National Meteorological and Oceanographic Center

Melbourne, Australia

National Meteorological and Oceanographic Center

Melbourne, Australia
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Gloster J.,UK Met Office | Jones A.,UK Met Office | Redington A.,UK Met Office | Burgin L.,UK Met Office | And 11 more authors.
Veterinary Journal | Year: 2010

Foot-and-mouth disease virus (FMDV) spreads by direct contact between animals, by animal products (milk, meat and semen), by mechanical transfer on people or fomites and by the airborne route, with the relative importance of each mechanism depending on the particular outbreak characteristics. Atmospheric dispersion models have been developed to assess airborne spread of FMDV in a number of countries, including the UK, Denmark, Australia, New Zealand, USA and Canada. These models were compared at a Workshop hosted by the Institute for Animal Health/Met Office in 2008. Each modeller was provided with data relating to the 1967 outbreak of FMD in Hampshire, UK, and asked to predict the spread of FMDV by the airborne route. A number of key issues emerged from the Workshop and subsequent modelling work: (1) in general all models predicted similar directions for livestock at risk, with much of the remaining differences strongly related to differences in the meteorological data used; (2) determination of an accurate sequence of events on the infected premises is highly important, especially if the meteorological conditions vary substantially during the virus emission period; (3) differences in assumptions made about virus release, environmental fate and susceptibility to airborne infection can substantially modify the size and location of the downwind risk area. All of the atmospheric dispersion models compared at the Workshop can be used to assess windborne spread of FMDV and provide scientific advice to those responsible for making control and eradication decisions in the event of an outbreak of disease. Crown Copyright © 2008.

Davidson N.E.,Center for Australian Weather and Climate Research | Xiao Y.,Center for Australian Weather and Climate Research | Ma Y.,Center for Australian Weather and Climate Research | Weber H.C.,University of the Federal Public Administration | And 9 more authors.
Monthly Weather Review | Year: 2014

The Australian Community Climate and Earth System Simulator (ACCESS) has been adapted for operational and research applications on tropical cyclones. The base systemruns at a resolution of 0.11 ° and 50 levels. The domain is relocatable and nested in coarser-resolution ACCESS forecasts. Initialization consists of five cycles of four-dimensional variational data assimilation (4DVAR) over 24 h. Forecasts to 72 h are made. Without vortex specification, initial conditions usually contain a weak and misplaced circulation pattern. Significant effort has been devoted to building physically based, synthetic inner-core structures, validated using historical dropsonde data and surface analyses from the Atlantic. Based on estimates of central pressure and storm size, vortex specification is used to filter the analyzed circulation from the original analysis, construct an inner core of the storm, locate it to the observed position, andmerge itwith the large-scale analysis at outer radii. Using all available conventional observations and only synthetic surface pressure observations from the idealized vortex to correct the initial location and structure of the storm, the 4DVAR builds a balanced, intense 3D vortex with maximum wind at the radius of maximum wind and with a well-developed secondary circulation. Mean track and intensity errors for Australian region and northwest Pacific storms have been encouraging, as are recent real-time results from the Australian National Meteorological and Oceanographic Centre. The system became fully operational in November 2011. From preliminary diagnostics, some interesting structure change features are illustrated. Current limitations, future enhancements, and research applications are also discussed. © 2014 American Meteorological Society.

Ma Y.,Center for Australian Weather and Climate Research | Huang X.,National Meteorological and Oceanographic Center | Mills G.A.,Center for Australian Weather and Climate Research | Parkyn K.,Victorian Regional Forecast Center
Weather and Forecasting | Year: 2010

During a wildfire, a sharp wind change can lead to an abrupt increase in fire activity and change the rate of spread, endangering firefighters working on what had been the flank of the fire. In southeastern Australia, routine forecast of cold-frontal wind change arrival times is a critical component of the fire weather forecasting service, and mesoscale NWP model predictions are integral to this forecast process. An event-based verification method has been developed in order to verify these mesoscale NWP model forecasts of wind changes. The approach is based on fuzzy-rule techniques and objectively determines the timing of significant (fire weather) wind changes from time series of observations at a single surface station. In this paper these rules are applied to observational and NWP model forecast time series at observation locations over five fire seasons to determine objective "observed wind change times" and "forecast wind change times" for significant frontal wind changes in southeastern Australia. These forecast wind change times are compared with those observed, and also with those determined subjectively by forecasters at the Victorian Regional Forecast Centre. This provides an objective verification of NWP wind change forecasts and a measure of contemporary NWP model skill against which future model improvements may be measured. Case studies of two wind change events at selected stations are also presented to demonstrate some of the strengths, weaknesses, and characteristics of this verification technique. © 2010 American Meteorological Society.

Miao Y.,Center for Australian Weather and Climate Research | Potts R.,Center for Australian Weather and Climate Research | Huang X.,National Meteorological and Oceanographic Center | Elliott G.,Western Australia Regional Office | Rivett R.,Western Australia Regional Office
Pure and Applied Geophysics | Year: 2012

Perth Airport is a major airport along the southwest coast of Australia. Even though, on average, fog only occurs about twelve times a year, the lack of suitable alternate aerodromes nearby for diversion makes fog forecasts for Perth Airport very important to long-haul international flights. Fog is most likely to form in the cool season between April and October. This study developed an objective fuzzy logic fog forecasting model for Perth Airport for the cool season. The fuzzy logic fog model was based on outputs from a high-resolution operational NWP model called LAPS125 that ran twice daily at 00 and 12 UTC, but fuzzy logic was employed to deal with the inaccuracy of NWP prediction and uncertainties associated with relationships between fog predictors and fog occurrence. The outcome of the fuzzy logic fog model is in one of the four categories from low to high fog risk as FM0, FM5, FM15 or FM30, intended to map to approximate fog probability of 0, 5, 15 and 30%, respectively. The model was found useful in its 5 year performance in the cool seasons between 2004 and 2008 and required little recalibration if mist was treated as if it were also a fog event in the skill evaluation. To generate an operational fog forecast for Perth Airport, the outcome of the fuzzy logic fog model was averaged with the outcomes of two other fog forecasting methods using a simple consensus approach. Fog forecast so generated is known as the operational consensus forecast. Skill assessment using frequency distribution diagram, Hansen and Kuiper skill score, and Relative Operating Characteristic curve showed that the operational consensus forecast outperformed all three individual methods. Out of the three methods, the fuzzy logic fog model ranked second. It performed better than the other objective method called GASM but worse than the subjective method which relied on forecaster's subjective assessment. The skills of the fuzzy logic fog model can be further improved with the tuning of fuzzy functions. In addition, similar models can be customised for other airports. The study also suggested the use of the simple consensus approach to enhance forecasting skills for other stations or weather phenomena if there were two or more independent forecasting methods available. © 2011 Springer Basel AG.

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