euler314
/

typhoon-predict

@@ -44,7 +44,9 @@ This selected example illustrates the model, not typical skill. Observations are
 Trackformer 1.1 predicts track and scalar intensity/structure outputs. **1.2 adds evolving sea-level-pressure fields and a moving pressure core**, giving the route forecast a spatial weather representation that can be inspected on a map.
-![Trackformer 1.1, 1.2 and WeatherNext Cyclones Mini: pressure intensity, track position and track direction on matched daily forecasts](https://huggingface.co/euler314/typhoon-predict/resolve/f1448bae8d3468d430180199fb73eedf0c45d14a/evaluation/released_daily/model_1_2_benchmark.png)
 | Development metric | 1.1 | 1.2 · mean of 50 | DeepMind Mini · one member | Shared coverage |
 | --- | ---: | ---: | ---: | --- |
@@ -81,7 +83,7 @@ The remaining 1,336 days / 230 historical storms overlap Mini's fitting years. J
 ## How Trackformer 1.2 works
-![Trackformer 1.2: causal weather history, coupled basin and moving core, pressure-based readout and autoregressive rollout](https://huggingface.co/euler314/typhoon-predict/resolve/f1448bae8d3468d430180199fb73eedf0c45d14a/docs/trackformer_1_2_architecture.svg)
 1. **Initialize from the past.** Nine six-hour analyses provide MSLP, 500 hPa height and winds at 850/500/200 hPa, ending at issue time. Static geography, the current observed centre, recent motion, current intensity and validity masks accompany the history. Native regional pressure detail is used when available; missing detail stays flagged. Current observations initialize the core, not future labels.
 2. **Evolve the environment.** A convolutional recurrent network combines steering-based advection with learned pressure tendencies. Multiscale spatial tokens and attention condition the environmental memory.

 Trackformer 1.1 predicts track and scalar intensity/structure outputs. **1.2 adds evolving sea-level-pressure fields and a moving pressure core**, giving the route forecast a spatial weather representation that can be inspected on a map.
+**The three-model comparison is complete and verified:** Trackformer 1.1, Trackformer 1.2 and Google's WeatherNext Cyclones Mini `<2024` were scored on **1,473 daily starts / 270 storms**. The DeepMind results below are completed RTX 3070 CUDA forecasts, not estimates or a pending run. [Completion receipt](https://huggingface.co/euler314/typhoon-predict/blob/main/evaluation/deepmind_daily/verification.json) · [Independent publication audit](https://huggingface.co/euler314/typhoon-predict/blob/main/evaluation/deepmind_daily/publication_audit.json).
+![Trackformer 1.1, 1.2 and WeatherNext Cyclones Mini: pressure intensity, track position and track direction on matched daily forecasts](https://huggingface.co/euler314/typhoon-predict/resolve/5066a907ceacffa4378159f0a67bfe3ffb561bb9/evaluation/released_daily/model_1_2_benchmark.png)
 | Development metric | 1.1 | 1.2 · mean of 50 | DeepMind Mini · one member | Shared coverage |
 | --- | ---: | ---: | ---: | --- |
 ## How Trackformer 1.2 works
+![Trackformer 1.2: causal weather history, coupled basin and moving core, pressure-based readout and autoregressive rollout](https://huggingface.co/euler314/typhoon-predict/resolve/5066a907ceacffa4378159f0a67bfe3ffb561bb9/docs/trackformer_1_2_architecture.svg)
 1. **Initialize from the past.** Nine six-hour analyses provide MSLP, 500 hPa height and winds at 850/500/200 hPa, ending at issue time. Static geography, the current observed centre, recent motion, current intensity and validity masks accompany the history. Native regional pressure detail is used when available; missing detail stays flagged. Current observations initialize the core, not future labels.
 2. **Evolve the environment.** A convolutional recurrent network combines steering-based advection with learned pressure tendencies. Multiscale spatial tokens and attention condition the environmental memory.