Stations In Australia Research Articles

Single-Station Back-Azimuth Determination with the Receiver Function Rotation Technique Validated by the Locations of Earthquakes, Impacts, and Explosions

Abstract The success of seismometer installations on the ocean floor, polar regions, remote continental areas, and even other planets’ surfaces has sparked renewed interest in determining the location via the azimuthal direction of a seismic event recorded by a single station, also known as the back azimuth (BAZ). However, classical algorithms for the BAZ estimate, like principal component and polarization analyses based on P-wave particle motions, are prone to ambiguities of 180°. Motivated by the sensor orientation correction techniques used for ocean-bottom seismometers and land stations for known event locations, we explore a receiver function rotation (RFR) method to determine the BAZ for events recorded by a single station. It is a parameter search over a range of horizontal component rotation angles from 0° to 360°. The fundamental feature of the method is that the direct P wave in the radial receiver function (RF) will have the maximum amplitude when the rotation from the ZNE system (vertical, north–south, and east–west) to ZRT (vertical, radial, and tangential) is aligned with the BAZ of the incoming P wave. Hence, the largest amplitude at zero time of the ensemble of RFs computed for different horizontal component rotations shows the optimal BAZ, which is consequently free of the 180° ambiguities. The technique’s performance is validated using the well-documented location of the 2017 Democratic People’s Republic of Korea nuclear explosion and over 1200 cataloged earthquakes on the two permanent stations in Australia. We further benchmark the RFR algorithm by the locations of two ground-truth Martian impact events documented by the orbital camera and recorded by InSight’s seismometer. Our method helps enhance the reliability of BAZ estimation as a complementary scheme to other methods. It can be used in remote areas on Earth and on the future missions to the Moon and other planets.

Modelling non-stationarity in extreme rainfall using large-scale climate drivers

Flood estimates used in engineering design are commonly based on intensity–duration–frequency (IDF) curves derived from historical extreme rainfalls. Under global warming these extreme rainfalls are increasing, threatening the capacity of existing infrastructure to resist failure as IDF curves traditionally assume no change in rainfall magnitude. Hence, there is a need to investigate the implications of non-stationarity in extreme rainfalls used to derive IDFs across storm durations and annual exceedance probabilities (AEPs). One way of doing this is to incorporate covariates into the fitted probability distribution. However, there are few studies which examine non-stationarity in extreme rainfall using large-scale climate drivers as covariates, with little consensus on which covariate is the most appropriate. Here we evaluate non-stationarity in extreme rainfall across different durations, from the 1 in 5 AEP to the 1 in 100 AEP, using potential large-scale climate drivers including global and continental mean temperature, continental diurnal temperature range, continental dewpoint temperature, continental precipitable water, the Indian Ocean Dipole, the El Niño Southern Oscillation, and the Southern Annular Mode. These covariates are linked to the three parameters of the Generalized Extreme Value distribution to identify the most appropriate form of non-stationary probability model. We analyse 16 different storm durations from 6 min to 7 day annual maxima across 46 stations in Australia. Based on the Akaike Information Criteria, precipitable water is the superior covariate at a large proportion of stations irrespective of storm duration. However, when the modelled quantile changes are inspected, only global temperature is able to adequately capture the variability in changes across both storm duration and annual exceedance probability. Further, when regional average values of mean temperature, diurnal temperature range, dewpoint temperature and precipitable water were used as covariates, there was no improvement in the model performance compared to continental-wide covariates, particularly for short durations. The results using a mean global temperature covariate show that stationary historical rainfall quantile estimates are underestimated by 12 % – 9 % for frequent (1 in 5 AEP) and 23 % – 13 % for rare (1 in 100 AEP) short duration (6 min – 30 min) events. Moving forward our results suggests infrastructure design needs to incorporate non-stationarity in IDFs to ensure infrastructure and flood planning levels are not under-designed.

Temporal and spatial variations in dust activity in Australia based on remote sensing and reanalysis datasets

Abstract. Spatial and temporal variations in the level of dust activity can provide valuable information for policymaking and climate research. Recently, Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products have been successfully used for retrieving dust aerosol optical depth (DAOD), especially over bright dust source areas, and Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) aerosol reanalysis provides DAOD and additionally other dust-aerosol-related parameters. In this study, spatial and temporal variations in dust activity in Australia were analyzed using MODIS and MERRA-2 combined (M&M) DAOD and MERRA-2 near-surface dust concentrations and estimated PM10 for the period 1980–2020. Validation results show that M&M DAOD has an expected error of ±(0.016+0.15τ) compared to the ground observations at the AErosol RObotic NETwork (AERONET) dust storms on populated areas sites. MERRA-2 near-surface dust concentrations show a power-law relationship with visibility data collected at meteorological stations with an r2 value from 0.18 to 0.44, and the estimated MERRA-2 PM10 shows similar temporal variations and correlates with ground-based PM10 data with an r2 value from 0.14 to 0.44 at six selected stations in Australia. Moreover, MERRA-2 horizontal dust flux shows the same major dust pathways as those in previous studies and similar dust emissions and deposition areas identified using ground-based observations. Dust events based on DAOD over eastern Australia are concentrated in the north in December, are concentrated in the south in February, and can occur anywhere in January. Near-surface dust concentration was found to be the highest (over 200 µg m−3) over the center of Lake Eyre basin in central Australia and radially decreased toward the coast to below 20 µg m−3 via the two main pathways in the southwest and northeast. The ratio of near-surface dust concentration to PM10 shows a similar spatial pattern. Total dust emission was estimated to be 40 Mt (megatonnes) per year over the period 1980–2020, of which nearly 50 % was deposited on land and the rest exported away from the Australian continent.

Investigating the influences of wind function parameterization on evaporation estimation using the generalized complementary principle

The complementary principle (CR) is the most practical method for estimating regional evaporation (E) based only on routine meteorological observations. However, CR-based models have certain problems that must be solved for widespread application, such as the influence of wind function types and parameterization. In this study, the generalized CR with polynomial forms (GCR) as a case study is applied to investigate the influences of two different parameterization schemes of wind function—the Penman wind function (fPM) and theoretical wind function (fSM)—on evaporation estimation. The observed daily E values from nine eddy flux stations in Australia with contrasting climates and land cover types were collected to quantify the influences. The results showed that the GCR successfully estimated the E value of flux stations with both the fPM and fSM, and the E value estimated with fSM was more sensitive to wind speed than that with fPM. The estimated aerodynamic term (EA), estimated evaporation error (Eerror), and parameter αc showed marked differences between the two wind functions. The EA-SM (hereafter, subscripts PM and SM represent the wind function type) is approximately 1.05- to 5.06-fold greater than the EA-PM across stations, with a mean of 2.60 ± 1.33 (mean ± standard deviation). The correlation between Eerror-SM with wind speed (u) was positive with an average slope of 0.19, while that of fSM was negative with an average slope of −0.13. The difference in the calibrated parameter of the GCR (i.e., αc) between the two wind functions varied site-to-site, ranging from 0.02 to 0.31. This study highlights that both wind function parameterization schemes successfully estimate regional evaporation, but highly differed in evaporation components, errors, and parameter. These results can help to understand and adopt proper wind parameterization schemes for wider applications of GCR method. Additional studies are required to generalize the spatial variability of different wind function parameters and their influence on the applicability of the GCR.

The use of information technology for analysis and prediction of climate changes

The article provides a brief overview of the issue of global warming and the factors that influencing climate change. The relevance of the research lies in the need for a comprehensive analysis of climate data and prediction of further trends, both in the short term and the long term, as climate affects evolutionary processes, biosphere balance, the availability of resources essential for the functioning of the technosphere, the amount of primary production produced by producers, all of which collectively ensure the existence of human civilization. Significant fluctuations in the climate, both towards warming and cooling, can disrupt stability and lead to the destruction of the technosphere, economic and social upheavals. The research is based on previous studies conducted using data from 1 100 meteorological stations in Australia, which showed the need to revise the approach to grouping monitoring stations: trends in average temperature changes across the continent, and monitoring stations grouped according to their distance from the coast, showed a general synchronicity of warming and cooling periods, but different dynamic patterns in recent decades. It was decided to study the climate data of one state (Victoria) and compare the results with previous studies. Python programming language packages were used for data processing, analysis, visualization, and forecasting. Data from two monitoring stations in cities located in the south and north of the state were separately analyzed, showing a general trend of slight warming. The trend in temperature changes in the state showed a pattern different from the continental dynamics obtained in previous studies, which confirmed the validity of the chosen approach to studying the climate on the continent. Based on the information obtained, the average temperature was predicted for both the entire state and selected cities.

Developing a statistical approach of evaluating daily maximum and minimum temperature observations from third‐party automatic weather stations in Australia

AbstractThird‐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.

Inheritance and Inhabitants: The Material Place-Making Practices of Community Radio

ABSTRACT Community radio, a “third space” outside of commercial and state-based broadcasting, contributes to democratizing media access. Physical space plays a key role in this, yet the materiality of community radio stations is often liminal and precarious. DIY place-making practices are vital in establishing the accessibility and feel of the physical space, and, in turn, the participatory and deliberative environment. This article explores the processes and impacts of place-making practices within community radio stations in Australia and India. The findings suggest the constructed materiality of community radio has significant impacts on how space is used by practitioners, and the broader community.

STRESS ANALYSIS AND CHARACTERISTICS DUE TO THE SOUTH JAVA EARTHQUAKE, APRIL 10, 2021

The April 10, 2021, earthquake in the south of East Java was classified as destructive. The secondary impact of this earthquake was quite significant. Many houses collapsed, and not a few casualties. This earthquake is unique because usually, destructive earthquakes occur at shallow depths, but earthquakes with a magnitude of 6.1 are classified as medium-depth earthquakes at sea. The earthquake in the south of East Java is classified as an intraplate earthquake because it is located on the continental plate, not in the plate contact area. The question is whether the damage that occurred to the building was purely due to the magnitude of the stress released by the earthquake or whether there were other factors. This study uses seismogram data for the earthquake south of East Java on April 10, 2021, with a radius (∆) of 300-1000 recorded at MEEK, MORW, and ARMA stations in Australia. It calculates the amount of stress based on the stress drop, while the stress column determines the stress mechanism. Calculation of stress drop from the source spectrum is obtained by the deconvolution method, namely the seismogram component separation technique in the form of Source (f), Path (f), Site (f), and Instrument (f). The analysis of the observed displacement spectrum used the Nelder Mead Simplex nonlinear inversion method. Meanwhile, the Stress Columb calculation was obtained using the Columb 3.3 program from the United States Geological Survey (USGS). The result of this research is that the stress drop value is 1.69 MPa, with the type of focus mechanism being a thrust fault in the sea. The earthquake in the south of East Java was caused by rock activity in the intraplate. The value of the stress drop is more significant when compared to the subduction contact area. This area is of intraplate rock with various variations, and earthquakes are rare. This study aims to analyze the stress, both the magnitude of the stress drop and the mechanism of the column stress results, so that the stress caused by the earthquake can be known and why the earthquake in the south of East Java is destructive. The quake in Southeast Java is classified as dangerous, not because of the magnitude of the stress generated or its mechanism. The damage was due to the amplification of earthquake waves in the building. The injury occurred because most of the buildings were built on soft soil, especially in several areas in East Java, such as Lumajang, Pasuruan, Trenggalek, Probolinggo, Ponorogo, Jember, Tulunggagung, Nganjuk, Pacitan, and several urban areas, namely Blitar, Kediri, Malang, and Stone. So, there is a need for earthquake disaster mitigation, especially in densely populated areas that live on soft soil. This mitigation effort is to minimize the occurrence of casualties by building buildings according to earthquake-resistant standards and avoiding development in the regions that have the potential for amplification of earthquake waves.

Improving the Accuracy of MODIS Near-Infrared Water Vapor Product Under all Weather Conditions Based on Machine Learning Considering Multiple Dependence Parameters

We developed six machine learning based calibration models to improve the all-weather accuracy of precipitable water vapor (PWV) product from near-infrared (NIR) observations of the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument, i.e. MOD05 PWV. The six machine learning approaches are: Back Propagation Neural Network (BPNN), Gradient Boosting Decision Tree (GBDT), Generalized Regression Neural Network (GRNN), K-Nearest Neighbor (KNN), Multilayer Perceptron Neural Network (MLPNN), and eXtreme Gradient Boosting (XGBoost). The input of the models included MOD05 PWV, latitude, longitude, elevation, cloud, season, and solar zenith angle, in association with the quality of the MOD05 PWV product. PWV data measured from in-situ 453 Global Positioning System (GPS) stations in Australia in 2017 were utilized as the target water vapor data for model training. The validation results in Australia in 2018–2019 indicate that the models can significantly improve the all-weather quality (increased R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , reduced root-mean-square error (RMSE), and reduced mean bias (MB)) of the MOD05 PWV product, exhibiting a reduction in RMSE of 53.33% for BPNN, 55.25% for GBDT, 53.24% for GRNN, 37.81% for KNN, 54.98% for MLPNN, and 55.16% for XGBoost. The R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> was 0.58 ~ 0.79 and the MB was 0.12 mm ~ 0.72 mm, much better than the MOD05 all-weather PWV product (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.26 and MB = -1.75 mm). Different from previous studies that focused on clear-sky conditions only, this work is the first one to enhance the quality of official MODIS NIR PWV products under all weather conditions, reducing the impact of clouds on PWV products.

Statistical evaluation of proxies for estimating the rainfall erosivity factor

Considering the high-temporal-resolution rainfall data requirements for calculating the Rainfall Erosivity factor (that is, the R-factor), studies have developed a large number of proxies for the R-factor (PR). This study aims to evaluate 15 widely used proxies, which were developed in various countries using daily, monthly, or yearly rainfall data, in terms of correlation and statistical equality with the R-factor by using the 6-min pluviographic data from 28 stations in Australia. Meng’s test was applied to rank the correlations. Although the Meng’s test indicated that the correlation between Rainfall Erosivity (R) and Rainfall Erosivity calculated by the proxy model (PR) generally increased with a finer time resolution of the rainfall data (in the order of year, month, and day), the 15 PRs under examination were all highly correlated with R (r > 0.62, p < 0.004), implying that all of them can be reasonably used as an R predictor. A direct estimation of the R-factor using PRs produced a mean relative error (MRE), root mean square error (RMSE), and Nash–Sutcliffe efficiency coefficient (NSE) with a mean of 50.0%, 1392 MJ mm ha−1 h−1 a−1, and 0.17, respectively. The linear calibrations improved the accuracy of the estimation and produced an MRE, RMSE, and NSE with a mean of 36.0%, 887 MJ mm ha−1 h−1 a−1, and 0.70, respectively. Finally, suitable proxies for instances where only daily, monthly, or yearly rainfall data are available were recommended.

Complementary Relationship for evaporation performance at different spatial and temporal scales

Several versions of the Complementary Relationship (CR) between actual regional evaporation and apparent potential evaporation have recently been proposed. Few studies have compared multiple CR versions side-by-side using datasets spanning various climates and land surfaces. Filling this lack is one purpose of this project. It also investigates how various CR versions respond to changes in spatial and temporal averaging. This study uses multiple years of data from seven eddy-covariance flux stations in Australia, representing a wide range of biomes, along with global ERA5 reanalysis data products. Daily and monthly averages were used for both datasets, and the Australian observations also used weekly and yearly averages. The ERA5 data represent a scale of about 30 km, much larger than the scale represented by the flux station data. A set of five questions regarding the impact of spatial and temporal scaling on CR parameter values and performance are asked and assessed using the two datasets. Four recent CR versions are considered in answering the questions. Due to important differences between FLUXNET and ERA5 data, questions regarding temporal scaling were answered with greater confidence than those regarding spatial scaling. With these data, rescaled versions of the CR performed best overall.

Empirical estimation of soil temperature and its controlling factors in Australia: Implication for interaction between geographic setting and air temperature

Soil temperature is a key parameter affecting changes in the balance among energy flow, water flow, nutrient cycling, and ecological stability. In previous research on soil temperature estimation, field measurement has been valuable. However its value is uncertain for regional scale research. A regression equation algorithm approach introduces the environmental factors into consideration. The correlations between soil temperature and environmental covariates may not be linear, and most of them conform to a parabolic relationship. Therefore, based on 53 years' data (1958-2010) from 140 meteorological stations in Australia, this study applied a 3-parameter cosine wave model to fit the monthly air and soil temperature. Further, this study identified the correlations between the parameters and landscape factors using stepwise regression. The root mean square error (RMSE) and Nash-Sutcliffe efficiency coefficient (NS) were used to evaluate the accuracy of the model. The results showed that: (1) The cosine-wave function can effectively identify the monthly air/soil temperature fluctuation and the prediction accuracy increases gradually from north to south in Australia. (2) The precipitation, horizontal irradiation and elevation are predominant factors that affecting soil temperature fluctuation given a defined air temperature fluctuation. (3) Landscape factors regulate the hysteresis of air/soil temperature fluctuation. The hysteresis of soil temperature response to air temperature in Australia is in order of duration: central > south > north. Therefore, the application of the cosine function is a useful and novel technique for estimation of soil temperature, which could provide better understanding of how soil temperature fluctuation responds to the global warming.

Short-Term Photovoltaic Power Prediction Based on Similar Days and Improved SOA-DBN Model

Existing methods in predicting short-term photovoltaic (PV) power have low accuracy and cannot satisfy actual demand. Thus, a prediction model based on similar days and seagull optimization algorithm (SOA) is proposed to optimize a deep belief network (DBN). Fast correlation-based filter (FCBF) method is used to select a meteorological feature set with the best correlation with PV output and avoid redundancy among meteorological factors affecting PV output. In addition, a comprehensive similarity index combining European distance and gray correlation degree is proposed to select the similar day. Then, SOA is used to optimize the number of neurons and the learning rate parameters in DBN. Based on the nonuniform mutation and opposition-based learning method, an improved seagull optimization algorithm (ISOA) with higher optimization accuracy is proposed. Finally, the ISOA-DBN prediction model is established, and the experimental analysis is conducted using the actual data of PV power stations in Australia. Results show that compared with DBN, support vector machine (SVM), extreme learning machine (ELM), radial basis function (RBF), Elman, and back propagation (BP), the mean absolute percentage error indicator of ISOA-DBN is only 1.512% on a sunny day, 5.975 on a rainy day, 3.359 on a cloudy to sunny day, and 1.911% on a sunny to cloudy day. Therefore, the good accuracy of the proposed model is verified.

Factors Affecting Electric Vehicle Uptake: Insights from a Descriptive Analysis in Australia

Transport activities are among the major contributors of greenhouse gas emissions and the resulting global climate crisis. Despite some efforts in shifting from internal combustion engines to electric motors, the global market share of electric vehicles (EVs) is very low—about 1%. This figure even goes as low as 0.4% for some developed countries—e.g., Australia. There is a growing, but still limited, number of studies investigating the key factors affecting the uptake of EVs. Additionally, there is no regional analysis in late-moving countries, which would provide knowledge for a better understanding why some countries are falling behind in the EV market. This paper focuses on Australia as a late mover in the EV market and generates insights into a regional analysis of key factors affecting the uptake of EVs. The unit of analysis for this study is determined as the states and territories of Australia. The methodologic approach of the study includes a descriptive analysis of publicly accessible fast and slow charging stations in Australia, the distribution of renewable energy, as well as electric vehicle sales in Australia, along with further factors relating to differences in income and education and subsidies for EVs from the government. The findings of the study reveal that (a) EV uptake conditions is an emerging research topic; (b) renewable energy, EV subsidies, charging stations, income, and education do generally favor EV sales in Australia; (c) the Australian Capital Territory has the highest readiness level among all the Australian states and territories; and (d) future research should be conducted on a local government level to capture the local readiness levels accurately. The study findings inform policymakers, car manufacturers, the energy sector, and scholars on the critical success factors for the uptake of EVs in Australia.

Upper-mantle discontinuities beneath Australia from transdimensional Bayesian inversions using multimode surface waves and receiver functions

SUMMARYRadially anisotropic S-wave structures under the permanent seismic stations in Australia are reconstructed using multimode surface waves (SWs) and receiver functions (RFs) in a framework of the Bayesian inference. We have developed a fully nonlinear method of joint inversions incorporating P-RFs and multimode Rayleigh and Love waves, based on the transdimensional Hierarchical Bayesian formulation. The method allows us to estimate a probabilistic Earth model taking account of the complexity and uncertainty of Earth structure, by treating the model parameters and data errors as unknowns. The Parallel Tempering algorithm is employed for the effective parameter search based on the reversible-jump Markov Chain Monte Carlo method. The use of higher modes enables us to enhance the sensitivity to the depth below the continental asthenosphere. Synthetic experiments indicate the importance of higher mode SWs for the better recovery of radial anisotropy in the whole depth range of the upper mantle. The method is applied to five Global Seismographic Network stations in Australia. While the S-wave models in eastern Australia show shallow lithosphere–asthenosphere boundary (LAB) above 100 km depth, those in central and Western Australia exhibit both mid-lithosphere discontinuities (MLDs) and LAB. Also, seismic velocity jumps equivalent to the Lehmann discontinuity (L-D) are found in all seismic stations. The L-D under the Australian continents is found at around 200–300 km depth, depending on locations. Radial anisotropy in the depth range between LAB and L-D tends to show faster SH anomalies, which may indicate the effects of horizontal shear underneath the fast-moving Australian plate.

Measuring the spatial connectivity of extreme rainfall

The frequency and severity of extreme weather events have been noted to be changing in both time and space as a result of rising global temperatures. In this regard, the analysis of joint occurrences of extreme climate events has gained considerable importance for planning and emergency management as such events often result in system failures that extend beyond a single location in the region impacted. By their very definition, extremes cannot be sampled frequently, necessitating approaches that are robust and efficient and can take advantage of the Big Data movement we are now a part of. This study introduces a new concept for characterizing such dependence, termed relative connectivity, formulated using the theory of empirical copulas which represent a non-parametric method to describe the joint dependence of random variables. The spatial relative connectivity of extreme rainfall events across 2,708 rainfall stations in Australia is ascertained and conclusions drawn. The extreme rainfall events in Western Australia, southeast South Australia, and southwest Victoria exhibit higher relative connectivity than other regions, suggesting a stronger spatial dependence and higher possibility of concurrence when compared to other regions. The overall variation of relative connectivity is also examined under different low-frequency climatic anomalies (i.e. El Nino and La Nina events) and temperature states. We observe that spatial dependence across raingauges increases under both El Nino and La Nina periods, while it weakens at warmer temperatures, with localized lower relative connectivity at target raingauges.

Embedding trend into seasonal temperature forecasts through statistical calibration of GCM outputs

AbstractAccurate and reliable seasonal climate forecasts are frequently sought by climate‐sensitive sectors to support decision‐making under climate variability and change. Temperature trend is discernible globally over the past decades, but seasonal forecasts produced by a global climate model (GCM) generally underestimate such trend. Current statistical methods used for calibrating seasonal climate forecasts mostly do not explicitly account for climate trends. Consequently, the calibrated forecasts also fail to capture the observed trend. Solving this problem can enhance user confidence in seasonal climate forecasts. In this study, we extend the capability of the Bayesian joint probability (BJP) modelling approach for statistical calibration of seasonal climate forecasts. A trend component is introduced into the BJP algorithm for embedding the observed trend into calibrated ensemble forecasts. We apply the new model (named BJP‐t) to three test stations in Australia. Seasonal forecasts of daily maximum temperatures from the SEAS5 model, operated by the European Centre for Medium‐Range Weather Forecasts (ECMWF), are calibrated and evaluated. The BJP‐t calibrated ensemble forecasts can reproduce the observed trend, when the raw ensemble forecasts and the BJP calibrated ensemble forecasts both fail to do so. The BJP‐t calibration leads to more skilful, more reliable and sharper forecasts than the BJP calibration.

Development of a postprocessing system of daily rainfall forecasts for seasonal crop prediction in Australia

There is a need to postprocess seasonal rainfall forecasts from physical climate models to reduce bias, improve skill and restore daily variability for use as input for crop simulation at the farm scale. We develop an extended copula postprocessing (ECPP) method to deal with daily rainfall with numerous zero occurrences. By treating rainfall as a left-censored variable, we derive likelihood estimation and adjust simulation procedure with consideration of zero rainfall occurrences. In a case study for 50 representative agricultural stations in Australia, we test our method to postprocess daily rainfall forecasts with up to 186-day lead time. We demonstrate that the ECPP improves the overall forecast skill from raw rainfall forecasts and outperforms quantile mapping (QM) by checking various verification measures. Though the forecasts for daily amounts are hardly skilful except for the first few days, the forecasts for accumulated totals can be skilful from error averaging and propagating positive skill from short lead times. We also demonstrate that the ECPP can simulate rainfall forecasts with more realistic dry day distribution and daily rainfall intensity than QM. Further research directions including several opportunities to improve ECPP are discussed.

Making the Output of Seasonal Climate Models More Palatable to Agriculture: A Copula-Based Postprocessing Method

AbstractSeasonal climate forecasts from raw climate models at coarse grids are often biased and statistically unreliable for credible crop prediction at the farm scale. We develop a copula-based postprocessing (CPP) method to overcome this mismatch problem. The CPP forecasts are ensemble based and are generated from the predictive distribution conditioned on raw climate forecasts. CPP performs univariate postprocessing procedures at each station, lead time, and variable separately and then applies the Schaake shuffle to reorder ensemble sequence for a more realistic spatial, temporal, and cross-variable dependence structure. The use of copulas makes CPP free of strong distributional assumptions and flexible enough to describe complex dependence structures. In a case study, we apply CPP to postprocess rainfall, minimum temperature, maximum temperature, and radiation forecasts at a monthly level from the Australian Community Climate and Earth-System Simulator Seasonal model (ACCESS-S) to three representative stations in Australia. We evaluate forecast skill at lead times of 0–5 months on a cross-validation theme in the context of both univariate and multivariate forecast verification. When compared with forecasts that use climatological values as the predictor, the CPP forecast has positive skills, although the skills diminish with increasing lead times and finally become comparable at long lead times. When compared with the bias-corrected forecasts and the quantile-mapped forecasts, the CPP forecast is the overall best, with the smallest bias and greatest univariate forecast skill. As a result of the skill gain from univariate forecasts and the effect of the Schaake shuffle, CPP leads to the most skillful multivariate forecast as well. Further results investigate whether using ensemble mean or additional predictors can enhance forecast skill for CPP.

Assessing environmental contamination from metal emission and relevant regulations in major areas of coal mining and electricity generation in Australia

The Hunter and Latrobe Valleys have two of the richest coal deposits in Australia. They also host the largest coal-fired power stations in the country. We reconstructed metal deposition records in lake sediments in the Hunter and Latrobe Valleys to determine if metal deposition in freshwater lakes have increased in the region. The current regulatory arrangement applied to metal emissions from coal-fired power stations in Australia are presented, discussing their capacity to address future increases in metal deposition from these sources. Sediment records of spheroidal carbonaceous particles (SCPs), a component of fly-ash, were also used as an additional line of evidence to identify the contribution of industrial activities related to electricity generation to metal deposition in regions surrounding open-cut coal mines and coal-fired power stations. Sediment metal concentrations and SCP counts in the sedimentary records, from the Hunter and Latrobe Valleys, both indicated that open-cut coal mining and the subsequent combustion of coal in power stations has most likely resulted in an increase in atmospheric deposition of metals in the local region. In particular, the metalloids As and Se showed the greatest enrichment compared to before coal mining commenced. Although the introduction of bag filters at Liddell Power Station and the decommissioning of Hazelwood Power Station appear to have resulted in a decrease of metal deposition in nearby lakes, overall metal deposition in the environment is still increasing. The challenge for the years to come will be to develop better regulation policies and tools that will contribute to reduce metal emissions in these major electricity production centres in Australia.