Further Refinements to an Operational Orographic Precipitation Model

Orographically generated precipitation is a distinctive component of the overall precipitation accumulation in areas of varied topography. Accurately predicting this type of precipitation is an important operational forecast problem. These orographic snowfall events often have more significant impacts on transportation, recreation, avalanches, reservoir storage and river flows than do events associated with synoptic-scale migratory troughs and cyclones.

For several decades, operational forecasters in Colorado have been using an orographic precipitation model based on work done by Rhea in the late 1970s. This model has performed well throughout its long history, despite several shortcomings. Among these faults are an overly simplified depiction of mountain wave dynamics, simplified static stability parameters, flow confined to two dimensions, and the inability to easily incorporate synoptic-scale, convective and microphysical processes. In addition to these deficiencies in the model physics, the output has only recently been modified to be used in digital format for areas other than Colorado.

To overcome some of the aforementioned limitations, a new version of the model continues to be refined and tuned for use in other parts of the country and for non-cold season applications. Based on research and theoretical advances that have occurred over the last several years, this new rendition of the model will take advantage of computational resources presently available in local forecast offices. These factors will provide what is anticipated to be more accurate and useful quantitative precipitation forecasts for mountainous areas. Very high-resolution output will be in a digital format, making it possible to more directly incorporate the forecasts into the National Weather Service's National Digital Forecast Database and other digital forecast databases, possibly including river forecast models. Due to its speed, ensemble runs of the model are also possible, providing the opportunity to make probabilistic forecasts of orographic precipitation.