NOAA National Weather Service Central Region

Abstract A field research campaign, the Hail Spatial and Temporal Observing Network Effort (HailSTONE), was designed to obtain physical high-resolution hail measurements at the ground associated with convective storms to help address several operational challenges that remain unsatisfied through public storm reports. Field phases occurred over a 5-yr period, yielding hail measurements from 73 severe thunderstorms [hail diameter ≥ 1.00 in. (2.54 cm)]. These data provide unprecedented insight into the hailfall character of each storm and afford a baseline to explore the representativeness of the climatological hail database and hail forecasts in NWS warning products. Based upon the full analysis of HailSTONE observations, hail sizes recorded in Storm Data as well as hail size forecasts in NWS warnings frequently underestimated the maximum diameter hailfall occurring at the surface. NWS hail forecasts were generally conservative in size and at least partially calibrated to incoming hail reports. Storm mode played a notable role in determining the potential range of maximum hail size during the life span of each storm. Supercells overwhelmingly produced the largest hail diameters, with smaller maximum hail sizes observed as convection became progressively less organized. Warning forecasters may employ a storm-mode hail size forecast philosophy, in conjunction with other radar-based hail detection techniques, to better anticipate and forecast hail sizes during convective warning episodes.

One of the principle applications of climatological tornado data is in tornado-hazard assessment. To perform such a hazard-potential determination, historical tornado characteristics in either a regional or tom area are complied. A model is then used to determine a site-specific point probability of a tornado greater than a specified intensity occurring. Various models require different climatological input. However, a knowledge of the mean values of tornado track width, tornado track width, tornado affected area and tornado occurrence rate as both a function of tornado intensity and geographic area, along with a violence frequency distribution, enable Mod of the models to be applied. The NSSFC-NRC tornado data base is used to supply input for the determination of these parameters over the United States. This climatic data base has undergone extensive updating and quality control since it was last reported. For track parameters, internally redundant data were used to cheek consistency. Further, reports which derivated significantly from the mean wore individually checked. Intensity data have been compared with the University of Chicago DAPPLE tornado base. All tornadoes whose recorded intensifies differed by more than one category were reclassified by an independent scientist so that the two data sets are consistent.

Techniques that have evolved during the hundred years that scientific severe thunderstorm forecasts have been prepared are reviewed. The early empirical rules developed by Finley, Showalter and Fulks, Fawbush, Miller and Starrett, and others have been corroborated by more recent theoretical work. While significant efforts have been devoted to defining the severe thunderstorm environment, it is now obvious that these storms can occur under a variety of synoptic conditions. Severe thunderstorm forecasting consists in not only identifying the time and place that an environment compatible with such storms will exist but also in identifying suitable triggering mechanisms in that environment.

Abstract Corn is the most widely grown crop in the Americas, with annual production in the United States of approximately 332 million metric tons. Improved climate forecasts, together with climate-related decision tools for corn producers based on these improved forecasts, could substantially reduce uncertainty and increase profitability for corn producers. The purpose of this paper is to acquaint climate information developers, climate information users, and climate researchers with an overview of weather conditions throughout the year that affect corn production as well as forecast content and timing needed by producers. The authors provide a graphic depicting the climate-informed decision cycle, which they call the climate forecast–decision cycle calendar for corn.

This chapter contains sections titled: Risk Analysis Climatology The Future of Tornado Climatology and Risk Assessment

We present a knowledge-guided machine learning framework for operational hydrologic forecasting at the catchment scale. Our approach, a Factorized Hierarchical Neural Network (FHNN), has two main components: inverse and forward models. The inverse model uses observed precipitation, temperature, and streamflow data to generate a representation of the current underlying catchment state. The forward model predicts streamflow using the learned catchment state. The FHNN architecture is designed to model multi-scale processes and capture their interactions while providing explainability and interpretability. FHNN also improves forecasts based on real-time data through an inference-based data integration approach. FHNN’s data integration approach improves forecasts in response to observed data more efficiently than data assimilation methods (e.g., ensemble Kalman filtering) that require computationally intensive optimization. Once an inverse model is trained, it can quickly infer catchment states directly based on data in real-time. To show the operational performance of FHNN, we compare the FHNN forecasts with that of an expert human hydrologic forecaster using a physics-based model where both use the same imperfectly known future precipitation forecast in their modeling. The expert human forecaster creates a more accurate forecast within the first 18 hours of a forecast’s issuance, but FHNN has significantly better predictions at longer lead times. Additionally, FHNN internal states correlate strongly with internal physics-based model states, such as soil moisture, in a synthetic case. This research lays the groundwork for leveraging the predictive performance of AI-based models with the expertise in forecasting agencies to produce better river forecasts at all lead times.

The catchment approach has been traditionally limited to small, experimental catchments where water fluxes can be determined with high accuracy. However, larger catchments where landscape management occurs have emergent drivers of streamflow at scale, and thus may exhibit novel responses to land cover disturbance. We used statistical models of water yield and annual maximum peak streamflow for multiple forested catchments in the low-relief glaciated region of central North America to investigate how forest disturbance may affect water yield and peak flows in similar landscapes. We utilized linear models, linear mixed effects models, and probabilistic flood-frequency analysis, with Bayesian parameter estimation in two case studies in Minnesota, USA: 1) a wildfire comprising ~30% of a 650km 2 wilderness Upper Kawishiwi catchment, and 2) 11 catchments within the St. Louis River Basin ranging from 56 to 8,880 km 2 with a patchwork disturbance regime wherein ~0.25% to 1% of the catchment is harvested or converted to non-forest land use each year. We also assessed for the most likely hydrological recovery year after forest disturbance, and the relative importance of stationary and nonstationary drivers of streamflow. We found forest disturbance correlated with declines in water yield for low-level disturbance regimes, but that water yield increased in response to the large-scale wildfire. Positive and negative associations of forest disturbance with peak flows were observed, generally with low confidence. Hydrologic recovery time ranged from 5 to 12 years for water yield and peak flows following disturbance. Despite these effects of forest disturbance on streamflow, effects of climate variability and stationary catchment size factors were more prominent drivers of streamflow. Basins larger than ~50 km 2 in low-relief glaciated regions were resilient to forest cover change when it comprised <30% of basin area, but climate change may have a larger effect than could be mitigated by land management.