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X-WR-CALDESC:Events for NOAA Center for Earth System Sciences and Remote Sensing Technologies
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BEGIN:VEVENT
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/
LOCATION:WI
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/
LOCATION:WI
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/
LOCATION:WI
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/
LOCATION:WI
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:20261016T113000
DTEND;TZID=America/New_York:20261016T120000
DTSTAMP:20261009T133708Z
CREATED:20261009T133708Z
LAST-MODIFIED:20261009T133708Z
UID:6179-1792150200-1792152000@www.cessrst.org
SUMMARY:NOAA Seminar Series: Using AI/ML and Bayesian Modeling Techniques to Study Chemical Stressors and Their Impacts
DESCRIPTION:[vc_row][vc_column][vc_column_text css=””]\n\nTitle: Using AI/ML and Bayesian Modeling Techniques to Study Chemical Stressors and Their Impacts\n \nPresenter(s): Anais Ortega\, CESSRST II Graduate Fellow  \nRemote Access: Video call link: https://meet.google.com/nnd-owun-ryn\n\nAbstract: \nNOAA’s Mussel Watch Program has trackedcoastal contamination since the 1980s\, but its sediment chemistry\, toxicity\,benthic\, and tissue records have been analyzed separately. This project testedwhether they can support one national framework. Benthic condition was scoredwith AMBI and M-AMBI and linked to watershed urbanization at 732 sites fromEPA’s 2015 National Coastal Condition Assessment and 33 stations from a 1991NOAA survey of the Hudson-Raritan Estuary\, an urban system with legacySuperfund sediment. Random forest and Bayesian models showed that urbanizationpredicted benthic condition in the New York metropolitan area (ρ = 0.62) but not nationally (ρ = 0.05). A Delft3D model ofthe estuary reproduced observed tides (R = 0.97-0.98) and showed sea-level riseraising bed shear in the deep channels. \nThe results presented are from the NOAA EPP CSC NERTOgraduate internship project conducted under the mentorship of Dr. KimaniKimbrough and Dr. Erik Davenport\, National Ocean Service (NOS)\, NationalCenters for Coastal Ocean Science (NCCOS)\, Mussel Watch Program. This NERTOexperience aligns with the NOAA Cooperative Science Center in CESSRST-II\,supporting the Center’s goal of conducting NOAA mission-aligned collaborativeresearch to understand and predict changes in land and water\, oceans andcoasts. This project\, titled Using AI/ML and Bayesian Modeling Techniques toStudy Chemical Stressors and Their Impacts\, addressed the research question:does urbanization predict benthic condition\, and do sediment contaminants carrythat link? The work provides value to the scientific community and programstakeholders by providing a standardized national benthic metric and a modelfor testing whether Superfund cleanups hold under sea-level rise. Through thisinternship\, the student deepened their understanding of NOAA mission areas andgained enhanced skills in machine learning\, Bayesian statistics\, geospatialanalysis\, and hydrodynamic modeling. \n\n\n[/vc_column_text][/vc_column][/vc_row]
URL:https://www.cessrst.org/event/noaa-seminar-series-using-ai-ml-and-bayesian-modeling-techniques-to-study-chemical-stressors-and-their-impacts/
LOCATION:WI
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:20261016T123000
DTEND;TZID=America/New_York:20261016T130000
DTSTAMP:20261009T133946Z
CREATED:20261009T133946Z
LAST-MODIFIED:20261009T133946Z
UID:6182-1792153800-1792155600@www.cessrst.org
SUMMARY:NOAA Seminar Series: From Radiosonde Archives to Flight Commands: ML-Guided Adaptive Sampling for UAS Atmospheric Boundary-Layer Profiling
DESCRIPTION:[vc_row][vc_column][vc_column_text css=””]\n\nTitle: From Radiosonde Archives to Flight Commands: ML-Guided Adaptive Sampling for UAS Atmospheric Boundary-Layer Profiling\n \nPresenter(s):Dereck Guerra\, CESSRST II Graduate Fellow  \nRemote Access: Video call link: https://meet.google.com/ssp-uvxo-fxr\n\nAbstract: \nUncrewed Aircraft Systems (UAS) complement radiosondes for boundary-layer profiling\, but most UAS soundings sample at a fixed rate\, under-resolving the thin inversions\, hydrolapses\, and wind-shift transitions that matter most for severe-weather forecasting. This project\, part of the TADSS (Teaching Any Drone to Sample Smarter) framework\, tested whether a UAS could anticipate a boundary-layer point-of-interest’s location from its sounding shape so far\, matching it against a library of real historical radiosonde profiles to sample more densely where it matters. The approach uses K-Medoids(PAM) clustering of seasonally stratified IGRA2 radiosonde archives with a two-of-three POI voting scheme\, built on the BLISS CopterSonde platform and LAAIRS ground control station at NOAA’s National Severe Storms Laboratory (NSSL)\, Norman\, OK. A temperature-convention fix cut sounding-matching MAE from~18°C to 0.4–1.6°C; accuracy gains plateaued beyond ~15–20 medoids\, showing clustering resolution — not library size — as the main constraint. POI detection validated against field data\, with transition zones consistently at800–1\,100 m AGL\, and end-to-end matching now runs inside LAAIRS \n\n\n[/vc_column_text][/vc_column][/vc_row]
URL:https://www.cessrst.org/event/noaa-seminar-series-from-radiosonde-archives-to-flight-commands-ml-guided-adaptive-sampling-for-uas-atmospheric-boundary-layer-profiling/
LOCATION:WI
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:20261016T133000
DTEND;TZID=America/New_York:20261016T140000
DTSTAMP:20261009T134402Z
CREATED:20261009T134325Z
LAST-MODIFIED:20261009T134402Z
UID:6185-1792157400-1792159200@www.cessrst.org
SUMMARY:NOAA Seminar Series: Creating a METAR Climatology Targeting Gradients and Trends Between METARS in the Southwestern U.S. to Examine Impacts on Southern California Weather
DESCRIPTION:[vc_row][vc_column][vc_column_text css=””]\n\nTitle: Creating a METAR Climatology Targeting Gradients and Trends Between METARS in the Southwestern U.S. to Examine Impacts on Southern California Weather\n \nPresenter(s): Jose Hurtado\, CESSRST II Graduate Fellow  \nRemote Access: Video call link: https://meet.google.com/egy-frvh-dth\n\nAbstract: \nCarbon cycling is driven by plant biological activity (root respiration\, photosynthesis\, respiration due to decay)\, climatic and meteorological factors (pressure\, precipitation\, ambient air temperature)\, and the interaction of these two sets of variables. However\, ecology research field stations can have data gaps due to equipment failures or human interference (thieves or trespassers). For example\, the Sky Oaks Field Station in Warner Springs\, CA has sensors for barometric pressure\, temperature\, relative humidity\, wind speed\, and direction\, as well as atmospheric carbon cycling data. This NERTO internship was to develop a Linear Mixed Effects Model for the interpolation of missing barometric pressure and ambient air temperature values at the Sky Oaks Field Station (Warner Springs\, CA) based on METAR data from the National Weather Service stations surrounding the Sky Oaks Field Station as archived from the Iowa State University’s data hub\, Iowa Environmental Mesonet. This inverse distance weighting (IDW) interpolation method used geographic data from the United States Geological Survey 3DElevation Program\, which improved model accuracy. In addition\, one of the Eddy Covariance Towers at the Sky Oaks Field Station was restored to collect gas flux and meteorological data. The Linear Mixed Effects Model produced for barometric pressure was overall highly significant (p=0.007503)\, but the model’s mean absolute error needs improvement\, as it is above the acceptable value of 1 millibar (1.21 millibar). The Linear Mixed Model for the Sky Oaks Field Station temperature failed to find significant correlations between the Sky Oaks Field Station measurements\, and its mean absolute error was 2.60 F(1.44 C) \n\n\n[/vc_column_text][/vc_column][/vc_row]
URL:https://www.cessrst.org/event/noaa-seminar-series-creating-a-metar-climatology-targeting-gradients-and-trends-between-metars-in-the-southwestern-u-s-to-examine-impacts-on-southern-california-weather/
LOCATION:WI
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:20261016T143000
DTEND;TZID=America/New_York:20261016T150000
DTSTAMP:20261009T134632Z
CREATED:20261009T134632Z
LAST-MODIFIED:20261009T134632Z
UID:6188-1792161000-1792162800@www.cessrst.org
SUMMARY:NOAA Seminar Series: Leveraging Machine Learning and Artificial Intelligence Approaches to Improve Harmful Algal Bloom Monitoring and Forecasts
DESCRIPTION:[vc_row][vc_column][vc_column_text css=””]\n\nTitle: Leveraging Machine Learning and Artificial Intelligence Approaches to Improve Harmful Algal Bloom Monitoring and Forecasts\n \nPresenter(s): Haniel Cordero Nieves\, CESSRST II Graduate Fellow  \nRemote Access: Video call link: https://meet.google.com/out-xrvc-baz\n\nAbstract: \nHarmful algal blooms (HABs) in the Chesapeake Bay are challenging to characterize from satellite observations because of optically complex waters\, mixed phytoplankton communities\, variable environmental conditions\, and limited coincident field observations. This NERTO project developed a reproducible framework integrating historical phytoplankton observations\, NOAA satellite-derived algal bloom products\, geospatial analysis\, ecological information\, and machine-learning methods to support future AI-assisted discrimination of diatom- and dinoflagellate-dominated events. Field observations were matched with georeferenced satellite imagery and evaluated through a structured quality-control workflow addressing mixed pixels\, land contamination\, clouds\, missing data\, non-detect conditions\, saturation\, and coordinate inconsistencies. Of 31\,531 field-to-satellite records evaluated\, 860were initially classified as valid\, while neighboring-pixel analysis identified additional potentially recoverable observations. A preliminary Mini U-Net segmentation model demonstrated the feasibility of the approach but also revealed limitations associated with automatically generated labels\, motivating development of an interactive expert-review tool for event validation\, masking\, and labeling. Ecological analyses additionally explored seasonal phenology and environmental variables as potential features for future multimodal models \n\n\n[/vc_column_text][/vc_column][/vc_row]
URL:https://www.cessrst.org/event/noaa-seminar-series-leveraging-machine-learning-and-artificial-intelligence-approaches-to-improve-harmful-algal-bloom-monitoring-and-forecasts/
LOCATION:WI
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:20261104T130000
DTEND;TZID=America/New_York:20261104T143000
DTSTAMP:20261008T203657Z
CREATED:20261008T203603Z
LAST-MODIFIED:20261008T203657Z
UID:6173-1793797200-1793802600@www.cessrst.org
SUMMARY:CSC Education Experts’ Seminar Series: Resume Refresh: Stronger Sections\, Clearer Stories
DESCRIPTION:[vc_row][vc_column][vc_column_text css=””]\nPlease join for the next Education Expert Seminar: Resume Refresh: Stronger Sections\, Clearer Stories \nDownload Flyer\n\nJoin Zoom Meeting\nhttps://us02web.zoom.us/j/87574581955?pwd=MShWa2Oy7BcDToPUd2kcfX6RSnuj3D.1\n\nMeeting ID: 875 7458 1955\nPasscode: 693721\n[/vc_column_text][/vc_column][/vc_row]
URL:https://www.cessrst.org/event/csc-education-experts-seminar-series-resume-refresh-stronger-sections-clearer-stories/
LOCATION:WI
CATEGORIES:Informational Webinar,Seminar Series
ATTACH;FMTTYPE=image/png:https://www.cessrst.org/wp-content/uploads/2026/10/Seminar-11-04-2026.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20261112T123000
DTEND;TZID=America/New_York:20261112T133000
DTSTAMP:20261106T153027Z
CREATED:20261106T153027Z
LAST-MODIFIED:20261106T153027Z
UID:6206-1794486600-1794490200@www.cessrst.org
SUMMARY:NOAA Seminar Series: Hybrid Aviation Weather Climate Interface - HAWCI
DESCRIPTION:[vc_row][vc_column][vc_column_text css=””]\n\nTitle: Hybrid Aviation Weather Climate Interface – HAWCI\n \nPresenter(s): Drake Valencia\, CESSRST II Graduate Fellow  \nRemote Access: Video call link:  https://meet.google.com/cpt-znyu-stw\n\nAbstract: \n Crosswind\, ceiling\, and visibility constrain Southern California aviation\, but forecast guidance covers one airfield and one variable at a time\, leaving no quantified regional picture. METAR observations from 53 ASOS and AWOS stations (2006 to 2025\, 10\,403\,137records) were paired with FAA NASR runway headings to resolve crosswind trigonometrically\, then reduced to lagged conditional probabilities for all ordered station pairs\, stratified by wind direction\, season\, month\, and time of day. The domain packs dozens of civilian and military airfields across varied terrain. Calm wind\, not wind direction\, dominates inland low-visibility conditions\, where rates run three to four times the directional-wind rate. Crosswind co-occurrence is strongest among coastal fields\, while ceiling and visibility co-occurrence tracks shared elevation and terrain rather than distance. \nThe results presented are from the NOAA EPP CSC NERTO graduate internship project conducted under the mentorship of Ivory Small\, National Weather Service\, San Diego Weather Forecast Office. 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. This project\, titled HAWCI-Hybrid Aviation Weather Climate Interface\, addressed the research question: how often do Southern California airfields exceed operational crosswind\, ceiling\, and visibility thresholds\, and which other airfields follow\, and after how long? The work provides value to the scientific community and program stakeholders by converting two decades of routine observations into a quantified regional climatology and a standalone operational tool. Through this internship\, the student deepened their understanding of NOAA mission areas and gained enhanced skills in large-scale data pipeline development\, Python programming\, spatial statistics\, and operational software delivery. \n\n\n[/vc_column_text][/vc_column][/vc_row]
URL:https://www.cessrst.org/event/noaa-seminar-series-hybrid-aviation-weather-climate-interface-hawci/
LOCATION:WI
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:20261112T133000
DTEND;TZID=America/New_York:20261112T141500
DTSTAMP:20261106T153312Z
CREATED:20261106T153312Z
LAST-MODIFIED:20261106T153312Z
UID:6209-1794490200-1794492900@www.cessrst.org
SUMMARY:NOAA Seminar Series: From Satellites to Cells: a comparison of methods to measure phytoplankton and Harmful Algal Blooms in Charleston Harbor
DESCRIPTION:[vc_row][vc_column][vc_column_text css=””]\n\nTitle:From Satellites to Cells: a comparison of methods to measure phytoplankton and Harmful Algal Blooms in Charleston Harbor\n \nPresenter(s): Emilia Lepe\, CESSRST II Graduate Fellow  \nRemote Access: Video call link: https://meet.google.com/udt-cpam-jyi\n\nAbstract: \nCharleston Harbor\, South Carolina\, isa tidally flushed estuary where phytoplankton respond to both tidal flushing and nutrient inputs from land. Harmful algal blooms (HABs) can degrade water quality\, so monitoring must cover both space and time. Point sampling cannot easily track blooms across the harbor\, and satellite ocean color is limited in turbid\, optically complex water where colored dissolved organic matter and sediment can mask chlorophyll-a signals. This 12-week internship at the Hollings Marine Laboratory provided training in methods that characterize phytoplankton across scales: scanning electron microscopy\, flow imaging with a newly installed FlowCam 8100\, high-performance liquid chromatography (HPLC)pigment analysis\, smartphone-based reflectance measurements with the HydroColorapp\, and Sentinel-2 satellite imagery processed with ACOLITE atmospheric correction and the chl_oc2 algorithm. On June 23\, 2026\, the chlorophyll-a map showed lower values around the Plum Island Wastewater Treatment Plant diffusers than in the surrounding harbor. This pattern may reflect reduced phytoplankton growth where the effluent carries fewer nutrients\, or an optical artifact of rising bubbles and fresher water at the diffusers; a time series with optical correction and in-situ validation is needed to tell them apart. Electron microscopy showed that species-level identification depends on fine cell structure that no satellite pixel can resolve\, illustrating the trade-off between spatial coverage and taxonomic resolution and why these methods are most useful in combination. \n\n\n[/vc_column_text][/vc_column][/vc_row]
URL:https://www.cessrst.org/event/noaa-seminar-series-from-satellites-to-cells-a-comparison-of-methods-to-measure-phytoplankton-and-harmful-algal-blooms-in-charleston-harbor/
LOCATION:WI
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:20261112T150000
DTEND;TZID=America/New_York:20261112T160000
DTSTAMP:20261106T153604Z
CREATED:20261106T153604Z
LAST-MODIFIED:20261106T153604Z
UID:6212-1794495600-1794499200@www.cessrst.org
SUMMARY:NOAA Seminar Series: Integrating technology and imagery into evaluations of coral reef condition
DESCRIPTION:[vc_row][vc_column][vc_column_text css=””]\n\nTitle: Integrating technology and imagery into evaluations of coral reef condition\n \nPresenter(s): Neidibel Martinez\, CESSRST II Graduate Fellow  \nRemote Access: Video call link: https://meet.google.com/uoq-gcdw-toa\n\nAbstract: \nCoral reef structural complexity supports habitat and ecological function\, so accurate measurement is essential for monitoring and restoration. In-water surveys face time\, logistical\, and safety constraints\, creating a need for scalable approaches such as large-area imaging (LAI) and Structure-from-Motion (SfM) photogrammetry. NOAA needs consistent workflows to monitor restoration areas\, integrate imagery from different platforms\, and distinguish ecological change from methodological variability. Using the SfM section of NOAA’s Mission: Iconic Reefs LAI guidance as a reference\, this NERTO project developed a reproducible framework for comparing diver- and ROV-based imagery while quantifying processing variability. The analysis used imagery from reef restoration sites\, a relevant setting for fine-scale structural monitoring. We processed images in Agisoft Metashape using different alignment\, depth-map quality\, and filtering configurations. Reconstructions were screened using checkpoint RMSE\, reprojection error\, coverage\, point density and confidence\, completeness\, noise\, processing time\, and file size. We compared leading products as standardized digital elevation models in R using a common region of interest and surface complexity ratio. Higher-detail reconstruction produced approximately four times more points and a larger file but changed surface complexity by only about 3%. Preliminary diver–ROV results showed broadly similar structural patterns\, but the areas were not spatially paired and therefore cannot demonstrate platform agreement. These findings provide NOAA with a stronger QA/QC basis for selecting efficient processing settings and designing paired comparisons that support reproducible\, scalable coral reef restoration monitoring. \nThe results presented are from the NOAA EPP CSC NERTO graduate internship conducted under the mentorship of Dr. Shay Viehman at NOAA’s NCCOS Beaufort Laboratory\, NC. This project supports the NOAA CESSRST-II mission of understanding and predicting environmental changes\, particularly in oceans and coastal ecosystems. The project investigated how variations in SfM processing parameters and diver- versus ROV-based imagery influence the accuracy\, consistency\, and efficiency of coral reef structural measurements\, with the goal of developing standardized workflows for reproducible coral reef restoration monitoring. \n\n\n[/vc_column_text][/vc_column][/vc_row]
URL:https://www.cessrst.org/event/noaa-seminar-series-integrating-technology-and-imagery-into-evaluations-of-coral-reef-condition/
LOCATION:WI
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
END:VCALENDAR