Update README.md
Browse files
README.md
CHANGED
@@ -12,6 +12,8 @@ pipeline_tag: graph-ml
|
|
12 |
Here, we introduce the **Artificial Intelligence Forecasting System (AIFS)**, a data driven forecast
|
13 |
model developed by the European Centre for Medium-Range Weather Forecasts (ECMWF).
|
14 |
|
|
|
|
|
15 |
We show that AIFS produces highly skilled forecasts for upper-air variables, surface weather parameters and
|
16 |
tropical cyclone tracks. AIFS is run four times daily alongside ECMWF’s physics-based NWP model and forecasts
|
17 |
are available to the public under ECMWF’s open data policy.
|
@@ -24,6 +26,12 @@ are available to the public under ECMWF’s open data policy.
|
|
24 |
|
25 |
AIFS is based on a graph neural network (GNN) encoder and decoder, and a sliding window transformer processor,
|
26 |
and is trained on ECMWF’s ERA5 re-analysis and ECMWF’s operational numerical weather prediction (NWP) analyses.
|
|
|
|
|
|
|
|
|
|
|
|
|
27 |
It has a flexible and modular design and supports several levels of parallelism to enable training on
|
28 |
high resolution input data. AIFS forecast skill is assessed by comparing its forecasts to NWP analyses
|
29 |
and direct observational data.
|
|
|
12 |
Here, we introduce the **Artificial Intelligence Forecasting System (AIFS)**, a data driven forecast
|
13 |
model developed by the European Centre for Medium-Range Weather Forecasts (ECMWF).
|
14 |
|
15 |
+
![AIFS 10 days forecast](aifs_10days.gif)
|
16 |
+
|
17 |
We show that AIFS produces highly skilled forecasts for upper-air variables, surface weather parameters and
|
18 |
tropical cyclone tracks. AIFS is run four times daily alongside ECMWF’s physics-based NWP model and forecasts
|
19 |
are available to the public under ECMWF’s open data policy.
|
|
|
26 |
|
27 |
AIFS is based on a graph neural network (GNN) encoder and decoder, and a sliding window transformer processor,
|
28 |
and is trained on ECMWF’s ERA5 re-analysis and ECMWF’s operational numerical weather prediction (NWP) analyses.
|
29 |
+
|
30 |
+
<div style="display: flex;">
|
31 |
+
<img src="encoder_graph.jpeg" alt="Encoder graph" style="width: 50%;"/>
|
32 |
+
<img src="decoder_graph.jpeg" alt="Decoder graph" style="width: 50%;"/>
|
33 |
+
</div>
|
34 |
+
|
35 |
It has a flexible and modular design and supports several levels of parallelism to enable training on
|
36 |
high resolution input data. AIFS forecast skill is assessed by comparing its forecasts to NWP analyses
|
37 |
and direct observational data.
|