NOAA Center for Earth System Sciences and Remote Sensing Technologies

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 (America/New_York) | NY
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 | NY
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 | NY
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.

Seminar on Building Coastal Research Partnerships with NOAA
2026-03-06 13:00 - 14:30 | Grove School of Engineering, 160 Convent Avenue, New York, NY, 10031, United States
Seminar on Building Coastal Research Partnerships with NOAA,
Date: March 6, 2026 from 1:00pm
Location: Exhibit Room, Steinman Hall, Grove School of Engineering
Presenter(s): Dr. Lonnie Gonsalves (NOS), Artara Johnson, Dr. Veeshan Narinesingh, and Matthew Harrison (GFDL)
Join Zoom Meeting
https://us02web.zoom.us/j/87612364051?pwd=[REDACTED]
Meeting ID: 876 1236 4051
Passcode: [REDACTED]

Line offices across NOAA will engage in a seminar and networking session aimed at strengthening collaboration and building coastal research partnerships across mission areas.

Dr. 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.

Additional 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.

CSC Education Experts’ Seminar Series: The New USA Jobs + The Federal Resume
2026-04-13 13:00 - 14:30 | NY
Please join the April 13th Education Experts’ Seminar Series on the New USA Jobs and Federal Resume
Join Zoom Meeting
https://us02web.zoom.us/j/84938459252?pwd=[REDACTED]
Meeting ID: 849 3845 9252
Passcode: [REDACTED]
