The Detection and Tracking of Satellite Image Features Associated with Extreme Physical Events for Sensor Web Targeting Observing AIST-QRS ESTO Interim Review Principle Investigator: John F Moses, GSFC Code 586 Data Systems Co-Investigator:Liping Di, George Mason University CSISS Co-Investigator : Wayne Feltz, U of Wisconsin SSEC/CIMSS Co-Investigator : Jason Brunner, U of Wisconsin SSEC/CIMSS Co-Investigator: Robert Rabin, National Severe Storms Laboratory

2 The Detection and Tracking of Satellite Image Features Associated with Extreme Physical Events for Sensor Web Targeting Observing Objective Key Milestones TRL in = 2 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 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. Approach 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 cross- discipline 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. Liping Di / GMU Wayne Feltz, Jason Brunner / U. of WI Robert Rabin / NOAA PI: John Moses, GSFC Co-I’s/Partners Dec 2006 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.

3 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

4 Detector Project Formulation Web Service Studies (GMU) Science Case Studies (UofWisc) Cross-Correlation methods (UofWisc) Contour Integral methods (GSFC) Tracking (NSSL)

5 Technical Status: Prototype

6 Web Service Architecture

7 Web Processing Service (WPS) WPS Web Processing Service GetCapabilities Communication over the web using HTTP ExecuteDescribeProcess Algorithms Repository … Tracking Detection Data Handler Repository … … GML Data Handler WPS-CLIENT  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.

8 WPS USE Example Here is a request to execute new-added “DetectContour” and its process result. ecute”&store=“true”&Identifier=“DetectContour”&DataInputs=contourURL, Input:grid.txt Output: CloudSlice (Top)

9 NetCDF ingest added to WCS

10 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.

11 Standard Process to Access Data through WCS WCS server : Use example: One route to access data – – getCapabilities (find coverage)->describeCoverage( describe coverage)->getCoverage(get coverage data) bin/wcs110?service=WCS&request=getCap abilities&version=1.1.0 NETCDF:"/Data/G12IR04D nc":Band4_TEMP bin/wcs110?service=WCS&request=DescribeCoverage&version=1.1.0&identifier= NETCDF:"/Data/G12IR04D nc":Band4_TEMP bin/wcs110?service=WCS&version=1.1.0&request= GetCoverage&identifier=NETCDF:"/Data/G12IR04D nc":Band4_TEMP&format=image/ geotiff&BoundingBox= , , , ,urn:ogc:def:crs:EPSG::

12 WCS Client Function.

13 Use WCS Client API gmu.csiss.wcsclient.goes.getTIFF tiffwcs = new getTIFF(“data.laits.gmu.edu/cgi-bin/wcs110”,“WCS”, “1.1.0”,“NETCDF:\"LEOIR31D 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(); Data on WCS server imageData=

14 Swath Scaled integers 1354x2030 Enhanced-V on 25 May UTC (TERRA overpass) MOD021KM.A hdf 1.Select & download from GSFC LAADS Website to desktop 2.Subset to binary using hdf2bin utility for display MODIS Prototype Test Results Sink hole Enhanced-V peak 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 ) Swath subset Grid 30x30 Bow front 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

15 Detected Sinks (holes) 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) Enhanced-V feature: MODIS Prototype Contour Integral Results Albers Equal Area Black Body Temperature with MB Enhancement 2243x URL request to GMU WCS for subset on grid 2.Downloaded from WCS to desktop for display Gulf Coast Center peak - Cloud top event #0 Bow shape - Cloud top event #1 Cloud # Subset 1km Grid 40x60

16 NetCDF Integrated Data Viewer (from UCAR) From Modis L1b Band 31 reformated to NetCDF CF metadata in LEOIR31D nc by U of Wisconsin McIDAS Enhanced-V peak Sink hole Bow front MODIS Test Results

17 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 sub- satellite 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

18 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)

19 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

20 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

21 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)

22 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 UTC over Oklahoma * Case 2: 10 May UTC over South Dakota/Nebraska/Iowa border * Case 3: 6 May 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

23 Enhanced-V Test Case # May 2004, 0424 UTC

24 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) 13A (30x30) 0.5NN0.4NY0.3YY 13B (50x50) 0.5NN0.4YN0.3YN 13C (15x15) 0.5NN0.4NY0.3NY 23A (30x30) 0.5NY0.4NY0.3NY 23B (50x50) 0.5NN0.4NN0.3NN 23C (15x15) 0.5NN0.4NN0.3YY 33A (30x30) 0.5YN0.4YY0.3YY 33B (50x50) 0.5NN0.4NN0.3YN 33C (15x15) 0.5NN0.4NN0.3Y/NY

25 Case 3: MOD3A: 0.5 Run (+) Enhanced-V detected correctly

26 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

27 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

28 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) Null3A (30x30) 0.5N0.4Y0.3Y Null3B (50x50) 0.5N0.4Y0.3Y Null3C (15x15) 0.5N0.4N0.3Y 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

 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 1  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 (Use Overshooting Top Pixel Location)  1D Array Thermal Couplet Values  1D Array Angle Orientations Of Thermal Couplets 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 STEP 3  Image Line/Elem Locations Of Identified Enhanced-V Pixels  Statistical Correlation Values Of Identified Enhanced-V Pixels

30 MODIS Test Cases for Revised Enhanced-V Algorithm/Pre- Processing (Using Methodology in Process Flow Chart on Previous Slide) Case 1: 25 May UTC (TERRA overpass) * Enhanced-V over Oklahoma Case 2: 7 April UTC (TERRA overpass) * Numerous enhanced-Vs over Tennessee and Kentucky Case 3: 7 April 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

31 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

32 GOES-12 CI Nowcasting Using 1-Min Resolution Data 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 30 MINS LATER

33 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

34 Milestone Status

35 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

36 Cost and Schedule Status