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Background: The Australian Fire Danger Rating System program (AFDRS) has built a new fire danger rating system for Australia. A live trial of the system's Research Prototype (AFDRSRP), based on fire behaviour thresholds, was run and evaluated between October 2017 and March 2018. Aims: Live trial results are critically analysed, and knowledge gaps and recommendations for future work discussed. Methods: Australian bushfire experts assessed wildfires and prescribed burns across a range of vegetation types and weather conditions. Forecast fire danger ratings calculated using: (1) AFDRSRP; and (2) Forest Fire Danger Index (FFDI) and Grassland Fire Danger Index (GFDI) were compared against ratings derived by expert opinion for each evaluation fire (n = 336). Key results: Overall performance of AFDRSRP was superior to the FFDI/GFDI system (56 vs 43% correct), with a tendency to over-predict rather than under-predict fire potential. AFDRSRP also demonstrated its value to assess fire danger in fuel types not conforming to current grassland or forest models; e.g. for fuels that were grouped to use mallee-heath, spinifex and shrubland fire spread models. Conclusions: The AFDRSRP live trial was successful, outperforming the existing operational fire danger system. Implications: Identified improvements would further enhance AFDRSRP performance, ensuring readiness for operational implementation. A Research Prototype of the Australian Fire Danger Rating System (AFDRSRP) was designed and tested in a live trial. Fire danger ratings derived from the AFDRSRP and FFDI/GFDI systems were compared to expert-derived ratings for 336 fires. In this paper the results of the live trial are presented and discussed. This article belongs to the Collection Australian Fire Danger Rating System. [ABSTRACT FROM AUTHOR]

Background: Development of the Australian Fire Danger Rating System began in 2017 with a project aimed at demonstrating the feasibility of a new fire danger rating system through a Research Prototype (AFDRSRP) that accounted for variability in Australian vegetation types, was nationally applicable, modular and open to continuous improvement. Aims: In this manuscript, we identify and define transition points and categories for the AFDRSRP. We discuss user responses to the categorisation during a live trial evaluation of the AFDRSRP and reflect on limitations and potential improvements. Methods: A review of available literature, broad consultation with stakeholders and reanalysis of fire impact data were used to determine suitable thresholds for categorising fire danger within the AFDRSRP. Key results: Fire danger categories within the AFDRSRP reflect transitions in fire behaviour that result in application of different fire management strategies or are associated with variation in serious consequences and impacts. Conclusions: The AFDRSRP incorporated the best available science, supported by a well-defined framework for categorising and defining fire danger making it suitable for application across Australian fire jurisdictions and range of fuel types. Implications: The framework allows fire managers to assess the accuracy and appropriateness of forecasted fire danger. We identify and present defined categories and thresholds for the Australian Fire Danger Rating System: Research Prototype based on reflecting transitions in fire behaviour that result in application of different fire management strategies or associated with variation in potential for serious consequences and impacts. This article belongs to the Collection Australian Fire Danger Rating System. [ABSTRACT FROM AUTHOR]

Background: Fire danger rating systems are used daily across Australia to support fire management operations and communications to the general public regarding potential fire danger. Aims: In this paper, we introduce the Australian Fire Danger Rating System (AFDRS), providing a short historical account of fire danger rating in Australia as well as the requirements for an improved forecast system. Methods: The AFDRS combines nationally consistent, spatially explicit fuel information with forecast weather and advanced fire behaviour models and knowledge to produce locally relevant ratings of fire behaviour potential. Key results: A well-defined framework is essential for categorising and defining fire danger based on operational response, the potential for impact and observable characteristics of fire incidents. The AFDRS is modular, supporting continuous and incremental improvements and allowing upgrades to components in response to new science. Conclusions: The AFDRS provides a new method to estimate fire danger based on the best available fire behaviour models, leading to potentially significant improvements in the way fire danger is calculated, forecast and interpreted. Implications: The Australian Fire Danger Rating System was implemented in 2022, the most significant change to fire danger forecasting in Australia in more than 50 years. We introduce the Australian Fire Danger Rating System (AFDRS), providing a history of fire danger rating in Australia and requirement for advancements. We describe development, design principles and the supporting framework of the AFDRS as an introduction to subsequent papers in this series. This article belongs to the Collection Australian Fire Danger Rating System. [ABSTRACT FROM AUTHOR]

The European Space Agency's Aeolus mission, launched in August 2018, provides the first global horizontal line‐of‐sight wind profile measurements. Previous studies have shown that Aeolus winds in global forecast systems improve the overall forecast skill, especially in the upper tropospheric tropics and in other data‐sparse regions. In this study, we use a series of observing system experiments with the latest version of the reprocessed Aeolus wind product (2B11) to better characterize the locations and drivers of improved skill from Aeolus with Environment and Climate Change Canada's Global Deterministic Prediction System. Observing system experiments that test the impact of Aeolus winds and the impact of all operational wind observations are carried out, covering the period summer 2019 and winter 2019–2020. Assimilation of operational winds improves the tropospheric wind forecast over the Tropics by a reduction of 8% in the forecast error, and adding the Aeolus winds to the assimilations results in an extra 0.7–0.9%. Aeolus wind impacts are improvements are 0.7–0.9% for the Arctic, and 0.4–0.6% over the Northern and Southern Hemisphere extratropics. The scale dependence of these impacts is investigated using spatial spectra (spherical harmonic decomposition). The improvement is quantified using the difference of the 250 hPa kinetic energy forecast error spectra between experiments. The operational winds largely improve the forecast of planetary scale to intermediate scale for spherical wave numbers between 1 and 20 in the short‐range forecasts. The operational wind impact decreases as the forecast lead time increases. On the other hand, the impact of Aeolus is mostly seen in the intermediate to large scale range with a peak around spherical wave number 8. The Aeolus‐related improvement around this wave number increases with forecast lead time. This analysis suggests that Aeolus winds provide estimates of the wind state that are valuable and complementary to that provided from current operational winds. [ABSTRACT FROM AUTHOR]

The El Niño–Southern Oscillation (ENSO) can cause climate anomalies on a global scale, and further affect human life and activities in coastal zones. Therefore, its forecast is of great significance for early disaster warning and coastal management. However, the frequent occurrence of central Pacific (CP) El Niño events increases the diversity and complexity of ENSO, severely reducing its prediction efficiency. In this study, an extended ensemble coupled data assimilation–forecast system was employed to investigate the prediction of different types of El Niño events, including eastern Pacific (EP) and CP events. The extended system was based on the fifth-generation Lamont–Doherty Earth Observation (LDEO5) model, in which an advanced ensemble Kalman filter was used to construct a multisource data assimilation system, and a stochastic optimal method was used to measure the influence of atmospheric stochastic processes on model prediction errors. The extended system was used to predict two types of El Niño events that occurred between January 1950 and December 2018. The results showed that the extended system was generally able to predict EP events with a higher accuracy than CP events for all lead times. The extended system successfully predicted the mature phase of EP events up to 12 months in advance but could only predict the mature phase of CP events up to 6 months in advance. The extended system was also able to depict the evolution of both EP and CP events, although the sea surface temperature anomalies were underestimated. The extended system not only provides a useful platform for improving ENSO prediction accuracies in association with El Niño diversity but also provides an important tool for disaster early warning and coastal management. • The advanced EnKF and SO methods are employed to extend the LDEO5 model. • The extended system successfully identifies the different flavors of El Niño in prediction. • Prediction errors are reduced over the equatorial central and eastern Pacific. • Accurately predicting the flavor and intensity of ENSO is important for disaster early warning and coastal management. [ABSTRACT FROM AUTHOR]