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In November 2016, an unprecedented epidemic thunderstorm asthma event in Victoria, Australia, resulted in many thousands of people developing breathing difficulties in a very short period of time, including 10 deaths, and created extreme demand across the Victorian health services. To better prepare for future events, a pilot forecasting system for epidemic thunderstorm asthma (ETSA) risk has been developed for Victoria. The system uses a categorical risk-based approach, combining operational forecasting of gusty winds in severe thunderstorms with statistical forecasts of high ambient grass pollen concentrations, which together generate the risk of epidemic thunderstorm asthma. This pilot system provides the first routine daily epidemic thunderstorm asthma risk forecasting service in the world that covers a wide area, and integrates into the health, ambulance, and emergency management sector. Epidemic thunderstorm asthma events have historically occurred infrequently, and no event of similar magnitude has impacted the Victorian health system since. However, during the first three years of the pilot, 2017–19, two high asthma presentation events and four moderate asthma presentation events were identified from public hospital emergency department records. The ETSA risk forecasts showed skill in discriminating between days with and without health impacts. However, even with hindsight of the actual weather and airborne grass pollen conditions, some high asthma presentation events occurred in districts that were assessed as low risk for ETSA, reflecting the challenge of predicting this unusual phenomenon.

This study evaluates the performance of the area-based, district heatwave forecasts that drive the Australian heatwave warning service. The analysis involves using a recently developed approach of scoring multicategorical forecasts using the FIxed Risk Multicategorical (FIRM) scoring framework. Additionally, we quantify the stability of the district forecasts between forecast updates. Notably, at longer lead times, a discernible overforecast bias exists that leads to issuing severe and extreme heatwave district forecasts too frequently. Consequently, at shorter lead times forecast heatwave categories are frequently downgraded with subsequent revisions. To address these issues, we demonstrate how isotonic regression can be used to conditionally bias correct the district forecasts. Finally, using synthetic experiments, we illustrate that even if an area warning is derived from a perfectly calibrated gridded forecast, the area warning will be biased in most situations. We show how these biases can also be corrected using isotonic regression which could lead to a better warning service. Importantly, the evaluation and bias correction approaches demonstrated in this paper are relevant to forecast parameters other than heat indices.

`scores` is a Python package containing mathematical functions for the verification, evaluation and optimisation of forecasts, predictions or models. It supports labelled n-dimensional (multidimensional) data, which is used in many scientific fields and in machine learning. At present, `scores` primarily supports the geoscience communities; in particular, the meteorological, climatological and oceanographic communities. `scores` not only includes common scores (e.g., Mean Absolute Error), it also includes novel scores not commonly found elsewhere (e.g., FIxed Risk Multicategorical (FIRM) score, Flip-Flop Index), complex scores (e.g., threshold-weighted continuous ranked probability score), and statistical tests (such as the Diebold Mariano test). It also contains isotonic regression which is becoming an increasingly important tool in forecast verification and can be used to generate stable reliability diagrams. Additionally, it provides pre-processing tools for preparing data for scores in a variety of formats including cumulative distribution functions (CDF). At the time of writing, `scores` includes over 50 metrics, statistical techniques and data processing tools. All of the scores and statistical techniques in this package have undergone a thorough scientific and software review. Every score has a companion Jupyter Notebook tutorial that demonstrates its use in practice. `scores` supports `xarray` datatypes, allowing it to work with Earth system data in a range of formats including NetCDF4, HDF5, Zarr and GRIB among others. `scores` uses Dask for scaling and performance. Support for `pandas` is being introduced. The `scores` software repository can be found at https://github.com/nci/scores/

Forecast verification is critical for continuous improvement in meteorological organizations. The Jive verification system was originally developed to assess the accuracy of public weather forecasts issued by the Australian Bureau of Meteorology. It started as a research project in 2015 and gradually evolved to be a Bureau operational verification system in 2022. The system includes daily verification dashboards for forecasters to visualize recent forecast performance and \"Evidence Targeted Automation\" dashboards for exploring the performance of competing forecast systems. Additionally, Jive includes a Jupyter Notebook server with the Jive Python library which supports research experiments, case studies, and the development of new verification metrics and tools. This paper describes the Jive verification system and how it helped bring verification to the forefront at the Bureau of Meteorology, leading to more accurate, streamlined forecasts. Jive has provided evidence to support forecast automation decisions and has helped to understand the evolving role of meteorologists in the forecast process. It has given operational meteorologists tools for evaluating forecast processes, including identifying when and how manual interventions lead to superior predictions. Work on Jive led to new verification science, including novel metrics that are decision-focused, including diagnostics for extreme conditions. Jive also provided the Bureau with an enterprise-wide data analysis environment and has prompted a clarification of forecast definitions. These collective impacts have resulted in more accurate forecasts, ultimately benefiting society, and building trust with forecast users. These positive outcomes highlight the importance of meteorological organizations investing in verification science and technology.

Fanconi anaemia (FA), a model syndrome of genome instability, is caused by a deficiency in DNA interstrand crosslink repair resulting in chromosome breakage 1 – 3 . The FA repair pathway protects against endogenous and exogenous carcinogenic aldehydes 4 – 7 . Individuals with FA are hundreds to thousands fold more likely to develop head and neck (HNSCC), oesophageal and anogenital squamous cell carcinomas 8 (SCCs). Molecular studies of SCCs from individuals with FA (FA SCCs) are limited, and it is unclear how FA SCCs relate to sporadic HNSCCs primarily driven by tobacco and alcohol exposure or infection with human papillomavirus 9 (HPV). Here, by sequencing genomes and exomes of FA SCCs, we demonstrate that the primary genomic signature of FA repair deficiency is the presence of high numbers of structural variants. Structural variants are enriched for small deletions, unbalanced translocations and fold-back inversions, and are often connected, thereby forming complex rearrangements. They arise in the context of TP53 loss, but not in the context of HPV infection, and lead to somatic copy-number alterations of HNSCC driver genes. We further show that FA pathway deficiency may lead to epithelial-to-mesenchymal transition and enhanced keratinocyte-intrinsic inflammatory signalling, which would contribute to the aggressive nature of FA SCCs. We propose that the genomic instability in sporadic HPV-negative HNSCC may arise as a result of the FA repair pathway being overwhelmed by DNA interstrand crosslink damage caused by alcohol and tobacco-derived aldehydes, making FA SCC a powerful model to study tumorigenesis resulting from DNA-crosslinking damage. Defective DNA interstrand crosslink repair in Fanconi anaemia drives extensive genomic rearrangements, thereby substantially increasing the risk of cancer development.