A numerical weather prediction (NWP) system that represents continental Australia at a convection-permitting resolution presents both advantages and challenges. It must represent diverse weather regimes over areas of variable observation coverage, which complicates the land and atmospheric modelling and data assimilation. A single-domain prototype system, ACCESS-A (Australia Community Climate and Earth System Simulator – Australia), has been developed and tested. Compared with the current operational NWP system comprising seven small domains (ACCESS-C), ACCESS-A incorporates improvements to satellite, conventional and radar data assimilation and uses an upgraded model science configuration. ACCESS-A was extensively evaluated over two 3-month periods. Qualitative and quantitative precipitation verification indicates that ACCESS-A reproduces the seasons’ weather patterns and observed behaviour of convective precipitation. Objective verification of defined subdomains shows that forecast skill of near-surface weather is variable across the continent. It is found more skilful in better-observed regions that coincide with areas dominated by more predictable, synoptically driven weather systems. Regions with lower skill, particularly corresponding to areas not covered by ACCESS-C, suggest a focus for future research. A comparison with ACCESS-C confirmed the anticipated improved skill related to the forecast model’s upgraded land and atmospheric physics, and provides confidence in the combined impact of all upgrades implemented in ACCESS-A. ACCESS-A is demonstrated to be ready to prepare for operational NWP and ongoing research at the Australian Bureau of Meteorology.

Insights from Forecast Demonstration Projects and Research Development Projects, training workshops, and symposia, conducted between 2000 and 2024 are summarized. The projects were organized by the Nowcasting and Mesoscale Research Working Group of the World Weather Research Programme of the World Meteorological Organization. The objective was to advance, promote, and build capacity in nowcasting and very short-range forecasting. The projects were associated with the Olympic Games, emergency management, and aviation services. They brought international experts together to work in a collaborative fashion. Extensive interaction with end users and decision-makers expanded and extended the scope of services from traditional weather hazards (heavy rain, wind, hail, lightning) to include specific user needs (e.g., visibility in complex terrain or airport runways, periods of calm winds or light rain, heat stress). Substantial progress has been made in many areas including advanced radar nowcasting algorithms, stochastic nowcasts, kilometric and hectometric numerical weather prediction models, blending of observations and models, and multimodel systems. Verification was a key and valuable component of the projects quantifying the results. Also, the types of services have expanded to include both summer and winter services, complex terrain and urban environments, air transport, air quality, hydrology, and health. Insights are presented in all aspects of nowcasting and very short-range forecasting from end-user decision-making, critical role of the forecaster, forecast systems (models, heuristics, observations), to science and knowledge gaps.

Operational ensemble numerical weather prediction models are typically underspread near the land surface, with the Australian Bureau of Meteorology’s (BoM) global system being a typical example. In this study, land surface fraction values, representing the estimated proportions of various land cover types, are perturbed with the aim of increasing the ensemble spread at the surface. The perturbations are achieved by multiplying the existing land surface fraction estimates by spatially correlated random error structures that represent the uncertainties in these estimates. The methodology was trialed over a 75-day period during the Australian summer of 2017–2018 when both perturbed and unperturbed forecasting cycling experiments were run. The results showed that land surface fraction perturbations increased surface temperature, sensible heat flux, and latent heat flux ensemble spread significantly, especially in the tropics and over the Australian region. The screen-level temperature ensemble spread also increased, albeit by a relatively smaller magnitude compared to the surface temperature ensemble spread. Root-mean square error values—as measured relative to reanalysis data—were also found to be smaller in the perturbed runs, leading to significantly improved spread-to-skill ratio values.

Bureau of Meteorology high-resolution Atmospheric Regional Reanalysis for Australia version 2 (BARRA2) is a new regional reanalysis, nested in ERA5 (ECMWF (European Centre for Medium-Range Weather Forecasts) Reanalysis ver. 5), extending from 1979 to near present. It is developed for the Australasia domain, including Australia, New Zealand and parts of the Maritime Continent, at a horizontal grid resolution of 12 km, with a finer 4.4-km grid over Australia. Building on its predecessor (BARRA1), BARRA2 introduces significant improvements by incorporating a broader range of observations and previously unavailable pre-processed data sets. BARRA2 employs a four-dimension-variational assimilation method for the atmosphere and an extended Kalman filter for the land surface, integrating conventional observations and satellite-based radiances, atmospheric motion vector winds, satellite and ground-based Global Navigation Satellite System measurements, and satellite soil moisture data. The modelling system is based on the UK Met Office Unified Model (UM) and the Joint UK Land Simulator (JULES), with updated physics configurations that address limitations of BARRA1. This also includes a convection-permitting configuration for the 4.4-km model. This paper describes the BARRA2 system and assesses the quality of its deterministic outputs for key near-surface meteorological parameters, including temperature, wind and precipitation. The added value of BARRA2 over global reanalyses is evident in coastal and high-terrain regions and within the convection-permitting system. BARRA2 shows quality changes from approximately the year 2000, although these do not appear to negatively affect long-term temperature or rainfall trends at studied sites and regions. Remaining challenges, including modelling biases and data assimilation limitations, are discussed to inform future development.

Third-party automatic weather stations (TPAWS) provide a compelling data source for scientists and practitioners to observe and estimate more accurate fine-scale atmospheric conditions, including daily maximum and minimum temperature (denoted as Tmax and Tmin, respectively), than the current primary weather observation network can offer. Several uncertainties and errors arise in data from TPAWS as the quality control applied to these stations may be inadequate or ad hoc. In this study, we develop a statistical approach to evaluate the quality of daily Tmax and Tmin observations collected from TPAWS in Australia. Our approach compares a target observation with multiple types of reliable reference data, including neighbouring primary weather observations from the official Bureau of Meteorology of Australia stations, Australian Gridded Climate Data, and numerical weather prediction data. Guided by the operational requirements in terms of automation, interpretability, and simplicity as well as expandability, a separate test is formed for each type of reference data and then all the individual tests are merged to generate a single result based on a Gaussian mixture model that is used to provide the final overall assessment for each TPAWS observation. The overall assessment is made in the form of a p-value-based confidence score that measures the difference between the target observation and trusted reference data. Our method is validated by synthetic datasets based on high-quality observations and is also applied to daily Tmax and Tmin observations from 184 TPAWS owned by the Department of Primary Industries and Regional Development of Western Australia. The framework can be readily applied to different regions with different reliable or trusted data sources. We present a statistical method assessing daily Tmax and Tmin data from third-party automatic weather stations (TPAWS). Our approach employs p-value-based confidence scores, detecting disparities between TPAWS observations and trusted references. The figure depicts 2019 daily Tmax data from a TPAWS, highlighting eight potentially erroneous readings (labelled in red; Section 5 elaborates). Some anomalies may not be immediately evident in time series trends alone, but comparing them with surrounding reference observations highlights the significance of our method's contribution. image

The new scheme for deriving the near-surface wind profiles discussed in Ma (in review) is applied to an Australian Bureau of Meteorology operational convective scale model over various domains. Both the new and conventional schemes' diagnostic 10-m winds are then verified against Australia-wide automatic weather station observations. Analyses of bulk statistics reveal that the new scheme's 10-m wind forecasts have generally better accuracy than the current conventional scheme with a consistent reduction of biases over all domains. A widely recognised diurnal bias pattern of surface wind speed over the land is substantially reduced, and the inclusion of Ekman spiral effect on the 10-m wind marginally improves statistics of the wind direction during the nighttime. The new scheme introduces no systemic bias, given the histogram of a bulk mean bias is analogiased to a Gaussian distribution, and moves the distribution of diagnostic wind speed closer to that observed. A newly proposed scheme for deriving the near-surface wind distribution is applied in the Australian high-resolution operational model for obtaining the model diagnostic 10-m wind. Verifications of the winds from both the new and conventional schemes against the nationwide automatic weather station observations demonstrate that the new scheme makes a consistent reduction of biases, substantially reduces a widely recognised diurnal bias pattern of surface wind speed over the land, and moves the histogram distribution of diagnostic wind speed closer to that observed, shown in the plot. image

Third-party rainfall observations could provide an improvement of the current official observation network for rainfall monitoring. Although third-party weather stations can provide large quantities of near-real-time rainfall observations at fine temporal and spatial resolutions, the quality of these data is susceptible due to variations in quality control applied and there is a need to provide greater confidence in them. In this study, we develop an automatic quality evaluation procedure for daily rainfall observations collected from third-party stations in near real time. Australian Gridded Climate Data (AGCD) and radar Rainfields data have been identified as two reliable data sources that can be used for assessing third-party observations in Australia. To achieve better model interpretability and scalability, these reference data sources are used to provide separate tests rather than a complex single test on a third-party data point. Based on the assumption that the error of a data source follows a Gaussian distribution after a log-sinh transformation, each test issues a p -value-based confidence score as a measure of quality and the confidence of the third-party data observation. The maximum of confidence scores from individual tests is used to merge these tests into a single result which provides overall assessment. We validate our method with synthetic datasets based on high-quality rainfall observations from 100 Bureau of Meteorology (BoM) of Australia stations across Australia and apply it to evaluate real third-party rainfall observations owned by the Department of Primary Industries and regional development (DPIRD) of Western Australia. Our method works well with the synthetic datasets and can detect 76.7% erroneous data while keeping the false alarm rate as low as 1.7%. We also discuss the possibility of using other reference datasets, such as numerical weather prediction data and satellite rainfall data.

Abstract Third-party rainfall observations could provide an improvement of the current official observation network for rainfall monitoring. Although third-party weather stations can provide large quantities of near-real-time rainfall observations at fine temporal and spatial resolutions, the quality of these data is susceptible because of there is a need to provide greater confidence in them due to variations in quality control applied. In this study, we develop a statistical method for an automated quality control system to evaluate daily rainfall observations collected from third-party stations in near real time. Australian Gridded Climate Data (AGCD) and radar Rainfields data have been identified as two reliable data sources that can be used for assessing third-party observations in Australia. To achieve better model interpretability and scalability, the reference data sources are used to provide three separate tests rather than a complex single test on a third-party data point. Based on the assumption that the error of a data source follows a Gaussian distribution after a log-sinh transformation, each test issues a p-value-based confidence score as a measure of quality and the confidence of the third-party data point. The maximum of confidence scores from individual tests is used to merge these tests into a single result which provides overall assessment. We validate our method with synthetic datasets based on high-quality rainfall observations from 100 Bureau of Meteorology (BoM) of Australia stations across Australia and apply it to evaluate real third-party rainfall observations owned by the Department of Primary Industries and regional development (DPIRD) of Western Australia. Our method works well with the synthetic datasets and can detect 76.7% erroneous data while keeping the false alarm rate as low as 1.7%. We also discuss the possibility of using other reference datasets, such as numerical weather prediction data and satellite rainfall data.

The Australian Bureau of Meteorology’s ‘Australian Parallel Suite’ (APS) operational numerical weather prediction regional Australian Community Climate and Earth-System Simulator (ACCESS) city-based system (APS1 ACCESS-C1) was updated in August 2017 with the commissioning of the APS2 ACCESS-C2. ACCESS-C2 runs over six regional domains. Significant upgrade changes included implementation of Unified Model 8.2 code; nesting in the 12 km resolution APS2 ACCESS-R2 regional model; and, importantly, an increased horizontal resolution from 4 to 1.5 km, enabling C2 to become the first Australian operational convection-permitting model (CPM). Traditional rainfall verification metrics and Fractions Skill Score show C2 forecast skill over ACCESS-C domains in summer and winter was generally, and in many cases, significantly better than C1. Case studies showed that C2 forecasts had better-detailed wind and precipitation fields, particularly at longer forecast ranges and higher rain rates. The improvements in C2 forecasts were principally due to its CPM ability to simulate high temporal and spatial resolution features, which continue to be of great interest to forecasters. C2 also laid the groundwork for the present day APS3 ACCESS-C forecast C3 and ensemble CE3 models and further development of higher resolution (down to 300 m) fire weather and urban models.

Abstract High‐resolution regional reanalysis datasets have the potential to provide valuable guidance to emergency management agencies, highlighting areas at risk of severe weather, including estimates of return periods of various hazardous weather phenomena. The BARRA regional reanalysis for Australia comprises a reanalysis for a broad region around Australia at moderately high spatial and temporal resolution (12 km/hourly), together with four subdomains at high resolution (1.5 km/1 h). Here, we document four applications of BARRA developed for emergency management: optimal placement of portable automatic weather stations for fire weather monitoring; climatology of low‐level wind shear conducive to cool‐season tornadogenesis; development of rainfall intensity–frequency–duration curves based on the gridded reanalysis data; and development of a climatology across Australia of parameters associated with severe thunderstorm occurrence.

The Australian Bureau of Meteorology recently upgraded its convection-allowing numerical weather prediction system, known as the Australian Community Climate and Earth System Simulator (ACCESS-C). ACCESS-C includes seven domains covering major population centers, nested inside the Bureau's global NWP system. The upgrade included the introduction of data assimilation, with hourly cycling 4D-Var. With a much newer version of the Unified Model to provide the forecast, a range of storm attribute diagnostics to improve forecasting of severe weather events could be introduced. This paper details the configuration of the new version of ACCESS-C. Some verification compared with its predecessor (a downscaling system of comparable resolution) is presented. Of greater note is an exploration of the differences in the model characteristics between the new and old systems, which will affect how users interpret the outputs.

Regional reanalyses provide a dynamically consistent recreation of past weather observations at scales useful for local-scale environmental applications. The development of convection-permitting models (CPMs) in numerical weather prediction has facilitated the creation of kilometrescale (1–4 km) regional reanalysis and climate projections. The Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) also aims to realize the benefits of these high-resolution models over Australian sub-regions for applications such as fire danger research by nesting them in BARRA’s 12 km regional reanalysis (BARRA-R). Four midlatitude sub-regions are centred on Perth in Western Australia, Adelaide in South Australia, Sydney in New South Wales (NSW), and Tasmania. The resulting 29-year 1.5 km downscaled reanalyses (BARRA-C) are assessed for their added skill over BARRA-R and global reanalyses for near-surface parameters (temperature, wind, and precipitation) at observation locations and against independent 5 km gridded analyses. BARRA-C demonstrates better agreement with point observations for temperature and wind, particularly in topographically complex regions and coastal regions. BARRA-C also improves upon BARRA-R in terms of the intensity and timing of precipitation during the thunderstorm seasons in NSW and spatial patterns of sub-daily rain fields during storm events. BARRA-C reflects known issues of CPMs: overestimation of heavy rain rates and rain cells, as well as underestimation of light rain occurrence. As a hindcast-only system, BARRA-C largely inherits the domain-averaged bias pattern from BARRA-R but does produce different climatological extremes for temperature and precipitation. An added-value analysis of temperature and precipitation extremes shows that BARRA-C provides additional skill over BARRA-R when compared to gridded observations. The spatial patterns of BARRA-C warm temperature extremes and wet precipitation extremes are more highly correlated with observations. BARRA-C adds value in the representation of the spatial pattern of cold extremes over coastal regions but remains biased in terms of magnitude.

The Bureau of Meteorology is now embarking on developing the next regional ensemblebased atmospheric reanalysis, to improve upon its first deterministic reanalysis – Bureau of Meteorology Atmospheric Regional high-resolution Reanalysis for Australia (BARRA) – completed in 2019. This will lead to a foundational dataset to provide a consistent, detailed characterisation of historical hazards and continuous intelligence. It is a part of a strategic initiative to establish a single authoritative source of information, analysis and expertise on climate and natural disaster risks that is focused on relief, recovery and resilience. The enhanced reanalysis is expected to span the era of modern meteorological satellites from 1979 and will be kept up to date as an operationally supported system. The reanalysis will use the same modelling components as those in other modelling systems in the Bureau including nowcasting, weather forecasting, seasonal forecasting and regional climate projections, reflecting the Bureau’s strategy for a common modelling approach across all time scales from historical analysis to multi-decadal outlooks to support seamlessness in services. Here we report on the quality of BARRA and the development of the enhanced reanalysis, and benchmarking results against the Bureau’s global numerical weather prediction system and the global reanalyses. We also describe how BARRA is disseminated to users and used to inform climate risk.

The influence of anthropogenic climate change on extreme bushfire weather in Australia is assessed using a standardised method for projections information.The method steps comprise a review and synthesis of a comprehensive range of factors based on observations, modelling and physical process understanding.The resultant lines of evidence are then used to guide the production of projections data and confidence assessments.Projections are produced based on global climate model output as well as dynamical downscaling data using three regional climate modelling approaches (CCAM, BARPA and NARCliM/WRF).The projections data are calibrated using quantile matching methods trained on observations-based data, with a particular focus on the accurate representation of extremes.The resultant projections data include nationally consistent maps of bushfire weather indices corresponding to the 10-year average recurrence interval (i.e., return period) around the middle of this century (2040-2059), with a focus of the discussion on regions around southern and eastern Australia during summer as needed for some risk assessment applications.The projections data are also available for other seasons and time periods throughout this century, as well as for other metrics of extreme or average conditions.The results for southern and eastern Australia during summer show more dangerous bushfire conditions (high confidence in southern Australia; medium confidence in eastern Australia) attributable to increasing greenhouse gas emissions.

Aircraft reports are an important source of information for numerical weather prediction (NWP). From March 2020, the COVID‐19 pandemic resulted in a large loss of aircraft data but despite this it is difficult to see any evidence of significant degradation in the forecast skill of global NWP systems. This apparent discrepancy is partly because forecast skill is very variable, showing both day‐to‐day noise and lower frequency dependence on the mean state of the atmosphere. The definitive way to cleanly assess aircraft impact is using a data denial experiment, which shows that the largest impact is in the upper troposphere. The method used by Chen (2020, https://doi.org/10.1029/2020gl088613 ) to estimate the impact of COVID‐19 is oversimplistic. Chen understates the huge importance of satellite data for modern weather forecasts and raises more alarm than necessary about a drop in forecast accuracy.

Improving the forecasting and communication of weather hazards such as urban floods and extreme winds has been recognized by the World Meteorological Organization (WMO) as a priority for international weather research. The WMO has established a 10-yr High-Impact Weather Project (HIWeather) to address global challenges and accelerate progress on scientific and social solutions. In this review, key challenges in hazard forecasting are first illustrated and summarized via four examples of high-impact weather events. Following this, a synthesis of the requirements, current status, and future research in observations, multiscale data assimilation, multiscale ensemble forecasting, and multiscale coupled hazard modeling is provided.

The impact of Doppler radar wind observations on forecasts from a developmental, high-resolution numerical weather prediction (NWP) system is assessed. The new 1.5-km limited-area model will be Australia’s first such operational NWP system to include data assimilation. During development, the assimilation of radar wind observations was trialed over a 2-month period to approve the initial inclusion of these observations. Three trials were run: the first with no radar data, the second with radial wind observations from precipitation echoes, and the third with radial winds from both precipitation and insect echoes. The forecasts were verified against surface observations from automatic weather stations, against rainfall accumulations using fractions skill scores, and against satellite cloud observations. These methods encompassed verification across a range of vertical levels. Additionally, a case study was examined more closely. Overall results showed little statistical difference in skill between the trials, and the net impact was neutral. While the new observations clearly affected the forecast, the objective and subjective analyses showed a neutral impact on the forecast overall. As a first step, this result is satisfactory for the operational implementation. In future, upgrades to the radar network will start to reduce the observation error, and further improvements to the data assimilation are planned, which may be expected to improve the impact.

The United States Air Force (USAF) has a proud and storied tradition of enabling significant advancements in the area of characterizing and modeling land state information. 557th Weather Wing (557 WW; DoD’s Executive Agent for Land Information) provides routine geospatial intelligence information to warfighters, planners, and decision makers at all echelons and services of the U.S. military, government and intelligence community. 557 WW and its predecessors have been home to the DoD’s only operational regional and global land data analysis systems since January 1958. As a trusted partner since 2005, Air Force Weather (AFW) has relied on the Hydrological Sciences Laboratory at NASA/GSFC to lead the interagency scientific collaboration known as the Land Information System (LIS). LIS is an advanced software framework for high performance land surface modeling and data assimilation of geospatial intelligence (GEOINT) information.