NOAA Center for Earth System Sciences and Remote Sensing Technologies

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 (America/New_York)
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.
