We will develop a robust indicator of extreme precipitation over the continental united states (US) based on gridded precipitation data from nasas tropical rainfall measurement mission (trmm) and…

This is a $302,078.02 grant awarded by the National Aeronautics and Space Administration (NASA) to Portland State University (PSU) to develop a "robust indicator of extreme precipitation over the Continental United States" based on data from NASA's Tropical Rainfall Measurement Mission (TRMM) and Global Precipitation Measurement (GPM) programs. The indicator will categorize extreme precipitation events similar to the Saffir-Simpson hurricane scale, allowing tracking of trends in frequency and severity. The grant supports NASA's contributions to the U.S. National Climate Assessment process, which aims to monitor and report on the impacts of climate change. The work will be incorporated into the open-source Regional Climate Model Evaluation System (RCMES) to ensure transparency and enable customization. This grant does not have a set-aside designation, indicating PSU competed for the award through open competition. As a subunit of the Oregon University System, PSU has extensive experience providing research and technical services to federal agencies across various domains.

We will develop a robust indicator of extreme precipitation over the continental united states (US) based on gridded precipitation data from nasas tropical rainfall measurement mission (trmm) and the forthcoming global precipitation measurement (GPM) product to support nasas contribution to the sustained US national climate assessment (NCA) process. Extreme precipitation is associated with a multitude of severe impacts on multiple US sectors at local, regional, and national scales. These impacts include flash flooding, landslides and debris flows, large scale river flooding affecting human life and property, agricultural productivity, and national infrastructure. Extreme precipitation also represents a critical contribution to the water budget for some parts of the US. Furthermore, IT is anticipated that changes in the frequency, severity, and spatial distribution of extreme precipitation events will result from anthropogenic global warming, with a greater proportion of total rainfall occurring in extreme events overall. Monitoring of trends in the frequency and severity of extreme precipitation at scales relevant to society is therefore of critical national import and is highly consistent with the goals of the US NCA process. However, the current proposed heavy precipitation indicatorcontained in the NCA indicators system report does not provide regionally relevant or customizable information and therefore does not address the majority of the indicators system goals. We propose an indicator that meets the stated goals of the indicators systemand provides the level of local, regional, and national information that a phenomenon as nationally important as extreme precipitation deserves. Our indicator will rank extreme precipitation events by category (p CAT) based on 3 day storm totals, similar to the saffir simpson ranking scale used to describe hurricane intensity. The rankings will be based on five s cats with s CAT 1 identifying the lightest and s CAT 5 the heaviest extreme precipitation storm total. Changes in the frequency of each category will then be monitored through time. We will develop a simple and user friendly web interface to allow users to customize the way they obtain and visualize information provided by this indicator. By assigning categories to extreme precipitation events, this is an easily and intuitively interpretable indicator of change in the character of extreme precipitation across the continental US. For example, an increase in s CAT 5 events over time would be indicative of a tendency towards more frequent high end extreme precipitation events. Furthermore, the seamless temporal continuity provided by trmm and its forthcoming transition to GPM will allow for an increasingly long measurement record, enhancing the robustness and statistical confidence of the indicator over time. We will ensure the transparent reproducibility of this indicator by incorporating the software and data used in this project into the regional climate model evaluation system (rcmes), a NASA enabling tool tasked with supporting the NCA. Additionally, the code base of rcmes is completely open source allowing users to customize the indicator software to address their specific needs and to easily use this indicator to evaluate climate model simulations.