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Acacia S. Pepler and Peter T. May

Abstract

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.

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Acacia S. Pepler, Peter T. May, and Merhala Thurai

Abstract

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.

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Pandora Hope, Mitchell T. Black, Eun-Pa Lim, Andrew Dowdy, Guomin Wang, Robert J. B. Fawcett, and Acacia S. Pepler
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Acacia S. Pepler, Alejandro Di Luca, Fei Ji, Lisa V. Alexander, Jason P. Evans, and Steven C. Sherwood

Abstract

The Australian east coast low (ECL) is both a major cause of damaging severe weather and an important contributor to rainfall and dam inflow along the east coast, and is of interest to a wide range of groups including catchment managers and emergency services. For this reason, several studies in recent years have developed and interrogated databases of east coast lows using a variety of automated cyclone detection methods and identification criteria. This paper retunes each method so that all yield a similar event frequency within the ECL region, to enable a detailed intercomparison of the similarities, differences, and relative advantages of each method. All methods are shown to have substantial skill at identifying ECL events leading to major impacts or explosive development, but the choice of method significantly affects both the seasonal and interannual variation of detected ECL numbers. This must be taken into consideration in studies on trends or variability in ECLs, with a subcategorization of ECL events by synoptic situation of key importance.

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Agus Santoso, Harry Hendon, Andrew Watkins, Scott Power, Dietmar Dommenget, Matthew H. England, Leela Frankcombe, Neil J. Holbrook, Ryan Holmes, Pandora Hope, Eun-Pa Lim, Jing-Jia Luo, Shayne McGregor, Sonja Neske, Hanh Nguyen, Acacia Pepler, Harun Rashid, Alex Sen Gupta, Andréa S. Taschetto, Guomin Wang, Esteban Abellán, Arnold Sullivan, Maurice F. Huguenin, Felicity Gamble, and Francois Delage

Abstract

El Niño and La Niña, the warm and cold phases of El Niño–Southern Oscillation (ENSO), cause significant year-to-year disruptions in global climate, including in the atmosphere, oceans, and cryosphere. Australia is one of the countries where its climate, including droughts and flooding rains, is highly sensitive to the temporal and spatial variations of ENSO. The dramatic impacts of ENSO on the environment, society, health, and economies worldwide make the application of reliable ENSO predictions a powerful way to manage risks and resources. An improved understanding of ENSO dynamics in a changing climate has the potential to lead to more accurate and reliable ENSO predictions by facilitating improved forecast systems. This motivated an Australian national workshop on ENSO dynamics and prediction that was held in Sydney, Australia, in November 2017. This workshop followed the aftermath of the 2015/16 extreme El Niño, which exhibited different characteristics to previous extreme El Niños and whose early evolution since 2014 was challenging to predict. This essay summarizes the collective workshop perspective on recent progress and challenges in understanding ENSO dynamics and predictability and improving forecast systems. While this essay discusses key issues from an Australian perspective, many of the same issues are important for other ENSO-affected countries and for the international ENSO research community.

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