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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241021T140000
DTEND;TZID=America/New_York:20241021T143500
DTSTAMP:20260405T123533
CREATED:20241021T132243Z
LAST-MODIFIED:20241021T132243Z
UID:5240-1729519200-1729521300@www.cessrst.org
SUMMARY:NOAA Seminar Series: Phytoplankton monitoring and mapping using NOAA OAR/AOML satellite data products for urban waters ecosystems.
DESCRIPTION:Title: Phytoplankton monitoring and mapping using NOAA OAR/AOML satellite data products for urban waters ecosystems.\n \nPresenter(s): Carolina Perez \nDate: 21 October 2024 2:05 pm – 2:35 pm ET\n \nRemote Access: Google Meet joining info \nVideo call link:  https://meet.google.com/yjc-yvec-nkm \nOr dial: (US) +1 929-269-1887 PIN: 429 915 143#  \nMore phone numbers: https://tel.meet/yjc-yvec-nkm?pin=1379180679958 \nAbout Speaker: Carolina Perez \nAbstract:  Harmful Algal Blooms (HABs) significantly threaten coastal communities and public health\, impacting marine ecosystems and local economies. These blooms indicate potentially toxic phytoplankton and reflect broader environmental issues such as eutrophication\, runoff\, and the effects of weather events on coastal areas. My research focuses on the monitoring of HABs in New York City waterbodies\, specifically Randall’s Island and the Gowanus Canal\, in collaboration with NOAA’s National Centers for Coastal Ocean Science (NCCOS)and the National Phytoplankton Monitoring Network (PMN). The Gowanus Canal\, a notable study area\, was designated a Superfund site by the EPA in 2010 due to its severe contamination from runoff and combined sewage overflow.During my time at AOML\, as part of my NERTO research project\, I helped develop a research technique by combining satellite data and ground-based data to develop interactive web tools to enhance the real-time monitoring of marine ecosystems. While a NOAA AOML NERTO Intern\, I contributed to enhancing sustainability and resilience for marine ecosystems and coastal communities. AOML’s Ocean Chemistry and Ecosystem Dynamics (OCED) branch employs satellite observations to analyze sea surface temperature (SST) and chlorophyll-a (CHL) levels in water bodies. Using the Environmental Research Division’s Data Access Program (ERDDAP)\, data on SST and CHL I was able to generate detailed maps of these parameters using MATLAB. Integrating satellite-based data with in-situ measurements offers a powerful approach to understanding the health of both open ocean and coastal ecosystems. Combining these data sources\, the research aims to create synthesis products that examine algal blooms and their dynamics.The results are from the NOAA EPP/MSI CSC NERTO graduate internship project that was conducted with NOAA mentors Dr. Chris Kelble and Dr. Enrique Montes of the NOAA Atlantic Oceanographic and Meteorological Laboratory\, Ocean Chemistry and Ecosystem Dynamics (OCED) branch. This NERTO aligns with NOAA CSC’s goal of Resilient Coastal Communities and Economies through the theme of Oceans and Coasts. The NERTO deepened the intern’s understanding of NOAA’s research practices by giving me firsthand experience with the teamwork and dedication of a NOAA research team.
URL:https://www.cessrst.org/event/noaa-seminar-series-phytoplankton-monitoring-and-mapping-using-noaa-oar-aoml-satellite-data-products-for-urban-waters-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:20241024T140000
DTEND;TZID=America/New_York:20241024T143000
DTSTAMP:20260405T123533
CREATED:20241021T142845Z
LAST-MODIFIED:20241021T142845Z
UID:5242-1729778400-1729780200@www.cessrst.org
SUMMARY:NOAA Seminar Series: Societal Data Insights: Data Integration for Inland Flooding.
DESCRIPTION:Title:Societal Data Insights: Data Integration for Inland Flooding.\n \nPresenter(s): Isabel Lopez \nDate: 24 October 2024 2:00 pm – 2:30 pm ET\n \nRemote Access: Google Meet joining info \nVideo call link: https://meet.google.com/xsy-nupc-von   \nOr dial: (US) +1 234-276-0398PIN: 436 710 044#  \nMore phone numbers: https://tel.meet/xsy-nupc-von?pin=2132911046548 \nAbout Speaker: Isabel Lopez \nAbstract: Urban recurrent flooding presents a complex challenge distinct from nuisance flooding\, typically associated with coastal areas. Unlike nuisance flooding\, which is often predictable and localized\, urban recurrent flooding involves adynamic interplay of factors such as dense infrastructure\, varied land use\, and heterogeneous topography. These elements contribute to unpredictable flood patterns that are more difficult to model and manage. The complexity of urban environments amplifies the challenges in assessing flood risks and potential impacts\, necessitating a more sophisticated analytical approach. This research adapts the Topographic Wetness Index (TWI) to highlight areas prone to flooding based on flow direction and water accumulation. Additionally\, it incorporates the Curve Number (CN) method to estimate runoff volumes from precipitation events\, providing refined tools for measuring surface runoff and predicting flooding potential. Recognizing that urban flooding significantly impacts communities\, this study integrates social data to capture the broader societal effects\, particularly on vulnerable populations. The proposed framework is designed for flexibility\, allowing its application across diverse urban areas with varying geographic and social characteristics. By combining geospatial analysis with social data\, this research offers a comprehensive approach to flood risk assessment\, providing valuable insights for policymakers and urban planners.The results are from the NOAA EPP/MSI CSC NERTO graduate internship project that was conducted with NOAA mentors Dr. Jonathon Mote and Dr. Kyle Metta of the Weather ProgramOffice (WPO) in Silver Spring\, MD. The NERTO aligns with NOAA CSC CESSRST-II’s goal to understand changes in climate and weather and to share that knowledge and information with others. The NERTO project enhanced the intern’s ability to integrate social data with physical data\, providing deeper insights into developing methods that combine social\, weather\, and climate data for more comprehensive analyses.
URL:https://www.cessrst.org/event/noaa-seminar-series-societal-data-insights-data-integration-for-inland-flooding/
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:20241024T143500
DTEND;TZID=America/New_York:20241024T150500
DTSTAMP:20260405T123533
CREATED:20241021T143329Z
LAST-MODIFIED:20241021T143329Z
UID:5244-1729780500-1729782300@www.cessrst.org
SUMMARY:NOAA Seminar Series: Historical Data Reconstruction for the California Coastal Currents using 3D Empirical Orthogonal Functions and Multivariate Regression
DESCRIPTION:Title: Historical Data Reconstruction for the California Coastal Currents using 3D Empirical Orthogonal Functions and Multivariate Regression\n \nPresenter(s): Danielle Lafarga\, \nDate: 24 October 2024 2:35 pm – 3:05 pm ET\n \nRemote Access: Google Meet joining info \nVideo call link:  https://meet.google.com/new-qbkh-azj \n Or dial: (US) +1 440-482-5511 PIN: 303 375 204#  \nMore phone numbers: https://tel.meet/new-qbkh-azj?pin=5643412593662 \nAbout Speaker: Danielle Lafarga\, \nAbstract: Many studies analyze ocean temperature variance\, computing empirical orthogonal functions (EOFs) one layer at a time(2D). However\, surface phenomena like El Nio extend into deeper layers\, exemplifying how crucial it is to examine their three-dimensional structure to fully understand their impact. This research aims to compute 3D EOFs for different areas of the Pacific Ocean to answer how much and what variability can be explored across ocean layers using ahigh-resolution\, eddy-resolving model known as the Global Ocean Physics Reanalysis (GLORYS). The model’s fine resolution allows for detailed analysis of smaller-scale dynamics\, such as those along the coasts of California\, Oaxaca\, and Costa Rica. Nevertheless\, the volume of data presents a memory challenge for 3D calculations. To address this\, we propose an algorithm that enables 3D EOF computation on computers with limited memory (16GB RAM)\, making high-resolution analysis feasible.Computing 3D EOFs is crucial for understanding our oceans and how ocean dynamics can extend through multiple layers. This research aligns with NOAA’s mission to understand and predict changes in climate\, weather\, oceans\, and coasts. By providing a more comprehensive view of ocean variability\, the results also contribute valuable insights into the habitats of fish species protected by NOAA Fisheries\, aiding in the preservation and management of marine ecosystems.The results are from the NOAA EPP/MSI CSC NERTO graduate internship project that was conducted with NOAA mentor\, Dr. Michael Jacox of NOAA SWFSC Environmental Research Division\, and NOAA collaborator Dr. Michael Alexander of NOAA Atmosphere Ocean Processes and Predictability (AOPP) Division. The NERTO aligns NOAA CSCCESSRST-II’s goal of to understand and predict changes in climate and weather. The NERTO project deepened the intern’s understanding of remote sensing technology\, big data computing\, and participation in NOAA mission-aligned activities through extensive collaborations with NOAA employees.
URL:https://www.cessrst.org/event/noaa-seminar-series-historical-data-reconstruction-for-the-california-coastal-currents-using-3d-empirical-orthogonal-functions-and-multivariate-regression/
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:20241024T151500
DTEND;TZID=America/New_York:20241024T154500
DTSTAMP:20260405T123533
CREATED:20241021T143731Z
LAST-MODIFIED:20241021T143731Z
UID:5246-1729782900-1729784700@www.cessrst.org
SUMMARY:NOAA Seminar Series: Identifying local and synoptic-scale meteorological and land cover conditions favorable for the occurrence of large fires in California
DESCRIPTION:Title: Identifying local and synoptic-scale meteorological and land cover conditions favorable for the occurrence of large fires in California\n \nPresenter(s):  E’lysha Guerrero \nDate: 24 October 2024 3:15 pm – 3:45 pm ET\n \nRemote Access: Google Meet joining info \nVideo call link: https://meet.google.com/dft-obqy-fhb \nOr dial: (US) +1 650-535-0909PIN: 928 542 289#  \nMore phone numbers: https://tel.meet/dft-obqy-fhb?pin=5001908281383 \nAbout Speaker: E’lysha Guerrero\, \nAbstract: Whilst global warming projections lead to continuous warming trends and California wildfire activity is expected to increase\, the state of wildfire predictions will need to be enhanced to keep up with the ever-changing climate conditions. This research project aims to characterize meteorological and land conditions related to large wildfires in California and identify their connection to predictable climate patterns\, potentially enhancing future wildfire predictions. We utilize historical wildfire perimeter data (2000 “2022) and apply the K-means Clustering Algorithm on localized meteorological variables to group wildfires based on similar conditions. Larger-scale synoptic meteorology is analyzed to identify potential predictors for future wildfire occurrences. The research questions addressed during the NERTO are: (a) What are the local regional and seasonal characteristics of California’s historically larger wildfires from 2000 – 2022? and (b) What are the typical large-scale circulation patterns associated with each California clustered group?The value of this research lies in its contribution to NOAA’s mission to understand and predict climate and weather changes\, specifically through advancing wildfire prediction capabilities. The insights gained can improve both prediction models and wildfire management strategies\, supporting NOAA’s broader goal of mitigating the impacts of extreme weather and natural hazards. Additionally\, the use of machine learning techniques\, like K-means clustering\, fosters innovation in predictive skills\, aligning with the NOAA Physical Sciences Laboratory’s mission to develop new knowledge and tools for forecasting extreme events such as wildfires. The results are from the NOAA EPP/MSI CSC NERTO graduate internship project that was conducted with NOAA mentor Dr. Andrew Hoell\, Dr. Rochelle Worsnop\, and Dr. Melissa Breeden of NOAA Physical Sciences Laboratory\, Boulder\, CO. The NERTO aligns with NOAA CSC CESSRST-II’s goal to understand and predict changes in climate and weather. The NERTO project deepened the intern’s understanding and increased the research skill sets of data acquisition\, preprocessing\, analyses\, and validation techniques required for earth system science research.
URL:https://www.cessrst.org/event/noaa-seminar-series-identifying-local-and-synoptic-scale-meteorological-and-land-cover-conditions-favorable-for-the-occurrence-of-large-fires-in-california/
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:20241025T133000
DTEND;TZID=America/New_York:20241025T140000
DTSTAMP:20260405T123533
CREATED:20241021T152307Z
LAST-MODIFIED:20241021T152323Z
UID:5249-1729863000-1729864800@www.cessrst.org
SUMMARY:NOAA Seminar Series: Remote sensing of extreme weather events on CDOM in Long Island Sound
DESCRIPTION:Title: Remote sensing of extreme weather events on CDOM in Long Island Sound \n \nPresenter(s):  Charlotte Rhoads \nDate: 25 October 2024 1:30 pm – 2:00 pm ET\n \nRemote Access: Google Meet joining info \nVideo call link: https://meet.google.com/gnq-msir-wvj  \nOr dial: (US) +1 267-553-4621 PIN: 434 202 737#  \nMore phone numbers: https://tel.meet/gnq-msir-wvj?pin=9872489736362\n \nAbout Speaker: Charlotte Rhoads \nAbstract: Coastal areas are critical in global carbon cycling\, particularly as climate change alters carbon fluxes through rising temperatures\, shifting precipitation patterns\, and sea-level rise. These changes affect microbial activity\, carbon remineralization\, and transportation pathways\, influencing the fate of carbon in coastal environments. Dissolved organic matter (DOM)\, sourced from land and sea\, regulates ecosystem functioning\, while colored dissolved organic matter (CDOM) acts as an optical proxy for tracking DOM dynamics. Extreme weather events modify DOM quantity and quality\, impacting harmful algal blooms (HABs). In Long Island Sound (LIS)\, a eutrophic estuary\, high-resolution satellite remote sensing\, and regionally optimized algorithms enable monitoring of DOM and its response to extreme precipitation events. By analyzing CDOM’s optical properties\, such as aCDOM(300)\,DOC\, S275″295\, and SR\, changes in DOM can be linked to carbon cycling and water quality. Increased DOM flux during heavy rains drives dinoflagellate blooms\, posing threats to water quality and aquaculture. Identifying CDOM optical signatures of harmful dinoflagellates like Prorocentrumcordatum and Margalefidinium polykrikoides may allow for monitoring via remote sensing. The results are from the NOAA EPP/MSI CSC NERTO graduate internship project that was conducted with NOAA mentors Veronica Lance and Jonathan Sherman of NESDIS and Gary Wilkfors and Lisa Guy of NOAA Fisheries. The NERTO aligns with NOAACSC CESSRST-II’s goal of providing actionable remote sensing-based research to stakeholders. The NERTO deepened the intern’s understanding of NOAA’s mission of managing and protecting coastal ecosystems and resources.
URL:https://www.cessrst.org/event/noaa-seminar-series-remote-sensing-of-extreme-weather-events-on-cdom-in-long-island-sound/
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:20241114T120000
DTEND;TZID=America/New_York:20241114T130000
DTSTAMP:20260405T123533
CREATED:20240910T142016Z
LAST-MODIFIED:20250320T133425Z
UID:5157-1731585600-1731589200@www.cessrst.org
SUMMARY:Climate Seminar Series: The Expanding Reach of Ocean Acidification
DESCRIPTION:Download Flyer \nView details \nEvent: NOAA EPP/MSI CSC Climate Change Seminar Series \nTitle:  THE EXPANDING REACH OF OCEAN ACIDIFICATION \nPresenter: Andrea Fassbender\,  OAR Pacific  Marine Environment Lab (Hosted by CCME) \nDate: November 14\, 2024 \nTime: 12:00pm-1:00 PM \nRegister: Click here to register \n After registering\, you will receive a confirmation email with details to join the event \n About the Seminar Series. \nJoin NOAA’s EPP/MSI Cooperative Science Centers for this lecture series featuring scientists from NOAA Fisheries and NOAA Research. These monthly seminars will be hosted by the lead institution\, virtually or hybrid throughout the 2025-2025 academic year.  For more information\, please visit\, https://research.noaa.gov/noaa-csc-24-25/ \n 
URL:https://www.cessrst.org/event/climate-change-seminar-series-the-expanding-reach-of-ocean-acidification/
CATEGORIES:NOAA Seminar Series,Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://www.cessrst.org/wp-content/uploads/2024/09/Climate-Seminar-Series-Fall-2024.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241205T120000
DTEND;TZID=America/New_York:20241205T130000
DTSTAMP:20260405T123533
CREATED:20240910T142253Z
LAST-MODIFIED:20250320T133411Z
UID:5159-1733400000-1733403600@www.cessrst.org
SUMMARY:Climate Seminar Series: An overview of the Tropospheric Ozone Pollution Project
DESCRIPTION:Download Flyer \nView details \nEvent: NOAA EPP/MSI CSC Climate Change Seminar Series \nTitle: AN OVERVIEW OF THE TROPOSPHERIC OZONE POLLUTION PROJECT – USING BALLOON SONDES TO UNDERSTAND INFLUENCES ON AIR QUALITY \nPresenter: Gary Morris\,  OAR Global Monitoring Lab (Hosted by CESSRST-II) \nDate: December 5\, 2024 \nTime: 12:00pm-1:00 PM \nRegister: Click here to register \n After registering\, you will receive a confirmation email with details to join the event \n About the Seminar Series. \nJoin NOAA’s EPP/MSI Cooperative Science Centers for this lecture series featuring scientists from NOAA Fisheries and NOAA Research. These monthly seminars will be hosted by the lead institution\, virtually or hybrid throughout the 2025-2025 academic year.  For more information\, please visit\, https://research.noaa.gov/noaa-csc-24-25/ \n 
URL:https://www.cessrst.org/event/climate-change-seminar-series-an-overview-of-the-tropospheric-ozone-pollution-project/
CATEGORIES:NOAA Seminar Series,Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://www.cessrst.org/wp-content/uploads/2024/09/Climate-Seminar-Series-Fall-2024.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250213T120000
DTEND;TZID=America/New_York:20250213T130000
DTSTAMP:20260405T123533
CREATED:20240910T150056Z
LAST-MODIFIED:20250320T133357Z
UID:5161-1739448000-1739451600@www.cessrst.org
SUMMARY:Climate Seminar Series: Sub seasonal to Seasonal Prediction of Extreme Events
DESCRIPTION:Download Flyer \nView details \nEvent: NOAA EPP/MSI CSC Climate Change Seminar Series \nTitle: SUBSEASONAL TO SEASONAL PREDICTION OF EXTREME EVENTS: ADVANCES AND CHALLENGES \nPresenter: Mark Olsen\,  OAR Weather Program Office (Hosted by NCAS-M) \nDate: February 13\, 2025 \nTime: 12:00pm-1:00 PM \nRegister: Click here to register \n After registering\, you will receive a confirmation email with details to join the event \n About the Seminar Series. \nJoin NOAA’s EPP/MSI Cooperative Science Centers for this lecture series featuring scientists from NOAA Fisheries and NOAA Research. These monthly seminars will be hosted by the lead institution\, virtually or hybrid throughout the 2025-2025 academic year.  For more information\, please visit\, https://research.noaa.gov/noaa-csc-24-25/ \n 
URL:https://www.cessrst.org/event/climate-change-seminar-series-sub-seasonal-to-seasonal-prediction-of-extreme-events/
CATEGORIES:NOAA Seminar Series,Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://www.cessrst.org/wp-content/uploads/2024/09/Climate-Seminar-Series-Fall-2024.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250811T150000
DTEND;TZID=America/New_York:20250811T160000
DTSTAMP:20260405T123533
CREATED:20250801T160221Z
LAST-MODIFIED:20250807T160452Z
UID:5548-1754924400-1754928000@www.cessrst.org
SUMMARY:NOAA Seminar Series: Mapping Cumulative Impacts of Essential Fish Habitat Consultations in the Pacific Islands Region
DESCRIPTION:Title: Mapping Cumulative Impacts of Essential Fish Habitat Consultations in the Pacific Islands Region\n \nPresenter(s):  Amy Carrillo \nDate: 11 August 2025\, 3:00 pm – 4:00 pm ET\n \nRemote Access: Google Meet joining info \nVideo call link: https://meet.google.com/prg-psrb-moa \nAbout Speaker:  Amy Carrillo \nAbstract: The cumulative impacts of federally funded projects\, reviewed by the Essential Fish Habitat (EFH) team\, on EFH in the Pacific Islands region were visualized and evaluated. Using data from the Environmental Consultation Organizer (ECO)\, covering projects from 2017 to the present\, interactive dashboards and a story map were created to visualize the spatial distribution and intensity of these projects across the region. Three tools were created\, first a dashboard that displays all the EFH consultations as points covering the Pacific Island region to allow for visualization of project locations. Second\, on Oahu\, where each consultation action area is mapped as polygons\, scored for adverse impacts on EFH\, and supplemented with detailed information on the type of activity\, impact level\, and other relevant data. Finally\, on Honolulu Harbor\, presenting a story map of one of Oahu’s most heavily impacted and managed areas\, which also hosts a large coral nursery. By processing over 500 project records and creating these tools\, the project provides a comprehensive overview of the scale and impact of federal activities on EFH. The dashboards and story maps are designed to support decision-making by offering accessible tools for conservation efforts and the EFH team’s effective coastal resource management.
URL:https://www.cessrst.org/event/noaa-seminar-series-mapping-cumulative-impacts-of-essential-fish-habitat-consultations-in-the-pacific-islands-region/
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:20250820T100000
DTEND;TZID=America/New_York:20250820T110000
DTSTAMP:20260405T123533
CREATED:20250801T160655Z
LAST-MODIFIED:20250807T160844Z
UID:5550-1755684000-1755687600@www.cessrst.org
SUMMARY:NOAA Seminar Series: Marine Heatwaves in the Tropical Atlantic: Detection\, Characteristics\, and Trends in a Warming Ocean.
DESCRIPTION:Title: Marine Heatwaves in the Tropical Atlantic: Detection\, Characteristics\, and Trends in a Warming Ocean.\n \nDate:  August 20\, 2025\, 10:00 am – 11:00 am ET\n \nPresenter(s): Keneshia Hibbert\, CESSRST-II Graduate Fellow\n\nRemote Access: : https://meet.google.com/jyf-ojbj-wzn\n\nAbstract: Marine heatwaves (MHWs) are prolonged periods of anomalously warm sea surface temperatures (SSTs) that can have profound ecological and climatic consequences. This study presents a comprehensive assessment of MHW characteristics across the tropical Atlantic Ocean from 1982to 2024\, employing a consistent methodology based on the framework established by Hobday et al. (2016). Daily SST data were analyzed against a seasonally varying climatological 90th percentile threshold to detect MHW events and quantify key metrics\, including event frequency\, duration\, and spatial extent. Our domain-level approach identifies and tracks contiguous periods of elevated SSTs across the entire basin\, applying strict temporal criteria to ensure scientific robustness. Results reveal distinct seasonal and interannual variability in MHW occurrence\, with several multi-week events observed during the boreal summer and fall months. The spatial extent of MHWs was found to fluctuate considerably over time\, occasionally covering large portions of the tropical Atlantic Basin. These findings provide a critical foundation for understanding the temporal evolution and physical characteristics of marine heatwaves in a region of high climate sensitivity. This work lays the groundwork for future efforts to investigate the role of large-scale climate modes and anthropogenic warming in shaping the dynamics of MHW in the tropical Atlantic.
URL:https://www.cessrst.org/event/noaa-seminar-series-marine-heatwaves-in-the-tropical-atlantic-detection-characteristics-and-trends-in-a-warming-ocean/
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:20250828T130000
DTEND;TZID=America/New_York:20250828T140000
DTSTAMP:20260405T123533
CREATED:20250807T162057Z
LAST-MODIFIED:20250807T162057Z
UID:5552-1756386000-1756389600@www.cessrst.org
SUMMARY:NOAA Seminar Series: Spatial Resolution Impacts on Remotely Sensed Product Uncertainty and Representativeness
DESCRIPTION:Title: Spatial Resolution Impacts on Remotely Sensed Product Uncertainty and Representativeness \nPresenter(s): Biajani Gonzalez\, CESSRST II Graduate Fellow  \nGoogle Meet :  https://meet.google.com/ugm-keyg-pgr\n\nAbstract: Satellite remote sensing\, while providing broad geographic coverage\, faces limitations in spatial resolution for detailed benthic mapping\, particularly in coastal regions such as Puerto Rico. Small unmanned aerial systems (UAS) offer a promising solution due to their ability to capture high-resolution imagery with flexibility. This study examines the impact of spatial resolution and classifier training strategies on the accuracy and consistency of benthic habitat classifications derived from drone-based imagery. It determines the optimal airborne sampling parameters ” balancing effective spatial resolution and flight parameters ” when using UAS for marine habitat mapping. Using high-resolution RGB orthomosaics (0.036 m/pixel) collected via UAS and upscaled to coarser resolutions (0.5 m to 10 m)\, we assessed the classification performance of coral\, sand\, seagrass\, and substrate using Support Vector Machine (SVM) classifiers under four case-study scenarios. Spatial metrics (total area\, patch count) and accuracy assessment indicators (self-transition and Critical Success Index) were applied to quantify classification degradation across scales and scenes. Results show fine-scale features\, especially coral and seagrass\, rapidly degrade beyond 1 meter\, while more homogeneous classes\, such as sand and substrate\, remain relatively stable. 
URL:https://www.cessrst.org/event/noaa-seminar-series-spatial-resolution-impacts-on-remotely-sensed-product-uncertainty-and-representativeness/
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:20250926T143000
DTEND;TZID=America/New_York:20250926T150000
DTSTAMP:20260405T123533
CREATED:20250816T130848Z
LAST-MODIFIED:20250918T203051Z
UID:5580-1758897000-1758898800@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\n\nTitle: Quantifying the accuracy of satellite-observed sea surface salinity against in situ observations by saildrones \nPresenter(s): Andrew Dixon\, CESSRST II Graduate Fellow  \nGoogle Meet : https://meet.google.com/rxj-wgmk-day \nOr dial: ‪(US) +1 318-367-3080 PIN: ‪865 081 511# \n\nAbstract:  \nSea surface salinity (SSS) regulates upper-ocean stratification\, influencing vertical mixing and the heat exchange critical to the development of tropical cyclones. Satellite salinity observations from NASA’s SMAP observatory offer global coverage and have been validated for general conditions in certain areas\, but benefit from validation under storm conditions. This study uses NOAA Saildrones (uncrewed surface vehicles)\, capable of targeting specific storms and collecting near-continuous data\, to compare in situ SSS with two SMAP products. Results show strong agreement with the 8-day averaged dataset and moderate but positive agreement with the near-real-time product. Collocations during storm encounters indicate that SMAP could be useful in tropical cyclone forecasting. By validating satellite data with uncrewed systems\, this work advances NOAA’s mission of enabling a weather-ready nation through expanded hurricane predictions and fostering collaborations for future satellite data validation projects. \nThe results are from the NOAA EPP/MSI CSCNERTO graduate internship project that was conducted with NOAA mentor\, Dr. Chidong Zhang of NOAA Research’s Pacific Marine Environmental Laboratory. The NERTO aligns with NOAA CSC CESSRST-II’s goal of conducting NOAA mission-aligned collaborative research to understand and predict changes in climate\, weather\, oceans\, and coasts. The NERTO “Quantifying the accuracy of satellite observed sea surface salinity against in situ observations by Saildrones” also deepened the intern’s understanding of NOAA’s databases and development steps toward further use of uncrewed systems. \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/
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:20251121T140000
DTEND;TZID=America/New_York:20251121T143000
DTSTAMP:20260405T123533
CREATED:20251118T221001Z
LAST-MODIFIED:20251119T221323Z
UID:5689-1763733600-1763735400@www.cessrst.org
SUMMARY:NOAA Seminar Series: Species distribution models of deep-sea coral and sponge(DSCS)species of the northeast continental shelf (USA)
DESCRIPTION:[vc_row][vc_column][vc_column_text css=””]\n\nTitle:  Species distribution models of deep-sea coral and sponge(DSCS)species of the northeast continental shelf (USA) \nPresenter(s): James Herlan\, CESSRST II Graduate Fellow  \nRemote Access: Video call link: https://meet.google.com/wde-shmw-vcw\nOr dial: (US)+1 435-562-1268 PIN: 906485 620\n\n#More phone numbers: https://tel.meet/wde-shmw-vcw?pin=3526342705011 \nAbstract: Deep-sea coral distributions in the Northwest Atlantic remain poorly characterized due to sampling limitations. We developed species distribution models (SDMs) for the cold-water coral Desmophyllum dianthus using generalized additive models (GAMs) to identify key environmental drivers and predict suitable habitat. We analyzed 81\,112 presence-absence records from multiple research cruises\, evaluating 28 environmental predictors through variance inflation factor (VIF) screening and univariate assessment. The final model incorporated depth\, rugosity index (rie)\, slope (qslp)\, and bottom total alkalinity (btm_talk_ann) as smoothed terms\, with and without spatial coordinates. Model performance was strong (AUC = 0.878 with location\, 0.849without)\, with 26.0% deviance explained. D. dianthus showed a unimodal depth response peaking at 500 ” 700 m\, positive associations with seafloor rugosity and slope\, and a narrow alkalinity optimum (2.400 ” 2.405 mol m-3). Spatial structure accounted for 18.5% of explained deviance\, suggesting unmeasured environmental gradients or dispersal limitations contribute moderately to distribution patterns. The 4.8 percentage point deviance improvement and 0.029AUC increase when including spatial terms demonstrates the value of incorporating geographic structure in deep-sea SDMs\, though environmental predictors remain primary drivers. Our results provide critical baseline information for conservation planning and highlight the importance of topographic complexity and oceanographic conditions in determining cold-water coral distributions. The results are from the NOAA EPP/MSI CSC NERTO graduate internship project\, which was conducted under the guidance of NOAA mentor James Vasslides of James J. Howard Marine Sciences Laboratory at Sandy Hook. The NERTO aligns with NOAA CSC Center for Earth System Sciences and Remote Sensing Technologies (CESSRST-II) award’s goal of becoming a scientist. The NERTO also deepened the intern’s understanding of multiple modeling approaches that include generalized linear models (GLMs)\, generalized additive models(GAMs)\, boosted regression trees (BRTs)\, and random forest models (RFs)\, using has been collected as part of the larger Northeast Deep Sea Coral Initiative\, he will interact with scientists from other parts of NOAA Fisheries\, NCCOS\, and international collaborators. \n  \n\n\n[/vc_column_text][/vc_column][/vc_row]
URL:https://www.cessrst.org/event/noaa-seminar-series-species-distribution-models-of-deep-sea-coral-and-spongedscsspecies-of-the-northeast-continental-shelf-usa/
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:20251203T103000
DTEND;TZID=America/New_York:20251203T110000
DTSTAMP:20260405T123533
CREATED:20251120T215646Z
LAST-MODIFIED:20251120T220006Z
UID:5691-1764757800-1764759600@www.cessrst.org
SUMMARY:NOAA Seminar Series: Assessment of High-Resolution Rapid Refresh (HRRR) Precipitation Forecasts for Urban Coastal Areas: New York City Testbed
DESCRIPTION:[vc_row][vc_column][vc_column_text css=””]\n\nTitle: Assessment of High-Resolution Rapid Refresh (HRRR) Precipitation Forecasts for Urban Coastal Areas: New York City Testbed \nPresenter(s): Sebastian Makrides\, CESSRST II Graduate Fellow  \nRemote Access: Video call link: https://meet.google.com/fnt-grnx-fdd\n\nAbstract: Accurate precipitation forecasting is critical for managing flood risks in New York City (NYC). NYC’s approximately 72% impervious surface area often routes runoff directly to sewer systems with limited capacity (~ 44.45 mm/hr). NOAA’s High-Resolution Rapid Refresh (HRRR) model\, a3-km grid spacing hourly-updating convection-allowing forecast system\, provides quantitative precipitation forecasts (QPF) alongside other predicted variables for the continental United States. While the HRRR’s QPF performance has been evaluated over broad regions\, assessments over small-scale urban coastal environments like NYC remain limited. Therefore\, this research assesses HRRR performance in predicting where\, when\, and how much precipitation reaches NYC. This study evaluates HRRR QPF by comparing it with the gridded Analysis of Record for Calibration (AORC) dataset. Multi-year precipitation data are extracted\, temporally and spatially aligned\, and assessed via statistical and numerical analysis to evaluate HRRR’s accuracy in predicting timing\, intensity\, and spatial placement of rainfall. Additionally\, the use of self-organizing maps is explored for the spatial verification of extreme events based on shared seasonal behavior\, facilitating analysis despite their rarity and localized nature. The results expected from such methods will provide insight into potential systematic biases and spatial inaccuracies that may limit the HRRR’s performance for NYC\, where limited drainage infrastructure and vulnerable populations heighten the need for more accurate precipitation forecasts. Understanding HRRR performance for urban hydrometeorology and its associated forecasting strengths and limitations will support improved flood preparedness\, aid in future model developments\, and drive enhancements in verification techniques for the HRRR and other numerical weather prediction models alike. The results are from the NOAA EPP/MSI CSC NERTO graduate internship project that was conducted with NOAA mentors\, Dave Turner and Kelly Mahoney of Earth System Research Laboratories (ESRL)\, Oceanic and Atmospheric Research (OAR). The NERTO aligns with NOAA CESSRST’s goal to conduct NOAA mission-aligned collaborative research. The NERTO Assessment of High-Resolution Rapid Refresh (HRRR) Precipitation Forecasts for Urban Coastal Areas: New York City Testbed also deepened the intern’s understanding of NOAA’s operational forecasting systems\, data assimilation techniques\, and model verification processes\, while enhancing competencies in statistical analysis\, geospatial data integration\, and the interpretation of high-resolution numerical weather prediction outputs for urban hydrometeorological applications.\n \n  \n\n\n[/vc_column_text][/vc_column][/vc_row]
URL:https://www.cessrst.org/event/noaa-seminar-series-assessment-of-high-resolution-rapid-refresh-hrrr-precipitation-forecasts-for-urban-coastal-areas-new-york-city-testbed/
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:20251203T110000
DTEND;TZID=America/New_York:20251203T113000
DTSTAMP:20260405T123533
CREATED:20251120T215925Z
LAST-MODIFIED:20251120T215948Z
UID:5693-1764759600-1764761400@www.cessrst.org
SUMMARY:NOAA Seminar Series: Retrieving Humidity from Existing Wireless Transmissions
DESCRIPTION:[vc_row][vc_column][vc_column_text css=””]\n\nTitle: Retrieving Humidity from Existing Wireless Transmissions \nPresenter(s): Lasbati Djiwa CESSRST II Graduate Fellow  \nRemote Access: Video call link: https://meet.google.com/gxm-azan-dct\n\nAbstract: \n This graduate internship NERTO project addressed the research question: Can radio frequency (RF) phase shifts from existing wireless transmission systems be used to retrieve atmospheric humidity in real time? It has been previously demonstrated that the attenuation of signals such as cellular transmissions and wireless backhaul can be used to retrieve rainfall rates. This project aims to extend that concept to determine whether humidity can also be measured by monitoring the phase shifts of transmitted signals. The NERTO project involved designing and testing a 24.5 GHz experimental RF system to measure phase variations caused by humidity changes along a wireless path. Ground-truth humidity data from a commercial sensor were used for calibration. Linear regression and temperature-compensated models showed that RF-derived phase data can provide reasonable humidity estimates\, with improved accuracy when temperature effects are included. The results are from the NERTO graduate internship project that was conducted with the mentorship of J. Rafael Mendoza\, Cesar M Salazar Aquino\, and Gerald Kunkle at the National Severe Storms Laboratory in Norman\, Oklahoma. The NERTO aligns with NOAA CSC”CESSRST’s goal of advancing innovative environmental observations and developing next-generation atmospheric sensing technologies. The NERTO project also deepened the intern’s understanding of NOAA’s role in engineering-based environmental monitoring\, strengthened technical skills in RF systems and signal analysis\, and enhanced interdisciplinary collaboration with atmospheric scientists. This work adds value to NOAA’s mission and the broader science community by exploring a low-cost\, scalable approach to improve weather forecasting and climate monitoring capabilities. \n\n\n[/vc_column_text][/vc_column][/vc_row]
URL:https://www.cessrst.org/event/noaa-seminar-series-retrieving-humidity-from-existing-wireless-transmissions/
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:20251203T113000
DTEND;TZID=America/New_York:20251203T120000
DTSTAMP:20260405T123533
CREATED:20251120T220245Z
LAST-MODIFIED:20251120T220245Z
UID:5695-1764761400-1764763200@www.cessrst.org
SUMMARY:NOAA Seminar Series: Detection of Seals and Polar Bears in Multispectral Aerial Imagery
DESCRIPTION:[vc_row][vc_column][vc_column_text css=””]\n\nTitle:  Detection of Seals and Polar Bears in Multispectral Aerial Imagery \nPresenter(s): Leah Porras\, CESSRST II Graduate Fellow  \nRemote Access: Video call link: https://meet.google.com/ias-uysw-ehc\n\nAbstract: \nIce seals (ribbon\, ringed\, spotted\, & bearded) use spring sea ice as a platform for pupping\, resting\, and their annual molt. Seals play vital roles in Arctic and subarctic marine ecosystems & are a resource for Alaska Native communities. To manage & conserve these species\, reliable population estimates and distribution maps are needed for management and understanding how they respond to climate change and other human impacts. The Polar Ecosystems Program at NOAA’s Alaska Fisheries Science Center conducts aerial surveys to estimate the abundance and distribution of ice-associated seals and polar bears in the Bering\, Chukchi\, and Beaufort seas. Millions of images are collected using color (RGB)\, thermal infrared (IR)\, & ultraviolet (UV) cameras. Current IR machine learning (ML)models struggle to detect rare animals\, including unattended white-coat seal pups\, because of their size\, and polar bears because of their variable thermal signatures. UV imagery has been introduced to address these challenges. This project seeks to enhance ML detection models by integrating UV\, RGB\, and IR imagery\, using annotated datasets developed by NOAA researchers for training and validation. The goal is to create a robust\, open-source model capable of detecting 80% known animals with fewer than 40%false positives. This system will improve survey efficiency & accuracy\, providing reliable population estimates & supporting conservation efforts. With the expected outcome of more precise abundance estimates for decision-making\, this project supports NOAA’s mission to understand and predict species distribution under changing sea ice conditions. The results are from the NOAA EPP/MSI CSC NERTO graduate internship project conducted with NOAA mentor Ms. Erin Moreland of the Alaska Fisheries Science Center\, Marine Mammal Laboratory\, Seattle\, WA. The NERTO aligns with NOAA CSC CESSRST-II’s goal for research on Coastal and Marine Habitat and Ecosystem Goods & Services. The research conducted supports NOAA’s mission by utilizing multidisciplinary tools to enhance the monitoring\, understanding\, and conservation of coastal and marine resources and habitats that are especially vulnerable to both natural and human-induced stressors. Through the NERTO Detection of Seals and Polar Bears in Multispectral Imagery project\, the intern advanced mission-aligned research skills at NOAA. She developed new competencies in artificial intelligence/machine learning for object detection and classification\, and employed remote sensing and computer vision methods to interrogate and validate multispectral datasets. \n\n\n[/vc_column_text][/vc_column][/vc_row]
URL:https://www.cessrst.org/event/noaa-seminar-series-detection-of-seals-and-polar-bears-in-multispectral-aerial-imagery/
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:20251203T123000
DTEND;TZID=America/New_York:20251203T130000
DTSTAMP:20260405T123533
CREATED:20251125T181634Z
LAST-MODIFIED:20251125T181634Z
UID:5705-1764765000-1764766800@www.cessrst.org
SUMMARY:NOAA Seminar Series: Alaskan Arctic Patterns: Remote Sensing and Greenhouse Gas Emissions from Thermokarst Landscape
DESCRIPTION:[vc_row][vc_column][vc_column_text css=””]\n\nTitle: Alaskan Arctic Patterns: Remote Sensing and Greenhouse Gas Emissions from Thermokarst Landscape \nPresenter(s): Francia Tenorio\, CESSRST II Graduate Fellow  \nRemote Access: Video call link: https://meet.google.com/emg-xjzf-phb\n\n\nAbstract: \nArctic soils are one of the largest terrestrial reservoirs of organic carbon. This carbon is climate-sensitive\, and much effort has been made to investigate its release by establishing baselines for monitoring carbon dioxide (CO2) and methane (CH4) emissions from polar regions. Nitrous oxide (N2O)\, an ozone-depleting greenhouse gas with a global warming potential of 273 times that of CO2\, has traditionally been considered negligible in Arctic ecosystems due to low nitrogen mineralization rates and intense competition for inorganic nitrogen. Recent studies suggest otherwise\, indicating that the Arctic can be a significant source of N2O emissions\, particularly in landforms resulting from permafrost thawing\, such as thermokarst-affected areas with unvegetated surfaces. However\, much remains unknown about these processes in polar regions. This NERTO project aims to investigate the spatial variability of greenhouse gas emissions (CO2\, CH4\, and N2O) from thermokarst-affected landscapes\, particularly retrogressive thaw slumps across the North Slope of Alaska\, via in situ measurements using interdisciplinary approaches. The results are from the NOAA EPP/MSI CSC NERTO graduate internship project\, conducted under the guidance of NOAA mentor Bryan Thomas\, Station Lead of NOAA’s Office of Oceanic and Atmospheric Research\, Global Monitoring Laboratory – Barrow Atmospheric Baseline Observatory. The NERTO program deepened the intern’s understanding of Arctic emissions and thermokarst processes while strengthening their research skills in a collaborative environment. Given the global warming potential of these potent greenhouse gases\, particularly N2O\, which has been overlooked in Arctic ecosystems\, and the vast amount of carbon stored in Arctic landforms\, the results from the NERTO provide valuable ground observations on the patterns and controls of emissions across the region’s landscape\, improving current models on the carbon budget and thereby contributing to climate resiliency\, mitigation\, and adaptation efforts\, aligning with NOAA’s mission of science\, service\, and stewardship.\n \n\n\n[/vc_column_text][/vc_column][/vc_row]
URL:https://www.cessrst.org/event/noaa-seminar-series-alaskan-arctic-patterns-remote-sensing-and-greenhouse-gas-emissions-from-thermokarst-landscape/
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:20251203T130000
DTEND;TZID=America/New_York:20251203T133000
DTSTAMP:20260405T123533
CREATED:20251125T182444Z
LAST-MODIFIED:20251125T182444Z
UID:5709-1764766800-1764768600@www.cessrst.org
SUMMARY:NOAA Seminar Series: Leveraging Satellite Earth Observations to Understand Wetland Ecosystem Services for Coastal Resilience
DESCRIPTION:[vc_row][vc_column][vc_column_text css=””]\n\nTitle: Leveraging Satellite Earth Observations to Understand Wetland Ecosystem Services for Coastal Resilience\n \nPresenter(s): Nadia Samaroo\, CESSRST II Graduate Fellow  \nRemote Access: Video call link: https://meet.google.com/kim-pxkd-vwh\n\n\nAbstract: \nNew York City’s coastal wetlands “centered on Jamaica Bay’s tidal marshes” provide storm buffering\, carbon storage\, water filtration\, and habitat but have been degraded by relative sea-level rise\, sediment alteration\, eutrophication\, and urbanization. We test a reproducible\, multi-sensor workflow to map vegetation and track dynamics with two pipelines:(1) PlanetScope surface-reflectance imagery stacked with a USGS DEM and classified in R using a trained Random Forest to produce class and confidence GeoTIFFs; and (2) Sentinel-2 composites in Google Earth Engine generating seasonal NDVI (2016″2024) and annual NDWI (2016″2024) with robust cloud/cirrus masking. A Chesapeake Bay benchmark produced accurate five-class maps. In Jamaica Bay\, the model reliably separated open water from low marsh (Spartina alterniflora) but under-represented higher-elevation and edge communities\, indicating domain-shift and feature-set limits. NDVI showed strong seasonality (summer peaks\, winter minimal) and interannual variability consistent with restoration gains and edge erosion; NDWI captured dynamic wetness\, including expanding/contracting ponds and wave-washed fringes. The approach supports post-Sandy management by delivering repeatable indicators of marsh extent\, condition\, and hydrologic state. It highlights priorities for Jamaica Bay” specific retraining\, probability-aware mapping\, expanded predictors (texture\, tidal frequency\, LiDAR)\, and spatial cross-validation. The results are from the NOAA EPP/MSI CSC NERTO graduate internship project conducted with NOAA mentor Dr. Veronica Lance of the CoastWatch/OceanWatch/PolarWatch Program\, National Environmental Satellite\, Data\, and Information Service (NESDIS). The NERTO aligns with NOAA CSC CESSRST-II’s mission to advance earth system science\, remote sensing\, and data-driven environmental solutions\, in support of NOAA’s goals of a Weather-Ready Nation\, Resilient Coastal Communities\, and Climate Adaptation. The NERTO\, Leveraging Satellite Earth Observations to Understand Wetland Ecosystem Services for Coastal Resilience\, strengthened the intern’s skills by applying a multi-sensor workflow to map vegetation and track marsh dynamics in Jamaica Bay using PlanetScope classification in R andSentinel-2 NDVI/NDWI time series in Google Earth Engine. The project improved understanding of wetland change drivers and NOAA research practices while enhancing scientific communication\, technical reporting\, and collaborative analysis for coastal resilience.\n \n\n\n[/vc_column_text][/vc_column][/vc_row]
URL:https://www.cessrst.org/event/noaa-seminar-series-leveraging-satellite-earth-observations-to-understand-wetland-ecosystem-services-for-coastal-resilience/
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:20251205T130000
DTEND;TZID=America/New_York:20251205T133000
DTSTAMP:20260405T123533
CREATED:20251125T181921Z
LAST-MODIFIED:20251125T181921Z
UID:5707-1764939600-1764941400@www.cessrst.org
SUMMARY:NOAA Seminar Series: Exploring sampling approaches for NSSL's UAS applications
DESCRIPTION:[vc_row][vc_column][vc_column_text css=””]\n\nTitle: Exploring sampling approaches for NSSL’s UAS applications\n \nPresenter(s): Alejandro Medina\, CESSRST II Graduate Fellow  \nRemote Access: Video call link: https://meet.google.com/jiv-gdsq-yqn\n\n\nAbstract: \nThis research\, conducted at NOAA’s National Severe Storms Laboratory (NSSL)\, developed a framework to close critical data gaps in boundary-layer observations\, a region essential for predicting severe weather. Traditional uncrewed aerial system (UAS) flights follow fixed paths\, limiting adaptability to evolving atmospheric conditions. To address this\, we introduced an unsupervised clustering algorithm trained on radiosonde data from the Integrated Global Radiosonde Archive (IGRA). The algorithm detects structural patterns in vertical temperature profiles and informs adaptive sampling strategies. The framework allows a UAS to first collect a baseline profile\, then compare new flight data against clustering results to determine which atmospheric layers are undersampled or highly variable. The UAS can then adjust its flight behavior\, spending more time in regions with sparse data rather than distributing measurements uniformly. Implementation combined Python-based preprocessing and clustering workflows with MATLAB and ArduPilot simulations\, integrating the approach into CopterSonde\, a boundary-layer UAS platform already used in NOAA OAR laboratories. Early tests show the feasibility of real-time adaptive sampling. This work paves the way for UAS operations that actively reduce data gaps\, sharpen the resolution of boundary-layer measurements\, and strengthen NOAA’s forecasting capabilities in support of the Weather-Ready Nation initiative. The results are from the NOAA EPP/MSI CSCNERTO graduate internship project conducted with NOAA mentor Dr. Elizabeth Smith of the Oceanic and Atmospheric Research\, National Severe Storms Laboratory. The NERTO aligns with NOAA CSC CESSRST-II’s mission to advance earth system science\, remote sensing\, and data-driven environmental solutions\, in support of NOAA’s goals of a Weather-Ready Nation\, Resilient Coastal Communities\, and Climate Adaptation. The NERTO Exploring sampling approaches for NSSL’s UAS applications also deepened the intern’s understanding of NOAA operational workflows\, scientific communication\, and collaborative research environments\, while strengthening professional skills such as technical reporting\, cross-disciplinary teamwork\, and real-time presentation of scientific results.\n \n\n\n[/vc_column_text][/vc_column][/vc_row]
URL:https://www.cessrst.org/event/noaa-seminar-series-exploring-sampling-approaches-for-nssls-uas-applications/
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:20251222T110000
DTEND;TZID=America/New_York:20251222T113000
DTSTAMP:20260405T123533
CREATED:20251218T155802Z
LAST-MODIFIED:20251218T155802Z
UID:5731-1766401200-1766403000@www.cessrst.org
SUMMARY:NOAA Seminar Series: Short-Term Soil Moisture Dry-Down Prediction Using LSTM to Enhance Land-Surface Model Initialization and Verification
DESCRIPTION:[vc_row][vc_column][vc_column_text css=””]\n\nTitle: Short-Term Soil Moisture Dry-Down Prediction Using LSTM to Enhance Land-Surface Model Initialization and Verification\n \nPresenter(s): Stephanie Marquez\, CESSRST II Graduate Fellow  \nRemote Access: Video call link: https://meet.google.com/tin-sgmk-jpd\n\n\nAbstract: \nShort-term soil moisture prediction is essential for drought monitoring\, land “surface model initialization\, and improving the accuracy of NOAA’s environmental forecasting systems. This NERTO project was motivated by the operational need to better represent soil moisture dry-down behavior in NOAA models such as HRRR and Noah-MP\, which rely on accurate land-surface states but often lack sufficient observational constraints. The study focused on U.S. Climate Reference Network (USCRN) stations across the South west\, a region characterized by highly dynamic soil moisture conditions and strong land “atmosphere coupling\, making it an ideal domain for evaluating model skill. Using USCRN multi-depth soil moisture\, meteorological data\, and soil characteristics\, I developed a data-driven Long Short-Term Memory(LSTM) framework to forecast soil moisture recession at depths of 5″100 cm. The model used a 7-day lookback window to generate 1″30-day ahead predictions. Results show that the LSTM captures depth-dependent drying behavior\, achieving KGE values of 0.87″0.97 and RMSE values of 0.015″0.029\, with the highest skill in deeper soil layers where moisture changes more gradually. The model generalized well across stations and reproduced realistic dry-down trajectories\, revealing consistent patterns between soil depth\, atmospheric drivers\, and forecast skill. These findings demonstrate the value of machine-learning approaches for improving short-term soil moisture prediction and highlight their potential to support NOAA operations by enhancing land-surface model initialization\, bias detection\, and drought-related decision-making. The results presented are from the NOAA EPP/MSI CSC NERTO graduate internship project conducted under the mentorship of Dr. Dave Turner\, NOAA/OAR/Global Systems Laboratory\, and Dr. Michael Barlage\, NOAA/NWS/Environmental Modeling Center. This NERTO experience aligns with the NOAA Cooperative Science Center in CESSRST-II\, supporting the Center’s goal of conducting NOA A mission-aligned research to understand and predict changes in land and water systems. This project\, titled Short-Term Soil Moisture Dry-Down Prediction Using LSTM to Enhance Land-Surface Model Initialization and Verification\, addressed the research question: How accurately can an event-based\, multi-depth LSTM framework forecast soil moisture recession across USCRN sites\, and how can these predictions support NOAA land-surface modeling? The work provides value to the scientific community and program stakeholders by demonstrating a scalable\, data-driven approach for improving soil moisture representation in NOAA modeling systems\, supporting drought monitoring\, environmental prediction\, and land” atmosphere process understanding. \n\n\n[/vc_column_text][/vc_column][/vc_row]
URL:https://www.cessrst.org/event/noaa-seminar-series-short-term-soil-moisture-dry-down-prediction-using-lstm-to-enhance-land-surface-model-initialization-and-verification/
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:20260108T110000
DTEND;TZID=America/New_York:20260108T113000
DTSTAMP:20260405T123533
CREATED:20251218T160238Z
LAST-MODIFIED:20251218T160238Z
UID:5733-1767870000-1767871800@www.cessrst.org
SUMMARY:NOAA Seminar Series: Machine Learning Techniques to Identify Solar Filaments
DESCRIPTION:[vc_row][vc_column][vc_column_text css=””]\n\nTitle:  Machine Learning Techniques to Identify Solar Filaments\n \nPresenter(s): Ryan Goldberg\, CESSRST II Graduate Fellow  \nRemote Access: Video call link: https://meet.google.com/npq-acov-qvq\n\n\nAbstract: \nSolar filaments are a regularly occurring feature of the solar atmosphere that provides crucial information on changes in solar activity and helps forecast solar weather. Most notably\, filaments can give rise to coronal mass ejections (CMEs)\, a large expulsion of plasma and magnetic field from the Sun’s corona that can heavily impact Earth’s magnetosphere. However\, filaments can be hard to detect across the entire solar disc\, and methods that rely on human annotations\, which are inherently costly and time-consuming\, can lead to inconsistent mapping of solar phenomena. This project uses imagery from the Global Oscillation Network Group (GONG)\, which observes the full solar disk in the H-alpha band where filaments are most prominent. The first step uses preprocessing techniques to highlight filament features\, along with the Segment Anything Model(SAM)\, to produce a first-pass filament segmentation. These SAM predictions are improved by incorporating physical constraints from known filament shapes\, often connecting closely but separately located prediction masks. The second step trains a U-Net model on the machine-generated pseudo-labels to produce refined filament predictions. This model is validated against existing human-annotated filament mapping of the GONG H-alpha solar images. This self-training pipeline offers a scalable alternative to human annotations for filament mapping and the creation of a consistent\, large-scale dataset. The dataset can serve as a new benchmark for solar filament detection models\, and the self-training model can be adapted for automated analysis. The results are from the NOAA EPP CSC NERTO (in-residence at NOAA graduate internship) project conducted with NOAA mentor Rob Redmon of the NOAA National Centers for Environmental Information (NCEI). The NERTO aligns with NOAA CSC CESSRST-II’s goal to advance environmental data science and develop innovative remote sensing and machine learning capabilities that support NOAA’s mission. The NERTO project\, A Self-Trained Deep-Learning Methodology for Automated Solar Filament Detection and Dataset Generation\, also deepened the intern’s understanding of NOAA’s data stewardship practices\, solar-terrestrial monitoring needs\, and the application of artificial intelligence to large-scale environmental information systems. \n\n\n[/vc_column_text][/vc_column][/vc_row]
URL:https://www.cessrst.org/event/noaa-seminar-series-machine-learning-techniques-to-identify-solar-filaments/
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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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260108T130000
DTEND;TZID=America/New_York:20260108T133000
DTSTAMP:20260405T123533
CREATED:20251229T140934Z
LAST-MODIFIED:20251229T140934Z
UID:5736-1767877200-1767879000@www.cessrst.org
SUMMARY:NOAA Seminar Series: Evaluation of Wind Profile and Boundary Layer Height from an Airborne Doppler Lidar
DESCRIPTION:[vc_row][vc_column][vc_column_text css=””]\n\nTitle:  Evaluation of Wind Profile and Boundary Layer Height from an Airborne Doppler Lidar for Atmospheric Dynamics\, Weather and Air Quality\n \nPresenter(s): Kevin Herrera\, CESSRST II Graduate Fellow  \nRemote Access: Video call link: https://meet.google.com/vqj-aqdf-afz\n\n\nAbstract: \nThe planetary boundary layer (PBL) regulates the exchange of momentum\, heat\, moisture\, and pollutants between the Earth’s surface and the free atmosphere\, making accurate identification of the PBL height critical for weather forecasting\, air quality assessment\, and climate studies. This project was motivated by the need to better understand both the physical basis and practical limitations of PBLH retrievals derived from Doppler wind lidar. Using airborne observations from the2023 Coastal Urban Plume Dynamics Study (CUPiDS) over the New York City region and ground-based measurements from the Pick-Up based Mobile Atmospheric Sounder(PUMAS) collected during the 2024 Airborne Methane Mass Balance Emissions in Colorado (AMMBEC) campaign along the Colorado Front Range\, this study examined boundary-layer structure across urban\, coastal\, and continental environments. The analysis applied the Haar wavelet covariance method to range-corrected lidar intensity as a gradient-based approach for identifying the PBL top\, alongside variance-based diagnostics\, including vertical velocity variance and velocity azimuth display fit variance to characterize turbulence and flow heterogeneity. Results show that while Haar-based methods can reliably identify PBL transitions under ideal conditions\, their performance degrades in the presence of clouds and multi-layered aerosol structure. Variance-based products provide valuable complementary context\, with fit variance being a promising \,but underexplored diagnostic that qualitatively echoes established vertical velocity variance behavior. These findings contribute to improved understanding of foundational PBL height retrieval techniques and support the development of more robust approaches relevant to NOAA OAR’s role in developing and validating new measurement strategies and improving interpretation of complex observations for purposes of weather\, air quality\, and climate. The results presented are from the NOAA EPP/MSI CSC NERTO graduate internship project conducted under the mentorship of Brian Carroll and Steven Brown\, Office of Oceanic and Atmospheric Research\, Chemical Sciences Laboratory. \n\n\n[/vc_column_text][/vc_column][/vc_row]
URL:https://www.cessrst.org/event/noaa-seminar-series-evaluation-of-wind-profile-and-boundary-layer-height-from-an-airborne-doppler-lidar/
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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END:VCALENDAR