CNES_CLS 2022 Mean Sea Surface ...
The mean sea surface MSS_CNES_CLS2022 has been computed using a 29-year [1993-2021] period of altimetric data. As usually, the mean profiles from ERM (Exact Repeat Mission) missions (T/P, J1, 2, 3, ERS-2, Envisat, AltiKa, GFO) that provide the most accurate estimate of the oceanic mean content are u...
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CNES
2022
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| Online Access: | https://dx.doi.org/10.24400/527896/a01-2022.017 https://www.aviso.altimetry.fr/en/data/products/auxiliary-products/mss.html |
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ftdatacite:10.24400/527896/a01-2022.017 2024-04-28T08:10:06+00:00 CNES_CLS 2022 Mean Sea Surface ... CLS 2022 NetCDF https://dx.doi.org/10.24400/527896/a01-2022.017 https://www.aviso.altimetry.fr/en/data/products/auxiliary-products/mss.html unknown CNES https://dx.doi.org/10.3390/rs15112910 dataset Dataset Auxiliary product 2022 ftdatacite https://doi.org/10.24400/527896/a01-2022.01710.3390/rs15112910 2024-04-02T09:38:10Z The mean sea surface MSS_CNES_CLS2022 has been computed using a 29-year [1993-2021] period of altimetric data. As usually, the mean profiles from ERM (Exact Repeat Mission) missions (T/P, J1, 2, 3, ERS-2, Envisat, AltiKa, GFO) that provide the most accurate estimate of the oceanic mean content are used. This version includes new High Resolution datasets from CryoSat-2 and AltiKa which are respectively sampled at 20 Hz and 40 Hz. However, these data had to be filtered at 5 Hz to obtain a better signal to noise ratio. This new solution is also improved in arctic region using an optimal combination between conventional SSH and LEADS (linear fractures) over free ice areas (missions Sentinel-3A, B). The mapping method is based on a local least square collocation technique which also provides an estimation of calibrated error. ... Dataset Arctic DataCite Metadata Store (German National Library of Science and Technology) |
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Open Polar |
| collection |
DataCite Metadata Store (German National Library of Science and Technology) |
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ftdatacite |
| language |
unknown |
| description |
The mean sea surface MSS_CNES_CLS2022 has been computed using a 29-year [1993-2021] period of altimetric data. As usually, the mean profiles from ERM (Exact Repeat Mission) missions (T/P, J1, 2, 3, ERS-2, Envisat, AltiKa, GFO) that provide the most accurate estimate of the oceanic mean content are used. This version includes new High Resolution datasets from CryoSat-2 and AltiKa which are respectively sampled at 20 Hz and 40 Hz. However, these data had to be filtered at 5 Hz to obtain a better signal to noise ratio. This new solution is also improved in arctic region using an optimal combination between conventional SSH and LEADS (linear fractures) over free ice areas (missions Sentinel-3A, B). The mapping method is based on a local least square collocation technique which also provides an estimation of calibrated error. ... |
| format |
Dataset |
| author |
CLS |
| spellingShingle |
CLS CNES_CLS 2022 Mean Sea Surface ... |
| author_facet |
CLS |
| author_sort |
CLS |
| title |
CNES_CLS 2022 Mean Sea Surface ... |
| title_short |
CNES_CLS 2022 Mean Sea Surface ... |
| title_full |
CNES_CLS 2022 Mean Sea Surface ... |
| title_fullStr |
CNES_CLS 2022 Mean Sea Surface ... |
| title_full_unstemmed |
CNES_CLS 2022 Mean Sea Surface ... |
| title_sort |
cnes_cls 2022 mean sea surface ... |
| publisher |
CNES |
| publishDate |
2022 |
| url |
https://dx.doi.org/10.24400/527896/a01-2022.017 https://www.aviso.altimetry.fr/en/data/products/auxiliary-products/mss.html |
| genre |
Arctic |
| genre_facet |
Arctic |
| op_relation |
https://dx.doi.org/10.3390/rs15112910 |
| op_doi |
https://doi.org/10.24400/527896/a01-2022.01710.3390/rs15112910 |
| _version_ |
1797578154280222720 |