April 25, 2017 - 9:00 am
April 25, 2017 - 10:00 am
AddressNational Weather Center, 120 David L. Boren Blvd., Room 5820, Norman, OK 73072 View map
A Sub-Daily Severe Weather Climatology and its Implications for Forecasting
While there has been an abundance of research dedicated to the seasonal climatology of severe weather, very little has been done to study severe weather probabilities on smaller scales. Using a similar process to the Brooks et al. (2003a) creation of local daily climatological tornado estimates, local hourly climatological estimates of tornado and hail event probabilities were developed using storm reports from the NOAA Storm Prediction Center. These estimates begin the process of analyzing tornado and hail frequencies on a sub-24 hour scale.
Further work began to investigate characteristics of the local climatology, including how the diurnal cycle varies in space and time. Hourly tornado probabilities are very peaked for both the annual and diurnal cycles in the Northern and Southern Plains. However, this pattern breaks down quickly in the southeast United States (US), where there is an extremely variable pattern in tornado probabilities. These differences in the annual and diurnal cycle create forecasting and community response challenges unique to each region. These challenges are briefly discussed along with the associated climatological risks.
The same process was repeated using hail reports, yielding similar results to the tornado climatology. While the annual and diurnal cycles were once again more variable in the southeast US than in the Plains, they were much more peaked than the tornado probabilities.
This work is part of a larger effort to provide background information for probabilistic forecasts of hazardous weather that are meaningful over broad time and space scales, with a focus on scales broader than the typical time and space scales of the events of interest (including current products on the “watch” scale). A large challenge remains to continue describing probabilities as the time and space scales of the forecast become comparable to the scale of the event.