ORCID Profile
0000-0003-2504-0466
Current Organisations
Bureau of Meteorology
,
University of Melbourne
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Publisher: American Geophysical Union (AGU)
Date: 25-10-2016
DOI: 10.1002/2016JD025382
Publisher: CSIRO Publishing
Date: 20-09-2021
DOI: 10.1071/ES21007
Abstract: Reanalyses are important tools for understanding past weather and climate variability, but detailed verification of near surface humidity variables have not been published. This is particularly concerning in tropical regions where humid conditions impact meteorology and human activities. In this study, we used screen level temperature and humidity data from a high-resolution atmospheric regional reanalysis, the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA), validated against automatic weather stations (AWS) data for 32 sites across northern Australia. Overall, the BARRA data was reliable, with the time series from the AWS and BARRA being very highly correlated, but there were some seasonal and diurnally varying biases. The variability of the differences also changed from location to location and as a function of time of day and season, but much less than the biases. This variability was less than the ‘weather signal’ as evidenced by the high correlations. In particular, the litude of the diurnal cycle was overestimated, particularly in the dry (winter) season. In general, the differences in temperature were larger than those of the dew point temperature, and the wet bulb temperature had the least uncertainty. Overall, this study contributes to a better understanding of the effectiveness of reanalyses for examining the impact of moist variables on tropical climate variability.
Publisher: Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc.
Date: 12-2013
Publisher: Elsevier BV
Date: 07-2013
Publisher: IEEE
Date: 02-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2014
Publisher: American Physical Society (APS)
Date: 09-09-2009
Publisher: The Optical Society
Date: 27-02-2013
DOI: 10.1364/OE.21.005575
Publisher: The Optical Society
Date: 21-03-2011
DOI: 10.1364/OE.19.006354
Publisher: Elsevier BV
Date: 09-2016
Publisher: American Physical Society (APS)
Date: 08-2012
Publisher: American Physical Society (APS)
Date: 31-10-2014
Publisher: American Geophysical Union (AGU)
Date: 27-10-2015
DOI: 10.1002/2015JD023512
Publisher: Modelling and Simulation Society of Australia and New Zealand
Date: 29-11-2015
Publisher: Modelling and Simulation Society of Australia and New Zealand
Date: 29-11-2015
Publisher: American Geophysical Union (AGU)
Date: 26-07-2013
DOI: 10.1002/GRL.50695
Publisher: American Physical Society (APS)
Date: 09-11-2009
Publisher: The Optical Society
Date: 18-04-2008
DOI: 10.1364/OE.16.006240
Abstract: We analyze a nitrogen-vacancy (NV-) colour centre based single photon source based on cavity Purcell enhancement of the zero phonon line and suppression of other transitions. Optimal performance conditions of the cavity-centre system are analyzed using Master equation and quantum trajectory methods. By coupling the centre strongly to a high-finesse optical cavity [Q approximately O(10(4) - 10(5)), V approximately lambda (3)] and using sub-picosecond optical excitation the system has striking performance, including effective lifetime of 70 ps, linewidth of 0.01 nm, near unit single photon emission probability and small [O(10(-5))] multi-photon probability.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2016
Publisher: Copernicus GmbH
Date: 06-01-2015
Abstract: Abstract. Remote sensing, in situ networks and models are now providing unprecedented information for environmental monitoring. To conjunctively use multi-source data nominally representing an identical variable, one must resolve biases existing between these disparate sources, and the characteristics of the biases can be non-trivial due to spatio-temporal variability of the target variable, inter-sensor differences with variable measurement supports. One such ex le is of soil moisture (SM) monitoring. Triple collocation (TC) based bias correction is a powerful statistical method that is increasingly being used to address this issue, but is only applicable to the linear regime, whereas the non-linear method of statistical moment matching is susceptible to unintended biases originating from measurement error. Since different physical processes that influence SM dynamics may be distinguishable by their characteristic spatio-temporal scales, we propose a multi-timescale linear bias model in the framework of a wavelet-based multi-resolution analysis (MRA). The joint MRA-TC analysis was applied to demonstrate scale-dependent biases between in situ, remotely sensed and modelled SM, the influence of various prospective bias correction schemes on these biases, and lastly to enable multi-scale bias correction and data-adaptive, non-linear de-noising via wavelet thresholding.
Publisher: Elsevier BV
Date: 06-2015
Publisher: American Geophysical Union (AGU)
Date: 06-2016
DOI: 10.1002/2015WR018067
Publisher: American Geophysical Union (AGU)
Date: 10-06-2014
DOI: 10.1002/2013JD021043
Publisher: Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc.
Date: 12-2013
Publisher: American Physical Society (APS)
Date: 28-12-2009
Publisher: American Physical Society (APS)
Date: 20-01-2006
Publisher: The Optical Society
Date: 17-04-2009
DOI: 10.1364/OE.17.007295
Abstract: To take existing quantum optical experiments and devices into a more practical regimes requires the construction of robust, solid-state implementations. In particular, to observe the strong-coupling regime of tom-photon interactions requires very small cavities and large quality factors. Here we show that the slot-waveguide geometry recently introduced for photonic applications is also promising for quantum optical applications in the visible regime. We study diamond- and GaP-based slot-waveguide cavities (SWCs) compatible with diamond colour centres e.g. nitrogen-vacancy (NV) defect. We show that one can achieve increased single-photon Rabi frequencies of order O(10(11)) rad s(-1) in ultra-small cavity modal volumes, nearly 2 orders of magnitude smaller than previously studied diamond-based photonic crystal cavities.
Publisher: SPIE
Date: 11-02-2010
DOI: 10.1117/12.843013
Publisher: American Geophysical Union (AGU)
Date: 11-2015
DOI: 10.1002/2015WR016944
Publisher: American Geophysical Union (AGU)
Date: 07-2016
DOI: 10.1002/2015WR018429
Publisher: Copernicus GmbH
Date: 24-05-2019
Abstract: Abstract. The Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) is the first atmospheric regional reanalysis over a large region covering Australia, New Zealand, and Southeast Asia. The production of the reanalysis with approximately 12 km horizontal resolution – BARRA-R – is well underway with completion expected in 2019. This paper describes the numerical weather forecast model, the data assimilation methods, the forcing and observational data used to produce BARRA-R, and analyses results from the 2003–2016 reanalysis. BARRA-R provides a realistic depiction of the meteorology at and near the surface over land as diagnosed by temperature, wind speed, surface pressure, and precipitation. Comparing against the global reanalyses ERA-Interim and MERRA-2, BARRA-R scores lower root mean square errors when evaluated against (point-scale) 2 m temperature, 10 m wind speed, and surface pressure observations. It also shows reduced biases in daily 2 m temperature maximum and minimum at 5 km resolution and a higher frequency of very heavy precipitation days at 5 and 25 km resolution when compared to gridded satellite and gauge analyses. Some issues with BARRA-R are also identified: biases in 10 m wind, lower precipitation than observed over the tropical oceans, and higher precipitation over regions with higher elevations in south Asia and New Zealand. Some of these issues could be improved through dynamical downscaling of BARRA-R fields using convective-scale ( km) models.
Publisher: American Geophysical Union (AGU)
Date: 09-02-2016
DOI: 10.1002/2015JD024027
Publisher: IEEE
Date: 08-2011
Publisher: Elsevier BV
Date: 09-2017
Publisher: American Meteorological Society
Date: 25-05-2016
Abstract: The error characterization of soil moisture products, for ex le, obtained from microwave remote sensing data, is a key requirement for using these products in applications like numerical weather prediction. The error variance and root-mean-square error are among the most popular metrics: they can be estimated consistently for three datasets using triple collocation (TC) without assuming any dataset to be free of errors. This technique can account for additive and multiplicative biases that is, it assumes that the three products are linearly related. However, its susceptibility to nonlinear relations (e.g., due to sensor saturation and scale mismatch) has not been addressed. Here, a simulation study investigates the impact of quadratic relations on the TC error estimates [also when the products are first rescaled using the nonlinear cumulative distribution function (CDF) matching technique] and on those by two novel methods. These methods—based on error-in-variables regression and probabilistic factor analysis—extend standard TC by also accounting for nonlinear relations using quadratic polynomials. The relative differences between the error estimates of the ASCAT remotely sensed product by the quadratic and the linear methods are predominantly smaller than 10% in a case study based on remotely sensed, reanalysis, and in situ measured soil moisture over the contiguous United States. Exceptions with larger discrepancies indicate that nonlinear relations can pose a challenge to traditional TC analyses, as the simulations show they can introduce biases of either sign. In such cases, the use of nonlinear methods may complement traditional approaches for the error characterization of soil moisture products.
Publisher: Elsevier BV
Date: 06-2015
Publisher: Elsevier BV
Date: 11-2014
Publisher: Elsevier BV
Date: 04-2017
Publisher: Elsevier BV
Date: 2017
Publisher: Copernicus GmbH
Date: 09-04-2015
DOI: 10.5194/HESS-19-1659-2015
Abstract: Abstract. Assimilation of remotely sensed soil moisture data (SM-DA) to correct soil water stores of rainfall-runoff models has shown skill in improving streamflow prediction. In the case of large and sparsely monitored catchments, SM-DA is a particularly attractive tool. Within this context, we assimilate satellite soil moisture (SM) retrievals from the Advanced Microwave Scanning Radiometer (AMSR-E), the Advanced Scatterometer (ASCAT) and the Soil Moisture and Ocean Salinity (SMOS) instrument, using an Ensemble Kalman filter to improve operational flood prediction within a large ( 40 000 km2) semi-arid catchment in Australia. We assess the importance of accounting for channel routing and the spatial distribution of forcing data by applying SM-DA to a lumped and a semi-distributed scheme of the probability distributed model (PDM). Our scheme also accounts for model error representation by explicitly correcting bias in soil moisture and streamflow in the ensemble generation process, and for seasonal biases and errors in the satellite data. Before assimilation, the semi-distributed model provided a more accurate streamflow prediction (Nash–Sutcliffe efficiency, NSE = 0.77) than the lumped model (NSE = 0.67) at the catchment outlet. However, this did not ensure good performance at the "ungauged" inner catchments (two of them with NSE below 0.3). After SM-DA, the streamflow ensemble prediction at the outlet was improved in both the lumped and the semi-distributed schemes: the root mean square error of the ensemble was reduced by 22 and 24%, respectively the false alarm ratio was reduced by 9% in both cases the peak volume error was reduced by 58 and 1%, respectively the ensemble skill was improved (evidenced by 12 and 13% reductions in the continuous ranked probability scores, respectively) and the ensemble reliability was increased in both cases (expressed by flatter rank histograms). SM-DA did not improve NSE. Our findings imply that even when rainfall is the main driver of flooding in semi-arid catchments, adequately processed satellite SM can be used to reduce errors in the model soil moisture, which in turn provides better streamflow ensemble prediction. We demonstrate that SM-DA efficacy is enhanced when the spatial distribution in forcing data and routing processes are accounted for. At ungauged locations, SM-DA is effective at improving some characteristics of the streamflow ensemble prediction however, the updated prediction is still poor since SM-DA does not address the systematic errors found in the model prior to assimilation.
Publisher: IOP Publishing
Date: 14-06-2011
Publisher: The Optical Society
Date: 23-05-2011
DOI: 10.1364/OE.19.011018
Publisher: Copernicus GmbH
Date: 19-08-2019
DOI: 10.5194/HESS-23-3387-2019
Abstract: Abstract. An accurate representation of spatio-temporal characteristics of precipitation fields is fundamental for many hydro-meteorological analyses but is often limited by the paucity of gauges. Reanalysis models provide systematic methods of representing atmospheric processes to produce datasets of spatio-temporal precipitation estimates. The precipitation from the reanalysis datasets should, however, be evaluated thoroughly before use because it is inferred from physical parameterization. In this paper, we evaluated the precipitation dataset from the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) and compared it against (a) gauged point observations, (b) an interpolated gridded dataset based on gauged point observations (AWAP – Australian Water Availability Project), and (c) a global reanalysis dataset (ERA-Interim). We utilized a range of evaluation metrics such as continuous metrics (correlation, bias, variability, and modified Kling–Gupta efficiency), categorical metrics, and other statistics (wet-day frequency, transition probabilities, and quantiles) to ascertain the quality of the dataset. BARRA, in comparison with ERA-Interim, shows a better representation of rainfall of larger magnitude at both the point and grid scale of 5 km. BARRA also more closely reproduces the distribution of wet days and transition probabilities. The performance of BARRA varies spatially, with better performance in the temperate zone than in the arid and tropical zones. A point-to-grid evaluation based on correlation, bias, and modified Kling–Gupta efficiency (KGE′) indicates that ERA-Interim performs on par or better than BARRA. However, on a spatial scale, BARRA outperforms ERA-Interim in terms of the KGE′ score and the components of the KGE′ score. Our evaluation illustrates that BARRA, with richer spatial variations in climatology of daily precipitation, provides an improved representation of precipitation compared with the coarser ERA-Interim. It is a useful complement to existing precipitation datasets for Australia, especially in sparsely gauged regions.
Publisher: American Physical Society (APS)
Date: 23-12-2008
Publisher: American Geophysical Union (AGU)
Date: 03-11-2016
DOI: 10.1002/2016GL070458
Abstract: Climate Data Records (CDR) that blend multiple satellite products are invaluable for climate studies, trend analysis and risk assessments. Knowledge of any inhomogeneities in the CDR is therefore critical for making correct inferences. This work proposes a methodology to identify the spatiotemporal extent of the inhomogeneities in a 36 year, global multisatellite soil moisture CDR as the result of changing observing systems. Inhomogeneities are detected at up to 24% of the tested pixels with spatial extent varying with satellite changeover times. Nevertheless, the contiguous periods without inhomogeneities at changeover times are generally longer than 10 years. Although the inhomogeneities have measurable impact on the derived trends, these trends are similar to those observed in ground data and land surface reanalysis, with an average error less than 0.003 m 3 m −3 y −1 . These results strengthen the basis of using the product for long‐term studies and demonstrate the necessity of homogeneity testing of multisatellite CDRs in general.
Publisher: American Geophysical Union (AGU)
Date: 08-2016
DOI: 10.1002/2015WR018177
Publisher: Elsevier BV
Date: 03-2016
Publisher: SPIE
Date: 19-08-2010
DOI: 10.1117/12.865609
No related grants have been discovered for Chun-Hsu Su.