Comparison of Weighted/Unweighted and Interpolated Grid Data at Regional and Global Scales

Uniform grid data are widely used in climate science and related interdisciplinary fields. Such data usually describe the hydrometeorological states averaged over uniform latitude–longitude grids. While these data have larger grid areas in the tropics than other high-latitude regions, less attention...
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Autor*in:	Rui Wei [verfasserIn] Yuxin Li [verfasserIn] Jun Yin [verfasserIn] Xieyao Ma [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	gridded datasets weighted sample weighted histogram weighted sample distribution climate data

Übergeordnetes Werk:	In: Atmosphere - MDPI AG, 2011, 13(2022), 12, p 2071
Übergeordnetes Werk:	volume:13 ; year:2022 ; number:12, p 2071

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/atmos13122071

Katalog-ID:	DOAJ08323490X

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520			\|a Uniform grid data are widely used in climate science and related interdisciplinary fields. Such data usually describe the hydrometeorological states averaged over uniform latitude–longitude grids. While these data have larger grid areas in the tropics than other high-latitude regions, less attention has been paid to the areal weights of these grid data. Here, we revisited two methods available for processing these uniform grid data, including weighted sample statistics and grid interpolation. The former directly considers the grid area differences using geodetic weights; the latter converts the uniform grids to equal-area grids for conventional data analysis. When applied to global temperature and precipitation data, we found larger differences between weighted and unweighted samples and smaller differences between weighted and interpolated samples, highlighting the importance of areal weights in grid data analysis. Given the different results from various methods, we call for explicit clarification of the grid data processing methods to improve reproducibility in climate research.
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spelling	10.3390/atmos13122071 doi (DE-627)DOAJ08323490X (DE-599)DOAJc456598fe97b4bb8af359002f5512505 DE-627 ger DE-627 rakwb eng QC851-999 Rui Wei verfasserin aut Comparison of Weighted/Unweighted and Interpolated Grid Data at Regional and Global Scales 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Uniform grid data are widely used in climate science and related interdisciplinary fields. Such data usually describe the hydrometeorological states averaged over uniform latitude–longitude grids. While these data have larger grid areas in the tropics than other high-latitude regions, less attention has been paid to the areal weights of these grid data. Here, we revisited two methods available for processing these uniform grid data, including weighted sample statistics and grid interpolation. The former directly considers the grid area differences using geodetic weights; the latter converts the uniform grids to equal-area grids for conventional data analysis. When applied to global temperature and precipitation data, we found larger differences between weighted and unweighted samples and smaller differences between weighted and interpolated samples, highlighting the importance of areal weights in grid data analysis. Given the different results from various methods, we call for explicit clarification of the grid data processing methods to improve reproducibility in climate research. gridded datasets weighted sample weighted histogram weighted sample distribution climate data Meteorology. Climatology Yuxin Li verfasserin aut Jun Yin verfasserin aut Xieyao Ma verfasserin aut In Atmosphere MDPI AG, 2011 13(2022), 12, p 2071 (DE-627)657584010 (DE-600)2605928-9 20734433 nnns volume:13 year:2022 number:12, p 2071 https://doi.org/10.3390/atmos13122071 kostenfrei https://doaj.org/article/c456598fe97b4bb8af359002f5512505 kostenfrei https://www.mdpi.com/2073-4433/13/12/2071 kostenfrei https://doaj.org/toc/2073-4433 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2022 12, p 2071
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allfieldsGer	10.3390/atmos13122071 doi (DE-627)DOAJ08323490X (DE-599)DOAJc456598fe97b4bb8af359002f5512505 DE-627 ger DE-627 rakwb eng QC851-999 Rui Wei verfasserin aut Comparison of Weighted/Unweighted and Interpolated Grid Data at Regional and Global Scales 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Uniform grid data are widely used in climate science and related interdisciplinary fields. Such data usually describe the hydrometeorological states averaged over uniform latitude–longitude grids. While these data have larger grid areas in the tropics than other high-latitude regions, less attention has been paid to the areal weights of these grid data. Here, we revisited two methods available for processing these uniform grid data, including weighted sample statistics and grid interpolation. The former directly considers the grid area differences using geodetic weights; the latter converts the uniform grids to equal-area grids for conventional data analysis. When applied to global temperature and precipitation data, we found larger differences between weighted and unweighted samples and smaller differences between weighted and interpolated samples, highlighting the importance of areal weights in grid data analysis. Given the different results from various methods, we call for explicit clarification of the grid data processing methods to improve reproducibility in climate research. gridded datasets weighted sample weighted histogram weighted sample distribution climate data Meteorology. Climatology Yuxin Li verfasserin aut Jun Yin verfasserin aut Xieyao Ma verfasserin aut In Atmosphere MDPI AG, 2011 13(2022), 12, p 2071 (DE-627)657584010 (DE-600)2605928-9 20734433 nnns volume:13 year:2022 number:12, p 2071 https://doi.org/10.3390/atmos13122071 kostenfrei https://doaj.org/article/c456598fe97b4bb8af359002f5512505 kostenfrei https://www.mdpi.com/2073-4433/13/12/2071 kostenfrei https://doaj.org/toc/2073-4433 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2022 12, p 2071
allfieldsSound	10.3390/atmos13122071 doi (DE-627)DOAJ08323490X (DE-599)DOAJc456598fe97b4bb8af359002f5512505 DE-627 ger DE-627 rakwb eng QC851-999 Rui Wei verfasserin aut Comparison of Weighted/Unweighted and Interpolated Grid Data at Regional and Global Scales 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Uniform grid data are widely used in climate science and related interdisciplinary fields. Such data usually describe the hydrometeorological states averaged over uniform latitude–longitude grids. While these data have larger grid areas in the tropics than other high-latitude regions, less attention has been paid to the areal weights of these grid data. Here, we revisited two methods available for processing these uniform grid data, including weighted sample statistics and grid interpolation. The former directly considers the grid area differences using geodetic weights; the latter converts the uniform grids to equal-area grids for conventional data analysis. When applied to global temperature and precipitation data, we found larger differences between weighted and unweighted samples and smaller differences between weighted and interpolated samples, highlighting the importance of areal weights in grid data analysis. Given the different results from various methods, we call for explicit clarification of the grid data processing methods to improve reproducibility in climate research. gridded datasets weighted sample weighted histogram weighted sample distribution climate data Meteorology. Climatology Yuxin Li verfasserin aut Jun Yin verfasserin aut Xieyao Ma verfasserin aut In Atmosphere MDPI AG, 2011 13(2022), 12, p 2071 (DE-627)657584010 (DE-600)2605928-9 20734433 nnns volume:13 year:2022 number:12, p 2071 https://doi.org/10.3390/atmos13122071 kostenfrei https://doaj.org/article/c456598fe97b4bb8af359002f5512505 kostenfrei https://www.mdpi.com/2073-4433/13/12/2071 kostenfrei https://doaj.org/toc/2073-4433 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2022 12, p 2071
language	English
source	In Atmosphere 13(2022), 12, p 2071 volume:13 year:2022 number:12, p 2071
sourceStr	In Atmosphere 13(2022), 12, p 2071 volume:13 year:2022 number:12, p 2071
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	gridded datasets weighted sample weighted histogram weighted sample distribution climate data Meteorology. Climatology
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container_title	Atmosphere
authorswithroles_txt_mv	Rui Wei @@aut@@ Yuxin Li @@aut@@ Jun Yin @@aut@@ Xieyao Ma @@aut@@
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callnumber-first	Q - Science
author	Rui Wei
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topic_title	QC851-999 Comparison of Weighted/Unweighted and Interpolated Grid Data at Regional and Global Scales gridded datasets weighted sample weighted histogram weighted sample distribution climate data
topic	misc QC851-999 misc gridded datasets misc weighted sample misc weighted histogram misc weighted sample distribution misc climate data misc Meteorology. Climatology
topic_unstemmed	misc QC851-999 misc gridded datasets misc weighted sample misc weighted histogram misc weighted sample distribution misc climate data misc Meteorology. Climatology
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title	Comparison of Weighted/Unweighted and Interpolated Grid Data at Regional and Global Scales
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title_sort	comparison of weighted/unweighted and interpolated grid data at regional and global scales
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title_auth	Comparison of Weighted/Unweighted and Interpolated Grid Data at Regional and Global Scales
abstract	Uniform grid data are widely used in climate science and related interdisciplinary fields. Such data usually describe the hydrometeorological states averaged over uniform latitude–longitude grids. While these data have larger grid areas in the tropics than other high-latitude regions, less attention has been paid to the areal weights of these grid data. Here, we revisited two methods available for processing these uniform grid data, including weighted sample statistics and grid interpolation. The former directly considers the grid area differences using geodetic weights; the latter converts the uniform grids to equal-area grids for conventional data analysis. When applied to global temperature and precipitation data, we found larger differences between weighted and unweighted samples and smaller differences between weighted and interpolated samples, highlighting the importance of areal weights in grid data analysis. Given the different results from various methods, we call for explicit clarification of the grid data processing methods to improve reproducibility in climate research.
abstractGer	Uniform grid data are widely used in climate science and related interdisciplinary fields. Such data usually describe the hydrometeorological states averaged over uniform latitude–longitude grids. While these data have larger grid areas in the tropics than other high-latitude regions, less attention has been paid to the areal weights of these grid data. Here, we revisited two methods available for processing these uniform grid data, including weighted sample statistics and grid interpolation. The former directly considers the grid area differences using geodetic weights; the latter converts the uniform grids to equal-area grids for conventional data analysis. When applied to global temperature and precipitation data, we found larger differences between weighted and unweighted samples and smaller differences between weighted and interpolated samples, highlighting the importance of areal weights in grid data analysis. Given the different results from various methods, we call for explicit clarification of the grid data processing methods to improve reproducibility in climate research.
abstract_unstemmed	Uniform grid data are widely used in climate science and related interdisciplinary fields. Such data usually describe the hydrometeorological states averaged over uniform latitude–longitude grids. While these data have larger grid areas in the tropics than other high-latitude regions, less attention has been paid to the areal weights of these grid data. Here, we revisited two methods available for processing these uniform grid data, including weighted sample statistics and grid interpolation. The former directly considers the grid area differences using geodetic weights; the latter converts the uniform grids to equal-area grids for conventional data analysis. When applied to global temperature and precipitation data, we found larger differences between weighted and unweighted samples and smaller differences between weighted and interpolated samples, highlighting the importance of areal weights in grid data analysis. Given the different results from various methods, we call for explicit clarification of the grid data processing methods to improve reproducibility in climate research.
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title_short	Comparison of Weighted/Unweighted and Interpolated Grid Data at Regional and Global Scales
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Comparison of Weighted/Unweighted and Interpolated Grid Data at Regional and Global Scales

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Zugang & Verfügbarkeit

Vorhandene Bände

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