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Assimilation of All-sky Geostationary Satellite Infrared Radiances for Convection-Permitting Initialization and Prediction of Hurricane Joaquin(2015)

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Advances in Atmospheric sciences 2022年第11期39卷 1859-1872页

作者： Lei ZHU Zhiyong MENG Yonghui WENG Fuqing ZHANG Key Laboratory of Meteorological Disaster Ministry of Education(KLME)/Joint International Research Laboratory of Climate and Environmental Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD)Nanjing University of Information Science and TechnologyNanjing 210044China Laboratory for Climate and Ocean–Atmosphere Studies Department of Atmospheric and Oceanic SciencesSchool of PhysicsPeking UniversityBeijing 100871China I.M.System Group(IMSG) at Environmental Modeling Center(EMC)NCEPNWSNOAACollege ParkMD 20740USA Department of Meteorology and Atmospheric Science and Center for Advanced Data Assimilation and Predictability TechniquesThe Pennsylvania State UniversityUniversity ParkPennsylvania 16802USA

Intensity forecasting is one of the most challenging aspects of tropical cyclone(TC) forecasting. This work examines the impact of assimilating high-resolution all-sky infrared radiance observations from geostationary satellite GOES-13 on the convection-permitting initialization and prediction of Hurricane Joaquin(2015) with an ensemble Kalman filter(EnKF)based on the Weather Research and Forecasting(WRF) model. Given that almost all operational global and regional models struggled to capture Hurricane Joaquin(2015)'s intensity, this study examines the potential in improving Joaquin's prediction when assimilating all-sky infrared radiances from GOES-13's water vapor channel. It is demonstrated that, after a few 3-hour cycles assimilating all-sky radiance, the WRF model was able to forecast reasonably well Joaquin's intensity,including its rapid intensification(RI). The improvement was largely due to a more realistic initial hurricane structure with a stronger, warmer, and more compact inner-core. Ensemble forecasts were used to further explore the important physical mechanisms driving the hurricane's RI. Results showed that the RI forecasts were greatly impacted by the initial inner-core vortex structure.

关键词： all-sky infrared radiance TC intensity EnKF inner-core structure

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Gradient-flow adaptive importance sampling for Bayesian leave one out cross-validation with application to sigmoidal classification models

arXiv

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arXiv 2024年

作者： Chang, Joshua C. Li, Xiangting Xu, Shixin Yao, Hao-Ren Porcino, Julia Chow, Carson C. NIH Clinical Center Rehabilitation Medicine Epidemiology and Biostatistics Section BethesdaMD United States UCLA Department of Computational Medicine Los AngelesCA United States Data Science Research Center Duke Kunshan University Jiangsu Kunshan China NIH NIDDK Laboratory of Biological Modeling BethesdaMD United States

We introduce gradient-flow-guided adaptive importance sampling (IS) transformations for stabilizing Monte-Carlo approximations of leave-one-out (LOO) cross-validated predictions for Bayesian models. After defining two variational problems, we derive corresponding simple nonlinear transformations that utilize gradient information to shift a model’s pre-trained full-data posterior closer to the target LOO posterior predictive distributions. In doing so, the transformations stabilize importance weights. The resulting Monte Carlo integrals depend on Jacobian determinants with respect to the model Hessian. We derive closed-form exact formulae for these Jacobian determinants in the cases of logistic regression and shallow ReLU-activated artificial neural networks, and provide a simple approximation that sidesteps the need to compute full Hessian matrices and their spectra. We test the methodology on an n p dataset that is known to produce unstable LOO IS weights. © 2024, CC BY.

关键词： Importance sampling

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A Bayesian Circadian Hidden Markov Model to Infer Rest-Activity Rhythms Using 24-hour Actigraphy data

arXiv

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arXiv 2023年

作者： Lu, Jiachen Xiao, Qian Bauer, Cici Department of Biostatistics and Data Science The University of Texas Health Science Center Houston School of Public Health United States Department of Epidemiology Human Genetics and Environmental Sciences The University of Texas Health Science Center Houston School of Public Health United States Center for Spatial-temporal Modeling for Applications in Population Sciences The University of Texas Health Science Center Houston School of Public Health United States

24-hour actigraphy data collected by wearable devices offer valuable insights into physical activity types, intensity levels, and rest-activity rhythms (RAR). RARs, or patterns of rest and activity exhibited over a 24-hour period, are regulated by the body’s circadian system, synchronizing physiological processes with external cues like the light-dark cycle. Disruptions to these rhythms, such as irregular sleep patterns, daytime drowsiness or shift work, have been linked to adverse health outcomes including metabolic disorders, cardiovascular disease, depression, and even cancer, making RARs a critical area of health research. In this study, we propose a Bayesian Circadian Hidden Markov Model (BCHMM) that explicitly incorporates 24-hour circadian oscillators mirroring human biological rhythms. The model assumes that observed activity counts are conditional on hidden activity states through Gaussian emission densities, with transition probabilities modeled by state-specific sinusoidal functions. Our comprehensive simulation study reveals that BCHMM outperforms frequentist approaches in identifying the underlying hidden states, particularly when the activity states are difficult to separate. BCHMM also excels with smaller Kullback-Leibler divergence on estimated densities. With the Bayesian framework, we address the label-switching problem inherent to hidden Markov models via a positive constraint on mean parameters. From the proposed BCHMM, we can infer the 24-hour rest-activity profile via time-varying state probabilities, to characterize the person-level RAR. We demonstrate the utility of the proposed BCHMM using 2011-2014 National Health and Nutrition Examination Survey (NHANES) data, where worsened RAR, indicated by lower probabilities in low-activity state during the day and higher probabilities in high-activity state at night, is associated with an increased risk of diabetes. © 2023, CC BY.

关键词： Hidden Markov models

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The northern boundary of the Asian summer monsoon and division of westerlies and monsoon regimes over the Tibetan Plateau in present-day

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science China Earth sciences 2023年第4期66卷 882-893页

作者： Lingxin HUANG Jie CHEN Kun YANG Yujie YANG Wei HUANG Xu ZHANG Fahu CHEN Key Laboratory of Western China’s Environmental Systems(Ministry of Education) College of Earth and Environmental SciencesLanzhou UniversityLanzhou 730000China Group of Alpine Paleoecology and Human Adaptation(ALPHA) State Key Laboratory of Tibetan Plateau Earth SystemEnvironment and Resources(TPESER)Institute of Tibetan Plateau Research(ITPCAS)Chinese Academy of Sciences(CAS)Beijing 100101China College of Atmospheric Sciences Lanzhou UniversityLanzhou 730000China Department of Earth System Science Ministry of Education Key Laboratory for Earth System ModelingInstitute for Global Change StudiesTsinghua UniversityBeijing 100084China National Tibetan Plateau Data Center State Key Laboratory of Tibetan Plateau Earth SystemEnvironment and Resources(TPESER)Institute of Tibetan Plateau Research(ITPCAS)Chinese Academy of Sciences(CAS)Beijing 100101China

Precipitation patterns and their variations over the Tibetan Plateau(TP) are mainly dominated by the Asian summer monsoon, westerlies, and their interactions. The exact extent of the Asian summer monsoon’s influence, however, remains undetermined. Referencing the climatological northern boundary index of the East Asian summer monsoon, we demonstrate that the 300 mm precipitation isoline from May to September can be utilized as an indicator of the northern boundary of the Asian summer monsoon over the TP, allowing for an analysis of the spatial distribution characteristics of the climatological and interannual northern boundary. Our results indicate that the climatological northern boundary of the Asian summer monsoon over the TP lies along the eastern Qilian Mountains-Tanggula Mountains-Qiangtang Plateau-Gangdise Mountains-Western Himalayas during 2001–2020. This position corresponds well with the position of the convergence of westerly(westerlies) and southerly wind(monsoon) in the lower troposphere, representing the interface between dry and wet regions in the rainy season over the TP. There is a significant positive correlation between changes in the zonal/meridional water vapor budget and variations in precipitation to the north/south of the climatological northern boundary, respectively. Additionally, a close relationship exists between the interannual fluctuation range of the northern boundary and the distribution of vegetation across the TP. Compared to the northern boundary of the summer monsoon defined by meteorological criteria, which is established based on 5-day(pentad)mean precipitation(exceeding 4 mm day^(-1)), our climatological northern boundary offers a more objective portrayal of the region that experiences persistent influence from the summer monsoon. These indicate that climatological northern boundary has a clear significance for natural geographical distribution such as the westerlies-monsoon circulation, ecology, and climate. Based on the inte

关键词： Tibetan Plateau Asian summer monsoon Climatological northern boundary index Westerlies domain Monsoon domain

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Identification of soybean in Argentina using Sentinel-2 composite images

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International Journal of Agricultural and Biological Engineering 2024年第5期17卷 266-274页

作者： Linsheng Huang Yue Chen Yuhao Pan Zihang Lou Shijun Zheng Xiaoyang Zhang Le Yu Shengwei Liu Dailiang Peng National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui UniversityHefei 230601China Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China Research Area of Ecology and Biodiversity School of Biological SciencesThe University of Hong KongPokfulamHong KongChina College of Environmental and Resource Sciences Zhejiang UniversityHangzhou 310058China Department of Earth System Science Ministry of Education Key Laboratory for Earth System ModelingInstitute for Global Change StudiesTsinghua UniversityBeijing 100084China Geospatial Sciences Center of Excellence Department of Geography&Geospatial SciencesSouth Dakota State UniversityBrookingsSD 57007USA Ministry of Education Ecological Field Station for East Asian Migratory Birds Beijing 100084China Tsinghua University(Department of Earth System Science)-Xi’an Institute of Surveying and Mapping Joint Research Center for Next-Generation Smart Mapping Beijing 100084China School of Surveying and Land Information Engineering Henan Polytechnic UniversityJiaozuo 454000HenanChina

Soybean is one of the most important oil crops, and Argentina is the third-largest soybean producer in the world, accounting for 17% of the global soybean yield. Timely and accurate information on soybean spatial distribution is critical for ensuring global food security. Sentinel-2 multispectral data and machine learning classification models are used to investigate the potential of soybean identification in the early stage of the growing season in Argentina, with the help of Google Earth Engine (GEE). The earliest time window and optimal feature set for soybean identification are explored. Results are as follows: 1) the random forest (RF) classification model demonstrated the highest level of classification accuracy compared to the backpropagation neural network (BPNN), support vector machine (SVM), and naive Bayes (NB) models;2) Soybean can be accurately identified as early as the end of February (filling stage), which is approximately one month before harvest;3) The optimal feature-subset can reduce the amount of input data by 80% while maintaining high classification accuracy. The overall accuracy (OA) of the RF classification model is 85.87%, and the relative error between the estimated soybean planting area and the agricultural statistics is 3.45%. This study provided a high-precision method for early-season identification of soybeans over large scales. The results can provide a data support for early futures trading and agricultural insurance, as well as a reference for policy-making to ensure global soybean food security.

关键词： soybean machine learning time window feature selection Sentinel-2 Google Earth Engine

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Cross-Validation Approach to Evaluate Clustering Algorithms: An Experimental Study Using Multi-Label datasets

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SN Computer science 2020年第5期1卷 1-9页

作者： Tarekegn, Adane Nega Michalak, Krzysztof Giacobini, Mario Modelling and Data Science Department of Mathematics University of Turin Turin Italy Department of Information Technologies Wroclaw University of Economics Wroclaw Poland Data Analysis and Modeling Unit Department of Veterinary Sciences University of Turin Turin Italy

Clustering validation is one of the most important and challenging parts of clustering analysis, as there is no ground truth knowledge to compare the results with. Up till now, the evaluation methods for clustering algorithms have been used for determining the optimal number of clusters in the data, assessing the quality of clustering results through various validity criteria, comparison of results with other clustering schemes, etc. It is also often practically important to build a model on a large amount of training data and then apply the model repeatedly to smaller amounts of new data. This is similar to assigning new data points to existing clusters which are constructed on the training set. However, very little practical guidance is available to measure the prediction strength of the constructed model to predict cluster labels for new samples. In this study, we proposed an extension of the cross-validation procedure to evaluate the quality of the clustering model in predicting cluster membership for new data points. The performance score was measured in terms of the root mean squared error based on the information from multiple labels of the training and testing samples. The principal component analysis (PCA) followed by k-means clustering algorithm was used to evaluate the proposed method. The clustering model was tested using three benchmark multi-label datasets and has shown promising results with overall RMSE of less than 0.075 and MAPE of less than 12.5% in three datasets. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Clustering analysis Clustering validation Cross-validation Multi-label data

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Variational Quality Control of Non-Gaussian Innovations in the GRAPES m3DVAR System: Mass Field Evaluation of Assimilation Experiments

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Advances in Atmospheric sciences 2021年第9期38卷 1510-1524页

作者： Jie HE Xulin MA Xuyang GE Juanjuan LIU Wei CHENG Man-Yau CHAN Ziniu XIAO Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Key Laboratory of Meteorological DisasterNanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Beijing Institute of Applied Meteorology Beijing 100029China Department of Meteorology and Atmospheric Science and Center for Advanced Data Assimilation and Predictability TechniquesThe Pennsylvania State UniversityUniversity ParkPA 16801USA

The existence of outliers can seriously influence the analysis of variational data *** control allows us to effectively eliminate or absorb these outliers to produce better analysis *** particular,variational quality control(VarQC) can process gray zone outliers and is thus broadly used in variational data assimilation *** this study,governing equations are derived for two VarQC algorithms that utilize different contaminated Gaussian distributions(CGDs): Gaussian plus flat distribution and Huber norm *** such,these VarQC algorithms can handle outliers that have non-Gaussian ***,these VarQC algorithms are implemented in the Global/Regional Assimilation and PrEdiction System(GRAPES) model-level three-dimensional variational data assimilation(m3 DVAR) *** using artificial observations indicate that the VarQC method using the Huber distribution has stronger robustness for including outliers to improve posterior analysis than the VarQC method using the Gaussian plus flat ***,real observation experiments show that the distribution of observation analysis weights conform well with theory,indicating that the application of VarQC is effective in the GRAPES m3 DVAR *** case study and longperiod data assimilation experiments show that the spatial distribution and amplitude of the observation analysis weights are related to the analysis increments of the mass field(geopotential height and temperature).Compared to the control experiment,VarQC experiments have noticeably better posterior mass ***,the VarQC method using the Huber distribution is superior to the VarQC method using the Gaussian plus flat distribution,especially at the middle and lower levels.

关键词： variational quality control non-Gaussian distribution innovation outlier data assimilation

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Joint Collaboration on Coupled data Assimilation and modeling

Joint Collaboration on Coupled Data Assimilation and Modelin...

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作者： Sims, Jamese Lee, Tsengdar Koch, Dorothy Gross, Brian Stajner, Ivanka Considine, David Pawson, Steven Kleist, Daryl Gelaro, Ron Flampouris, Stylianos Jung, Youngsun Gasbarro, Marc NOAA/NWS/OSTI Modeling Program Silver SpringMD United States Weather Data for Operation and Assessment Program NASA PO WashingtonDC United States NOAA/OAR/Weather Program Office Silver SpringMD United States NOAA/NWS/EMC College ParkMD United States Modeling Analysis and Prediction Program NASA PO WashingtonDC United States Global Modeling and Assimilation Office NASA Goddard Space Flight Center GreenbeltMD United States Tomorrow.io BostonMA United States NOAA/NWS/OSTI Silver SpringMD United States NOAA/NWS/OSTI/Science and Technology Corp. Silver SpringMD United States

NOAA-NASA Coupled data Assimilation and modeling Workshop: What: Scientists came together to discuss potential ideas and specific subjects on data assimilation and Earth system modeling. The workshop was organized by center leads and program managers from NOAA and NASA. When: 20 April 2021 Where: Online. © 2022 American Meteorological Society. All rights reserved.

关键词： NASA

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Mining experimental data from Materials science literature with Large Language Models: an evaluation study

arXiv

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arXiv 2024年

作者： Foppiano, Luca Lambard, Guillaume Amagasa, Toshiyuki Ishii, Masashi Materials Modeling Group Center for Basic Research on Materials National Institute for Materials Science Ibaraki-ken Tsukuba-shi 1-1 Namiki 305-0044 Japan Centre for Computational Sciences University of Tsukuba Japan Data-driven Materials Design Group Center for Basic Research on Materials National Institute for Materials Science Ibaraki-ken Tsukuba-shi 1-1 Namiki 305-0044 Japan

This study is dedicated to assessing the capabilities of large language models (LLMs) such as GPT-3.5-Turbo, GPT-4, and GPT-4-Turbo in extracting structured information from scientific documents in materials science. To this end, we primarily focus on two critical tasks of information extraction: (i) a named entity recognition (NER) of studied materials and physical properties and (ii) a relation extraction (RE) between these entities. Due to the evident lack of datasets within Materials Informatics (MI), we evaluated using SuperMat, based on superconductor research, and MeasEval, a generic measurement evaluation corpus. The performance of LLMs in executing these tasks is benchmarked against traditional models based on the BERT architecture and rule-based approaches (baseline). We introduce a novel methodology for the comparative analysis of intricate material expressions, emphasising the standardisation of chemical formulas to tackle the complexities inherent in materials science information assessment. For NER, LLMs fail to outperform the baseline with zero-shot prompting and exhibit only limited improvement with few-shot prompting. However, a GPT-3.5-Turbo fine-tuned with the appropriate strategy for RE outperforms all models, including the baseline. Without any fine-tuning, GPT-4 and GPT-4-Turbo display remarkable reasoning and relationship extraction capabilities after being provided with merely a couple of examples, surpassing the baseline. Overall, the results suggest that although LLMs demonstrate relevant reasoning skills in connecting concepts, specialised models are currently a better choice for tasks requiring extracting complex domain-specific entities like materials. These insights provide initial guidance applicable to other materials science sub-domains in future work. © 2024, CC BY.

关键词： Computational linguistics

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Multi-dimensional Graph Linear Canonical Transform

arXiv

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arXiv 2024年

作者： Li, Na Zhang, Zhichao Han, Jie Chen, Yunjie Cao, Chunzheng School of Mathematics and Statistics Center for Applied Mathematics of Jiangsu Province Jiangsu International Joint Laboratory on System Modeling and Data Analysis Nanjing University of Information Science and Technology Nanjing210044 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Key Laboratory of Computational Science and Application of Hainan Province Haikou571158 China School of Remote Sensing and Geomatics Engineering Nanjing University of Information Science and Technology Nanjing210044 China

Many multi-dimensional (M-D) graph signals appear in the real world, such as digital images, sensor network measurements and temperature records from weather observation stations. It is a key challenge to design a transform method for processing these graph M-D signals in the linear canonical transform domain. This paper proposes the two-dimensional graph linear canonical transform based on the central discrete dilated Hermite function (2-D CDDHFs-GLCT) and the two-dimensional graph linear canonical transform based on chirp multiplication-chirp convolution-chirp multiplication decomposition (2-D CM-CC-CM-GLCT). Then, extending 2-D CDDHFs-GLCT and 2-D CM-CC-CM-GLCT to M-D CDDHFs-GLCT and M-D CM-CC-CM-GLCT. In terms of the computational complexity, additivity and reversibility, M-D CDDHFs-GLCT and M-D CM-CC-CM-GLCT are compared. Theoretical analysis shows that the computational complexity of M-D CM-CC-CM-GLCT algorithm is obviously reduced. Simulation results indicate that M-D CM-CC-CM-GLCT achieves comparable additivity to M-D CDDHFs-GLCT, while M-D CM-CC-CM-GLCT exhibits better reversibility. Finally, M-D GLCT is applied to data compression to show its application advantages. The experimental results reflect the superiority of M-D GLCT in the algorithm design and implementation of data compression. Copyright © 2024, The Authors. All rights reserved.

关键词： Chirp modulation

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