Forecasting the extreme rainfall, low temperatures, and strong winds associated with the northern Queensland floods of February 2019

Cowan, Tim; Wheeler, Matthew C.; Alves, Oscar; Narsey, Sugata; Burgh-Day, Catherine de; Griffiths, Morwenna; Jarvis, Chelsea; Cobon, D. H.; Hawcroft, Matt

doi:10.1016/j.wace.2019.100232

Cited by 33 publications

(64 citation statements)

References 27 publications

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“…For instance, the compound high-temperature and severe precipitation events have vital effects on plants during the growing season (Madden and Williams, 1978). A compound event with low temperatures, strong wind, and following extreme precipitation in Queensland of Australia caused the deaths of half a million cattle (Cowan et al, 2019). While most existing studies paid much attention to individual events, few focused on compound events with magnified impacts compared to the individual events (Weber et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Increasing Compound Heat and Precipitation Extremes Elevated by Urbanization in South China

Chan

Zhang

et al. 2021

Front. Earth Sci.

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Compared with individual events, compound weather and climate extremes may impose more serious influences on natural systems and human society, especially in populated areas. In this study, we examine the changes in the compound precipitation events that follow extremely hot weather within several days during 1961–2017 in South China by taking the Guangdong Province as an example. Additionally, we assess the impacts of urbanization on these changes. It is found that extreme precipitation events in Guangdong are often preceded by hot weather, with an average fraction of 28.25%. The fraction of such compound events is even larger in more populated and urbanized areas such as the Pearl River Delta (PRD) region. Moreover, our results reveal significant increases in the frequency and fraction of the compound extreme heat and precipitation events. These increases are especially stronger in more developed areas (e.g., PRD), and their increasing trends tend to accelerate in recent decades. Furthermore, the local urbanization contributes to 40.91 and 49.38% of the increases in the frequency and fraction of the compound events, respectively. Our findings provide scientific references for policy-makers and urban planners to mitigate the influences of the compound heat and precipitation extremes by considering their increasing risks under the context of global climate change and local urbanization.

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Section: Introductionmentioning

confidence: 99%

Increasing Compound Heat and Precipitation Extremes Elevated by Urbanization in South China

Chan

Zhang

et al. 2021

Front. Earth Sci.

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“…Ensemble-mean weekly rainfall amounts, however, were considerably underestimated by the prediction system, even in forecasts initialised at the start of the peak flooding week, consistent with other state-of-the-art dynamical prediction systems. Cowan et al (2019) concluded that predicting this exceptional event beyond two weeks appears beyond our current capability, despite the dynamical system forecasts showing good skill in forecasting the broadscale atmospheric conditions north of Australia a week prior.…”

Section: Climate Drivers and Predictabilitymentioning

confidence: 87%

“…Lau and Wu (2011) found using Tropical Rainfall Measuring Mission data (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009) that extreme rain events are most sensitive to the changes in tropical mean SST. Cowan et al (2019) reported that the tropical convective signal of the MJO was over the western Pacific, and likely contributed to the heavy rainfall. Over the northern Tasman Sea, an anticyclone helped maintain a positive phase of the SAM and promoted onshore easterly flow.…”

Section: Climate Drivers and Predictabilitymentioning

confidence: 99%

Weather systems and extreme rainfall generation in the 2019 north Queensland floods compared with historical north Queensland record floods

Callaghan¹

2021

J. South. Hemisph. Earth Syst. Sci.

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Earlier papers have addressed floods from warm-air advection (WAA) in southeast Australia and around the globe, and extreme rainfall in US hurricanes and Australian tropical cyclones (TCs). This is the first paper to address the WAA phenomena in causing monsoon and TC floods and in TC-like systems which develop over the interior of northern Australia. The inland events help explain Australia’s worst tropical flooding disaster in 1916. A disastrous series of floods during late January and early February 2019 caused widespread damage in tropical north Queensland both in inland regions and along the coast. This occurred when some large-scale climate influences, including the sea surface temperatures suggested conditions would not lead to major flooding. Therefore, it is important to focus on the weather systems to understand the processes that resulted in the extreme rainfall responsible for the flooding. The structure of weather systems in most areas involved a pattern in which the winds turned in an anticyclonic sense as they ascended from the low to middle levels of the atmosphere (often referred to as WAA) which was maintained over large areas for 11 days. HYSPLIT air parcel trajectory observations were employed to confirm these ascent analyses. Examination of a period during which the heaviest rain was reported and compared with climatology showed a much stronger monsoon circulation, widespread WAA through tropical Queensland where normally its descending equivalent of cold-air advection is found, and higher mean sea level pressures along the south Queensland coast. The monsoon low was located between strong deep monsoon westerlies to the north and strong deep easterlies to the south which ensured its slow movement. This non-TC event produced heavy inland rainfall. Extreme inland rainfall is rare in this region. Dare et al. (2012), using data from 1969/70 to 2009/10, showed that over north Queensland non-TC events produced a large percentage of the total rainfall. The vertical structure associated with one of the earlier events that occurred in 2008 had sufficient data to detect strong and widespread WAA overlying an onshore moist tropical airstream. This appears to have played a crucial role in such extreme rainfall extending well inland and perhaps gives insight to the cause of a 1916 flooding disaster at Clermont which claimed around 70 lives. Several other events over the inland Tropics with strong WAA also help explain the 1916 disaster.

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“…( + 1) = ( ) + 1 ⋅ ⋅ (26) Next, it is necessary to integrate the training rules (26), the error minimization functions (18) and the weight adjustment of the synapse coefficients (25) in order to increase the accuracy of training and, as a result, the predicted values:…”

Section: Neural Network Training For Predicting Water Levelsmentioning

confidence: 99%

“…To solve this problem, many approaches and methods are used, such as numerical methods, regression models, etc. [2,[4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. But due to the lack of works containing a description of the method of early detection of threats based on their early forecasting, it becomes relevant to use neural network approaches and technologies to solve this problem.…”

Section: Introductionmentioning

confidence: 99%

Threat prediction in complex distributed systems using artificial neural network technology

Palchevsky

Khristodulo

Pavlov

2020

CPT2020 the 8th International Scientific Conference on Computing in Physics and Technology Proceedings

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In the context of this article, a method for detecting threats based on their forecasting and development in complex distributed systems is proposed. Initially, the relevance of the research topic is substantiated from the point of view of the prospective use of various methods in the framework of threat management and their forecasting in complex distributed systems. Based on the analysis of these methods, a proprietary forecasting method based on the second generation recurrent neural network (RNN) was proposed. The mathematical formulation of the problem is presented, as well as the structure of this neural network and its mathematical model of self-learning, which allows achieving more accurate (with less error) results in the framework of threat prediction (in this case, the level of water rise at gauging stations) in complex distributed systems. An analysis was also made of the effectiveness of the existing and proposed forecasting methods, which showed the stability of the neural network in relation to other forecasting methods: the error of the neural network is 3-20% of actual (real) water levels; the least squares method reaches up to 34.5%, the numerical method in a generalized form - up to 36%; linear regression model – up to 47.5%. Thus, the neural network allows a fairly stable forecast of the flood situation over several days, which allows special services to carry out flood control measures.

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Forecasting the extreme rainfall, low temperatures, and strong winds associated with the northern Queensland floods of February 2019

Cited by 33 publications

References 27 publications

Increasing Compound Heat and Precipitation Extremes Elevated by Urbanization in South China

Increasing Compound Heat and Precipitation Extremes Elevated by Urbanization in South China

Weather systems and extreme rainfall generation in the 2019 north Queensland floods compared with historical north Queensland record floods

Threat prediction in complex distributed systems using artificial neural network technology

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