Update README.md

README.md

@@ -148,20 +148,20 @@ All data has daily and 1.5-degree resolution.
 ## 💡 Baseline Models
 In addition to climatology and persistence, we evaluate the following:
 
-    - Physics-based models (including control/perturbed forecasts):
-        - [x] UKMO: UK Meteorological Office
-        - [x] NCEP: National Centers for Environmental Prediction
-        - [x] CMA: China Meteorological Administration
-        - [x] ECMWF: European Centre for Medium-Range Weather Forecasts
-    - Data-driven models:
-        - [x] Lagged-Autoencoder
-        - [x] Fourier Neural Operator (FNO)
-        - [x] ResNet
-        - [x] UNet
-        - [x] ViT/ClimaX
-        - [x] PanguWeather
-        - [x] GraphCast
-        - [x] Fourcastnetv2
+1. __Physics-based models (including control/perturbed forecasts)__:
+    - [x] UKMO: UK Meteorological Office
+    - [x] NCEP: National Centers for Environmental Prediction
+    - [x] CMA: China Meteorological Administration
+    - [x] ECMWF: European Centre for Medium-Range Weather Forecasts
+2. __Data-driven models__:
+    - [x] Lagged-Autoencoder
+    - [x] Fourier Neural Operator (FNO)
+    - [x] ResNet
+    - [x] UNet
+    - [x] ViT/ClimaX
+    - [x] PanguWeather
+    - [x] GraphCast
+    - [x] Fourcastnetv2
 
 ## 🏅 Evaluation Metrics
 We divide our metrics into 3 classes: (1) Deterministic-based, which cover evaluation used in conventional deterministic forecasting tasks, (2) Physics-based, which are aimed to construct a more physically-faithful and explainable data-driven forecast, and (3) Probabilistic-based, which account for the skillfulness of ensemble forecasts.