Subseasonal to Seasonal Climate Forecasts Provide the Backbone of a Near-Real-Time Event Explainer Service

The Bureau of Meteorology serves the Australian community to reduce its climate risk and is developing a suite of tools to explain the drivers of extreme events. Dynamical sub-seasonal to seasonal forecasts form the backbone of the service, potentially enabling it to be run in near real time

Dynamics and predictability of El Nino-Southern Oscillation: an Australian perspective on progress and challenges

Many scientific challenges remain for managing the risk of future ENSO impacts in countries like Australia that are strongly affected by ENSO event diversity

A robust error-based rain estimation method for polarimetric radar. Part II : case study

Rainfall estimation using polarimetric radar involves the combination of a number of estimators with differing error characteristics to optimize rainfall estimates at all rain rates. In Part I of this paper, a new technique for such combinations was proposed that weights algorithms by the inverse of their theoretical errors. In this paper, the derived algorithms are validated using the "CP2" polarimetric radar in Queensland, Australia, and a collocated rain gauge network for two heavy-rain events during November 2008 and a larger statistical analysis that is based on data from between 2007 and 2009. Use of a weighted combination of polarimetric algorithms offers some improvement over composite methods that are based on decision-tree logic, particularly at moderate to high rain rates and during severe-thunderstorm events.12 page(s

A Robust error-based rain estimation method for polarimetric radar. Part I: Development of a method

The algorithms used to estimate rainfall from polarimetric radar variables show significant variance in error characteristics over the range of naturally occurring rain rates. As a consequence, to improve rainfall estimation accuracy using polarimetric radar, it is necessary to optimally combine a number of different algorithms. In this study, a new composite method is proposed that weights the algorithms by the inverse of their theoretical error. A number of approaches are discussed and are investigated using simulated radar data calculated from disdrometer measurements. The resultant algorithms show modest improvement over composite methods based on decision-tree logic-in particular, at rain rates above 20 mm h-1.12 page(s

The ability of a multi-model seasonal forecasting ensemble to forecast the frequency of warm, cold and wet extremes

Dynamical models are now widely used to provide forecasts of above or below average seasonal mean temperatures and precipitation, with growing interest in their ability to forecast climate extremes on a seasonal time scale. This study assesses the skill of the ENSEMBLES multi-model ensemble to forecast the 90th and 10th percentiles of both seasonal temperature and precipitation, using a number of metrics of ‘extremeness’. Skill is generally similar or slightly lower to that for seasonal means, with skill strongly influenced by the El Niño-Southern Oscillation. As documented in previous studies, much of the skill in forecasting extremes can be related to skill in forecasting the seasonal mean value, with skill for extremes generally lower although still significant. Despite this, little relationship is found between the skill of forecasting the upper and lower tails of the distribution of daily values