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Application of the improved dung beetle optimizer,muti-head attention and hybrid deep learning algorithms to groundwater depth prediction in the Ningxia area,China

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Atmospheric and Oceanic Science Letters 2025年第1期18卷 18-23页

作者： Jiarui Cai Bo Sun Huijun Wang Yi Zheng Siyu Zhou Huixin Li Yanyan Huang Peishu Zong Collaborative Innovation center on forecast and Evaluation of meteorological disasters/Key Laboratory of meteorological disaster Ministry of Education/Joint International Research Laboratory of Climate and Environment ChangeNanjing University of Information Science and TechnologyNanjingChina Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) ZhuhaiChina China meteorological Administration Key Laboratory of Transportation MeteorologyNanjingChina Jiangsu meteorological Observatory NanjingChina

Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical *** learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many *** this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,*** two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction *** addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.

关键词： Groundwater depth Multi-head attention Improved dung beetle optimizer CNN-LSTM CNN-GRU Ningxia

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A robust statistical prediction model for late-summer heavy precipitation days in North China

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Science China Earth Sciences 2025年第1期68卷 158-171页

作者： Shunli JIANG Tingting HAN Xin ZHOU Huijun WANG Zhicong YIN Xiaolei SONG Collaborative Innovation center on Forecast and Evaluation of meteorological disasters/Key Laboratory of meteorological disaster Ministry of Education Nanjing University for Information Science and Technology Nansen-Zhu International Research Centre Institute of Atmospheric Physics Chinese Academy of Sciences Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)

Recently, heavy precipitation(HP) events have occurred frequently in North China(NC), causing devastating economic losses and human fatalities. However, the short-term climate prediction of HP is quite limited. Combining year-to-year increment(DY) method and sliding correlations, we developed a robust seasonal prediction model for late-summer HP days(HPDs) in NC during 1982–2022, utilizing three independent predictors—February sea surface temperature(SST) in the Indian Ocean(SST＿IO), February snow depth over North Asia(SDE＿NA), and May melted snow depth in NC(MSDE＿NC). The SST＿IO anomalies affect NC's precipitation through the Pacific-Japan pattern. The SDE＿NA anomalies are associated with East Asian anomalous anticyclone by southeastern propagation of Rossby wave at Eurasia. The MSDE＿NC anomalies are followed by vertical motion and moisture anomalies in situ and thereby cause precipitation anomalies. This prediction model can well simulate the variations of the HPDs, with a correlation coefficient(CC) of 0.81(0.65) between the observed and predicted HPDs＿DY(HPDs＿anomaly). The percentage with the same sign for 15 extreme HPDs＿anomaly years(PSSE) is 100%.Moreover, in the cross-validation test during 1982–2022, the PSSE for HPDs＿anomaly is as high as 100%, along with a low rootmean-square error of 1.14. For independent hindcasts during 2013–2022, the CC between the observed and predicted HPDs＿DY(HPDs＿anomaly) is 0.93(0.83), together with high Nash-Sutcliffe efficiency(0.82) and agreement index(0.89). Specifically, the predictions are broadly consistent with the observations for 2015, 2016, 2017, 2021, and 2022, reflecting excellent performance of this prediction model of HPDs in NC.

关键词： Heavy precipitation at North China Year-to-year increment approach Robust seasonal prediction Sea surface temperature Snow depth

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Diagnostic Analysis of Wave Action Density During Heavy Rainfall Caused by Landfalling Typhoon

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Journal of Tropical Meteorology 2022年第3期28卷 364-376页

作者： ZHOU Guan-bo JIAO Ya-yin XU Ying-long National meteorological center Beijing 100081 China Key Laboratory of South China Sea meteorological disaster prevention and Mitigation of Hainan province Hainan 570203 China hebei meteorological disaster prevention and environmental Meteorology center Hebei 050033 China

Based on prior investigation,this work defined a new thermodynamic shear advection parameter,which combines the vertical component of convective vorticity vector,horizontal divergence,and vertical gradient of generalized potential *** interaction between waves and fundamental states was computed for the heavyrainfall event generated by landfalling typhoon“Morakot”.The analysis data was produced by ADAS[ARPS(Advanced Regional Prediction System)Data Analysis System]combined with the NCEP/NCAR final analysis data(1°×1°,26 vertical pressure levels and 6-hour interval)with the routine observations of surface and *** it may describe the typical vertical structure of dynamical and thermodynamic fields,the result indicates that the parameter is intimately related to precipitation *** parameter’s positive high-value area closely matches the reported 6-hour accumulated surface *** the statistical analysis reveals a certain correspondence between the thermodynamic shear advection parameter and the observed 6-hour accumulated surface rainfall in the summer of *** implies that the parameter can predict and indicate the rainfall area,as well as the initiation and evolution of precipitation systems.

关键词： disturbance thermodynamic shear advection parameter wave action density generalized potential temperature wave-flow interaction

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Raindrop Size Distribution Measurements at High Altitudes in the Northeastern Tibetan Plateau during Summer

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Advances in Atmospheric Sciences 2023年第7期40卷 1244-1256页

作者： Huibang HAN Yuxin ZHANG Jianbing TIAN Xiaoyan KANG meteorological disaster prevention Technology center in Qinghai province Xining 810000China Key Laboratory for disaster prevention and Mitigation in Qinghai province Xining 810000China

Characteristics of raindrop size distribution during summer are studied by using the data from six Parsivel disdrometers located in the northeastern Tibetan *** analysis focuses on convective and stratiform precipitation at high altitudes from 2434 m to 4202 *** results show that the contribution of stratiform and convective precipitation with rain rate between 1≤R<5 mm h^(-1) to the total precipitation increases with altitude,and the raindrop scale and number concentration of convective precipitation is larger than that for stratiform *** droplet size spectra of both stratiform and convective precipitation shows a single peak with a peak particle size between 0.31–0.50 mm,and they have essentially the same peak particle size and number concentration at the same *** maximum spectral widths of stratiform clouds are between 4 mm and 5 mm,while those of convective clouds range from 4 mm to 8 *** Gamma distribution is more suitable than the Marshall-Palmer distribution in terms of the actual raindrop spectrum *** stratiform precipitation particles are smaller with higher number concentration,while the opposite is true for the convective precipitation *** convective precipitation particles drop faster than stratiform precipitation particles when the particle size exceeds 2 mm,and the falling velocity of raindrops after standard curve fitting is underestimated during the observation ***,conventional radar estimation methods would underestimate the precipitation in the Northeastern Tibetan Plateau.

关键词： raindrop size distribution precipitation microphysics Northeastern Tibetan Plateau fall velocity

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Crowd sensing data delivery based on tangle DAG network

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The Journal of China Universities of Posts and Telecommunications 2023年第3期30卷 88-98页

作者： Chen Hui Jiang Xiaoling Wu Tianting Mou Xingyu Operation Monitoring Department Jilin Province Meteorological Information Network CenterChangchun 130062China Appraisal Department Jilin Province Meteorological Disaster Prevention Technology CenterChangchun 130062China

In view of the fact that current data delivery methods are not enough to meet the security requirements of today’s distributed crowd sensing,and the data delivery methods are not flexible enough,this paper proposes a crowd sensing data interaction method based on tangle directed acyclic graph(DAG)*** this method,users and platforms are regarded as nodes of the network in the process of performing crowd sensing ***,the heaviest chain is generated through the main chain strategy to ensure the stability of the ***,the hidden Markov model(HMM)prediction model is used to improve the correlation of the perceived data to improve the ***,the confidential transaction and commitment algorithm is used to ensure the reliability of the transaction,overcome the security risks faced by the trusted third party,and simplify the group intelligence aware transaction ***,through simulation experiments,the security and feasibility of the group intelligence aware data delivery method based on tangle DAG network are verified.

关键词： data delivery crowd sensing tangle directed acyclic graph(DAG) confidential transactions

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Regional Rainfall Damage Functions to Estimate Direct Economic Losses in Rainstorms:A Case Study of the 2016 Extreme Rainfall Event in hebei province of China

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International Journal of disaster Risk Science 2024年第4期15卷 508-520页

作者： Xiaojuan Chen Yifu Xu Ting Li Jun Wei Jidong Wu China meteorological Administration Xiong’an Atmospheric Boundary Layer Key Laboratory Xiong’an New Area 071800China Key Laboratory of Meteorology and Ecological environment of hebei province Shijiazhuang 050021China meteorological disaster prevention and environment meteorological center of hebei province Shijiazhuang 050021China Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance Beijing Normal University at ZhuhaiZhuhai 519087China

Developing a regional damage function to quickly estimate direct economic losses(DELs) caused by heavy rain and floods is crucial for providing scientific supports in effective disaster response and risk reduction. This study investigated the factors that influence regional rainfall-induced damage and developed a calibrated regional rainfall damage function(RDF) using data from the 2016 extreme rainfall event in hebei province, China. The analysis revealed that total precipitation, asset value exposure, per capita GDP, and historical geological disaster density at both the township and county levels significantly affect regional rainfall-induced damage. The coefficients of the calibrated RDF indicate that doubling the values of these factors leads to varying increases or decreases in rainfall-induced damage. Furthermore, the study demonstrated a spatial scale dependency in the coefficients of the RDF, with increased elasticity values for asset value exposure and per capita GDP at the county level compared to the township level. The findings emphasize the challenges of applying RDFs across multiple scales and highlight the importance of considering socioeconomic factors in assessing rainfall-induced damage. Despite the limitations and uncertainties of the RDF developed, this study contributes to our understanding of the relationship between physical and socioeconomic factors and rainfall-induced damage. Future research should prioritize enhancing exposure estimation and calibrating RDFs for various types of rainfall-induced disasters to improve model accuracy and *** study also acknowledges the variation in RDF performance across different physical environments, especially concerning geological disasters and slope stability.

关键词： Regional rainfall damage function Loss attribution Direct economic losses Rainfall intensity Asset value Vulnerability

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MOISTURE TRANSPORT PATHS AND SOURCES OF SOUTH CHINA ANNUALLY FIRST RAINY SEASON AND THEIR RELATIONSHIP WITH THE ONSET OF THE SOUTH CHINA SEA SUMMER MONSOON

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Journal of Tropical Meteorology 2019年第2期25卷 171-179页

作者： LI Xiao-fan JIANG Zhi-hong SHI Yi Key Laboratory of meteorological disaster Ministry of Education/Joint International Research Laboratory of Climate and environment Change/Collaborative Innovation center on Forecast and Evaluation of meteorological disasters Nanjing University of Information Science and Technology hebei Climate center hebei Provincial Key Laboratory for Meteorology and Eco-environment meteorological Institute of hebei province

This study simulated the moisture transport process of southern China annually first rainy season(SCAFRS)using a Lagrangian airflow trajectory model(Hybrid Single Particle Lagrangian Integrated Trajectory: HYSPLIT), to determine SCAFRS moisture transport characteristics and their relationship with South China Sea summer monsoon(SCSSM). It is found that the moisture transport paths and sources of SCAFRS are closely related to the onset of SCSSM. Divided by SCSSM onset dates, the moisture transport characteristics of SCAFRS are compared *** the onset of SCSSM, precipitation of SCAFRS mainly comes from western Pacific and eastern China. Their contributions are 24% and 25%, respectively. The amount of water vapor carried along the path coming from Bay of Bengal-South China Sea(BSC) is relatively high, but the contribution rate of this path to SCAFRS precipitation is relatively low. Mainly due to strong precipitation over Bay of Bengal before the onset of SCSSM, this region is a moisture sink, which makes most moisture deposit in this region and only a small portion of water vapor transported to southern China. After the onset of SCSSM, most water vapor is transported to southern China by the southwesterly paths. The Indian Ocean is the main moisture source, which contributes almost 25% to SCAFRS precipitation. The contributions of moisture originating from BSC and eastern China to southern China precipitation after the onset of SCSSM are 21% and 18%, respectively.

关键词： moisture transport Lagrangian method southern China annually first rainy season onset of the South China Sea summer monsoon

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Changing characteristics and spatial differentiation of spring precipitation in Southwest China during 1961-2012

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Chinese Physics B 2015年第2期24卷 526-533页

作者：刘洪兰张强张俊国胡文超郭俊琴王胜 Zhangye meteorological Bureau Institute of Arid Meteorology China Meteorological AdministrationKey Laboratory of Arid Climatic Change and Disaster Reduction of Gansu ProvinceKey Laboratory of Arid Climate Change and Disaster Reduction of China Meteorological Administration Zhangye Middle School meteorological Service center of Gansu province Northwest Regional Climate center Jinta County meteorological Bureau

In this study,we analyze spring precipitation from 92 meteorological stations spanning between 1961 and 2012 to understand temporal-spatial variability and change of spring precipitation over Southwest *** analysis methods are used for different purposes,including empirical orthogonal function（EOF） analysis and rotated EOF（REOF） for analyzing spatial structure change of precipitation anomaly,and the Mann-Kendall testing method to determine whether there were abrupt changes during the analyzed time *** find that the first spatial mode of the precipitation has a domain uniform structure;the second is dominated by a spatial dipole;and the third contains five variability *** 2000 s is the decade with the largest amount of precipitation while the 1990 s is the decade with the smallest amount of *** year-to-year difference of that region is large：the amount of the largest precipitation year doubles that of the smallest precipitation *** also find that spring precipitation in Southwest China experienced a few abrupt changes：a sudden increase at 1966,a sudden decrease at 1979,and a sudden increase at *** speculate that the spring precipitation will increase gradually in the next two decades.

关键词： spring precipitation temporal-spatial structure 500-hPa level Southwest region

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Study on Quantitative Precipitation Estimation by Polarimetric Radar Using Deep Learning

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Advances in Atmospheric Sciences 2024年第6期41卷 1147-1160页

作者： Jiang HUANGFU Zhiqun HU Jiafeng ZHENG Lirong WANG Yongjie ZHU State Key Lab of Severe Weather Chinese Academy of Meteorological SciencesBeijing 100081China meteorological observatory of Jiangxi province Nanchang 330096China hebei meteorological disaster prevention and environmental meteorological center Shijiazhuang 050021China School of Atmospheric Sciences Chengdu University of Information TechnologyChengdu 610225China

Accurate radar quantitative precipitation estimation(QPE)plays an essential role in disaster prevention and *** this paper,two deep learning-based QPE networks including a single-parameter network and a multi-parameter network are ***,a self-defined loss function(SLF)is proposed during *** dataset includes Shijiazhuang S-band dual polarimetric radar(CINRAD/SAD)data and rain gauge data within the radar’s 100-km detection range during the flood season of 2021 in North *** that the specific propagation phase shift(KDP)has a roughly linear relationship with the precipitation intensity,KDP is set to 0.5°km^(-1 )as a threshold value to divide all the rain data(AR)into a heavy rain(HR)and light rain(LR)***,12 deep learning-based QPE models are trained according to the input radar parameters,the precipitation datasets,and whether an SLF was adopted,*** results suggest that the effects of QPE after distinguishing rainfall intensity are better than those without distinguishing,and the effects of using SLF are better than those that used MSE as a loss function.A Z-R relationship and a ZH-KDP-R synthesis method are compared with deep learning-based *** mean relative errors(MRE)of AR models using SLF are improved by 61.90%,51.21%,and 56.34%compared with the Z-R relational method,and by 38.63%,42.55%,and 47.49%compared with the synthesis ***,the models are further evaluated in three precipitation processes,which manifest that the deep learning-based models have significant advantages over the traditional empirical formula methods.

关键词： polarimetric radar quantitative precipitation estimation deep learning single-parameter network multi-parameter network

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Effects of Optimized Root Water Uptake Parameterization Schemes on Water and Heat Flux Simulation in a Maize Agroecosystem

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Journal of meteorological Research 2017年第2期31卷 363-377页

作者： Fu CAI Huiqing MING Na MI Yanbing XIE Yushu ZHANG Rongping LI Institute of Atmospheric environment China Meteorological Administration Shenyang 110166 Liaoning province meteorological Service center Shenyang 110166 Institute of meteorological Science of Liaoning province Shenyang 110166

As root water uptake（RWU）is an important link in the water and heat exchange between plants and ambient air,improving its parameterization is key to enhancing the performance of land surface model *** different types of RWU functions have been adopted in land surface models,there is no evidence as to which scheme most applicable to maize farmland *** on the 2007–09 data collected at the farmland ecosystem field station in Jinzhou,the RWU function in the Common Land Model（Co LM）was optimized with scheme options in light of factors determining whether roots absorb water from a certain soil layer（W_x）and whether the baseline cumulative root efficiency required for maximum plant transpiration（W_c）is *** sensibility of the parameters of the optimization scheme was investigated,and then the effects of the optimized RWU function on water and heat flux simulation were *** results indicate that the model simulation was not sensitive to W_x but was significantly impacted by W_*** the original model,soil humidity was somewhat underestimated for precipitation-free days;soil temperature was simulated with obvious interannual and seasonal differences and remarkable underestimations for the maize late-growth stage;and sensible and latent heat fluxes were overestimated and underestimated,respectively,for years with relatively less precipitation,and both were simulated with high accuracy for years with relatively more *** optimized RWU process resulted in a significant improvement of Co LM’s performance in simulating soil humidity,temperature,sensible heat,and latent heat,for dry *** conclusion,the optimized RWU scheme available for the Co LM model is applicable to the simulation of water and heat flux for maize farmland ecosystems in arid areas.

关键词： root water uptake land surface model water flux heat flux maize agroecosystem

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