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DTSTART;TZID=America/New_York:20260108T110000
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DTSTAMP:20260425T165738
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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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DTSTART;TZID=America/New_York:20260108T130000
DTEND;TZID=America/New_York:20260108T133000
DTSTAMP:20260425T165738
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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