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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.

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.

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.

Computer P u b l i s h e d b y t h e I E E E C o m p u t e r S o c i e t yLEAD establish an interactive closed loop between the forecast analysis and the instruments: The data drives the instruments, which, to make more accurate predictions, refocus in a repeated cycle.The "Hypothetical CASA-LEAD Scenario" sidebar provides an example of the unprecedented capabilities these changes afford.Mesoscale meteorology is the study of smaller-scale weather phenomena such as severe storms, tornadoes, and hurricanes. System-level science in this context involves the responsiveness of the forecast models to the weather at hand as well as conditions on the network at large and the large-scale computational resources on which forecasts rely. This responsiveness can be broken down into four narrowly defined goals:• Dynamic workflow adaptivity. Forecasts execute in the context of a workflow, or task graph. Workflows should be able to dynamically reconfigure in response to new events.• Dynamic resource allocation. The system should be able to dynamically allocate resources, including radars and remote observing technologies, to optiTwo closely linked projects aim to dramatically improve storm forecasting speed and accuracy. CASA is creating a distributed, collaborative, adaptive sensor network of lowpower, high-resolution radars that respond to user needs. LEAD offers dynamic workflow orchestration and data management in a Web services framework designed to support on-demand, real-time, dynamically adaptive systems.

The ability to provide advanced warning on tornadoes can be impacted by variations in storm mode. This research evaluates 2 yr of National Weather Service (NWS) tornado warnings, verification reports, and radar-derived convective modes to appraise the ability of the NWS to warn across a variety of convective modes and environmental conditions. Several specific hypotheses are considered: (i) supercell morphologies are the easiest convective modes to warn for tornadoes and yield the greatest lead times, while tornadoes from more linear, nonsupercell convective modes, such as quasi-linear convective systems, are more difficult to warn for; (ii) parameters such as tornado distance from radar, population density, and tornado intensity (F scale) introduce significant and complex variability into warning statistics as a function of storm mode; and (iii) tornadoes from stronger storms, as measured by their mesocyclone strength (when present), convective available potential energy (CAPE), vertical wind shear, and significant tornado parameter (STP) are easier to warn for than tornadoes from weaker systems. Results confirmed these hypotheses. Supercell morphologies caused 97% of tornado fatalities, 96% of injuries, and 92% of damage during the study period. Tornado warnings for supercells had a statistically higher probability of detection (POD) and lead time than tornado warnings for nonsupercells; among supercell storms, tornadoes from supercells in lines were slightly more difficult to warn for than tornadoes from discrete or clusters of supercells. F-scale intensity and distance from radar had some impact on POD, with less impact on lead times. Higher mesocyclone strength (when applicable), CAPE, wind shear, and STP values were associated with greater tornado POD and lead times.

The Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) is a multiyear engineering research center established by the National Science Foundation for the development of small, inexpensive, low-power radars designed to improve the scanning of the lowest levels (,3 km AGL) of the atmosphere. Instead of sensing autonomously, CASA radars are designed to operate as a network, collectively adapting to the changing needs of end users and the environment; this network approach to scanning is known as distributed collaborative adaptive sensing (DCAS). DCAS optimizes the low-level volume coverage scanning and maximizes the utility of each scanning cycle. A test bed of four prototype CASA radars was deployed in southwestern Oklahoma in 2006 and operated continuously while in DCAS mode from March through June of 2007.This paper analyzes three convective events observed during April-May 2007, during CASA's intense operation period (IOP), with a special focus on evaluating the benefits and weaknesses of CASA radar system deployment and DCAS scanning strategy of detecting and tracking low-level circulations. Data collected from nearby Weather Surveillance Radar-1988 Doppler (WSR-88D) and CASA radars are compared for mesoscyclones, misocyclones, and low-level vortices. Initial results indicate that the dense, overlapping coverage at low levels provided by the CASA radars and the high temporal (60 s) resolution provided by DCAS give forecasters more detailed feature continuity and tracking. Moreover, the CASA system is able to resolve a whole class of circulations-misocyclones-far better than the WSR-88Ds. In fact, many of these are probably missed completely by the WSR-88D. The impacts of this increased detail on severe weather warnings are under investigation. Ongoing efforts include enhancing the DCAS data quality and scanning strategy, improving the DCAS data visualization, and developing a robust infrastructure to better support forecast and warning operations.