Ensemble Forecast of a Typhoon Flood Event

Mackey, Brian; Krishnamurti, T. N.

doi:10.1175/1520-0434(2001)016<0399:efoatf>2.0.co;2

Cited by 23 publications

(19 citation statements)

References 21 publications

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“…For example, some studies (e.g., Krishnamurti et al 2000;Goerss 2000;Aberson 2001;Vijaya Kumar et al 2003;Williford et al 2003;Weber 2003Weber , 2005a have demonstrated that scalar position and intensity forecasts computed from multimodel ensembles are better than those from individual ensemble members. Additional work (e.g., Krishnamurti et al 1997;Zhang and Krishnamurti 1999;Mackey and Krishnamurti 2001) has shown that the ensemble mean from an individual model can significantly improve on deterministic forecasts of tropical cyclones.…”

Section: B the Usefulness Of Ensemblesmentioning

confidence: 99%

A Probabilistic Analysis of the Dynamics and Predictability of Tropical Cyclogenesis

Sippel

Zhang

2008

Journal of the Atmospheric Sciences

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Using methods unique for tropical cyclone studies in peer-reviewed literature, this study examines the dynamics and predictability of a nondeveloping tropical disturbance in the Gulf of Mexico during the 2004 hurricane season. Short-range ensemble forecasts are performed with a mesoscale model at low resolution with parameterized moist convection and at high resolution with explicit convection. Taking advantage of discrepancies between ensemble members, statistical correlation is used to elucidate why some ensemble members strengthen the disturbance into a tropical cyclone or hurricane and others do not.It is found that the two most important factors in the initial conditions for genesis in this case are the presence of deep moisture and high CAPE. These factors combine to yield more active initial convection and a quick spinup during the first 6-12 h. Because these factors result in quicker genesis in some ensemble members than others, they are also the primary source for spread early in the ensemble. Discrepancies after 12 h are amplified by differences in convection that are related to fluxes of sensible and latent heat. Eventually the wind-induced surface heat exchange mechanism results in even larger ensemble spread.

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Section: B the Usefulness Of Ensemblesmentioning

confidence: 99%

A Probabilistic Analysis of the Dynamics and Predictability of Tropical Cyclogenesis

Sippel

Zhang

2008

Journal of the Atmospheric Sciences

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“…Assuming floods as stochastic processes, they can be predicted using certain probability distributions from historical streamflow data [29]. For instance, the climatology average method (CLIM) [28], empirical orthogonal function (EOF) [30], multiple linear regressions (MLR), quantile regression techniques (QRT) [31], and Bayesian forecasting models [32] are widely used for predicting major floods. However, they were reported to be unsuitable for short-term prediction, and, in this context, they need major improvement due to the lack of accuracy, complexity of the usage, computation cost, and robustness of the method.…”

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confidence: 99%

Flood Prediction Using Machine Learning Models: Literature Review

Mosavi

Öztürk

Chau

2018

Water

1,030

469

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Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models contributed to risk reduction, policy suggestion, minimization of the loss of human life, and reduction the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods contributed highly in the advancement of prediction systems providing better performance and cost-effective solutions. Due to the vast benefits and potential of ML, its popularity dramatically increased among hydrologists.Researchers through introducing novel ML methods and hybridizing of the existing ones aim at discovering more accurate and efficient prediction models. The main contribution of this paper is to demonstrate the state of the art of ML models in flood prediction and to give insight into the most suitable models. In this paper, the literature where ML models were benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed are particularly investigated to provide an extensive overview on the various ML algorithms used in the field. The performance comparison of ML models presents an in-depth understanding of the different techniques within the framework of a comprehensive evaluation and discussion. As a result, this paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported as the most effective strategies for the improvement of ML methods. This survey can be used as a guideline for hydrologists as well as climate scientists in choosing the proper ML method according to the prediction task. Keywords: flood prediction; flood forecasting; flash-flood model, big flood management; hydrologic model; rainfall-runoff, hybrid & ensemble machine learning; artificial neural networks (ANNs); support vector machines (SVM); natural hazards & disasters; adaptive neuro-fuzzy inference system (ANFIS); decision trees (DT); internet of things (IoT); random forest (RF); survey; classification and regression trees (CART), data science; deep learning; big data; bagging, boosting, artificial intelligence (AI); soft computing; extreme event management; time series prediction; multilayer perceptron (MLP); simulated annealing (SA); multivariate adaptive regression splines (MARS), supervised learning IntroductionAmong the natural disasters, floods are the most destructive, causing massive damage to human life, infrastructure, agriculture, and the socioeconomic system.Governments, therefore, are under pressure to develop reliable and accurate maps of flood risk areas and further plan for sustainable flood risk management focusing on prevention, protection, and preparedness [1]. Flood prediction models are of significant impor...

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“…Many studies have documented results from these applications. The ensemble forecasting was used in short-range ensemble forecasting (SREF) [62][63][64][65][66][67], tropical cyclone forecasts [68,69], as well as the flooding warning [70], and so forth. The ensemblebased Kalman filtering techniques were also applied for the studying and numerical simulation of hurricanes (see, e.g,.…”

Section: Applications Of Ensemble Forecasting and Ensemble Kalman Filmentioning

confidence: 99%

Beating the Uncertainties: Ensemble Forecasting and Ensemble‐Based Data Assimilation in Modern Numerical Weather Prediction

Zhang

2010

Advances in Meteorology

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Accurate numerical weather forecasting is of great importance. Due to inadequate observations, our limited understanding of the physical processes of the atmosphere, and the chaotic nature of atmospheric flow, uncertainties always exist in modern numerical weather prediction (NWP). Recent developments in ensemble forecasting and ensemble-based data assimilation have proved that there are promising ways to beat the forecast uncertainties in NWP. This paper gives a brief overview of fundamental problems and recent progress associated with ensemble forecasting and ensemble-based data assimilation. The usefulness of these methods in improving high-impact weather forecasting is also discussed.

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Ensemble Forecast of a Typhoon Flood Event

Cited by 23 publications

References 21 publications

A Probabilistic Analysis of the Dynamics and Predictability of Tropical Cyclogenesis

A Probabilistic Analysis of the Dynamics and Predictability of Tropical Cyclogenesis

Flood Prediction Using Machine Learning Models: Literature Review

Beating the Uncertainties: Ensemble Forecasting and Ensemble‐Based Data Assimilation in Modern Numerical Weather Prediction

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