NOAA: Assessment of High-Resolution Rapid Refresh (HRRR) Precipitation Forecasts for Urban Coastal Areas: New York City Testbed

Presenter(s): Sebastian Makrides, Graduate Fellow, NOAA Office of Education, Educational Partnership Program with Minority Serving Institutions (EPP), NOAA Cooperative Science Center for Earth System Science and Remote Sensing Technologies II (CESSRST II) Cohort 3 Fellow at the City College of New York

Sponsor(s): NOAA EPP/MSI Cooperative Science Centers

Seminar Contact(s): Audrey.Trotman@noaa.gov and oed.epp10@noaa.gov

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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.

Bio(s): Sebastian Makrides is a NOAA Cooperative Science Center Fellow for the Center for Earth System Science and Remote Sensing Technologies (CESSRST-II). He is an MS student in Earth Systems Science and Environmental Engineering at The City College of New York (CCNY).Building upon skills from his undergraduate degree in Systems Engineering at George Washington University, his research is primarily focused on urban hydrometeorology and performance assessments of forecast models, as well as in-situ data collected from CCNY’s New York Urban Hydro-meteorological Testbed of autonomous weather stations. More specifically, his interests include leveraging statistical analysis, geographic information systems, and artificial intelligence for the assessment of environmental data and related issues. Beyond his current work, he is greatly interested in applying remote sensing to bridge the gap between science and society, including research into NYC’s combined sewer overflow events, their connection to the city’s green infrastructure, and the potential impacts of future climate risks.Sebastian Makrides is supported as a Cohort 3 Graduate Fellow in the NOAA Center for Earth System Sciences and Remote Sensing Technologies (CESSRST-II) award.