No abstract

With the unusually violent tornado season of 2011, there has been a renewed national interest, through such programs as NOAA's Weather Ready Nation initiative, to reevaluate and improve our tornado warning process. This literature review provides an interdisciplinary, end-to-end examination of the tornado warning process. Following the steps outlined by the Integrated Warning System, current research in tornado prediction and detection, the warning decision process, warning dissemination, and public response are reviewed, and some of the major challenges for improving each stage are highlighted. The progress and challenges in multi-day to short-term tornado prediction are discussed, followed by an examination of tornado detection, focused primarily upon the contributions made by weather radar and storm spotters. Next is a review of the warning decision process and the challenges associated with dissemination of the warning, followed by a discussion of the complexities associated with understanding public response. Finally, several research opportunities are considered, with emphases on understanding acceptable risk, greater community and personal preparation, and personalization of the hazard risk.

Tornado warnings are currently issued an average of 13 min in advance of a tornado and are based on a warn-on-detection paradigm. However, computer model improvements may allow for a new warning paradigm, warn-on-forecast, to be established in the future. This would mean that tornado warnings could be issued one to two hours in advance, prior to storm initiation. In anticipation of the technological innovation, this study inquires whether the warn-on-forecast paradigm for tornado warnings may be preferred by the public (i.e., individuals and households). The authors sample is drawn from visitors to the National Weather Center in Norman, Oklahoma. During the summer and fall of 2009, surveys were distributed to 320 participants to assess their understanding and perception of weather risks and preferred tornado warning lead time. Responses were analyzed according to several different parameters including age, region of residency, educational level, number of children, and prior tornado experience. A majority of the respondents answered many of the weather risk questions correctly. They seemed to be familiar with tornado seasons; however, they were unaware of the relative number of fatalities caused by tornadoes and several additional weather phenomena each year in the United States. The preferred lead time was 34.3 min according to average survey responses. This suggests that while the general public may currently prefer a longer average lead time than the present system offers, the preference does not extend to the 1–2-h time frame theoretically offered by the warn-on-forecast system. When asked what they would do if given a 1-h lead time, respondents reported that taking shelter was a lesser priority than when given a 15-min lead time, and fleeing the area became a slightly more popular alternative. A majority of respondents also reported the situation would feel less life threatening if given a 1-h lead time. These results suggest that how the public responds to longer lead times may be complex and situationally dependent, and further study must be conducted to ascertain the users for whom the longer lead times would carry the most value. These results form the basis of an informative stated-preference approach to predicting public response to long (>1 h) warning lead times, using public understanding of the risks posed by severe weather events to contextualize lead-time demand.

[1] Oklahoma Atmospheric Surface-Layer Instrumentation System (OASIS) measurements of net radiation (R n ), latent heat flux (LH), sensible heat flux (SH), and ground heat flux (GH) were used to validate the NOAH-Oregon State University Land Surface Model (NOAH-OSU LSM). A 1-year study period was used. R n , LH, SH and GH data from seven sites were screened based on an energy balance closure criterion (daily/ hourly sum of the flux components within the range of À10 to +10 W m À2). The vegetation fraction used in the model was computed using both the Gutman-Ignatov (G-I) and the Carlson-Ripley (C-R) schemes. The simulated net radiation and ground heat fluxes seem less sensitive to the choice of schemes for computing green vegetation fraction, while latent and sensible heat flux show more sensitivity particularly for soil dry-down period. Therefore, the G-I approach was used for the validation study, as it is widely used and linear in its form. The daily aggregated model outputs showed that the predicted R n had a positive bias of 0.

A significant challenge with dynamical downscaling of climate simulations is the ability to accurately represent convection and precipitation. The use of convection‐permitting resolutions avoids cumulus parameterization, which is known to be a large source of uncertainty. A regional climate model (RCM) based on the Weather Research and Forecasting model is configured with a 4 km grid spacing and applied to the U.S. Great Plains, a region characterized by many forms of weather and climate extremes. The 4 km RCM is evaluated by running it in a hindcast mode over the central U.S. region for a 10 year period, forced at the boundary by the 32 km North America Regional Reanalysis. The model is also run at a 25 km grid spacing, but with cumulus parameterization turned on for comparison. The 4 km run more successfully reproduces certain observed features of the Great Plains May‐through‐August precipitation. In particular, the magnitude of extreme precipitation and the diurnal cycle of precipitation over the Great Plains are better simulated. The 4 km run more realistically simulates the low‐level jet and related atmospheric circulations that transport and redistribute moisture from Gulf of Mexico. The convection‐permitting RCM may therefore produce better dynamical downscaling of future climate when nested within global model climate projections, especially for extreme precipitation magnitudes. The 4 km and 25 km simulations do share similar precipitation biases, including low biases over the central Great Plains and high biases over the Rockies. These biases appear linked to circulation biases in the simulations, but determining of the exact causes will require extensive, separate studies.