kashif HF Staff commited on
Commit
a5734df
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1 Parent(s): a1a2f7b

Add all four ensemble members as subfolders

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README.md CHANGED
@@ -28,8 +28,7 @@ simply several draws — which here is just the batch dimension.
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  | [WeatherNextCyclones](https://huggingface.co/kashif/weathernext-cyclones) | 0.25° (721×1440) | 40,962 | 183.8M | no |
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  These weights correspond to `WeatherNext2_<2025_model1`, trained on data through 2024 and fine-tuned for
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- initialization from operational ECMWF HRES analysis. The published ensemble uses four independently trained members
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- (`model1`–`model4`); this repository holds the first.
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  ## Usage
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@@ -61,17 +60,6 @@ forecast = processor.postprocess(outputs.prediction, state)
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  print(forecast["2m_temperature"].shape) # (1, 721, 1440)
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  ```
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- ### Ensembles
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-
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- Repeat the inputs along the batch axis; each member gets its own noise draw.
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-
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- ```python
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- members = 8
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- inputs = {key: value.repeat(members, *([1] * (value.ndim - 1))) for key, value in inputs.items()}
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- with torch.no_grad():
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- outputs = model(**inputs, generator=torch.Generator().manual_seed(0))
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- ```
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-
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  ### Autoregressive rollout
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  Each 6-hour step draws fresh noise. `advance_state` drops the oldest frame, appends the forecast, recomputes the clock
@@ -88,6 +76,53 @@ for step in range(20): # 5 days
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  state = processor.advance_state(state, forecast, valid_time)
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  ```
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  ## Model details
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  - **Architecture**: encode–process–decode graph network. The lat/lon grid is encoded, projected onto an icosahedral
 
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  | [WeatherNextCyclones](https://huggingface.co/kashif/weathernext-cyclones) | 0.25° (721×1440) | 40,962 | 183.8M | no |
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  These weights correspond to `WeatherNext2_<2025_model1`, trained on data through 2024 and fine-tuned for
31
+ initialization from operational ECMWF HRES analysis. All four independently trained members (`model1`–`model4`) are included; see [Ensembles](#ensembles).
 
32
 
33
  ## Usage
34
 
 
60
  print(forecast["2m_temperature"].shape) # (1, 721, 1440)
61
  ```
62
 
 
 
 
 
 
 
 
 
 
 
 
63
  ### Autoregressive rollout
64
 
65
  Each 6-hour step draws fresh noise. `advance_state` drops the oldest frame, appends the forecast, recomputes the clock
 
76
  state = processor.advance_state(state, forecast, valid_time)
77
  ```
78
 
79
+ ## Ensembles
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+
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+ There are two independent ensembles here, and useful forecasts combine both.
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+
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+ **1. Noise ensemble (within one checkpoint).** This is the FGN mechanism: each member is one draw of the
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+ 32-dimensional noise vector through the same weights. Members are the batch axis, and stay independent through an
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+ autoregressive rollout.
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+
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+ ```python
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+ members = 8
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+ inputs = processor(state, seconds_since_epoch=valid_time)
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+ inputs = {key: value.repeat(members, *([1] * (value.ndim - 1))) for key, value in inputs.items()}
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+ with torch.no_grad():
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+ outputs = model(**inputs, generator=torch.Generator().manual_seed(0))
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+ ```
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+
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+ At 0.25° one member needs roughly 50 GB, so batching all members at once usually will not fit on a single device.
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+ Loop over draws instead (or shard them across devices); the result is identical.
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+
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+ ```python
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+ predictions = []
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+ for member in range(members):
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+ noise = torch.randn(1, model.config.noise_channels, generator=torch.Generator().manual_seed(member))
102
+ with torch.no_grad():
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+ predictions.append(model(**inputs, noise=noise).prediction)
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+ ```
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+
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+ **2. Multi-model ensemble (across checkpoints).** The released product is four independently trained networks. Member 1
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+ is at the repository root; all four are also available as subfolders, so you can loop uniformly.
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+
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+ ```python
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+ REPO = "kashif/weathernext2"
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+
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+ def load_member(member: int, revision: str = "main"):
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+ return WeatherNext2ForWeatherForecasting.from_pretrained(
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+ REPO, subfolder=f"model{member}", revision=revision
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+ ).eval()
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+
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+ models = [load_member(i) for i in range(1, 5)] # or a subset, they are ~700 MB each
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+ ```
119
+
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+ `subfolder` works the same way for [`WeatherNext2Processor`] and `AutoConfig`, since each subfolder holds its own
121
+ `config.json` and `preprocessor_config.json`. The processors are identical across members, so loading one is enough.
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+
123
+ Combining the two gives the full ensemble: `num_models x num_noise_draws` trajectories, from which you take the
124
+ ensemble mean, spread, or whatever quantiles the downstream product needs.
125
+
126
  ## Model details
127
 
128
  - **Architecture**: encode–process–decode graph network. The lat/lon grid is encoded, projected onto an icosahedral
model1/config.json ADDED
@@ -0,0 +1,121 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "aggregate_normalization": 4.0,
3
+ "architectures": [
4
+ "WeatherNext2ForWeatherForecasting"
5
+ ],
6
+ "atmospheric_variables": [
7
+ "temperature",
8
+ "geopotential",
9
+ "u_component_of_wind",
10
+ "v_component_of_wind",
11
+ "vertical_velocity",
12
+ "specific_humidity"
13
+ ],
14
+ "attention_dropout": 0.0,
15
+ "attention_k_hop": 32,
16
+ "ball_query_radius_fraction": 0.6,
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+ "dtype": "float32",
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+ "edge_hidden_size": 32,
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+ "forcing_variables": [
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+ "year_progress_sin",
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+ "year_progress_cos",
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+ "day_progress_sin",
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+ "day_progress_cos"
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+ ],
25
+ "global_variables": [
26
+ "year_progress_sin",
27
+ "year_progress_cos"
28
+ ],
29
+ "grid_latitudes": 721,
30
+ "grid_longitudes": 1440,
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+ "hidden_act": "gelu_pytorch_tanh",
32
+ "hidden_size": 768,
33
+ "initializer_range": 0.02,
34
+ "input_variables": [
35
+ "temperature",
36
+ "geopotential",
37
+ "u_component_of_wind",
38
+ "v_component_of_wind",
39
+ "vertical_velocity",
40
+ "specific_humidity",
41
+ "2m_temperature",
42
+ "mean_sea_level_pressure",
43
+ "10m_v_component_of_wind",
44
+ "10m_u_component_of_wind",
45
+ "sea_surface_temperature",
46
+ "100m_u_component_of_wind",
47
+ "100m_v_component_of_wind",
48
+ "geopotential_at_surface",
49
+ "land_sea_mask",
50
+ "year_progress_sin",
51
+ "year_progress_cos",
52
+ "day_progress_sin",
53
+ "day_progress_cos"
54
+ ],
55
+ "intermediate_size": 3072,
56
+ "layer_norm_eps": 1e-05,
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+ "mesh_splits": 6,
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+ "mlp_act": "silu",
59
+ "model_type": "weathernext2",
60
+ "noise_channels": 32,
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+ "num_attention_heads": 6,
62
+ "num_hidden_layers": 24,
63
+ "num_input_timesteps": 2,
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+ "pressure_levels": [
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+ 50,
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+ 100,
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+ 150,
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+ 200,
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+ 250,
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+ 300,
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+ 400,
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+ 500,
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+ 600,
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+ 700,
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+ 850,
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+ 925,
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+ 1000
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+ ],
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+ "sigmoid_shifted_outputs": {
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+ "cyclone_exists_gaussian_unit_mode": 2.0
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+ },
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+ "static_variables": [
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+ "geopotential_at_surface",
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+ "land_sea_mask"
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+ ],
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+ "target_variables": [
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+ "temperature",
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+ "geopotential",
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+ "u_component_of_wind",
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+ "v_component_of_wind",
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+ "vertical_velocity",
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+ "specific_humidity",
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+ "2m_temperature",
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+ "mean_sea_level_pressure",
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+ "10m_v_component_of_wind",
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+ "10m_u_component_of_wind",
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+ "sea_surface_temperature",
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+ "100m_u_component_of_wind",
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+ "100m_v_component_of_wind",
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+ "total_precipitation_6hr",
101
+ "cyclone_exists_gaussian_unit_mode",
102
+ "cyclone_all_wind_disc",
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+ "cyclone_usa_wind_disc",
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+ "cyclone_usa_r34_ne_radius_disc",
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+ "cyclone_usa_r34_se_radius_disc",
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+ "cyclone_usa_r34_sw_radius_disc",
107
+ "cyclone_usa_r34_nw_radius_disc",
108
+ "cyclone_usa_r50_ne_radius_disc",
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+ "cyclone_usa_r50_se_radius_disc",
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+ "cyclone_usa_r50_sw_radius_disc",
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+ "cyclone_usa_r50_nw_radius_disc",
112
+ "cyclone_usa_r64_ne_radius_disc",
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+ "cyclone_usa_r64_se_radius_disc",
114
+ "cyclone_usa_r64_sw_radius_disc",
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+ "cyclone_usa_r64_nw_radius_disc",
116
+ "cyclone_usa_rmw_disc",
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+ "cyclone_usa_pres_disc"
118
+ ],
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+ "time_step_hours": 6,
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+ "transformers_version": "5.15.0.dev0"
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+ }
model1/model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:3465621ef4a5082192f015cbef2fba1dd2999b47db7e2a9726f517599f5eb1fa
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+ size 735190748
model1/preprocessor_config.json ADDED
@@ -0,0 +1,1083 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "atmospheric_variables": [
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+ "temperature",
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+ "geopotential",
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+ "u_component_of_wind",
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+ "v_component_of_wind",
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+ "vertical_velocity",
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+ "specific_humidity"
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+ ],
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+ "diffs_stddev_by_level": {
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+ "100m_u_component_of_wind": 2.8724644772751793,
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+ "100m_v_component_of_wind": 3.1648334389975545,
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+ "10m_u_component_of_wind": 2.2499901294404236,
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+ "10m_v_component_of_wind": 2.477609082148393,
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+ "10m_wind_gust_since_previous_post_processing": 2.545839214725464,
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+ "2m_dewpoint_temperature": 1.8065216717852064,
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+ "2m_temperature": 2.5883234840491407,
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+ "clear_sky_direct_solar_radiation_at_surface": 1289174.214185969,
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+ "clear_sky_direct_solar_radiation_at_surface_12hr": 8340957.42302206,
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+ "clear_sky_direct_solar_radiation_at_surface_1hr": 1289174.2141859706,
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+ "clear_sky_direct_solar_radiation_at_surface_24hr": 167095.398938028,
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+ "clear_sky_direct_solar_radiation_at_surface_3hr": 3704104.4479462453,
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+ "clear_sky_direct_solar_radiation_at_surface_6hr": 6462412.430241175,
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+ "convective_available_potential_energy": 187.68694596776757,
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+ "convective_inhibition": 128.01245802376496,
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+ "convective_precipitation": 0.00028841567987796993,
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+ "cyclone_all_wind_disc": 2.5184570234783696,
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+ "cyclone_all_wind_sparse": 2.4397211372759102,
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+ "cyclone_bom_exists_disc": 0.0015831270177122782,
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+ "cyclone_bom_exists_gaussian_probability": 0.00016628465412838172,
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model2/config.json ADDED
@@ -0,0 +1,121 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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