Science and Prediction of Heavy Rainfall over China: Research Progress since the Reform and Opening-Up of New China

Luo, Yali; Sun, Junyi; Li, Ying; Xia, Rudi; Du, Yu; Yang, Shuai; Zhang, Yuanchun; Chen, Jing; Dai, Kun; Shen, Xueshun; Chen, Haoming; Zhou, Feifan; Liu, Yimin; Fu, Shenming; Wu, Meng‐Wen; Tong, Xiao; Chen, Yangruixue; Li, Huiqi; Li, Mingxin

doi:10.1007/s13351-020-0006-x

Cited by 54 publications

(34 citation statements)

References 188 publications

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“…We speculate that it is because the coarser resolution outside the refinement region may alter certain large-scale controlling systems that have systematic impacts on the rainfall over South China, especially during this five-year period. This speculation is drawn from model development experience that rainfall over South China is sensitive to the global climate background, which is also understood from the observational studies (e.g., Ding & Chan, 2005;Luo et al, 2020).…”

Section: South Chinamentioning

confidence: 99%

“…It has distinctive geographical and topographic features, under the influences of land and sea, plateau and plain. The precipitation processes are related to various static and dynamic factors of influence, including the topography, surface heating, diurnal variations in the monsoon circulations, and organized convective systems (e.g., Ding & Chan, 2005; Luo et al., 2020; Wang & LinHo, 2002; Yu et al., 2014).…”

Section: Introductionmentioning

confidence: 99%

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AMIP Simulations of a Global Model for Unified Weather‐Climate Forecast: Understanding Precipitation Characteristics and Sensitivity Over East Asia

Zhang

Jian

et al. 2021

J Adv Model Earth Syst

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A global model formulation for unified weather‐climate forecast is evaluated, with emphasis on the climate simulations at typical hydrostatic resolutions. The internal sensitivity is explored by considering different dynamical configurations (resolution, solver type, transport scheme). After a basic assessment of the global mean climate, a detailed analysis of precipitation characteristics is extended to East Asia. The model shows a reasonable mean state, seasonal variation, frequency–intensity structure, and diurnal phase time. The artificial rainfall around the steep slopes of the Tibetan Plateau can be improved through choices in the dynamical configuration. The regional features characterized by “afternoon versus nocturnal‐to‐early‐morning peaks” are properly distinguished. The hourly climatic features are comparable to super‐parameterized CAM5. Different dynamical configurations demonstrate unique sensitivities related to underling physical mechanisms, which are studied from the perspective of the diurnal cycle for three representative regions. Over South China, the higher‐resolution models decrease the weak‐precipitation while increase intense rainfall, thus reducing the dry biases. This is contributed by enhanced grid and sub‐grid scale motions associated with daytime convection progression. Over central western China, the variable‐resolution model better simulates the eastward propagating episodes characterized by a transition from convective to stratiform rainfall along the eastern slope of the Plateau. This reduces the positive biases at the high topography of the Plateau and alleviates the negative biases at the lower foot. Over central eastern China, the model replicates the dominant role of large‐scale governing factors in regulating the early morning rainfall peaks, and produces stratiform heating patterns.

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Section: South Chinamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

AMIP Simulations of a Global Model for Unified Weather‐Climate Forecast: Understanding Precipitation Characteristics and Sensitivity Over East Asia

Zhang

Jian

et al. 2021

J Adv Model Earth Syst

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“…Numerous attempts have been made for forecasting rainfall using various physical, empirical, and physio-empirical models. Such models have been used for forecasting rainfall at annual, seasonal, monthly, and daily scale in different regions of the world [9,10]. Physical models are developed considering the synoptic climate and physical mechanism of interactions of different climatic variables causing rainfall in a region [11].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Regularized Neural Network Model Development for Predicting Daily Rainfall from Sea Level Pressure Data: Investigation on Solving Complex Hydrology Problem

Jabbar

Zahra

et al. 2021

Complexity

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Prediction of daily rainfall is important for flood forecasting, reservoir operation, and many other hydrological applications. The artificial intelligence (AI) algorithm is generally used for stochastic forecasting rainfall which is not capable to simulate unseen extreme rainfall events which become common due to climate change. A new model is developed in this study for prediction of daily rainfall for different lead times based on sea level pressure (SLP) which is physically related to rainfall on land and thus able to predict unseen rainfall events. Daily rainfall of east coast of Peninsular Malaysia (PM) was predicted using SLP data over the climate domain. Five advanced AI algorithms such as extreme learning machine (ELM), Bayesian regularized neural networks (BRNNs), Bayesian additive regression trees (BART), extreme gradient boosting (xgBoost), and hybrid neural fuzzy inference system (HNFIS) were used considering the complex relationship of rainfall with sea level pressure. Principle components of SLP domain correlated with daily rainfall were used as predictors. The results revealed that the efficacy of AI models is predicting daily rainfall one day before. The relative performance of the models revealed the higher performance of BRNN with normalized root mean square error (NRMSE) of 0.678 compared with HNFIS (NRMSE = 0.708), BART (NRMSE = 0.784), xgBoost (NRMSE = 0.803), and ELM (NRMSE = 0.915). Visual inspection of predicted rainfall during model validation using density-scatter plot and other novel ways of visual comparison revealed the ability of BRNN to predict daily rainfall one day before reliably.

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“…As one of the most serious natural disasters, the typhoon is the strongest tropical cyclone in the tropical ocean and has the characteristics of strong, sudden, and destructive power. China is one of the countries suffering from the most severe global tropical cyclone disasters and there is an average of 7.2 typhoons landing in the southeast coastal areas of China every year, which causes a direct loss of about 0.4% of GDP [1]. Besides, the typhoon has a great impact on the operation security, stability, and economy of power systems.…”

Section: Introduction 1impact Of Natural Disasters On the Power Grid And The Increase In Installed Capacity Of Renewable Energy Power Genmentioning

confidence: 99%

Optimal Coordinated Dispatching Strategy of Multi-Sources Power System with Wind, Hydro and Thermal Power Based on CVaR in Typhoon Environment

Qian¹,

Chen²,

Chen

et al. 2021

Energies

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Typhoons and other natural disasters affect the normal operation of power systems thus it is an important goal for strong and intelligent power grid construction to improve the ability of power systems to resist typhoons and other natural disasters. Especially, an effective coordinated and optimized dispatching strategy for a multi-source power system is greatly helpful to cope with the impact of typhoons and other natural disasters on power system operation. Given this background, a typhoon wind circle model considering the temporal and spatial distribution of typhoons is established to obtain the input wind speed of the wind farm at first. Second, based on the initial input wind speed of wind farms, a typical scenario set of wind power output is constructed to reflect its fluctuation and uncertainty. Next, an optimal coordinated dispatching model of a multi-source power system with wind, hydro and thermal power based on the conditional value at risk (CVaR) is established with the target of minimizing the total cost of system dispatching, in which a 72 h pre-dispatching mode is studied to optimize the system operation for 72 h on the day before, on and after the typhoon. Finally, a revised 24-node transmission network system in a coastal area with typhoon is served as a case for demonstrating the effectiveness of the proposed model, and the simulation result shows that the proposed model could take the advantages of the coordination and complementarity of multi-sources power system and decrease the total cost of system dispatching and improve the renewable energy consumption level.

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Science and Prediction of Heavy Rainfall over China: Research Progress since the Reform and Opening-Up of New China

Cited by 54 publications

References 188 publications

AMIP Simulations of a Global Model for Unified Weather‐Climate Forecast: Understanding Precipitation Characteristics and Sensitivity Over East Asia

AMIP Simulations of a Global Model for Unified Weather‐Climate Forecast: Understanding Precipitation Characteristics and Sensitivity Over East Asia

Bayesian Regularized Neural Network Model Development for Predicting Daily Rainfall from Sea Level Pressure Data: Investigation on Solving Complex Hydrology Problem

Optimal Coordinated Dispatching Strategy of Multi-Sources Power System with Wind, Hydro and Thermal Power Based on CVaR in Typhoon Environment

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