Australian Bureau Of Meteorology Research Articles

Increased levels of solar radiation are associated with reduced type-2 diabetes prevalence: A cross-sectional study of Australian postcodes

Type-2 diabetes is a leading cause of death and disability. Emerging evidence suggests that ultraviolet radiation or sun exposure may limit its development. We used freely available online datasets to evaluate the associations between solar radiation and type-2 diabetes prevalence across Australia. We extracted prevalence data for 1822 postcodes from the Australian Diabetes Map on 25 January 2020. Daily solar radiation data averaged over 30-years (1990–2019) were collated from online databases (Australian Bureau of Meteorology). Population-weighted linear regression models were adjusted for covariates at the postcode level including socioeconomic status (IRSAD), remoteness, mean age, gender, Aboriginal and Torres Strait Islander status, as well as mean annual ambient temperature (1961–1990) and rainfall (1981–2010). A consistent inverse correlation was observed between type-2 diabetes prevalence and solar radiation, after adjusting for these covariates (ß (coefficient of regression) = −0.045; 95% CI: −0.086, −0.0051; p = 0.027). However, the relative contribution of solar radiation towards type-2 diabetes prevalence was small (2.1%) in this model. Other significant correlations between type-2 diabetes prevalence and covariates included: socioeconomic status (ß = −0.017; 95% CI: −0.017, −0.016; p < 0.001), mean age (ß = 0.041; 95% CI: 0.028, 0.054; p < 0.015), remoteness (ß = −0.05; 95% CI: −0.088, −0.011; p < 0.001) and rainfall (ß = −0.0008; 95% CI: −0.00097, −0.00067; p < 0.001). In conclusion, in Australian postcodes, higher levels of solar radiation and rainfall was associated with reduced type-2 diabetes prevalence. Further studies are needed that consider lifestyle covariates such as physical activity.

A national hydrological projections service for Australia

Australia’s water management and infrastructure decision making needs detailed high-resolution climate and water forecasts and projections to inform the process of water-sensitive decision making, raise awareness and understand future risks, and to mitigate, adapt, and capitalise on the impacts of a changing climate in industries ranging from agriculture and water management to fire risk. Until now no nationally consistent information on hydrological change has existed preventing standardised and comparable impact assessments across multiple spatial and temporal scales, particularly for the management of hazards and risks spanning jurisdictional boundaries. Information available for Australian hydrological impact assessments to date has been produced through state and territory based regional downscaling efforts, across limited timescales and using different interpretation methods, particularly regarding downscaling and assessing hydrological impacts.To address this need, the Australian Bureau of Meteorology has released an innovative, seamless national landscape water service called the Australian Water Outlook. This service provides historical data on water availability with forecast products, as well as hydrological impact projections, using the Bureau’s operational Australian Water Landscape Water Balance model (AWRA-L). The service combines state-of-the-art hydrological research with a user-centred design approach to develop the service and user interface to meet the needs of Australia water resource managers. The innovative science underpinning the hydrological projections service includes nationally consistent downscaling and bias-correction approaches using three statistical methods in an operational framework to produce a future projections dataset including climate (rainfall, solar radiation, temperature, and wind) and water (soil moisture, potential evapotranspiration, and runoff) information in a consistent format.The hydrological projections component of the service showcases novel ways of communicating the plausible impacts of climate change on Australia’s water resources through a ‘storylines’ approach. As part of the service development the Bureau of Meteorology is also working with early adopters and priority customers to demonstrate the application of the service to water management. This paper describes the inception and development of the service informed by a process of user-centred design, and provides examples of storyline approaches for water resources management.

Multi-Sensor Sea Surface Temperature Products from the Australian Bureau of Meteorology

Sea surface temperature (SST) products that can resolve fine scale features, such as sub-mesoscale eddies, ocean fronts and coastal upwelling, are increasingly in demand. In response to user requirements for gap-free, highest spatial resolution, best quality and highest accuracy SST data, the Australian Bureau of Meteorology (BoM) produces operational, real-time Multi-sensor SST level 3 products by compositing SST from Advanced Very-High-Resolution Radiometer (AVHRR) sensors on Meteorological Operational satellite (MetOp)-B and National Oceanic and Atmospheric Administration (NOAA) 18, along with SST from Visible Infrared Imaging Radiometer Suite (VIIRS) sensors on the Suomi National Polar-orbiting Partnership (Suomi NPP) and NOAA 20 polar-orbiting satellites for the Australian Integrated Marine Observing System (IMOS) project. Here we discuss our method to combine data from different sensors and present validation of the satellite-derived SST against in situ SST data. The Multi-sensor Level 3 Super Collated (L3S) SSTs exhibit significantly greater spatial coverage and improved accuracy compared with the pre-existing IMOS AVHRR-only L3S SSTs. When compared to the Geo Polar Blended level 4 analysis SST data over the Great Barrier Reef, Multi-sensor L3S SST differs by less than 1 °C while exhibiting a wider range of SSTs over the region. It shows more variability and restores small-scale features better than the Geo Polar Blended level 4 analysis SST data. The operational Multi-sensor L3S SST products are used as input for applications such as IMOS OceanCurrent and the BoM ReefTemp Next-Generation Coral Bleaching Nowcasting service and provide useful insight into the study of marine heatwaves and ocean upwelling in near-coastal regions.

Calibrating anomalies improves forecasting of daily reference crop evapotranspiration

• We develop a new calibration strategy to enhance ET o forecasting. • This strategy improves the representation of ET o seasonality. • This strategy adds skills to calibrated ET o forecasts. • We validate the effectiveness and robustness of the strategy. Forecasting of short-term reference crop evapotranspiration (ET o ) provides valuable information for hydrological, agricultural, and ecological applications. ET o forecasts can be derived from weather forecasts of Numerical Weather Prediction (NWP) models, but such raw forecasts need to be calibrated to correct errors and improve reliability. This study calibrates the short-term ET o forecasts constructed with weather forecasts from the Australian Bureau of Meteorology's Australian Community Climate and Earth-System Simulator G2 version (ACCESS-G2) model, using the recently developed Seasonally Coherent Calibration (SCC) model. The monthly parameterization of the SCC model will not be able to capture the increasing or decreasing trends in ET o at the submonthly scale, posing a challenge for effective forecast calibration. To address this challenge, we developed a new calibration strategy based on ET o anomalies and climatological mean. We thoroughly evaluated this strategy at both the continental and weather station scales. Results indicate that calibrating ET o anomalies improves the correlation coefficient between calibrated forecasts and observations by up to 10%, and increases forecast skill scores by up to 200%, with more significant improvements found at longer lead times (9-day lead time vs. 1-day lead time). Improvements in forecast quality in calibrations across two spatial scales based on different types of observations (gridded observations vs. weather station observations) validate the effectiveness and robustness of the developed strategy. We anticipate that this strategy will be applicable to other calibration models to enhance NWP-based ET o forecasting.

The structure of strong Indian monsoon low‐pressure systems in subseasonal‐to‐seasonal prediction models

AbstractThe structure of strong Indian monsoon low‐pressure systems (LPSs) up to forecast lead times of 15 days in 11 models of the Subseasonal‐to‐Seasonal (S2S) Prediction Project is analysed. Strong LPS (SLPS) tracks are obtained from a catalogue of LPSs tracked in all ensemble members of the S2S models during a common reforecast period of June–September 1999–2010. SLPSs, which have a minimum intensity equal to at least the upper‐quartile intensity of all LPSs, are then composited to generate horizontal and vertical structures of several dynamic and thermodynamic fields. The evolution of fields with forecast lead time and during LPS lifecycle is analysed. Furthermore, the simulation of the lower‐tropospheric monsoon circulation, precipitation biases, and the precipitation contribution of LPSs are analysed. All S2S models and the multimodel mean simulate the lower‐tropospheric monsoon circulation, but prominent dry biases are observed in the Australian Bureau of Meteorology and Environment and Climate Change Canada models. The precipitation contribution of LPSs to the summer mean precipitation is smaller in all S2S models than in tracks derived from ERA‐Interim reanalysis. The location and amplitude of the lower‐tropospheric cold core and the location of maximum precipitation are not well simulated by many models, particularly by the Hydrometeorological Centre of Russia model, in which the cold core is missing altogether. The structure of relative vorticity anomaly in all S2S models and the multimodel mean is shallower and weaker than in ERA‐Interim and MERRA‐2 reanalyses. Though the cold core intensifies through the LPS lifecycle in all models, the warm core features a midlife maximum, except in models such as Australian Bureau of Meteorology and China Meteorological Administration. These results demonstrate the potential for S2S models at simulating the structure of SLPSs, benefiting stakeholders that use S2S models for forecasting.

Monthly Rainfall Prediction at Catchment Level with the Facebook Prophet Model Using Observed and CMIP5 Decadal Data

Early prediction of rainfall is important for the planning of agriculture, water infrastructure, and other socio-economic developments. The near-term prediction (e.g., 10 years) of hydrologic data is a recent development in GCM (General Circulation Model) simulations, e.g., the CMIP5 (Coupled Modelled Intercomparison Project Phase 5) decadal experiments. The prediction of monthly rainfall on a decadal time scale is an important step for catchment management. Previous studies have considered stochastic models using observed time series data only for rainfall prediction, but no studies have used GCM decadal data together with observed data at the catchment level. This study used the Facebook Prophet (FBP) model and six machine learning (ML) regression algorithms for the prediction of monthly rainfall on a decadal time scale for the Brisbane River catchment in Queensland, Australia. Monthly hindcast decadal precipitation data of eight GCMs (EC-EARTH MIROC4h, MRI-CGCM3, MPI-ESM-LR, MPI-ESM-MR, MIROC5, CanCM4, and CMCC-CM) were downloaded from the CMIP5 data portal, and the observed data were collected from the Australian Bureau of Meteorology. At first, the FBP model was used for predictions based on: (i) the observed data only; and (ii) a combination of observed and CMIP5 decadal data. In the next step, predictions were performed through ML regressions where CMIP5 decadal data were used as features and corresponding observed data were used as target variables. The prediction skills were assessed through several skill tests, including Pearson Correlation Coefficient (PCC), Anomaly Correlation Coefficient (ACC), Index of Agreement (IA), and Mean Absolute Error (MAE). Upon comparing the skills, this study found that predictions based on a combination of observed and CMIP5 decadal data through the FBP model provided better skills than the predictions based on the observed data only. The optimal performance of the FBP model, especially for the dry periods, was mainly due to its multiplicative seasonality function.

Pioneer attempt of incorporating four variables in generalised equations for predicting water savings through rainwater tanks

ABSTRACT This paper presents the development of robust equations for calculating expected water savings through rainwater tanks, which are dependent on annual rainfall, rainwater demand, tank size and roof area. The city of Brisbane (Australia) was selected for this purpose. A modified version of the earlier developed daily water balance model, eTank was used to calculate annual water savings under different scenarios using daily rainfall data collected from the Australian Bureau of Meteorology. Several sets of charts on varying roof size, demand, tank size and annual rainfall were produced from the simulated results. Eventually, the sets of charts were converted to a single generalised equation, each for one of the four selected regions in Brisbane. Results from the developed equations were seen matching with the model (eTank) simulated results. Such equations can be easily incorporated into mobile apps or computer programs to help quick calculations for expected water savings.

Does game-day circadian misalignment or environmental temperature influence team performance in the Australian Football League? An examination of historical data

ObjectivesExamine Australian Football League results for evidence that game-day circadian misalignment or environmental temperature influence games with competing teams from each of Western Australia and Victoria. DesignRetrospective observational study. MethodsData were obtained for games (n = 791) contested by Western Australian and Victorian teams; including game location, start time, and outcome. Start times were categorised as afternoon, twilight, or night. Game-day maximum temperature (°C) for afternoon games was obtained from the Australian Bureau of Meteorology. Mixed effects generalised linear models examined evidence for a circadian advantage to Victorian teams in afternoon games, and to Western Australian teams in night games. Models examined evidence for an advantage to Western Australian teams as game-day temperature increases. ResultsOdds of winning and point margin for home games played in the afternoon, twilight, and night, were not different between Victorian and Western Australian teams (p > 0.05). For afternoon games, each 1 °C increase in temperature improved odds of Western Australian teams winning by 11% (p < 0.001) and their point margin by 2.1 points (p < 0.001). For games played in Victoria, each 1 °C increase in temperature improved odds of Western Australian teams winning by 6% (p = 0.028) and their point margin by 1.7 points (p = 0.005). ConclusionsThere is no evidence here that circadian misalignment between teams influences game outcomes in the AFL. Western Australian teams are more likely to win afternoon games played in warm conditions. This may reflect superior heat acclimatisation among AFL players based in Western Australia.

Development in a state of climate change: an Australian case study of government response

Purpose This research seeks to understand the potential impact to investors from government responses to climate change risk, as reflected in changes to planning processes made after significant weather events.Design/methodology/approach The research examines the land planning responses within a select local government authority (“LGA”) area following four significant weather events, in order to identify any changes made, and the impact on future development proposals. The LGA selected is the Central Coast Council, which is a coastal LGA in the Australian State of New South Wales. The research engaged with the publicly accessible records available on the Central Coast Council, Australian Bureau of Meteorology and other websites; and extant literature.Findings The research reveals that some adjustments were made by the Central Coast Council, and or the State government, to relevant laws, policies and processes following these events. These changes, however, tended to focus on imposing additional requirements on future development applications, rather than on requiring changes to current structures, or prohibiting further development works.Research limitations/implications The research has three limitations: (1) land law in Australia varies, as each State and Territory, and LGA, has specific laws, policies and processes; (2) as laws and policies are subject to change, it was necessary to select points in time at which to engage with those laws and processes; and (3) COVID-19's impact on domestic Australian travel [the authors could not travel interstate] meant only documents available on the Internet were considered, however, not all documents relating to development; or changes to laws and processes were easily accessible online. As the research focussed on one case study area, this may limit the applicability of the results to other areas. However, as extreme events are international, the related issues are a concern in all areas.Practical implications This research confirms the results of other extant research, which observed that some risks cannot be properly mitigated, such that any development in an at-risk area remains at risk. It also identifies that more current, accurate and publicly accessible data are required to enable investors to more easily and accurately identify all risks affecting a property.Originality/value The research provides a snapshot of one LGA's response to the physical risks arising from climate change events. As investors and other organisations integrate and build up their analysis of climate risks to their portfolios and organisations, governments become more aware of the long-term effects of climate change and consistently with extant research; this research indicates that a greater awareness is required of current risks and action to manage the short-term effects and cost challenges, in addition to the long-term adaptation requirements.

Past and future coastal flooding in Pacific Small-Island Nations: insights from the Pacific Sea Level and Geodetic Monitoring (PSLGM) Project tide gauges

Sea level rise is increasing the frequency of coastal flooding globally, and low-lying communities are particularly vulnerable. We present an assessment of historical and projected changes in coastal flooding in 11 Pacific small-island nations, using tide gauge data from the Australian Bureau of Meteorology. We derive impact-, event- and percentile-based thresholds to calculate historical exceedance frequencies. Projections of future exceedance frequencies are then made using the recent suite of Shared Socioeconomic Pathways (SSPs) emission scenarios (Sixth Assessment Report). We find that exceedances of the percentile thresholds have increased in the last decade at all locations, with sites seeing exceedances in months where exceedances were previously rare or unseen in the sea level record. In the future, daily threshold exceedances occur after 50–115 cm of sea level rise, depending on location. Such levels are currently projected to be reached between 2080 and 2130 according to high emissions scenario SSP5–8.5. Low emissions scenario, SSP1–1.9, shows sea level rise resulting in 25–75 days of exceedances by 2050 for the 11 locations. This increased frequency of coastal flooding highlights the changing nature of coastal flood risk in the Pacific, with extreme weather and wave events being increasingly unnecessary for inundation to occur. Further, this work highlights how underlying increases in coastal flooding frequency pose a growing risk of exacerbating inundation associated with extreme weather or waves. Better flood monitoring and reporting will improve the accuracy of impact thresholds, strengthening the relevance of the results presented here for coastal emergency and planning managers.

APS2-ACCESS-C2: the first Australian operational NWP convection-permitting model

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.

Trend Analysis and Temporal and Spatial Distribution of Wet Bulb Globe Temperature as a Heat Stress Index in Iran during the Summer Season over a 30-year Period

Introduction: Global warming is one of the most important environmental problems that have raised researchers’ attention. The present study aimed to analyze heat stress trends using the Wet Bulb Globe Temperature (WBGT) index in the country of Iran during the summer over a 30-year period.&#x0D; Materials and Methods: Daily summertime statistical data regarding mean temperature and mean relative humidity, taken from 40 synoptic meteorological stations across Iran during a 30-year period were obtained from the Iranian National Meteorological Department. The De Martonne climate classification system was used to categorize various climate regions of Iran. The WBGT index was calculated using the formula given by the Australian Bureau of Meteorology. The Mann-Kendall statistical test and the Sen's slope estimator were used to analyze the trends of the WBGT index.&#x0D; Results: The WBGT index had an upward trend during the three months of June, July, and August in 71.42%, 57.14%, and 66.66% of all stations and this trend was statistically significant in 53.32%, 50%, and 42.85% of those stations, respectively. Moreover, throughout the summer, 45% of the WBGT index measurements were in the medium range (18-23°C), 37.5% were in the high range (23-28°C), and 17.5% were in the very high range (&gt; 28°C).&#x0D; Conclusion: The WBGT index followed an upward trend during the summer, especially in semi-arid regions of Iran. Considering the phenomenon of global warming, it is essential to monitor, plan ahead, and take necessary precaution measures for sensitive populations who are at high risk areas of the country.

Extending a joint probability modelling approach for post-processing ensemble precipitation forecasts from numerical weather prediction models

Statistical post-processing has been widely employed to correct bias and dispersion errors in raw ensemble precipitation forecasts from numerical weather prediction models. One prominent post-processing scheme is to establish a joint probability model by fitting a bivariate distribution of raw forecasts and corresponding observations. However, current joint probability models only incorporate ensemble mean as the predictor, and ensemble spread is not considered. This is a major disadvantage of joint probability models as ensemble spread can be informative for forecast uncertainty. In this paper, we propose a two-step calibration approach to combine the strengths of joint probability models and the useful information included in the ensemble spread. In the first step, we take the seasonally coherent calibration (SCC) model as an example of joint probability models to calibrate the ensemble mean. As SCC for precipitation forecasts involves transformations for data normalization and special treatments of zero values, we explore three different ways to estimate ensemble mean values when establishing the SCC model. In the second step, we re-calibrate the ensemble forecasts produced in the first step to incorporate ensemble spread information from the raw forecasts. The performance of this two-step calibration is evaluated using ensemble precipitation forecasts from the Australian Bureau of Meteorology. We find that forecasts calibrated using the two-step calibration have better skills than SCC calibrated forecasts, especially for heavy precipitation events. Strengths of joint probability models and raw ensemble spread information are well utilized in the proposed two-step calibration approach.

Healthcare Costs of Emergency Department Presentations Related to Heatwave Intensity and Severity

BACKGROUND AND AIM: The health impacts of heatwaves are a growing public health concern. However, little is known about the healthcare costs of heatwave-related morbidity. This study aims to examine the relationship between heatwaves and costs of emergency department (ED) presentations and to quantify heat-attributable presentations and costs in Adelaide, South Australia. METHODS: Daily data on ED presentations and associated costs for the period 2014-2017 were obtained from the South Australian Department of Health and Wellbeing. Heatwave severity and intensity were determined using the excess heat factor (EHF) index, obtained from the Australian Bureau of Meteorology. A distributed lag non-linear model (DLNM) was used to quantify the cumulative risk of heatwave-intensity over a lag of 0-7 days on ED presentations and costs. Effects of heatwaves were estimated relative to no heatwave. The number of ED presentations and costs attributable to heatwaves was calculated separately for two EHF severity categories (low-intensity and severe heatwaves). Subgroup analyses by disease-diagnosis groups and age categories were performed. RESULTS:For most disease diagnosis and age categories, low-intensity and severe heatwaves were associated with higher rates of ED presentations and costs. We estimated a total of 1,161 (95% empirical confidence interval (eCI): 342, 1,944) heatwave-attributable all-cause ED presentations and an associated healthcare costs of AU$1,020,330 (95% eCI: 224,851, 1,804,745) during the warm seasons of 2014-2017. Heat-related illness was the disease category contributing most to ED presentations and costs. CONCLUSIONS:Heatwaves produced a significant case-load and cost burden to the ED. The young and old peoples were most susceptible to heat. Developing tailored interventions for the most vulnerable populations may help reduce the health impacts of heatwaves and to minimise the cost burden to the healthcare system. KEYWORDS: Temperature, Climate, Temperature extremes

Cumulative positive contributions of propagating ISO to the quick low-level atmospheric response during El Niño developing years

Based on the Australian Bureau of Meteorology (BoM) El Niño alert system, this study investigates the atmospheric and oceanic conditions during El Niño developing years between 1982 and 2016. It is found that there is a 2–5-month (subseasonal-to-seasonal) lag to establish the steady low-level atmospheric (or the Southern Oscillation Index, SOI) response than the steady El Niño-pattern Sea Surface Temperature Anomaly (SSTA), which is defined as the critical period in this research. According to the duration of this critical period, the quick and slow steady atmospheric response years can be identified among all El Niño–Southern Oscillation (ENSO) developing events. The quick establishments of the Sea Level Pressure Anomaly (SLPA) in the tropical atmosphere are proved to be closely related to the tropical atmospheric Intra-seasonal Oscillation (ISO) events. In the quick response years, the ISO events can even propagate to the eastern tropical Pacific, which lead to the cumulative negative Outgoing Longwave Radiation (OLR) and SLP anomalies there, and make a positive contribution to the quick atmospheric response at the end of the critical period. However, the eastward-propagation of ISO events is mainly restricted in the tropical Western Pacific in the slow response years, causing slow steady atmospheric response with almost no contributions from ISO. Furthermore, observations and several simulations are used to understand this propagation differences of the ISO between quick and slow response years.

Intercomparison of drift correction alternatives for CMIP5 decadal precipitation

AbstractDecadal experiments' output of the coupled model inter‐comparison project phase‐5 (CMIP5) contains significant model drift. For practical use of CMIP5 decadal climate variables, it is necessary to correct this model drift. In previous studies, drift correction of CMIP5 decadal data of temperature and temperature‐based climate indices have been investigated, but there is no study that investigated the drift correction of decadal precipitation at a catchment scale. This study investigates the practical use of CMIP5 decadal precipitation data on seasonal scale using different drift correction alternatives for Brisbane catchment in Australia. For this, decadal monthly precipitation data from eight GCMs (EC‐EARTH, MRI‐CGCM3, MPI‐ESM‐LR, MPI‐ESM‐MR, MIROC4h, MIROC5 and CanCM4) were cut for the Australian region. By using the bi‐linear interpolation, the GCM data were re‐gridded to 0.05° × 0.05° spatial resolution matching with the observed gridded precipitation data collected from the Australian Bureau of Meteorology. Both model and observed data were subset for the Brisbane catchment and aggregated into seasonal means for Australian seasons. Four drift correction alternatives including one new modified method were employed for the selected GCM models and the models were categorized based on their performances using different skill scores. The results revealed that the performance of drift correction alternatives vary among different models and initialization years. Although some drift correction methods showed better performances than others for a given model but it is still difficult to suggest the most suitable method for seasonal precipitation. Drift correction approaches for other time scales such as monthly precipitation, and their application for individual ensemble members may be investigated further to better understand the implications of alternative drift corrections for decadal forecasting.

Assessing one-minute diffuse fraction models based on worldwide climate features

Despite the variety of irradiance separation models available in the literature, there is not yet a model that performs equally well for every location worldwide. Nevertheless, separation models still represent an interesting approach when only global irradiance is measured. This work presents further developments of the recently proposed Boland-Ridley-Lauret minute model and proposes 1-min diffuse fraction models for each climate zone in the globe. To accomplish this, 1-min worldwide irradiance data from 51 the Baseline Surface Rresearch Network stations and one from Australian Bureau of Meteorology were used. The Köppen-Geiger classification was used to determine the climate zone of each station: both the simple climate classification (A, B, C, D, E) and the complete classification (Aw, Bsh, etc.). Furthermore, a robust nonlinear regression method was used to build the separation models without removing measurement outliers in advance. The climate-specific models proposed herein present better performance than other models from the literature, such as the BRL, Perez and Skartveit models (developed using hourly irradiance data) and the Engerer model (developed using minute data). Generally, the models proposed for climate zones in the complete Köppen classification presented better similitude with the measured data than the models for the simple classification, especially for stations within climate zones B and E.

Comparison of the Prediction of Indian Monsoon Low Pressure Systems by Subseasonal-to-Seasonal Prediction Models

AbstractThis study analyzes the prediction of Indian monsoon low pressure systems (LPSs) on an extended time scale of 15 days by models of the Subseasonal-to-Seasonal (S2S) prediction project. Using a feature-tracking algorithm, LPSs are identified in 11 S2S models during a common reforecast period of June–September 1999–2010, and then compared with 290 and 281 LPSs tracked in ERA-Interim and MERRA-2 reanalysis datasets. The results show that all S2S models underestimate the frequency of LPSs. They are able to represent transits, genesis, and lysis of LPSs; however, large biases are observed in the Australian Bureau of Meteorology, China Meteorological Administration (CMA), and Hydrometeorological Centre of Russia (HMCR) models. The CMA model exhibits large LPS track position error and the intensity of LPSs is overestimated (underestimated) by most models when verified against ERA-Interim (MERRA-2). The European Centre for Medium-Range Weather Forecasts and Met Office models have the best ensemble spread–error relationship for the track position and intensity, whereas the HMCR model has the worst. Most S2S models are underdispersive—more so for the intensity than the position. We find the influence of errors in the LPS simulation on the pattern of total precipitation biases in all S2S models. In most models, precipitation biases increase with forecast lead time over most of the monsoon core zone. These results demonstrate the potential for S2S models at simulating LPSs, thereby giving the possibility of improved disaster preparedness and water resources planning.

Going Dark

Going Dark

Achieving effective calibration of precipitation forecasts over a continental scale

Study regionAustralia. Study focusWe developed the Seasonally Coherent Calibration (SCC) model to post-process precipitation forecasts from numerical weather prediction (NWP) models. The SCC model is capable of generating high-quality calibrated forecasts which are coherent in climatology, including seasonality, consistent with observations, despite only a short period of forecasts being available. In this study, we post-process deterministic precipitation forecasts from the Australian Bureau of Meteorology’s Australian Community Climate and Earth System Simulator G2 version (ACCESS-G2) NWP model to generate calibrated ensemble forecasts across Australia. We evaluate the effectiveness, robustness, and computational feasibility of forecast post-processing with the SCC model at a high spatial resolution across the continental scale. New hydrological insights for the regionInadequate representation of forecast seasonality in calibration models poses a challenge for the calibration of NWP forecasts. Through this investigation, the statistical assumptions and reparameterization algorithms of the SCC model are confirmed to be effective and robust in addressing the challenge. Calibrated precipitation forecasts could be used to support operational water balance forecasting in Australia. Post-processing of NWP precipitation forecasts with the SCC model will also benefit a broad range of forecast users in Australia by providing accurate, skillful, and reliable ensemble forecasts. Computation strategies adopted in this study will help develop effective procedures for future post-processing of NWP forecasts with sophisticated calibration models at a large spatial scale.