Update README.md

README.md

@@ -86,7 +86,17 @@ Use the code below to get started with the model.
 
 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
 
-{{ training_data | default("[More Information Needed]", true)}}
+AIFS is trained to produce 6-hour forecasts. It receives as input a representation of the atmospheric states
+at \\(t_{−6h}\\), \\(t_{0}\\), and then forecasts the state at time \\(t_{+6h}\\). 
+
+The full list of input and output fields is shown below:
+
+| Field                                                                                                                                                       | Level type                                                                   | Input/Output |
+|-------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------|--------------|
+| Geopotential, horizontal and vertical wind components, specific humidity, temperature                                                                       | Pressure level: 50,100, 150, 200, 250,300, 400, 500, 600,700, 850, 925, 1000 | Both         |
+| Surface pressure, mean sea-level pressure, skin temperature, 2 m temperature, 2 m dewpoint temperature, 10 m horizontal wind components, total column water | Surface                                                                      | Both         |
+| Total precipitation, convective precipitation                                                                                                               | Surface                                                                      | Output       |
+| Land-sea mask, orography, standard deviation of sub-grid orography, slope of sub-scale orography, insolation, latitude/longitude, time of day/day of year    | Surface                                                                      | Input        |
 
 ### Training Procedure