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Colloquium|Department of Atmospheric Science

Seminar: Fri., March 7, 3:10  pm, EN6085 

Mesoscale Numerical Weather Model for Probable Maximum Precipitation Estimation

Noriaki Ohara

Civil and Architectural Engineering
University of Wyoming 

 

A Probable Maximum Precipitation (PMP) is typically defined as a physical upper bound of possible precipitation over a given size storm area. The PMP is mainly used for designing the emergency spillways of reservoirs. Although PMP implies the “probabilistic” limitation of heaviest precipitation, PMP has been mainly determined by the “physics” of the weather system. This presentation will discuss a possibility of mesoscale weather model application for PMP estimation. As an example case, a model-based 72-hour PMP was estimated for American River Watershed (ARW) in California. First, a mesoscale weather model, MM5, was calibrated and validated for a historical major storm event for the ARW, based on the NCAR reanalysis data. The model-simulated precipitation field in ARW was successfully validated at nine individual rain gauge stations in the watershed. Then, the initial and boundary conditions in the outer nesting domain of the atmospheric model were modified to maximize the precipitation over the watershed. In this demonstrative study, the boundary conditions were modified by three methods: 1) maximizing the atmospheric moisture by setting the relative humidity at 100 percent, 2) maintaining the atmospheric boundary conditions corresponding to the state of the heaviest precipitation (maintaining equilibrium conditions), and 3) spatially shifting the atmospheric conditions in order to render the atmospheric moisture flux hit the watershed. These different maximization methods produced similar 72-hour precipitation depths which were 549 mm by the combination of 100% relative humidity and equilibrium high precipitation conditions at the outer boundary of the model domain, and 541 mm from shifting the atmospheric conditions to south by 5.0 degrees. It was demonstrated that the presented modeling approach can be a potential alternative to the standard Probable Maximum Precipitation (PMP) estimation without depending upon the linear relationships required in the standard PMP method.

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