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X-WR-CALNAME:NOAA Center for Earth System Sciences and Remote Sensing Technologies
X-ORIGINAL-URL:https://www.cessrst.org
X-WR-CALDESC:Events for NOAA Center for Earth System Sciences and Remote Sensing Technologies
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DTSTART;TZID=America/New_York:20251222T110000
DTEND;TZID=America/New_York:20251222T113000
DTSTAMP:20260406T061321
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:20260406T061321
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260108T130000
DTEND;TZID=America/New_York:20260108T133000
DTSTAMP:20260406T061321
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260306T130000
DTEND;TZID=America/New_York:20260306T143000
DTSTAMP:20260406T061321
CREATED:20260212T221300Z
LAST-MODIFIED:20260225T224449Z
UID:5826-1772802000-1772807400@www.cessrst.org
SUMMARY:Seminar on Building Coastal Research Partnerships with NOAA
DESCRIPTION:[vc_row][vc_column][vc_column_text css=””]Seminar on Building Coastal Research Partnerships with NOAA\, \nDate: March 6\, 2026 from 1:00pm \nLocation: Exhibit Room\, Steinman Hall\, Grove School of Engineering \nPresenter(s): Dr.  Lonnie Gonsalves (NOS)\, Artara Johnson\, Dr. Veeshan Narinesingh\, and Matthew Harrison (GFDL) \nJoin Zoom Meeting \nhttps://us02web.zoom.us/j/87612364051?pwd=OX8TAqkiPdMcSYHLxtSQe8wbnWTgVq.1  \nMeeting ID: 876 1236 4051 \nPasscode: 539611 \n\n\nLine offices across NOAA will engage in a seminar and networking session aimed at strengthening collaboration and building coastal research partnerships across mission areas. \n\n\nDr. Lonnie Gonsalves\, Division Chief within NOAA’s National Ocean Service (NOS)\, and Artara Johnson  will address priority mission areas\, key technical skill areas\, and the communities served across NOS offices. Lonnie will focus predominantly on NCCOS\, with connectivity to work in coastal states\, national marine sanctuaries\, disaster response\, and forecasting. Tara will discuss environmental intelligence and prediction from the F4 offices perspective\, with applications to economic services (ports and shipping)\, resilience\, and disaster response. \n\n\nAdditional perspectives will be provided by Physical Scientists Dr. Veeshan Narinesingh\, and Matthew Harrison of NOAA’s Office of Oceanic and Atmospheric Research (OAR) and the Geophysical Fluid Dynamics Laboratory (GFDL). They will highlight research and operational integration across OAR and GFDL\, with discussion of observing systems\, modeling\, remote sensing\, engineering innovations\, and applications relevant to environmental intelligence and Earth system science. \n\n\n[/vc_column_text][/vc_column][/vc_row]
URL:https://www.cessrst.org/event/seminar-on-building-coastal-research-partnerships-with-noaa/
LOCATION:Grove School of Engineering\, 160 Convent Avenue\, New York\, NY\, 10031\, United States
CATEGORIES:Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://www.cessrst.org/wp-content/uploads/2026/02/March-6th-Flyer_T.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260319T100000
DTEND;TZID=America/New_York:20260319T163000
DTSTAMP:20260406T061321
CREATED:20260302T194207Z
LAST-MODIFIED:20260302T194252Z
UID:5839-1773914400-1773937800@www.cessrst.org
SUMMARY:8th NOAA AI Workshop
DESCRIPTION:[vc_row][vc_column][vc_column_text css=””]\n\n\n\n\n8th NOAA AI Hybrid Workshop –\nThis year’s NOAA AI Workshop will host virtual events in March and June. The Workshop event series will conclude with a 3-day hybrid event in Boulder\, Colorado with panels and sessions spanning all topics around AI for environmental sciences. \nEvent schedule: \n\nMarch 19\, 2026 (10:30–16:00 ET\, virtual*) – see Agenda below / Register\nJune 18\, 2026 (TBD\, virtual*)\nAugust 25-27\, 2026 (hybrid; virtual* and in person)\n\n  \n\n\n\n\n[/vc_column_text][/vc_column][/vc_row]
URL:https://www.cessrst.org/event/8th-noaa-ai-workshop/
CATEGORIES:Workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260420T043000
DTEND;TZID=America/New_York:20260421T133000
DTSTAMP:20260406T061321
CREATED:20260215T134438Z
LAST-MODIFIED:20260225T134836Z
UID:5810-1776659400-1776778200@www.cessrst.org
SUMMARY:Fourth CESSRST II Annual Meeting
DESCRIPTION:[vc_row][vc_column][vc_column_text css=””]Third Annual CESSRST-II Meeting \nDate: April 8-10\, 2025 \nTime: 8:30AM ET \nVenue: Silver Spring\, MD \nMeeting Objects and Expected Outcomes \n\nTo share the Center’s year 4 updates\nTo create better collaborations and connection between CESSRST Scientist and NOAA Collaborators and Subject Matter Experts (SMEs)\n\n[/vc_column_text][/vc_column][/vc_row]
URL:https://www.cessrst.org/event/fourth-cessrst-ii-annual-meeting/
LOCATION:NOAA SSMC\, Silver Springs\, MD\, 1315 East-West Highway\, Silver Spring\, MD\, 20910
CATEGORIES:Conference/Symposium
ATTACH;FMTTYPE=image/png:https://www.cessrst.org/wp-content/uploads/2023/02/logo-htext-sm.png
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:20260720T080000
DTEND;TZID=America/New_York:20260723T170000
DTSTAMP:20260406T061321
CREATED:20260217T135108Z
LAST-MODIFIED:20260305T162930Z
UID:5813-1784534400-1784826000@www.cessrst.org
SUMMARY:2026 EPP Biennial Education and Science Forum
DESCRIPTION:[vc_row][vc_column][vc_column_text css=””]Save the date: The 12th Biennial NOAA EPP Education and Science Forum will be held in-person on the campus of  the City College of New York (CUNY) on July 20 – 23\, 2026 \nThis year’s forum is a celebration of the Program’s 25th Anniversary. \nThe Biennial Education and Science Forum focuses on the important role educational partners such as the four EPP Cooperative Science Centers (CSCs)  contribute to the NOAA community. The EPP Forum supports NOAA’s objective to  prepare a strong pipeline of qualified candidates for the future workforce. Since 2001\, EPP institutions have graduated over 2\,000 students in NOAA mission fields.[/vc_column_text][/vc_column][/vc_row]
URL:https://www.cessrst.org/event/2026-epp-biennial-education-and-science-forum/
LOCATION:The City College of New York\, 160 Convent Avenue\, New York\, NY\, 10031\, United States
CATEGORIES:Conference/Symposium,Informational Webinar,Workshop
ATTACH;FMTTYPE=image/jpeg:https://www.cessrst.org/wp-content/uploads/2026/02/EPP-Forum-2026-Save-the-Date.jpg
ORGANIZER;CN="Center for Earth System Sciences and Remote Sensing Technologies (CESSRST)":MAILTO:cessrst@ccny.cuny.edu
END:VEVENT
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