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In Vanuatu, communities are predicted to be at high risk of more frequent and severe Marine Heat Wave (MHW) impacts in the future, as a result of climate change. A critical sector at risk in Vanuatu is fisheries, which vitally support food security and livelihoods. To sustain local communities, the MHW risk for Vanuatu fisheries must be extensively explored. In this study, an efficient MHW risk assessment methodology is demonstrated specifically for assessing MHW risk to Vanuatu fisheries. The fisheries specific MHW risk assessment was conducted on the local area council scale for two retrospective case study periods: 2015–2017 and 2020–2022. An integrated GIS-based approach was taken to calculating and mapping monthly hazard, vulnerability, exposure, and overall risk indices. Key areas and time periods of concern for MHW impacts are identified. Area councils in the Shefa province area are particularly concerning, displaying consistently high-risk levels throughout both case studies. Risk levels in 2022 were the most concerning, with most months displaying peak risk to MHW impacts. A sensitivity analysis is employed to validate the selection and weighting of the indicators used. However, it is recommended that a more comprehensive validation of the retrospective risk assessment results, using multiple ground-truth sources, be conducted in the future. Once results are sufficiently validated, management recommendations for fisheries resilience can be made. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) can be devastating events for vulnerable countries like Vanuatu, impacting their population, livelihoods, and infrastructure, leaving the country in need of aid and recovery. Despite this, comprehensive risk information on the nuanced impacts of each region is not well understood. Every TC event is different, and understanding the potential for impact at each location empowers decision makers in the lead-up to an event or during off-season planning to make more informed decisions to direct disaster risk reduction efforts. TC hazard model data typically describe intensity and likelihood, which can be fed into risk assessment frameworks to describe probabilistic risk. This study instead uses freely available remote sensing data to create proxies for the TC hazards of storm surge and flooding and to describe only the intensity of the hazard if the event occurs at the location. This hazard susceptibility index is fed into a risk assessment framework with Vanuatu exposure and vulnerability data for domains of populations, housing, and roads. These methods allow for the risk to be estimated for each month, as well as during specific historical time periods of TC Pam, TC Harold, and the TCs Judy and Kevin, enabling future impact validation. The results show households to have the highest risk, followed by roads and population domains, while a TC-induced surge risk is overall higher than TC-induced flooding, particularly in the road domain. The results, however, show a likely underestimation of event hazards and an overestimation of Port Vila's resistance to impacts, which is a subject of future investigation and validation. [ABSTRACT FROM AUTHOR]

Most existing drought forecast systems rely only on observed or forecast rainfall, losing valuable context gained from considering both. The lack of a direct link between observed and forecast rainfall reduces the physical consistency of a system, motivating the development of a methodology that can directly link the two. The methodology developed in this study allows the comparison of the calibrated ensemble forecasts of rainfall totals from a dynamical climate model to observed rainfall deficiencies from a gridded rainfall analysis. The methodology is used to create a probabilistic product that forecasts the chance of entering meteorological drought, with lead times of one month (monthly forecast) and three months (seasonal forecast). Existing deficiency areas are included to facilitate analysis of how these areas are forecast to change. The performance of the developed methodology was verified using Percent Correct (PC), Brier Score (BS), and Relative Operating Characteristic (ROC) statistics. Analysis of the forecast plots was also completed visually. Forecast performance for areas with existing deficiencies as well as for non-deficiency areas was promising (PC rates of >79% and >97%, respectively). Although PC rates for observed deficiencies were low across most months, the mean forecast probability for these areas was 36%, indicating the system had value and outperformed climatology. A calibrated, coupled product like the one scoped in this study has not been explored and we note that it could be an invaluable tool for quantifying meteorological drought onset and persistence in Australia. [ABSTRACT FROM AUTHOR]

Satellites provide a useful way of estimating rainfall where the availability of in situ data is low but their indirect nature of estimation means there can be substantial biases. Consequently, the assimilation of in situ data is an important step in improving the accuracy of the satellite rainfall analysis. The effectiveness of this step varies with gauge density, and this study investigated the effectiveness of statistical interpolation (SI), also known as optimal interpolation (OI), on a monthly time scale when gauge density is extremely low using Papua New Guinea (PNG) as a study region. The topography of the region presented an additional challenge to the algorithm. An open-source implementation of SI was developed on Python 3 and confirmed to be consistent with an existing implementation, addressing a lack of open-source implementation for this classical algorithm. The effectiveness of the analysis produced by this algorithm was then compared to the pure satellite analysis over PNG from 2001 to 2014. When performance over the entire study domain was considered, the improvement from using SI was close to imperceptible because of the small number of stations available for assimilation and the small radius of influence of each station (imposed by the topography present in the domain). However, there was still value in using OI as performance around each of the stations was noticeably improved, with the error consistently being reduced along with a general increase in the correlation metric. Furthermore, in an operational context, the use of OI provides an important function of ensuring consistency between in situ data and the gridded analysis. Significance Statement: The blending of satellite and gauge rainfall data through a process known as statistical interpolation (SI) is known to be capable of producing a more accurate dataset that facilitates better estimation of rainfall. However, the performance of this algorithm over a domain such as Papua New Guinea, where gauge density is extremely low, is not often explored. This study reveals that, although an improvement over the entire Papua New Guinea domain was slight, the algorithm is still valuable as there was a consistent improvement around the stations. Additionally, an adaptable and open-source version of the algorithm is provided, allowing users to blend their own satellite and gauge data and create better geospatial datasets for their own purposes. [ABSTRACT FROM AUTHOR]

One of the hazards associated with tropical cyclones (TCs) is a storm surge, which leads to coastal inundation and often results in loss of life and damage to infrastructure. In this study, we used GIS-based bathtub models and tide-gauge-derived water levels to assess coastal inundation scenarios for the landfall region of TC Debbie. The three scenarios modelled what could have happened if the TC's maximum storm surge had coincided with the maximum storm tide for that day, month, or TC season, where the water levels were determined through analysis of tide gauge data, using a new method called the variable enhanced Bathtub Model. Additionally, this study analysed the impact of excluding the correction of water levels with the Australian Height Datum. Our study found that between the least and most severe scenarios, with the input water-level difference for the model along the coastline being 0.43 m, the observed inundation depth of the analysed populated region increased from 0.25 m to 1 m. Ultimately, it was found that in the worst-case scenario, the study region could have experienced coastal inundation 0.63 m higher than it did, inundating 72.53 km2 of the coast. The results of this study support the consensus that coastal inundation is highly dependent on the characteristics of the terrain, and that coastal inundation modelling, such as that completed in this study, needs to be performed to better inform decision makers and communities of the potential impacts of TC-induced storm surges. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) are one of the most destructive natural hazards to impact on Australia's population, infrastructure, and the environment. To examine potential TC impacts, it is important to understand which assets are exposed to the hazard and of these, which are vulnerable to damage. The aim of this study is to improve TC risk assessments through developing an exposure–vulnerability index, utilising a case study for the six Local Government Areas (LGAs) impacted by the landfall of TC Debbie in 2017: Burdekin Shire, Charters Towers Region, Isaac Region, Mackay Region, City of Townsville, and Whitsunday Region. This study utilised a natural hazard risk assessment methodology, linking exposure and vulnerability indicators related to social factors, infrastructure, and the environment. The two LGAs with the most extreme exposure–vulnerability values were the coastal regions of Mackay Region and the City of Townsville. This is consistent with urbanisation and city development trends, with these LGAs having more people (social) and infrastructure exposed, while the environmental domain was more exposed and vulnerable to TC impacts in rural LGAs. Therefore, further resilience protocols and mitigation strategies are required, particularly for Mackay Region and the City of Townsville, to reduce the damage and ultimate loss of lives and livelihoods from TC impacts. This study serves as a framework for developing a TC risk index based on hazard, exposure, and vulnerability indices, and insight into the improved mitigation strategies for communities to implement in order to build resilience to the impacts of future TCs. [ABSTRACT FROM AUTHOR]

This study investigated tropical cyclone (TC)-induced flooding in coastal regions of Australia due to the impact of TC Debbie in 2017 utilising a differential evolution-optimised random forest to model flood susceptibility in the region of Bowen, Airlie Beach, and Mackay in North Queensland. Model performance was evaluated using a receiver operating characteristic curve, which showed an area under the curve of 0.925 and an overall accuracy score of 80%. The important flood-influencing factors (FIFs) were investigated using both feature importance scores and the SHapely Additive exPlanations method (SHAP), creating a flood hazard map of the region and a map of SHAP contributions. It was found that the elevation, slope, and normalised difference vegetation index were the most important FIFs overall. However, in some regions, the distance to the river and the stream power index dominated for a similar flood hazard susceptibility outcome. Validation using SHAP to test the physical reasoning of the model confirmed the reliability of the flood hazard map. This study shows that explainable artificial intelligence allows for improved interpretation of model predictions, assisting decision-makers in better understanding machine learning-based flood hazard assessments and ultimately aiding in mitigating adverse impacts of flooding in coastal regions affected by TCs. [ABSTRACT FROM AUTHOR]

Seasonal tropical cyclone (TC) forecasting has evolved substantially since its commencement in the early 1980s. However, present operational seasonal TC forecasting services still do not meet the requirements of society and stakeholders: current operational products are mainly basin-scale information, while more detailed sub-basin scale information such as potential risks of TC landfall is anticipated for decision making. To fill this gap and make the TC science and services move forward, this paper reviews recent research and development in seasonal tropical cyclone (TC) forecasting. In particular, this paper features new research topics on seasonal TC predictability in neutral conditions of El Ni˜no-Southern Oscillation (ENSO), emerging forecasting techniques of seasonal TC activity including Machine Learning/Artificial Intelligence, and multi-annual TC predictions. We also review the skill of forecast systems at predicting landfalling statistics for certain regions of the North Atlantic, Western North Pacific and South Indian oceans and discuss the gap that remains between current products and potential user's expectations. New knowledge and advanced forecasting techniques are expected to further enhance the capability of seasonal TC forecasting and lead to more actionable and fit-for-purpose products. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) have long posed a significant threat to Australia's population, infrastructure, and environment. This threat may grow under climate change as projections indicate continuing rises in sea level and increases in rainfall during TC events. Previous Australian TC risk assessment efforts have focused on the risk from wind, whereas a holistic approach requires multi-hazard risk assessments that also consider impacts of other TC-related hazards. This study assessed and mapped TC risk nationwide, focusing on the impacts on population and infrastructure from the TC-related hazards of wind, storm surges, flooding, and landslides. Risk maps were created at the Local Government Area (LGA) level for all of Australia, using collated data on multiple hazards, exposure, and vulnerability. The results demonstrated that the risk posed by all hazards was highest for coastal LGAs of eastern Queensland and New South Wales, followed by medium risk across Northern Territory and north-western Western Australia. Further enhancement and validation of risk maps developed in this study will provide decision makers with the information needed to reduce TC risk, save lives, and prevent damage to infrastructure. [ABSTRACT FROM AUTHOR]

Climate change is increasing the frequency and intensity of natural hazards, causing disastrous impacts on vulnerable communities. Pacific Small Island Developing States (SIDS) are of particular concern, requiring resilient disaster risk management consisting of two key elements: proactivity and suitability. Drought risk knowledge can inform resilient risk management, but it is currently underexplored in Pacific SIDS, particularly in the highly vulnerable nation of Papua New Guinea (PNG). A tailored, meaning highly specific to the area under investigation, drought risk assessment methodology is key for expanding risk knowledge in vulnerable communities. A semi-dynamic and tailored drought risk assessment methodology to be utilised in PNG was developed in this research. Representative hazard, vulnerability, and exposure indicators were selected, and integrated geographic information system (GIS) processes were used to produce hazard, vulnerability, exposure, and risk indices and maps. The validity of the risk assessment was investigated with a retrospective risk assessment of drought in PNG (from 2014–2020) paired with a literature assessment (as a ground-truth source), and a sensitivity analysis. The preliminary drought risk assessment methodology demonstrated in this study was overall deemed valid and robust, with supplementary improvements proposed for consideration in future investigation. The developed methodology makes strides in addressing methodological knowledge gaps in drought risk assessment, for global assessments and those specific for PNG, and demonstrates the potential for risk assessment to inform resilient drought management practices in at-risk areas. Overall, the results of this study directly contribute to enhancing provincial drought risk knowledge in PNG. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of extracting trends from time series data involving missing values. In particular, we investigate a general class of procedures that impute the missing data and then extract trends using seasonal-trend decomposition based on loess (STL), where loess stands for locally weighted smoothing, a popular tool for describing the regression relationship between two variables by a smooth curve. We refer to them as the imputation-STL procedures. Two results are obtained in this paper. First, we settle a theoretical issue, namely the connection between imputation error and the overall error from estimating the trend. Specifically, we derive the bounds for the overall error in terms of the imputation error. This subsequently facilitates the error analysis of any imputation-STL procedure and justifies its use in practice. Second, we investigate loess-STL, a particular imputation-STL procedure with the imputation also being performed using loess. Through both theoretical arguments and simulation results, we show that loess-STL has the capacity of handling a high proportion of missing data and providing reliable trend estimates if the underlying trend is smooth and the missing data are dispersed over the time series. In addition to mathematical derivations and simulation study, we apply our loess-STL procedure to profile radiosonde records of upper air temperature at 22 Antarctic research stations covering the past 50 years. For purpose of illustration, we present in this paper only the results for Novolazaravskaja station which has temperature records with more than 8.4% dispersed missing values at 8 pressure levels from October/1969 to March/2011. [ABSTRACT FROM AUTHOR]

Floods are the most common and costliest natural disaster in Australia. Australian flood risk assessments (FRAs) are mostly conducted on relatively small scales using modelling outputs. The aim of this study was to develop a novel approach of index-based analysis using a multi-criteria decision-making (MCDM) method for FRA on a large spatial domain. The selected case study area was the Hawkesbury-Nepean Catchment (HNC) in New South Wales, which is historically one of the most flood-prone regions of Australia. The HNC's high flood risk was made distinctly clear during recent significant flood events in 2021 and 2022. Using a MCDM method, an overall Flood Risk Index (FRI) for the HNC was calculated based on flood hazard, flood exposure, and flood vulnerability indices. Inputs for the indices were selected to ensure that they are scalable and replicable, allowing them to be applied elsewhere for future flood management plans. The results of this study demonstrate that the HNC displays high flood risk, especially on its urbanised floodplain. For the examined March 2021 flood event, the HNC was found to have over 73% (or over 15,900 km2) of its area at 'Severe' or 'Extreme' flood risk. Validating the developed FRI for correspondence to actual flooding observations during the March 2021 flood event using the Receiver Operating Characteristic (ROC) statistical test, a value of 0.803 was obtained (i.e., very good). The developed proof-of-concept methodology for flood risk assessment on a large spatial scale has the potential to be used as a framework for further index-based FRA approaches. [ABSTRACT FROM AUTHOR]

Floods are the most common and costliest natural hazard in Australia. However, the Flood Resilience Assessments (FReAs) employed to manage them lack a focus on adaptive capacity and tend not to be incorporated into established flood risk frameworks. This leaves potential for Australian FReAs to make better use of a methodology which holistically incorporates more accurate flood resilience characterisations into flood risk frameworks. In this study, a FReA and mapping for the Hawkesbury-Nepean Catchment (HNC), a flood-prone region in Australia, were conducted. Nine flood resilience indicators were selected to derive the Flood Resilience Index (FReI). Results demonstrated that Statistical Areas Level 2 (SA2s) on or near the floodplain, located near the eastern border of the HNC, present moderate to very high levels of resilience due to increased socio-economic development and urbanisation in the region. Ultimately, this novel FReI can contribute to knowledge bolstering flood resilience in the HNC, as well as assist in flood risk reduction. Additionally, the developed scalable and replicable methodology can be applied to other flood-prone regions of Australia. [ABSTRACT FROM AUTHOR]

In this study, we attempt to expand tropical cyclone (TC) risk assessment methodology and build an understanding of TC risk to Australia's natural environment by focusing on coral reefs. TCs are natural hazards known to have the potential to bring destruction due to associated gale-force winds, torrential rain, and storm surge. The focus of TC risk assessment studies has commonly centred around impacts on human livelihoods and infrastructure exposed to TC events. In our earlier study, we created a framework for assessing multi-hazard TC risk to the Australian population and infrastructure at the Local Government Area level. This methodology is used in this study with coral reefs as the focus. TC hazard, exposure, and vulnerability indices were created from selected coral-related datasets to calculate an overall TC risk index for the Ningaloo Reef (NR) and the Great Barrier Reef (GBR) regions. The obtained results demonstrate that the northern NR and the southern GBR had the highest risk values within the study area; however, limitations in data quality have meant that results are estimates at best. The study has shown the potential benefits of such a TC risk assessment framework that can be improved upon, as coral data collection becomes more readily available. [ABSTRACT FROM AUTHOR]

A novel model selection and averaging approach is proposed—through integrating the corrected Akaike information criterion (AICc), the Gibbs sampler, and the Poisson regression models, to improve tropical cyclone seasonal forecasting in the Australian and the South Pacific Ocean regions and sub-regions. It has been found by the new approach that indices which describe tropical cyclone inter-annual variability such as the Dipole Mode Index (DMI) and the El Niño Modoki Index (EMI) are among the most important predictors used by the selected models. The core computational method underlying the proposed approach is a new stochastic search algorithm that we have developed, and is named Metropolis–Gibbs random scan (MGRS). By applying MGRS to minimize AICc over all candidate models, a set of the most important predictors are identified which can form a small number of optimal Poisson regression models. These optimal models are then averaged to improve their overall predictability. Results from our case study of tropical cyclone seasonal forecasting show that the MGRS-AICc method performs significantly better than the commonly used step-wise AICc method. [ABSTRACT FROM AUTHOR]

Floods are one of the most destructive natural hazards to which Australia is exposed. The frequency of extreme rainfall events and consequential floods are projected to increase into the future as a result of anthropogenic climate change. This highlights the need for more holistic risk assessments of flood affected regions. Flood risk assessments (FRAs) are used to inform decision makers and stakeholders when creating mitigation and adaptation strategies for at-risk communities. When assessing flood risk, previous FRAs from Australia's most flood prone regions were generally focused on the flood hazard itself, and rarely considering flood vulnerability (FV). This study assessed FV in one of Australia's most flood prone regions—the Hawkesbury-Nepean catchment, and investigated indicator-based approaches as a proxy method for Australian FV assessment instead of hydrological modelling. Four indicators were selected with the intention of representing environmental and socio-economic characteristics: elevation, degree of slope, index of relative socio-economic disadvantage (IRSD), and hydrologic soil groups (HSGs). It was found that combination of low elevation, low degree of slope, low IRSD score, and very-low infiltration soils resulted in very high levels of vulnerability. FV was shown to be at its highest in the Hawkesbury-Nepean valley flood plain region on the outskirts of Greater Western Sydney, particularly in Blacktown, Penrith, and Liverpool. This actionable risk data which resulted from the final FV index supported the practicality and serviceability of the proxy indicator-based approach. The developed methodology for FV assessment is replicable and has the potential to help inform decision makers of flood-prone communities in Australia, particularly in data scarce areas. [ABSTRACT FROM AUTHOR]

Floods are among the costliest natural hazards, in Australia and globally. In this study, we used an indicator-based method to assess flood hazard risk in Australia's Hawkesbury-Nepean catchment (HNC). Australian flood risk assessments are typically spatially constrained through the common use of resource-intensive flood modelling. The large spatial scale of this study area is the primary element of novelty in this research. The indicators of maximum 3-day precipitation (M3DP), distance to river—elevation weighted (DREW), and soil moisture (SM) were used to create the final Flood Hazard Index (FHI). The 17–26 March 2021 flood event in the HNC was used as a case study. It was found that almost 85% of the HNC was classified by the FHI at 'severe' or 'extreme' level, illustrating the extremity of the studied event. The urbanised floodplain area in the central-east of the HNC had the highest FHI values. Conversely, regions along the western border of the catchment had the lowest flood hazard risk. The DREW indicator strongly correlated with the FHI. The M3DP indicator displayed strong trends of extreme rainfall totals increasing towards the eastern catchment border. The SM indicator was highly variable, but featured extreme values in conservation areas of the HNC. This study introduces a method of large-scale proxy flood hazard assessment that is novel in an Australian context. A proof-of-concept methodology of flood hazard assessment developed for the HNC is replicable and could be applied to other flood-prone areas elsewhere. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is critical in monitoring the time-lagged impacts of agrometeorological drought. In Australia and several south-west Pacific Small Island Developing States (SIDS), there are a limited number of in situ SM stations that can adequately assess soil-water availability in a near-real-time context. Satellite SM datasets provide a viable alternative for SM monitoring and agrometeorological drought provision in these regions. In this study, we investigated the performance of Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Operational Products System (SMOPS), SM from the Advanced Microwave Scanning Radiometer 2 (AMSR-2) and SM from the Advanced Scatterometer (ASCAT) over Australia and south-west Pacific SIDS. Products were first evaluated in Australia, given the presence of several in-situ SM monitoring stations and a state-of-the-art hydrological model—the Australian Water Resources Assessment Landscape modelling system (AWRA-L). We further investigated the accuracy of SM satellite datasets in Australia and the south-west Pacific through Triple Collocation analysis with two other SM reference datasets—ERA5 reanalysis SM data and model data from the Global Land Data Assimilation System (GLDAS) dataset. All datasets have differing observation periods ranging from 1911-now, with a common period of observations between 2015–2021. Results demonstrated that ASCAT and SMOS were consistently superior in their performance. Analysis in the six south-west Pacific SIDS indicated reduced performance for all products, with ASCAT and SMOS still performing better than others for most SIDS with median R values ranging between 0.3–0.9. We conducted a case study of the 2015 El Niño and Positive Indian Ocean Dipole-induced drought in Papua New Guinea. It was shown that ASCAT is a valuable dataset indicative of agrometeorological drought for the nation, highlighting the value of using satellite SM products to provide early warning of drought in data-sparse regions in the south-west Pacific. [ABSTRACT FROM AUTHOR]

This study investigates the relationship between the Normalized Difference Vegetation Index (NDVI) and meteorological drought category to identify NDVI thresholds that correspond to varying drought categories. The gridded evaluation was performed across a 34-year period from 1982 to 2016 on a monthly time scale for Grassland and Temperate regions in Australia. To label the drought category for each grid inside the climate zone, we use the Australian Gridded Climate Dataset (AGCD) across a 120-year period from 1900 to 2020 on a monthly scale and calculate percentiles corresponding to drought categories. The drought category classification model takes NDVI data as the input and outputs of drought categories. Then, we propose a threshold selection algorithm to distinguish the NDVI threshold to indicate the boundary between two adjacent drought categories. The performance of the drought category classification model is evaluated using the accuracy metric, and visual interpretation is performed using the heat map. The drought classification model provides a concept to evaluate drought severity, as well as the relationship between NDVI data and drought severity. The results of this study demonstrate the potential application of this concept toward early drought warning systems. [ABSTRACT FROM AUTHOR]

The validation of precipitation estimates is necessary for the selection of the most appropriate dataset, as well as for having confidence in its selection. Traditional validation against gauges or radars is much less effective when the quality of these references (which are considered the 'truth') degrades, such as in areas of poor coverage. In scenarios like this where the 'truth' is unreliable or unknown, triple collocation analysis (TCA) facilitates a relative ranking of independent datasets based on their similarity to each other. TCA has been successfully employed for precipitation error estimation in earlier studies, but a thorough evaluation of its effectiveness over Australia has not been completed before. This study assesses the use of TCA for precipitation verification over Australia using satellite datasets in combination with reanalysis data (ERA5) and rain gauge data (AGCD) on a monthly timescale from 2001 to 2020. Both the additive and multiplicative models for TCA are evaluated. These results are compared against the traditional verification method using gauge data and Multi-Source Weighted-Ensemble Precipitation (MSWEP) as references. AGCD (KGE = 0.861), CMORPH-BLD (0.835), CHIRPS (0.743), and GSMaP (0.708) were respectively found to have the highest KGE when compared to MSWEP. The ranking of the datasets, as well as the relative difference in performance amongst the datasets as derived from TCA, can largely be reconciled with the traditional verification methods, illustrating that TCA is a valid verification method for precipitation over Australia. Additionally, the additive model was less prone to outliers and provided a spatial pattern that was more consistent with the traditional methods. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) have long posed a significant threat to Australia's population, infrastructure, and natural environment. This threat may grow under climate change as projections indicate continuing sea level rise and increases in rainfall during TC events. Previous TC risk reduction efforts have focused on the risk from wind alone, whereas a holistic approach requires multi-hazard risk assessments that also consider impacts of other TC-related hazards. This study assessed and mapped TC risk nationwide, focusing on the impacts on population and infrastructure from the TC-related hazards of wind, storm surge, flooding and landslides. Risk maps were created at the Local Government Area (LGA) level for all of Australia, using collated data on multiple hazards, exposure and vulnerability. The study demonstrated that the risk posed by all hazards was highest for coastal LGAs of eastern Queensland and New South Wales followed by medium risk across Northern Territory and north-west of Western Australia, with flood and landslide hazards also affecting several inland LGAs. The resulting maps of risk will provide decision-makers with the information needed to further reduce TC risk, save lives, protect the environment, and reduce economic losses. [ABSTRACT FROM AUTHOR]

An approach to developing a blended satellite-rainfall dataset over Australia that could be suitable for operational use is presented. In this study, Global Satellite Mapping of Precipitation (GSMaP) satellite precipitation estimates were blended with station-based rain gauge data over Australia, using operational station data that has not been harnessed by other blended products. A two-step method was utilized. First, GSMaP satellite precipitation estimates were adjusted using rain gauge data through multiplicative ratios that were gridded using ordinary kriging. This step resulted in reducing dry biases, especially over topography. The adjusted GSMaP data was then blended with the Australian Gridded Climate Dataset (AGCD) rainfall analysis, an operational station-based gridded rain gauge dataset, using an inverse error variance weighting method to further remove biases. A validation that was performed using a 20-year range (2001 to 2020) showed the proposed approach was successful; the resulting blended dataset displayed superior performance compared to other non-gauge-based datasets with respect to stations as well as displaying more realistic patterns of rainfall than the AGCD in areas with no rain gauges. The average mean absolute error (MAE) against station data was reduced from 0.89 to 0.31. The greatest bias reductions were obtained for extreme precipitation totals and over mountainous regions, provided sufficient rain gauge availability. The newly produced dataset supported the identification of a general positive bias in the AGCD over the north-west interior of Australia. [ABSTRACT FROM AUTHOR]

Global navigation satellite system (GNSS) signals in the radio occultation (RO) technique using new measurements from constellation observing system for meteorology, ionosphere & climate (COSMIC-2) mission were observed very deep below the Earth’s limb. Selected occultations collocated with severe tropical cyclones showed the existence of signal-to-noise ratio (SNR) variations at or below −200 km in terms of height of straight line (HSL) connecting a pair of occulting satellites. The presence of such signals is considered as indicative of sharp inversion layers associated with planetary boundary layer. We investigate the potential application of deep occultation signals for detection of tropical cyclones often resulting in strong vertical gradients of refractivity. The most prominent deep signatures computed using 1 s running mean filter can reach 400 V/V, whereas the majority of deep signals exceed the noise level by a factor of two. The cross-satellite interference is important mechanism affecting the structure of deep signals, especially for global positioning system (GPS) occultations. [ABSTRACT FROM AUTHOR]

Climate change is increasing the frequency and intensity of natural hazards, causing adverse impacts on vulnerable communities. Pacific Small Island Developing States (SIDS) are of particular concern, requiring resilient disaster risk management consisting of two key elements: proactivity and suitability. User-centred Integrated Early Warning Systems (I-EWSs) can inform resilient risk management. However, an EWS is only effectively integrated when all components are functioning adequately. In Pacific SIDS, the risk knowledge component of an I-EWS is underexplored. Risk knowledge is improved through efficient risk assessment. A case study assessing drought risk in PNG provinces was conducted to demonstrate the development and validate the application of a tailored risk assessment methodology. Hazard, vulnerability, and exposure indicators appropriate for monitoring drought in PNG provinces were selected. Risk indices for past years (2014-2020) were calculated and mapped in Geographic Information Systems (GIS). Risk assessment results were validated with a literature investigation of sources presenting information on previous droughts in PNG. The risk assessment indicated a strong drought event in 2015-2016, and a moderate event in 2019-2020. The literature corroborated this, confirming the validity of the risk assessment methodology. The methodology and results can be used to inform improved disaster risk management in PNG, by advising decision-makers of their risk and policymakers on which provinces are of priority for resource allocation. The methodology can also be used to enhance the risk knowledge component of a user-centred I-EWS and guide the implementation of such a system for drought in PNG and other Pacific SIDS. [ABSTRACT FROM AUTHOR]

Subseasonal tropical cyclone forecasts from two operational forecast models are verified for the 2017/18 and 2018/19 Southern Hemisphere cyclone seasons. The forecasts are generated using the ECMWF’s Medium- and Extended-Range Ensemble Integrated Forecasting System (IFS), and the Bureau of Meteorology’s seasonal forecasting system ACCESS-S1. Results show the IFS is more skillful than ACCESS-S1, which is attributed to the IFS’s greater ensemble size, increased spatial resolution, and data assimilation schemes. Applying a lagged ensemble with ACCESS-S1 increases forecast reliability, with the optimum number of lagged members being dependent on forecast lead time. To investigate the impacts of atmospheric assimilation at shorter lead times, comparisons were made between the Bureau of Meteorology’s ACCESS-S1 and ACCESS-GE2 systems, the latter a global Numerical Weather Prediction system running with the same resolution and model physics as ACCESS-S1 but using an ensemble Kalman filter for data assimilation. This comparison showed the data assimilation scheme used in the GE2 system gave improvements in forecast skill for days 8–10, despite the smaller ensemble size used in GE2 (24 members per forecast compared to 33). Finally, a multimodel ensemble was created by combining forecasts from both the IFS and ACCESS-S1. Using the multimodel ensemble gave improvements in forecast skill and reliability. This improvement is attributed to complementary spatial errors in both systems occurring across much of the Southern Hemisphere as well as an increase in the ensemble size. SIGNIFICANCE STATEMENT Advances in model development allow skillful forecasts of tropical cyclone activity beyond the normal limit of weather prediction (typically 14 days, or a two-week forecast) and into the ‘‘subseasonal’’ time frame. This is achieved by coupling high-resolution ensemble global forecast models to global ocean models. These subseasonal forecasts fill the gap between traditional weather forecasts and monthly climate outlooks. This study verifies two separate subseasonal cyclone forecasting system for the 2017/18 and 2018/19 cyclone seasons over the Southern Hemisphere. Both systems showed good skill in forecasting cyclone activity out to three weeks in advance. By combining the results of both models, forecast skill is further improved. [ABSTRACT FROM AUTHOR]

The skill of predicting the weekly occurrence of tropical cyclone (TC) activity in the Southern Hemisphere is investigated in the Australian Bureau of Meteorology seasonal forecasting system (ACCESS‐S1). On multiweek time‐scales, the Madden–Julian Oscillation (MJO) has previously been shown to be a major driver of TC variability. ACCESS‐S1 shows high skill for predictions of the MJO out to a lead time of ∼30 days and is able to reproduce the observed modulation of TC activity by the MJO in the Southern Hemisphere. In particular, ACCESS‐S1 shows a clear eastward propagation of increased TC activity with the enhanced convective phase of the MJO. MJO modulated changes in the large‐scale environment associated with TC genesis, such as 850‐hPa absolute vorticity, 600‐hPa relative humidity and 850–200 hPa vertical wind shear, are well captured by ACCESS‐S1 except for off the northwest coast of Australia. There, the change in the large‐scale environment and associated TC activity is too weak in the model. Probabilistic forecast verification shows that ACCESS‐S1 is able to provide skilful forecasts of TC occurrence into week 5 if forecasts are calibrated to take into account model biases in TC frequency. Two different calibration strategies are tested: the first is a simple constant scaling factor applied across the Southern Hemisphere for all forecast leads; the second applies a different scaling factor for different regions and lead times. Results of this study suggest that ACCESS‐S1 can provide skilful multiweek forecasts of TC occurrence for the Southern Hemisphere, greatly extending the current TC forecast availability beyond the 1–5 day time‐scale. We examine the Australian Bureau of Meteorology (BoM) seasonal forecasting system ACCESS‐S1 to predict tropical cyclone (TC) activity on multi‐week timescales in the Southern Hemisphere. At this timescale the Madden–Julian Oscillation (MJO) is a key component of TC variability. Results show that ACCESS‐S1 has skill over climatology for predicting TC occurrence out to week 5. Results impact directly on operational forecasts at the BoM, greatly extending the current TC forecast capability beyond the 1–5 day timescale. [ABSTRACT FROM AUTHOR]