2022
DOI: 10.1038/s41597-022-01419-x
|View full text |Cite
|
Sign up to set email alerts
|

A global long-term ocean surface daily/0.05° net radiation product from 1983–2020

Abstract: The all-wave net radiation (Rn) on the ocean surface characterizes the available radiative energy balance and is important to understand the Earth’s climate system. Considering the shortcomings of available ocean surface Rn datasets (e.g., coarse spatial resolutions, discrepancy in accuracy, inconsistency, and short duration), a new long-term global daily Rn product at a spatial resolution of 0.05° from 1983 to 2020, as part of the Global High Resolution Ocean Surface Energy (GHOSE) products suite, was generat… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2023
2023
2023
2023

Publication Types

Select...
4

Relationship

0
4

Authors

Journals

citations
Cited by 4 publications
(2 citation statements)
references
References 78 publications
0
2
0
Order By: Relevance
“…ERA5 can be used to fill in SSR data from the oceans and Antarctica and carry out the global reconstruction, taking into account its high spatial resolution and the reliable performance of SSR (Jiao et al, 2022;Liang et al, 2022). After the reconstruction, we removed the data for the ocean reanalysis and maintained the data only in the land area (except for Antarctica).…”
Section: Reanalysismentioning
confidence: 99%
See 1 more Smart Citation
“…ERA5 can be used to fill in SSR data from the oceans and Antarctica and carry out the global reconstruction, taking into account its high spatial resolution and the reliable performance of SSR (Jiao et al, 2022;Liang et al, 2022). After the reconstruction, we removed the data for the ocean reanalysis and maintained the data only in the land area (except for Antarctica).…”
Section: Reanalysismentioning
confidence: 99%
“…Comparisons show that ERA5 has a high spatial resolution and relatively reliable performance in the temporal variations and long-term trends (Liang et al, 2022;Jiao et al, 2022). To obtain a higher data coverage and ensure that the AI model runs well, we used the ERA5 to fill the SSR of the homogenized global gridded SSR in the Antarctic and ocean areas.…”
Section: Data Pre-processingmentioning
confidence: 99%