Datasets:
LEAP
/

ArXiv:
License:
juannat7 commited on
Commit
defe1de
1 Parent(s): ac74290

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +10 -9
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 - 2023), we include ERA5/LRA5/ORAS5 reanalysis for a fully-coupled Earth system emulation (atmosphere-terrestrial-sea-ice)
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
+