Update README.md

README.md

@@ -28,19 +28,16 @@ It has a flexible and modular design and supports several levels of parallelism
 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)}}
-- **Shared by [optional]:** {{ shared_by | default("[More Information Needed]", true)}}
-- **Model type:** {{ model_type | default("[More Information Needed]", true)}}
-- **Language(s) (NLP):** {{ language | default("[More Information Needed]", true)}}
-- **License:** {{ license | default("[More Information Needed]", true)}}
-- **Finetuned from model [optional]:** {{ base_model | default("[More Information Needed]", true)}}
+- **Developed by:** ECMWF
+- **Model type:** Encoder-processor-decoder model
+- **License:** CC BY-SA 4.0
+
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** https://anemoi-docs.readthedocs.io/en/latest/index.html
+- **Repository:** [Anemoi](https://anemoi-docs.readthedocs.io/en/latest/index.html)
 - **Paper:** https://arxiv.org/pdf/2406.01465
 
 ## Uses
@@ -96,15 +93,21 @@ The full list of input and output fields is shown below:
 | 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        |
+| 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
 
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 
-#### Preprocessing [optional]
-
-{{ preprocessing | default("[More Information Needed]", true)}}
+- Pre-training was performed on ERA5 for the years 1979 to 2020 with a cosine learning rate (LR) schedule and a total
+of 260,000 steps. The LR is increased from 0 to \\(10^{-4}\\) during the first 1000 steps, then it is annealed to a minimum
+of \\(3 × 10^{-7}\\).
+- The pre-training is then followed by rollout on ERA5 for the years 1979 to 2018, this time with a LR
+of \\(6 × 10^{-7}\\). As in [Lam et al. [2023]](https://doi.org/10.48550/arXiv.2212.12794) we increase the
+rollout every 1000 training steps up to a maximum of 72 h (12 auto-regressive steps).
+- Finally, to further improve forecast performance, we fine-tune the model on operational real-time IFS NWP
+analyses. This is done via another round of rollout training, this time using IFS operational analysis data
+from 2019 and 2020
 
 
 #### Training Hyperparameters