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improvement of cloud microphysical parameterization and its advantages in simulating precipitation along the Sichuan-Xizang Railway

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Science China Earth Sciences 2024年第3期67卷 856-873页

作者： Xiaoqi XU Zhiwei HENG Yueqing LI Shunjiu WANG Jian LI Yuan WANG Jinghua CHEN Peiwen ZHANG Chunsong LU China Meteorological Administration Transportation Meteorology Key Laboratory Nanjing Joint Institute for Atmospheric SciencesNanjing 210041China Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province Institute of Plateau MeteorologyCMAChengdu 610072China State Key Laboratory of Disaster Weather Chinese Academy of Meteorological SciencesBeijing 100081China Collaborative Innovation Center for Western Ecological Safety Lanzhou UniversityLanzhou 730000China China Meteorological Administration Aerosol-Cloud and Precipitation Key Laboratory and Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD) Nanjing University of Information Science and TechnologyNanjing 210044China

The Sichuan-Xizang Railway is an important part of the railway network in China, and geological disasters, such as mountain floods and landslides, frequently occur in this region. Precipitation is an important cause of these disasters;therefore,accurate simulation of the precipitation in this region is highly important. In this study, the descriptions for uncertain processes in the cloud microphysics scheme are improved;these processes include cloud droplet activation, cloud-rain autoconversion, rain accretion by cloud droplets, and the entrainment-mixing process. In the default scheme, the cloud water content of different sizes corresponds to the same cloud droplet concentration, which is inconsistent with the actual content;this results in excessive cloud droplet size, unreasonable related conversion rates of microphysical process(such as cloud-rain autoconversion), and an overestimation of precipitation. Our new scheme overcomes the problem of excessive cloud droplet size. The processes of cloudrain autoconversion and rain accretion by cloud droplets are similar to the stochastic collection equation, and the mixing mechanism of cloud droplets is more consistent with that occurred during the actual physical process in the cloud. Based on the new and old schemes, multiple precipitation processes in the flood season of 2021 along the Sichuan-Xizang Railway are simulated, and the results are evaluated using ground observations and satellite data. Compared to the default scheme, the new scheme is more suitable for the simulation of cloud physics, reducing the simulation deviation of the liquid water path and droplet radius from 2 times to less than 1 time and significantly alleviating the overestimation of precipitation intensity and range of precipitation center. The average root-mean-square error is reduced by 22%. Our results can provide a scientific reference for improving precipitation forecasting and disaster prevention in this region.

关键词： The Sichuan-Xizang Railway Cloud microphysics Precipitation model improvement

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Research on Plant Species Identification Based on Improved Convolutional Neural Network

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Phyton-International Journal of Experimental Botany 2023年第4期92卷 1037-1058页

作者： Chuangchuang Yuan Tonghai Liu Shuang Song Fangyu Gao Rui Zhang College of Computer and Information Engineering Tianjin Agricultural UniversityTianjin300392China

Plant species recognition is an important research area in image recognition in recent years.However,the existing plant species recognition methods have low recognition accuracy and do not meet professional requirements in terms of recognition accuracy.Therefore,ShuffleNetV2 was improved by combining the current hot concern mechanism,convolution kernel size adjustment,convolution tailoring,and CSP technology to improve the accuracy and reduce the amount of computation in this study.Six convolutional neural network models with sufficient trainable parameters were designed for differentiation learning.The SGD algorithm is used to optimize the training process to avoid overfitting or falling into the local optimum.In this paper,a conventional plant image dataset TJAU10 collected by cell phones in a natural context was constructed,containing 3000 images of 10 plant species on the campus of Tianjin Agricultural University.Finally,the improved model is compared with the baseline version of the model,which achieves better results in terms of improving accuracy and reducing the computational effort.The recognition accuracy tested on the TJAU10 dataset reaches up to 98.3%,and the recognition precision reaches up to 93.6%,which is 5.1%better than the original model and reduces the computational effort by about 31%compared with the original model.In addition,the experimental results were evaluated using metrics such as the confusion matrix,which can meet the requirements of professionals for the accurate identification of plant species.

关键词： Deep learning convolutional neural network plant identification model improvement

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Corpus Augmentation for Improving Neural Machine Translation

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Computers, Materials & Continua 2020年第7期64卷 637-650页

作者： Zijian Li Chengying Chi Yunyun Zhan University of Science and Technology Liaoning Anshan114031China College of Science&Health Technological University DublinDublinD08 X622Ireland

The translation quality of neural machine translation(NMT)systems depends largely on the quality of large-scale bilingual parallel corpora available.Research shows that under the condition of limited resources,the performance of NMT is greatly reduced,and a large amount of high-quality bilingual parallel data is needed to train a competitive translation model.However,not all languages have large-scale and high-quality bilingual corpus resources available.In these cases,improving the quality of the corpora has become the main focus to increase the accuracy of the NMT results.This paper proposes a new method to improve the quality of data by using data cleaning,data expansion,and other measures to expand the data at the word and sentence-level,thus improving the richness of the bilingual data.The long short-term memory(LSTM)language model is also used to ensure the smoothness of sentence construction in the process of sentence construction.At the same time,it uses a variety of processing methods to improve the quality of the bilingual data.Experiments using three standard test sets are conducted to validate the proposed method;the most advanced fairseq-transformer NMT system is used in the training.The results show that the proposed method has worked well on improving the translation results.Compared with the state-of-the-art methods,the BLEU value of our method is increased by 2.34 compared with that of the baseline.

关键词： Neural machine translation corpus argumentation model improvement deep learning data cleaning

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modeling the effects of extreme high-temperature stress at anthesis and grain filling on grain protein in winter wheat

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The Crop Journal 2021年第4期9卷 889-900页

作者： Raheel Osman Yan Zhu Weixing Cao Zhifeng Ding Meng Wang Leilei Liu Liang Tang Bing Liu National Engineering and Technology Center for Information Agriculture/Engineering Research Center of Smart Agriculture Ministry of Education/Key Laboratory for Crop System Analysis and Decision MakingMinistry of Agriculture and Rural Affairs/Jiangsu Key Laboratory for Information Agriculture/Jiangsu Collaborative Innovation Center for Modern Crop ProductionNanjing Agricultural UniversityNanjing 210095JiangsuChina

Extreme high-temperature stress(HTS) associated with climate change poses potential threats to wheat grain yield and quality. Wheat grain protein concentration(GPC) is a determinant of wheat quality for human nutrition and is often neglected in attempts to assess climate change impacts on wheat production. Crop models are useful tools for quantification of temperature impacts on grain yield and quality.Current crop models either cannot simulate or can simulate only partially the effects of HTS on crop N dynamics and grain N accumulation. There is a paucity of observational data on crop N and grain quality collected under systematic HTS scenarios to develop algorithms for model improvement as well as evaluate crop models. Two-year phytotron experiments were conducted with two wheat cultivars under HTS at anthesis, grain filling, and both stages. HTS significantly reduced total aboveground N and increased the rate of grain N accumulation, while total aboveground N and the rate of grain N accumulation were more sensitive to HTS at anthesis than at grain filling. The observed relationships between total aboveground N, rate of grain N accumulation, and HTS were quantified and incorporated into the WheatGrow model. The new HTS routines improved simulation of the dynamics of total aboveground N, grain N accumulation, and GPC by the model. The improved model provided better estimates of total aboveground N, grain N accumulation, and GPC under HTS(the normalized root mean square error was reduced by 40%, 85%, and 80%, respectively) than the original WheatGrow model. The improvements in the model enhance its applicability to the assessment of climate change effects on wheat grain quality by reducing the uncertainties of simulating N dynamics and grain quality under HTS.

关键词： Heat stress Total aboveground N Grain N accumulation Grain protein concentration model improvement WheatGrow model

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