NOAA STAR SOCD OceanView: The Level-4 thermal fronts module Prasanjit Dash1,2, Marouan Bouali3, Jorge Vazquez4, Paul DiGiacomo1 1NOAA STAR SOCD, College Park, MD, USA; 2CSU CIRA, Fort Collins, CO, USA; 3IOUSP, São Paulo, Brazil; 4JPL/Caltech, Pasadena, CA, USA Preamble: The NOAA STAR SOCD OceanView (OV) v1.0 was publicly released in May-2021 at https://www.star.nesdis.noaa.gov/socd/ov/. The OceanView is a web-based application delivering an integrated display of remote sensing, in situ, and model data over oceans, polar areas, and coastal and inland waterways [1]. It incorporates products primarily from NOAA and some from NASA and other sources. The application comprises several modules, including an experimental thermal fronts component capable of deriving and interactively visualizing level-4 SST fronts. This L4 fronts module will potentially contribute to the GHRSST Climatology and L4 Inter-comparison Task Team (IC-TT) in discussion with its participants and other enthusiasts. Here, we demonstrate the existing capability with only one L4 SST and aim to gauge features of interest and potential extension to meet GHRSST TT objectives. S3. Calibration, validation and product assessment Abstract ID 42; virtual The Satellite Oceanography and Climate Division (SOCD) of NOAA STAR released the OceanView v1.0 in 2021. The L4 fronts module of OceanView currently has only one L4 SST front (NOAA GOES-POES blended). However, the system is scalable and will be expanded depending on identified needs and the path forward. This preliminary illustration aims to gauge features of interest and potential extension to meet GHRSST IC-TT objectives. The work is experimental and will likely undergo improvements in both algorithm and validation approaches. References [1] Dash, P. and DiGiacomo, P, 2021: The NOAA STAR SOCD OceanView (OV): an application for integrated visualization of satellite, in situ, and model data & ocean events – the debut release.; GHRSST XXII Proc. Virtual meeting, 7-11 June 2021. https://doi.org/10.5281/zenodo.5750123 [2] Maturi, E., and Coauthors, 2017: A New High-Resolution Sea Surface Temperature Blended Analysis. BAMS, 77–80, 21–30, https://doi.org/10.1175/BAMS-D-15-00002.1 [3] Martin, M., and Coauthors, 2012: Group for High Resolution Sea Surface temperature (GHRSST) analysis fields inter-comparisons. Part 1: A GHRSST multi-product ensemble (GMPE). DSR. II, 77–80, 21–30, https://doi.org/10.1016/j.dsr2.2012.04.013 [4] Dash, P., and Coauthors, 2012: Group for High Resolution Sea Surface Temperature (GHRSST) analysis fields inter-comparisons. Part 2: Near real time web-based level 4 SST Quality Monitor. DSR. II, 77–80, 31–43, https://doi.org/10.1016/j.dsr2.2012.04.002 Acknowledgment JPSS program, Ocean Remote Sensing (funding); GHRSST IC-TT members for discussions. 23rd International Science Team Meeting (GHRSST XXIII), Barcelona 27 June – 1 July 2022 and virtual; contact: prasanjit.dash@noaa.gov (https://orcid.org/0000-0002-8789-0506) Simplified overview of: Science Technology Example Summary Experimental L4 front module • interactive profiler for individual fronts • filters for avg length, gradient & avg. SST 1. Calculate gradient values per pixel using finite difference, with central differences in the interior and first differences at the boundaries. 2. Use Sobel operator to detect edges. Canny was also tested (Marouan B.) but did not seem to provide significant improvement. Numerical operations 3. Adaptive threshold for Sobel filter using cumulative distribution function 4. Binarize to 1 or 0 using the above threshold 5. Skeletonize to make it thinner (1-pixel) 6. Contourize the detected edges (computer vision) Morphological operations 7. Store gradient values & location info for each point on the contours (for web interactivity) 8. Store average SST and length for each contour as attributes (for web interactivity) 9. Save all features in a GeoJSON file for web-display *These steps enable web-visualization & provide in