Subduction processes in the Southern Ocean transfer oxygen, heat, and anthropogenic carbon into the ocean interior. The future response of upper-ocean subduction, in the Subantarctic Mode Water (SAMW) and Antarctic Intermediate Water (AAIW) classes, is dependent on the evolution of the combined surface buoyancy forcing and overlying westerly wind stress. Here, the recently observed pattern of a poleward intensification of the westerly winds is divided into its shift and increase components. SAMW and AAIW formation occurs in regional “hot spots” in deep mixed layer zones, primarily in the southeast Indian and Pacific. It is found that the mixed layer depth responds differently to wind stress perturbations across these regional formation zones. An increase only in the westerly winds in the Indian sector steepens isopycnals and increases the local circulation, driving deeper mixed layers and increased subduction. Conversely, in the same region, a poleward shift and poleward intensification of the westerly winds reduces heat loss and increases freshwater input, thus decreasing the mixed layer depth and consequently the associated SAMW and AAIW subduction. In the Pacific sector, all wind stress perturbations lead to increases in heat loss and decreases in freshwater input, resulting in a net increase in SAMW and AAIW subduction. Overall, the poleward shift in the westerly wind stress dominates the SAMW subduction changes, rather than the increase in wind stress. The net decrease in SAMW subduction across all basins would likely decrease anthropogenic carbon sequestration; however, the net AAIW subduction changes across the Southern Ocean are overall minor.

There is a growing need to better understand and quantify risks associated with extreme weather, including severe thunderstorm-related hazards such as hail and lightning. Hail occurrence based on a long-term archive of radar observations is presented for the first time in many temperate and subtropical regions of Australia, together with lightning observations from a ground-based network of sensors. Mean monthly and hourly occurrence frequencies are examined for hail and lightning. Environmental conditions obtained from hourly reanalysis data indicate stronger wind shear on average for hail than lightning. The environmental conditions also indicate higher freezing levels on average for lightning than hail. These environmental differences provide plausible physical reasons for observed differences between hail and lightning climatology through the year. The study results are intended to help inform future planning and preparedness for thunderstorm-related risks, including for severe weather forecasting and climate risk applications. Plain Language Summary Natural hazards caused by thunderstorms, such as hail and lightning, can have severe impacts on society. Radar data are used to examine hail events at 10 Australian locations, and comparisons are made with lightning observations. Results are averaged over several years to calculate average annual values, including for the hourly and the monthly number of the hail and the lightning events. We find differences between hail and lightning events in the environmental conditions that they occur in. These environmental differences are found to help explain differences through the year in the average risk of occurrence of hail or lightning. The results are intended to enhance resilience in relation to thunderstorm-related risks and provide guidance for extreme weather modeling and climate adaptation purposes.

A spatial mismatch between radar-based hail swaths and surface hail reports is commonly noted in meteorological literature. The discrepancy is partly due to hailstone advection and melting between detection aloft and observation at the ground. The present study aims to mitigate this problem by introducing a model named HailTrack, which estimates hailfall at the surface using radar observations. The model operates by detecting, tracking and collating hailstone trajectories using dual-polarised, dual-Doppler radar retrievals. Notable improvements in hailfall forecasts were observed through the use of HailTrack, and initialising the model with hail differential reflectivity (HDR) radar retrievals was found to produce the most accurate hailfall estimates. The analysis of a case study in Brisbane, Australia demonstrated that trajectory modelling significantly improved the correlation between hail swaths and hail-related insurance losses, increasing Heidke skill scores from 0.48 to 0.58. The accumulated kinetic energy of hailstone impacts also showed some skill in identifying areas that were exposed to particularly severe hailfall. Other unique impact estimates are presented such as hailstone advection information and hailstone impact angle statistics. The potential to run the model in real time and produce short-term (10-15 minute) nowcasts is also introduced. Model applications include improving radar-based hail climatologies, validating hail detection techniques and insurance claims data, and providing real-time hail impact maps to improve public awareness of hail risk.

<p><span>The size, shape and ground-impact location of each hailstone is characterised by its trajectory </span><span>through the parent hailstorm</span><span>. This trajectory determines whether the hailstone passes through regions of the storm that are more favorable for growth or even miss out entirely. Recent simulation-based studies have demonstrated the diversity of trajectories and how certain pathways </span><span>exist</span><span> in response to storm processes. </span><span>Hail trajectories can also be simulated from radar observations, and this has been shown to significantly improve the accuracy of the estimated ground hail swath for case studies. O</span><span>perational hail analysis techniques </span><span>currently </span><span>do not consider trajectories, leaving a degree of uncertainty when estimating ground impact. </span><span> The lack of robust observational datasets to verify trajectories is one factor that limits the transition of this new science into operations.</span></p><p><span>This talk </span><span>will </span><span>introduce an innovative approach to measuring trajectories within a hailstorm using hailstone-shaped probes called “HailSondes”. I</span><span>mprovements</span><span> in low-energy </span><span>radio</span><span>, </span><span>energy</span> <span>storage</span><span> and electronics miniaturization are combined to make this new sensor possible, which, until recently, was the realm of fantasy for meteorologists</span><span>. </span><span>HailSonde measurements will provide critical validation for the practical application radar-derived trajectories for hailstorm analysis and nowcasting, supporting the transition to future hail services and benefiting a wide range of sectors from aviation, risk management, transport and public safety. </span><span>The design challenges, simulations, prototype development and deployment of HailSondes </span><span>within field experiments</span><span> are discussed.</span></p>

Abstract. This study uses ship-based weather radar observations collected from research vessel Investigator to evaluate the Australian weather radar network calibration monitoring technique that uses spaceborne radar observations from the NASA Global Precipitation Mission (GPM). Quantitative operational applications such as rainfall and hail nowcasting require a calibration accuracy of ±1 dB for radars of the Australian network covering capital cities. Seven ground-based radars along the western coast of Australia and the ship-based OceanPOL radar are first calibrated independently using GPM radar overpasses over a 3-month period. The calibration difference between the OceanPOL radar (used as a moving reference for the second step of the study) and each of the seven operational radars is then estimated using collocated, gridded, radar observations to quantify the accuracy of the GPM technique. For all seven radars the calibration difference with the ship radar lies within ±0.5 dB, therefore fulfilling the 1 dB requirement. This result validates the concept of using the GPM spaceborne radar observations to calibrate national weather radar networks (provided that the spaceborne radar maintains a high calibration accuracy). The analysis of the day-to-day and hourly variability of calibration differences between the OceanPOL and Darwin (Berrimah) radars also demonstrates that quantitative comparisons of gridded radar observations can accurately track daily and hourly calibration differences between pairs of operational radars with overlapping coverage (daily and hourly standard deviations of ∼ 0.3 and ∼ 1 dB, respectively).