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Comparison of CYGNSS and Jason-3 Wind Speed Measurements via Gaussian Processes 基于高斯过程的CYGNSS和Jason-3风速测量的比较

Data science in science Pub Date : 2022-03-07 DOI: 10.1080/26941899.2023.2194349

William Bekerman, J. Guinness

{"title":"Comparison of CYGNSS and Jason-3 Wind Speed Measurements via Gaussian Processes","authors":"William Bekerman, J. Guinness","doi":"10.1080/26941899.2023.2194349","DOIUrl":"https://doi.org/10.1080/26941899.2023.2194349","url":null,"abstract":"Wind is a critical component of the Earth system and has unmistakable impacts on everyday life. The CYGNSS satellite mission improves observational coverage of ocean winds via a fleet of eight micro-satellites that use reflected GNSS signals to infer surface wind speed. We present analyses characterizing variability in wind speed measurements among the eight CYGNSS satellites and between antennas. In particular, we use a carefully constructed Gaussian process model that leverages comparisons between CYGNSS and Jason-3 during a one-year period from September 2019 to September 2020. The CYGNSS sensors exhibit a range of biases, most of them between -1.0 m/s and +0.2 m/s with respect to Jason-3, indicating that some CYGNSS sensors are biased with respect to one another and with respect to Jason-3. The biases between the starboard and port antennas within a CYGNSS satellite are smaller. Our results are consistent with, yet sharper than, a more traditional paired comparison analysis. We also explore the possibility that the bias depends on wind speed, finding some evidence that CYGNSS satellites have positive biases with respect to Jason-3 at low wind speeds. However, we argue that there are subtle issues associated with estimating wind speed-dependent biases, so additional careful statistical modeling and analysis is warranted.","PeriodicalId":72770,"journal":{"name":"Data science in science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43811000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quantifying Distances Between Non-Elliptical Clusters to Enhance the Identification of Meaningful Emotional Reactivity Subtypes. 量化非椭圆聚类之间的距离，加强对有意义的情绪反应亚型的识别。

Data science in science Pub Date : 2022-01-01 Epub Date: 2023-01-18 DOI: 10.1080/26941899.2022.2157349

M L Wallace, L McTeague, J L Graves, N Kissel, C Tortora, B Wheeler, S Iyengar

{"title":"Quantifying Distances Between Non-Elliptical Clusters to Enhance the Identification of Meaningful Emotional Reactivity Subtypes.","authors":"M L Wallace, L McTeague, J L Graves, N Kissel, C Tortora, B Wheeler, S Iyengar","doi":"10.1080/26941899.2022.2157349","DOIUrl":"10.1080/26941899.2022.2157349","url":null,"abstract":"<p><p>Coordinated emotional responses across psychophysiological and subjective indices is a cornerstone of adaptive emotional functioning. Using clustering to identify cross-diagnostic subgroups with similar emotion response profiles may suggest novel underlying mechanisms and treatments.However, many psychophysiological measures are non-normal even in homogenous samples, and over-reliance on traditional elliptical clustering approaches may inhibit the identification of meaningful subgroups. Finite mixture models that allow for non-elliptical cluster distributions is an emerging methodological field that may overcome this hurdle. Furthermore, succinctly quantifying pairwise cluster separation could enhance the clinical utility of the clustering solutions. However, a comprehensive examination of distance measures in the context of elliptical and non-elliptical model-based clustering is needed to provide practical guidance on the computation, benefits, and disadvantages of existing measures. We summarize several measures that can quantify the multivariate distance between two clusters and suggest practical computational tools. Through a simulation study, we evaluate the measures across three scenarios that allow for clusters to differ in location, scale, skewness, and rotation. We then demonstrate our approaches using psychophysiological and subjective responses to emotional imagery captured through the Transdiagnostic Anxiety Study. Finally, we synthesize findings to provide guidance on how to use distance measures in clustering applications.</p>","PeriodicalId":72770,"journal":{"name":"Data science in science","volume":"1 1","pages":"34-59"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10166186/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9450718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Role of Variable Renewable Energy Penetration on Electricity Price and its Volatility across Independent System Operators in the United States 可变可再生能源渗透率对美国独立系统运营商电价及其波动性的影响

Data science in science Pub Date : 2021-11-10 DOI: 10.1080/26941899.2022.2158145

Olukunle O. Owolabi, Toryn L. J. Schafer, Georgia E. Smits, Sanhita Sengupta, Sean E. Ryan, Lang Wang, D. Matteson, Mila Getmansky Sherman, D. Sunter

{"title":"Role of Variable Renewable Energy Penetration on Electricity Price and its Volatility across Independent System Operators in the United States","authors":"Olukunle O. Owolabi, Toryn L. J. Schafer, Georgia E. Smits, Sanhita Sengupta, Sean E. Ryan, Lang Wang, D. Matteson, Mila Getmansky Sherman, D. Sunter","doi":"10.1080/26941899.2022.2158145","DOIUrl":"https://doi.org/10.1080/26941899.2022.2158145","url":null,"abstract":"The U.S. electrical grid has undergone substantial transformation with increased penetration of wind and solar -- forms of variable renewable energy (VRE). Despite the benefits of VRE for decarbonization, it has garnered some controversy for inducing unwanted effects in regional electricity markets. In this study, the role of VRE penetration is examined on the system electricity price and price volatility based on hourly, real-time, historical data from six Independent System Operators (ISOs) in the U.S. using quantile and skew t-distribution regressions. After correcting for temporal effects, we found an increase in VRE penetration is associated with decrease in system electricity price in all ISOs studied. The increase in VRE penetration is associated with decrease in temporal price volatility in five out of six ISOs studied. The relationships are non-linear. These results are consistent with the modern portfolio theory where diverse volatile assets may lead to more stable and less risky portfolios.","PeriodicalId":72770,"journal":{"name":"Data science in science","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49445134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

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