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Supporting data used in the paper: Xi Chen, 2020, The LMARS based shallow-water dynamical core on generic gnomonic cubed-sphere geometry
No full text# Simulation results of the unstaggered shallow water model
This repository contains the supporting data used in the paper: Xi Chen, 2020, The LMARS based shallow‐water dynamical core on generic gnomonic cubed‐sphere geometry, DOI: 10.1029/2020MS002280
Organization of the repository:
The tar archive with this data submission has a:
doc directory contains a README.md with information regarding naming conventions to label the model configurations for a shallow water test simulation. Additional information can also be found in README.md. Table 4 in the paper provides additional details.
The data directory contains the supporting data files (NetCDF format).Disclaimer: "This was prepared by Xi Chen under award NA18OAR4320123 from the National Oceanic and Atmospheric Administration, U.S. Department of Commerce. The statements, findings, conclusions, and recommendations are those of the author(s) and do not necessarily reflect the views of the National Oceanic and Atmospheric Administration, or the U.S. Department of Commerce.
Felhívás! XI., kerület dolgozói! Munkások! Egyetemisták! Értelmiségiek! Katonák! Barátaink!
No full text1956. okt. 28.XI. kerület Ideiglenes Nemzeti Bizottsá
XI. kerület dolgozói! Munkások! Diákok! Katonák!
No full text1956. okt. 29.XI. kerületi Ideiglenes Forradalmi Nemzeti Bizottsá
Lun xi qu fan ying wei da qun zhong shi dai wen ti. v.1
No full text戲劇報編輯部戲曲硏究编委会编.附簡譜歌曲.Xi ju bao bian ji bu Xi qu yan jiu bian wei hui bian.Fu jian pu ge qu
Data for: Empowering Knowledge: Political Leaders, Education, and Economic Liberalization
No full textData set: "Empowering Knowledge: Political Leaders, Education, and Economic Liberalization" by Jingheng Li, Tianyang Xi, and Yang Yao (2018
Global aeolian dust variations and trends: a revisit of dust event and visibility observations from surface weather stations
No full textDr. Xin Xi at Michigan Tech is leading an effort to create a homogenized weather station-based dust-climate dataset (duISD) in support of wind erosion monitoring, dust source mapping, and dust-climate analysis at local to global scales. Current version (v1) of duISD consists of CSV files documenting the monthly dust event frequency and inverse visibility derived from the SYNOP report data of the NOAA Integrated Surface Database (ISD). The temporal coverage varies by station, with some going back to the 1950s. Refer to the README file for details.
duISD v1 is associated with a research paper, titled "Global aeolian dust variations and trends: a revisit of dust event and visibility observations from surface weather stations", currently under review at Atmospheric Chemistry and Physics (https://acp.copernicus.org/preprints/acp-2020-813/).
Please use appropriate citation when using this dataset, and contact Dr. Xin Xi, ([email protected], https://ixnix.github.io/abci/ ) if you have questions
Dataset for dissertation - Yue Xi (u1975213)
No full textThis dataset is for archiving the questionnaire data in the dissertation "An investigation into how Young Consumers’ Purchasing Intentions can be Affected by Sponsorship in eSports Tournaments – Taking the 2020 League of Legends World Championship as an Example". This research looked at the relationship between consumers’ purchase intention and sponsorship in eSports games. The purpose of conducting such research is demonstrated in the first section, followed by huge amount of secondary research. Literature related to the theories of sponsorship, hierarchy of effects were reviewed. During the literature review, three previous sponsorship effects models were found and modified into the conceptual model that this research was aimed to test. The data analysis chapter illustrated the outcome of the research with the help of SPSS
Global aeolian dust variations and trends: a revisit of dust event and visibility observations from surface weather stations
No full textThis dataset is associated with a manuscript titled "Global aeolian dust variations and trends: a revisit of dust event and visibility observations from surface weather stations", currently under review at Atmospheric Chemistry and Physics
Appropriate citation is required for using this dataset.
Contact: Xin Xi, ([email protected])
https://ixnix.github.io/abci
Global aeolian dust variations and trends: a revisit of dust event and visibility observations from surface weather stations
No full textThis dataset is associated with a manuscript titled "Global aeolian dust variations and trends: a revisit of dust event and visibility observations from surface weather stations", currently under review at Atmospheric Chemistry and Physics
Appropriate citation is required for using this dataset.
Contact: Xin Xi, ([email protected])
https://ixnix.github.io/abci
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