####################################################################################################
## software
# R
@manual{software_R,
	title        = {R: A Language and Environment for Statistical Computing},
	author       = {{R Core Team}},
	year         = 2023,
	address      = {Vienna, Austria},
	url          = {https://www.R-project.org/},
	note         = {R version 4.3.2 (2023-10-31)},
	organization = {R Foundation for Statistical Computing}
}
# CRAN
@manual{software_CRAN,
	title        = {The Comprehensive {R} Archive Network},
	author       = {Benjamin Altmann and Kurt Hornik and Tomas Kalibera and Konstanze Lauseker and Uwe Ligges and Brian D. Ripley and Simon Urbanek},
	address      = {Vienna, Austria},
	url          = {https://cran.r-project.org/}
}

@manual{software_exactextract,
	author       = {Baston, Daniel},
	title        = {exactextract},
	month        = may,
	year         = 2024,
	publisher    = {Zenodo},
	version      = {0.1.0},
	doi          = {10.5281/zenodo.11396420},
	url          = {https://doi.org/10.5281/zenodo.11396420}
}

@manual{software_tigerline,
  author       = {U.S. Census Bureau},
  title        = {2020 {TIGER/Line} Shapefiles (machine-readable data files)},
  year         = {2020},
  url          = {https://www.census.gov/programs-surveys/geography/technical-documentation/complete-technical-documentation/tiger-geo-line.2020.html}
}

# R packages
@manual{package_RAQSAPI,
	title        = {A {R} Interface to the {US} {EPA} Air Quality System Data Mart {API}},
	author       = {Clinton Mccrowey and Timothy Sharac and Nick Mangus and Doug Jager and Ryan Brown and Daniel Garver and Benjamin Wells and Hayley Brittingham},
	year         = 2021,
	url          = {https://cran.r-project.org/package=RAQSAPI}
}
@manual{package_Reanalysis,
	title        = {Reanalysis},
	author       = {Kevin Shook},
	year         = 2022,
	url          = {https://github.com/CentreForHydrology/Reanalysis},
	note         = {R package version 1.4.1}
}
@article{package_rNOMADS,
	title        = {Near real time weather and ocean model data access with {rNOMADS}},
	author       = {Daniel C. Bowman and Jonathan M. Lees},
	year         = 2015,
	journal      = {{Computers \& Geosciences}},
	volume       = 78,
	pages        = {88--95},
	doi          = {10.1016/j.cageo.2015.02.013}
}
@article{package_Kusch2021KrigRaTF,
	title        = {{KrigR}—a tool for downloading and statistically downscaling climate reanalysis data},
	author       = {Erik Kusch and Richard Davy},
	year         = 2021,
	journal      = {Environmental Research Letters},
	booktitle    = {Environmental Research Letters},
	volume       = 17
}
@article{package_daymetr,
	title        = {An integrated phenology modelling framework in {R}: modelling vegetation phenology with {phenor}},
	author       = {Koen Hufkens and David Basler and Tom Milliman and Eli K. Melaas and Andrew D. Richardson},
	year         = 2018,
	journal      = {{Methods in Ecology \& Evolution}},
	volume       = 9,
	pages        = {1--10},
	doi          = {10.1111/2041-210X.12970}
}
@manual{package_climateR,
	title        = {{climateR}: climateR},
	author       = {Mike Johnson},
	year         = 2023,
	url          = {https://github.com/mikejohnson51/climateR},
	note         = {R package version 0.3.2}
}
@manual{package_RClimChange,
	title        = {{RClimChange}: A package to manipulate Global Climate Models from {NCCS} {THREDDS} {NEX-GDDP-CMIP6}},
	author       = {Harold Llauca},
	year         = 2024,
	note         = {R package version 3.2.2}
}
@manual{package_ecmwfr,
	title        = {The ecwmfr package: an interface to {ECMWF} {API} endpoints},
	author       = {Koen Hufkens and Reto Stauffer and Elio Campitelli},
	year         = 2019,
	doi          = {10.5281/zenodo.2647531},
	url          = {https://bluegreen-labs.github.io/ecmwfr/},
	note         = {R package version 1.5.0}
}
@manual{package_dataRetrieval,
	title        = {{dataRetrieval}: {R} packages for discovering and retrieving water data available from U.S. federal hydrologic web services},
	author       = {Laura DeCicco and Robert Hirsch and David Lorenz and David Watkins and Mike Johnson},
	year         = 2024,
	publisher    = {U.S. Geological Survey},
	address      = {Reston, VA},
	doi          = {10.5066/P9X4L3GE},
	url          = {https://code.usgs.gov/water/dataRetrieval},
	version      = {2.7.15},
	institution  = {U.S. Geological Survey}
}
@manual{package_luna,
	title        = {{luna}: Tools for Satellite Remote Sensing (Earth Observation) Data Processing},
	author       = {Robert J. Hijmans and Aniruddha Ghosh},
	year         = 2023,
	note         = {R package version 0.3-6}
}
@manual{package_FedData,
	title        = {{FedData}: Functions to Automate Downloading Geospatial Data Available from Several Federated Data Sources},
	author       = {R. Kyle Bocinsky},
	year         = 2023,
	url          = {https://CRAN.R-project.org/package=FedData},
	note         = {R package version 4.0.0}
}
@manual{package_raqs,
	title        = {{raqs}: Interface to the {US EPA} Air Quality System ({AQS}) {API}},
	author       = {Jaehyun Joo and Blanca Himes},
	year         = 2023,
	url          = {https://CRAN.R-project.org/package=raqs},
	note         = {R package version 1.0.2}
}
@manual{package_geodata,
	title        = {{geodata}: Download Geographic Data},
	author       = {Robert J. Hijmans and Márcia Barbosa and Aniruddha Ghosh and Alex Mandel},
	year         = 2023,
	url          = {https://CRAN.R-project.org/package=geodata},
	note         = {R package version 0.5-9}
}
@manual{package_terra,
	title        = {{terra}: Spatial Data Analysis},
	author       = {Robert J. Hijmans},
	year         = 2023,
	url          = {https://CRAN.R-project.org/package=terra},
	note         = {R package version 1.7-65}
}
@article{package_sf,
	title        = {{Simple Features for {R}: Standardized Support for Spatial Vector Data}},
	author       = {Edzer Pebesma},
	year         = 2018,
	journal      = {{The R Journal}},
	volume       = 10,
	number       = 1,
	pages        = {439--446},
	doi          = {10.32614/RJ-2018-009},
	url          = {https://doi.org/10.32614/RJ-2018-009}
}
@book{package_stars,
	title        = {{Spatial Data Science: With applications in {R}}},
	author       = {Edzer Pebesma and Roger Bivand},
	year         = 2023,
	publisher    = {Chapman and Hall/CRC},
	address      = {London},
	pages        = 352,
	doi          = {10.1201/9780429459016},
	url          = {https://r-spatial.org/book/}
}
@manual{package_MODISTools,
	title        = {The {MODISTools} package: an interface to the {MODIS} Land Products Subsets Web Services},
	author       = {Koen Hufkens},
	year         = 2023,
	month        = {Jan},
	publisher    = {Zenodo},
	doi          = {10.5281/zenodo.7551165},
	url          = {https://doi.org/10.5281/zenodo.7551165},
	version      = {R package version 1.1.4}
}
@article{package_MODIStsp,
	title        = {{MODIStsp}: an {R} package for preprocessing of {MODIS} Land Products time series},
	author       = {Lorenzo Busetto and Luigi Ranghetti},
	year         = 2016,
	journal      = {{Computers \& Geosciences}},
	volume       = 97,
	pages        = {40--48},
	doi          = {10.1016/j.cageo.2016.08.020},
	issn         = {0098-3004},
	url          = {https://github.com/ropensci/MODIStsp}
}
@manual{package_merra2ools,
	title        = {{merra2ools}: {R}-tools to process {MERRA-2} data ({NASA}) with focus on renewable energy},
	author       = {Oleg Lugovoy and Shuo Gao},
	year         = 2023,
	url          = {https://energyRt.github.io/merra2ools/},
	note         = {R package version 0.1.03}
}
@article{package_testthat,
	title        = {testthat: Get Started with Testing},
	author       = {Hadley Wickham},
	year         = 2011,
	journal      = {The R Journal},
	volume       = 3,
	pages        = {5--10},
	url          = {https://journal.r-project.org/archive/2011-1/RJournal_2011-1_Wickham.pdf}
}
@manual{package_pkgdown,
	title        = {{pkgdown}: Make Static {HTML} Documentation for a Package},
	author       = {Hadley Wickham and Jay Hesselberth and Maëlle Salmon},
	year         = 2022,
	url          = {https://CRAN.R-project.org/package=pkgdown},
	note         = {R package version 2.0.7}
}
@manual{package_roxygen2,
	title        = {{roxygen2}: In-Line Documentation for {R}},
	author       = {Hadley Wickham and Peter Danenberg and Gábor Csárdi and Manuel Eugster},
	year         = 2024,
	url          = {https://CRAN.R-project.org/package=roxygen2},
	note         = {R package version 7.3.1}
}
@manual{package_httr,
	title        = {{httr}: Tools for Working with {URLs} and {HTTP}},
	author       = {Hadley Wickham},
	year         = 2023,
	url          = {https://CRAN.R-project.org/package=httr},
	note         = {R package version 1.4.7}
}
@manual{package_qbms,
	title        = {{QBMS}: Query the Breeding Management System(s)},
	author       = {Khaled Al-Shamaa},
	year         = 2024,
	url          = {https://CRAN.R-project.org/package=QBMS},
	note         = {R package version 1.0.0}
}
@manual{package_exactextractr,
	title        = {exactextractr: Fast Extraction from Raster Datasets using Polygons},
	author       = {{Daniel Baston}},
	year         = 2023,
	url          = {https://CRAN.R-project.org/package=exactextractr},
	note         = {R package version 0.10.0}
}
@manual{package_tigris,
	title        = {{tigris}: Load {Census} {TIGER/Line} {Shapefiles}},
	author       = {Kyle Walker},
	year         = 2024,
	url          = {https://CRAN.R-project.org/package=tigris},
	note         = {R package version 2.1}
}
@manual{package_covr,
	title        = {{covr}: Test Coverage for Packages},
	author       = {Jim Hester},
	year         = 2023,
	url          = {https://CRAN.R-project.org/package=covr},
	note         = {R package version 3.6.4}
}
@manual{package_tidycensus,
	title        = {tidycensus: Load US Census Boundary and Attribute Data as 'tidyverse' and 'sf'-Ready Data Frames},
	author       = {Kyle Walker and Matt Herman},
	year         = 2024,
	url          = {https://CRAN.R-project.org/package=tidycensus},
	note         = {R package version 1.6.3}
}

# R functions
@manual{function_NARRdownloadNetCDF,
	title        = {{NARRdownloadNetCDF}: Downloads {NARR} {NetCDF} files},
	author       = {Kevin Shook and Bastien Ferland-Raymond},
	year         = 2022,
	url          = {https://rdrr.io/github/CentreForHydrology/Reanalysis/man/NARRdownloadNetCDF.html}
}
@manual{function_downloadDEM,
	title        = {download\_{DEM}: Downloading {DEM} data from {USGS} servers},
	author       = {Erik Kusch},
	year         = 2021,
	url          = {https://rdrr.io/github/ErikKusch/KrigR/man/download_DEM.html}
}
@manual{function_mt_subset,
	title        = {mt\_subset: Download {MODIS} Land Product subsets},
	author       = {Koen Hufkens},
	year         = 2023,
	url          = {https://bluegreen-labs.github.io/MODISTools/reference/mt_subset.html}
}
# R vignettes
@manual{vignette_RAQSAPI,
	title        = {Introduction to the {RAQSAPI} package},
	author       = {Clinton Mccrowey},
	publisher    = {US Environmental Protection Agency},
	url          = {https://cran.r-project.org/web/packages/RAQSAPI/vignettes/RAQSAPIvignette.html#RAQSAPI%20functional%20families}
}

####################################################################################################
## datasets
@manual{data_SEDAC_population,
	title        = {Gridded Population of the World, Version 4 (GPWv4): Population Density Adjusted to Match 2015 Revision UN WPP Country Totals, Revision 11},
	author       = {Center for International Earth Science Information Network - CIESIN - Columbia University},
	year         = 2018,
	month        = jun,
	journal      = {{AGU} Advances},
	publisher    = {NASA Socioeconomic Data and Applications Center (SEDAC)},
	doi          = {10.7927/H4F47M65},
	url          = {https://doi.org/10.7927/H4F47M65},
	howpublished = {\url{https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density-adjusted-to-2015-unwpp-country-totals-rev11}}
}
@manual{data_koppen_geiger,
	title        = {Present and future K{\"o}ppen-Geiger climate classification maps at 1-km resolution},
	author       = {Beck, Hylke E and Zimmermann, Niklaus E and McVicar, Tim R and Vergopolan, Noemi and Berg, Alexis and Wood, Eric F},
	year         = 2018,
	journal      = {Scientific data},
	publisher    = {Nature Publishing Group},
	volume       = 5,
	number       = 1,
	pages        = {1--12},
	doi          = {10.6084/m9.figshare.6396959},
	url          = {https://doi.org/10.6084/m9.figshare.6396959},
	howpublished = {\url{https://figshare.com/articles/dataset/Present_and_future_K_ppen-Geiger_climate_classification_maps_at_1-km_resolution/6396959/2}}
}
@manual{data_acsdp05,
	title        = {ACS Demographic and Housing Estimates (DP05)},
	author       = {U.S. Census Bureau},
	year         = 2019,
	howpublished = {\url{https://data.census.gov/cedsci/table?q=DP05}}
}

####################################################################################################
## web pages
@misc{web_SEDAC_GPWv4,
	title        = {{Gridded Population of the World (GPW)}, v4},
	author       = {Center for International Earth Science Information Network - CIESIN - Columbia University},
	publisher    = {NASA Socioeconomic Data and Applications Center (SEDAC)},
	url          = {https://sedac.ciesin.columbia.edu/data/collection/gpw-v4/documentation},
	howpublished = {\url{https://sedac.ciesin.columbia.edu/data/collection/gpw-v4/documentation}}
}
@misc{web_Sruthi2024,
	title        = {Top 20 Best Programming Languages To Learn in 2024},
	author       = {Sruthi Veeraraghavan},
	year         = 2024,
	url          = {https://www.simplilearn.com/best-programming-languages-start-learning-today-article},
	howpublished = {\url{https://www.simplilearn.com/best-programming-languages-start-learning-today-article}}
}
@misc{web_NARRcrs,
	title        = {Lambert Conformal Format},
	publisher    = {NOAA Physical Sciences Laboratory},
	url          = {https://psl.noaa.gov/data/narr/format.html},
	howpublished = {\url{https://psl.noaa.gov/data/narr/format.html}}
}
@misc{web_NARRabout,
	title        = {NCEP North American Regional Reanalysis (NARR)},
	publisher    = {NOAA Physical Sciences Laboratory},
	url          = {https://psl.noaa.gov/data/gridded/data.narr.html},
	howpublished = {\url{https://psl.noaa.gov/data/gridded/data.narr.html}}
}
@misc{web_HMSabout,
	title        = {Hazard Mapping System Fire and Smoke Product: Hazard Mapping System},
	publisher    = {NOAA Office of Satellite And Product Operations},
	url          = {https://www.ospo.noaa.gov/products/land/hms.html#about},
	howpublished = {\url{https://www.ospo.noaa.gov/products/land/hms.html#about}}
}
@misc{web_ESRIshapefile,
	title        = {Shapefile file extensions},
	publisher    = {Esri},
	year         = {2021},
	url          = {https://desktop.arcgis.com/en/arcmap/latest/manage-data/shapefiles/shapefile-file-extensions.htm},
	howpublished = {\url{https://desktop.arcgis.com/en/arcmap/latest/manage-data/shapefiles/shapefile-file-extensions.htm}}
}
@misc{web_HTTPresponse,
	title        = {{HTTP} response status codes},
	year         = 2023,
	publisher    = {MDN Web Docs},
	url          = {https://developer.mozilla.org/en-US/docs/Web/HTTP/Status},
	howpublished = {\url{https://developer.mozilla.org/en-US/docs/Web/HTTP/Status}}
}
@misc{web_NIEHSplan,
	title        = {Theme One: Advancing Environmental Health Sciences},
	year         = 2018,
	publisher    = {National Institute of Environmental Health Sciences},
	url          = {https://www.niehs.nih.gov/about/strategicplan/2018-2023_theme1}
}
@misc{web_sedctutorial,
	title = {Euclidean Sum of Exponentially Decaying Contributions Tutorial},
	author = {Corinne Wiesner},
	url = {https://mserre.sph.unc.edu/BMElab_web/SEDCtutorial/index.html}
}
@misc{web_sedctutorial_package,
	title = {Euclidean Sum of Exponentially Decaying Contributions Tutorial},
	author = {Corinne Wiesner}
}
@misc{web_ropensci,
	title        = {Packages},
	publisher    = {rOpenSci},
	url          = {https://ropensci.org/packages/},
	howpublished = {\url{https://ropensci.org/packages/}}
}
@misc{web_wktformat,
	title        = {Well-known text ({WKT}) format},
	publisher    = {IBM},
	url          = {https://www.ibm.com/docs/en/i/7.5?topic=formats-well-known-text-wkt-format},
	howpublished = {\url{https://www.ibm.com/docs/en/i/7.5?topic=formats-well-known-text-wkt-format}}
}
@misc{web_exactextractr,
	title        = {exactextractr},
	author       = {{Daniel Baston}},
	publisher    = {isciences},
	url          = {https://isciences.gitlab.io/exactextractr/},
	howpublished = {\url{https://isciences.gitlab.io/exactextractr/}}
}
@misc{web_exactextractr_background,
	title        = {exactextractr - Background},
	author       = {{Daniel Baston}},
	url          = {https://github.com/isciences/exactextract#background},
	howpublished = {\url{https://github.com/isciences/exactextract#background}}
}
@misc{web_FedData,
	title        = {{FedData}},
	author       = {R. Kyle Bocinsky},
	year         = 2024,
	url          = {https://github.com/ropensci/FedData},
	howpublished = {\url{https://github.com/ropensci/FedData}}
}
@misc{web_geoids,
	title        = {Geographic Identifiers},
	author       = {U.S. Census Bureau},
	year         = {n.d.},
	note         = {Accessed: 2024-07-01},
	howpublished = {\url{https://www.census.gov/programs-surveys/geography/guidance/geo-identifiers.html}}
}

####################################################################################################
## books
@book{book_mayer2013big,
	title        = {Big data: {A} revolution that will transform how we live, work, and think},
	author       = {Mayer-Sch{\"o}nberger, Viktor and Cukier, Kenneth},
	year         = 2013,
	publisher    = {Houghton Mifflin Harcourt}
}
@incollection{book_RPackages_13,
	title        = {Testing basics},
	author       = {Hadley Wickham and Jennifer Bryan},
	year         = 2023,
	booktitle    = {R Packages, 2nd Edition},
	publisher    = {O'Reilly Media, Inc.},
	isbn         = 9781098134945,
	url          = {https://r-pkgs.org/},
	chapter      = 13
}
@book{book_RMarkdown,
	title        = {{R Markdown}: The Definitive Guide},
	author       = {Yihui Xie and J.J. Allaire and Garrett Grolemund},
	year         = 2019,
	publisher    = {{Chapman \& Hall/CRC}},
	isbn         = {978-1-138-35933-0},
	url          = {https://bookdown.org/yihui/rmarkdown/}
}

####################################################################################################
## journal articles
@article{article_beck2018present,
	title        = {Present and future K{\"o}ppen-Geiger climate classification maps at 1-km resolution},
	author       = {Beck, Hylke E and Zimmermann, Niklaus E and McVicar, Tim R and Vergopolan, Noemi and Berg, Alexis and Wood, Eric F},
	year         = 2018,
	journal      = {Scientific data},
	publisher    = {Nature Publishing Group},
	volume       = 5,
	number       = 1,
	pages        = {1--12},
	doi          = {10.1038/sdata.2018.214},
	url          = {https://doi.org/10.1038/sdata.2018.214}
}
@article{article_Giorgi2022TheRL,
	title        = {The {R} Language: An Engine for Bioinformatics and Data Science},
	author       = {F. Giorgi and Carmine Ceraolo and D. Mercatelli},
	year         = 2022,
	journal      = {Life},
	volume       = 12,
	doi          = {10.3390/life12050648},
	url          = {https://www.mdpi.com/2075-1729/12/5/648}
}
@article{article_Lai2019Eval,
	title        = {Evaluating the popularity of {R} in ecology},
	author       = {Lai, Jiangshan and Lortie, Christopher J. and Muenchen, Robert A. and Yang, Jian and Ma, Keping},
	year         = 2019,
	journal      = {Ecosphere},
	volume       = 10,
	number       = 1,
	pages        = {e02567},
	doi          = {https://doi.org/10.1002/ecs2.2567},
	url          = {https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1002/ecs2.2567},
	keywords     = {code, ecology journal, open science, packages, R, reproducibility, statistical programming, Web of Science},
	eprint       = {https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1002/ecs2.2567},
	abstract     = {Abstract The programming language R is widely used in many fields. We explored the extent of reported R use in the field of ecology using the Web of Science and text mining. We analyzed the frequencies of R packages reported in more than 60,000 peer-reviewed articles published in 30 ecology journals during a 10-yr period ending in 2017. The number of studies reported using R as their primary tool in data analysis increased linearly from 11.4\% in 2008 to 58.0\% in 2017. The top 10 packages reported were lme4, vegan, nlme, ape, MuMIn, MASS, mgcv, ade4, multcomp, and car. The increasing popularity of R has most likely furthered open science in ecological research because it can improve reproducibility of analyses and captures workflows when scripts and codes are included and shared. These findings may not be entirely unique to R because there are other programming languages used by ecologists, but they do strongly suggest that given the relatively high frequency of reported use of R, it is a significant component of contemporary analytics in the field of ecology.}
}
@article{article_Stieb2017PromiseAP,
	title        = {Promise and pitfalls in the application of big data to occupational and environmental health},
	author       = {D. Stieb and C. Boot and M. Turner},
	year         = 2017,
	journal      = {BMC Public Health},
	volume       = 17,
	doi          = {10.1186/s12889-017-4286-8},
	url          = {https://doi.org/10.1186/s12889-017-4286-8}
}
@article{article_Comess2020BringingBD,
	title        = {Bringing Big Data to Bear in Environmental Public Health: Challenges and Recommendations},
	author       = {S. Comess and Alexia Akbay and Melpomene Vasiliou and R. Hines and L. Joppa and V. Vasiliou and N. Kleinstreuer},
	year         = 2020,
	journal      = {Frontiers in Artificial Intelligence},
	volume       = 3,
	doi          = {10.3389/frai.2020.00031},
	url          = {https://doi.org/10.3389/frai.2020.00031}
}
@article{artcile_Tange053447,
	title        = {{Big Data Reality Check (BDRC)} for public health: to what extent the environmental health and health services research did meet the {\textquoteleft}V{\textquoteright} criteria for big data? A study protocol},
	author       = {Pui Pui Tang and I Lam Tam and Yongliang Jia and Siu-wai Leung},
	year         = 2022,
	journal      = {BMJ Open},
	publisher    = {British Medical Journal Publishing Group},
	volume       = 12,
	number       = 3,
	doi          = {10.1136/bmjopen-2021-053447},
	issn         = {2044-6055},
	url          = {https://bmjopen.bmj.com/content/12/3/e053447},
	elocation-id = {e053447},
	abstract     = {Introduction Big data technologies have been talked up in the fields of science and medicine. The V-criteria (volume, variety, velocity and veracity, etc) for defining big data have been well-known and even quoted in most research articles; however, big data research into public health is often misrepresented due to certain common misconceptions. Such misrepresentations and misconceptions would mislead study designs, research findings and healthcare decision-making. This study aims to identify the V-eligibility of big data studies and their technologies applied to environmental health and health services research that explicitly claim to be big data studies.Methods and analysis Our protocol follows Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P). Scoping review and/or systematic review will be conducted. The results will be reported using PRISMA for Scoping Reviews (PRISMA-ScR), or PRISMA 2020 and Synthesis Without Meta-analysis guideline. Web of Science, PubMed, Medline and ProQuest Central will be searched for the articles from the database inception to 2021. Two reviewers will independently select eligible studies and extract specified data. The numeric data will be analysed with R statistical software. The text data will be analysed with NVivo wherever applicable.Ethics and dissemination This study will review the literature of big data research related to both environmental health and health services. Ethics approval is not required as all data are publicly available and involves confidential personal data. We will disseminate our findings in a peer-reviewed journal.PROSPERO registration number CRD42021202306.},
	eprint       = {https://bmjopen.bmj.com/content/12/3/e053447.full.pdf}
}
@article{article_BigDatainEnvironmentandHumanHealth,
	title        = {Big Data in Environment and Human Health},
	author       = {Lora Fleming and Niccolò Tempini and Harriet Gordon-Brown and Gordon L. Nichols and Christophe Sarran and Paolo Vineis and Giovanni Leonardi and Brian Golding and Andy Haines and Anthony Kessel and Virginia Murray and Michael Depledge and Sabina Leonelli},
	year         = 2017,
	month        = {07},
	journal      = {Oxford Research Encyclopedia of Environmental Science},
	publisher    = {Oxford University Press},
	doi          = {10.1093/acrefore/9780199389414.013.541},
	url          = {https://oxfordre.com/environmentalscience/view/10.1093/acrefore/9780199389414.001.0001/acrefore-9780199389414-e-541}
}
@article{article_Piel2020Using,
	title        = {{Using large and complex datasets for small-area environment-health studies: from theory to practice}},
	author       = {Piel, Frédéric B and Cockings, Samantha},
	year         = 2020,
	month        = {04},
	journal      = {International Journal of Epidemiology},
	volume       = 49,
	number       = {Supplement_1},
	pages        = {i1-i3},
	doi          = {10.1093/ije/dyaa018},
	issn         = {0300-5771},
	url          = {https://doi.org/10.1093/ije/dyaa018},
	abstract     = {{Humans are exposed to a wide range of pollutants throughout their lifetime, many of which pose a potential risk to their health. Such hazards include features of the natural, human-modified, social and economic environments. In this supplement, we are primarily concerned with risks to human health resulting from hazards of the human-modified environment, although many of the concepts, methods and tools are equally applicable to investigations of the health impacts of other types of environmental hazards. Amongst human-modified environmental hazards, air pollution has been identified as the world’s largest killer, being responsible for an estimated 6.4 million deaths per year (1 in 9 deaths).1 According to the World Health Organization, two billion children live in areas where outdoor air pollution exceeds recommended international limits and 300 million children live in areas where outdoor air pollution exceeds six times those international limits. Other hazards of the human-modified environment include water pollutants, such as chemicals and microplastics; radiation from mobile phones, powerlines or nearby nuclear installations; and soil contaminants such as heavy metals.}},
	eprint       = {https://academic.oup.com/ije/article-pdf/49/Supplement\_1/i1/33050268/dyaa018.pdf}
}
@article{article_Fleming2014Data,
	title        = {Data Mashups: Potential Contribution to Decision Support on Climate Change and Health},
	author       = {Fleming, Lora E. and Haines, Andy and Golding, Brian and Kessel, Anthony and Cichowska, Anna and Sabel, Clive E. and Depledge, Michael H. and Sarran, Christophe and Osborne, Nicholas J. and Whitmore, Ceri and Cocksedge, Nicola and Bloomfield, Daniel},
	year         = 2014,
	journal      = {International Journal of Environmental Research and Public Health},
	volume       = 11,
	number       = 2,
	pages        = {1725--1746},
	doi          = {10.3390/ijerph110201725},
	issn         = {1660-4601},
	url          = {https://www.mdpi.com/1660-4601/11/2/1725},
	pubmedid     = 24499879,
	abstract     = {Linking environmental, socioeconomic and health datasets provides new insights into the potential associations between climate change and human health and wellbeing, and underpins the development of decision support tools that will promote resilience to climate change, and thus enable more effective adaptation. This paper outlines the challenges and opportunities presented by advances in data collection, storage, analysis, and access, particularly focusing on “data mashups”. These data mashups are integrations of different types and sources of data, frequently using open application programming interfaces and data sources, to produce enriched results that were not necessarily the original reason for assembling the raw source data. As an illustration of this potential, this paper describes a recently funded initiative to create such a facility in the UK for use in decision support around climate change and health, and provides examples of suitable sources of data and the purposes to which they can be directed, particularly for policy makers and public health decision makers.}
}
@article{article_Fleming2017BigDI,
	title        = {{Big Data in Environment and Human Health}},
	author       = {Lora Fleming and Niccoló Tempini and Harriet Gordon-Brown and Gordon L. Nichols and Christophe Sarran and Paolo Vineis and Giovanni Leonardi and Brian Golding and Andy Haines and Anthony Kessel and Virginia Murray and Michael Depledge and Sabina Leonelli},
	year         = 2017,
	journal      = {Oxford Research Encyclopedia of Environmental Science},
	doi          = {10.1093/acrefore/9780199389414.013.541},
	url          = {https://doi.org/10.1093/acrefore/9780199389414.013.541}
}
@article{article_wilkinson2016fair,
	title        = {The {FAIR} Guiding Principles for scientific data management and stewardship},
	author       = {Wilkinson, Mark D and Dumontier, Michel and Aalbersberg, IJsbrand Jan and Appleton, Gabrielle and Axton, Myles and Baak, Arie and Blomberg, Niklas and Boiten, Jan-Willem and da Silva Santos, Luiz Bonino and Bourne, Philip E and others},
	year         = 2016,
	journal      = {Scientific data},
	publisher    = {Nature Publishing Group},
	volume       = 3,
	number       = 1,
	pages        = {1--9}
}
@article{article_cui2022integrating,
	title        = {Integrating multiscale geospatial environmental data into large population health studies: challenges and opportunities},
	author       = {Cui, Yuxia and Eccles, Kristin M and Kwok, Richard K and Joubert, Bonnie R and Messier, Kyle P and Balshaw, David M},
	year         = 2022,
	journal      = {Toxics},
	publisher    = {MDPI},
	volume       = 10,
	number       = 7,
	pages        = 403
}
@techreport{report_USGS2002,
	title        = {Methods to achieve accurate projection of regional and global raster databases},
	author       = {Usery, E Lynn and Seong, Jeong Chang and Steinwand, Dan},
	year         = 2002,
	doi          = {10.3133/ofr01181},
	url          = {https://pubs.usgs.gov/publication/ofr01181},
	institution  = {US Geological Survey}
}
@article{article_messier2012integrating,
	title        = {Integrating address geocoding, land use regression, and spatiotemporal geostatistical estimation for groundwater tetrachloroethylene},
	author       = {Messier, Kyle P and Akita, Yasuyuki and Serre, Marc L},
	year         = 2012,
	journal      = {Environmental science \& technology},
	publisher    = {ACS Publications},
	volume       = 46,
	number       = 5,
	pages        = {2772--2780}
}
@article{article_abatzoglou2013development,
	title        = {Development of gridded surface meteorological data for ecological applications and modelling},
	author       = {Abatzoglou, John T},
	year         = 2013,
	journal      = {International journal of climatology},
	publisher    = {Wiley Online Library},
	volume       = 33,
	number       = 1,
	pages        = {121--131}
}
@article{article_abatzoglou2018terraclimate,
	title        = {{TerraClimate}, a high-resolution global dataset of monthly climate and climatic water balance from 1958--2015},
	author       = {Abatzoglou, John T and Dobrowski, Solomon Z and Parks, Sean A and Hegewisch, Katherine C},
	year         = 2018,
	journal      = {Scientific data},
	publisher    = {Nature Publishing Group},
	volume       = 5,
	number       = 1,
	pages        = {1--12}
}
@article{article_christman2012error,
	title        = {Error propagation in raster data integration},
	author       = {Christman, Zachary J and Rogan, John},
	year         = 2012,
	journal      = {Photogrammetric Engineering \& Remote Sensing},
	publisher    = {American Society for Photogrammetry and Remote Sensing},
	volume       = 78,
	number       = 6,
	pages        = {617--624}
}
@article{article_steinwand1995reprojection,
	title        = {Map Projections for Global and Gontinental Data Sets and an Analysis of Pixel Distortion Caused by Reproiection},
	author       = {Steinwand, Daniel R and Hutchinson, John A and Snyder, John P},
	year         = 1995,
	journal      = {Photogrammetric Engineering \& Remote Sensing},
	volume       = 61,
	number       = 12,
	pages        = {1487--1497}
}

@article{article_cruzalonso2023easyclimate,
title = {The easyclimate {R} package: Easy access to high-resolution daily climate data for Europe},
journal = {Environmental Modelling \& Software},
volume = {161},
pages = {105627},
year = {2023},
issn = {1364-8152},
doi = {https://doi.org/10.1016/j.envsoft.2023.105627},
url = {https://www.sciencedirect.com/science/article/pii/S1364815223000130},
author = {Verónica Cruz-Alonso and Christoph Pucher and Sophia Ratcliffe and Paloma Ruiz-Benito and Julen Astigarraga and Mathias Neumann and Hubert Hasenauer and Francisco Rodríguez-Sánchez},
keywords = {R package, Climate, Europe, Cloud-optimized geoTIFF, Daily data, Reproducibility},
abstract = {In recent decades there has been an increasing demand in environmental sciences for harmonized climatic data at large spatial scales and long periods. Here we present easyclimate, a software package to obtain daily climatic data at high resolution (0.0083°, ∼1 km) with R. The package facilitates the downloading and processing of precipitation, minimum and maximum temperatures for Europe from 1950 to 2020. Using easyclimate and given a set of coordinates (points or polygons) and dates (days or years), the user can download the climatic information as a tidy table or a raster object. In this package we implemented Cloud-Optimized GeoTIFFs which provide access to daily climate data for thousands of sites/days, without having to download huge rasters. Daily climate data are not available in many of the current climate databases and are essential for many research questions and applications in environmental modelling, forestry, and ecological and vegetation studies across Europe.}
}

@article{article_nandi2024imdlib,
title = {{IMDLIB}: {A}n open-source library for retrieval, processing and spatiotemporal exploratory assessments of gridded meteorological observation datasets over India},
journal = {Environmental Modelling \& Software},
volume = {171},
pages = {105869},
year = {2024},
issn = {1364-8152},
doi = {https://doi.org/10.1016/j.envsoft.2023.105869},
url = {https://www.sciencedirect.com/science/article/pii/S1364815223002554},
author = {Saswata Nandi and Pratiman Patel and Sabyasachi Swain},
keywords = {IMD gridded datasets, Hydrometeorology, Python, Trend, Extreme indices, SDGs},
abstract = {Addressing a pressing need for streamlined access to observed meteorological data in India, we present IMDLIB, an open-source Python library that operates on gridded datasets from the India Meteorological Department (IMD). IMDLIB simplifies the retrieval, management, and processing of IMD's rainfall, maximum and minimum temperature data at varying scales (point, regional, catchment, and country scales). IMDLIB stands out by directly interfacing with IMD's database through robust APIs, streamlining data acquisition. It offers transformation of IMD's binary data into common formats (e.g., NetCDF/GeoTIFF/CSV) and advanced analytics, including trend analysis, spatiotemporal data interpolation, calculation of meteorological indices and extreme characteristics, showcasing superior capabilities compared to traditional meteorological data processing tool available for the region. To underline its robustness, IMDLIB's application was illustrated in two distinct regions: Rajasthan State and the Godavari River Basin. Through IMDLIB, we envision enhanced data accessibility, leading to more insightful hydro-climatic research and analysis.}
}

@article{article_vargasgodoy2023precipe,
title = {pRecipe: A global precipitation climatology toolbox and database},
journal = {Environmental Modelling \& Software},
volume = {165},
pages = {105711},
year = {2023},
issn = {1364-8152},
doi = {https://doi.org/10.1016/j.envsoft.2023.105711},
url = {https://www.sciencedirect.com/science/article/pii/S136481522300097X},
author = {Mijael Rodrigo {Vargas Godoy} and Yannis Markonis},
keywords = {Multi-source data, Data set validation, Global precipitation, Satellite remote sensing, Reanalyses, Hydroclimatology},
abstract = {Remote sensing data and model simulations of precipitation complemented the traditional surface measurements and offer unprecedented coverage on a global scale. However, the substantial heterogeneity among data products has hindered this unique opportunity to obtain a robust quantification of the climatologic properties of precipitation globally. Herein we present pRecipe, a package developed in R with reproducible science as our standard. pRecipe provides functions to download, explore, process, and visualize a database of 24 precipitation data sets at monthly time step and 0.25∘ resolution (20CR v3, CHIRPS v2.0, CMAP, CMORPH, CPC-Global, CRU TS v4.06, EM-EARTH, ERA-20C, ERA5, GHCN v2, GLDAS-CLSM, GLDAS-NOAH, GLDAS-VIC, GPCC v2020, GPCP v2.3, GPM IMERGM v06, MSWEP v2.8, NCEP/DOE R2, NCEP/NCAR R1, PERSIANN-CDR, PREC/L, TerraClimate, TRMM 3B43 v7, UDel v5.01).}
}


@article{article_sayyed2024satellite,
	title = {Satellite data for environmental justice: a scoping review of the literature in the {United} {States}},
	volume = {19},
	issn = {1748-9326},
	shorttitle = {Satellite data for environmental justice},
	url = {https://iopscience.iop.org/article/10.1088/1748-9326/ad1fa4},
	doi = {10.1088/1748-9326/ad1fa4},
	abstract = {Abstract
            In support of the environmental justice (EJ) movement, researchers, activists, and policymakers often use environmental data to document evidence of the unequal distribution of environmental burdens and benefits along lines of race, class, and other socioeconomic characteristics. Numerous limitations, such as spatial or temporal discontinuities, exist with commonly used data measurement techniques, which include ground monitoring and federal screening tools. Satellite data is well poised to address these gaps in EJ measurement and monitoring; however, little is known about how satellite data has advanced findings in EJ or can help to promote EJ through interventions. Thus, this scoping review aims to (1) explore trends in study design, topics, geographic scope, and satellite datasets used to research EJ, (2) synthesize findings from studies that use satellite data to characterize disparities and inequities across socio-demographic groups for various environmental categories, and (3) capture how satellite data are relevant to policy and real-world impact. Following PRISMA extension guidelines for scoping reviews, we retrieved 81 articles that applied satellite data for EJ research in the United States from 2000 to 2022. The majority of the studies leveraged the technical advantages of satellite data to identify socio-demographic disparities in exposure to environmental risk factors, such as air pollution, and access to environmental benefits, such as green space, at wider coverage and with greater precision than previously possible. These disparities in exposure and access are associated with health outcomes such as increased cardiovascular and respiratory diseases, mental illness, and mortality. Research using satellite data to illuminate EJ concerns can contribute to efforts to mitigate environmental inequalities and reduce health disparities. Satellite data for EJ research can therefore support targeted interventions or influence planning and policy changes, but significant work remains to facilitate the application of satellite data for policy and community impact.},
	number = {3},
	urldate = {2024-06-06},
	journal = {Environmental Research Letters},
	author = {Sayyed, Tanya Kreutzer and Ovienmhada, Ufuoma and Kashani, Mitra and Vohra, Karn and Kerr, Gaige Hunter and O’Donnell, Catherine and Harris, Maria H and Gladson, Laura and Titus, Andrea R and Adamo, Susana B and Fong, Kelvin C and Gargulinski, Emily M and Soja, Amber J and Anenberg, Susan and Kuwayama, Yusuke},
	month = mar,
	year = {2024},
	pages = {033001}
}

@article{article_thomas2022burning,
	title = {A burning issue: {Reviewing} the socio-demographic and environmental justice aspects of the wildfire literature},
	volume = {17},
	issn = {1932-6203},
	shorttitle = {A burning issue},
	url = {https://dx.plos.org/10.1371/journal.pone.0271019},
	doi = {10.1371/journal.pone.0271019},
	abstract = {Larger and more severe wildfires are becoming more frequent and impacting different communities and human settlements. Much of the scientific literature and media on wildfires has focused on area of ecosystems burned and numbers of structures destroyed. Equally unprecedented, but often less reported, are the increasing socioeconomic impacts different people and communities face from wildfires. Such information seems to indicate an emerging need to account for wildfire effects on peri-urban or wildland urban interface (WUI) areas, newer socio-demographic groups, and disadvantaged communities. To address this, we reviewed the socio-demographic dimensions of the wildfire literature using an environmental justice (EJ) lens. Specifically using a literature review of wildfires, human communities, social vulnerability, and homeowner mitigation, we conducted bibliometric and statistical analyses of 299 publications. The majority of publications were from the United States, followed by Canada and Australia, and most dealt with homeowner mitigation of risk, defensible space, and fuel treatments in WUI areas. Most publications studied the direct effects of wildfire related damage. Secondary impacts such as smoke, rural and urban communities, and the role of poverty and language were less studied. Based on a proposed wildfire-relevant EJ definition, the first EJ publication was in 2004, but the term was first used as a keyword in 2018. Studies in WUI communities statistically decreased the likelihood that a publication was EJ relevant. There was a significant relationship between EJ designation and inclusion of race/ethnicity and poverty variables in the study. Complexity across the various definitions of EJ suggest that it should not be used as a quantitative or binary metric; but as a lens to better understand socio-ecological impacts to diverse communities. We present a wildfire-relevant definition to potentially guide policy formulation and account for social and environmental justice issues.},
	language = {en},
	number = {7},
	urldate = {2024-06-06},
	journal = {PLOS ONE},
	author = {Thomas, Alyssa S. and Escobedo, Francisco J. and Sloggy, Matthew R. and Sánchez, José J.},
	editor = {Jones, Julia A.},
	month = jul,
	year = {2022},
	pages = {e0271019}
}



@misc{data_mod09ga2021,
	title = {{MODIS}/{Terra} {Surface} {Reflectance} {Daily} {L2G} {Global} 1km and 500m {SIN} {Grid} {V061}},
	url = {https://www.earthdata.nasa.gov/data/catalog/lpcloud-mod09ga-061},
	doi = {10.5067/MODIS/MOD09GA.061},
	urldate = {2024-06-24},
	publisher = {NASA EOSDIS Land Processes Distributed Active Archive Center},
	author = {Vermote, Eric and Wolfe, Robert},
	year = {2021},
}

@misc{data_mod13a22021,
	title = {{MODIS}/{Terra} {Vegetation} {Indices} 16-{Day} {L3} {Global} 1km {SIN} {Grid} {V061}},
	url = {https://www.earthdata.nasa.gov/data/catalog/lpcloud-mod13a2-061},
	doi = {10.5067/MODIS/MOD13A2.061},
	urldate = {2024-06-24},
	publisher = {NASA EOSDIS Land Processes Distributed Active Archive Center},
	author = {Didan, Kamel},
	year = {2021},
}

@misc{data_mod11a12021,
	title = {{MODIS}/{Terra} {Land} {Surface} {Temperature}/{Emissivity} {Daily} {L3} {Global} 1km {SIN} {Grid} {V061}},
	url = {https://www.earthdata.nasa.gov/data/catalog/lpcloud-mod11a1-061},
	doi = {10.5067/MODIS/MOD11A1.061},
	urldate = {2024-06-24},
	publisher = {NASA EOSDIS Land Processes Distributed Active Archive Center},
	author = {Wan, Zhengming and Hook, Simon and Hulley, Glynn},
	year = {2021},
}

@misc{data_mcd19a22021,
	title = {{MODIS}/{Terra}+{Aqua} {Land} {Aerosol} {Optical} {Depth} {Daily} {L2G} {Global} 1km {SIN} {Grid} {V061}},
	url = {https://www.earthdata.nasa.gov/data/catalog/lpcloud-mcd19a2-061},
	doi = {10.5067/MODIS/MCD19A2.061},
	urldate = {2024-06-24},
	publisher = {NASA EOSDIS Land Processes Distributed Active Archive Center},
	author = {Lyapustin, Alexei and Wang, Yujie},
	year = {2022},
}

@misc{data_mod06l2_2017,
	title = {{MODIS}/{Terra} {Clouds} 5-{Min} {L2} {Swath} 1km and 5km},
	url = {https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MOD06_L2},
	doi = {10.5067/MODIS/MOD06_L2.061},
	urldate = {2024-06-24},
	publisher = {NASA Level 1 and Atmosphere Archive and Distribution System Distributed Active Archive Center},
	author = {{MODIS Atmosphere Science Team}},
	year = {2017},
}

@article{article_roman2018vnp46,
	title = {{NASA}'s {Black} {Marble} nighttime lights product suite},
	volume = {210},
	issn = {00344257},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S003442571830110X},
	doi = {10.1016/j.rse.2018.03.017},
	language = {en},
	urldate = {2024-06-24},
	journal = {Remote Sensing of Environment},
	author = {Román, Miguel O. and Wang, Zhuosen and Sun, Qingsong and Kalb, Virginia and Miller, Steven D. and Molthan, Andrew and Schultz, Lori and Bell, Jordan and Stokes, Eleanor C. and Pandey, Bhartendu and Seto, Karen C. and Hall, Dorothy and Oda, Tomohiro and Wolfe, Robert E. and Lin, Gary and Golpayegani, Navid and Devadiga, Sadashiva and Davidson, Carol and Sarkar, Sudipta and Praderas, Cid and Schmaltz, Jeffrey and Boller, Ryan and Stevens, Joshua and Ramos González, Olga M. and Padilla, Elizabeth and Alonso, José and Detrés, Yasmín and Armstrong, Roy and Miranda, Ismael and Conte, Yasmín and Marrero, Nitza and MacManus, Kytt and Esch, Thomas and Masuoka, Edward J.},
	month = jun,
	year = {2018},
	pages = {113--143},
}

@misc{web_usepa2024nei,
	title = {Air {Emissions} {Inventories}},
	url = {https://www.epa.gov/air-emissions-inventories},
	urldate = {2024-06-24},
	author = {{United States Environmental Protection Agency}},
	year = {2024},
}

@misc{data_ciesin2013groads,
	title = {Global {Roads} {Open} {Access} {Data} {Set}, {Version} 1 ({gROADSv1})},
	copyright = {Users may use and redistribute these data without explicit written permission from CIESIN or Information Technology Outreach Services (ITOS)/University of Georgia, with the exception of roads data associated with countries that are listed in the data documentation as requiring additional credits or holding special restrictions. Users are advised to consult the data documentation for further information and to obtain necessary permissions or adhere to relevant restrictions that apply to each of those data sets.},
	url = {https://data.nasa.gov/dataset/global-roads-open-access-data-set-version-1-groadsv1},
	doi = {10.7927/H4VD6WCT},
	abstract = {The Global Roads Open Access Data Set, Version 1 (gROADSv1) was developed under the auspices of the CODATA Global Roads Data Development Task Group. The data set combines the best available roads data by country into a global roads coverage, using the UN Spatial Data Infrastructure Transport (UNSDI-T) version 2 as a common data model. All country road networks have been joined topologically at the borders, and many countries have been edited for internal topology. Source data for each country are provided in the documentation, and users are encouraged to refer to the readme file for use constraints that apply to a small number of countries. Because the data are compiled from multiple sources, the date range for road network representations ranges from the 1980s to 2010 depending on the country (most countries have no confirmed date), and spatial accuracy varies. The baseline global data set was compiled by the Information Technology Outreach Services (ITOS) of the University of Georgia. Updated data for 27 countries and 6 smaller geographic entities were assembled by Columbia University's Center for International Earth Science Information Network (CIESIN), with a focus largely on developing countries with the poorest data coverage.},
	urldate = {2024-06-24},
	publisher = {Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC)},
	author = {{Center For International Earth Science Information Network-CIESIN-Columbia University} and {Information Technology Outreach Services-ITOS-University Of Georgia}},
	year = {2013},
	keywords = {poverty, agriculture, conservation, health, infrastructure, urban},
}

@article{article_omernik2014ecoregions,
	title = {Ecoregions of the {Conterminous} {United} {States}: {Evolution} of a {Hierarchical} {Spatial} {Framework}},
	volume = {54},
	issn = {0364-152X, 1432-1009},
	shorttitle = {Ecoregions of the {Conterminous} {United} {States}},
	url = {https://link.springer.com/article/10.1007/s00267-014-0364-1},
	doi = {10.1007/s00267-014-0364-1},
	language = {en},
	number = {6},
	urldate = {2024-06-24},
	journal = {Environmental Management},
	author = {Omernik, James M. and Griffith, Glenn E.},
	month = dec,
	year = {2014},
	pages = {1249--1266},
}

@article{article_beck2023koppen,
	title = {High-resolution (1 km) {Köppen}-{Geiger} maps for 1901–2099 based on constrained {CMIP6} projections},
	volume = {10},
	issn = {2052-4463},
	url = {https://www.nature.com/articles/s41597-023-02549-6},
	doi = {10.1038/s41597-023-02549-6},
	abstract = {Abstract
            
              We introduce Version 2 of our widely used 1-km Köppen-Geiger climate classification maps for historical and future climate conditions. The historical maps (encompassing 1901–1930, 1931–1960, 1961–1990, and 1991–2020) are based on high-resolution, observation-based climatologies, while the future maps (encompassing 2041–2070 and 2071–2099) are based on downscaled and bias-corrected climate projections for seven shared socio-economic pathways (SSPs). We evaluated 67 climate models from the Coupled Model Intercomparison Project phase 6 (CMIP6) and kept a subset of 42 with the most plausible CO
              2
              -induced warming rates. We estimate that from 1901–1930 to 1991–2020, approximately 5\% of the global land surface (excluding Antarctica) transitioned to a different major Köppen-Geiger class. Furthermore, we project that from 1991–2020 to 2071–2099, 5\% of the land surface will transition to a different major class under the low-emissions SSP1-2.6 scenario, 8\% under the middle-of-the-road SSP2-4.5 scenario, and 13\% under the high-emissions SSP5-8.5 scenario. The Köppen-Geiger maps, along with associated confidence estimates, underlying monthly air temperature and precipitation data, and sensitivity metrics for the CMIP6 models, can be accessed at
              www.gloh2o.org/koppen
              .},
	language = {en},
	number = {1},
	urldate = {2024-06-24},
	journal = {Scientific Data},
	author = {Beck, Hylke E. and McVicar, Tim R. and Vergopolan, Noemi and Berg, Alexis and Lutsko, Nicholas J. and Dufour, Ambroise and Zeng, Zhenzhong and Jiang, Xin and Van Dijk, Albert I. J. M. and Miralles, Diego G.},
	month = oct,
	year = {2023},
	pages = {724}
}


@article{article_daly2000prism,
	title = {HIGH-QUALITY SPATIAL CLIMATE DATA SETS FOR THE {U}NITED {S}TATES AND BEYOND},
	volume = {43},
	issn = {2151-0059},
	url = {http://elibrary.asabe.org/abstract.asp??JID=3&AID=3101&CID=t2000&v=43&i=6&T=1},
	doi = {10.13031/2013.3101},
	language = {en},
	number = {6},
	urldate = {2024-06-24},
	journal = {Transactions of the ASAE},
	author = {Daly, C. and Taylor, G. H. and Gibson, W. P. and Parzybok, T. W. and Johnson, G. L. and Pasteris, P. A.},
	year = {2000},
	pages = {1957--1962},
}

@misc{web_usepa2024tri,
	title = {{TRI} {Basic} {Data} {Files}: {Calendar} {Years} {1987 -- Present}},
	url = {https://www.epa.gov/toxics-release-inventory-tri-program/tri-data-action-0},
	urldate = {2024-06-24},
	author = {{United States Environmental Protection Agency}},
	year = {2024},
}

@misc{web_edgarv8_1ap,
	title = {{EDGAR} {Global} {Air} {Pollutant} {Emissions} {Version} {8.1}},
	url = {https://edgar.jrc.ec.europa.eu/index.php/dataset_ap81},
	urldate = {2025-05-20},
	author = {{European Comission Joint Research Centre}},
	year = {2023},
}

@misc{web_edgarv8_1voc,
	title = {{Emissions Database for Global Atmospheric Research} {Global} {Speciated} {NMVOC} {Emissions} {Version} {8.1}},
	url = {https://edgar.jrc.ec.europa.eu/dataset_ap81_VOC_spec#sources},
	urldate = {2025-05-20},
	author = {{European Comission Joint Research Centre}},
	year = {2025},
}

@book{data_hengl2023openlandmap,
  author       = {Hengl, T. and Parente, L. and Ho, Y.-F. and Simoes, R. and contributors},
  title        = {{OpenLandMap Open Land Data services}},
  year         = {2023},
  publisher    = {OpenGeoHub foundation},
  address      = {Wageningen},
  version      = {v0.2},
  doi          = {10.5281/zenodo.10522799},
  url          = {https://openlandmap.github.io/book/}
}

@misc{web_stacspec2024,
	title = {{STAC}: {SpatioTemporal} {Asset} {Catalogs}},
	url = {https://www.stacspec.org/en},
	urldate = {2024-06-24},
	year = {2024}
}

@inproceedings{abstract_simoes2021rstac,
	address = {Brussels, Belgium},
	title = {Rstac: {An} {R} {Package} to {Access} {Spatiotemporal} {Asset} {Catalog} {Satellite} {Imagery}},
	copyright = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html},
	isbn = {978-1-66540-369-6},
	shorttitle = {Rstac},
	url = {https://ieeexplore.ieee.org/document/9553518/},
	doi = {10.1109/IGARSS47720.2021.9553518},
	urldate = {2024-06-24},
	booktitle = {2021 {IEEE} {International} {Geoscience} and {Remote} {Sensing} {Symposium} {IGARSS}},
	publisher = {IEEE},
	author = {Simoes, Rolf and De Souza, Felipe Carvalho and Zaglia, Matheus and De Queiroz, Gilberto Ribeiro and Dos Santos, Rafael D. C. and Ferreira, Karine Reis},
	month = jul,
	year = {2021},
	pages = {7674--7677},
}

@misc{data_usgs2023nhd,
  author = {{U.S. Geological Survey}},
  title = {{National Hydrography Dataset (NHD) -- USGS National Map Downloadable Data Collection}},
  url = {https://www.usgs.gov/national-hydrography},
  year = {2023}
}

@misc{data_usepa2023airdata,
  author = {{U.S. Environmental Protection Agency}},
  title = {{Air Quality System Data Mart [internet database]}},
  url = {https://www.epa.gov/outdoor-air-quality-data},
  urldate = {2024-06-24},
  year = {2023}
}


@misc{data_ciesin2017gpwv4,
	title = {Gridded {Population} of the {World}, {Version} 4 ({GPWv4}): {Population} {Density}, {Revision} 11},
	copyright = {This work is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0). Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.},
	shorttitle = {Gridded {Population} of the {World}, {Version} 4 ({GPWv4})},
	url = {https://earthdata.nasa.gov/data/catalog/sedac-ciesin-sedac-gpwv4-popdens-r11-4.11},
	doi = {10.7927/H49C6VHW},
	abstract = {The Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11 consists of estimates of human population density (number of persons per square kilometer) based on counts consistent with national censuses and population registers, for the years 2000, 2005, 2010, 2015, and 2020. A proportional allocation gridding algorithm, utilizing approximately 13.5 million national and sub-national administrative units, was used to assign population counts to 30 arc-second grid cells. The population density rasters were created by dividing the population count raster for a given target year by the land area raster. The data files were produced as global rasters at 30 arc-second ({\textasciitilde}1 km at the equator) resolution. To enable faster global processing, and in support of research communities, the 30 arc-second count data were aggregated to 2.5 arc-minute, 15 arc-minute, 30 arc-minute and 1 degree resolutions to produce density rasters at these resolutions.},
	urldate = {2024-06-24},
	publisher = {Palisades, NY:  Socioeconomic Data and Applications Center (SEDAC)},
	author = {{Center For International Earth Science Information Network-CIESIN-Columbia University}},
	year = {2017},
	keywords = {population},
}



@misc{data_gmao_merra-inst1_2d_asm_Nx,
    title = {{MERRA}-2 inst1\_2d\_asm\_Nx: 2d,3-{Hourly},{Instantaneous},{Single}-{Level},{Assimilation},{Single}-{Level} {Diagnostics} {V5}.12.4},
    shorttitle = {{MERRA}-2 inst1\_2d\_asm\_Nx},
    url = {https://disc.gsfc.nasa.gov/datasets/M2I1NXASM_5.12.4/summary},
    doi = {10.5067/3Z173KIE2TPD},
    urldate = {2024-06-24},
    publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
    author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
    year = {2015},
}

@misc{data_gmao_merra-inst1_2d_int_Nx,
    title = {{MERRA}-2 inst1\_2d\_int\_Nx: 2d,1-{Hourly},{Instantaneous},{Single}-{Level},{Assimilation},{Vertically} {Integrated} {Diagnostics} {V5}.12.4},
    shorttitle = {{MERRA}-2 inst1\_2d\_int\_Nx},
    url = {https://disc.gsfc.nasa.gov/datasets/M2I1NXINT_5.12.4/summary},
    doi = {10.5067/G0U6NGQ3BLE0},
    urldate = {2024-06-24},
    publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
    author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
    year = {2015},
}

@misc{data_gmao_merra-inst1_2d_lfo_Nx,
    title = {{MERRA}-2 inst1\_2d\_lfo\_Nx: 2d,1-{Hourly},{Instantaneous},{Single}-{Level},{Assimilation},{Land} {Surface} {Forcings} {V5}.12.4},
    shorttitle = {{MERRA}-2 inst1\_2d\_lfo\_Nx},
    url = {https://disc.gsfc.nasa.gov/datasets/M2I1NXLFO_5.12.4/summary},
    doi = {10.5067/RCMZA6TL70BG},
    urldate = {2024-06-24},
    publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
    author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
    year = {2015},
}

@misc{data_gmao_merra-inst3_3d_asm_Np,
    title = {{MERRA}-2 inst3\_3d\_asm\_Np: 3d,3-{Hourly},{Instantaneous},{Pressure}-{Level},{Assimilation},{Assimilated} {Meteorological} {Fields} {V5}.12.4},
    shorttitle = {{MERRA}-2 inst3\_3d\_asm\_Np},
    url = {https://disc.gsfc.nasa.gov/datasets/M2I3NPASM_5.12.4/summary},
    doi = {10.5067/QBZ6MG944HW0},
    urldate = {2024-06-24},
    publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
    author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
    year = {2015},
}

@misc{data_gmao_merra-inst3_3d_aer_Nv,
    title = {{MERRA}-2 inst3\_3d\_aer\_Nv: 3d,3-{Hourly},{Instantaneous},{Model}-{Level},{Assimilation},{Aerosol} {Mixing} {Ratio} {V5}.12.4},
    shorttitle = {{MERRA}-2 inst3\_3d\_aer\_Nv},
    url = {https://disc.gsfc.nasa.gov/datasets/M2I3NVAER_5.12.4/summary},
    doi = {10.5067/LTVB4GPCOTK2},
    urldate = {2024-06-24},
    publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
    author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
    year = {2015},
}

@misc{data_gmao_merra-inst3_3d_asm_Nv,
    title = {{MERRA}-2 inst3\_3d\_asm\_Nv: 3d,3-{Hourly},{Instantaneous},{Model}-{Level},{Assimilation},{Assimilated} {Meteorological} {Fields} {V5}.12.4},
    shorttitle = {{MERRA}-2 inst3\_3d\_asm\_Nv},
    url = {https://disc.gsfc.nasa.gov/datasets/M2I3NVASM_5.12.4/summary},
    doi = {10.5067/WWQSXQ8IVFW8},
    urldate = {2024-06-24},
    publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
    author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
    year = {2015},
}

@misc{data_gmao_merra-inst3_3d_chm_Nv,
    title = {{MERRA}-2 inst3\_3d\_chm\_Nv: 3d,3-{Hourly},{Instantaneous},{Model}-{Level},{Assimilation},{Carbon} {Monoxide} and {Ozone} {Mixing} {Ratio} {V5}.12.4},
    shorttitle = {{MERRA}-2 inst3\_3d\_chm\_Nv},
    url = {https://disc.gsfc.nasa.gov/datasets/M2I3NVCHM_5.12.4/summary},
    doi = {10.5067/HO9OVZWF3KW2},
    urldate = {2024-06-24},
    publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
    author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
    year = {2015},
}

@misc{data_gmao_merra-inst3_3d_gas_Nv,
    title = {{MERRA}-2 inst3\_3d\_gas\_Nv: 3d,3-{Hourly},{Instantaneous},{Model}-{Level},{Assimilation},{Aerosol} {Mixing} {Ratio} {Analysis} {Increments} {V5}.12.4},
    shorttitle = {{MERRA}-2 inst3\_3d\_gas\_Nv},
    url = {https://disc.gsfc.nasa.gov/datasets/M2I3NVGAS_5.12.4/summary},
    doi = {10.5067/96BUID8HGGX5},
    urldate = {2024-06-24},
    publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
    author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
    year = {2015},
}

@misc{data_gmao_merra-inst3_2d_gas_Nx,
    title = {{MERRA}-2 inst3\_2d\_gas\_Nx: 2d,3-{Hourly},{Instantaneous},{Single}-{Level},{Assimilation},{Aerosol} {Optical} {Depth} {Analysis} {V5}.12.4},
    shorttitle = {{MERRA}-2 inst3\_2d\_gas\_Nx},
    url = {https://disc.gsfc.nasa.gov/datasets/M2I3NXGAS_5.12.4/summary},
    doi = {10.5067/HNGA0EWW0R09},
    urldate = {2024-06-24},
    publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
    author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
    year = {2015},
}

@misc{data_gmao_merra-inst6_3d_ana_Np,
    title = {{MERRA}-2 inst6\_3d\_ana\_Np: 3d,6-{Hourly},{Instantaneous},{Pressure}-{Level},{Analysis},{Analyzed} {Meteorological} {Fields} {V5}.12.4},
    shorttitle = {{MERRA}-2 inst6\_3d\_ana\_Np},
    url = {https://disc.gsfc.nasa.gov/datasets/M2I6NPANA_5.12.4/summary},
    doi = {10.5067/A7S6XP56VZWS},
    urldate = {2024-06-24},
    publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
    author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
    year = {2015},
}

@misc{data_gmao_merra-inst6_3d_ana_Nv,
    title = {{MERRA}-2 inst6\_3d\_ana\_Nv: 3d,6-{Hourly},{Instantaneous},{Model}-{Level},{Analysis},{Analyzed} {Meteorological} {Fields} {V5}.12.4},
    shorttitle = {{MERRA}-2 inst6\_3d\_ana\_Nv},
    url = {https://disc.gsfc.nasa.gov/datasets/M2I6NVANA_5.12.4/summary},
    doi = {10.5067/IUUF4WB9FT4W},
    urldate = {2024-06-24},
    publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
    author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
    year = {2015},
}

@misc{data_gmao_merra-statD_2d_slv_Nx_d,
    title = {{MERRA}-2 {statD}\_2d\_slv\_Nx: 2d,{Daily},{Aggregated} {Statistics},{Single}-{Level},{Assimilation},{Single}-{Level} {Diagnostics} {V5}.12.4},
    shorttitle = {{MERRA}-2 {statD}\_2d\_slv\_Nx},
    url = {https://disc.gsfc.nasa.gov/datasets/M2SDNXSLV_5.12.4/summary},
    doi = {10.5067/9SC1VNTWGWV3},
    urldate = {2024-06-24},
    publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
    author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
    year = {2015},
}

@misc{data_gmao_merra-tavg1_2d_adg_Nx,
    title = {{MERRA}-2 tavg1\_2d\_adg\_Nx: 2d,3-{Hourly},{Time}-averaged,{Single}-{Level},{Assimilation},{Aerosol} {Diagnostics} (extended) {V5}.12.4},
    shorttitle = {{MERRA}-2 tavg1\_2d\_adg\_Nx},
    url = {https://disc.gsfc.nasa.gov/datasets/M2T1NXADG_5.12.4/summary},
    doi = {10.5067/HM00OHQBHKTP},
    urldate = {2024-06-24},
    publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
    author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
    year = {2015},
}

@misc{data_gmao_merra-tavg1_2d_aer_Nx,
    title = {{MERRA}-2 tavg1\_2d\_aer\_Nx: 2d,1-{Hourly},{Time}-averaged,{Single}-{Level},{Assimilation},{Aerosol} {Diagnostics} {V5}.12.4},
    shorttitle = {{MERRA}-2 tavg1\_2d\_aer\_Nx},
    url = {https://disc.gsfc.nasa.gov/datasets/M2T1NXAER_5.12.4/summary},
    doi = {10.5067/KLICLTZ8EM9D},
    urldate = {2024-06-24},
    publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
    author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
    year = {2015},
}

@misc{data_gmao_merra-tavg1_2d_chm_Nx,
    title = {{MERRA}-2 tavg1\_2d\_chm\_Nx: 2d,3-{Hourly},{Time}-{Averaged},{Single}-{Level},{Assimilation},{Carbon} {Monoxide} and {Ozone} {Diagnostics} {V5}.12.4},
    shorttitle = {{MERRA}-2 tavg1\_2d\_chm\_Nx},
    url = {https://disc.gsfc.nasa.gov/datasets/M2T1NXCHM_5.12.4/summary},
    doi = {10.5067/3RQ5YS674DGQ},
    urldate = {2024-06-24},
    publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
    author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
    year = {2015},
}

@misc{data_gmao_merra-tavg1_2d_csp_Nx,
    title = {{MERRA}-2 tavg1\_2d\_csp\_Nx: 2d,1-{Hourly},{Time}-averaged,{Single}-{Level},{Assimilation},{COSP} {Satellite} {Simulator} {V5}.12.4},
    shorttitle = {{MERRA}-2 tavg1\_2d\_csp\_Nx},
    url = {https://disc.gsfc.nasa.gov/datasets/M2T1NXCSP_5.12.4/summary},
    doi = {10.5067/H0VVAD8F6MX5},
    urldate = {2024-06-24},
    publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
    author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
    year = {2015},
}

@misc{data_gmao_merra-tavg1_2d_flx_Nx,
    title = {{MERRA}-2 tavg1\_2d\_flx\_Nx: 2d,1-{Hourly},{Time}-{Averaged},{Single}-{Level},{Assimilation},{Surface} {Flux} {Diagnostics} {V5}.12.4},
    shorttitle = {{MERRA}-2 tavg1\_2d\_flx\_Nx},
    url = {https://disc.gsfc.nasa.gov/datasets/M2T1NXFLX_5.12.4/summary},
    doi = {10.5067/7MCPBJ41Y0K6},
    urldate = {2024-06-24},
    publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
    author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
    year = {2015},
}

@misc{data_gmao_merra-tavg1_2d_int_Nx,
    title = {{MERRA}-2 tavg1\_2d\_int\_Nx: 2d,1-{Hourly},{Time}-averaged,{Single}-{Level},{Assimilation},{Vertically} {Integrated} {Diagnostics} {V5}.12.4},
    shorttitle = {{MERRA}-2 tavg1\_2d\_int\_Nx},
    url = {https://disc.gsfc.nasa.gov/datasets/M2T1NXINT_5.12.4/summary},
    doi = {10.5067/Q5GVUVUIVGO7},
    urldate = {2024-06-24},
    publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
    author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
    year = {2015},
}

@misc{pawson_merra-2_2020,
    title = {{MERRA}-2 tavg1\_2d\_lfo\_Nx: 2d,1-{Hourly},{Time}-{Averaged},{Single}-{Level},{Assimilation},{Land} {Surface} {Forcings} {V5}.12.4},
    shorttitle = {{MERRA}-2 tavg1\_2d\_lfo\_Nx},
    url = {https://disc.gsfc.nasa.gov/datasets/M2T1NXLFO_5.12.4/summary},
    doi = {10.5067/L0T5GEG1NYFA},
    urldate = {2024-06-24},
    publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
    author = {Pawson, Steven},
    year = {2020},
}

@misc{data_gmao_merra-tavg1_2d_lnd_Nx,
    title = {{MERRA}-2 tavg1\_2d\_lnd\_Nx: 2d,1-{Hourly},{Time}-{Averaged},{Single}-{Level},{Assimilation},{Land} {Surface} {Diagnostics} {V5}.12.4},
    shorttitle = {{MERRA}-2 tavg1\_2d\_lnd\_Nx},
    url = {https://disc.gsfc.nasa.gov/datasets/M2T1NXLND_5.12.4/summary},
    doi = {10.5067/RKPHT8KC1Y1T},
    urldate = {2024-06-24},
    publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
    author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
    year = {2015},
}

@misc{data_gmao_merra-tavg1_2d_ocn_Nx,
    title = {{MERRA}-2 tavg1\_2d\_ocn\_Nx: 2d,1-{Hourly},{Time}-{Averaged},{Single}-{Level},{Assimilation},{Ocean} {Surface} {Diagnostics} {V5}.12.4},
    shorttitle = {{MERRA}-2 tavg1\_2d\_ocn\_Nx},
    url = {https://disc.gsfc.nasa.gov/datasets/M2T1NXOCN_5.12.4/summary},
    doi = {10.5067/Y67YQ1L3ZZ4R},
    urldate = {2024-06-24},
    publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
    author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
    year = {2015},
}

@misc{data_gmao_merra-tavg1_2d_rad_Nx,
    title = {{MERRA}-2 tavg1\_2d\_rad\_Nx: 2d,1-{Hourly},{Time}-{Averaged},{Single}-{Level},{Assimilation},{Radiation} {Diagnostics} {V5}.12.4},
    shorttitle = {{MERRA}-2 tavg1\_2d\_rad\_Nx},
    url = {https://disc.gsfc.nasa.gov/datasets/M2T1NXRAD_5.12.4/summary},
    doi = {10.5067/Q9QMY5PBNV1T},
    urldate = {2024-06-24},
    publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
    author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
    year = {2015},
}

@misc{data_gmao_merra-tavg1_2d_slv_Nx,
    title = {{MERRA}-2 tavg1\_2d\_slv\_Nx: 2d,1-{Hourly},{Time}-{Averaged},{Single}-{Level},{Assimilation},{Single}-{Level} {Diagnostics} {V5}.12.4},
    shorttitle = {{MERRA}-2 tavg1\_2d\_slv\_Nx},
    url = {https://disc.gsfc.nasa.gov/datasets/M2T1NXSLV_5.12.4/summary},
    doi = {10.5067/VJAFPLI1CSIV},
    urldate = {2024-06-24},
    publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
    author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
    year = {2015},
}

@misc{data_gmao_merra-tavg3_3d_mst_Ne,
    title = {{MERRA}-2 tavg3\_3d\_mst\_Ne: 3d,3-{Hourly},{Time}-{Averaged},{Model}-{Level} {Edge},{Assimilation},{Moist} {Processes} {Diagnostics} {V5}.12.4},
    shorttitle = {{MERRA}-2 tavg3\_3d\_mst\_Ne},
    url = {https://disc.gsfc.nasa.gov/datasets/M2T3NEMST_5.12.4/summary},
    doi = {10.5067/JRUZ3SJ3ZJ72},
    urldate = {2024-06-24},
    publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
    author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
    year = {2015},
}

@misc{data_gmao_merra-tavg3_3d_trb_Ne,
    title = {{MERRA}-2 tavg3\_3d\_trb\_Ne: 3d,3-{Hourly},{Time}-{Averaged},{Model}-{Level} {Edge},{Assimilation},{Turbulence} {Diagnostics} {V5}.12.4},
    shorttitle = {{MERRA}-2 tavg3\_3d\_trb\_Ne},
    url = {https://disc.gsfc.nasa.gov/datasets/M2T3NETRB_5.12.4/summary},
    doi = {10.5067/4I7ZI35QRH8K},
    urldate = {2024-06-24},
    publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
    author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
    year = {2015},
}

@misc{data_gmao_merra-tavg3_3d_nav_Ne,
    title = {{MERRA}-2 tavg3\_3d\_nav\_Ne: 3d,3-{Hourly},{Time}-{Averaged}, {Vertical} {Coordinates} {V5}.12.4},
    shorttitle = {{MERRA}-2 tavg3\_3d\_nav\_Ne},
    url = {https://disc.gsfc.nasa.gov/datasets/M2T3NENAV_5.12.4/summary},
    doi = {10.5067/N5WAKNS1UYQN},
    urldate = {2024-06-24},
    publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
    author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
    year = {2015},
}

@misc{data_gmao_merra-tavg3_3d_cld_Np,
    title = {{MERRA}-2 tavg3\_3d\_cld\_Np: 3d,3-{Hourly},{Time}-{Averaged},{Pressure}-{Level},{Assimilation},{Cloud} {Diagnostics} {V5}.12.4},
    shorttitle = {{MERRA}-2 tavg3\_3d\_cld\_Np},
    url = {https://disc.gsfc.nasa.gov/datasets/M2T3NPCLD_5.12.4/summary},
    doi = {10.5067/TX10URJSKT53},
    urldate = {2024-06-24},
    publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
    author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
    year = {2015},
}

@misc{data_gmao_merra-tavg3_3d_mst_Np,
    title = {{MERRA}-2 tavg3\_3d\_mst\_Np: 3d,3-{Hourly},{Time}-{Averaged},{Pressure}-{Level},{Assimilation},{Moist} {Processes} {Diagnostics} {V5}.12.4},
    shorttitle = {{MERRA}-2 tavg3\_3d\_mst\_Np},
    url = {https://disc.gsfc.nasa.gov/datasets/M2T3NPMST_5.12.4/summary},
    doi = {10.5067/0TUFO90Q2PMS},
    urldate = {2024-06-24},
    publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
    author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
    year = {2015},
}

@misc{data_gmao_merra-tavg3_3d_rad_Np,
    title = {{MERRA}-2 tavg3\_3d\_rad\_Np: 3d,3-{Hourly},{Time}-{Averaged},{Pressure}-{Level},{Assimilation},{Radiation} {Diagnostics} {V5}.12.4},
    shorttitle = {{MERRA}-2 tavg3\_3d\_rad\_Np},
    url = {https://disc.gsfc.nasa.gov/datasets/M2T3NPRAD_5.12.4/summary},
    doi = {10.5067/3UGE8WQXZAOK},
    urldate = {2024-06-24},
    publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
    author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
    year = {2015},
}

@misc{data_gmao_merra-tavg3_3d_tdt_Np,
    title = {{MERRA}-2 tavg3\_3d\_tdt\_Np: 3d,3-{Hourly},{Time}-{Averaged},{Pressure}-{Level},{Assimilation},{Temperature} {Tendencies} {V5}.12.4},
    shorttitle = {{MERRA}-2 tavg3\_3d\_tdt\_Np},
    url = {https://disc.gsfc.nasa.gov/datasets/M2T3NPTDT_5.12.4/summary},
    doi = {10.5067/9NCR9DDDOPFI},
    urldate = {2024-06-24},
    publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
    author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
    year = {2015},
}

@misc{data_gmao_merra-tavg3_3d_trb_Np,
    title = {{MERRA}-2 tavg3\_3d\_trb\_Np: 3d,3-{Hourly},{Time}-{Averaged},{Pressure}-{Level},{Assimilation},{Turbulence} {Diagnostics} {V5}.12.4},
    shorttitle = {{MERRA}-2 tavg3\_3d\_trb\_Np},
    url = {https://disc.gsfc.nasa.gov/datasets/M2T3NPTRB_5.12.4/summary},
    doi = {10.5067/ZRRJPGWL8AVL},
    urldate = {2024-06-24},
    publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
    author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
    year = {2015},
}

@misc{data_gmao_merra-tavg3_3d_udt_Np,
    title = {{MERRA}-2 tavg3\_3d\_udt\_Np: 3d,3-{Hourly},{Time}-{Averaged},{Pressure}-{Level},{Assimilation},{Wind} {Tendencies} {V5}.12.4},
    shorttitle = {{MERRA}-2 tavg3\_3d\_udt\_Np},
    url = {https://disc.gsfc.nasa.gov/datasets/M2T3NPUDT_5.12.4/summary},
    doi = {10.5067/CWV0G3PPPWFW},
    urldate = {2024-06-24},
    publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
    author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
    year = {2015},
}

@misc{data_gmao_merra-tavg3_3d_odt_Np,
	title = {{MERRA}-2 tavg3\_3d\_odt\_Np: 3d,3-{Hourly},{Time}-{Averaged},{Pressure}-{Level},{Assimilation},{Ozone} {Tendencies} {V5}.12.4},
	shorttitle = {{MERRA}-2 tavg3\_3d\_odt\_Np},
	url = {https://disc.gsfc.nasa.gov/datasets/M2T3NPODT_5.12.4/summary},
	doi = {10.5067/S0LYTK57786Z},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavg3_3d_qdt_Np,
	title = {{MERRA}-2 tavg3\_3d\_qdt\_Np: 3d,3-{Hourly},{Time}-{Averaged},{Pressure}-{Level},{Assimilation},{Moist} {Tendencies} {V5}.12.4},
	shorttitle = {{MERRA}-2 tavg3\_3d\_qdt\_Np},
	url = {https://disc.gsfc.nasa.gov/datasets/M2T3NPQDT_5.12.4/summary},
	doi = {10.5067/A9KWADY78YHQ},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavg3_3d_asm_Nv,
	title = {{MERRA}-2 tavg3\_3d\_asm\_Nv: 3d,3-{Hourly},{Time}-{Averaged},{Model}-{Level},{Assimilation},{Assimilated} {Meteorological} {Fields} {V5}.12.4},
	shorttitle = {{MERRA}-2 tavg3\_3d\_asm\_Nv},
	url = {https://disc.gsfc.nasa.gov/datasets/M2T3NVASM_5.12.4/summary},
	doi = {10.5067/SUOQESM06LPK},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavg3_3d_cld_Nv,
	title = {{MERRA}-2 tavg3\_3d\_cld\_Nv: 3d,3-{Hourly},{Time}-{Averaged},{Model}-{Level},{Assimilation},{Cloud} {Diagnostics} {V5}.12.4},
	shorttitle = {{MERRA}-2 tavg3\_3d\_cld\_Nv},
	url = {https://disc.gsfc.nasa.gov/datasets/M2T3NVCLD_5.12.4/summary},
	doi = {10.5067/F9353J0FAHIH},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavg3_3d_mst_Nv,
	title = {{MERRA}-2 tavg3\_3d\_mst\_Nv: 3d,3-{Hourly},{Time}-{Averaged},{Model}-{Level},{Assimilation},{Moist} {Processes} {Diagnostics} {V5}.12.4},
	shorttitle = {{MERRA}-2 tavg3\_3d\_mst\_Nv},
	url = {https://disc.gsfc.nasa.gov/datasets/M2T3NVMST_5.12.4/summary},
	doi = {10.5067/ZXTJ28TQR1TR},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavg3_3d_rad_Nv,
	title = {{MERRA}-2 tavg3\_3d\_rad\_Nv: 3d,3-{Hourly},{Time}-{Averaged},{Model}-{Level},{Assimilation},{Radiation} {Diagnostics} {V5}.12.4},
	shorttitle = {{MERRA}-2 tavg3\_3d\_rad\_Nv},
	url = {https://disc.gsfc.nasa.gov/datasets/M2T3NVRAD_5.12.4/summary},
	doi = {10.5067/7GFQKO1T43RW},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavg3_2d_glc_Nx,
	title = {{MERRA}-2 tavg3\_2d\_glc\_Nx: 2d,3-{Hourly},{Time}-{Averaged},{Single}-{Level},{Assimilation},{Land} {Ice} {Surface} {Diagnostics} {V5}.12.4},
	shorttitle = {{MERRA}-2 tavg3\_2d\_glc\_Nx},
	url = {https://disc.gsfc.nasa.gov/datasets/M2T3NXGLC_5.12.4/summary},
	doi = {10.5067/9ETB4TT5J6US},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-instM_2d_asm_Nx,
	title = {{MERRA}-2 {instM}\_2d\_asm\_Nx: 2d,{Monthly} mean,{Single}-{Level},{Assimilation},{Single}-{Level} {Diagnostics} {V5}.12.4},
	shorttitle = {{MERRA}-2 {instM}\_2d\_asm\_Nx},
	url = {https://disc.gsfc.nasa.gov/datasets/M2IMNXASM_5.12.4/summary},
	doi = {10.5067/5ESKGQTZG7FO},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-instM_2d_int_Nx,
	title = {{MERRA}-2 {instM}\_2d\_int\_Nx: 2d,{Monthly} mean,{Instantaneous},{Single}-{Level},{Assimilation},{Vertically} {Integrated} {Diagnostics} {V5}.12.4},
	shorttitle = {{MERRA}-2 {instM}\_2d\_int\_Nx},
	url = {https://disc.gsfc.nasa.gov/datasets/M2IMNXINT_5.12.4/summary},
	doi = {10.5067/KVTU1A8BWFSJ},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-instM_2d_lfo_Nx,
	title = {{MERRA}-2 {instM}\_2d\_lfo\_Nx: 2d,{Monthly} mean,{Instantaneous},{Single}-{Level},{Assimilation},{Land} {Surface} {Forcings} {V5}.12.4},
	shorttitle = {{MERRA}-2 {instM}\_2d\_lfo\_Nx},
	url = {https://disc.gsfc.nasa.gov/datasets/M2IMNXLFO_5.12.4/summary},
	doi = {10.5067/11F99Y6TXN99},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-instM_2d_gas_Nx,
	title = {{MERRA}-2 {instM}\_2d\_gas\_Nx: 2d,{Monthly} mean,{Instantaneous},{Single}-{Level},{Assimilation},{Aerosol} {Optical} {Depth} {Analysis} {V5}.12.4},
	shorttitle = {{MERRA}-2 {instM}\_2d\_gas\_Nx},
	url = {https://disc.gsfc.nasa.gov/datasets/M2IMNXGAS_5.12.4/summary},
	doi = {10.5067/XOGNBQEPLUC5},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-instM_3d_asm_Np,
	title = {{MERRA}-2 {instM}\_3d\_asm\_Np: 3d,{Monthly} mean,{Instantaneous},{Pressure}-{Level},{Assimilation},{Assimilated} {Meteorological} {Fields} {V5}.12.4},
	shorttitle = {{MERRA}-2 {instM}\_3d\_asm\_Np},
	url = {https://disc.gsfc.nasa.gov/datasets/M2IMNPASM_5.12.4/summary},
	doi = {10.5067/2E096JV59PK7},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-instM_3d_ana_Np,
	title = {{MERRA}-2 {instM}\_3d\_ana\_Np: 3d,{Monthly} mean,{Instantaneous},{Pressure}-{Level},{Analysis},{Analyzed} {Meteorological} {Fields} {V5}.12.4},
	shorttitle = {{MERRA}-2 {instM}\_3d\_ana\_Np},
	url = {https://disc.gsfc.nasa.gov/datasets/M2IMNPANA_5.12.4/summary},
	doi = {10.5067/V92O8XZ30XBI},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgM_2d_adg_Nx,
	title = {{MERRA}-2 {tavgM}\_2d\_adg\_Nx: 2d,{Monthly} mean,{Time}-averaged,{Single}-{Level},{Assimilation},{Aerosol} {Diagnostics} (extended) {V5}.12.4},
	shorttitle = {{MERRA}-2 {tavgM}\_2d\_adg\_Nx},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TMNXADG_5.12.4/summary},
	doi = {10.5067/RZIK2TV7PP38},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgM_2d_aer_Nx,
	title = {{MERRA}-2 {tavgM}\_2d\_aer\_Nx: 2d,{Monthly} mean,{Time}-averaged,{Single}-{Level},{Assimilation},{Aerosol} {Diagnostics} {V5}.12.4},
	shorttitle = {{MERRA}-2 {tavgM}\_2d\_aer\_Nx},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TMNXAER_5.12.4/summary},
	doi = {10.5067/FH9A0MLJPC7N},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgM_2d_chm_Nx,
	title = {{MERRA}-2 {tavgM}\_2d\_chm\_Nx: 2d,{Monthly} mean,{Time}-{Averaged},{Single}-{Level},{Assimilation},{Carbon} {Monoxide} and {Ozone} {Diagnostics} {V5}.12.4},
	shorttitle = {{MERRA}-2 {tavgM}\_2d\_chm\_Nx},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TMNXCHM_5.12.4/summary},
	doi = {10.5067/WMT31RKEXK8I},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgM_2d_csp_Nx,
	title = {{MERRA}-2 {tavgM}\_2d\_csp\_Nx: 2d,{Monthly} mean,{Time}-averaged,{Single}-{Level},{Assimilation},{COSP} {Satellite} {Simulator} {V5}.12.4},
	shorttitle = {{MERRA}-2 {tavgM}\_2d\_csp\_Nx},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TMNXCSP_5.12.4/summary},
	doi = {10.5067/BZPOTGJOQKLU},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgM_2d_flx_Nx,
	title = {{MERRA}-2 {tavgM}\_2d\_flx\_Nx: 2d,{Monthly} mean,{Time}-{Averaged},{Single}-{Level},{Assimilation},{Surface} {Flux} {Diagnostics} {V5}.12.4},
	shorttitle = {{MERRA}-2 {tavgM}\_2d\_flx\_Nx},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TMNXFLX_5.12.4/summary},
	doi = {10.5067/0JRLVL8YV2Y4},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgM_2d_int_Nx,
	title = {{MERRA}-2 {tavgM}\_2d\_int\_Nx: 2d,{Monthly} mean,{Time}-{Averaged},{Single}-{Level},{Assimilation},{Vertically} {Integrated} {Diagnostics} {V5}.12.4},
	shorttitle = {{MERRA}-2 {tavgM}\_2d\_int\_Nx},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TMNXINT_5.12.4/summary},
	doi = {10.5067/FQPTQ4OJ22TL},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgM_2d_lfo_Nx,
	title = {{MERRA}-2 {tavgM}\_2d\_lfo\_Nx: 2d,{Monthly} mean,{Time}-{Averaged},{Single}-{Level},{Assimilation},{Land} {Surface} {Forcings} {V5}.12.4},
	shorttitle = {{MERRA}-2 {tavgM}\_2d\_lfo\_Nx},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TMNXLFO_5.12.4/summary},
	doi = {10.5067/5V7K6LJD44SY},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgM_2d_lnd_Nx,
	title = {{MERRA}-2 {tavgM}\_2d\_lnd\_Nx: 2d,{Monthly} mean,{Time}-{Averaged},{Single}-{Level},{Assimilation},{Land} {Surface} {Diagnostics} {V5}.12.4},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TMNXLND_5.12.4/summary},
	doi = {10.5067/8S35XF81C28F},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgM_2d_ocn_Nx,
	title = {{MERRA}-2 {tavgM}\_2d\_ocn\_Nx: 2d,{Monthly} mean,{Time}-{Averaged},{Single}-{Level},{Assimilation},{Ocean} {Surface} {Diagnostics} {V5}.12.4},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TMNXOCN_5.12.4/summary},
	doi = {10.5067/4IASLIDL8EEC},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgM_2d_rad_Nx,
	title = {{MERRA}-2 {tavgM}\_2d\_rad\_Nx: 2d,{Monthly} mean,{Time}-{Averaged},{Single}-{Level},{Assimilation},{Radiation} {Diagnostics} {V5}.12.4},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TMNXRAD_5.12.4/summary},
	doi = {10.5067/OU3HJDS973O0},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgM_2d_slv_Nx,
	title = {{MERRA}-2 {tavgM}\_2d\_slv\_Nx: 2d,{Monthly} mean,{Time}-{Averaged},{Single}-{Level},{Assimilation},{Single}-{Level} {Diagnostics} {V5}.12.4},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TMNXSLV_5.12.4/summary},
	doi = {10.5067/AP1B0BA5PD2K},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgM_2d_glc_Nx,
	title = {{MERRA}-2 {tavgM}\_2d\_glc\_Nx: 2d,{Monthly} mean,{Time}-{Averaged},{Single}-{Level},{Assimilation},{Land} {Ice} {Surface} {Diagnostics} {V5}.12.4},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TMNXGLC_5.12.4/summary},
	doi = {10.5067/5W8Q3I9WUFGX},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgM_3d_cld_Np,
	title = {{MERRA}-2 {tavgM}\_3d\_cld\_Np: 3d,{Monthly} mean,{Time}-{Averaged},{Pressure}-{Level},{Assimilation},{Cloud} {Diagnostics} {V5}.12.4},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TMNPCLD_5.12.4/summary},
	doi = {10.5067/J9R0LXGH48JR},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgM_3d_mst_Np,
	title = {{MERRA}-2 {tavgM}\_3d\_mst\_Np: 3d,{Monthly} mean,{Time}-{Averaged},{Pressure}-{Level},{Assimilation},{Moist} {Processes} {Diagnostics} {V5}.12.4},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TMNPMST_5.12.4/summary},
	doi = {10.5067/ZRZGD0DCK1CG},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgM_3d_rad_Np,
	title = {{MERRA}-2 {tavgM}\_3d\_rad\_Np: 3d,{Monthly} mean,{Time}-{Averaged},{Pressure}-{Level},{Assimilation},{Radiation} {Diagnostics} {V5}.12.4},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TMNPRAD_5.12.4/summary},
	doi = {10.5067/H3YGROBVBGFJ},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgM_3d_tdt_Np,
	title = {{MERRA}-2 {tavgM}\_3d\_tdt\_Np: 3d,{Monthly} mean,{Time}-{Averaged},{Pressure}-{Level},{Assimilation},{Temperature} {Tendencies} {V5}.12.4},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TMNPTDT_5.12.4/summary},
	doi = {10.5067/VILT59HI2MOY},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgM_3d_trb_Np,
	title = {{MERRA}-2 {tavgM}\_3d\_trb\_Np: 3d,{Monthly} mean,{Time}-{Averaged},{Pressure}-{Level},{Assimilation},{Turbulence} {Diagnostics} {V5}.12.4},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TMNPTRB_5.12.4/summary},
	doi = {10.5067/2YOIQB5C3ACN},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgM_3d_udt_Np,
	title = {{MERRA}-2 {tavgM}\_3d\_udt\_Np: 3d,{Monthly} mean,{Time}-{Averaged},{Pressure}-{Level},{Assimilation},{Wind} {Tendencies} {V5}.12.4},
	shorttitle = {{MERRA}-2 {tavgM}\_3d\_udt\_Np},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TMNPUDT_5.12.4/summary},
	doi = {10.5067/YSR6IA5057XX},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgM_3d_odt_Np,
	title = {{MERRA}-2 {tavgM}\_3d\_odt\_Np: 3d,{Monthly} mean,{Time}-{Averaged},{Pressure}-{Level},{Assimilation},{Ozone} {Tendencies} {V5}.12.4},
	shorttitle = {{MERRA}-2 {tavgM}\_3d\_odt\_Np},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TMNPODT_5.12.4/summary},
	doi = {10.5067/Z2KCWAV4GPD2},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgM_3d_qdt_Np,
	title = {{MERRA}-2 {tavgM}\_3d\_qdt\_Np: 3d,{Monthly} mean,{Time}-{Averaged},{Pressure}-{Level},{Assimilation},{Moist} {Tendencies} {V5}.12.4},
	shorttitle = {{MERRA}-2 {tavgM}\_3d\_qdt\_Np},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TMNPQDT_5.12.4/summary},
	doi = {10.5067/2ZTU87V69ATP},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-statD_2d_slv_Nx_m,
	title = {{MERRA}-2 {statD}\_2d\_slv\_Nx: 2d,{Monthly},{Aggregated} {Statistics},{Single}-{Level},{Assimilation},{Single}-{Level} {Diagnostics} {V5}.12.4},
	shorttitle = {{MERRA}-2 {statD}\_2d\_slv\_Nx},
	url = {https://disc.gsfc.nasa.gov/datasets/M2SMNXSLV_5.12.4/summary},
	doi = {10.5067/KVIMOMCUO83U},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-const_2d_asm_Nx,
	title = {{MERRA}-2 const\_2d\_asm\_Nx: 2d, constants},
	shorttitle = {{MERRA}-2 const\_2d\_asm\_Nx},
	url = {https://disc.gsfc.nasa.gov/datasets/M2C0NXASM_5.12.4/summary},
	doi = {10.5067/ME5QX6Q5IGGU},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-instU_2d_asm_Nx,
	title = {{MERRA}-2 {instU}\_2d\_asm\_Nx: 2d,{Diurnal},{Instantaneous},{Single}-{Level},{Assimilation},{Single}-{Level} {Diagnostics} {V5}.12.4},
	shorttitle = {{MERRA}-2 {instU}\_2d\_asm\_Nx},
	url = {https://disc.gsfc.nasa.gov/datasets/M2IUNXASM_5.12.4/summary},
	doi = {10.5067/BOJSTZAO2L8R},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-instU_2d_int_Nx,
	title = {{MERRA}-2 {instU}\_2d\_int\_Nx: 2d,{Diurnal},{Instantaneous},{Single}-{Level},{Assimilation},{Vertically} {Integrated} {Diagnostics} {V5}.12.4},
	shorttitle = {{MERRA}-2 {instU}\_2d\_int\_Nx},
	url = {https://disc.gsfc.nasa.gov/datasets/M2IUNXINT_5.12.4/summary},
	doi = {10.5067/DGAB3HFEYMLY},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-instU_2d_lfo_Nx,
	title = {{MERRA}-2 {instU}\_2d\_lfo\_Nx: 2d,{Diurnal},{Instantaneous},{Single}-{Level},{Assimilation},{Land} {Surface} {Forcings} {V5}.12.4},
	shorttitle = {{MERRA}-2 {instU}\_2d\_lfo\_Nx},
	url = {https://disc.gsfc.nasa.gov/datasets/M2IUNXLFO_5.12.4/summary},
	doi = {10.5067/FC3BVJ88Y8A2},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-instU_2d_gas_Nx,
	title = {{MERRA}-2 {instU}\_2d\_gas\_Nx: 2d,{Diurnal},{Instantaneous},{Single}-{Level},{Assimilation},{Aerosol} {Optical} {Depth} {Analysis} {V5}.12.4},
	shorttitle = {{MERRA}-2 {instU}\_2d\_gas\_Nx},
	url = {https://disc.gsfc.nasa.gov/datasets/M2IUNXGAS_5.12.4/summary},
	doi = {10.5067/TVJ4MHBED39L},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-instU_3d_asm_Np,
	title = {{MERRA}-2 {instU}\_3d\_asm\_Np: 3d,{Diurnal},{Instantaneous},{Pressure}-{Level},{Assimilation},{Assimilated} {Meteorological} {Fields} {V5}.12.4},
	shorttitle = {{MERRA}-2 {instU}\_3d\_asm\_Np},
	url = {https://disc.gsfc.nasa.gov/datasets/M2IUNPASM_5.12.4/summary},
	doi = {10.5067/6EGRBNEBMIYS},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-instU_3d_ana_Np,
	title = {{MERRA}-2 {instU}\_3d\_ana\_Np: 3d,{Diurnal},{Instantaneous},{Pressure}-{Level},{Analysis},{Analyzed} {Meteorological} {Fields} {V5}.12.4},
	shorttitle = {{MERRA}-2 {instU}\_3d\_ana\_Np},
	url = {https://disc.gsfc.nasa.gov/datasets/M2IUNPANA_5.12.4/summary},
	doi = {10.5067/TRD91YO9S6E7},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgU_2d_adg_Nx,
	title = {{MERRA}-2 {tavgU}\_2d\_adg\_Nx: 2d,{Diurnal},{Time}-averaged,{Single}-{Level},{Assimilation},{Aerosol} {Diagnostics} (extended) {V5}.12.4},
	shorttitle = {{MERRA}-2 {tavgU}\_2d\_adg\_Nx},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TUNXADG_5.12.4/summary},
	doi = {10.5067/YZJJXZTFCX6B},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgU_2d_aer_Nx,
	title = {{MERRA}-2 {tavgU}\_2d\_aer\_Nx: 2d,{Diurnal},{Time}-averaged,{Single}-{Level},{Assimilation},{Aerosol} {Diagnostics} {V5}.12.4},
	shorttitle = {{MERRA}-2 {tavgU}\_2d\_aer\_Nx},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TUNXAER_5.12.4/summary},
	doi = {10.5067/KPUMVXFEQLA1},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgU_2d_chm_Nx,
	title = {{MERRA}-2 {tavgU}\_2d\_chm\_Nx: 2d,{Diurnal},{Time}-{Averaged},{Single}-{Level},{Assimilation},{Carbon} {Monoxide} and {Ozone} {Diagnostics} {V5}.12.4},
	shorttitle = {{MERRA}-2 {tavgU}\_2d\_chm\_Nx},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TUNXCHM_5.12.4/summary},
	doi = {10.5067/5KFZ6GXRHZKN},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgU_2d_csp_Nx,
	title = {{MERRA}-2 {tavgU}\_2d\_csp\_Nx: 2d,{Diurnal},{Time}-averaged,{Single}-{Level},{Assimilation},{COSP} {Satellite} {Simulator} {V5}.12.4},
	shorttitle = {{MERRA}-2 {tavgU}\_2d\_csp\_Nx},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TUNXCSP_5.12.4/summary},
	doi = {10.5067/9PH5QU4CL9E8},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgU_2d_flx_Nx,
	title = {{MERRA}-2 {tavgU}\_2d\_flx\_Nx: 2d,{Diurnal},{Time}-{Averaged},{Single}-{Level},{Assimilation},{Surface} {Flux} {Diagnostics} {V5}.12.4},
	shorttitle = {{MERRA}-2 {tavgU}\_2d\_flx\_Nx},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TUNXFLX_5.12.4/summary},
	doi = {10.5067/LUHPNWAKYIO3},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgU_2d_int_Nx,
	title = {{MERRA}-2 {tavgU}\_2d\_int\_Nx: 2d,{Diurnal},{Time}-{Averaged},{Single}-{Level},{Assimilation},{Vertically} {Integrated} {Diagnostics} {V5}.12.4},
	shorttitle = {{MERRA}-2 {tavgU}\_2d\_int\_Nx},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TUNXINT_5.12.4/summary},
	doi = {10.5067/R2MPVU4EOSWT},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgU_2d_lfo_Nx,
	title = {{MERRA}-2 {tavgU}\_2d\_lfo\_Nx: 2d,{Diurnal},{Time}-{Averaged},{Single}-{Level},{Assimilation},{Land} {Surface} {Forcings} {V5}.12.4},
	shorttitle = {{MERRA}-2 {tavgU}\_2d\_lfo\_Nx},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TUNXLFO_5.12.4/summary},
	doi = {10.5067/BTSNKAJND3ME},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgU_2d_lnd_Nx,
	title = {{MERRA}-2 {tavgU}\_2d\_lnd\_Nx: 2d,{Diurnal},{Time}-{Averaged},{Single}-{Level},{Assimilation},{Land} {Surface} {Diagnostics} {V5}.12.4},
	shorttitle = {{MERRA}-2 {tavgU}\_2d\_lnd\_Nx},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TUNXLND_5.12.4/summary},
	doi = {10.5067/W0J15047CF6N},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgU_2d_ocn_Nx,
	title = {{MERRA}-2 {tavgU}\_2d\_ocn\_Nx: 2d,{Diurnal},{Time}-{Averaged},{Single}-{Level},{Assimilation},{Ocean} {Surface} {Diagnostics} {V5}.12.4},
	shorttitle = {{MERRA}-2 {tavgU}\_2d\_ocn\_Nx},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TUNXOCN_5.12.4/summary},
	doi = {10.5067/KLNAVGAX7J66},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgU_2d_rad_Nx,
	title = {{MERRA}-2 {tavgU}\_2d\_rad\_Nx: 2d,{Diurnal},{Time}-{Averaged},{Single}-{Level},{Assimilation},{Radiation} {Diagnostics} {V5}.12.4},
	shorttitle = {{MERRA}-2 {tavgU}\_2d\_rad\_Nx},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TUNXRAD_5.12.4/summary},
	doi = {10.5067/4SDCJYK8P9QU},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgU_2d_slv_Nx,
	title = {{MERRA}-2 {tavgU}\_2d\_slv\_Nx: 2d,{Diurnal},{Time}-{Averaged},{Single}-{Level},{Assimilation},{Single}-{Level} {Diagnostics} {V5}.12.4},
	shorttitle = {{MERRA}-2 {tavgU}\_2d\_slv\_Nx},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TUNXSLV_5.12.4/summary},
	doi = {10.5067/AFOK0TPEVQEK},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgU_2d_glc_Nx,
	title = {{MERRA}-2 {tavgU}\_2d\_glc\_Nx: 2d,{Diurnal},{Time}-{Averaged},{Single}-{Level},{Assimilation},{Land} {Ice} {Surface} {Diagnostics} {V5}.12.4},
	shorttitle = {{MERRA}-2 {tavgU}\_2d\_glc\_Nx},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TUNXGLC_5.12.4/summary},
	doi = {10.5067/7VUPQC736SWX},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgU_3d_cld_Np,
	title = {{MERRA}-2 {tavgU}\_3d\_cld\_Np: 3d,{Diurnal},{Time}-{Averaged},{Pressure}-{Level},{Assimilation},{Cloud} {Diagnostics} {V5}.12.4},
	shorttitle = {{MERRA}-2 {tavgU}\_3d\_cld\_Np},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TUNPCLD_5.12.4/summary},
	doi = {10.5067/EPW7T5UO0C0N},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgU_3d_mst_Np,
	title = {{MERRA}-2 {tavgU}\_3d\_mst\_Np: 3d,{Diurnal},{Time}-{Averaged},{Pressure}-{Level},{Assimilation},{Moist} {Processes} {Diagnostics} {V5}.12.4},
	shorttitle = {{MERRA}-2 {tavgU}\_3d\_mst\_Np},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TUNPMST_5.12.4/summary},
	doi = {10.5067/ZRSN0JU27DK2},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgU_3d_rad_Np,
	title = {{MERRA}-2 {tavgU}\_3d\_rad\_Np: 3d,{Diurnal},{Time}-{Averaged},{Pressure}-{Level},{Assimilation},{Radiation} {Diagnostics} {V5}.12.4},
	shorttitle = {{MERRA}-2 {tavgU}\_3d\_rad\_Np},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TUNPRAD_5.12.4/summary},
	doi = {10.5067/H140JMDOWB0Y},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgU_3d_tdt_Np,
	title = {{MERRA}-2 {tavgU}\_3d\_tdt\_Np: 3d,{Diurnal},{Time}-{Averaged},{Pressure}-{Level},{Assimilation},{Temperature} {Tendencies} {V5}.12.4},
	shorttitle = {{MERRA}-2 {tavgU}\_3d\_tdt\_Np},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TUNPTDT_5.12.4/summary},
	doi = {10.5067/QPO9E5TPZ8OF},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgU_3d_trb_Np,
	title = {{MERRA}-2 {tavgU}\_3d\_trb\_Np: 3d,{Diurnal},{Time}-{Averaged},{Pressure}-{Level},{Assimilation},{Turbulence} {Diagnostics} {V5}.12.4},
	shorttitle = {{MERRA}-2 {tavgU}\_3d\_trb\_Np},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TUNPTRB_5.12.4/summary},
	doi = {10.5067/2A99C60CG7WC},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgU_3d_udt_Np,
	title = {{MERRA}-2 {tavgU}\_3d\_udt\_Np: 3d,{Diurnal},{Time}-{Averaged},{Pressure}-{Level},{Assimilation},{Wind} {Tendencies} {V5}.12.4},
	shorttitle = {{MERRA}-2 {tavgU}\_3d\_udt\_Np},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TUNPUDT_5.12.4/summary},
	doi = {10.5067/DO715T7T5PG8},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgU_3d_odt_Np,
	title = {{MERRA}-2 {tavgU}\_3d\_odt\_Np: 3d,{Diurnal},{Time}-{Averaged},{Pressure}-{Level},{Assimilation},{Ozone} {Tendencies} {V5}.12.4},
	shorttitle = {{MERRA}-2 {tavgU}\_3d\_odt\_Np},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TUNPODT_5.12.4/summary},
	doi = {10.5067/M8OJ09GZP23E},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{data_gmao_merra-tavgU_3d_qdt_Np,
	title = {{MERRA}-2 {tavgU}\_3d\_qdt\_Np: 3d,{Diurnal},{Time}-{Averaged},{Pressure}-{Level},{Assimilation},{Moist} {Tendencies} {V5}.12.4},
	shorttitle = {{MERRA}-2 {tavgU}\_3d\_qdt\_Np},
	url = {https://disc.gsfc.nasa.gov/datasets/M2TUNPQDT_5.12.4/summary},
	doi = {10.5067/S8HJXIR0BFTS},
	urldate = {2024-06-24},
	publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
	author = {{Global Modeling And Assimilation Office} and Pawson, Steven},
	year = {2015},
}

@misc{dewitz_national_2024,
	title = {National {Land} {Cover} {Database} ({NLCD}) 2019 {Products} (ver. 3.0, {February} 2024)},
	doi = {10.5066/P9KZCM54},
	abstract = {The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91\% overall landcover accuracy, with the developed classes also showing a 91\% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.},
	publisher = {U.S. Geological Survey},
	author = {Dewitz, J.},
	month = {March},
	year = {2024},
	keywords = {land use change},
}

@misc{dewitz_national_2023,
	title = {National {Land} {Cover} {Database} ({NLCD}) 2021 {Products}},
	doi = {10.5066/P9JZ7AO3},
	abstract = {The U.S. Geological Survey (USGS), in partnership with several federal agencies, has now developed and released seven National Land Cover Database (NLCD) products: NLCD 1992, 2001, 2006, 2011, 2016, 2019, and 2021. Beginning with the 2016 release, land cover products were created for two-to-three-year intervals between 2001 and the most recent year. These products provide spatially explicit and reliable information on the Nation?s land cover and land cover change. NLCD continues to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database. The NLCD 2021 release is update based, so the Land Cover and Impervious Surface products�released in�2019 are unchanged and used directly with NLCD 2021 for change analysis though the NLCD timespan.�Science products and the change index are updated and will need to be reacquired to contain the additional 2021 change. These new products use a streamlined compositing process for assembling and preprocessing Landsat imagery and geospatial ancillary datasets; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a theme-based post-classification protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and a scripted operational system. The overall accuracy of the 2019 Level I land cover was 91\%. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2021 operational mapping (see https://doi.org/10.1080/15481603.2023.2181143 for the latest accuracy assessment publication). Questions about the NLCD 2021 land cover product can be directed to the NLCD 2021 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details. Please see�National Land Cover Database (NLCD) 2019 Products (ver. 2.0, June 2021) - ScienceBase-Catalog�for the 2019 release of NLCD which is used with the 2021 release for comparisons through the years.� Also refer to the larger NLCD Community page for all things NLCD related�National Land Cover Database (NLCD) - ScienceBase-Catalog.�},
	publisher = {U.S. Geological Survey},
	author = {Dewitz, Jon},
	year = {2023},
	keywords = {Land Use Change},
}

@article{mesinger_north_2006,
	title = {North {American} {Regional} {Reanalysis}},
	volume = {87},
	issn = {0003-0007, 1520-0477},
	url = {https://doi.org/10.1175/BAMS-87-3-343},
	doi = {10.1175/BAMS-87-3-343},
	language = {en},
	number = {3},
	journal = {Bulletin of the American Meteorological Society},
	author = {Mesinger, Fedor and DiMego, Geoff and Kalnay, Eugenia and Mitchell, Kenneth and Shafran, Perry C. and Ebisuzaki, Wesley and Jović, Dušan and Woollen, Jack and Rogers, Eric and Berbery, Ernesto H. and Ek, Michael B. and Fan, Yun and Grumbine, Robert and Higgins, Wayne and Li, Hong and Lin, Ying and Manikin, Geoff and Parrish, David and Shi, Wei},
	month = {March},
	year = {2006},
	pages = {343--360}
}

@techreport{danielson_global_2011,
	type = {Open-{File} {Report}},
	title = {Global multi-resolution terrain elevation data 2010 ({GMTED2010})},
	url = {https://doi.org/10.3133/ofr20111073},
	language = {en},
	number = {2011-1073},
	institution = {U.S. Geological Survey},
	author = {Danielson, Jeffrey J. and Gesch, Dean B.},
	year = {2011},
	note = {Series: Open-File Report},
	pages = {iv, 23 p.; Appendix},
}


@article{keller_description_2021,
	title = {Description of the {NASA} {GEOS} {Composition} {Forecast} {Modeling} {System} {GEOS}‐{CF} v1.0},
	volume = {13},
	issn = {1942-2466, 1942-2466},
	url = {https://doi.org/10.1029/2020MS002413},
	doi = {10.1029/2020MS002413},
	abstract = {Abstract
            
              The Goddard Earth Observing System composition forecast (GEOS‐CF) system is a high‐resolution (0.25°) global constituent prediction system from NASA's Global Modeling and Assimilation Office (GMAO). GEOS‐CF offers a new tool for atmospheric chemistry research, with the goal to supplement NASA's broad range of space‐based and in‐situ observations. GEOS‐CF expands on the GEOS weather and aerosol modeling system by introducing the GEOS‐Chem chemistry module to provide hindcasts and 5‐days forecasts of atmospheric constituents including ozone (O
              3
              ), carbon monoxide (CO), nitrogen dioxide (NO
              2
              ), sulfur dioxide (SO
              2
              ), and fine particulate matter (PM
              2.5
              ). The chemistry module integrated in GEOS‐CF is identical to the offline GEOS‐Chem model and readily benefits from the innovations provided by the GEOS‐Chem community. Evaluation of GEOS‐CF against satellite, ozonesonde and surface observations for years 2018–2019 show realistic simulated concentrations of O
              3
              , NO
              2
              , and CO, with normalized mean biases of −0.1 to 0.3, normalized root mean square errors between 0.1–0.4, and correlations between 0.3–0.8. Comparisons against surface observations highlight the successful representation of air pollutants in many regions of the world and during all seasons, yet also highlight current limitations, such as a global high bias in SO
              2
              and an overprediction of summertime O
              3
              over the Southeast United States. GEOS‐CF v1.0 generally overestimates aerosols by 20\%–50\% due to known issues in GEOS‐Chem v12.0.1 that have been addressed in later versions. The 5‐days forecasts have skill scores comparable to the 1‐day hindcast. Model skills can be improved significantly by applying a bias‐correction to the surface model output using a machine‐learning approach.
            
          , 
            Plain Language Summary
            
              Accurate forecasting of the compostion of the atmosphere is important for a variety of applications, including air pollution mitigation, support of satellite and other remote‐sensing observations, and research applications. Producing such forecasts is computationally expensive due to the complexity of atmospheric chemistry, which interacts with weather on all scales. Here we present the NASA Goddard Earth Observing System composition forecast (GEOS‐CF) system, which produces global forecasts of major atmospheric constituents such as ozone (O
              3
              ), nitrogen dioxide (NO
              2
              ), and fine particulate matter (PM
              2.5
              ). On a daily basis, the model tracks the atmospheric concentrations of more than 250 chemical species in more than 55 million model grid cells, computing the interactions between those species using the state‐of‐the‐science GEOS‐Chem chemistry model. We present an in‐depth evaluation of the GEOS‐CF model through comparison against independent observations. We show how the model captures many observed features of atmospheric composition, such as spatio‐temporal variations in air pollution due to changes in pollutant emissions, weather, and chemistry. We also highlight some of the model deficiencies, for example, with respect to the simulation of aerosol particles. Finally, we demonstrate how surface observations and model data can be combined using machine learning to provide improved local air quality forecasts.
            
          , 
            Key Points
            
              
                
                  
                    GEOS‐CF is a new modeling system that produces global forecasts of atmospheric composition at 25 km
                    2
                    horizontal resolution
                  
                
                
                  GEOS‐CF model output is freely available and offers a new tool for academic researchers, air quality managers, and the public},
	language = {en},
	number = {4},
	journal = {Journal of Advances in Modeling Earth Systems},
	author = {Keller, Christoph A. and Knowland, K. Emma and Duncan, Bryan N. and Liu, Junhua and Anderson, Daniel C. and Das, Sampa and Lucchesi, Robert A. and Lundgren, Elizabeth W. and Nicely, Julie M. and Nielsen, Eric and Ott, Lesley E. and Saunders, Emily and Strode, Sarah A. and Wales, Pamela A. and Jacob, Daniel J. and Pawson, Steven},
	month = {April},
	year = {2021},
	pages = {e2020MS002413},
}

# Figure 1 icons
@misc{Freepik_Cloud,
  author       = {Freepik},
  title        = {Cloud icons},
  howpublished = {\url{https://www.flaticon.com/free-icons/cloud}},
  note         = {Accessed: 2024-07-10}
}

@misc{Freepik_Heat,
  author       = {Freepik},
  title        = {Heat icons},
  howpublished = {\url{https://www.flaticon.com/free-icons/heat}},
  note         = {Accessed: 2024-07-10}
}

@misc{Freepik_Snowstorm,
  author       = {Freepik},
  title        = {Snowstorm icons},
  howpublished = {\url{https://www.flaticon.com/free-icons/snowstorm}},
  note         = {Accessed: 2024-07-10}
}

@misc{Freepik_Forest,
  author       = {Freepik},
  title        = {Forest icons},
  howpublished = {\url{https://www.flaticon.com/free-icons/forest}},
  note         = {Accessed: 2024-07-10}
}

@misc{Freepik_CO2,
  author       = {Freepik},
  title        = {Co2 icons},
  howpublished = {\url{https://www.flaticon.com/free-icons/co2}},
  note         = {Accessed: 2024-07-10}
}

@misc{Freepik_Download,
  author       = {Freepik},
  title        = {Download icons},
  howpublished = {\url{https://www.flaticon.com/free-icons/download}},
  note         = {Accessed: 2024-07-10}
}

@misc{Freepik_Database,
  author       = {Freepik},
  title        = {Database icons},
  howpublished = {\url{https://www.flaticon.com/free-icons/database}},
  note         = {Accessed: 2024-07-10}
}

@misc{Freepik_GIS,
  author       = {Freepik},
  title        = {Gis icons},
  howpublished = {\url{https://www.flaticon.com/free-icons/gis}},
  note         = {Accessed: 2024-07-10}
}

@misc{Freepik_Excel,
  author       = {Freepik},
  title        = {Excel icons},
  howpublished = {\url{https://www.flaticon.com/free-icons/excel}},
  note         = {Accessed: 2024-07-10}
}

@article{messier2012integrating,
  author    = {Kyle P. Messier and Yasuyuki Akita and Marc L. Serre},
  title     = {Integrating Address Geocoding, Land Use Regression, and Spatiotemporal Geostatistical Estimation for Groundwater Tetrachloroethylene},
  journal   = {Environmental Science \& Technology},
  year      = {2012},
  volume    = {46},
  number    = {5},
  pages     = {2772--2780},
  doi       = {10.1021/es203152a},
  url       = {https://doi.org/10.1021/es203152a},
  publisher = {American Chemical Society},
  issn      = {0013-936X}
}