Update README.md

README.md

@@ -12,12 +12,6 @@ pipeline_tag: graph-ml
 Here, we introduce the **Artificial Intelligence Forecasting System (AIFS)**, a data driven forecast
 model developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). 
 
-AIFS is based on a graph neural network (GNN) encoder and decoder, and a sliding window transformer processor, 
-and is trained on ECMWF’s ERA5 re-analysis and ECMWF’s operational numerical weather prediction (NWP) analyses. 
-It has a flexible and modular design and supports several levels of parallelism to enable training on
-high resolution input data. AIFS forecast skill is assessed by comparing its forecasts to NWP analyses
-and direct observational data. 
-
 We show that AIFS produces highly skilled forecasts for upper-air variables, surface weather parameters and
 tropical cyclone tracks. AIFS is run four times daily alongside ECMWF’s physics-based NWP model and forecasts
 are available to the public under ECMWF’s open data policy.
@@ -28,7 +22,11 @@ are available to the public under ECMWF’s open data policy.
 
 <!-- Provide a longer summary of what this model is. -->
 
-{{ model_description | default("", true) }}
+AIFS is based on a graph neural network (GNN) encoder and decoder, and a sliding window transformer processor, 
+and is trained on ECMWF’s ERA5 re-analysis and ECMWF’s operational numerical weather prediction (NWP) analyses. 
+It has a flexible and modular design and supports several levels of parallelism to enable training on
+high resolution input data. AIFS forecast skill is assessed by comparing its forecasts to NWP analyses
+and direct observational data. 
 
 - **Developed by:** {{ developers | default("[More Information Needed]", true)}}
 - **Funded by [optional]:** {{ funded_by | default("[More Information Needed]", true)}}