This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).
Improved forecasting of orographic precipitation requires continued integration of theory, field studies, and long term observations. While much of the previous work on orographic precipitation has focused on steady-state processes, little work has been done on intermittent precipitation cells embedded in broad regions of precipitation. Current numerical models cannot consistently reproduce orographic precipitation patterns at time scales of individual storms and spatial scales corresponding to individual terrain ridges. The working hypothesis is that these problems are in large part a result of errors in the representation of steady state and intermittent processes within the models.
An integrated modeling and observational study will be conducted in coastal Oregon. Observations from the National Weather Service weather radar network and upper air sounding network, and observations from specially-deployed vertically pointing radars and disdrometers will be used to characterize intermittent precipitation cells and to evaluate model output. As a result of the study intermittent orographic precipitation and its relative importance compared to total orographic precipitation will be quantified. The numerical model configuration needed to realistically model intermittent orographic precipitation will be defined, along with the subset of variations in environmental conditions needed for capturing the forecast ensemble uncertainty in the Oregon region. The proposed work also will address the parameterization of ice microphysics in numerical weather prediction models and mesoscale predictability of orographic precipitation.
The overall long-term goal of this joint orographic precipitation research is to improve winter quantitative precipitation forecasts in mountainous terrain, which is a broad impact. Improved quality of these forecasts also has broader impacts for flood forecasts and mitigation, avalanche control, fresh water resources management, and climate change impacts. Other broader impacts include support and training of graduate students as research assistants. Several undergraduate research assistants will also be trained to do data analysis. Results will be presented by students as posters at research symposia such as the ones held by North Carolina State University and SUNY Stony Brook each spring and summer. The real-time forecast model runs centered on Stony Brook, NY will be regularly used as part of forecast discussions during weekly student meetings on campus and by the regional offices of the National Weather Service. Educational modules on evaluation of model output with operational radar data to be used in undergraduate and graduate classes will be created under this proposal. Project funds will be used to enhance campus research infrastructure in terms of upgrading data server storage capacity and maintenance of field instruments. Results will be disseminated in conference presentations and journal articles.
