Mr. David R. Pogorzala Profile

David R. Pogorzala

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Author

Publications (16)

Proceedings Article | 20 September 2020 Presentation + Paper

Mitigating the GOES-17 ABI thermal anomaly using predictive calibration

David Pogorzala, Jon Fulbright, Elizabeth Kline, Boryana Efremova, Joel McCorkel, John Van Naarden

Proceedings Volume 11530, 115300O (2020) https://doi.org/10.1117/12.2574160

KEYWORDS: Calibration, Sensors, Temperature metrology, Imaging systems, Cooling systems, Detection and tracking algorithms, Image quality

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The GOES-17 Advanced Baseline Imager (ABI) has an anomaly with its on-board cooling system that prevents it from maintaining its Focal Plane Modules (FPMs) at cold, optimal temperatures. Because of this, during certain times of the year the FPMs and their detectors warm and cool throughout the day. Changing the detectors’ temperature changes their response to incoming radiance, which leads to calibration errors over time and degrades the imagery. Numerous mitigation strategies have been implemented to reduce the solar insolation on the instrument and to mitigate image degradation, including semi-annual yaw flips and changing the integration time of the detectors twice daily. These and other mitigations all work with the baseline calibration algorithms currently in place on the GOES-R Ground System. In an attempt to reduce the image degradation even further, the ABI vendor designed a new calibration scheme that predicts key parameters forward in time to account for the drifting FPM temperatures. These parameters, the linear gain term and dark current scene, are nominally updated on orbit every 5 minutes and 30 seconds, respectively. However, even at these relatively short cadences the detectors can change temperature, thereby rendering the parameters invalid for accurate calibration. By projecting these parameters forward in time the radiometric bias is reduced and image quality improves. This Predictive Calibration modification was deployed to operations on July 25, 2019, following several months of extensive testing and optimization by the GOES-R science teams. During this time several parameters and thresholds were tuned to ensure Predictive Calibration was turning on and off at the optimal times. Since going into operations users have seen noticeable improvement to the imagery and its calibration. This paper will discuss the fundamental assumptions behind the baseline equations and highlight the changes introduced by Predictive Calibration. Results will show the improvements to the calibration of the operational L1b products and reduction in image degradation.

Proceedings Article | 20 September 2020 Presentation + Paper

Product maturity process for ABI L1b and CMI products for GOES-R series

Jon Fulbright, Elizabeth Kline, Mathew Gunshor, Timothy Schmit, Xiangqian Wu, Katherine Pitts, David Pogorzala

Proceedings Volume 11530, 115300P (2020) https://doi.org/10.1117/12.2573804

KEYWORDS: Satellites, Imaging systems, Satellite imaging, Clouds, Calibration, Analytical research

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SPIE Journal Paper | 28 August 2020 Open Access

GOES-R series ABI Imagery artifacts

Mathew Gunshor, Timothy Schmit, David Pogorzala, Scott Lindstrom, James Nelson

JARS, Vol. 14, Issue 03, 032411, (August 2020) https://doi.org/10.1117/12.10.1117/1.JRS.14.032411

KEYWORDS: Satellites, Calibration, Sensors, Stray light, Infrared imaging, Sun, Satellite imaging, Visualization, Imaging systems, Clouds

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Proceedings Article | 7 September 2018 Presentation + Paper

Characterization of GOES-16 ABI detector-level uniformity from post-launch north south scan collections of several earth targets

Jason Casey, Francis Padula, Monica Cook, Brent Bartlett, Aaron Pearlman, Dave Pogorzala, Changyong Cao

Proceedings Volume 10764, 107640D (2018) https://doi.org/10.1117/12.2323673

KEYWORDS: Sensors, Target detection, Calibration, Imaging systems, Staring arrays, Sensor performance, Image sensors, Mirrors, Image processing, Data processing

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Proceedings Article | 7 September 2018 Presentation + Paper

Independent validation of the advanced baseline imager (ABI) on NOAA's GOES-16: post-launch ABI airborne science field campaign results

Brent Bartlett, Jason Casey, Francis Padula, Aaron Pearlman, Dave Pogorzala, Changyong Cao

Proceedings Volume 10764, 107640H (2018) https://doi.org/10.1117/12.2323672

KEYWORDS: Sensors, Databases, Spectrometers, Imaging systems, Calibration, Reflectivity, Image segmentation, Atmospheric corrections, Image processing, Space operations

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Proceedings Article | 5 September 2017 Presentation + Paper

Calibration/validation status for GOES-16 L1b data products

Jon Fulbright, Elizabeth Kline, David Pogorzala, Wayne MacKenzie, Jamese Sims, Robert Garnett, Curtiss Burnett, Matthew Seybold

Proceedings Volume 10402, 104020T (2017) https://doi.org/10.1117/12.2274358

KEYWORDS: Satellites, Calibration, Sensors, Meteorological satellites

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Proceedings Article | 19 October 2016 Presentation + Paper

Calibration/validation strategy for GOES-R L1b data products

Jon Fulbright, Elizabeth Kline, David Pogorzala, Wayne MacKenzie, Ryan Williams, Kathryn Mozer, Dawn Carter, Randall Race, Jamese Sims, Matthew Seybold

Proceedings Volume 10000, 100000T (2016) https://doi.org/10.1117/12.2242140

KEYWORDS: Satellites, Calibration, Imaging systems, Sensors, Aerospace engineering, Meteorological satellites, Extreme ultraviolet, Magnetometers, Data modeling, Particles

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The Geostationary Operational Environmental Satellite-R series (GOES-R) will be the next generation of NOAA geostationary environmental satellites. The first satellite in the series is planned for launch in November 2016. The satellite will carry six instruments dedicated to the study of the Earth’s weather, lightning mapping, solar observations, and space weather monitoring. Each of the six instruments require specialized calibration plans to achieve their product quality requirements. In this talk we will describe the overall on-orbit calibration program and data product release schedule of the GOES-R program, as well as an overview of the strategies of the individual instrument science teams. The Advanced Baseline Imager (ABI) is the primary Earth-viewing weather imaging instrument on GOES-R. Compared to the present on-orbit GOES imagers, ABI will provide three times the spectral bands, four times the spatial resolution, and operate five times faster. The increased data demands and product requirements necessitate an aggressive and innovative calibration campaign. The Geostationary Lightning Mapper (GLM) will provide continuous rapid lightning detection information covering the Americas and nearby ocean regions. The frequency of lightning activity points to the intensification of storms and may improve tornado warning lead time. The calibration of GLM will involve intercomparisons with ground-based lightning detectors, an airborne field campaign, and a ground-based laser beacon campaign. GOES-R also carries four instruments dedicated to the study of the space environment. The Solar Ultraviolet Imager (SUVI) and the Extreme Ultraviolet and X-Ray Irradiance Sensors (EXIS) will study solar activity that may affect power grids, communication, and spaceflight. The Space Environment In-Situ Suite (SEISS) and the Magnetometer (MAG) study the in-situ space weather environment. These instruments follow a calibration and validation (cal/val) program that relies on intercomparisons with other space-based sensors and utilize special spacecraft maneuvers. Given the importance of cal/val to the success of GOES-R, the mission is committed to a long-term effort. This commitment enhances our knowledge of the long-term data quality and builds user confidence. The plan is a collaborative effort amongst the National Oceanic and Atmospheric Administration (NOAA), the National Institute of Standards and Technology (NIST), and the National Aeronautics and Space Administration (NASA). It is being developed based on the experience and lessons-learned from the heritage GOES and Polar-orbiting Operational Environmental Satellite (POES) systems, as well as other programs. The methodologies described in the plan encompass both traditional approaches and the current state-of-the-art in cal/val.

Proceedings Article | 20 May 2011 Paper

Realism, utility, and evolution of remotely sensed simulations

Erin Ontiveros, Michael Gartely, Scott Brown, Rolando Raqueño, David Pogorzala

Proceedings Volume 8048, 80481L (2011) https://doi.org/10.1117/12.884178

KEYWORDS: Data modeling, Computer simulations, Image resolution, 3D modeling, Remote sensing, Visualization, Atmospheric modeling, Digital imaging, Databases, Scene simulation

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Proceedings Article | 20 May 2011 Paper

A scalable hierarchical approach for leveraging low resolution imagery for image classification

Francis Padula, Harry Gross, Curt Munechika, David Pogorzala

Proceedings Volume 8048, 804810 (2011) https://doi.org/10.1117/12.883466

KEYWORDS: Image resolution, MODIS, Image classification, Landsat, Image processing, Earth observing sensors, Satellite imaging, Satellites, Detection and tracking algorithms, Data archive systems

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Proceedings Article | 26 April 2010 Paper

Dynamic scene generation, multimodal sensor design, and target tracking demonstration for hyperspectral/polarimetric performance-driven sensing

Michael Presnar, Alan Raisanen, David Pogorzala, John Kerekes, Andrew Rice

Proceedings Volume 7672, 76720T (2010) https://doi.org/10.1117/12.849469

KEYWORDS: Sensors, Polarizers, Micromirrors, Image sensors, 3D modeling, Polarization, Atmospheric modeling, Image processing, Hyperspectral imaging, Polarimetry

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Proceedings Article | 27 April 2009 Paper

Sensor modeling and demonstration of a multi-object spectrometer for performance-driven sensing

John Kerekes, Michael Presnar, Kenneth Fourspring, Zoran Ninkov, David Pogorzala, Alan Raisanen, Andrew Rice, Juan Vasquez, Jeffrey Patel, Robert MacIntyre, Scott Brown

Proceedings Volume 7334, 73340J (2009) https://doi.org/10.1117/12.819265

KEYWORDS: Sensors, Micromirrors, Spectroscopy, Digital micromirror devices, Performance modeling, Video, Data modeling, Mirrors, Molybdenum, Detection and tracking algorithms

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Proceedings Article | 12 September 2007 Paper

Synthetic data generation of high-resolution hyperspectral data using DIRSIG

Marek Jakubowski, David Pogorzala, Timothy Hattenberger, Scott Brown, John Schott

Proceedings Volume 6661, 66610G (2007) https://doi.org/10.1117/12.735264

KEYWORDS: Sensors, Vegetation, Digital imaging, Detection and tracking algorithms, Data modeling, Remote sensing, Reflectivity, Algorithm development, Atmospheric modeling, Calibration

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Proceedings Article | 7 May 2007 Paper

A framework for polarized radiance signature prediction for natural scenes

Chabitha Devaraj, Scott Brown, David Messinger, Adam Goodenough, David Pogorzala

Proceedings Volume 6565, 65650Y (2007) https://doi.org/10.1117/12.719798

KEYWORDS: Polarization, Sun, Scattering, Cameras, Bidirectional reflectance transmission function, Rayleigh scattering, Atmospheric modeling, Electro optical modeling, Sensors, Reflection

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Proceedings Article | 7 May 2007 Paper

Recreation of a nominal polarimetric scene using synthetic modeling tools

David Pogorzala, Scott Brown, David Messinger, Chabitha Devaraj

Proceedings Volume 6565, 65650Z (2007) https://doi.org/10.1117/12.721207

KEYWORDS: Data modeling, Polarimetry, Polarization, Bidirectional reflectance transmission function, Sensors, Atmospheric modeling, Reflectivity, RGB color model, Imaging systems, Electro optical modeling

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Proceedings Article | 1 June 2005 Paper

Gas plume species identification in airborne LWIR imagery using constrained stepwise regression analyses

David Pogorzala, David Messinger, Carl Salvaggio, John Schott

Proceedings Volume 5806, (2005) https://doi.org/10.1117/12.603661

KEYWORDS: Gases, Absorption, Long wavelength infrared, Sensors, Hyperspectral imaging, Algorithm development, Digital imaging, Remote sensing, Detection and tracking algorithms, Solid modeling

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Proceedings Article | 12 August 2004 Paper

Gas plume species identification by regression analyses

David Pogorzala, David Messinger, Carl Salvaggio, John Schott

Proceedings Volume 5425, (2004) https://doi.org/10.1117/12.541641

KEYWORDS: Gases, Absorption, Algorithm development, Sensors, Hyperspectral imaging, Digital imaging, Remote sensing, Long wavelength infrared, Data modeling, Solid modeling

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Showing 5 of 16 publications

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