NOAA Center for Earth System Sciences and Remote Sensing Technologies

Social Science Community of Practice Meeting (CoP)
2025-10-03 12:00 - 13:00 (America/New_York)
CESSRT-II Social Science Meeting: Student Cohort Community of Practice Meeting
Social science community of practice meeting.
Date: October 3, 2025 at 2pm,
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CSC Education Experts’ Seminar Series: Alumni Career Panel
2025-10-23 13:30 - 14:30
Please join the Education Experts’ Seminar Series on Thursday, October 23, 2024, for a NOAA EPP/MSI Cooperative Science Center (CSC) Alumni Career Panel (flyer is attached). The purpose of this panel is to showcase career pathways that align with NOAA’s mission, beyond traditional NOAA federal employment. Panelists who are alumni from CSCs, including CESSRST, will discuss:

Their career journey since completing the EPP/MSI CSC program
How their current role contributes to NOAA’s mission
Insights into navigating career decisions and building professional networks
Advice for Fellows exploring non-federal career options

Please be sure to engage with your questions. Also, please remember to:

Appropriately introduce yourself before engaging verbally or in the chat (Name, Identify yourself as a CESSRST-II fellow, Identify your cohort number, and your institution)
Use the CESSRST-II Virtual Background (You may download the background appropriate to your institution from this folder)

Meeting details:
https://ccny.zoom.us/j/88623680869?pwd=[REDACTED]
Meeting ID: 886 2368 0869
Passcode: [REDACTED]

Write it Right Workshop: Using MS Word Strategically for CESSRST-II Reporting
2025-10-24 13:00 - 14:30 | City College of New York, 160 Convent Avenue, New York, 10031
Description: Effective use of Microsoft Word is essential for producing professional-quality reports, manuscripts, and academic documents. In addition to your academic work, your CESSRST-II fellowship requires several types reports that need to meet professional standards. This workshop will provide a practical guide on leveraging MS Word’s advanced features to enhance the clarity, consistency, and impact of written work. Fellows will learn how to organize documents using structured headings, apply automatic formatting styles, and create well-formatted tables with appropriate captions and cross-references for figures and tables. This workshop will focus on reporting for the post NOAA Experiential Research and Training Opportunities (NERTO) report that is required by the NOAA EPP/MSI Program. It will review crafting clear and concise abstracts, and managing citations and references using Word’s built-in tools and external citation managers such as Mendeley. Emphasis will be placed on maintaining uniform document styles, integrating visuals effectively, and adhering to academic writing conventions. By the end of this session, attendees will be equipped with the knowledge and skills to efficiently draft, format, and finalize the post NERTO report.
Who should attend?

Fellows who have recently completed a NERTO and are in the feedback phase with the Center before making the finalized submission, and
Fellows who plan to complete a NERTO or NOAA Undergraduate Research Internship (NURI) by August 2026

Facilitators: Dr. Tarendra Lakhankar, Center Research Coordinator
Format: https://ccny.zoom.us/j/83921420413?pwd=[REDACTED]
Meeting ID: 839 2142 0413
Passcode: [REDACTED]

Seminar on Characterizing the Dust Storm: An Integrated Ground-Based, Satellite, and Model Study
2025-10-28 12:30 - 13:30 | Grove School of Engineering, 160 Convent Avenue, New York, NY, 10031, United States
Seminar on Characterizing the ‘Godzilla’ Dust Storm: An Integrated Ground-Based, Satellite, and Model Study to Advance our Understanding of the Impact of African Dust on the Tropical Western Hemisphere
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Date: October 28, 2025 from 12:30pm
Location: Exhibit Room, Steinman Hall, Grove School of Engineering
Presenter(s): Dr. Olga L. Mayo-Bracero, Atmospheric Scientist, Brookhaven National Laboratory
Zoom: https://ccny.zoom.us/j/85976950243?pwd=[REDACTED] Abstract:
African dust transport across the Atlantic significantly impacts air quality, weather, and climate in the Caribbean and surrounding regions. However, gaps remain in understanding the intensity, variability, and health effects of these huge aerosol events. Addressing these uncertainties requires a comprehensive approach that combines multiple observational and modeling techniques to track dust movement and assess its impacts effectively. Long-term research and monitoring efforts in the Caribbean have been instrumental in improving our understanding of African dust transport and its regional consequences.
The unprecedented June 2020 “Godzilla” African dust event provided a unique opportunity to evaluate the effectiveness of an integrated approach using ground-based aerosol measurements, satellite observations, and forecast models. The NASA-funded CALIMA campaign built on prior research efforts, integrating data from nine Caribbean locations, satellite sensors (CALIOP, MODIS, MISR), and dust forecast models (NASA GEOS, WRF-Chem) to characterize dust transport and its impact on air quality. Surface and columnar measurements revealed severe air quality degradation across the Caribbean, southern United States, northern South America, and Central America, posing significant public health risks. Model evaluations exposed substantial discrepancies in dust forecasts, highlighting the need for improved prediction capabilities.
This study demonstrates the value of combining observational networks with satellite remote sensing and modeling to characterize the spatial and temporal variability of African dust transport to the western hemisphere. Strengthening such coordinated efforts is essential to refine forecasts, mitigate health and environmental risks, and better understand how transatlantic dust transport may evolve under a changing climate.
About Presenter:
Dr. Olga L. Mayol-Bracero is an atmospheric scientist and the Lead of the Aerosol Observations Group in the Environmental Science and Technologies Department at Brookhaven National Laboratory (BNL), where she also serves as the Director of the Center for Aerosol Measurement Science (CAMS). She is the Lead Mentor of the Aerosol Observing Systems (AOS) of the DOE Atmospheric Radiation Measurement (ARM) User Facility and a participant in the DOE Atmospheric System Research (ASR) program. She joined BNL in August 2021. Before joining BNL, Dr. Mayol-Bracero was a Full Professor in the Department of Environmental Science at the University of Puerto Rico–Río Piedras (UPR-RP). There, she directed the Atmospheric Chemistry and Aerosols Research Group and led both the Cape San Juan Atmospheric Observatory—a site that is part of NOAA ESRL’s aerosol network, NASA’s AERONET, Pandora, and MPLNET networks, and a WMO GAW regional station—and the Pico del Este Cloud Forest Station.
Dr. Mayol-Bracero’s research focuses on the temporal and spatial variability of atmospheric aerosols, including the characterization of African dust, biomass burning, marine, urban, and biogenic aerosols, with particular emphasis on tropical regions. Her expertise spans size-resolved aerosol composition and sources, carbonaceous aerosols (organic and black carbon), aerosol–cloud–precipitation interactions, and air quality.
She has extensive experience leading aerosol field projects and managing field atmospheric observatories, participating in numerous ground-based and airborne field campaigns across diverse environments, including the Maldives, the Amazon Basin, and the Caribbean region. Dr. Mayol-Bracero also holds several international leadership roles, serving as a member of the International Commission on Atmospheric Chemistry and Global Pollution (iCACGP), the World Meteorological Organization’s Scientific Advisory Group on Aerosols, and the Americas Working Group of the International Global Atmospheric Chemistry (IGAC) project. She is the author of more than 60 peer-reviewed publications, one book chapter, and over 300 presentations at national and international conferences. Dr. Mayol-Bracero earned her B.S. and M.S. in Chemistry and her Ph.D. in Chemistry from the University of Puerto Rico–Río Piedras and the Lawrence Berkeley National Laboratory. She completed her postdoctoral fellowship at the Max Planck Institute for Chemistry in Mainz, Germany.

CSC Education Experts’ Seminar Series: Connecting Minds - AI insights for CSC Fellows
2025-11-19 13:00 - 14:30
Please join next week’s Education Experts’ Seminar Series (EESS) session titled “Connecting Minds – AI insights for CSC Fellows” with presentations from Dr. Luke Madaus (Research Science, RWE AI Lab) and Dr. David Die, (DRS at LMRCSC)
Download Flyer for Speaker information.
Meeting Information:

Zoom Link
Meeting ID: 867 5670 9117
Passcode: [REDACTED]

NOAA Seminar Series: Species distribution models of deep-sea coral and sponge(DSCS)species of the northeast continental shelf (USA)
2025-11-21 14:00 - 14:30
Title: Species distribution models of deep-sea coral and sponge(DSCS)species of the northeast continental shelf (USA)
Presenter(s): James Herlan, CESSRST II Graduate Fellow
Remote Access: Video call link: https://meet.google.com/wde-shmw-vcw
Or dial: (US)+1 435-562-1268 PIN: [REDACTED] 620

#More phone numbers: https://tel.meet/wde-shmw-vcw?pin=3526342705011
Abstract: 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.

NOAA Seminar Series: Assessment of High-Resolution Rapid Refresh (HRRR) Precipitation Forecasts for Urban Coastal Areas: New York City Testbed
2025-12-03 10:30 - 11:00
Title: Assessment of High-Resolution Rapid Refresh (HRRR) Precipitation Forecasts for Urban Coastal Areas: New York City Testbed
Presenter(s): Sebastian Makrides, CESSRST II Graduate Fellow
Remote Access: Video call link: https://meet.google.com/fnt-grnx-fdd

Abstract: 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.

NOAA Seminar Series: Retrieving Humidity from Existing Wireless Transmissions
2025-12-03 11:00 - 11:30
Title: Retrieving Humidity from Existing Wireless Transmissions
Presenter(s): Lasbati Djiwa CESSRST II Graduate Fellow
Remote Access: Video call link: https://meet.google.com/gxm-azan-dct

Abstract:
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.

NOAA Seminar Series: Detection of Seals and Polar Bears in Multispectral Aerial Imagery
2025-12-03 11:30 - 12:00
Title: Detection of Seals and Polar Bears in Multispectral Aerial Imagery
Presenter(s): Leah Porras, CESSRST II Graduate Fellow
Remote Access: Video call link: https://meet.google.com/ias-uysw-ehc

Abstract:
Ice 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.

NOAA Seminar Series: Alaskan Arctic Patterns: Remote Sensing and Greenhouse Gas Emissions from Thermokarst Landscape
2025-12-03 12:30 - 13:00
Title: Alaskan Arctic Patterns: Remote Sensing and Greenhouse Gas Emissions from Thermokarst Landscape
Presenter(s): Francia Tenorio, CESSRST II Graduate Fellow
Remote Access: Video call link: https://meet.google.com/emg-xjzf-phb

Abstract:
Arctic 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.

NOAA Seminar Series: Leveraging Satellite Earth Observations to Understand Wetland Ecosystem Services for Coastal Resilience
2025-12-03 13:00 - 13:30
Title: Leveraging Satellite Earth Observations to Understand Wetland Ecosystem Services for Coastal Resilience

Presenter(s): Nadia Samaroo, CESSRST II Graduate Fellow
Remote Access: Video call link: https://meet.google.com/kim-pxkd-vwh

Abstract:
New 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.

NOAA Seminar Series: Exploring sampling approaches for NSSL's UAS applications
2025-12-05 13:00 - 13:30
Title: Exploring sampling approaches for NSSL’s UAS applications

Presenter(s): Alejandro Medina, CESSRST II Graduate Fellow
Remote Access: Video call link: https://meet.google.com/jiv-gdsq-yqn

Abstract:
This 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.

NOAA Seminar Series: Short-Term Soil Moisture Dry-Down Prediction Using LSTM to Enhance Land-Surface Model Initialization and Verification
2025-12-22 11:00 - 11:30
Title: Short-Term Soil Moisture Dry-Down Prediction Using LSTM to Enhance Land-Surface Model Initialization and Verification

Presenter(s): Stephanie Marquez, CESSRST II Graduate Fellow
Remote Access: Video call link: https://meet.google.com/tin-sgmk-jpd

Abstract:
Short-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.

NOAA Seminar Series: Machine Learning Techniques to Identify Solar Filaments
2026-01-08 11:00 - 11:30
Title: Machine Learning Techniques to Identify Solar Filaments

Presenter(s): Ryan Goldberg, CESSRST II Graduate Fellow
Remote Access: Video call link: https://meet.google.com/npq-acov-qvq

Abstract:
Solar 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.

NOAA Seminar Series: Evaluation of Wind Profile and Boundary Layer Height from an Airborne Doppler Lidar
2026-01-08 13:00 - 13:30
Title: Evaluation of Wind Profile and Boundary Layer Height from an Airborne Doppler Lidar for Atmospheric Dynamics, Weather and Air Quality

Presenter(s): Kevin Herrera, CESSRST II Graduate Fellow
Remote Access: Video call link: https://meet.google.com/vqj-aqdf-afz

Abstract:
The 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.
