Update README.md

README.md

@@ -80,7 +80,11 @@ ai-models anemoi --checkpoint aifs_single_v0.2.1.ckpt --file example_20241107_12
 
 **Note** we train AIFS using `flash_attention` (https://github.com/Dao-AILab/flash-attention).
 There are currently some issues when trying to install flash attention with the latest PyTorch version 2.5 and CUDA 12.4 (https://github.com/Dao-AILab/flash-attention/issues/1330).
-For that reason, we recommend you install PyTorch 2.4.
+For that reason, we recommend you install PyTorch 2.4. 
+Additonally the use of 'Flash Attention' package also imposes certain requirements in terms of software and hardware. Those can be found under #Installation and Features in https://github.com/Dao-AILab/flash-attention
+
+
+
 
 After running the `ai-models` command the output of the forecast should be written into `anemoi.grib` 
 Below you can find an example to read that file and load it as numpy array or xarray. 
@@ -178,7 +182,7 @@ sdaf) can be found in e.g Ben Bouallegue et al. ` [2024].
 </div>
 
 
-Forecasts are initialised on 00 and 12 UTC. Shown are relative score changes as function of lead time (day 1 to 10) for northern extra-tropics (n.hem),
+Forecasts are initialised on 00 and 12 UTC. The scorecard show relative score changes as function of lead time (day 1 to 10) for northern extra-tropics (n.hem),
 southern extra-tropics (s.hem), tropics and Europe. Blue colours mark score improvements and red colours score
 degradations. Purple colours indicate an increased in standard deviation of forecast anomaly, while green colours
 indicate a reduction. Framed rectangles indicate 95% significance level. Variables are geopotential (z), temperature
@@ -188,6 +192,13 @@ suffix indicates verification against IFS NWP analyses (an) or radiosonde and SY
 shown are anomaly correlation (ccaf), SEEPS (seeps, for precipitation), RMSE (rmsef) and standard deviation of
 forecast anomaly (sdaf, see text for more explanation).
 
+Additional evaluation analysis including tropycal cyclone performance or comparison against other popular data-driven models can be found in AIFS preprint (https://arxiv.org/pdf/2406.01465v1) section 4.
+
+# Known limitations
+- This version of AIFS shares certain limitations with some of the other data-driven weather forecast models that are trained with a weighted MSE loss, such as blurring of the forecast fields at longer lead times.
+- AIFS exhibits reduced forecast skill in the stratosphere forecast owing to the linear loss scaling with height
+- AIFS currently provides reduced intensity of some high-impact systems such as tropical cyclones.
+
 ## Technical Specifications
 
 ### Hardware