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DTSTART;TZID=America/New_York:20241024T140000
DTEND;TZID=America/New_York:20241024T143000
DTSTAMP:20260429T234918
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UID:5242-1729778400-1729780200@www.cessrst.org
SUMMARY:NOAA Seminar Series: Societal Data Insights: Data Integration for Inland Flooding.
DESCRIPTION:Title:Societal Data Insights: Data Integration for Inland Flooding.\n \nPresenter(s): Isabel Lopez \nDate: 24 October 2024 2:00 pm – 2:30 pm ET\n \nRemote Access: Google Meet joining info \nVideo call link: https://meet.google.com/xsy-nupc-von   \nOr dial: (US) +1 234-276-0398PIN: 436 710 044#  \nMore phone numbers: https://tel.meet/xsy-nupc-von?pin=2132911046548 \nAbout Speaker: Isabel Lopez \nAbstract: Urban recurrent flooding presents a complex challenge distinct from nuisance flooding\, typically associated with coastal areas. Unlike nuisance flooding\, which is often predictable and localized\, urban recurrent flooding involves adynamic interplay of factors such as dense infrastructure\, varied land use\, and heterogeneous topography. These elements contribute to unpredictable flood patterns that are more difficult to model and manage. The complexity of urban environments amplifies the challenges in assessing flood risks and potential impacts\, necessitating a more sophisticated analytical approach. This research adapts the Topographic Wetness Index (TWI) to highlight areas prone to flooding based on flow direction and water accumulation. Additionally\, it incorporates the Curve Number (CN) method to estimate runoff volumes from precipitation events\, providing refined tools for measuring surface runoff and predicting flooding potential. Recognizing that urban flooding significantly impacts communities\, this study integrates social data to capture the broader societal effects\, particularly on vulnerable populations. The proposed framework is designed for flexibility\, allowing its application across diverse urban areas with varying geographic and social characteristics. By combining geospatial analysis with social data\, this research offers a comprehensive approach to flood risk assessment\, providing valuable insights for policymakers and urban planners.The results are from the NOAA EPP/MSI CSC NERTO graduate internship project that was conducted with NOAA mentors Dr. Jonathon Mote and Dr. Kyle Metta of the Weather ProgramOffice (WPO) in Silver Spring\, MD. The NERTO aligns with NOAA CSC CESSRST-II’s goal to understand changes in climate and weather and to share that knowledge and information with others. The NERTO project enhanced the intern’s ability to integrate social data with physical data\, providing deeper insights into developing methods that combine social\, weather\, and climate data for more comprehensive analyses.
URL:https://www.cessrst.org/event/noaa-seminar-series-societal-data-insights-data-integration-for-inland-flooding/
LOCATION:NY
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:20241024T143500
DTEND;TZID=America/New_York:20241024T150500
DTSTAMP:20260429T234918
CREATED:20241021T143329Z
LAST-MODIFIED:20241021T143329Z
UID:5244-1729780500-1729782300@www.cessrst.org
SUMMARY:NOAA Seminar Series: Historical Data Reconstruction for the California Coastal Currents using 3D Empirical Orthogonal Functions and Multivariate Regression
DESCRIPTION:Title: Historical Data Reconstruction for the California Coastal Currents using 3D Empirical Orthogonal Functions and Multivariate Regression\n \nPresenter(s): Danielle Lafarga\, \nDate: 24 October 2024 2:35 pm – 3:05 pm ET\n \nRemote Access: Google Meet joining info \nVideo call link:  https://meet.google.com/new-qbkh-azj \n Or dial: (US) +1 440-482-5511 PIN: 303 375 204#  \nMore phone numbers: https://tel.meet/new-qbkh-azj?pin=5643412593662 \nAbout Speaker: Danielle Lafarga\, \nAbstract: Many studies analyze ocean temperature variance\, computing empirical orthogonal functions (EOFs) one layer at a time(2D). However\, surface phenomena like El Nio extend into deeper layers\, exemplifying how crucial it is to examine their three-dimensional structure to fully understand their impact. This research aims to compute 3D EOFs for different areas of the Pacific Ocean to answer how much and what variability can be explored across ocean layers using ahigh-resolution\, eddy-resolving model known as the Global Ocean Physics Reanalysis (GLORYS). The model’s fine resolution allows for detailed analysis of smaller-scale dynamics\, such as those along the coasts of California\, Oaxaca\, and Costa Rica. Nevertheless\, the volume of data presents a memory challenge for 3D calculations. To address this\, we propose an algorithm that enables 3D EOF computation on computers with limited memory (16GB RAM)\, making high-resolution analysis feasible.Computing 3D EOFs is crucial for understanding our oceans and how ocean dynamics can extend through multiple layers. This research aligns with NOAA’s mission to understand and predict changes in climate\, weather\, oceans\, and coasts. By providing a more comprehensive view of ocean variability\, the results also contribute valuable insights into the habitats of fish species protected by NOAA Fisheries\, aiding in the preservation and management of marine ecosystems.The results are from the NOAA EPP/MSI CSC NERTO graduate internship project that was conducted with NOAA mentor\, Dr. Michael Jacox of NOAA SWFSC Environmental Research Division\, and NOAA collaborator Dr. Michael Alexander of NOAA Atmosphere Ocean Processes and Predictability (AOPP) Division. The NERTO aligns NOAA CSCCESSRST-II’s goal of to understand and predict changes in climate and weather. The NERTO project deepened the intern’s understanding of remote sensing technology\, big data computing\, and participation in NOAA mission-aligned activities through extensive collaborations with NOAA employees.
URL:https://www.cessrst.org/event/noaa-seminar-series-historical-data-reconstruction-for-the-california-coastal-currents-using-3d-empirical-orthogonal-functions-and-multivariate-regression/
LOCATION:NY
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:20241024T151500
DTEND;TZID=America/New_York:20241024T154500
DTSTAMP:20260429T234918
CREATED:20241021T143731Z
LAST-MODIFIED:20241021T143731Z
UID:5246-1729782900-1729784700@www.cessrst.org
SUMMARY:NOAA Seminar Series: Identifying local and synoptic-scale meteorological and land cover conditions favorable for the occurrence of large fires in California
DESCRIPTION:Title: Identifying local and synoptic-scale meteorological and land cover conditions favorable for the occurrence of large fires in California\n \nPresenter(s):  E’lysha Guerrero \nDate: 24 October 2024 3:15 pm – 3:45 pm ET\n \nRemote Access: Google Meet joining info \nVideo call link: https://meet.google.com/dft-obqy-fhb \nOr dial: (US) +1 650-535-0909PIN: 928 542 289#  \nMore phone numbers: https://tel.meet/dft-obqy-fhb?pin=5001908281383 \nAbout Speaker: E’lysha Guerrero\, \nAbstract: Whilst global warming projections lead to continuous warming trends and California wildfire activity is expected to increase\, the state of wildfire predictions will need to be enhanced to keep up with the ever-changing climate conditions. This research project aims to characterize meteorological and land conditions related to large wildfires in California and identify their connection to predictable climate patterns\, potentially enhancing future wildfire predictions. We utilize historical wildfire perimeter data (2000 “2022) and apply the K-means Clustering Algorithm on localized meteorological variables to group wildfires based on similar conditions. Larger-scale synoptic meteorology is analyzed to identify potential predictors for future wildfire occurrences. The research questions addressed during the NERTO are: (a) What are the local regional and seasonal characteristics of California’s historically larger wildfires from 2000 – 2022? and (b) What are the typical large-scale circulation patterns associated with each California clustered group?The value of this research lies in its contribution to NOAA’s mission to understand and predict climate and weather changes\, specifically through advancing wildfire prediction capabilities. The insights gained can improve both prediction models and wildfire management strategies\, supporting NOAA’s broader goal of mitigating the impacts of extreme weather and natural hazards. Additionally\, the use of machine learning techniques\, like K-means clustering\, fosters innovation in predictive skills\, aligning with the NOAA Physical Sciences Laboratory’s mission to develop new knowledge and tools for forecasting extreme events such as wildfires. The results are from the NOAA EPP/MSI CSC NERTO graduate internship project that was conducted with NOAA mentor Dr. Andrew Hoell\, Dr. Rochelle Worsnop\, and Dr. Melissa Breeden of NOAA Physical Sciences Laboratory\, Boulder\, CO. The NERTO aligns with NOAA CSC CESSRST-II’s goal to understand and predict changes in climate and weather. The NERTO project deepened the intern’s understanding and increased the research skill sets of data acquisition\, preprocessing\, analyses\, and validation techniques required for earth system science research.
URL:https://www.cessrst.org/event/noaa-seminar-series-identifying-local-and-synoptic-scale-meteorological-and-land-cover-conditions-favorable-for-the-occurrence-of-large-fires-in-california/
LOCATION:NY
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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