Australian Bureau Of Meteorology Research Articles

Observed Associations between Fire Danger and Climate Modes and Their Representation in ACCESS-S2

Abstract The increasing frequency and severity of wildfires in Australia, driven by climate change, pose a significant threat to ecosystems, lives, and property. This study examines the impact of climate drivers, specifically El Niño–Southern Oscillation (ENSO), Indian Ocean dipole (IOD), Southern Annular Mode (SAM), Madden–Julian oscillation (MJO), and two modes of persistent high pressure in the Australia–Pacific region, on extreme fire danger. By analyzing observed and simulated fire danger in relation to these climate drivers, we aim to enhance our understanding of climate–fire mechanisms and contribute to Australia’s bushfire preparedness. Our findings indicate that all assessed drivers influence extreme fire danger, with key influences related to the drivers’ established relationships with rainfall and temperature. El Niño, positive IOD, and negative SAM events generally increase extreme fire danger across most of Australia and in most seasons. The two modes of Australian blocking exhibit similar effects, varying spatially. Specific phases of the MJO have significant seasonal relationships with fire danger. In some instances, increased fire danger is not directly linked to temperature or precipitation changes but rather driven by remote teleconnections, airflow, or pressure anomalies. Evaluating the accuracy of weather and climate forecasting systems in representing these relationships is crucial for the effective prediction and mitigation of fire hazards. The leading Australian climate simulation model effectively reproduces observed relationships but reveals biases in capturing certain aspects of climate variability. Advancements in this field would enhance fire weather forecasts, including those by the Australian Bureau of Meteorology with lead times of up to 4 months. Significance Statement This assessment of climate–fire relationships and modeling accuracy is pivotal in understanding the factors behind severe fire events and improving future predictions and mitigation. Prior research has already established links between elevated fire weather conditions and these climate drivers, highlighting their potential for predicting severe fire events. This study aims to deepen our comprehension of the factors driving severe fire events and facilitate improved bushfire risk management in Australia.

The Jive Verification System and Its Transformative Impact on Weather Forecasting Operations

Abstract 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.

Impact of ENSO on Cloud Distribution and Rainfall Variability in Tangerang Regency

A type of climate variation in the Pacific Ocean known as El Niño Southern Oscillation (ENSO) is defined by an increase in sea surface temperature (SST) in the Central and Eastern equatorial areas, which affects the amount of rainfall that falls and increases. In Indonesia, the rainy season typically persists from October to March, whereas the dry season persists from April to September. Research on the influence of ENSO on rainfall has been carried out in several areas, but has not been carried out in the Tangerang area. This study aims to ascertain how the ENSO phenomena affect rainfall fluctuations and cloud distribution in the Regency of Tangerang. The secondary data, which includes information on rainfall, cloud cover, wind direction, and speed, was acquired from BMKG Meteorology Budiarto. In addition, data from the Australian Bureau of Meteorology (BOM) website's Southern Oscillation Index (SOI) was used. Surface weather analysis and Pearson correlation analysis are the data analysis techniques used. The results presented that based on surface weather study, ENSO events had a consequence on cloud distribution in Tangerang Regency. The clouds lead when the ENSO phenomenon occurs, namely Cumulus and Nimbostratus clouds with a 4-8 octa cloud cover. Meanwhile, the correlation test results show that ENSO influences seasonal and annual rainfall variations in the district of Tangerang. The largest correlation between SOI and annual rainfall occurred in 2015 with a correlation value of r-0.84 (very strong). Meanwhile, the greatest correlation between SOI and seasonal rainfall occurred in July-September with a correlation value of r=0.67 (strong).

Predictability of the 2020 Strong Vortex in the Antarctic Stratosphere and the Role of Ozone

AbstractThe Antarctic vortex of October–December 2020 was the strongest on record in the satellite era for the season in the mid‐ to lower stratosphere. However, it was poorly predicted by the Australian Bureau of Meteorology's operational seasonal climate forecast system of that time, ACCESS‐S1, even at a short lead time of a month. Using the current operational forecast system, ACCESS‐S2, we have, therefore, tried to find a primary cause of the limited predictability of this event and conducted forecast sensitivity experiments to understand the potential role of ozone in the event and its associated anomalies of the Southern Annular Mode (SAM) and rainfall over south–eastern Australia and western Patagonia. Here, we show that the 2020 strong vortex event did not follow the canonical dynamical evolution seen in previous strong vortex events in spring but suddenly appeared as a result of the record‐low upward propagating wave activity in September 2020. The ACCESS‐S2 forecasts significantly underestimated the negative wave forcing in September even at zero lead time, irrespective of the ozone configuration, therefore falling short in predicting the record strength of the polar vortex in late spring 2020. Nevertheless, ACCESS‐S2 with prescribed realistic ozone that had large anomalies in the Antarctic stratosphere significantly better predicted the strong vortex and the subsequent positive SAM and related rainfall anomalies over south–eastern Australia and western Patagonia in the austral summer of 2020–21. This highlights the potentially important role of ozone variations for seasonal climate forecasting as a source of long‐lead predictability.

Impacts of Climate Change on Rainfall ‘Seasonality Index’ and Its Potential Implications on Water Savings and Reliability through Household Rainwater Tanks

This study depicts potential climate change impacts on annual rainwater savings from household rainwater harvesting using two different climate projection models; ACCESS 1.0 and CSIRO-Mk3.6. This paper also investigates potential changes in the relationships of ‘water saving efficiency’ and reliability with rainfall ‘seasonality index’ under the mentioned climate change scenarios. The annual water savings were calculated for three weather conditions: dry, average, and wet. Historical daily rainfall amounts provided by the Australian Bureau of Meteorology were used for three locations within the city of Brisbane (Australia). For the same locations, projected future daily rainfall amounts were collected from an online data portal facilitated by the Australian government. Potential annual water savings, water saving efficiency, and reliability values for the selected locations were calculated through a widely used tool, eTank, developed on water balance methodology at a daily scale. It was found that for the coastal location, Manly, the future water savings are not likely to change significantly. However, for the inland location, Sunnybank, the future water savings are expected to decrease under all the weather conditions through both the considered climate projections. For the far inner location, Oxley, the water savings are likely to decrease in the dry year, whereas in wet year, they are likely to increase. Also, it was found that the overall average relationship of SI–water saving efficiency is steeper for ACCESS 1.0 projected data compared to that produced through CSIRO-Mk3.6 data, and that significant differences exist among individual relationships for each location. The overall reliabilities calculated through the model projected data show lower values compared to the reliabilities calculated using historical data.

Himawari-8 Sea Surface Temperature Products from the Australian Bureau of Meteorology

Himawari-8 Sea Surface Temperature Products from the Australian Bureau of Meteorology

Future Scenarios of Design Rainfall Due to Upcoming Climate Changes in NSW, Australia

The occurrence of rainfall is significantly affected by climate change around the world. While in some places this is likely to result in increases in rainfall, both winter and summer rainfall in most parts of New South Wales (NSW), Australia are projected to decrease considerably due to climate change. This has the potential to impact on a range of hydraulic and hydrologic design considerations for water engineers, such as the design and construction of stormwater management systems. These systems are currently planned based on past extreme rain event data, and changes in extreme rainfall amounts due to climate change could lead to systems being seriously undersized (if extreme precipitation events become more common and/or higher in magnitude) or oversized (if extreme rainfall events become less frequent or decrease in magnitude). Both outcomes would have potentially serious consequences. Consequently, safe, efficient, and cost-effective urban drainage system design requires the consideration of impacts arising from climate change on the approximation of design rainfall. This study examines the impacts of climate change on the probability of occurrence of daily extreme rainfall in New South Wales (NSW), Australia. The analysis was performed for 29 selected meteorological stations located across NSW. Future design rainfall in this research was determined from the projected rainfall for different time periods (2020 to 2039, 2040 to 2059, 2060 to 2079, and 2080 to 2099). The results of this study show that design rainfall for the standard return periods was, in most cases, lower than that derived employing the design rainfall obtained from the Australian Bureau of Meteorology (BoM). While most of the analysed meteorological stations showed significantly different outcomes using the climate change scenario data, this varied considerably between stations and different time periods. This suggests that more work needs to be performed at the local scale to incorporate climate change predicted rainfall data into future stormwater system designs to ensure the best outcomes.

ACCESS-S2 seasonal forecasts of rainfall and the SAM–rainfall relationship during the grain growing season in south-west Western Australia

South-west Western Australia (SWWA) is home to a world class grains industry that is significantly affected by periods of drought. Previous research has shown a link between the Southern Annular Mode (SAM) and rainfall in SWWA, especially during winter months. Hence, the predictability of the SAM and its relationship to SWWA rainfall can potentially improve forecasts of SWWA drought, which would provide valuable information for farmers. In this paper, focusing on the 0-month lead time forecast, we assess the bias and skill of ACCESS-S2, the Australian Bureau of Meteorology’s current operational sub-seasonal to seasonal forecasting system, in simulating seasonal rainfall for SWWA during the growing season (May–October). We then analyse the relationship between the SAM and SWWA precipitation and how well this is captured in ACCESS-S2 as well as how well ACCESS-S2 forecasts the monthly SAM index. Finally, ACCESS-S2 rainfall forecasts and the simulation of SAM are assessed for a case study of extreme drought in 2010. Our results show that forecasts tend to have greater skill in the earlier part of the season (May–July). ACCESS-S2 captures the significant inverse SAM–rainfall relationship but underestimates its strength. The model also shows overall skill in forecasting the monthly SAM index and simulating the MSLP and 850-hPa wind anomaly patterns associated with positive and negative SAM phases. However, for the 2010 drought case study, ACCESS-S2 does not indicate strong likelihoods of the upcoming dry conditions, particularly for later in the growing season, despite predicting a positive (although weaker than observed) SAM index. Although ACCESS-S2 is shown to skillfully depict the SAM–SWWA rainfall relationship and generally forecast the SAM index well, the seasonal rainfall forecasts still show limited skill. Hence it is likely that model errors unrelated to the SAM are contributing to limited skill in seasonal rainfall forecasts for SWWA, as well as the generally low seasonal-timescale predictability for the region.

The impact of rainfall on beef cattle growth across diverse climate zones

Genetics, animal husbandry and the feedbase all impact cattle growth. Australia’s cattle feedbase covers 40% of the continent and encompasses diverse climates and landscapes, making stocking rate decisions challenging. Of the factors contributing to climate change, rainfall is a primary determinant of feedbase growth and, with this, cattle growth. Understanding the interplay between rainfall and cattle growth across the diverse Australian landscape is thus critical to aid farmer decision-making. However, revealing such interactions between landscape and rainfall for cattle growth for such decision-making has until now been infeasible due to a lack of sufficient temporal and spatial cattle growth data. The Optiweigh (OW) system has been deployed across Australia’s extensive beef production systems as a voluntary weighing unit, opportunistically monitoring cattle liveweight (LW). This study determined the impact of rainfall on the temporal and spatial variability of beef cattle growth across three of Australia’s agro-climatic zones (grassland, subtropical and temperate), aiming to describe the diverse feedbase through patterns of LW variability. A total of 1.3 million cattle LW observations were collected from 82 026 cattle over 2 years (2020–2022). Rainfall data from the Australian Bureau of Meteorology were also collated for the closest meteorological station to each OW unit. Cattle LW average daily gain (ADG) was clustered by season and zone. A series of linear mixed models were used to examine ADG for each zone-season combination, with random effects for individual animals and farms, and fixed effects for climate zone and current and lagged rainfall. The overall mean ADG for the dataset was 0.68 kg/day, with greater growth variability between farms within a zone (SD: 0.349 ± 0.021 kg/day, estimate ± SE) than between cattle within a farm (SD: 0.229 ± 0.004 kg/day). This ADG variability can be partly attributed to the timing and amount of rainfall, with agro-climatic zones showing unique interactions between rainfall and ADG. Seasonal lagged rainfall effects were present in the grassland and temperate zones, while rainfall in the temperate zone had a year-round effect on cattle growth. Further, season-wise lagged rainfall had mixed effects on ADG, whereas rain occurring in a season reduced growth in the same season across zones (P < 0.001). These findings provide valuable initial insights into the variability of ADG across the landscape over time and markedly improve our understanding of the interplay between climate and Australia’s diverse feedbase, contributing to improved management strategies.

Prediction of Sea Level Using Double Data Decomposition and Hybrid Deep Learning Model for Northern Territory, Australia

Sea level rise (SLR) attributed to the melting of ice caps and thermal expansion of seawater is of great global significance to vast populations of people residing along the world’s coastlines. The extent of SLR’s impact on physical coastal areas is determined by multiple factors such as geographical location, coastal structure, wetland vegetation and related oceanic changes. For coastal communities at risk of inundation and coastal erosion due to SLR, the modelling and projection of future sea levels can provide the information necessary to prepare and adapt to gradual sea level rise over several years. In the following study, a new model for predicting future sea levels is presented, which focusses on two tide gauge locations (Darwin and Milner Bay) in the Northern Territory (NT), Australia. Historical data from the Australian Bureau of Meteorology (BOM) from 1990 to 2022 are used for data training and prediction using artificial intelligence models and computation of mean sea level (MSL) linear projection. The study employs a new double data decomposition approach using Multivariate Variational Mode Decomposition (MVMD) and Successive Variational Mode Decomposition (SVMD) with dimensionality reduction techniques of Principal Component Analysis (PCA) for data modelling using four artificial intelligence models (Support Vector Regression (SVR), Adaptive Boosting Regressor (AdaBoost), Multilayer Perceptron (MLP), and Convolutional Neural Network–Bidirectional Gated Recurrent Unit (CNN-BiGRU). It proposes a deep learning hybrid CNN-BiGRU model for sea level prediction, which is benchmarked by SVR, AdaBoost, and MLP. MVMD-SVMD-CNN-BiGRU hybrid models achieved the highest performance values of 0.9979 (d), 0.996 (NS), 0.9409 (L); and 0.998 (d), 0.9959 (NS), 0.9413 (L) for Milner Bay and Darwin, respectively. It also attained the lowest error values of 0.1016 (RMSE), 0.0782 (MABE), 2.3699 (RRMSE), and 2.4123 (MAPE) for Darwin and 0.0248 (RMSE), 0.0189 (MABE), 1.9901 (RRMSE), and 1.7486 (MAPE) for Milner Bay. The mean sea level (MSL) trend analysis showed a rise of 6.1 ± 1.1 mm and 5.6 ± 1.5 mm for Darwin and Milner Bay, respectively, from 1990 to 2022.

Downscaled numerical weather predictions can improve forecasts of sugarcane irrigation indices

Efficient irrigation reduces energy and water costs, increases profit margins and delivers better environmental outcomes. Whilst many growers rely on weather forecasts to make decisions, few studies have sought to incorporate weather forecast uncertainty into the optimisation of irrigation management or to evaluate weather forecasts through the lens of irrigation indices. Therefore, in this study, we seek to generate and evaluate ensemble forecasts of irrigation indices produced by coupling numerical weather prediction (NWP) forecasts with a biophysical process model (APSIM). We investigate a case study application for sugarcane in northeastern Australia. As a first step, three and a half years of forecasts from the Australian Bureau of Meteorology’s ACCESS-G3 model are statistically post-processed to generate 7-day forecasts that are downscaled and calibrated to local climate zones. In addition, the forecast post-processor converts the deterministic forecasts into an ensemble, thus quantifying forecast uncertainty. The generated forecasts are then used as forcing for the APSIM crop model to produce ensemble forecasts of soil water deficit (SWD), crop water use (CWU) and crop stress (Stress) for a simulated sugarcane crop. Through cross-validation, the post-processed weather forecasts demonstrate improved forecast accuracy compared to naive climatology and raw NWP forecasts for daily rainfall, maximum and minimum temperature and solar radiation; with the added benefit of providing a reliable uncertainty estimate. Improvement to an even greater degree is observed for the derived irrigation indices, particularly CWU and Stress, for which the forecasts are also reliable. The developed irrigation indices based on NWP can be used directly for decision making or, alternatively, may be used further in machine learning for optimisation of irrigation schedules in conjunction with other remotely sensed variables.

Skilful multiweek predictions of tropical cyclone frequency in the Northern Hemisphere using ACCESS‐S2

AbstractThe skill of subseasonal (multiweek) forecasts of tropical‐cyclone (TC) occurrence over the Northern Hemisphere is examined in the Australian Bureau of Meteorology's (BoM) multiweek to seasonal prediction system, ACCESS‐S2. ACCESS‐S2 shows a good representation of the spatial distribution of TCs in the Northern Hemisphere; however, TC track frequency is generally underpredicted in the western North Pacific to the east of the Philippines and in the eastern North Pacific. The reduced activity relative to observations could be due to a significant positive bias in 850–200‐hPa wind shear in both of these regions, as well as a significant negative sea‐surface temperature (SST) bias in the eastern North Pacific. Despite biases in climatological TC frequency, the observed change in TC track frequency across the Northern Hemisphere with the phase of the Madden–Julian Oscillation (MJO) is well captured by ACCESS‐S2. Changes in the large‐scale environment (e.g., precipitation, 600‐hPa relative humidity, 850‐hPa absolute vorticity and 850–200‐hPa wind shear) are also well represented, with the location and size of the anomalies comparable to ERA‐Interim, apart from SST which shows a different response during some phases. ACCESS‐S2 shows skill relative to climatology for multiweek predictions of TC occurrence out to week 5 in the western North Pacific, eastern North Pacific and North Atlantic; and out to week 2 for the North Indian Ocean. Assessment of real‐time forecasts for Typhoon Rai (December 2021) showed that ACCESS‐S2 provided good guidance of the development and potential landfall of a TC in the Philippines at four weeks lead time.

Continental-scale bias-corrected climate and hydrological projections for Australia

Abstract. The Australian Bureau of Meteorology has developed a national hydrological projections (NHP) service for Australia. The NHP aimed to provide nationally consistent hydrological projections across jurisdictional boundaries to support planning of water-dependent industries. NHP is complementary to those previously produced by federal and state governments, universities, and other organisations for limited geographical domains. The projections comprise an ensemble of application-ready bias-corrected climate model data, derived hydrological projections at daily temporal and 0.05° × 0.05° spatial resolution for the period 1960–2099, and two emission scenarios (Representative Concentration Pathway (RCP) 4.5 and RCP8.5). The spatial resolution of the projections matches that of gridded historical reference data used to perform the bias correction and the Bureau of Meteorology's operational gridded hydrological model. Three bias correction techniques were applied to four CMIP5 global climate models (GCMs), and one method was applied to a regional climate model (RCM) forced by the same four GCMs, resulting in a 16-member ensemble of bias-corrected GCM data for each emission scenario. The bias correction was applied to fields of precipitation, minimum and maximum temperature, downwelling shortwave radiation, and surface winds. These variables are required inputs to the Bureau of Meteorology's landscape water balance hydrological model (AWRA-L), which was forced using the bias-corrected GCM and RCM data to produce a 16-member ensemble of hydrological output. The hydrological output variables include root zone soil moisture (moisture in the top 1 m soil layer), potential evapotranspiration, and runoff. Here we present an overview of the production of the hydrological projections, including GCM selection, bias correction methods and their evaluation, technical aspects of their implementation, and examples of analysis performed to construct the NHP service. The data are publicly available on the National Computing Infrastructure (https://doi.org/10.25914/6130680dc5a51, Bureau of Meteorology, 2021), and a user interface is accessible at https://awo.bom.gov.au/products/projection/ (last access: 24 November 2023).

The meteorology of the 2019 North Queensland floods

AbstractIn January–February 2019, a monsoon low developed over northeastern Australia and brought extreme flooding to much of tropical Queensland. The European Centre for Medium Range Weather Forecasts reanalyses are used to explain the formation and severity of the event. Strong anticyclonic Rossby wave breaking to the southeast of Australia produced weak steering winds over northern Australia, and consequently the low remained nearly stationary for over a week, contributing to the extreme rainfall. Furthermore, the tropical moist margin was deformed by a series of disturbances along the monsoon trough, which drew moist maritime air over land. The event examined has much in common with the composite mean of slow‐moving potential vorticity anomalies in North Queensland, including weak background winds and heavy precipitation. An ensemble subseasonal forecast with the Australian Bureau of Meteorology's Australian Community Climate and Earth System Simulator Seasonal prediction version system 1 shows the same relationship between the background flow, the steering, and the subsequent rainfall. Hence, accurately forecasting the background winds is a prerequisite to forecasting such extreme rainfall.

Atmospheric water vapour transport in ACCESS‐S2 and the potential for enhancing skill of subseasonal forecasts of precipitation

AbstractExtended warning of above‐average and extreme precipitation is valuable to a wide range of stakeholders. However, the sporadic nature of precipitation makes it difficult to forecast skilfully beyond one week. Subseasonal forecasting is a growing area of science that aims to predict average weather conditions multiple weeks in advance using dynamical models. Building on recent work in this area, we test the hypothesis that using large‐scale horizontal moisture transport as a predictor for precipitation may increase the forecast skill of the above‐median and high‐precipitation weeks on subseasonal time‐scales. We analysed retrospective forecast (hindcast) sets from the Australian Bureau of Meteorology's latest operational subseasonal‐to‐seasonal forecasting model, ACCESS‐S2, to compare the forecast skill of precipitation using integrated water vapour transport (IVT) as a proxy, compared to using precipitation forecasts directly. We show that ACCESS‐S2 precipitation generally produces more skilful forecasts, except over some regions where IVT could be a useful additional diagnostic for warning of heavy precipitation events.

Multiweek tropical cyclone prediction for the Southern Hemisphere in ACCESS‐S2: Maintaining operational skill and continuity of service

AbstractThe skill of subseasonal (multiweek) forecasts of tropical cyclone (TC) occurrence over the Southern Hemisphere is examined in the Australian Bureau of Meteorology's (BoM) multiweek to seasonal prediction system, ACCESS‐S2. Relative to its predecessor, ACCESS‐S1, ACCESS‐S2 shows improved biases in spatial TC frequency in the South Pacific and Southwest Indian Ocean. However, there is no improvement to the known negative bias in TC frequency off the coast of NW Australia. The ability of ACCESS‐S2 to provide probabilistic forecasts of TC occurrence for the Southern Hemisphere on multiweek timescales is examined using reliability measures and Brier skill scores. For the period November–February 1990–2012, both ACCESS‐S1 and ACCESS‐S2 show positive skill relative to climatology for calibrated forecasts out to week 5. However, the skill of ACCESS‐S2 is slightly reduced compared to ACCESS‐S1 at all lead times, which may be due to the fewer number of ensemble members available. For the full ACCESS‐S2 hindcast period, November–April 1981–2018, ACCESS‐S2 again shows positive skill of calibrated forecasts over climatology out to week 5. For weeks 1–2, skill is reduced compared to the shorter 1990–2012 period; whereas it is marginally improved for longer lead times (weeks 3–5). Use of lagged ensembles, an alternative linear regression calibration, as well as removing weaker model TCs were examined to potentially improve the skill of ACCESS‐S2 forecasts; however, none of these methods were able to significantly increase skill at all lead times. Continued use of the original calibration method is therefore recommended in order to retain skill and continuity of service of the BoM operational and public multiweek TC forecasts.

Verification and intercomparison of global ocean Eulerian near-surface currents

The OceanPredict task team for Intercomparison and Validation (IV-TT) has established the CLASS4 data standard for routine forecast verification against selected reference observing platforms. The set of CLASS4 reference data has been recently extended to include near-surface currents derived from the trajectories of drifting buoys drogued at 15 m. We have applied these data to the Ocean Model, Analysis and Prediction System (OceanMAPS) at the Australian Bureau of Meteorology (ABoM) for verification and inter-comparison with Mercator Océan International ocean forecast system (MOi), the operational models of the Met Office, UK: Forecast Ocean Assimilation Model (FOAM) and Coupled Atmosphere-Land-Ocean-Ice Data Assimilation (CPLDA) systems, and the Global Ice Ocean Prediction System (GDPS-GIOPS) at the Canadian Centre for Meteorological and Environmental Prediction (CCMEP). The aims for this verification analysis are to extend the routine monitoring of the operational system; to assess the OceanMAPS skill against other models; and to inform our stakeholders of the OceanMAPS performance.We have evaluated the impacts of adding Stokes drift and tidal currents from separate global wave and global tide models to the model currents on the verification of currents. Including Stokes drift leads to significant improvements in representation and the metrics for model skill while inclusion of tides has no significant impact on the global statistics. Zonal currents have a stronger signal than the meridional currents as indicated by their high correlation with observations, and lower errors. This indicates zonal currents on average are more predictable and persistent than the meridional currents, resulting in improved representation in the models.Overall, MOi, and the new version of OceanMAPS (4.0i), and FOAM show marginally better performance against the observations relative to other models. Although there are significant differences in the configurations of the eight models under evaluation, all the models are shown to be remarkably statistically equivalent with consistent spatial and temporal patterns indicating the main differences are attributable to unrepresented processes. We therefore conclude that there is significant scope to further improve the representation of the modelled currents with the observations.

Real-Time Monitoring of Weather Radar Network Calibration and Antenna Pointing

Abstract We present an integrated framework that leverages multiple weather radar calibration and monitoring techniques to provide real-time diagnostics on reflectivity calibration, antenna pointing, and dual-polarization moments. This framework uses a volume-matching technique to track the absolute calibration of radar reflectivity with respect to the Global Precipitation Measurement (GPM) spaceborne radar, the relative calibration adjustment (RCA) technique to track relative changes in the radar calibration constant, the solar calibration technique to track daily change in solar power and antenna pointing error, and techniques that track properties of light-rain medium to monitor the differential reflectivity and dual-polarization moments. This framework allows for an evaluation of various calibration and monitoring techniques. For example, we found that a change in the RCA is highly correlated to a change in absolute calibration, with respect to GPM, if a change in antenna pointing can first be ruled out. It is currently monitoring 67+ radars from the Australian radar network. Because of the diverse and evolving nature of the Australian radar network, flexibility and modularity are at the core of the calibration framework. The framework can tailor its diagnostics to the specific characteristics of a radar (band, beamwidth, etc.). Because of its modularity, it can be expanded with new techniques to provide additional diagnostics (e.g., monitoring of radar sensitivity). The results are presented in an interactive dashboard at different level of details for a wide and diverse audience (radar engineers, researchers, forecasters, and management), and it is operational at the Australian Bureau of Meteorology. Significance Statement Weather radars, like all instruments, require maintenance and upgrades. Rainfall measurements are highly variable and sensitive to change, and this can lead to inconsistencies within a radar network. Calibration is the process to counteract those inconsistencies. Any calibration requires a fixed standard to which the changed/upgraded radar can be compared. The SCAR calibration framework presented herein makes use of several standards to retrieve a full set of diagnostics about the radar data. We apply these techniques over the entire Australian weather radar network and demonstrate that, by using this integrated approach, absolute calibration can be achieved to within 1 dBZ of reflectivity, antenna pointing can be monitored within 0.1°, and the various measurements of the radars can be quality controlled.

P-120 Seasonal effects at the time of oocyte collection on the success of frozen embryo transfers

Abstract Study question Does season and duration of daylight hours at the time of oocyte collection impact on live birth rates following frozen embryo transfer? Summary answer Frozen embryo transfer following oocyte collection in summer had 30% increased odds of live birth compared to oocytes collected in autumn. What is known already Season and conditions at the time of fresh or frozen embryo transfer do not appear to impact live birth rates. Recent data from the northwest USA suggest increased live birth rates following frozen embryo transfer from oocytes collected in summer, independent of the season at the time of transfer. Study design, size, duration Retrospective cohort study of all frozen embryo transfers at a single centre in Western Australia from 2013 - 2021 for which oocyte collection also occurred between these dates. This study included 3,659 frozen embryo transfers with embryos generated from 2,155 IVF cycles in 1,835 patients. Data were analysed by season, temperature and recorded sunshine hours at the time of oocyte collection and embryo transfer. Participants/materials, setting, methods All transfers during the study period were included; demographics, IVF cycle characteristics, embryological data and clinical outcomes were collected from the clinic database. Weather data were recorded by the Australian Bureau of Meteorology. Statistical adjustment was performed for factors known to affect live birth rates, and we corrected for multiple cycles in the same patient and conditions at embryo transfer. Main results and the role of chance Compared to frozen embryo transfer (FET) with oocyte retrieval dates in autumn, FET with oocyte retrieval dates in summer had 30% increased odds of live birth (OR: 1.30, 95% CI: 1.04-1.62). Temperature at the time of oocyte retrieval did not affect LBR; there was a 28% increase in odds of live birth when the sunshine hours were in the highest tertile compared to the lowest on the day of oocyte retrieval (OR: 1.28, 95% CI: 1.06-1.53). These findings remained consistent when adjusted for season or sunshine hours on the day of FET respectively. Odds of livebirth were decreased by 18% when the minimum temperature on the day of transfer was high compared with low (OR: 0.82, 95% CI: 0.69−0.99). The duration of bright sunshine on the day of oocyte retrieval appeared to drive the seasonal variations, whereas ambient temperature was not associated with clinical outcomes. Limitations, reasons for caution This was a retrospective study, however study populations across seasons, temperature tertiles and sunshine tertiles were similar. We did not analyse subgroups of low prognosis patients. We also did not analyse environmental pollutants in this study, nor did we assess impacts on sperm quality. These may all affect the results. Wider implications of the findings Optimal conditions for livebirth appear to be associated with summer and increased sunshine hours on the day of oocyte retrieval. Consideration could be given to timing oocyte retrieval to optimise outcomes. The mechanisms remain to be clarified, though may involve melatonin and its impact on oocyte quality. Trial registration number 2022/ET000980

Validation of Himawari-8 Sea Surface Temperature Retrievals Using Infrared SST Autonomous Radiometer Measurements

This study has evaluated five years (2016–2020) of Himawari-8 (H8) Sea Surface Temperature (SST) Level 2 Pre-processed (L2P) data produced by the Australian Bureau of Meteorology (Bureau) against shipborne radiometer SST measurements obtained from the Infrared SST Autonomous Radiometer (ISAR) onboard research vessel RV Investigator. Before being used, all data sets employed in this study have gone through careful quality control, and only the most trustworthy measurements are retained. With a large matchup database (31,871 collocations in total, including 16,418 during daytime and 15,453 during night-time), it is found that the Bureau H8 SST product is of good quality, with a mean bias ± standard deviation (SD) of −0.12 °C ± 0.47 °C for the daytime and −0.04 °C ± 0.37 °C for the night-time. The performance of the H8 data under different environmental conditions, determined by the observations obtained concurrently from RV Investigator, is examined. Daytime and night-time satellite data behave slightly differently. During the daytime, a cold bias can be seen under almost all environmental conditions, including for most values of wind speed, SST, and relative humidity. On the other hand, the performance of the night-time H8 SST product is consistently more stable under most meteorological conditions with the mean bias usually close to zero.