################################################################################ TEMP_SURFACE Validation Statistics ################################################################################ Format: 1.1 Created: 2026-05-06 10:52:36 Author: RT Project: OceanICU Institute: BB Area: AMM7 Experiment: NetSW_LW ################################################################################ ================================================================================ Period: 2016-2023 | Model: pyGETM ================================================================================ Analysed: 2026-05-06 10:52:36 --- OSTIA --- RMSE: 0.9415 Bias: +0.4208 MAE: 0.7398 Correlation: 0.9627 N points: 151,821,276 Model mean: 12.2915 Obs mean: 11.8707 Model std: 3.9080 Obs std: 3.8133 Error distribution: Min: -12.9817 5th pct: -0.9061 25th pct: -0.0742 Median: +0.4324 75th pct: +0.9404 95th pct: +1.7018 Max: +9.2566 Monthly breakdown: Month RMSE Bias MAE Corr -------------------------------------------- Jan 1.0564 +0.4409 0.8227 0.9485 Feb 1.0318 +0.4161 0.7851 0.9525 Mar 0.9651 +0.3636 0.7317 0.9572 Apr 0.8632 +0.2957 0.6639 0.9652 May 0.8248 +0.3558 0.6514 0.9702 Jun 1.0194 +0.5312 0.8090 0.9593 Jul 1.0607 +0.5550 0.8423 0.9580 Aug 0.9591 +0.4249 0.7414 0.9617 Sep 0.7970 +0.2961 0.6193 0.9741 Oct 0.7889 +0.3338 0.6222 0.9745 Nov 0.9222 +0.4741 0.7408 0.9647 Dec 1.0633 +0.5592 0.8480 0.9544 -------------------------------------------- All 0.9415 +0.4208 0.7398 0.9627 --- CCI-SST --- RMSE: 0.9283 Bias: +0.4006 MAE: 0.7288 Correlation: 0.9621 N points: 197,655,640 Model mean: 12.4765 Obs mean: 12.0759 Model std: 3.7919 Obs std: 3.7509 Error distribution: Min: -14.3565 5th pct: -0.9097 25th pct: -0.1167 Median: +0.4034 75th pct: +0.9249 95th pct: +1.6919 Max: +9.3532 Monthly breakdown: Month RMSE Bias MAE Corr -------------------------------------------- Jan 1.0161 +0.4097 0.7839 0.9490 Feb 0.9931 +0.4081 0.7525 0.9531 Mar 0.9465 +0.3793 0.7199 0.9568 Apr 0.8466 +0.3059 0.6541 0.9640 May 0.8181 +0.3624 0.6459 0.9686 Jun 0.9992 +0.5039 0.7926 0.9582 Jul 1.0285 +0.5264 0.8177 0.9591 Aug 0.9547 +0.4141 0.7398 0.9626 Sep 0.8154 +0.2718 0.6339 0.9730 Oct 0.8119 +0.2914 0.6401 0.9724 Nov 0.9148 +0.4188 0.7304 0.9625 Dec 1.0519 +0.5125 0.8328 0.9511 -------------------------------------------- All 0.9283 +0.4006 0.7288 0.9621 ================================================================================ Period: 2016 | Model: pyGETM ================================================================================ Analysed: 2026-05-06 10:52:36 --- OSTIA --- RMSE: 0.8488 Bias: +0.1464 MAE: 0.6590 Correlation: 0.9602 N points: 19,016,628 Model mean: 12.0200 Obs mean: 11.8735 Model std: 3.8818 Obs std: 3.7392 Error distribution: Min: -10.8903 5th pct: -1.1494 25th pct: -0.3265 Median: +0.1762 75th pct: +0.6929 95th pct: +1.4225 Max: +6.8282 --- CCI-SST --- RMSE: 0.8102 Bias: +0.1454 MAE: 0.6280 Correlation: 0.9633 N points: 24,757,704 Model mean: 12.1967 Obs mean: 12.0513 Model std: 3.7655 Obs std: 3.7266 Error distribution: Min: -10.8550 5th pct: -1.1084 25th pct: -0.3038 Median: +0.1764 75th pct: +0.6670 95th pct: +1.3590 Max: +7.4316 ================================================================================ Period: 2017 | Model: pyGETM ================================================================================ Analysed: 2026-05-06 10:52:36 --- OSTIA --- RMSE: 0.9122 Bias: +0.4188 MAE: 0.7204 Correlation: 0.9628 N points: 18,964,670 Model mean: 12.3162 Obs mean: 11.8974 Model std: 3.7273 Obs std: 3.6558 Error distribution: Min: -6.7119 5th pct: -0.8847 25th pct: -0.0655 Median: +0.4394 75th pct: +0.9462 95th pct: +1.7174 Max: +9.2566 --- CCI-SST --- RMSE: 0.8836 Bias: +0.3842 MAE: 0.6967 Correlation: 0.9640 N points: 24,690,059 Model mean: 12.5105 Obs mean: 12.1263 Model std: 3.6457 Obs std: 3.6290 Error distribution: Min: -7.9452 5th pct: -0.9117 25th pct: -0.1069 Median: +0.3939 75th pct: +0.9053 95th pct: +1.6614 Max: +7.1168 ================================================================================ Period: 2018 | Model: pyGETM ================================================================================ Analysed: 2026-05-06 10:52:36 --- OSTIA --- RMSE: 1.0061 Bias: +0.5127 MAE: 0.7935 Correlation: 0.9615 N points: 18,964,670 Model mean: 12.1566 Obs mean: 11.6439 Model std: 4.0553 Obs std: 3.9844 Error distribution: Min: -12.2141 5th pct: -0.8627 25th pct: -0.0095 Median: +0.5263 75th pct: +1.0548 95th pct: +1.8536 Max: +8.8416 --- CCI-SST --- RMSE: 0.9916 Bias: +0.5336 MAE: 0.7786 Correlation: 0.9631 N points: 24,690,050 Model mean: 12.3377 Obs mean: 11.8041 Model std: 3.9064 Obs std: 3.8476 Error distribution: Min: -12.0296 5th pct: -0.7528 25th pct: +0.0207 Median: +0.5329 75th pct: +1.0581 95th pct: +1.8693 Max: +7.4533 ================================================================================ Period: 2019 | Model: pyGETM ================================================================================ Analysed: 2026-05-06 10:52:36 --- OSTIA --- RMSE: 0.9338 Bias: +0.4929 MAE: 0.7384 Correlation: 0.9661 N points: 18,964,670 Model mean: 12.2560 Obs mean: 11.7631 Model std: 3.7895 Obs std: 3.6817 Error distribution: Min: -8.0217 5th pct: -0.7613 25th pct: +0.0106 Median: +0.5021 75th pct: +1.0138 95th pct: +1.7466 Max: +7.6184 --- CCI-SST --- RMSE: 0.9417 Bias: +0.5068 MAE: 0.7449 Correlation: 0.9640 N points: 24,690,060 Model mean: 12.4589 Obs mean: 11.9521 Model std: 3.6799 Obs std: 3.6177 Error distribution: Min: -9.7678 5th pct: -0.7272 25th pct: -0.0094 Median: +0.5087 75th pct: +1.0421 95th pct: +1.7834 Max: +6.9756 ================================================================================ Period: 2020 | Model: pyGETM ================================================================================ Analysed: 2026-05-06 10:52:36 --- OSTIA --- RMSE: 0.9412 Bias: +0.4488 MAE: 0.7341 Correlation: 0.9642 N points: 19,016,628 Model mean: 12.2282 Obs mean: 11.7794 Model std: 3.7892 Obs std: 3.6783 Error distribution: Min: -9.9017 5th pct: -0.8316 25th pct: -0.0329 Median: +0.4633 75th pct: +0.9674 95th pct: +1.7206 Max: +7.9620 --- CCI-SST --- RMSE: 0.9430 Bias: +0.4880 MAE: 0.7398 Correlation: 0.9644 N points: 24,757,704 Model mean: 12.4062 Obs mean: 11.9182 Model std: 3.6910 Obs std: 3.6279 Error distribution: Min: -14.3298 5th pct: -0.7581 25th pct: -0.0095 Median: +0.4921 75th pct: +1.0011 95th pct: +1.7472 Max: +7.7940 ================================================================================ Period: 2021 | Model: pyGETM ================================================================================ Analysed: 2026-05-06 10:52:36 --- OSTIA --- RMSE: 1.0497 Bias: +0.5446 MAE: 0.8199 Correlation: 0.9561 N points: 18,964,670 Model mean: 12.2797 Obs mean: 11.7351 Model std: 3.9846 Obs std: 3.8776 Error distribution: Min: -11.8210 5th pct: -0.8373 25th pct: +0.0189 Median: +0.5428 75th pct: +1.1011 95th pct: +1.9431 Max: +9.0611 --- CCI-SST --- RMSE: 1.0277 Bias: +0.4997 MAE: 0.7983 Correlation: 0.9553 N points: 24,689,971 Model mean: 12.4782 Obs mean: 11.9786 Model std: 3.8665 Obs std: 3.8267 Error distribution: Min: -11.6745 5th pct: -0.8838 25th pct: -0.0684 Median: +0.4860 75th pct: +1.0571 95th pct: +1.9357 Max: +8.5444 ================================================================================ Period: 2022 | Model: pyGETM ================================================================================ Analysed: 2026-05-06 10:52:36 --- OSTIA --- RMSE: 0.8771 Bias: +0.4041 MAE: 0.6910 Correlation: 0.9691 N points: 18,964,670 Model mean: 12.4558 Obs mean: 12.0517 Model std: 3.9214 Obs std: 3.8585 Error distribution: Min: -6.8852 5th pct: -0.8434 25th pct: -0.0608 Median: +0.4250 75th pct: +0.9047 95th pct: +1.6157 Max: +7.2476 --- CCI-SST --- RMSE: 0.8663 Bias: +0.3347 MAE: 0.6819 Correlation: 0.9656 N points: 24,690,060 Model mean: 12.6361 Obs mean: 12.3015 Model std: 3.7990 Obs std: 3.7716 Error distribution: Min: -7.7442 5th pct: -0.9426 25th pct: -0.1763 Median: +0.3444 75th pct: +0.8622 95th pct: +1.6026 Max: +7.6917 ================================================================================ Period: 2023 | Model: pyGETM ================================================================================ Analysed: 2026-05-06 10:52:36 --- OSTIA --- RMSE: 0.9612 Bias: +0.3987 MAE: 0.7625 Correlation: 0.9623 N points: 18,964,670 Model mean: 12.6208 Obs mean: 12.2221 Model std: 4.0710 Obs std: 3.9821 Error distribution: Min: -12.9817 5th pct: -1.0507 25th pct: -0.1016 Median: +0.4453 75th pct: +0.9589 95th pct: +1.7080 Max: +7.5824 --- CCI-SST --- RMSE: 0.9602 Bias: +0.3125 MAE: 0.7622 Correlation: 0.9575 N points: 24,690,032 Model mean: 12.7882 Obs mean: 12.4757 Model std: 3.9403 Obs std: 3.9041 Error distribution: Min: -14.3565 5th pct: -1.1693 25th pct: -0.2715 Median: +0.3480 75th pct: +0.9166 95th pct: +1.7121 Max: +9.3532