Update README.md
Browse files
README.md
CHANGED
@@ -13,9 +13,17 @@ viewer: false
|
|
13 |
|
14 |
ChaosBench is a benchmark project to improve and extend the predictability range of deep weather emulators to the subseasonal-to-seasonal (S2S) range. Predictability at this scale is more challenging due to its: (1) __double sensitivities__ to intial condition (in weather-scale) and boundary condition (in climate-scale), (2) __butterfly effect__, and our (3) __inherent lack of understanding__ of physical processes operating at this scale. Thus, given the __high socioeconomic stakes__ for accurate, reliable, and stable S2S forecasts (e.g., for disaster/extremes preparedness), this benchmark is timely for DL-accelerated solutions.
|
15 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
16 |
## ✨ Features
|
17 |
|
18 |
-
1️⃣ __Diverse Observations__. Spanning over 45 years (1979
|
19 |
|
20 |
2️⃣ __Diverse Baselines__. Wide selection of physics-based forecasts from leading national agencies in Europe, the UK, America, and Asia
|
21 |
|
@@ -198,11 +206,4 @@ We divide our metrics into 3 classes: (1) Deterministic-based, which cover evalu
|
|
198 |
- [x] Continuous Ranked Probability Skill Score (CRPSS)
|
199 |
- [x] Spread
|
200 |
- [x] Spread/Skill Ratio
|
201 |
-
|
202 |
-
|
203 |
-
## 🪜 Leaderboard
|
204 |
-
View our interactive leaderboard dashboard [here](https://leap-stc.github.io/ChaosBench/leaderboard.html)
|
205 |
-
|
206 |
-
You can access the full score and checkpoints in `logs/<MODEL_NAME>` within the following subdirectory:
|
207 |
-
- Scores: `eval/<METRIC>.csv`
|
208 |
-
- Model checkpoints: `lightning_logs/`
|
|
|
13 |
|
14 |
ChaosBench is a benchmark project to improve and extend the predictability range of deep weather emulators to the subseasonal-to-seasonal (S2S) range. Predictability at this scale is more challenging due to its: (1) __double sensitivities__ to intial condition (in weather-scale) and boundary condition (in climate-scale), (2) __butterfly effect__, and our (3) __inherent lack of understanding__ of physical processes operating at this scale. Thus, given the __high socioeconomic stakes__ for accurate, reliable, and stable S2S forecasts (e.g., for disaster/extremes preparedness), this benchmark is timely for DL-accelerated solutions.
|
15 |
|
16 |
+
## 🪜 Leaderboard
|
17 |
+
View our interactive leaderboard dashboard [here](https://leap-stc.github.io/ChaosBench/leaderboard.html)
|
18 |
+
|
19 |
+
You can access the full score and checkpoints in `logs/<MODEL_NAME>` within the following subdirectory:
|
20 |
+
- Scores: `eval/<METRIC>.csv`
|
21 |
+
- Model checkpoints: `lightning_logs/`
|
22 |
+
|
23 |
+
|
24 |
## ✨ Features
|
25 |
|
26 |
+
1️⃣ __Diverse Observations__. Spanning over 45 years (1979-), we include ERA5/LRA5/ORAS5 reanalysis for a fully-coupled Earth system emulation (atmosphere-terrestrial-sea-ice)
|
27 |
|
28 |
2️⃣ __Diverse Baselines__. Wide selection of physics-based forecasts from leading national agencies in Europe, the UK, America, and Asia
|
29 |
|
|
|
206 |
- [x] Continuous Ranked Probability Skill Score (CRPSS)
|
207 |
- [x] Spread
|
208 |
- [x] Spread/Skill Ratio
|
209 |
+
|
|
|
|
|
|
|
|
|
|
|
|
|
|