Update README.md

README.md

@@ -162,6 +162,8 @@ anemoi-training train --config-name=config_pretraining.yaml
 ```
 
 Now, you can fine-tune your model for rollout using the `run_id` of your previous run,
+Note - this run_id refers to the experiment tracker run_id. To reproduce the training recipe, we recommend you use Mlflow as your experiment tracker.
+For more details, please refer to https://anemoi.readthedocs.io/projects/training/en/latest/user-guide/training.html#restarting-a-training-run
 
 ```
 export PRETRAINING_RUN_ID=???????
@@ -278,13 +280,14 @@ 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.
 
+Please refer to https://confluence.ecmwf.int/display/FCST/Known+AIFS+Forecasting+Issues for further details
+
 ## Technical Specifications
 
 ### Hardware