A persistent feature of the Southern Hemisphere upper-level time-mean flow is the presence of a split jet across the South Pacific east of Australia during the austral winter. The split jet is composed of the subtropical jet (STJ) on its equatorward branch and the polar front jet (PFJ) on its poleward branch. The NCEP-NCAR reanalysis is used to investigate the structure and evolution of the split jet. Results show that the presence/ absence of the PFJ determines the degree of split flow, given that the STJ is a quasi-steady feature. A split-flow index (SFI) is developed to quantify the variability of the split jet, in which negative values represent strong split flow and positive values nonsplit flow. Correlations with teleconnection indices are investigated, with the SFI positively correlated to the Southern Oscillation index and negatively correlated to the Antarctic oscillation. The SFI is used to construct composites of heights, temperature, and wind for split-flow and non-split-flow days. The composites reveal that relatively cold conditions occur in the South Pacific in association with non-split-flow regimes, and split-flow regimes occur when relatively warm conditions prevail. In the latter situation cold air bottled up over Antarctica helps to augment the background tropospheric thickness gradient between Antarctica and the lower latitudes with a resulting increase in the thermal wind and the PFJ. It is surmised that frequent cold surges out of Antarctica moving into the South Pacific are associated with non-split-flow regimes. In this context, the variability of the split jet responds to large-scale baroclinic processes and is further modulated by synoptic-scale disturbances.

The organized behavior of differential radar reflectivity (ZDR) is documented in the cold regions of a wide variety of stratiform precipitation types occurring in both winter and summer. The radar targets and attendant cloud microphysical conditions are interpreted within the context of measurements of ice crystal types in laboratory diffusion chambers in which humidity and temperature are both stringently controlled. The overriding operational interest here is in the identification of regions prone to icing hazards with long horizontal paths. Two predominant regimes are identified: category A, which is typified by moderate reflectivity (from 10 to 30 dBZ) and modest 1ZDR values (from 0 to 13 dB) in which both supercooled water and dendritic ice crystals (and oriented aggregates of ice crystals) are present at a mean temperature of 2138C, and category B, which is typified by small reflectivity (from 210 to 110 dBZ) and the largest 1ZDR values (from 13 to 17 dB), in which supercooled water is dilute or absent and both flat-plate and dendritic crystals are likely. The predominant positive values for ZDR in many case studies suggest that the role of an electric field on ice particle orientation is small in comparison with gravity. The absence of robust 1ZDR signatures in the trailing stratiform regions of vigorous summer squall lines may be due both to the infusion of noncrystalline ice particles (i.e., graupel and rimed aggregates) from the leading deep convection and to the effects of the stronger electric fields expected in these situations. These polarimetric measurements and their interpretations underscore the need for the accurate calibration of ZDR.

A series of webinars and panel discussions were conducted on the topic of the evolving role of humans in weather prediction and communication, in recognition of the 100th anniversary of the founding of the AMS. One main theme that arose was the inevitability that new tools using artificial intelligence will improve data analysis, forecasting, and communication. We discussed what tools are being created, how they are being created, and how the tools will potentially affect various duties for operational meteorologists in multiple sectors of the profession. Even as artificial intelligence increases automation, humans will remain a vital part of the forecast process as that process changes over time. Additionally, both university training and professional development must be revised to accommodate the evolving forecasting process, including addressing the need for computing and data skills (including artificial intelligence and visualization), probabilistic and ensemble forecasting, decision support, and communication skills. These changing skill sets necessitate that both the U.S. government’s Meteorologist General Schedule-1340 requirements and the AMS standards for a bachelor’s degree need to be revised. Seven recommendations are presented for student and forecaster preparation and career planning, highlighting the need for students and operational meteorologists to be flexible life-long learners, acquire new skills, and be engaged in the changes to forecast technology in order to best serve the user community throughout their careers. The article closes with our vision for the ways that humans can maintain an essential role in weather prediction and communication, highlighting the interdependent relationship between computers and humans.

Disembedding, or recognizing patterns in a distracting background, is a spatial thinking skill that is particularly relevant to the interpretation of meteorological surface and upper-air maps. Difficulty “seeing” patterns such as cyclonic flow, thermal ridges, or pressure gradients can make weather analysis challenging for students. In this qualitative case study, we characterize how three undergraduate meteorology students with varying disembedding skill complete a series of meteorological tasks. Videos and transcribed verbal data collected during the task, as well as participant products, were analyzed for instances of disembedding and rule-based reasoning. Results demonstrate that the student with greater disembedding skill relied on observing patterns embedded in meteorological maps in conjunction with rule-based reasoning, whereas the two students with lower disembedding skill preferred generalized application of rules. These results can aid meteorology instructors in recognizing students who struggle with disembedding data and patterns and inform the development of instructional interventions in undergraduate meteorology classrooms.