This work evaluates the short-term forecast (#6 h) of the 29-30 June 2012 derecho event from the Advanced Research core of the Weather Research and Forecasting Model (WRF-ARW) when using two distinct data assimilation techniques at cloud-resolving scales (3-km horizontal grid). The first technique assimilates total lightning data using a smooth nudging function. The second method is a three-dimensional variational technique (3DVAR) that assimilates radar reflectivity and radial velocity data. A suite of sensitivity experiments revealed that the lightning assimilation was better able to capture the placement and intensity of the derecho up to 6 h of the forecast. All the simulations employing 3DVAR, however, best represented the storm's radar reflectivity structure at the analysis time. Detailed analysis revealed that a small feature in the velocity field from one of the six selected radars in the original 3DVAR experiment led to the development of spurious convection ahead of the parent mesoscale convective system, which significantly degraded the forecast. Thus, the relatively simple nudging scheme using lightning data complements the more complex variational technique. The much lower computational cost of the lightning scheme may permit its use alongside variational techniques in improving severe weather forecasts on days favorable for the development of outflow-dominated mesoscale convective systems.

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This work evaluates the performance of a recently developed cloud-scale lightning data assimilation technique implemented within the Weather Research and Forecasting Model running at convection-allowing scales (4-km grid spacing). Data provided by the Earth Networks Total Lightning Network for the contiguous United States (CONUS) were assimilated in real time over 67 days spanning the 2013 warm season (May–July). The lightning data were assimilated during the first 2 h of simulations each day. Bias-corrected, neighborhood-based, equitable threat scores (BC-ETSs) were the chief metric used to quantify the skill of the forecasts utilizing this assimilation scheme. Owing to inferior observational data quality over mountainous terrain, this evaluation focused on the eastern two-thirds of the United States. During the first 3 h following the assimilation (i.e., 3-h forecasts), all the simulations suffered from a high wet bias in forecasted accumulated precipitation (APCP), particularly for the lightning assimilation run (LIGHT). Forecasts produced by LIGHT, however, had a noticeable, statistically significant (α = 0.05) improvement over those by the control run (CTRL) up to 6 h into the forecast with BC-ETS differences often exceeding 0.4. This improvement was seen independently of the APCP threshold (ranging from 2.5 to 50 mm) and the neighborhood radius (ranging from 0 to 40 km) selected. Past 6 h of the forecast, the APCP fields from LIGHT progressively converged to that of CTRL probably due to the longer-term evolution being bounded by the large-scale model environment. Thus, this computationally inexpensive lightning assimilation scheme shows considerable promise for routinely improving short-term (≤6 h) forecasts of high-impact weather by convection-allowing forecast models.

A new strategy for generating and presenting model diagnostic fields from convection-allowing forecast models is introduced. The fields are produced by computing temporal-maximum values for selected diagnostics at each horizontal grid point between scheduled output times. The two-dimensional arrays containing these maximum values are saved at the scheduled output times. The additional fields have minimal impacts on the size of the output files and the computation of most diagnostic quantities can be done very efficiently during integration of the Weather Research and Forecasting Model. Results show that these unique output fields facilitate the examination of features associated with convective storms, which can change dramatically within typical output intervals of 1-3 h.

For practical purposes, the convection initiation forecasting challenge should be franned in terms of the initiation of mesoscale convective events rather than the formation^^, and Hrov\/th of individual cumulonimbus clouds.

Hourly maximum fields of simulated storm diagnostics from experimental versions of convection-permitting models (CPMs) provide valuable information regarding severe weather potential. While past studies have focused on predicting any type of severe weather, this study uses a CPM-based Weather Research and Forecasting (WRF) Model ensemble initialized daily at the National Severe Storms Laboratory (NSSL) to derive tornado probabilities using a combination of simulated storm diagnostics and environmental parameters. Daily probabilistic tornado forecasts are developed from the NSSL-WRF ensemble using updraft helicity (UH) as a tornado proxy. The UH fields are combined with simulated environmental fields such as lifted condensation level (LCL) height, most unstable and surface-based CAPE (MUCAPE and SBCAPE, respectively), and multifield severe weather parameters such as the significant tornado parameter (STP). Varying thresholds of 2–5-km updraft helicity were tested with differing values of σ in the Gaussian smoother that was used to derive forecast probabilities, as well as different environmental information, with the aim of maximizing both forecast skill and reliability. The addition of environmental information improved the reliability and the critical success index (CSI) while slightly degrading the area under the receiver operating characteristic (ROC) curve across all UH thresholds and σ values. The probabilities accurately reflected the location of tornado reports, and three case studies demonstrate value to forecasters. Based on initial tests, four sets of tornado probabilities were chosen for evaluation by participants in the 2015 National Oceanic and Atmospheric Administration’s Hazardous Weather Testbed Spring Forecasting Experiment from 4 May to 5 June 2015. Participants found the probabilities useful and noted an overforecasting tendency.

Output from a real-time high-resolution numerical model is used to generate synthetic infrared satellite imagery. It is shown that this imagery helps to characterize model-simulated large-scale precursors to the formation of deep-convective storms as well as the subsequent development of storm systems. A strategy for using this imagery in the forecasting of severe convective weather is presented. This strategy involves comparing model-simulated precursors to their observed counterparts to help anticipate model errors in the timing and location of storm formation, while using the simulated storm evolution as guidance.

Attempts at probabilistic tornado forecasting using convection-allowing models (CAMs) have thus far used CAM attribute [e.g., hourly maximum 2–5-km updraft helicity (UH)] thresholds, treating them as binary events—either a grid point exceeds a given threshold or it does not. This study approaches these attributes probabilistically, using empirical observations of storm environment attributes and the subsequent climatological tornado occurrence frequency to assign a probability that a point will be within 40 km of a tornado, given the model-derived storm environment attributes. Combining empirical frequencies and forecast attributes produces better forecasts than solely using mid- or low-level UH, even if the UH is filtered using environmental parameter thresholds. Empirical tornado frequencies were derived using severe right-moving supercellular storms associated with a local storm report (LSR) of a tornado, severe wind, or severe hail for a given significant tornado parameter (STP) value from Storm Prediction Center (SPC) mesoanalysis grids in 2014–15. The NSSL–WRF ensemble produced the forecast STP values and simulated right-moving supercells, which were identified using a UH exceedance threshold. Model-derived probabilities are verified using tornado segment data from just right-moving supercells and from all tornadoes, as are the SPC-issued 0600 UTC tornado probabilities from the initial day 1 forecast valid 1200–1159 UTC the following day. The STP-based probabilistic forecasts perform comparably to SPC tornado probability forecasts in many skill metrics (e.g., reliability) and thus could be used as first-guess forecasts. Comparison with prior methodologies shows that probabilistic environmental information improves CAM-based tornado forecasts.