BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//NOAA Center for Earth System Sciences and Remote Sensing Technologies - ECPv6.17.5//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://www.cessrst.org
X-WR-CALDESC:Events for NOAA Center for Earth System Sciences and Remote Sensing Technologies
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20250309T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20251102T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20260308T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20261101T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20270314T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20271107T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260929T143000
DTEND;TZID=America/New_York:20260929T150000
DTSTAMP:20260921T184626Z
CREATED:20260921T184626Z
LAST-MODIFIED:20260921T184626Z
UID:6138-1790692200-1790694000@www.cessrst.org
SUMMARY:NOAA Seminar Series: Testing artificial intelligence tools for understanding and predicting oceanographic changes and their effects on marine ecosystems
DESCRIPTION:[vc_row][vc_column][vc_column_text css=””]\n\nTitle: Testing artificial intelligence tools for understanding and predicting oceanographic changes and their effects on marine ecosystems\n \nPresenter(s): Lewis Luis\, CESSRST II Graduate Fellow  \nRemote Access: Video call link: https://meet.google.com/mmo-ztub-ykw\n\n\nAbstract: \nGenerative AI (Gen AI) has undertaken rapid development in recent years with applications in commerce\, science\, research among other fields. One known design flaw\, pertaining to rigorous research\, is Gen AI’s tendency to hallucinate responses. A new paradigm has emerged as a response\, Retrieval-Augmented Generation (RAG) which serves as the bridge between Gen AI and the external database. We propose a novel RAG tool as an interface to be used in conjunction with scientific papers related to oceans. A key objective was to create a dedicated agentic pipeline that reads the chunked text files\, generates multiple choice questions\, conducts quality assurance and finally attempts to answer the generated questions using various types of agents. Benchmarking was conducted to verify accurate results. To compare\, our RAG to a no-context and perfect context agent was also tested. It was found that our RAG agent is effective at retrieval of accurate data with accuracy being greater than 80%. The no context agent was highly limited with best runs only achieving 70% accuracy. Limitations of the study include the token limit with the API tools used and the limited number of scientific papers. Further research may continue to build the framework of this tool; expanding it to encompass more scientific papers. The tool could be connected to a private LLM and have the user load data in. Hence\, when our RAG agent achieves greater accuracy\, it becomes usable as a tool for interpreting oceanic scientific papers at a greater level of efficiency. \nThe results presented are from the NOAA EPP CSC NERTO graduate internship project conducted under the mentorship of Andrew Ross\, Geophysical Fluid Dynamics Laboratory\, GFDL. This NERTO experience aligns with the NOAA Cooperative Science Center in CESSRST-II\, supporting the Center’s goal of collaborative research to understand and predict changes in our oceans and that impact on marine life. This project\, titled Testing artificial intelligence tools for understanding and predicting oceanographic changes and their effects on marine ecosystems addressed the research question: can we use Agentic AI systems with RAG tools to reliably understand changes in the ocean within the Northeastern U.S shelf region and can that system extrapolate conditions via data ingested. The work provides value to the scientific community and program stakeholders by expanding the knowledge NOAA has on AI tools and allowing researchers to utilize this Agentic RAG combination to accelerate their own research pipelines. Through this internship\, the student deepened their understanding of NOAA mission areas and gained enhanced skills in utilization of complex agentic AI systems and an increased understanding in oceanography. Additionally\, gained experience using cloud computing platforms and NVIDIA APIs. \n\n\n[/vc_column_text][/vc_column][/vc_row]
URL:https://www.cessrst.org/event/noaa-seminar-series-testing-artificial-intelligence-tools-for-understanding-and-predicting-oceanographic-changes-and-their-effects-on-marine-ecosystems/
LOCATION:NY
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:20260930T113000
DTEND;TZID=America/New_York:20260930T120000
DTSTAMP:20260921T185118Z
CREATED:20260921T184951Z
LAST-MODIFIED:20260921T185118Z
UID:6141-1790767800-1790769600@www.cessrst.org
SUMMARY:NOAA Seminar Series: Remote Sensing of Suspended Particulate Matter in Coastal Alaska with Ocean Color
DESCRIPTION:[vc_row][vc_column][vc_column_text css=””]\n\nTitle:Remote Sensing of Suspended Particulate Matter in Coastal Alaska with Ocean Color\n \nPresenter(s): Storm Heidinger\, CESSRST II Graduate Fellow  \nRemote Access: Video call link: https://meet.google.com/wvz-xeyd-kez\n\n\nAbstract: \nThe results presented are from the NOAA EPPCSC NERTO graduate internship project conducted under the mentorship of Dr. Menghua Wang and co-mentor Dr. Jianwei Wei\, Marine Ecosystems and Coastal Branch\, NOAA/NESDIS/STAR. This NERTO experience aligns with the NOAA Cooperative Science Center in CESSRST-II\, supporting the Center’s goal to understand and predict changes in oceans and coasts through science. This project\, titled “Remote Sensing of Suspended Particulate Matter in Coastal Alaska with Ocean Color” addresses the performance and uncertainty of suspended particulate matter (SPM) retrieval algorithms in the complex and challenging coastal Arctic Alaska region. \nSatellite ocean color remote sensing of suspended particulate matter (SPM) provides valuable spatial and temporal coverage for the study of coastal systems\, including water clarity\, riverine exports\, and erosion. Satellite remote sensing retrievals of water quality parameters are critical in Arctic Alaska due to the expansive spatial extent and challenging sampling logistics in remote regions. The NOAA NESDIS STAR Ocean Color Science Team developed an algorithm for global SPM retrieval using the NIR-RGB (near infrared\, red\, green\, and blue) bands of the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor. However\, the study noted the need for additional evaluation in high latitude regions such as Arctic Alaska due to remote sensing reflectance (Rrs) uncertainties. This project compiled an expanded validation data set for Arctic Alaska with in situ SPM and Rrs samples\, and granules from the NOAA VIIRS-SNPP (Suomi National Polar Orbiting Partnership) satellite sensor. \nThe NIR-RGB algorithm predicted SPM using in situ Rrs with strong agreement with in situ SPM samples based on fit statistics including the coefficient of determination (R2)\, bias\, and median absolute percent difference (MAPD). Using VIIRS-SNPP Rrs for SPM prediction\, R2was lower but bias and MAPD were consistent. The Rrs validation showed high alignment for the Rrs(671)/Rrs(551) band ratio\, which is the most important for SPM retrieval in turbid waters\, but weaker performance for the Rrs(551)/Rrs(443) band ratio used for SPM retrieval in clear waters\, potentially indicating a driver of the lower fit for satellite SPM retrievals. As a case study\, the project developed a spatial climatology and time series of SPM from 2012-2025in the Colville River Delta region of the Beaufort Sea using the NIR-RGB algorithm. \nThe work provides value to the scientific community and program stakeholders by assessing the uncertainty of the NIR-RGB suspended particulate matter (SPM) retrieval algorithm\, indicating the suitability of theLevel-2 VIIRS SPM product available in NOAA Coast Watch and OC View for fisheries and coastal management applications in the coastal Arctic Alaska region. Through this internship\, the student deepened their understanding of NOAA mission areas and gained skills in satellite remote sensing ocean color calibration and validation \n\n\n[/vc_column_text][/vc_column][/vc_row]
URL:https://www.cessrst.org/event/noaa-seminar-series-remote-sensing-of-suspended-particulate-matter-in-coastal-alaska-with-ocean-color/
LOCATION:NY
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:20260930T130000
DTEND;TZID=America/New_York:20260930T140000
DTSTAMP:20260921T125352Z
CREATED:20260908T153903Z
LAST-MODIFIED:20260921T125352Z
UID:6074-1790773200-1790776800@www.cessrst.org
SUMMARY:Seminar: Weather Ready NYC: Forecasting\, Warnings\, and Urban Weather Challenges
DESCRIPTION:[vc_row][vc_column][vc_column_text css=””]\nDownload Flyer \nJoin us for a seminar presented by NOAA NWS “Weather Ready NYC: Forecasting\, Warnings\, and Urban Weather Challenges” \nDate: September 30\, 2026 \nTime: 12pm \nAbstract: \nThis presentation by Dave Radell\, Meteorologist in Charge\, and Nelson Vaz\, Warning Coordination Meteorologist\, from National Weather Service (NWS) New York\, NY\, will provide an overview of NWS’s mission\, along with insights into forecast and warning operations for the Tri-State area.  NWS’s mission is to deliver forecast and warning information in a way that better supports emergency managers\, first responders\, government officials\, businesses and the public to make fast\, smart decisions that save lives and property and enhance livelihoods. NWS works closely with NYCEM to better prepare NYC residents for all seasons through its weather\, water and climate data\, forecasts and warnings for the protection of life and property.  The session will end with a discussion on current forecast challenges related to heavy rainfall forecasting in the urban environment. \n[/vc_column_text][/vc_column][/vc_row]
URL:https://www.cessrst.org/event/seminar-weather-ready-nyc-forecasting-warnings-and-urban-weather-challenges/
LOCATION:City College of New York\, 160 Convent Avenue\, New York\, 10031
CATEGORIES:Seminar Series,Workshop
ORGANIZER;CN="Center for Earth System Sciences and Remote Sensing Technologies (CESSRST)":MAILTO:cessrst@ccny.cuny.edu
END:VEVENT
END:VCALENDAR