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
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 | 0.061 | 0.117 | 0.029 | 0.002 | 0.033 | -0.014 | 0.003 | 0.058 | 0.133 | 0.11 | 0.072 | 0.129 |
| CMAandFDU | 1 | FengshunHybrid | 2 | 0.053 | 0.08 | 0.032 | 0.028 | 0.025 | 0.02 | 0.01 | 0.027 | 0.06 | 0.065 | 0.077 | 0.047 |
| CMAandFDU | 1 | Fengshun | 14 | -0.002 | 0.001 | 0.006 | 0.029 | 0.053 | -0.08 | 0.006 | -0.024 | 0.007 | -0.077 | 0.042 | 0.078 |
| MicroEnsemble | 2 | StillLearning | 3 | 0.051 | 0.099 | 0.006 | 0.039 | 0.045 | -0.022 | -0.019 | 0.019 | 0.095 | 0.083 | 0.055 | 0.094 |
| MicroEnsemble | 2 | MicroDuet | 4 | 0.045 | 0.087 | 0.0 | 0.047 | 0.042 | -0.013 | -0.026 | 0.046 | 0.114 | 0.017 | 0.044 | 0.103 |
| MicroEnsemble | 2 | Huracan | 8 | 0.009 | 0.021 | -0.017 | 0.053 | 0.032 | -0.044 | -0.047 | 0.032 | 0.048 | -0.06 | 0.001 | 0.059 |
| LP | 3 | LPM | 5 | 0.036 | 0.056 | 0.026 | 0.044 | 0.022 | -0.059 | -0.002 | 0.036 | 0.077 | 0.024 | 0.066 | 0.006 |
| AIFS | 4 | AIFSgaia | 6 | 0.024 | 0.04 | -0.013 | 0.032 | 0.029 | 0.029 | -0.017 | -0.051 | 0.118 | 0.009 | 0.033 | 0.009 |
| AIFS | 4 | AIFShera | 7 | 0.009 | 0.014 | -0.02 | 0.005 | 0.045 | -0.015 | 0.009 | -0.083 | 0.078 | 0.007 | 0.019 | 0.061 |
| AIFS | 4 | AIFSthalassa | 15 | -0.005 | -0.028 | 0.0 | -0.007 | 0.021 | 0.026 | 0.013 | -0.046 | 0.04 | -0.08 | 0.03 | -0.117 |
| KITKangu | 5 | KanguPlusPlus | 9 | 0.0 | -0.0 | -0.0 | -0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -0.0 | -0.0 | 0.0 | -0.0 |
| KITKangu | 5 | KanguParametricPrediction | 9 | 0.0 | -0.0 | -0.0 | -0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -0.0 | -0.0 | 0.0 | -0.0 |
| scienceAI | 5 | findforecast | 9 | 0.0 | -0.0 | -0.0 | -0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -0.0 | -0.0 | 0.0 | -0.0 |
| scienceAI | 5 | zephyr | 9 | 0.0 | -0.0 | -0.0 | -0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -0.0 | -0.0 | 0.0 | -0.0 |
| scienceAI | 5 | ngcm | 9 | 0.0 | -0.0 | -0.0 | -0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -0.0 | -0.0 | 0.0 | -0.0 |
| KITKangu | 5 | KanguS2SEasyUQ | 39 | -1.39 | -1.441 | -1.309 | -0.861 | -1.343 | -1.442 | -1.219 | -1.261 | -1.532 | -1.509 | -1.364 | -1.174 |
| CliMA | 7 | CliMAWeather | 16 | -0.1 | -0.138 | -0.114 | -0.127 | -0.031 | 0.024 | -0.122 | -0.12 | -0.117 | -0.269 | -0.103 | 0.089 |
| CliMA | 7 | CliMAWeather2 | 17 | -0.106 | -0.142 | -0.123 | -0.138 | -0.037 | 0.02 | -0.134 | -0.133 | -0.13 | -0.278 | -0.102 | 0.09 |
| FengWuW2S | 8 | FengWu2 | 18 | -0.264 | -0.203 | -0.457 | 0.008 | -0.308 | -0.054 | -0.301 | -0.058 | -0.098 | -0.276 | -0.616 | -0.282 |
| FengWuW2S | 8 | FengWu | 20 | -0.35 | -0.476 | -0.363 | -0.106 | -0.205 | 0.007 | -0.271 | -0.139 | -0.587 | -0.678 | -0.472 | -0.333 |
| NordicS2S | 9 | NordicS2S1 | 19 | -0.315 | -0.342 | -0.24 | -0.25 | -0.161 | -0.439 | -0.153 | -0.165 | -0.283 | -0.584 | -0.259 | -0.272 |
| NordicS2S | 9 | NordicS2S3 | 27 | -0.527 | -0.778 | -0.28 | -0.111 | -0.328 | -0.427 | -0.321 | -0.477 | -0.704 | -0.923 | -0.413 | -0.589 |
| NordicS2S | 9 | NordicS2S2 | 29 | -0.606 | -0.731 | -0.654 | -0.764 | -0.222 | -0.092 | -0.37 | -0.669 | -0.78 | -0.854 | -0.503 | -0.553 |
| Sibyl | 10 | ClimSDE | 21 | -0.386 | -0.392 | -0.319 | -0.779 | -0.261 | -0.506 | -0.276 | -0.345 | -0.634 | -0.409 | -0.22 | -0.299 |