This page provides an overview of regional forecast skill for the SON 2025 period. A detailed description of outputs automatically updated on this page can be found on the following section's overview.
Regional skill score files
All regional RPSSs are available to download via the following link:
SON 2025 Regional RPSSs (Excel format)
Top 10 teams of regional forecast window 1, period-aggregated, variable-averaged RPSSs
| Teamname | Team_rank | Modelname | Model_rank | Global | Tropics | NHem. ExTro. | SHem. ExTro. | NHem. Polar | SHem. Polar | Europe | N. Amer. | S. Amer. | Africa | Asia | Oceania |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AIFS | 1 | AIFShera | 1 | 0.0872657041634113 | 0.1077807249991681 | 0.0831543358259171 | -0.0586823080636266 | 0.0439612392074482 | 0.0238868231859243 | 0.0920495516059873 | -0.0279850632142148 | 0.055337637769575 | 0.0573159721902057 | 0.1467860156223119 | 0.0605456365590373 |
| AIFS | 1 | AIFSgaia | 3 | 0.0811298329908194 | 0.1333703876497428 | 0.0504444830112336 | -0.0801225039467007 | 0.0344557116485248 | -0.024433245810331 | 0.0914761565203339 | -0.0740618703693753 | -0.0295817417802678 | 0.1387195293447594 | 0.1555872698355637 | 0.0350405108436841 |
| AIFS | 1 | AIFSthalassa | 6 | 0.0751385836284376 | 0.1294099741103095 | 0.0366767243032266 | -0.1267808969940127 | 0.0293191002100561 | -0.0500263821530941 | 0.0392261920005742 | -0.0459863143231923 | -0.1126544870533037 | 0.1280366700836895 | 0.1294707914683695 | 0.0611139128695571 |
| CMAandFDU | 2 | FengshunAdjust | 2 | 0.0847126910944717 | 0.1710068414166666 | 0.0153947383208917 | -0.072585314450927 | 0.0050083394077634 | -0.0222683177614774 | 0.0627863228628453 | -0.0237073878441976 | 0.0946558406179435 | 0.0536585545497491 | 0.1134537672593039 | 0.0533048592404414 |
| CMAandFDU | 2 | FengshunHybrid | 7 | 0.0644852776892303 | 0.1246359615930289 | 0.0021708387747086 | -0.0465709611261634 | 0.0456063204474957 | -0.0210685144564061 | 0.0369279521838778 | -0.0259320243175089 | -0.0637763243975089 | 0.1070348981412967 | 0.1216146045074416 | -0.0231034247978232 |
| CMAandFDU | 2 | Fengshun | 10 | 0.0348315631134322 | 0.0864929057108073 | -0.0063617751429924 | -0.0821768637336775 | 0.0477925039899162 | -0.0405814617728486 | 0.0663661110225405 | -0.0410760213738255 | -0.0075118670400408 | 0.03008274716371 | 0.082893233834882 | 0.1039381855796275 |
| MicroEnsemble | 3 | MicroDuet | 4 | 0.0768420556086506 | 0.1084786667599793 | 0.0493394492178274 | -0.0218915839842206 | 0.032041757968749 | 0.0654175517429067 | 0.1050376652880791 | -0.0016552637686019 | -0.0189511975870761 | 0.0630243710135443 | 0.108598223942687 | 0.0921151542591087 |
| MicroEnsemble | 3 | StillLearning | 5 | 0.0768193934327012 | 0.1019736135028891 | 0.0580600132151527 | -0.0087950118325421 | 0.0400307847430897 | 0.0639440367741658 | 0.1185327042608914 | -0.0039094413916992 | -0.001889913598068 | 0.0560530860694973 | 0.1129082303234684 | 0.0817178780905799 |
| MicroEnsemble | 3 | Huracan | 9 | 0.047033814624337 | 0.0436935144917127 | 0.0598610877193079 | -0.0451467559652294 | 0.0201602170324967 | 0.0686882477798757 | 0.1041896923265447 | -0.005417425601323 | -0.089897720201524 | -0.015231671513704 | 0.0991885045423252 | 0.0402480851373969 |
| LP | 4 | LPM | 8 | 0.0622763282650112 | 0.0828449680972131 | 0.0613147734554885 | -0.0138902053315259 | 0.0243708463494071 | 0.016781459389786 | 0.0867450971585289 | 0.0192322401943349 | 0.0324249096369578 | 0.0206356235548932 | 0.0920613793896299 | -0.0205453160240804 |
| KITKangu | 5 | KanguPlusPlus | 11 | 3.666856966214974e-09 | -4.0358675660693645e-10 | -7.362577584639022e-09 | -8.457189837329793e-09 | 3.202370239356137e-08 | 3.4529600401178584e-08 | 4.1434213467657815e-09 | 1.7628015343736552e-09 | -7.819986880264194e-10 | -1.243628607502008e-09 | 2.605395269898262e-09 | -4.042379024108793e-09 |
| KITKangu | 5 | KanguParametricPrediction | 11 | 3.666856966214974e-09 | -4.0358675660693645e-10 | -7.362577584639022e-09 | -8.457189837329793e-09 | 3.202370239356137e-08 | 3.4529600401178584e-08 | 4.1434213467657815e-09 | 1.7628015343736552e-09 | -7.819986880264194e-10 | -1.243628607502008e-09 | 2.605395269898262e-09 | -4.042379024108793e-09 |
| scienceAI | 5 | findforecast | 11 | 3.666856966214974e-09 | -4.0358675660693645e-10 | -7.362577584639022e-09 | -8.457189837329793e-09 | 3.202370239356137e-08 | 3.4529600401178584e-08 | 4.1434213467657815e-09 | 1.7628015343736552e-09 | -7.819986880264194e-10 | -1.243628607502008e-09 | 2.605395269898262e-09 | -4.042379024108793e-09 |
| scienceAI | 5 | zephyr | 11 | 3.666856966214974e-09 | -4.0358675660693645e-10 | -7.362577584639022e-09 | -8.457189837329793e-09 | 3.202370239356137e-08 | 3.4529600401178584e-08 | 4.1434213467657815e-09 | 1.7628015343736552e-09 | -7.819986880264194e-10 | -1.243628607502008e-09 | 2.605395269898262e-09 | -4.042379024108793e-09 |
| scienceAI | 5 | ngcm | 11 | 3.666856966214974e-09 | -4.0358675660693645e-10 | -7.362577584639022e-09 | -8.457189837329793e-09 | 3.202370239356137e-08 | 3.4529600401178584e-08 | 4.1434213467657815e-09 | 1.7628015343736552e-09 | -7.819986880264194e-10 | -1.243628607502008e-09 | 2.605395269898262e-09 | -4.042379024108793e-09 |
| KITKangu | 5 | KanguS2SEasyUQ | 39 | -1.2347523823399784 | -1.3401195220637367 | -1.045133965154535 | -1.357285868673576 | -1.0937269217185723 | -1.2653733658246211 | -1.1939834961133735 | -0.8764893369277466 | -1.4926150553226716 | -1.445703410997419 | -1.1781797038225297 | -1.6726960282590275 |
| CliMA | 7 | CliMAWeather | 16 | -0.0401551400113346 | -0.0390112873313769 | -0.052132923056435 | -0.2400895905058521 | -0.037498413761689 | 0.0632659389093055 | 0.0107444990738656 | -0.0871304845380396 | -0.1510158815335493 | -0.0239612890986327 | -0.019111690258686 | -0.1370860032428479 |
| CliMA | 7 | CliMAWeather2 | 17 | -0.040318241889637 | -0.04106684186707 | -0.0486472605750094 | -0.2378184970076762 | -0.0385811666652545 | 0.0676703975281957 | 0.0082547294542645 | -0.0858899211504835 | -0.1587708066025433 | -0.0250689569388942 | -0.0170447349246089 | -0.1287299545129007 |
| WindBorne | 8 | WeatherMesh | 18 | -0.1246740662212984 | -0.0817395133477092 | -0.2059140701112232 | -0.2589037256803564 | -0.1525721142720525 | -0.1317549620568503 | -0.2020446607234841 | -0.1874665495628587 | -0.1400424871394033 | -0.1837295339440002 | -0.1406398784011505 | -0.3577949236071163 |
| FengWuW2S | 9 | FengWu2 | 19 | -0.2598175485147354 | -0.134728010855516 | -0.4344056052392832 | -0.2548453604793543 | -0.4705608400773706 | -0.2496801603621676 | -0.7317112133780895 | -0.0419558442846404 | -0.1238405224442392 | -0.4522045029611422 | -0.4910944634661558 | -0.125979124263102 |
| FengWuW2S | 9 | FengWu | 22 | -0.3297956763232948 | -0.3846686163227605 | -0.3308683411739114 | -0.3881415625866724 | -0.2703834267848625 | -0.1770237499050863 | -0.4621881409395296 | -0.1615819671620163 | -0.5237340307680335 | -0.6291286480279751 | -0.3931969434103262 | -0.2686038623275239 |
| Sibyl | 10 | ClimSDE | 20 | -0.2983663189800415 | -0.3437491277091165 | -0.3058353589781565 | -0.514741922074395 | -0.1896855823179573 | -0.3643652380831216 | -0.2772509411330016 | -0.1858118672283469 | -0.2795416448980184 | -0.5191503542767534 | -0.3152926171458969 | -0.5717426025937533 |
Top 10 teams of regional forecast window 2, period-aggregated, variable-averaged RPSSs
| Teamname | Team_rank | Modelname | Model_rank | Global | Tropics | NHem. ExTro. | SHem. ExTro. | NHem. Polar | SHem. Polar | Europe | N. Amer. | S. Amer. | Africa | Asia | Oceania |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| CMAandFDU | 1 | FengshunAdjust | 1 | 0.0609704538284342 | 0.117393332091908 | 0.0290144145871781 | 0.0021789788322219 | 0.0329664598681853 | -0.013654632678297 | 0.0027472023596456 | 0.0579614131295659 | 0.1326416096273614 | 0.1101404401676157 | 0.0724607101255174 | 0.1291803393447884 |
| CMAandFDU | 1 | FengshunHybrid | 2 | 0.0527583612632312 | 0.0795492419136108 | 0.0318531997655443 | 0.0280222931886769 | 0.0246930501049112 | 0.0203242646302143 | 0.0102752764068216 | 0.0273708465032861 | 0.0602399886026433 | 0.0646956343244963 | 0.0768472737834497 | 0.0471767410652232 |
| CMAandFDU | 1 | Fengshun | 14 | -0.0024703227562228 | 0.0010035000257188 | 0.0057860086159402 | 0.0290300134027464 | 0.0528683665772347 | -0.0802104767690924 | 0.0057550728709226 | -0.0236900708688099 | 0.0070644505770458 | -0.0767967082053618 | 0.0419895158234453 | 0.0782722632859353 |
| MicroEnsemble | 2 | StillLearning | 3 | 0.0514150687505273 | 0.0994368794231486 | 0.0058952885216756 | 0.0390823186782357 | 0.0454623875943802 | -0.0223098980709466 | -0.0188645900844365 | 0.0187466422482555 | 0.0951132465576866 | 0.0827033207464629 | 0.0550485982168662 | 0.0935829545319012 |
| MicroEnsemble | 2 | MicroDuet | 4 | 0.0453574274924767 | 0.0868847329704792 | 2.6171987126500512e-05 | 0.0466364158862867 | 0.0424746291161806 | -0.0127951477654476 | -0.0257086234725832 | 0.0462191831467234 | 0.114424848878271 | 0.0171453225657822 | 0.0443910842604825 | 0.1028292034557147 |
| MicroEnsemble | 2 | Huracan | 8 | 0.0088160778839042 | 0.0214323315030628 | -0.0168919738564884 | 0.052769551703249 | 0.0320281778374951 | -0.043590992662836 | -0.0469603674862009 | 0.0318434251385058 | 0.0482364649984184 | -0.0595537778051697 | 0.0014628388160801 | 0.059454537846756 |
| LP | 3 | LPM | 5 | 0.0359757073138679 | 0.0559889997164319 | 0.025555567586773 | 0.0442004488928816 | 0.022166670256691 | -0.0593037753391682 | -0.0021551249797733 | 0.0357581423581214 | 0.077140296483522 | 0.0238210406588864 | 0.0661306496363801 | 0.0060609838732028 |
| AIFS | 4 | AIFSgaia | 6 | 0.0243550828895415 | 0.0404042191841902 | -0.0130883674124977 | 0.0319799298924255 | 0.0289636238955928 | 0.0293269032816071 | -0.0168687276128939 | -0.0506688973797992 | 0.1183199481559452 | 0.0086054565768282 | 0.0327986486195891 | 0.0093627390311302 |
| AIFS | 4 | AIFShera | 7 | 0.0089699221170026 | 0.0141127415355947 | -0.0197811705129096 | 0.005385453602815 | 0.0451679852169668 | -0.0151711477921617 | 0.0090224910333509 | -0.0828559097438942 | 0.0780533585858897 | 0.0067065738790715 | 0.0190902424568871 | 0.0605170623404766 |
| AIFS | 4 | AIFSthalassa | 15 | -0.0045689093707475 | -0.0275378435840423 | 0.000431252264004 | -0.0074559730282795 | 0.0214832494753073 | 0.0264837272557239 | 0.0131558162937652 | -0.0457925207137526 | 0.0404753052720088 | -0.0799169231714984 | 0.0296227580306912 | -0.1172533776665263 |
| KITKangu | 5 | KanguPlusPlus | 9 | 3.6718625917586682e-09 | -9.11592653215128e-10 | -6.9456635258073155e-09 | -8.849109223163511e-09 | 3.2644538923894593e-08 | 3.3818281545509414e-08 | 6.216317534798084e-09 | 2.7036507107188372e-09 | -1.5391835953929938e-09 | -1.098188059008483e-09 | 2.2373580395769932e-09 | -5.1284800850481815e-09 |
| KITKangu | 5 | KanguParametricPrediction | 9 | 3.6718625917586682e-09 | -9.11592653215128e-10 | -6.9456635258073155e-09 | -8.849109223163511e-09 | 3.2644538923894593e-08 | 3.3818281545509414e-08 | 6.216317534798084e-09 | 2.7036507107188372e-09 | -1.5391835953929938e-09 | -1.098188059008483e-09 | 2.2373580395769932e-09 | -5.1284800850481815e-09 |
| scienceAI | 5 | findforecast | 9 | 3.6718625917586682e-09 | -9.11592653215128e-10 | -6.9456635258073155e-09 | -8.849109223163511e-09 | 3.2644538923894593e-08 | 3.3818281545509414e-08 | 6.216317534798084e-09 | 2.7036507107188372e-09 | -1.5391835953929938e-09 | -1.098188059008483e-09 | 2.2373580395769932e-09 | -5.1284800850481815e-09 |
| scienceAI | 5 | zephyr | 9 | 3.6718625917586682e-09 | -9.11592653215128e-10 | -6.9456635258073155e-09 | -8.849109223163511e-09 | 3.2644538923894593e-08 | 3.3818281545509414e-08 | 6.216317534798084e-09 | 2.7036507107188372e-09 | -1.5391835953929938e-09 | -1.098188059008483e-09 | 2.2373580395769932e-09 | -5.1284800850481815e-09 |
| scienceAI | 5 | ngcm | 9 | 3.6718625917586682e-09 | -9.11592653215128e-10 | -6.9456635258073155e-09 | -8.849109223163511e-09 | 3.2644538923894593e-08 | 3.3818281545509414e-08 | 6.216317534798084e-09 | 2.7036507107188372e-09 | -1.5391835953929938e-09 | -1.098188059008483e-09 | 2.2373580395769932e-09 | -5.1284800850481815e-09 |
| KITKangu | 5 | KanguS2SEasyUQ | 39 | -1.3898377989107802 | -1.441187434994885 | -1.3093072382340456 | -0.860630554294317 | -1.3427116980759906 | -1.4422746100474977 | -1.2186735698604043 | -1.2607653359586928 | -1.5319290385318312 | -1.5089622367155702 | -1.3642179502171723 | -1.1739736179229532 |
| CliMA | 7 | CliMAWeather | 16 | -0.0997201052792394 | -0.1377858110105982 | -0.1140509675418923 | -0.1273071718057634 | -0.0311224375091261 | 0.0243030786480396 | -0.1222154148202415 | -0.1197948503852352 | -0.1165648625704478 | -0.268687510871712 | -0.1030548454398337 | 0.0886144356093481 |
| CliMA | 7 | CliMAWeather2 | 17 | -0.1059069014291666 | -0.1419025408404863 | -0.1229622322352672 | -0.138326158039714 | -0.0365455034092823 | 0.0201287617477252 | -0.1337811845702122 | -0.1328807611088117 | -0.1296215688597378 | -0.2781686872827242 | -0.1021342020041919 | 0.0896058916967846 |
| FengWuW2S | 8 | FengWu2 | 18 | -0.2637982180490629 | -0.2033104087319254 | -0.4566306866878422 | 0.0082240677934922 | -0.3084377184380691 | -0.0543464215892999 | -0.3005374637541703 | -0.0575678629486624 | -0.0982088461358459 | -0.2759119544023418 | -0.6159696986850421 | -0.2824437317201644 |
| FengWuW2S | 8 | FengWu | 20 | -0.3504299211906538 | -0.4763511384737846 | -0.3626642293556934 | -0.1056370471891408 | -0.2049489872363674 | 0.00731284214924 | -0.2711071906489209 | -0.1387664854323166 | -0.5872330110348261 | -0.6784948352598686 | -0.4717580082311046 | -0.3325387705162455 |
| NordicS2S | 9 | NordicS2S1 | 19 | -0.315404475963272 | -0.3416426849164855 | -0.2397346021733059 | -0.2497766043761195 | -0.1610331796484567 | -0.4388890891503878 | -0.1530534219882934 | -0.1652146380614118 | -0.2834361503133122 | -0.584097045647085 | -0.2586756831282115 | -0.2724499367123978 |
| NordicS2S | 9 | NordicS2S3 | 27 | -0.5265507521364994 | -0.7781466803509519 | -0.2803937608639974 | -0.1109370105503585 | -0.3279495054293059 | -0.4273705113906661 | -0.3210480276202649 | -0.4773649561729151 | -0.7035491360084464 | -0.9232246580349408 | -0.413383558677425 | -0.5888730372499086 |
| NordicS2S | 9 | NordicS2S2 | 29 | -0.6055187799634721 | -0.7305827547936555 | -0.6544465859052372 | -0.7637247864748167 | -0.2216312591612375 | -0.0923550123927006 | -0.3700162228329868 | -0.6691351136680801 | -0.7803976959404016 | -0.8535560955625039 | -0.5034382684853603 | -0.5533929938645247 |
| Sibyl | 10 | ClimSDE | 21 | -0.3863240455654866 | -0.3921940542797756 | -0.3189914326490093 | -0.7794869395383307 | -0.260770625666276 | -0.5060688115995131 | -0.2755647156107741 | -0.3448042322040479 | -0.633935566134859 | -0.4091174055142876 | -0.2197063409780724 | -0.2985457795394503 |