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X-WR-CALDESC:Events for NOAA Center for Earth System Sciences and Remote Sensing Technologies
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DTSTART;TZID=America/New_York:20261007T113000
DTEND;TZID=America/New_York:20261007T120000
DTSTAMP:20261002T131643Z
CREATED:20261002T131643Z
LAST-MODIFIED:20261002T131643Z
UID:6149-1791372600-1791374400@www.cessrst.org
SUMMARY:NOAA Seminar Series: Quantifying the accuracy of satellite-observed sea surface salinity against in situ observations by saildrones
DESCRIPTION:[vc_row][vc_column][vc_column_text css=””]\n\nTitle: Quantifying the accuracy of satellite-observed sea surface salinity against in situ observations by sail drones\n \nPresenter(s): Kolawole Owoeye\, CESSRST II Graduate Fellow  \nRemote Access: Video call link:  https://meet.google.com/ipf-sjrq-ssu\n\nAbstract: \nSatellite observations provide broad ocean coverage for variables essential to weather and climate research\, but their accuracy can be difficult to evaluate under extreme environmental conditions\, when conventional in situ observations are sparse or unavailable. NOAA’s use of uncrewed observing systems\, particularly Saildrones deployed during hurricanes\, provides a unique opportunity to evaluate satellite-observed sea surface salinity (SSS) under these challenging conditions and supports NOAA’s Weather-Ready Nation goal. This NERTO project quantified the agreement between satellite SSS and in situ Saildrone observations and further investigated how SSS changes spatially and temporally during hurricane encounters. The analysis focused on Atlantic hurricanes observed during NOAA Saildrone missions from 2021–2024 and integrated Saildrone measurements with SMAP Level-2B and Level-3 CAP Version 5.0 SSS products and IBTrACS storm-track and intensity data. Satellite–Saildrone matchups and residual statistics were used to evaluate agreement\, while observations were transformed into a dynamic storm-relative coordinate system based on hurricane motion. SSS responses were examined before (−72 to −12 h)\, during (−12 to +12 h)\, and after (+12 to +72 h) the closest storm approach\, and by normalized distance from the storm center and left-front\, right-front\, left-rear\, and right-rear quadrants. Results showed that satellite–Saildrone agreement varies under hurricane conditions and that hurricane-induced SSS responses are spatially and temporally heterogeneous rather than characterized by a single uniform freshening or salinification response. Post-storm SSS exhibited greater variability\, with distinct responses among storm-relative quadrants. \n\n\n[/vc_column_text][/vc_column][/vc_row]
URL:https://www.cessrst.org/event/noaa-seminar-series-quantifying-the-accuracy-of-satellite-observed-sea-surface-salinity-against-in-situ-observations-by-saildrones-2/
CATEGORIES:NOAA Seminar Series,Seminar Series
ORGANIZER;CN="Center for Earth System Sciences and Remote Sensing Technologies (CESSRST)":MAILTO:cessrst@ccny.cuny.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20261007T121500
DTEND;TZID=America/New_York:20261007T124500
DTSTAMP:20261002T131929Z
CREATED:20261002T131929Z
LAST-MODIFIED:20261002T131929Z
UID:6152-1791375300-1791377100@www.cessrst.org
SUMMARY:NOAA Seminar Series: Quantifying Seafloor Visibility in Underwater Images: A Deep Learning Approach
DESCRIPTION:[vc_row][vc_column][vc_column_text css=””]\n\nTitle:Quantifying Seafloor Visibility in Underwater Images: A Deep Learning Approach\n \nPresenter(s): Zoya Shafique\, CESSRST II Graduate Fellow  \nRemote Access: Video call link:  https://meet.google.com/fwu-drwn-ict\n\nAbstract: \nThe results presented are from the NOAA EPP CSC NERTO graduate internship project conducted under the mentorship of Tim Battista\, National Centers for Coastal Ocean Science (Silver Spring). This NERTO experience aligns with the NOAA Cooperative Science Center in CESSRST-II\, supporting the Center’s goal of conserving and restoring coastal and marine ecosystems. This project\, titled Quantifying Seafloor Visibility in Underwater Images: A Deep Learning Approach\, addressed the research question: how can deep learning models be used to quantify turbidity in images of the seafloor. Images of the Gulf’s seafloor from field missions related to the Mesophotic and Deep Benthic Communities project were used to train a deep learning model to recognize different levels of turbidity. The model was then used to rank images based on the presence of turbidity to allow for a deeper understanding of what percentage of the data is usable for downstream analysis. \nThe work provides value to the scientific community and program stakeholders by providing an efficient method for data quality analysis in the case where multiple terabytes of data are being collected. Measuring data quality as it is being collected in the field allows the field team to monitor if the collected data is usable for downstream modelling. If not\, real-time changes can be made so make efficient use of resources and improve the quality of collected data. Through this internship\, the student deepened their understanding of NOAA mission areas and gained enhanced skills in data analysis\, modeling\, coding\, and remote sensing. \n\n\n[/vc_column_text][/vc_column][/vc_row]
URL:https://www.cessrst.org/event/noaa-seminar-series-quantifying-seafloor-visibility-in-underwater-images-a-deep-learning-approach/
CATEGORIES:NOAA Seminar Series,Seminar Series
ORGANIZER;CN="Center for Earth System Sciences and Remote Sensing Technologies (CESSRST)":MAILTO:cessrst@ccny.cuny.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20261007T130000
DTEND;TZID=America/New_York:20261007T133000
DTSTAMP:20261002T132152Z
CREATED:20261002T132152Z
LAST-MODIFIED:20261002T132152Z
UID:6155-1791378000-1791379800@www.cessrst.org
SUMMARY:NOAA Seminar Series: Satellite-Base Mapping of Land Surface Temperature (LST) Across Ecosystems
DESCRIPTION:[vc_row][vc_column][vc_column_text css=””]\n\nTitle: Satellite-Base Mapping of Land Surface Temperature (LST) Across Ecosystems\n \nPresenter(s): Giselle Cabrera Flores\, CESSRST II Graduate Fellow  \nRemote Access: Video call link:  https://meet.google.com/yza-zggc-rrp\n\nAbstract: \n Land Surface Temperature (LST) is an important variable for understanding land–atmosphere interactions and ecosystem processes. This study uses Landsat 8 and 9 observations to examine spatial and temporal variability in LST across Surface Energy Budget Network (SEBN) sites in 2024: the Audubon Research Ranch semiarid grassland in Arizona (Ameriflux ID: US-Aud)\, the Chestnut Ridge deciduous forest in eastern Tennessee (US-ChR)\, and the Bondville cropland in Illinois (US-Bo1). The Land Surface Temperature\, Normalized Difference Vegetation Index (NDVI)\, and surface albedo were analyzed across spatial scales ranging from 100 m to 5 km from the tower sites. Results showed that LST was generally spatially homogeneous near tower footprints. During the growing season\, LST showed stronger relationships with NDVI\, particularly at Chestnut Ridge and Bondville\, while surface characteristics became more evident during fall and winter. Comparisons between satellite and tower observations showed overall good agreement. This work supports NOAA ARL’s Surface Energy Budget Network (SEBN)\, which provides long-term observations to better understand energy\, water\, and carbon exchanges across different ecosystems. Through this internship\, the student deepened their understanding of NOAA mission areas and strengthened their skills in remote sensing\, GIS\, and data analysis\, including gaining experience with RStudio. \n\n\n[/vc_column_text][/vc_column][/vc_row]
URL:https://www.cessrst.org/event/noaa-seminar-series-satellite-base-mapping-of-land-surface-temperature-lst-across-ecosystems/
CATEGORIES:NOAA Seminar Series,Seminar Series
ORGANIZER;CN="Center for Earth System Sciences and Remote Sensing Technologies (CESSRST)":MAILTO:cessrst@ccny.cuny.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20261007T134500
DTEND;TZID=America/New_York:20261007T141500
DTSTAMP:20261002T132507Z
CREATED:20261002T132507Z
LAST-MODIFIED:20261002T132507Z
UID:6158-1791380700-1791382500@www.cessrst.org
SUMMARY:NOAA Seminar Series: National Water Model Assessment In Puerto Rico
DESCRIPTION:[vc_row][vc_column][vc_column_text css=””]\n\nTitle:  National Water Model Assessment In Puerto Rico\n \nPresenter(s): Gerardo Tross-Torres\, CESSRST II Graduate Fellow  \nRemote Access: Video call link: https://meet.google.com/ije-cjaj-fdc\n\nAbstract: \nThis NERTO project conducted an initial evaluation of FIMBox flood-inundation performance in Puerto Rico using National Water Model retrospective streamflow. Example-basin tests\, hydrofabric exploration\, input preparation\, and a Puerto Rico domain-selection adaptation supported four regional applications and a Hurricane Maria case with 20 hourly depth/inundation pairs. Original maps and high-water-mark sampling supported exploratory evaluation. During deliverable preparation\, an additional comparison used USGS recorded height above ground and maximum modeled depth in the archived window. Of 57 eligible riverine marks\, 43 had usable modeled values: mean absolute error was 6.47 m\, root mean square error was 8.10 m\, and mean signed difference was +6.47 m. These results show substantial positive differences in the selected sample. Additional NWS warning overlap supplied event context\, not accuracy evidence. Approximate observed ground heights\, terrain representation\, missing samples\, and partial event coverage limit generalization. The work provides a first assessment of model behavior to inform NOAA-aligned flood-mapping evaluation and model improvement. \nThe results presented are from the NOAA EPP CSC NERTO graduate internship project conducted under the mentorship of Derek Giardino Office of Water Prediction. This NERTO experience aligns with the NOAA Cooperative Science Center in CESSRST-II\, supporting the Center’s goal of conducting NOAA mission-aligned collaborative research to understand and predict changes in weather and atmosphere\, land and water\, oceans and coasts. This project\, titled National Water Model Assessment In Puerto Rico\, addressed the research question: How does flood inundation mapping model FIMBox performs under an extreme weather event in Puerto Rico?. The work provides value to the scientific community and program stakeholders by providing a first assessment of FIMBox performance in Puerto Rico. Through this internship\, the student deepened their understanding of NOAA mission areas and gained enhanced skills in land-and-water research skills in modeling\, programming\, GIS\, troubleshooting\, and scientific communication. Fieldwork experience included assisting USGS and NWS with river cross-section measurements in Caguas and Ciales/Morovis. \n\n\n[/vc_column_text][/vc_column][/vc_row]
URL:https://www.cessrst.org/event/noaa-seminar-series-national-water-model-assessment-in-puerto-rico/
CATEGORIES:NOAA Seminar Series,Seminar Series
ORGANIZER;CN="Center for Earth System Sciences and Remote Sensing Technologies (CESSRST)":MAILTO:cessrst@ccny.cuny.edu
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