4_TEMP&format=image/
geotiff&BoundingBox=-780000,150000,1070000,1587300,urn:ogc:def:crs:EPSG::63
71229
NETCDF:"/Data/G12IR04D20011009221100.nc":Band4_TEMP
11
WCS Client Function
.
12
Use WCS Client API
gmu.csiss.wcsclient.goes.getTIFF tiffwcs = new
getTIFF(“data.laits.gmu.edu/cgi-bin/wcs110”,“WCS”,
“1.1.0”,“NETCDF:\"LEOIR31D20040525043000.nc\":Band31_TEMP”,“Gtiff”,“0,
0,0,0,ogc:def:crs:OGC:0.0:imageCRS”,“”);
int imageH=tiffwcs.getImageW();
int imageW=tiffwcs.getImageH();
float[] imageData = new float[imageW*imageH];
imageData = tiffwcs.getDataFromImage();
imageData=
Data on WCS server
13
MODIS Prototype Test Results
Enhanced-V on 25 May 2004 0430 UTC (TERRA overpass)
MOD021KM.A2004146.0430.005.2007018113431.hdf
1.
2.
Select & download from GSFC LAADS Website to desktop
Subset to binary using hdf2bin utility for display
Swath subset Grid 30x30
Swath
Scaled integers
1354x2030
Detected Peaks (overshooting tops)
1. Contour maximum (cloud top)
1. Orange – single level
2. Red – multiple levels
2. Contour Integral max & min drawn to top
1. Blue – modes (max)
2. Green – nodes (min )
Detected Sinks (holes in anvil)
1.
Contour minimum (sink)
1.
Orange – single level
2.
Red – multiple levels
2.
Contour Integral max & min
1.
Blue – modes
2.
Green - nodes
14
MODIS Prototype Contour Integral Results
Subset 1km Grid 40x60
1.
2.
URL request to GMU WCS for subset on grid
Downloaded from WCS to desktop for display
Enhanced-V feature:
Center peak - Cloud top event #0
Bow shape - Cloud top event #1
Albers Equal Area
Black Body Temperature with
MB Enhancement 2243x2367
Detected Peaks (tops)
1.
Contour maximum (cloud top)
1.
Orange – single level
2.
Red – multiple levels
2.
Contour Integral max & min
1.
Drawn from
2.
Blue – modes (max)
3.
Green – nodes (min)
MODIS Detected Peak Events (cloud tops)
from WCS Alberts Equal Area Grid
1950
Cloud #
1960
Detected Sinks (holes)
brightness (tens of degrees K)
1970
0
1980
1
1990
2
3
2000
y = 18.101Ln(x) + 1960.2
2010
4
5
2020
6
2030
7
8
2040
Log. (0)
2050
2060
15
2070
0
10 20 30 40 50 60 70 80 90 100 110 120 130 140 150
area (~1KM pixels)
MODIS Test Results
NetCDF Integrated Data Viewer (from UCAR)
From Modis L1b Band 31 reformated to NetCDF CF metadata in
LEOIR31D20040525043000.nc by U of Wisconsin McIDAS
16
Technical Status: Case Studies
• Translated priority GOES and MODIS IR product
metadata and coordinate system information into
NetCDF Climate and Forecast standard and passed
through to WCS
– Assigned metadata, including domain corner lat/lon and subsatellite point
– Remapped MODIS and GOES to Albers Conic Equal Area 1 km
grid using VisAD tools and software (U of Wisc SSEC CIMSS)
– Adding AVHRR navigation capability (U of Wisc SSEC)
– Considered alternate data and software options with GSFC
GOES Project and with NOAA CLASS involving significantly
more development effort
• Selected MODIS and GOES IR rapid scan for detecting
the Enhanced V and the onset of convection
17
Technical Status:
Pearson Correlation Matrices
• Developed fabricated matrix to best represent what the enhanced-V
feature looks like in the imagery
• Three different matrices were developed – based on results of
quantitative parameters of enhanced-V features from 2003 and 2004
enhanced-V seasons (total of 450 cases looked at) (Brunner et al. 2007)
• GIF images of fabricated enhanced-Vs were created in Paint Shop Pro
and Jython code was used to create matrix of brightness temperature
values from GIF image (assigned each RGB color value in GIF image
to a brightness temperature)
• Mean/Median Enhanced-V Fabricated Matrix (30X30 pixel matrix MOD 3A)
• Maximum Enhanced-V Fabricated Matrix (50X50 pixel matrix MOD 3B)
• Minimum Enhanced-V Fabricated Matrix (15X15 pixel matrix MOD 3C)
18
Mean/Median Enhanced-V Fabricated Matrix (30X30
pixels)
TMIN (coldest cloud top temperature) – 201 K
TMAX (warmest cloud top temperature) – 217 K
TDIFF (warm-cold couplet) – 16 K
DIST (distance between warm and cold location) – 10 KM
DISTARMS (averaged distance of both V-arms) – 36 KM
ANGLEARMS (angle between both V-arms) – 75 Degrees
19
Enhanced-V Algorithm Inputs
• McIDAS AREA file of Low Earth Orbit (LEO)
satellite data (currently set up to input
150X150 pixel region), given a line/elem
value in image as upper left point of desired
region (line/elem/brightness temperature
value at each pixel in region is input)
• Enhanced-V Fabricated Matrix of brightness
temperatures (have three different versions),
ASCII file of brightness temperatures in
matrix
20
Enhanced-V Algorithm Statistical
Correlation Code: Output
* Algorithm takes 150X150 pixel region and steps through this region one
pixel at a time while comparing 10.7 micron brightness temperatures to a
fabricated enhanced-V matrix of brightness temperatures (looks for a similar
pattern in brightness temperature values)
* ASCII output file of line/elem/correlation value at every pixel
* ASCII filtered output file of line/elem/correlation value of all pixels that
exceed a certain correlation value threshold (such as >= 0.5 for example)
21
LEO Enhanced-V Proof of Concept
Test Cases
• Three Advanced Very High Resolution Radiometer (AVHRR)
enhanced-V cases were selected to test the enhanced-V statistical
correlation algorithm
* Case 1: 25 May 2004 0424 UTC over Oklahoma
* Case 2: 10 May 2004 0042 UTC over South Dakota/Nebraska/Iowa border
* Case 3: 6 May 2003 2218 UTC over northeastern Oklahoma
• The enhanced-V cases are East-West oriented enhanced-Vs and the
enhanced-V fabricated matrices are East-West oriented (“simple” case)
• In addition, a null case was used to test the enhanced-V algorithm
for thunderstorms (no enhanced-V features though) over southwest
Kansas on 10 May 2004 at 0042 UTC; Desired outcome is no detects
of enhanced-V features for the null case
22
Enhanced-V Test Case #1 - 25 May 2004, 0424 UTC
23
Summary Table of Results (3 cases, 3 enhanced-V matrices, 3
statistical correlation value thresholds; total of 27 runs)
Case #
Matrix
MOD
Statistic
al
Corr.
threshol
d
EV
Detected
(Y or N)
False
Detect
(Y or N)
Statistic
al
Corr.
threshol
d
EV
Detected
(Y or N)
False
Detect
(Y or N)
Statistic
al
Corr.
threshol
d
EV
Detected
(Y or N)
False
Detect
(Y or N)
1
3A
(30x30)
0.5
N
N
0.4
N
Y
0.3
Y
Y
1
3B
(50x50)
0.5
N
N
0.4
Y
N
0.3
Y
N
1
3C
(15x15)
0.5
N
N
0.4
N
Y
0.3
N
Y
2
3A
(30x30)
0.5
N
Y
0.4
N
Y
0.3
N
Y
2
3B
(50x50)
0.5
N
N
0.4
N
N
0.3
N
N
2
3C
(15x15)
0.5
N
N
0.4
N
N
0.3
Y
Y
3
3A
(30x30)
0.5
Y
N
0.4
Y
Y
0.3
Y
Y
3
3B
(50x50)
0.5
N
N
0.4
N
N
0.3
Y
N
3
3C
(15x15)
0.5
N
N
0.4
N
N
0.3
Y/N
Y
24
Case 3: MOD3A: 0.5 Run
• (+) Enhanced-V detected correctly
25
Case 1: MOD3A: 0.5/0.4/0.3 Runs
0.5 Run – No enhanced-V detected
0.4 Run – False detect in anvil region
0.3 Run – False detect in anvil and clear sky
regions; Enhanced-V detected correctly as
well
26
Case 2: MOD3A: 0.5/0.4/0.3 Runs
False detects of enhanced-V in anvil region in
all runs; the number of false detects increases
as the statistical correlation value threshold
decreases from 0.5 to 0.3
27
Summary Table of Results (1 null case, 3 enhanced-V matrices,
3 statistical correlation value thresholds; total of 9 runs)
Case #
Matrix
MOD
Statistic
al
Corr.
threshol
d
False
Detect
(Y or N)
Statistic
al
Corr.
threshol
d
False
Detect
(Y or N)
Statistic
al
Corr.
threshol
d
False
Detect
(Y or N)
Null
3A
(30x30)
0.5
N
0.4
Y
0.3
Y
Null
3B
(50x50)
0.5
N
0.4
Y
0.3
Y
Null
3C
(15x15)
0.5
N
0.4
N
0.3
Y
• For Null Case using MOD3A matrix; false detects of enhanced-V
found in anvil region for statistical correlation thresholds of 0.4 and 0.3
28
STEP 1
2D Array BT(6.7)
2D Array BT(10.7)
UL Image Line/Elem
BT(6.7) – BT(10.7)
To Isolate
Overshooting Top Pixels
STEP 3
Enhanced-V Statistical Correlation Algorithm
Search For Enhanced-V Features Around
Thermal Couplet Regions:
Orient Enhanced-V Fabricated Matrix In
Direction Of Thermal Couplet Angle Orientation
Search 50x50 Pixel Box Around Overshooting
Top Pixel Location
2D Array BT(6.7) – BT(10.7)
2D Array BT(10.7)
2D Array Image Line Values
2D Array Image Elem Values
STEP 2
Thermal Couplet Analysis
For Each Identified Overshooting Top Pixel
[BT(6.7) – BT(10.7) ≥ 4K];
Distance And Temperature Difference
Threshold Checks Performed:
Distance ≤ 25km
BT(10.7) Difference ≥ 12K And ≤ 35K
BT(6.7) – BT(10.7) ≥ -5K
Of Potential Warm Pixel
Additionally, Angle Orientation Of Detected
Thermal Couplet Is Calculated.
1D Array Thermal Couplet Image Line Locations
(Use Overshooting Top Pixel Location)
1D Array Thermal Couplet Image Elem Locations
Image Line/Elem Locations Of
Identified Enhanced-V Pixels
Statistical Correlation Values
Of Identified Enhanced-V Pixels
(Use Overshooting Top Pixel Location)
1D Array Thermal Couplet Values
1D Array Angle Orientations Of Thermal Couplets
MODIS Test Cases for
Revised Enhanced-V Algorithm/PreProcessing (Using Methodology in Process
Flow Chart on Previous Slide)
Case 1: 25 May 2004 0430 UTC (TERRA overpass)
* Enhanced-V over Oklahoma
Case 2: 7 April 2006 1702 UTC (TERRA overpass)
* Numerous enhanced-Vs over Tennessee and Kentucky
Case 3: 7 April 2006 1842 UTC (AQUA overpass)
* Numerous enhanced-Vs over Tennessee and Kentucky
Case 4: 12 October 2001 overpasses
* Enhanced-Vs over southern Plains, during GOES-12 SRSO Test period
Case 5: 24 April 2007 overpasses
* Enhanced-V over southwest Texas that was associated with Eagle Pass killer
tornado
Case 6: 9 October 2001 overpasses
* Enhanced-Vs over southern and central Plains, during GOES-12 SRSO Test
period
Case 7: 10 May 2006 overpasses
* Numerous enhanced-Vs over Texas and Gulf Coast, during GOES-12 RSO 30
Upcoming Conference
Presentations/Publications
• An oral presentation will be given at the upcoming Joint
EUMETSAT/AMS Satellite Conference in Amsterdam on the
enhanced-V detection algorithm work
• Peer-reviewed paper entitled “A Quantitative Analysis of the
Enhanced-V Feature in Relation to Severe Weather” has been
accepted for publication in Weather and Forecasting
31
GOES-12 CI Nowcasting Using 1-Min Resolution Data
30 MINS LATER
Mean cloud top cooling rate for small and
towering cumulus clouds are computed over .1
x .1 degree boxes over a 5-minute period
This allows cloud motion during this image
sequence to be disregarded, allowing us to
better identify developing cumulus clouds with
fewer false alarms than the previously
described method
32
Cooperation and Follow-on
• The objective overshooting top and enhanced-V algorithm
may satisfy future GOES-R Advanced Baseline Imager (ABI)
requirement
• Thunderstorm feature detection is complementing NASA
Advanced Satellite Aviation-weather Product (ASAP) research
toward improving airline safety through use of satellite data
• Overshooting top detection will also provide possible signal of
turbulent atmosphere which can be integrated into the NOAA
Aviation Weather Center Graphical Turbulence Guidance
(GTG) next generation product, used operationally by NOAA
and FAA
33
Milestone Status
Milestone
Months after
Task Start
Data sources and interface review
Design and architect the virtual sensor platform
January-07
February-07
Modify WCS server to work with GOES data and in-situ reports
March-07
Event detection data model design review
April-07
Install and configure virtual sensor platform
Integrate prototype detection and tracking algorithms with GOES,
MODIS sample data
Develop automated algorithms for Enhance V
Assess convection initiation detection skills
Finish tests on GEO and LEO case study datasets
June-07
July-07
September-07
September-07
November-07
Status
completed in March
completed in March
completed in April, revised to
use standard projection
(Alberts Equal Area)
conducted through a series of
telecons and meetings at GMU
and GSFC, completed in March
software modifications
completed in May, hardware
upgrades delayed until after
server relocation at GMU - in
August
started, no issues
started, on schedule
started, on schedule
34
Cost and Schedule Status
• One Year funding: $382,913
• Direct for NASA GSFC PI
• GMU/U of Wisc Grants, NSSL agreement
• Commitments $316k Jan 2007
• Cumulative Obligations: $316k Mar 2007
• The project is currently spending below the plan
due to accumulation of short delays:
–
–
–
–
–
Setup grants and awards
Data source/format
Temporary loss of the lead engineer at GMU
Location of GMU processing facility
$20K equipment purchase delayed until August
35
Cost and Schedule Status
Event Detector Cost Phasing
$350,000
$300,000
$250,000
Labor
$200,000
Equipment
Cum. Cost Plan
Funding
$150,000
Cum. Actual (est.)
$100,000
$50,000
$0
Dec-06
Jan-07
Feb-07
Mar-07
Apr-07
May-07
Jun-07
36