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X-WR-CALDESC:Events for NOAA Center for Earth System Sciences and Remote Sensing Technologies
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DTSTART;TZID=America/New_York:20261016T123000
DTEND;TZID=America/New_York:20261016T130000
DTSTAMP:20261009T133946Z
CREATED:20261009T133946Z
LAST-MODIFIED:20261009T133946Z
UID:6182-1792153800-1792155600@www.cessrst.org
SUMMARY:NOAA Seminar Series: From Radiosonde Archives to Flight Commands: ML-Guided Adaptive Sampling for UAS Atmospheric Boundary-Layer Profiling
DESCRIPTION:[vc_row][vc_column][vc_column_text css=””]\n\nTitle: From Radiosonde Archives to Flight Commands: ML-Guided Adaptive Sampling for UAS Atmospheric Boundary-Layer Profiling\n \nPresenter(s):Dereck Guerra\, CESSRST II Graduate Fellow  \nRemote Access: Video call link: https://meet.google.com/ssp-uvxo-fxr\n\nAbstract: \nUncrewed Aircraft Systems (UAS) complement radiosondes for boundary-layer profiling\, but most UAS soundings sample at a fixed rate\, under-resolving the thin inversions\, hydrolapses\, and wind-shift transitions that matter most for severe-weather forecasting. This project\, part of the TADSS (Teaching Any Drone to Sample Smarter) framework\, tested whether a UAS could anticipate a boundary-layer point-of-interest’s location from its sounding shape so far\, matching it against a library of real historical radiosonde profiles to sample more densely where it matters. The approach uses K-Medoids(PAM) clustering of seasonally stratified IGRA2 radiosonde archives with a two-of-three POI voting scheme\, built on the BLISS CopterSonde platform and LAAIRS ground control station at NOAA’s National Severe Storms Laboratory (NSSL)\, Norman\, OK. A temperature-convention fix cut sounding-matching MAE from~18°C to 0.4–1.6°C; accuracy gains plateaued beyond ~15–20 medoids\, showing clustering resolution — not library size — as the main constraint. POI detection validated against field data\, with transition zones consistently at800–1\,100 m AGL\, and end-to-end matching now runs inside LAAIRS \n\n\n[/vc_column_text][/vc_column][/vc_row]
URL:https://www.cessrst.org/event/noaa-seminar-series-from-radiosonde-archives-to-flight-commands-ml-guided-adaptive-sampling-for-uas-atmospheric-boundary-layer-profiling/
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:20261016T133000
DTEND;TZID=America/New_York:20261016T140000
DTSTAMP:20261009T134402Z
CREATED:20261009T134325Z
LAST-MODIFIED:20261009T134402Z
UID:6185-1792157400-1792159200@www.cessrst.org
SUMMARY:NOAA Seminar Series: Creating a METAR Climatology Targeting Gradients and Trends Between METARS in the Southwestern U.S. to Examine Impacts on Southern California Weather
DESCRIPTION:[vc_row][vc_column][vc_column_text css=””]\n\nTitle: Creating a METAR Climatology Targeting Gradients and Trends Between METARS in the Southwestern U.S. to Examine Impacts on Southern California Weather\n \nPresenter(s): Jose Hurtado\, CESSRST II Graduate Fellow  \nRemote Access: Video call link: https://meet.google.com/egy-frvh-dth\n\nAbstract: \nCarbon cycling is driven by plant biological activity (root respiration\, photosynthesis\, respiration due to decay)\, climatic and meteorological factors (pressure\, precipitation\, ambient air temperature)\, and the interaction of these two sets of variables. However\, ecology research field stations can have data gaps due to equipment failures or human interference (thieves or trespassers). For example\, the Sky Oaks Field Station in Warner Springs\, CA has sensors for barometric pressure\, temperature\, relative humidity\, wind speed\, and direction\, as well as atmospheric carbon cycling data. This NERTO internship was to develop a Linear Mixed Effects Model for the interpolation of missing barometric pressure and ambient air temperature values at the Sky Oaks Field Station (Warner Springs\, CA) based on METAR data from the National Weather Service stations surrounding the Sky Oaks Field Station as archived from the Iowa State University’s data hub\, Iowa Environmental Mesonet. This inverse distance weighting (IDW) interpolation method used geographic data from the United States Geological Survey 3DElevation Program\, which improved model accuracy. In addition\, one of the Eddy Covariance Towers at the Sky Oaks Field Station was restored to collect gas flux and meteorological data. The Linear Mixed Effects Model produced for barometric pressure was overall highly significant (p=0.007503)\, but the model’s mean absolute error needs improvement\, as it is above the acceptable value of 1 millibar (1.21 millibar). The Linear Mixed Model for the Sky Oaks Field Station temperature failed to find significant correlations between the Sky Oaks Field Station measurements\, and its mean absolute error was 2.60 F(1.44 C) \n\n\n[/vc_column_text][/vc_column][/vc_row]
URL:https://www.cessrst.org/event/noaa-seminar-series-creating-a-metar-climatology-targeting-gradients-and-trends-between-metars-in-the-southwestern-u-s-to-examine-impacts-on-southern-california-weather/
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:20261016T143000
DTEND;TZID=America/New_York:20261016T150000
DTSTAMP:20261009T134632Z
CREATED:20261009T134632Z
LAST-MODIFIED:20261009T134632Z
UID:6188-1792161000-1792162800@www.cessrst.org
SUMMARY:NOAA Seminar Series: Leveraging Machine Learning and Artificial Intelligence Approaches to Improve Harmful Algal Bloom Monitoring and Forecasts
DESCRIPTION:[vc_row][vc_column][vc_column_text css=””]\n\nTitle: Leveraging Machine Learning and Artificial Intelligence Approaches to Improve Harmful Algal Bloom Monitoring and Forecasts\n \nPresenter(s): Haniel Cordero Nieves\, CESSRST II Graduate Fellow  \nRemote Access: Video call link: https://meet.google.com/out-xrvc-baz\n\nAbstract: \nHarmful algal blooms (HABs) in the Chesapeake Bay are challenging to characterize from satellite observations because of optically complex waters\, mixed phytoplankton communities\, variable environmental conditions\, and limited coincident field observations. This NERTO project developed a reproducible framework integrating historical phytoplankton observations\, NOAA satellite-derived algal bloom products\, geospatial analysis\, ecological information\, and machine-learning methods to support future AI-assisted discrimination of diatom- and dinoflagellate-dominated events. Field observations were matched with georeferenced satellite imagery and evaluated through a structured quality-control workflow addressing mixed pixels\, land contamination\, clouds\, missing data\, non-detect conditions\, saturation\, and coordinate inconsistencies. Of 31\,531 field-to-satellite records evaluated\, 860were initially classified as valid\, while neighboring-pixel analysis identified additional potentially recoverable observations. A preliminary Mini U-Net segmentation model demonstrated the feasibility of the approach but also revealed limitations associated with automatically generated labels\, motivating development of an interactive expert-review tool for event validation\, masking\, and labeling. Ecological analyses additionally explored seasonal phenology and environmental variables as potential features for future multimodal models \n\n\n[/vc_column_text][/vc_column][/vc_row]
URL:https://www.cessrst.org/event/noaa-seminar-series-leveraging-machine-learning-and-artificial-intelligence-approaches-to-improve-harmful-algal-bloom-monitoring-and-forecasts/
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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