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--- |
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license: cc-by-4.0 |
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metrics: |
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- mse |
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pipeline_tag: graph-ml |
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language: |
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- en |
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library_name: anemoi |
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--- |
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# AIFS Single - v1.0 |
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<!-- Provide a quick summary of what the model is/does. --> |
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Here, we introduce the **Artificial Intelligence Forecasting System (AIFS)**, a data driven forecast |
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model developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). |
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The release of AIFS Single v1.0 marks the first operationally supported AIFS model. Version 1 |
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supersedes the existing experimental version, [0.2.1 AIFS-single](https://huggingface.co./ecmwf/aifs-single-0.2.1). |
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The new version, 1.0, brings changes to the AIFS Single model, including among many others: |
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- Improved performance for upper-level atmospheric variables (AIFS Single still uses 13 pressure-levels, so this improvement mainly refers to 50 and 100 hPa) |
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- Improved skill for total precipitation. |
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- Additional output variables, including 100 meter winds, snow-fall, surface solar-radiation and land variables such as soil-moisture and soil-temperature. |
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<div style="display: flex; justify-content: center;"> |
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<img src="assets/radiation_cloudcover.gif" alt="AIFS 10 days Forecast" style="width: 50%;"/> |
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</div> |
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AIFS produces highly skilled forecasts for upper-air variables, surface weather parameters and |
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tropical cyclone tracks. AIFS Single is run four times daily alongside ECMWF’s physics-based NWP model and forecasts |
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are available to the public under ECMWF’s open data policy (https://www.ecmwf.int/en/forecasts/datasets/open-data). |
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Note that due to the non-determinism of GPUs, users will be unable to exactly reproduce an official AIFS forecast |
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when running AIFS Single themselves. |
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For more details please refer to https://confluence.ecmwf.int/display/FCST/Implementation+of+AIFS+Single+v1 |
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## Data Details |
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### Data parameters |
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#### New parameters |
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More detailed information about the new parameters introduced with AIFS Single v1.0 is provided in the table below. |
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| Short Name | Name | Units | Component Type | Lev.Type | |
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|:----------:|:----:|:-----:|:--------------:|:--------:| |
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| ssrd | Surface short-wave (solar) radiation downwards | \\(J m^{-2}\\) | AIFS | sfc | |
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| strd | Surface long-wave (thermal) radiation downwards | \\(J m^{-2}\\) | AIFS | sfc | |
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| lcc | Low cloud cover | \\((0 - 1)\\) | AIFS | sfc | |
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| mcc | Medium cloud cover | \\((0 - 1)\\) | AIFS | sfc | |
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| hcc | High cloud cover | \\((0 - 1)\\) | AIFS | sfc | |
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| sf | Snowfall water equivalent | \\(kg m^{-2}\\) | AIFS | sfc | |
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| tcc | Total cloud cover | \\((0 - 1)\\) | AIFS | sfc | |
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| 100u | 100 metre U wind component | \\(m s^{-1}\\) | AIFS | sfc | |
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| 100v | 100 metre V wind component | \\(m s^{-1}\\) | AIFS | sfc | |
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| rowe | Runoff water equivalent (surface plus subsurface) | \\(kg m^{-2}\\) | AIFS | sfc | |
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| vsw | Volumetric soil moisture | \\(m^3 m^{-3}\\) | AIFS | sol | |
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| sot | Soil temperature | \\(K\\) | AIFS | sol | |
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#### Changes to existing parameters |
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There are no changes to existing parameters already introduced with AIFS Single v0.2.1. |
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**Note** |
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Regarding precipitation units, it's worth noting that AIFS model was trained on \\(m^{3}/m^{2}\\) and will therefore produce precip in that units. |
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If one wants to retrieve precipitation from Open data, the units will be \\(mm\\). |
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#### Discontinued parameters |
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No parameters have been discontinued with regards to the previous version of AIFS Single v0.2.1. |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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AIFS is based on a graph neural network (GNN) encoder and decoder, and a sliding window transformer processor, |
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and is trained on ECMWF’s ERA5 re-analysis and ECMWF’s operational numerical weather prediction (NWP) analyses. |
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<div style="display: flex; justify-content: center;"> |
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<img src="assets/encoder_graph.jpeg" alt="Encoder graph" style="width: 50%;"/> |
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<img src="assets/decoder_graph.jpeg" alt="Decoder graph" style="width: 50%;"/> |
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</div> |
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It has a flexible and modular design and supports several levels of parallelism to enable training on |
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high resolution input data. AIFS forecast skill is assessed by comparing its forecasts to NWP analyses |
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and direct observational data. |
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- **Developed by:** ECMWF |
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- **Model type:** Encoder-processor-decoder model |
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- **License:** These model weights are published under a Creative Commons Attribution 4.0 International (CC BY 4.0). |
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To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ |
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### Model resolution |
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There are no changes in resolution compared to previous version AIFS Single v0.2.1. |
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| | Component | Horizontal Resolution [kms] | Vertical Resolution [levels] | |
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|---|:---:|:---:|:---:| |
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| Atmosphere | AIFS-single v1.0 | ~ 36 | 13 | |
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### Model Sources |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** [Anemoi](https://anemoi-docs.readthedocs.io/en/latest/index.html) is an open-source framework for |
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creating machine learning (ML) weather forecasting systems, which ECMWF and a range of national meteorological |
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services across Europe have co-developed. |
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- **Paper:** https://arxiv.org/pdf/2406.01465 |
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## How to Get Started with the Model |
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To generate a new forecast using AIFS, you can use [anemoi-inference](https://github.com/ecmwf/anemoi-inference). In the [following notebook](run_AIFS_v1.ipynb), a |
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step-by-step workflow is specified to run the AIFS using the HuggingFace model: |
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1. **Install Required Packages and Imports** |
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2. **Retrieve Initial Conditions from ECMWF Open Data** |
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- Select a date |
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- Get the data from the [ECMWF Open Data API](https://www.ecmwf.int/en/forecasts/datasets/open-data) |
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- Get input fields |
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- Add the single levels fields and pressure levels fields |
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- Convert geopotential height into geopotential |
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- Create the initial state |
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3. **Load the Model and Run the Forecast** |
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- Download the Model's Checkpoint from Hugging Face |
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- Create a runner |
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- Run the forecast using anemoi-inference |
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4. **Inspect the generated forecast** |
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- Plot a field |
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🚨 **Note** we train AIFS using `flash_attention` (https://github.com/Dao-AILab/flash-attention). |
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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 |
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🚨 **Note** the `aifs_single_v1.0.ckpt` checkpoint just contains the model’s weights. |
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That file does not contain any information about the optimizer states, lr-scheduler states, etc. |
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## How to train AIFS Single v1.0 |
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To train this model you can use the configuration files included in this repository and the following Anemoi packages: |
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``` |
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anemoi-training==0.3.1 |
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anemoi-models==0.4.0 |
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anemoi-graphs==0.4.4 |
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``` |
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and run the pretraining stage as follows, |
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``` |
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export DATASETS_PATH=??????? |
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export OUTPUT_PATH=??????? |
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anemoi-training train --config-name=config_pretraining.yaml |
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``` |
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Now, you can fine-tune your model for rollout using the `run_id` of your previous run, |
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Note - this run_id refers to the run_id and you can find it looking at the checkpoint folder path. |
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For more details, please refer to https://anemoi.readthedocs.io/projects/training/en/latest/user-guide/training.html#restarting-a-training-run |
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``` |
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export PRETRAINING_RUN_ID=??????? |
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anemoi-training train --config-name=config_finetuning.yaml |
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``` |
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## Training Details |
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### Training Data |
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<!-- 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. --> |
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AIFS is trained to produce 6-hour forecasts. It receives as input a representation of the atmospheric states |
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at \\(t_{−6h}\\), \\(t_{0}\\), and then forecasts the state at time \\(t_{+6h}\\). |
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<div style="display: flex; justify-content: center;"> |
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<img src="assets/aifs_diagram.png" alt="AIFS 2m Temperature" style="width: 80%;"/> |
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</div> |
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The full list of input and output fields is shown below: |
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| Field | Level type | Input/Output | |
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|-------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------|--------------| |
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| 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 | |
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| 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 | |
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| Soil moisture and soil temperature (layers 1 & 2) | Surface | Both | |
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| 100m horizontal wind components, solar radiation (Surface short-wave (solar) radiation downwards and Surface long-wave (thermal) radiation downwards), cloud variables (tcc, hcc, mcc, lcc), runoff and snow fall | Surface | Output | |
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| Total precipitation, convective precipitation | Surface | Output | |
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| 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 | |
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Input and output states are normalised to unit variance and zero mean for each level. Some of |
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the forcing variables, like orography, are min-max normalised. |
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### Training Procedure |
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Based on the different experiments we have made - the final training recipe for AIFS Single v1.0 has deviated slightly |
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from the one used for AIFS Single v0.2.1 since we found that we could get a well trained model by skipping the ERA5 |
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rollout and directly doing the rollout on the operational-analysis (extended) dataset. When we say 'extended' we refer |
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to the fact that for AIFS Single v0.2.1 we used just operational-analysis data from 2019 to 2021, while in this new |
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release we have done the fine-tunning from 2016 to 2022. |
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The other important change in the fine-tuning stage is that for AIFS Single v0.2.1 after the 6hr model training the |
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optimiser was not restarted (ie. rollout was done with the minimal lr of \\(3 × 10^{-7}\\)). For this release we have seen |
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that restarting the optimiser for the rollout improves the model's performance. For the operational-fine tuning rollout |
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stage, the learning rate cycle is restarted, gradually decreasing to the minimum value at the end of rollout. |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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- **Pre-training**: It was performed on ERA5 for the years 1979 to 2022 with a cosine learning rate (LR) schedule and a |
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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 |
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minimum of \\(3 × 10^{-7}\\). The local learning rate used for this stage is \\(3.125 × 10^{-5}\\). |
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- **Fine-tuning**: The pre-training is then followed by rollout on operational real-time IFS NWP analyses for the years |
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2016 to 2022, this time with a local learning rate of \\(8 × 10^{−7}\\), which is decreased to \\(3 × 10^{−7}\\). Rollout steps |
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increase per epoch. In this second stage the warm up period of the optimiser is 100 steps to account for shorter length |
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of this stage. Optimizer step are equal to 7900 ( 12 epoch with ~630 steps per epoch). |
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As in the previous version of aifs-single for fine-tuning and initialisation of the model during inference, IFS fields |
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are interpolated from their native O1280 resolution (approximately \\(0.1°\\)) down to N320 (approximately \\(0.25°\\)). |
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#### Training Hyperparameters |
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- **Optimizer:** We use *AdamW* (Loshchilov and Hutter [2019]) with the \\(β\\)-coefficients set to 0.9 and 0.95. |
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- **Loss function:** The loss function is an area-weighted mean squared error (MSE) between the target atmospheric state |
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and prediction. |
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- **Loss scaling:** A loss scaling is applied for each output variable. The scaling was chosen empirically such that |
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all prognostic variables have roughly equal contributions to the loss, with the exception of the vertical velocities, |
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for which the weight was reduced. The loss weights also decrease linearly with height, which means that levels in |
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the upper atmosphere (e.g., 50 hPa) contribute relatively little to the total loss value. |
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#### Speeds, Sizes, Times |
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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Data parallelism is used for training, with a batch size of 16. One model instance is split across four 40GB A100 |
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GPUs within one node. Training is done using mixed precision (Micikevicius et al. [2018]), and the entire process |
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takes about one week, with 64 GPUs in total. The checkpoint size is 1.19 GB and as mentioned above, it does not include the optimizer |
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state. |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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AIFS is evaluated against ECMWF IFS (Integrated Forecast System) for 2022. The results of such evaluation are summarized in |
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the scorecard below that compares different forecast skill measures across a range of |
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variables. For verification, each system is compared against the operational ECMWF analysis from which the forecasts |
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are initialised. In addition, the forecasts are compared against radiosonde observations of geopotential, temperature |
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and windspeed, and SYNOP observations of 2 m temperature, 10 m wind and 24 h total precipitation. The definition |
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of the metrics, such as ACC (ccaf), RMSE (rmsef) and forecast activity (standard deviation of forecast anomaly, |
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sdaf) can be found in e.g Ben Bouallegue et al. ` [2024]. |
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### AIFS Single v1.0 vs AIFS Single v0.2.1 (2023) |
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<div style="display: flex; justify-content: center;"> |
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<img src="assets/scorecard_single1.0_vs_single0.2.1_2023.png" alt="Scorecard comparing forecast scores of AIFS versus IFS (2022)" style="width: 80%;"/> |
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</div> |
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### AIFS Single v1.0 vs IFS (2024) |
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<div style="display: flex; justify-content: center;"> |
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<img src="assets/scorecard_single1.0_vs_ifs_2024.png" alt="Scorecard comparing forecast scores of AIFS versus IFS (2022)" style="width: 80%;"/> |
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</div> |
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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), |
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southern extra-tropics (s.hem), tropics and Europe. Blue colours mark score improvements and red colours score |
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degradations. Purple colours indicate an increased in standard deviation of forecast anomaly, while green colours |
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indicate a reduction. Framed rectangles indicate 95% significance level. Variables are geopotential (z), temperature |
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(t), wind speed (ff), mean sea level pressure (msl), 2 m temperature (2t), 10 m wind speed (10ff) and 24 hr total |
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precipitation (tp). Numbers behind variable abbreviations indicate variables on pressure levels (e.g., 500 hPa), and |
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suffix indicates verification against IFS NWP analyses (an) or radiosonde and SYNOP observations (ob). Scores |
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shown are anomaly correlation (ccaf), SEEPS (seeps, for precipitation), RMSE (rmsef) and standard deviation of |
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forecast anomaly (sdaf, see text for more explanation). |
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# Known limitations |
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- 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. |
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- AIFS exhibits reduced forecast skill in the stratosphere forecast owing to the linear loss scaling with height |
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- AIFS currently provides reduced intensity of some high-impact systems such as tropical cyclones. |
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Please refer to https://confluence.ecmwf.int/display/FCST/Known+AIFS+Forecasting+Issues for further details |
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## Technical Specifications |
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### Hardware |
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<!-- {{ hardware_requirements | default("[More Information Needed]", true)}} --> |
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We acknowledge PRACE for awarding us access to Leonardo, CINECA, Italy. In particular, this version of the AIFS has been trained |
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on 64 A100 GPUs (40GB). |
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### Software |
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The model was developed and trained using the [AnemoI framework](https://anemoi-docs.readthedocs.io/en/latest/index.html). |
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AnemoI is a framework for developing machine learning weather forecasting models. It comprises of components or packages |
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for preparing training datasets, conducting ML model training and a registry for datasets and trained models. AnemoI |
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provides tools for operational inference, including interfacing to verification software. As a framework it seeks to |
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handle many of the complexities that meteorological organisations will share, allowing them to easily train models from |
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existing recipes but with their own data. |
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## Citation |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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If you use this model in your work, please cite it as follows: |
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**BibTeX:** |
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``` |
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@article{lang2024aifs, |
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title={AIFS-ECMWF's data-driven forecasting system}, |
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author={Lang, Simon and Alexe, Mihai and Chantry, Matthew and Dramsch, Jesper and Pinault, Florian and Raoult, Baudouin and Clare, Mariana CA and Lessig, Christian and Maier-Gerber, Michael and Magnusson, Linus and others}, |
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journal={arXiv preprint arXiv:2406.01465}, |
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year={2024} |
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} |
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``` |
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**APA:** |
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``` |
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Lang, S., Alexe, M., Chantry, M., Dramsch, J., Pinault, F., Raoult, B., ... & Rabier, F. (2024). AIFS-ECMWF's data-driven forecasting system. arXiv preprint arXiv:2406.01465. |
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``` |
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## More Information |
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[Find the paper here](https://arxiv.org/pdf/2406.01465) |
