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  • Open Access

    ARTICLE

    Deep Learning Framework for Precipitation Prediction Using Cloud Images

    Mirza Adnan Baig*, Ghulam Ali Mallah, Noor Ahmed Shaikh

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 4201-4213, 2022, DOI:10.32604/cmc.2022.026225 - 29 March 2022

    Abstract Precipitation prediction (PP) have become one of the significant research areas of deep learning (DL) and machine vision (MV) techniques are frequently used to predict the weather variables (WV). Since the climate change has left significant impact upon weather variables (WV) and continuously changes are observed in temperature, humidity, cloud patterns and other factors. Although cloud images contain sufficient information to predict the precipitation pattern but due to changes in climate, the complex cloud patterns and rapid shape changing behavior of clouds are difficult to consider for rainfall prediction. Prediction of rainfall would provide more… More >

  • Open Access

    ARTICLE

    Modelling the ZR Relationship of Precipitation Nowcasting Based on Deep Learning

    Jianbing Ma1,*, Xianghao Cui1, Nan Jiang2

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1939-1949, 2022, DOI:10.32604/cmc.2022.025206 - 24 February 2022

    Abstract Sudden precipitations may bring troubles or even huge harm to people's daily lives. Hence a timely and accurate precipitation nowcasting is expected to be an indispensable part of our modern life. Traditionally, the rainfall intensity estimation from weather radar is based on the relationship between radar reflectivity factor (Z) and rainfall rate (R), which is typically estimated by location-dependent experiential formula and arguably uncertain. Therefore, in this paper, we propose a deep learning-based method to model the ZR relation. To evaluate, we conducted our experiment with the Shenzhen precipitation dataset. We proposed a combined method More >

  • Open Access

    ARTICLE

    Diversity of Saxicolous Lichens along an Aridity Gradient in Central México

    José Carmen Soto-Correa1, Abraham Saldaña-Vega1, Víctor Hugo Cambrón-Sandoval1, Laura Concostrina-Zubiri2, Mariela Gómez-Romero3,*

    Phyton-International Journal of Experimental Botany, Vol.91, No.4, pp. 827-840, 2022, DOI:10.32604/phyton.2022.017929 - 09 December 2021

    Abstract Lichens are symbiotic organisms that comprise a fungus and a photosynthetic partner wich are recognized as a good indicator of climate change. However, our understanding of how aridity affects the diversity of saxicolous lichens in drylands is still limited. To evaluate the relationship between saxicolous lichen diversity and aridity in a central México dryland, a geographical transect was established of 100 km to build an aridity gradient in the semiarid zone of the State of Querétaro, Mexico, comprising ten sampling sites with a 10 km separation. Species richness, abundance and diversity of soil lichen species were recorded… More >

  • Open Access

    ARTICLE

    Characterization of endogenous nucleic acids that bind to NgAgo in Natronobacterium gregoryi sp2 cells

    LIXU JIANG1, LIN NING2, CHUNCHAO PU1, ZIXIN WANG1, BIFANG HE1,3, JIAN HUANG1,*

    BIOCELL, Vol.46, No.2, pp. 547-557, 2022, DOI:10.32604/biocell.2021.016500 - 20 October 2021

    Abstract As nucleic acid-guided endonucleases, some prokaryotic Argonautes have been used as programmable nucleases. Natronobacterium gregoryi Argonaute (NgAgo) has also been proposed for gene editing, but this remains very controversial. Until now, the endogenous nucleic acids that bind to NgAgo in Natronobacterium gregoryi sp2 (N. gregoryi sp2) have not been characterized. We expressed the conserved PIWI domain of NgAgo and used it to induce anti-PIWI antibody. We also cultured the N. gregoryi sp2 strain and performed immunoprecipitation, chromatin immunoprecipitation (ChIP), and RNA immunoprecipitation (RIP) assays. The nucleic acids that endogenously bound NgAgo in N. gregoryi sp2 cells were sequenced and analyzed.… More >

  • Open Access

    ARTICLE

    AttEF: Convolutional LSTM Encoder-Forecaster with Attention Module for Precipitation Nowcasting

    Wei Fang1,2,*, Lin Pang1, Weinan Yi1, Victor S. Sheng3

    Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 453-466, 2021, DOI:10.32604/iasc.2021.016589 - 11 August 2021

    Abstract Precipitation nowcasting has become an essential technology underlying various public services ranging from weather advisories to citywide rainfall alerts. The main challenge facing many algorithms is the high non-linearity and temporal-spatial complexity of the radar image. Convolutional Long Short-Term Memory (ConvLSTM) is appropriate for modeling spatiotemporal variations as it integrates the convolution operator into recurrent state transition functions. However, the technical characteristic of encoding the input sequence into a fixed-size vector cannot guarantee that ConvLSTM maintains adequate sequence representations in the information flow, which affects the performance of the task. In this paper, we propose… More >

  • Open Access

    ARTICLE

    Detection of Precipitation Cloud over the Tibet Based on the Improved U-Net

    Runzhe Tao1, *, Yonghong Zhang1, Lihua Wang1, Pengyan Cai1, Haowen Tan2

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2455-2474, 2020, DOI:10.32604/cmc.2020.011526 - 16 September 2020

    Abstract Aiming at the problem of radar base and ground observation stations on the Tibet is sparsely distributed and cannot achieve large-scale precipitation monitoring. UNet, an advanced machine learning (ML) method, is used to develop a robust and rapid algorithm for precipitating cloud detection based on the new-generation geostationary satellite of FengYun-4A (FY-4A). First, in this algorithm, the real-time multi-band infrared brightness temperature from FY-4A combined with the data of Digital Elevation Model (DEM) has been used as predictor variables for our model. Second, the efficiency of the feature was improved by changing the traditional convolution… More >

  • Open Access

    ARTICLE

    An Approach for Radar Quantitative Precipitation Estimation Based on Spatiotemporal Network

    Shengchun Wang1, Xiaozhong Yu1, Lianye Liu2, Jingui Huang1, *, Tsz Ho Wong3, Chengcheng Jiang1

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 459-479, 2020, DOI:10.32604/cmc.2020.010627 - 23 July 2020

    Abstract Radar quantitative precipitation estimation (QPE) is a key and challenging task for many designs and applications with meteorological purposes. Since the Z-R relation between radar and rain has a number of parameters on different areas, and the rainfall varies with seasons, the traditional methods are incapable of achieving high spatial and temporal resolution and thus difficult to obtain a refined rainfall estimation. This paper proposes a radar quantitative precipitation estimation algorithm based on the spatiotemporal network model (ST-QPE), which designs a convolutional time-series network QPE-Net8 and a multi-scale feature fusion time-series network QPE-Net22 to address More >

  • Open Access

    ARTICLE

    Responses of leaf δ13C and leaf traits to precipitation and temperature in arid ecosystem of northwestern China

    Xin ZM1,2, MH Liu2, Q Lu1,3, CA Busso5, YJ Zhu1,3, Z Li2, YR Huang2, XL Li2, FM Luo2, F Bao1, JQ Qian4*, YH Li1,3*

    Phyton-International Journal of Experimental Botany, Vol.87, pp. 144-155, 2018, DOI:10.32604/phyton.2018.87.144

    Abstract Leaf δ13C is widely used to explain plant strategies related to resource availability in different environments. However, the coupled response of leaf δ13C to precipitation and temperature as well as the relationship between leaf δ13C and leaf traits remain unclear. The leaf δ13C and its relationship with leaf traits [leaf size (LS), leaf length (LL), leaf width (LW), leaf length to width ratio (L:W), specific leaf area (SLA) and mass-based leaf nitrogen concentration (Nmass)] were investigated on the dominant shrub species Nitraria tangutorum Bobr. in the arid region (Dengkou and Minqin) of northwestern China under the simulated increasing precipitation… More >

  • Open Access

    ARTICLE

    Effects of precipitation changes on the dynamics of sparse elm woodland in Northeastern China

    Yi TANG1,*, Carlos Alberto BUSSO2

    BIOCELL, Vol.42, No.2, pp. 61-66, 2018, DOI:10.32604/biocell.2018.07015

    Abstract Elm (Ulmus pumila L.) is the dominant tree species in the sparse elm woodland, the original vegetation in the Horqin Sandy Land. The effects of changes in precipitation on U. pumila trees have not been fully studied. We determined a dynamic model by considering the five stages in the U. pumila life cycle, i.e. seed, seedling, and juvenile, mature and over-mature tree stages. The effects of changes in precipitation on population density and age structure were then evaluated. Population density, after averaging all study developmental morphology stages, ranged from 16.67 individuals/m2 to 25.01 individuals/m2 under More >

  • Open Access

    ARTICLE

    Lattice Boltzmann Simulation of a Gas-to-Solid Reaction and Precipitation Process in a Circular Tube

    Matthew D. Lindemer1, Suresh G. Advani2,*, Ajay K. Prasad2

    CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.3, pp. 527-553, 2018, DOI:10.31614/cmes.2018.00481

    Abstract The lattice Boltzmann method (LBM) is used to simulate the growth of a solid-deposit on the walls of a circular tube resulting from a gas-to-solid reaction and precipitation process. This process is of particular interest for the design of reactors for the production of hydrogen by the heterogeneous hydrolysis of steam with Zn vapor in the Zn/ZnO thermochemical cycle. The solid deposit of ZnO product on the tube wall evolves in time according to the temporally- and axially-varying convective-diffusive transport and reaction of Zn vapor with steam on the solid surface. The LBM is well-suited… More >

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