################################################################################ TEMP_SURFACE Validation Statistics ################################################################################ Format: 1.1 Created: 2026-10-07 10:44:08 Author: K.B. & R.T. Project: OceanICU Institute: BB Area: NSe Experiment: CMEMS/v01P ################################################################################ ================================================================================ Period: 2010-2013 | Model: pyGETM ================================================================================ Analysed: 2026-10-08 19:41:10 --- OSTIA --- RMSE: 1.0235 Bias: +0.0815 MAE: 0.7705 Correlation: 0.9080 N points: 42,772,236 Model mean: 10.7206 Obs mean: 10.6391 Model std: 4.4644 Obs std: 3.9865 Error distribution: Min: -9.0331 5th pct: -1.2623 25th pct: -0.3594 Median: +0.1129 75th pct: +0.5437 95th pct: +1.2877 Max: +8.4756 Monthly breakdown: Month RMSE Bias MAE Corr -------------------------------------------- Jan 1.0676 -0.3571 0.7941 0.9078 Feb 0.9721 -0.3978 0.7014 0.9304 Mar 0.8075 -0.3347 0.5734 0.9441 Apr 0.6502 -0.1716 0.4777 0.9466 May 0.8469 +0.2974 0.6264 0.9114 Jun 1.3200 +0.9144 1.0909 0.8532 Jul 1.4775 +1.0664 1.2364 0.8689 Aug 1.2488 +0.8768 1.0431 0.8746 Sep 0.8323 +0.3127 0.6417 0.9174 Oct 0.7055 -0.1143 0.5421 0.9330 Nov 0.8665 -0.4727 0.6667 0.9257 Dec 1.1138 -0.6757 0.8389 0.9197 -------------------------------------------- All 1.0235 +0.0815 0.7705 0.9080 --- CCI-SST --- RMSE: 1.0134 Bias: +0.1903 MAE: 0.7624 Correlation: 0.9145 N points: 43,019,271 Model mean: 10.7167 Obs mean: 10.5283 Model std: 4.4755 Obs std: 4.0016 Error distribution: Min: -8.7652 5th pct: -1.1065 25th pct: -0.2423 Median: +0.2193 75th pct: +0.6373 95th pct: +1.3719 Max: +8.5508 Monthly breakdown: Month RMSE Bias MAE Corr -------------------------------------------- Jan 1.0182 -0.2280 0.7534 0.9118 Feb 0.9300 -0.2840 0.6624 0.9324 Mar 0.7660 -0.2487 0.5337 0.9457 Apr 0.6117 -0.0923 0.4460 0.9508 May 0.8568 +0.3977 0.6436 0.9194 Jun 1.3698 +1.0155 1.1521 0.8641 Jul 1.5357 +1.1877 1.3194 0.8831 Aug 1.2876 +0.9562 1.0932 0.8833 Sep 0.8482 +0.3962 0.6646 0.9220 Oct 0.6752 -0.0101 0.5187 0.9374 Nov 0.7706 -0.3157 0.5877 0.9313 Dec 1.0163 -0.5255 0.7588 0.9232 -------------------------------------------- All 1.0134 +0.1903 0.7624 0.9145 --- OISST --- RMSE: 1.0914 Bias: +0.1890 MAE: 0.8373 Correlation: 0.9076 N points: 39,162,105 Model mean: 10.7219 Obs mean: 10.5329 Model std: 4.4110 Obs std: 3.8000 Error distribution: Min: -9.0298 5th pct: -1.1334 25th pct: -0.2513 Median: +0.2230 75th pct: +0.6591 95th pct: +1.3733 Max: +8.4328 Monthly breakdown: Month RMSE Bias MAE Corr -------------------------------------------- Jan 1.0989 -0.3326 0.8403 0.8938 Feb 0.9931 -0.5082 0.7390 0.9324 Mar 0.9174 -0.5707 0.7068 0.9440 Apr 0.6731 -0.2686 0.5166 0.9478 May 0.9127 +0.4396 0.7083 0.9070 Jun 1.5056 +1.1939 1.2939 0.8520 Jul 1.7306 +1.4153 1.5124 0.8630 Aug 1.3201 +1.0098 1.1302 0.8731 Sep 0.8922 +0.4790 0.7028 0.9191 Oct 0.6912 +0.1202 0.5404 0.9364 Nov 0.7520 -0.2480 0.5734 0.9279 Dec 1.0429 -0.5105 0.7666 0.9108 -------------------------------------------- All 1.0914 +0.1890 0.8373 0.9076 ================================================================================ Period: 2010 | Model: pyGETM ================================================================================ Analysed: 2026-10-08 19:41:10 --- OSTIA --- RMSE: 1.0286 Bias: +0.2294 MAE: 0.7525 Correlation: 0.9122 N points: 10,685,740 Model mean: 10.6620 Obs mean: 10.4326 Model std: 4.5449 Obs std: 4.1980 Error distribution: Min: -9.0331 5th pct: -1.2545 25th pct: -0.2750 Median: +0.2026 75th pct: +0.6317 95th pct: +1.4268 Max: +8.3935 --- CCI-SST --- RMSE: 1.0467 Bias: +0.3323 MAE: 0.7739 Correlation: 0.9161 N points: 10,744,418 Model mean: 10.6573 Obs mean: 10.3291 Model std: 4.5576 Obs std: 4.2313 Error distribution: Min: -8.7652 5th pct: -1.1274 25th pct: -0.1694 Median: +0.3014 75th pct: +0.7264 95th pct: +1.5511 Max: +8.5508 --- OISST --- RMSE: 1.0995 Bias: +0.2638 MAE: 0.8385 Correlation: 0.9116 N points: 9,783,825 Model mean: 10.6758 Obs mean: 10.4120 Model std: 4.4778 Obs std: 3.9746 Error distribution: Min: -9.0298 5th pct: -1.2742 25th pct: -0.2568 Median: +0.2273 75th pct: +0.6665 95th pct: +1.4132 Max: +7.8931 ================================================================================ Period: 2011 | Model: pyGETM ================================================================================ Analysed: 2026-10-08 19:41:10 --- OSTIA --- RMSE: 0.9444 Bias: +0.0363 MAE: 0.7180 Correlation: 0.9144 N points: 10,685,740 Model mean: 10.8613 Obs mean: 10.8250 Model std: 4.3834 Obs std: 3.8866 Error distribution: Min: -6.6380 5th pct: -1.2575 25th pct: -0.4146 Median: +0.0440 75th pct: +0.4477 95th pct: +1.1213 Max: +7.6276 --- CCI-SST --- RMSE: 0.9577 Bias: +0.1509 MAE: 0.7297 Correlation: 0.9172 N points: 10,747,789 Model mean: 10.8577 Obs mean: 10.7085 Model std: 4.3953 Obs std: 3.8794 Error distribution: Min: -7.0476 5th pct: -1.1272 25th pct: -0.2917 Median: +0.1610 75th pct: +0.5576 95th pct: +1.2350 Max: +7.2308 --- OISST --- RMSE: 1.0557 Bias: +0.1432 MAE: 0.8225 Correlation: 0.9070 N points: 9,783,825 Model mean: 10.8537 Obs mean: 10.7105 Model std: 4.3220 Obs std: 3.6444 Error distribution: Min: -7.1233 5th pct: -1.1736 25th pct: -0.3208 Median: +0.1529 75th pct: +0.5820 95th pct: +1.2248 Max: +6.2930 ================================================================================ Period: 2012 | Model: pyGETM ================================================================================ Analysed: 2026-10-08 19:41:10 --- OSTIA --- RMSE: 0.9663 Bias: +0.0211 MAE: 0.7419 Correlation: 0.9102 N points: 10,715,016 Model mean: 10.7707 Obs mean: 10.7496 Model std: 4.0994 Obs std: 3.6240 Error distribution: Min: -7.4930 5th pct: -1.2869 25th pct: -0.4614 Median: +0.0126 75th pct: +0.4415 95th pct: +1.1355 Max: +8.4756 --- CCI-SST --- RMSE: 0.9601 Bias: +0.1271 MAE: 0.7325 Correlation: 0.9157 N points: 10,778,210 Model mean: 10.7668 Obs mean: 10.6407 Model std: 4.1087 Obs std: 3.6238 Error distribution: Min: -7.0195 5th pct: -1.1264 25th pct: -0.3462 Median: +0.1260 75th pct: +0.5462 95th pct: +1.2260 Max: +6.2578 --- OISST --- RMSE: 0.9981 Bias: +0.1452 MAE: 0.7715 Correlation: 0.9127 N points: 9,810,630 Model mean: 10.7674 Obs mean: 10.6222 Model std: 4.0506 Obs std: 3.5272 Error distribution: Min: -6.6172 5th pct: -1.1106 25th pct: -0.3385 Median: +0.1320 75th pct: +0.5521 95th pct: +1.2272 Max: +6.8678 ================================================================================ Period: 2013 | Model: pyGETM ================================================================================ Analysed: 2026-10-08 19:41:10 --- OSTIA --- RMSE: 1.1433 Bias: +0.0394 MAE: 0.8696 Correlation: 0.8951 N points: 10,685,740 Model mean: 10.5885 Obs mean: 10.5491 Model std: 4.7974 Obs std: 4.1973 Error distribution: Min: -8.0403 5th pct: -1.4568 25th pct: -0.4670 Median: +0.0373 75th pct: +0.4936 95th pct: +1.3031 Max: +8.4393 --- CCI-SST --- RMSE: 1.0834 Bias: +0.1512 MAE: 0.8137 Correlation: 0.9084 N points: 10,748,854 Model mean: 10.5848 Obs mean: 10.4344 Model std: 4.8075 Obs std: 4.2285 Error distribution: Min: -7.6052 5th pct: -1.2384 25th pct: -0.3228 Median: +0.1521 75th pct: +0.5775 95th pct: +1.3387 Max: +7.0844 --- OISST --- RMSE: 1.2022 Bias: +0.2040 MAE: 0.9170 Correlation: 0.9010 N points: 9,783,825 Model mean: 10.5906 Obs mean: 10.3866 Model std: 4.7602 Obs std: 4.0213 Error distribution: Min: -7.9030 5th pct: -1.2197 25th pct: -0.2866 Median: +0.2048 75th pct: +0.6435 95th pct: +1.4210 Max: +8.4328