Project abstract accurate estimation of the potential upper limit for extreme precipitation is critical for DAM safety, high-hazard infrastructure planning, and water resources management

Colorado State University's Cooperative Institute (Inter-Agency Funded Activities) program (CFDA 11.405) award of $248,337, obligated on August 1, 2025, and concluding July 31, 2027, funds the development and testing of advanced weather modeling capabilities to modernize Probable Maximum Precipitation (PMP) estimation. Led by the National Oceanic and Atmospheric Administration (NOAA) Physical Sciences Laboratory in collaboration with the Cooperative Institute for Research in the Atmosphere (CIRA) at Colorado State University, this project delivers physics-based weather model improvements and machine learning-based post-processing tools designed to enhance the accuracy of extreme precipitation forecasts. The deliverables include enhanced data assimilation methods to improve model initial conditions, validation frameworks to assess model performance against historical extreme precipitation events, machine learning tools to correct systematic precipitation forecast errors, and comparative analyses of alternative weather model configurations to identify optimal approaches for PMP estimation.

These research and development activities directly support the Bipartisan Infrastructure Law's mandate to modernize PMP estimation methodologies, which are essential for dam safety assessment, high-hazard infrastructure planning, and water resources management. The award aligns with recommendations from the National Academies of Science, Engineering, and Medicine's 2024 study on modernizing PMP estimation and represents foundational technical work necessary to transition from outdated estimation methods to state-of-the-science simulation and machine learning approaches for predicting the theoretical upper limits of precipitation.

Methods used to estimate the theoretical upper limit of precipitation are often outdated and in need of improvement. The bipartisan infrastructure law (BIL) supports efforts to modernize probable maximum precipitation (PMP) estimation, led by NOAA PSL in collaboration with a diverse team of experts. In 2024 NOAA commissioned a study by the national academies of science, engineering, and medicine (NASEM), modernizing probable maximum precipitation estimation (DOI: https://DOI.ORG/10.17226/27460). One major component of the studys recommended approach is to estimate PMP by performing several simulations using a state-of-the-science, physics-based weather model, and then use machine learning approaches to correct for systematic errors in precipitation fields. To achieve this goal, IT is first necessary to develop and test a weather model that can meet the needs of estimating PMP as well as develop the necessary postprocessing to correct systematic errors. Cira, in collaboration with the partners listed above, will work towards developing and testing such a weather model. Specific activities will include: improving the initial state of the weather model through a process known as data assimilation. Better data assimilation will allow researchers to simulate past extreme precipitation events, which will be used to assess weather model performance. Developing a machine learning tool to correct systematic errors after a weather model forecast is created. Testing different weather model configurations. Developing tools to compare weather model output to past extreme precipitation events. This will inform which weather model configuration is best and what deficiencies still exist that need to be corrected with postprocessing these efforts are an important step towards realizing the recommendations of the NASEM report, which will result in better PMP estimates. These PMP estimates will then be used in the management and design of dams and other high-hazard infrastructure to ensure that these facilities are safe. Additional information about the BIL PMP modernization project can be found on NOAA psls website: https://PSL.NOAA.GOV/precip/PMP/.