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This research seeks to answer the basic question of how current-day extreme tornadic storm events might be realized under future anthropogenic climate change. The pseudo global warming (PGW) methodology was adapted for this purpose. Three contributions to the CMIP5 archive were used to obtain the mean 3D atmospheric state simulated during May 1990–99 and May 2090–99. The climate change differences (or Ds) in temperature, relative humidity, pressure, and winds were added to NWP analyses of three high-end tornadic storm events, and this modified atmospheric state was then used for initial and boundary conditions for realdata WRF Model simulations of the events at high resolution. Comparison of an ensemble of these simulations with control simulations (CTRL) facilitated assessment of PGW effects. In contrast to the robust development of supercellular convection in each CTRL, the combined effects of increased convective inhibition (CIN) and decreased parcel lifting under PGW led to a failure of convection initiation in many of the experiments. Those experiments that had sufficient matching between the CIN and lifting tended to generate stronger convective updrafts than CTRL, although not in proportion to the projected higher levels of convective available potential energy (CAPE) under PGW. In addition, the experiments with enhanced updrafts also tended to have enhanced vertical rotation. In fact, such supercellular convection was even found in simulations that were driven with PGW-reduced environmental wind shear. Notably, the PGW modifications did not induce a change in the convective morphology in any of the PGW experiments with significant convective storminess.

The effect of anthropogenically enhanced greenhouse gas concentrations on the frequency and intensity of hail depends on a range of physical processes and scales. These include the environmental support of the hailgenerating convective storms and the frequency of their initiation, the storm volume over which hail growth is promoted, and the depth of the lower atmosphere conducive to melting. Here, we use high-resolution (convection permitting) dynamical downscaling to simultaneously account for these effects. We find broad geographical areas of increases in the frequency of large hail (≥35-mm diameter) over the United States, during all four seasons. Increases in very large hail (≥50-mm diameter) are mostly confined to the central United States, during boreal spring and summer. And, although increases in moderate hail (≥20-mm diameter) are also found throughout the year, decreases occur over much of the eastern United States in summer. Such decreases result from a projected decrease in convective-storm frequency. Overall, these results suggest that the annual U.S. hail season may begin earlier in the year, be lengthened by more than a week, and exhibit more interannual variability in the future.

In this study we investigate convective environments and their corresponding climatological features over Europe and the United States. For this purpose, National Lightning Detection Network (NLDN) and Arrival Time Difference long-range lightning detection network (ATDnet) data, ERA5 hybrid-sigma levels, and severe weather reports from the European Severe Weather Database (ESWD) and Storm Prediction Center (SPC) Storm Data were combined on a common grid of 0.25° and 1-h steps over the period 1979–2018. The severity of convective hazards increases with increasing instability and wind shear (WMAXSHEAR), but climatological aspects of these features differ over both domains. Environments over the United States are characterized by higher moisture, CAPE, CIN, wind shear, and midtropospheric lapse rates. Conversely, 0–3-km CAPE and low-level lapse rates are higher over Europe. From the climatological perspective severe thunderstorm environments (hours) are around 3–4 times more frequent over the United States with peaks across the Great Plains, Midwest, and Southeast. Over Europe severe environments are the most common over the south with local maxima in northern Italy. Despite having lower CAPE (tail distribution of 3000–4000 J kg−1 compared to 6000–8000 J kg−1 over the United States), thunderstorms over Europe have a higher probability for convective initiation given a favorable environment. Conversely, the lowest probability for initiation is observed over the Great Plains, but, once a thunderstorm develops, the probability that it will become severe is much higher compared to Europe. Prime conditions for severe thunderstorms over the United States are between April and June, typically from 1200 to 2200 central standard time (CST), while across Europe favorable environments are observed from June to August, usually between 1400 and 2100 UTC.

In this work, long-term trends in convective parameters are compared between ERA5, MERRA-2, and observed rawinsonde profiles over Europe and the United States including surrounding areas. A 39-yr record (1980–2018) with 2.07 million quality-controlled measurements from 84 stations at 0000 and 1200 UTC is used for the comparison, along with collocated reanalysis profiles. Overall, reanalyses provide signals that are similar to observations, but ERA5 features lower biases. Over Europe, agreement in the trend signal between rawinsondes and the reanalyses is better, particularly with respect to instability (lifted index), low-level moisture (mixing ratio), and 0–3-km lapse rates as compared with mixed trends in the United States. However, consistent signals for all three datasets and both domains are found for robust increases in convective inhibition (CIN), downdraft CAPE (DCAPE), and decreases in mean 0–4-km relative humidity. Despite differing trends between continents, the reanalyses capture well changes in 0–6-km wind shear and 1–3-km mean wind with modest increases in the United States and decreases in Europe. However, these changes are mostly insignificant. All datasets indicate consistent warming of almost the entire tropospheric profile, which over Europe is the fastest near ground whereas across the Great Plains it is generally between 2 and 3 km above ground level, thus contributing to increases in CIN. Results of this work show the importance of intercomparing trends between various datasets, as the limitations associated with one reanalysis or observations may lead to uncertainties and lower our confidence in how parameters are changing over time.

Globally, on the order of 100 tropical cyclones (TCs) occur annually, yet the processes that control this number remain unknown. Here we test a simple hypothesis that this number is limited by the geography of thermodynamic environments favorable for TC formation and maintenance. First, climatologies of TC potential intensity and environmental ventilation are created from reanalyses and are used in conjunction with historical TC data to define the spatiotemporal geography of favorable environments. Based on a range of predefined separation distances, the geographic domain of environmental favorability is populated with randomly placed TCs assuming a fixed minimum separation distance to achieve a maximum daily packing density of storms. Inclusion of a fixed storm duration yields an annual “maximum potential genesis” (MPG) rate, which is found to be an order of magnitude larger than the observed rate on Earth. The mean daily packing density captures the seasonal cycle reasonably well for both the Northern and Southern Hemispheres, though it substantially overestimates TC counts outside of each hemisphere’s active seasons. Interannual variability in MPG is relatively small and is poorly correlated with annual storm count globally and across basins, though modest positive correlations are found in the North Atlantic and east Pacific basins. Overall, the spatiotemporal distribution of favorable environmental conditions appears to strongly modulate the seasonal cycle of TCs, which certainly strongly influences the TC climatology, though it does not explicitly constrain the global annual TC count. Our methodology provides the first estimate of an upper bound for annual TC frequency and outlines a framework for assessing how local and large-scale factors may act to limit global TC count below the maximum potential values found here.

The NOAA Warn-on-Forecast System (WoFS) is an experimental rapidly updating convection-allowing ensemble designed to provide probabilistic operational guidance on high-impact thunderstorm hazards. The current WoFS uses physics diversity to help maintain ensemble spread. We assess the systematic impacts of the three WoFS PBL schemes—YSU, MYJ, and MYNN—using novel, object-based methods tailored to thunderstorms. Very short forecast lead times of 0–3 h are examined, which limits phase errors and thereby facilitates comparisons of observed and model storms that occurred in the same area at the same time. This evaluation framework facilitates assessment of systematic PBL scheme impacts on storms and storm environments. Forecasts using all three PBL schemes exhibit overly narrow ranges of surface temperature, dewpoint, and wind speed. The surface biases do not generally decrease at later forecast initialization times, indicating that systematic PBL scheme errors are not well mitigated by data assimilation. The YSU scheme exhibits the least bias of the three in surface temperature and moisture and in many sounding-derived convective variables. Interscheme environmental differences are similar both near and far from storms and qualitatively resemble the differences analyzed in previous studies. The YSU environments exhibit stronger mixing, as expected of nonlocal PBL schemes; are slightly less favorable for storm intensification; and produce correspondingly weaker storms than the MYJ and MYNN environments. On the other hand, systematic interscheme differences in storm morphology and storm location forecast skill are negligible. Overall, the results suggest that calibrating forecasts to correct for systematic differences between PBL schemes may modestly improve WoFS and other convection-allowing ensemble guidance at short lead times.

Correlation was examined between detrended monthly surface temperature and monthly [E]F-1+ tornadoes and tornado days for several contiguous US regions during the period 1954–2022. This relatively simple, yet robust, analysis indicated that regional temperature fluctuations are moderately-to-strongly correlated with tornado days during some months and in certain regions. In general, surface temperatures during boreal cool (warm) season had a positive (negative) correlation with tornado days. Implications for using a continuous, simple scalar variable such as surface temperature for tornado prediction are discussed, as well as the potential utility for understanding changes in tornado frequency due to climate variability and change.

SFEs, co-led by the NWS/Storm Prediction Center (SPC) and OAR/National Severe Storm Laboratory (NSSL), aim to accelerate research-to-operations (R2O) by testing new prediction capabilities, studying how end-users apply and interpret severe weather guidance, and conducting numerous model evaluations. Furthermore, 2023 results were likely affected by a relatively quiet weather regime with weakly forced events in which the RRFS issues are more apparent. Because of irreconcilable issues NSSL identified with FV3 for rapid data assimilation and prediction at convection-allowing scales (i.e., grid spacing ≤ 3 km), NSSL began testing MPAS in early 2023 as a replacement for WRF in its next-generation WoFS. Adam J. Clark NOAA/OAR National Severe Storms Laboratory, and School of Meteorology, University of Oklahoma, Norman, Oklahoma; Israel L. Jirak NOAA/NWS/NCEP Storm Prediction Center, Norman, Oklahoma; Timothy A. Supinie NOAA/NWS/NCEP Storm Prediction Center, Norman, Oklahoma; Kent H. Knopfmeier NOAA/OAR National Severe Storms Laboratory, and Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma; Jake Vancil NOAA/NWS/NCEP Storm Prediction Center, and Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma; David Jahn NOAA/NWS/NCEP Storm Prediction Center, and Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma; David Harrison NOAA/NWS/NCEP Storm Prediction Center, and Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma; Allison Lynn Brannan NOAA/NWS/NCEP Storm Prediction Center, and Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma; Christopher D. Karstens NOAA/NWS/NCEP Storm Prediction Center, and School of Meteorology, University of Oklahoma, Norman, Oklahoma; Eric D. Loken NOAA/OAR National Severe Storms Laboratory, and Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma; Nathan A. Dahl NOAA/NWS/NCEP Storm Prediction Center, and Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma; Makenzie Krocak NOAA/OAR National Severe Storms Laboratory, Norman, Oklahoma; David Imy NOAA/OAR National Severe Storms Laboratory, Norman, Oklahoma; Andrew R. Wade NOAA/NWS/NCEP Storm Prediction Center, and Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma; Jeffrey M. Milne NOAA/NWS/NCEP Storm Prediction Center, and Cooperative Institute for Severe and High-Impact Weather Research and Operations, and School of Meteorology, University of Oklahoma, Norman, Oklahoma Kimberly A. Hoogewind NOAA/OAR National Severe Storms Laboratory, and Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma; Pamela L. Heinselman NOAA/OAR National Severe Storms Laboratory, and School of Meteorology, University of Oklahoma, Norman, Oklahoma; Montgomery Flora NOAA/OAR National Severe Storms Laboratory, and Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma; Joshua Martin NOAA/OAR National Severe Storms Laboratory, and Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma; Brian C. Matilla NOAA/OAR National Severe Storms Laboratory, and Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma; Joseph C. Picca NOAA/NWS/NCEP Storm Prediction Center, and Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma; Patrick S. Skinner NOAA/OAR National Severe Storms Laboratory, and Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma; , and Patrick Burke NOAA/OAR National Severe Storms Laboratory, Norman, Oklahoma;

The 2020 NOAA Hazardous Weather Testbed Spring Forecasting Experiment What: Severe weather research and forecasting experts convened virtually to evaluate convection-allowing modeling strategies and test short-term forecasting applications of a prototype Warn-on-Forecast System within a simulated, real-time forecasting environment. When: 27 April–29 May 2020 Where: Norman, Oklahoma The NWS/Storm Prediction Center (SPC) and OAR/National Severe Storms Laboratory (NSSL) co-led the 2020 NOAA Hazardous Weather Testbed Spring Forecasting Experiment (2020 SFE) virtually to evaluate new convection-allowing models (CAMs) and ensembles, post-processing strategies, and severe weather prediction tools for accelerated transition to operations. The 2020 CLUE enabled evaluation of different time-lagged and multimodel CAM ensemble configurations, diagnosis of forecast skill and sensitivities in versions of the Finite Volume Cubed Sphere Limited Area Model (FV3-LAM), examination of forecast sensitivity to different initial conditions and model cores, and evaluation of lightning data assimilation impacts at 0–12-h forecast lead times.