################################################################################ TEMP_SURFACE Validation Statistics ################################################################################ Format: 1.1 Created: 2026-05-28 09:43:10 Author: RT Project: OceanICU Institute: BB Area: AMM7 Experiment: CMEMS_prof ################################################################################ ================================================================================ Period: 2016-2023 | Model: pyGETM ================================================================================ Analysed: 2026-05-28 09:43:10 --- OSTIA --- RMSE: 1.0272 Bias: +0.4192 MAE: 0.8017 Correlation: 0.9479 N points: 132,856,606 Model mean: 12.2398 Obs mean: 11.8206 Model std: 3.6878 Obs std: 3.7859 Error distribution: Min: -12.5796 5th pct: -1.1147 25th pct: -0.0900 Median: +0.4479 75th pct: +0.9583 95th pct: +1.8836 Max: +9.9159 Monthly breakdown: Month RMSE Bias MAE Corr -------------------------------------------- Jan 0.9764 +0.3082 0.7456 0.9387 Feb 0.9233 +0.2761 0.6858 0.9479 Mar 0.8848 +0.2931 0.6505 0.9526 Apr 0.8361 +0.3726 0.6401 0.9615 May 0.9182 +0.5209 0.7384 0.9623 Jun 1.2008 +0.6604 0.9741 0.9433 Jul 1.3202 +0.6374 1.0694 0.9338 Aug 1.2043 +0.4679 0.9562 0.9392 Sep 1.0576 +0.3174 0.8241 0.9537 Oct 0.9766 +0.3090 0.7557 0.9569 Nov 1.0152 +0.4090 0.7741 0.9448 Dec 1.0395 +0.4489 0.7966 0.9357 -------------------------------------------- All 1.0272 +0.4192 0.8017 0.9479 --- CCI-SST --- RMSE: 1.0031 Bias: +0.4755 MAE: 0.7851 Correlation: 0.9550 N points: 172,965,608 Model mean: 12.4943 Obs mean: 12.0189 Model std: 3.6535 Obs std: 3.7251 Error distribution: Min: -12.9171 5th pct: -0.9264 25th pct: -0.0254 Median: +0.4851 75th pct: +0.9926 95th pct: +1.8553 Max: +9.4268 Monthly breakdown: Month RMSE Bias MAE Corr -------------------------------------------- Jan 0.9380 +0.3532 0.7133 0.9489 Feb 0.8946 +0.3490 0.6682 0.9564 Mar 0.8763 +0.3798 0.6520 0.9593 Apr 0.8495 +0.4423 0.6575 0.9654 May 0.9265 +0.5796 0.7471 0.9657 Jun 1.1568 +0.6971 0.9405 0.9498 Jul 1.2544 +0.6703 1.0156 0.9427 Aug 1.1670 +0.5356 0.9287 0.9479 Sep 1.0264 +0.3866 0.8008 0.9600 Oct 0.9634 +0.3649 0.7421 0.9619 Nov 0.9957 +0.4422 0.7592 0.9514 Dec 1.0222 +0.4958 0.7861 0.9448 -------------------------------------------- All 1.0031 +0.4755 0.7851 0.9550 ================================================================================ Period: 2016 | Model: pyGETM ================================================================================ Analysed: 2026-05-28 09:43:10 --- OSTIA --- RMSE: 0.9711 Bias: +0.2519 MAE: 0.7584 Correlation: 0.9475 N points: 19,016,628 Model mean: 12.1255 Obs mean: 11.8735 Model std: 3.7611 Obs std: 3.7392 Error distribution: Min: -10.6034 5th pct: -1.2672 25th pct: -0.2575 Median: +0.2846 75th pct: +0.8119 95th pct: +1.6858 Max: +6.4671 --- CCI-SST --- RMSE: 0.9101 Bias: +0.2927 MAE: 0.7083 Correlation: 0.9575 N points: 24,757,704 Model mean: 12.3440 Obs mean: 12.0513 Model std: 3.7325 Obs std: 3.7266 Error distribution: Min: -11.0251 5th pct: -1.0776 25th pct: -0.1841 Median: +0.3134 75th pct: +0.8145 95th pct: +1.5956 Max: +7.3013 ================================================================================ Period: 2017 | Model: pyGETM ================================================================================ Analysed: 2026-05-28 09:43:10 --- OSTIA --- RMSE: 0.9844 Bias: +0.3971 MAE: 0.7712 Correlation: 0.9490 N points: 18,964,670 Model mean: 12.2945 Obs mean: 11.8974 Model std: 3.5212 Obs std: 3.6558 Error distribution: Min: -11.1250 5th pct: -1.0841 25th pct: -0.0903 Median: +0.4354 75th pct: +0.9203 95th pct: +1.8100 Max: +9.0303 --- CCI-SST --- RMSE: 0.9464 Bias: +0.4140 MAE: 0.7441 Correlation: 0.9569 N points: 24,690,059 Model mean: 12.5403 Obs mean: 12.1263 Model std: 3.5342 Obs std: 3.6290 Error distribution: Min: -11.0290 5th pct: -0.9622 25th pct: -0.0733 Median: +0.4392 75th pct: +0.9222 95th pct: +1.7439 Max: +7.1730 ================================================================================ Period: 2018 | Model: pyGETM ================================================================================ Analysed: 2026-05-28 09:43:10 --- OSTIA --- RMSE: 1.1059 Bias: +0.4927 MAE: 0.8620 Correlation: 0.9444 N points: 18,964,670 Model mean: 12.1366 Obs mean: 11.6439 Model std: 3.8741 Obs std: 3.9844 Error distribution: Min: -10.3536 5th pct: -1.1036 25th pct: -0.0593 Median: +0.5111 75th pct: +1.0665 95th pct: +2.0356 Max: +9.1027 --- CCI-SST --- RMSE: 1.0858 Bias: +0.5573 MAE: 0.8480 Correlation: 0.9518 N points: 24,690,050 Model mean: 12.3614 Obs mean: 11.8041 Model std: 3.7917 Obs std: 3.8476 Error distribution: Min: -10.1898 5th pct: -0.8738 25th pct: +0.0181 Median: +0.5560 75th pct: +1.1075 95th pct: +2.0240 Max: +8.3808 ================================================================================ Period: 2019 | Model: pyGETM ================================================================================ Analysed: 2026-05-28 09:43:10 --- OSTIA --- RMSE: 1.0372 Bias: +0.4775 MAE: 0.8162 Correlation: 0.9480 N points: 18,964,670 Model mean: 12.2406 Obs mean: 11.7631 Model std: 3.5578 Obs std: 3.6817 Error distribution: Min: -10.1604 5th pct: -1.0140 25th pct: -0.0423 Median: +0.5012 75th pct: +1.0188 95th pct: +1.9494 Max: +7.0863 --- CCI-SST --- RMSE: 1.0284 Bias: +0.5445 MAE: 0.8085 Correlation: 0.9536 N points: 24,690,060 Model mean: 12.4965 Obs mean: 11.9521 Model std: 3.5237 Obs std: 3.6177 Error distribution: Min: -10.7550 5th pct: -0.8099 25th pct: +0.0178 Median: +0.5414 75th pct: +1.0724 95th pct: +1.9664 Max: +6.9857 ================================================================================ Period: 2020 | Model: pyGETM ================================================================================ Analysed: 2026-05-28 09:43:10 --- OSTIA --- RMSE: 0.9941 Bias: +0.3744 MAE: 0.7732 Correlation: 0.9482 N points: 19,016,628 Model mean: 12.1537 Obs mean: 11.7794 Model std: 3.5325 Obs std: 3.6783 Error distribution: Min: -12.5796 5th pct: -1.1772 25th pct: -0.1186 Median: +0.4096 75th pct: +0.9065 95th pct: +1.8162 Max: +8.0812 --- CCI-SST --- RMSE: 0.9897 Bias: +0.4743 MAE: 0.7769 Correlation: 0.9546 N points: 24,757,704 Model mean: 12.3926 Obs mean: 11.9182 Model std: 3.5221 Obs std: 3.6279 Error distribution: Min: -12.9171 5th pct: -0.9447 25th pct: -0.0113 Median: +0.4851 75th pct: +0.9818 95th pct: +1.8416 Max: +8.6647 ================================================================================ Period: 2021 | Model: pyGETM ================================================================================ Analysed: 2026-05-28 09:43:10 --- OSTIA --- RMSE: 1.0847 Bias: +0.5151 MAE: 0.8532 Correlation: 0.9444 N points: 18,964,670 Model mean: 12.2502 Obs mean: 11.7351 Model std: 3.8264 Obs std: 3.8776 Error distribution: Min: -9.4916 5th pct: -1.0401 25th pct: -0.0174 Median: +0.5381 75th pct: +1.0673 95th pct: +2.0778 Max: +9.9159 --- CCI-SST --- RMSE: 1.0759 Bias: +0.5751 MAE: 0.8461 Correlation: 0.9514 N points: 24,689,971 Model mean: 12.5536 Obs mean: 11.9786 Model std: 3.7981 Obs std: 3.8267 Error distribution: Min: -11.2869 5th pct: -0.8736 25th pct: +0.0470 Median: +0.5737 75th pct: +1.1068 95th pct: +2.0469 Max: +9.4268 ================================================================================ Period: 2022 | Model: pyGETM ================================================================================ Analysed: 2026-05-28 09:43:10 --- OSTIA --- RMSE: 1.0114 Bias: +0.4266 MAE: 0.7780 Correlation: 0.9547 N points: 18,964,670 Model mean: 12.4782 Obs mean: 12.0517 Model std: 3.7123 Obs std: 3.8585 Error distribution: Min: -12.4475 5th pct: -1.0535 25th pct: -0.0504 Median: +0.4487 75th pct: +0.9241 95th pct: +1.8781 Max: +8.5413 --- CCI-SST --- RMSE: 0.9847 Bias: +0.4708 MAE: 0.7637 Correlation: 0.9595 N points: 24,690,060 Model mean: 12.7723 Obs mean: 12.3015 Model std: 3.6414 Obs std: 3.7716 Error distribution: Min: -12.3007 5th pct: -0.8898 25th pct: -0.0004 Median: +0.4833 75th pct: +0.9501 95th pct: +1.8266 Max: +8.6596