################################################################################ TEMP_BOTTOM Validation Statistics ################################################################################ Format: 1.1 Created: 2026-10-08 20:11:12 Area: NSe Experiment: CMEMS/v01P ################################################################################ ================================================================================ Period: 2010-2013 | Model: pyGETM ================================================================================ Analysed: 2026-10-08 20:33:46 --- NWS-bottomT --- RMSE: 1.4277 Bias: -0.5865 MAE: 0.9517 Correlation: 0.9169 N points: 1,265,376 Model mean: 8.5984 Obs mean: 9.1850 Model std: 3.1628 Obs std: 3.2439 Error distribution: Min: -11.2410 5th pct: -3.1440 25th pct: -1.1726 Median: -0.2676 75th pct: +0.2105 95th pct: +0.9777 Max: +5.9366 Monthly breakdown: Month RMSE Bias MAE Corr -------------------------------------------- Jan 0.7159 +0.1706 0.5324 0.9130 Feb 0.5771 +0.1029 0.4297 0.9470 Mar 0.5847 +0.0848 0.3999 0.9417 Apr 0.6320 -0.1495 0.4562 0.9234 May 0.9545 -0.5222 0.7241 0.9085 Jun 1.5279 -0.9084 1.1396 0.8991 Jul 1.9929 -1.1814 1.4708 0.9034 Aug 2.2438 -1.3304 1.6514 0.9075 Sep 2.1359 -1.2724 1.5834 0.9075 Oct 1.8090 -1.0630 1.3449 0.9028 Nov 1.4044 -0.7317 1.0323 0.8766 Dec 0.9022 -0.2377 0.6558 0.8639 -------------------------------------------- All 1.4277 -0.5865 0.9517 0.9169 ================================================================================ Period: 2010 | Model: pyGETM ================================================================================ Analysed: 2026-10-08 20:33:46 --- NWS-bottomT --- RMSE: 1.4419 Bias: -0.4748 MAE: 0.9386 Correlation: 0.9092 N points: 316,344 Model mean: 8.4897 Obs mean: 8.9645 Model std: 3.0328 Obs std: 3.2670 Error distribution: Min: -10.9919 5th pct: -3.1673 25th pct: -0.9650 Median: -0.1296 75th pct: +0.3225 95th pct: +1.1342 Max: +5.2094 ================================================================================ Period: 2011 | Model: pyGETM ================================================================================ Analysed: 2026-10-08 20:33:46 --- NWS-bottomT --- RMSE: 1.5486 Bias: -0.7525 MAE: 1.0344 Correlation: 0.9169 N points: 316,344 Model mean: 8.7259 Obs mean: 9.4784 Model std: 3.3399 Obs std: 3.2960 Error distribution: Min: -11.2410 5th pct: -3.3827 25th pct: -1.3560 Median: -0.3896 75th pct: +0.1057 95th pct: +0.7945 Max: +4.6146 ================================================================================ Period: 2012 | Model: pyGETM ================================================================================ Analysed: 2026-10-08 20:33:46 --- NWS-bottomT --- RMSE: 1.2734 Bias: -0.5912 MAE: 0.8600 Correlation: 0.9279 N points: 316,344 Model mean: 8.9602 Obs mean: 9.5515 Model std: 2.9900 Obs std: 2.9447 Error distribution: Min: -10.0477 5th pct: -2.7068 25th pct: -1.0837 Median: -0.3445 75th pct: +0.0937 95th pct: +0.7879 Max: +3.8610 ================================================================================ Period: 2013 | Model: pyGETM ================================================================================ Analysed: 2026-10-08 20:33:46 --- NWS-bottomT --- RMSE: 1.4333 Bias: -0.5276 MAE: 0.9739 Correlation: 0.9197 N points: 316,344 Model mean: 8.2179 Obs mean: 8.7455 Model std: 3.2277 Obs std: 3.3796 Error distribution: Min: -10.1839 5th pct: -3.1710 25th pct: -1.1555 Median: -0.1833 75th pct: +0.2813 95th pct: +1.0903 Max: +5.9366