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Robert A. Iacovazzi, Jr. (ERT, Inc.) STAR Calibration Team

Architecture of the Integrated Cal/Val System. September 20, 2006. Robert A. Iacovazzi, Jr. (ERT, Inc.) STAR Calibration Team. Historical “Piecemeal” Cal/Val Approach. Anomaly study case by case Manual and time consuming process. CALIBRATION QUERIES and COMPLAINTS.

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Robert A. Iacovazzi, Jr. (ERT, Inc.) STAR Calibration Team

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  1. Architecture of the Integrated Cal/Val System September 20, 2006 Robert A. Iacovazzi, Jr. (ERT, Inc.) STAR Calibration Team

  2. Historical “Piecemeal” Cal/Val Approach Anomaly study case by case Manual and time consuming process CALIBRATION QUERIES and COMPLAINTS Cal/Val Reports/Journal Articles (ORA) Instrument parameter trending (OSDPD) User Community Satellite Data User interface fragmented Spacecraft & Instrument Status (OSO) Documentation & Archive (NCDC)

  3. Integrated Cal/Val System Architecture Calibration Opportunity Prediction Data Acquisition Scheduler Calibration Opportunity Register (CORE) Raw Data Acquisition for Calibration Analyses Stored Raw Data for Calibration Analyses SNO/ SCO Rad. Bias and Spectral Analysis Calibration Parameter Noise/ Stability Monitoring RTM Model Rad. at Calibration Reference Sites Inter-sensor Bias and Spectral Analysis Lunar Calibrationand Calibration Effects Geolocation Assessment (Coastlines, etc.) Independent Radiometric, Spectral, and Spatial Verification. Calibration links for POES, GOES, MetOP, NPP/NPOESS, GOES-R, DMSP, etc.

  4. Near Real-Time Calibration Opportunity Prediction Satellite Two-Line Elements (2 Sets for SNO, 1 Set for Overpass) 1 28654U 05018A 05182.45853142 .00000126 00000-0 94312-4 0 439 2 28654 098.7459 129.0912 0015218 133.0305 227.2127 14.10859063 5921 SNO Predictions Date Time Sat1 Lat/Lon Sat2 Lat/Lon Nadir Dist (km) Time Diff (sec) 02/22/2006 22:42:02 (-78.5,357.5) (-78.5,357.6) 2 8 02/25/2006 02:36:39 ( 78.4,119.5) ( 78.4,119.4) 2 22 03/01/2006 11:16:36 (-78.1,171.1) (-78.1,171.1) 1 30 03/03/2006 15:11:15 ( 78.1,292.5) ( 78.1,292.4) 2 16 03/05/2006 19:05:44 (-78.1, 53.5) (-78.1, 53.4) 3 9 03/07/2006 23:00:26 ( 78.3,174.1) ( 78.3,174.0) 1 23 03/12/2006 07:40:20 (-77.9,226.0) (-77.9,226.0) 0 28 03/14/2006 11:34:57 ( 77.9,347.3) ( 77.9,347.2) 2 12 Orbital Model Predictions Instrument Moonview Predictions Date Time Sat Lat/Lon Moon Lat/Lon Duration (sec) 03/29/2006 07:22:56 ( 41.0, 85.3) ( 40.8, 84.3) 300 04/04/2006 20:23:43 ( 40.8, 84.4) ( 40.8, 84.3) 724 04/05/2006 07:29:05 ( 40.7, 83.8) ( 40.8, 84.3) 421 Overpass of Calibration Reference Site Predictions Date Time Sat Lat/Lon Site Lat/Lon Nadir Distance (km) 03/29/2006 07:22:56 ( 41.0, 85.3) ( 40.8, 84.3) 87 04/04/2006 20:23:43 ( 40.8, 84.4) ( 40.8, 84.3) 11 04/05/2006 07:29:05 ( 40.7, 83.8) ( 40.8, 84.3) 39 Calibration Opportunity Register (CORE) Available to Data Users via a Web Interface!!!

  5. The Integrated Cal/Val Analysis Process Data Scheduling Module Cal/Val Opportunity Prediction Module Calibration Opportunity Register (CORE) Predicted Cal/Val Opportunities • Satellite SNO/SCO Bias and Spectral Analysis Module •RTM Satellite Data Modeling Module Applied to Cal. Reference Sites •Moon Calibration Module •Geolocation Assessment Module Scheduled Cal/Val Opportunities •Inter-sensor Bias and Spectral Analysis Module •Instrument Performance Monitoring Module Raw Data Acquisition Module (SFTP, Web, or Other Access) Mass Storage of Raw Data for Calibration Analyses Calibration Anomaly Correction Module Mass Storage of Calibration Analyses

  6. Integrated Cal/Val System Approach Online Cal/Val User Interface (available 24/7) AUTOMATED INTEGRATED CAL/VAL SYSTEM Satellite and In-situ Data CALIBRATION QUERIES and QUESTIONS Cal/Val Reports/Journal Articles Satellite Data User Community Cal/Val Team COLLABORATIVE CAL/VAL STUDIES Calibration-related R&D

  7. Example: POES and Aqua Instrument SNO Analysis SNO Raw Data Acquisition Software: General Architecture 0 Start Runscript: snoAcquireDataset GET SNO PREDICTIONS and CREATE SNO QUEUE ENTRIES 1.C) Update Short List of Unprocessed Predicted SNO Events to Data File: snoShortList.dat 0.A) Write Prescribed Parameters to Control File: sno_makeShortList.ctl 2.A) Write Prescribed Parameters to Control File: sno_acquireDataset.ctl 1.A) Get New SNO Prediction File and Find Potential New SNO Events: sno_makeShortList.pro 1 3.A) Gather SNO Raw Data from Local and Remote Sources for SNO #1 from SNO Short List: sno_acquireDataset.pro 1.B) End Runscript: snoDatasetCreation 3.B) Gather Available SNO Raw Data from Local and Remote Sources: sno_acquireDataset.pro #SNO Short List Entries GT 0 No OBTAIN L1B DATA 1 2 Yes 3.C) Gather Raw Data for Next SNO from SNO Short List: sno_acquireDataset.pro 0 2 #SNO Short List Entries EQ Last No Yes 4.A) End Runscript: snoAcquireDataset

  8. Example: POES and Aqua Instrument SNO Analysis SNO Dataset Creation Software: General Architecture SNO Raw Data Exists Start Runscript: snoMakeDataset.scr 2.B) Gather Available SNO Raw Data from Local and Remote Sources: sno_makeDataset.pro No 1 1.A) Write Prescribed Parameters to Control File: sno_makeDataset.ctl Yes 2.C) Find Nadir Location of SNO: sno_makeDataset.pro 2.A) Process Unprocessed SNO #1 from SNO Short List: sno_makeDataset.pro SNO Found No 1 Yes 2.F) Process Next Unprocessed SNO from SNO Short List: sno_makeDataset.pro SUBSET L1B DATA at SNO SITE 2.D) Subset and Match theRelevant SNO Data from the Raw Satellite Files: sno_makeDataset.pro 2 #SNO Short List Entries EQ Last No 2.E) Output SNO Data into an HDF File: sno_makeDataset.pro Yes 3.A) End Runscript: snoMakeDataset 2

  9. 0 SNOs to Analyze? No Example: POES and Aqua Instrument SNO Bias Analysis Yes 1.C) End Program and Runscript: sno_analyze.pro and snoAnalyze 1.D) Resize Relevant Variables and Structures sno_analyze.pro 1.E) Read in snoMasterList.dat Header sno_analyze.pro No SNO Dataset Analysis Software: General Architecture EOF of Master List Reached? #SNOs Analyzed GT 0 Yes Start Runscript: snoAnalyze No Yes 1.I) Compute and Compile Ensemble SNO Statistics: sno_analyze.pro 1.F) Read in a Record from SNO Master List snoMasterList.dat sno_analyze.pro 0.A) Write Prescribed Parameters to Control File: sno_analyze.dataInfo.ctl sno_analyze.plotInfo.ctl The SNO is Within Space/ Time Bounds? 1.J) Make Tables and Plots of Statistics: sno_analyze.pro PREPROCESSING SINGLE SNO STATS ENSEMBLE SNO STATS & PLOTS No 1.A) Read in Data from Control Files sno_analyze.dataInfo.ctl, sno_analyze.plotInfo.ctl and SNO_InstrSpecs/(instr Name).ctl, sno_analyze.pro 1.K) Output Tables and Plots sno_analyze.pro Yes 1.G) Read in Data Associated with the SNO sno_analyze.pro 1.B) Read in Data from SNO Master List snoMasterList.dat and Determine the Number of SNOs Within Time/Space Bounds sno_analyze.pro 1.L) End Program and Runscript: sno_analyze.pro and snoAnalyze 1.H) Compute and Compile Individual SNO Statistics: sno_analyze.pro 0

  10. EOS/Aqua convolved AIRS (CrIS Proxy) and N18/HIRS Infrared Sounder Example Result

  11. EOS/Aqua MODIS (VIIRS Proxy) and N18 AVHRR Infrared Imager Example Result all SNOs since NOAA-18 launch

  12. EOS/Aqua and N18/AMSU-A (ATMS Proxy) Microwave Sounder Example Result all SNOs since N18 launch.

  13. Integrated Cal/Val System Prototype: Current Phase

  14. Integrated Cal/Val System: Completion Schedule

  15. An automated integrated cal/val system is critical to improving accuracy of both existing and future weather and climate products for POES, NPP/NPOESS, and GOES-R. Summary The system architecture components:> Satellite position prediction and data scheduling; > Easy access to Level 1b and cal/val site data; > Up-to-date computing resources and shared mass data storage; > High-quality, well-documented calibration analysis software and RTM’s; and > Web user interface to near real-time data. Prototypes of portions of this cal/val system are up and running NOW.

  16. Intersatellite Calibration Bias Estimation in the Integrated Cal/Val System Part I: AMSU-A SNO Analysis Robert A. Iacovazzi, Jr. (ERT, Inc.) September 20, 2006

  17. Outline SNO-ensemble avg. biases between Aqua and POES AMSU-A instruments at sounding channels (Chs 3-14) Predicted and observed SNO-ensemble avg. biases between POES AMSU-A instruments at sounding channels SNO-ensemble biases inferred for AMSU-A1-2 and AMSU-A1-1 subunits on POES and Aqua. SNO analysis at AMSU-A surface channels

  18. SNO Events Between Concurrently Operating AMSU-A Instruments Time Period: May 21, 2005 to July 31, 2006 Locations: Mainly Around 800 North and South SNO Time Threshold: 30 Seconds Number of SNOs:

  19. Individual SNO Mean Bias Time Series for Aqua and N15 AMSU-A

  20. POES and Aqua AMSU-A SNO-ensemble Mean Biases and 99% Confidence Intervals

  21. Observed and Predicted AMSU-A SNO biases using Aqua/AMSU-A as a Calibration Transfer Radiometer

  22. Channel aggregated SNO biases for AMSU-A1-1 and AMSU-A1-2 subunits

  23. Uncertainties for Surface Channels Percentage of SNO Bias Variance due to NEDT

  24. Reducing the SNO bias uncertainty for the surface channels: Data interpolation and QC Collocation schemes : 1) No Interpolation (simple nearest neighbor matching), 2) Bilinear Interpolation, and 3) Bilinear Interpolation with Quality Control (based on maximum gradient value). SNO-ensemble statistics were computed from SNO events between N18 and EOS-Aqua AMSU-A instruments for the Northern and Southern Hemispheres.

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