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DTSTART;TZID=America/New_York:20251203T103000
DTEND;TZID=America/New_York:20251203T110000
DTSTAMP:20260404T051806
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:20260404T051806
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:20260404T051806
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:20260404T051806
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:20260404T051806
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:20260404T051806
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:20260404T051806
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:20260404T051806
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:20260404T051806
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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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260306T130000
DTEND;TZID=America/New_York:20260306T143000
DTSTAMP:20260404T051806
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
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