Plot meteograms

config/showcase.yaml

@@ -0,0 +1,54 @@
+# yaml-language-server: $schema=../workflow/tools/config.schema.json
+description: |
+  Basic config for showcasing M-1 forecaster.
+
+dates:
+  - 2020-02-03T00:00 # Storm Petra
+  - 2020-02-07T00:00 # Storm Sabine
+  - 2020-10-01T00:00 # Storm Brigitte
+
+runs:
+  - interpolator:
+      mlflow_id: 8d1e0410ca7d4f74b368b3079878259a
+      label: M-2 interpolator
+      steps: 0/120/1
+      config: resources/inference/configs/interpolator_stretched_with_global.yaml
+      forecaster:
+        mlflow_id: d0846032fc7248a58b089cbe8fa4c511
+        config: resources/inference/configs/forecaster_with_global.yaml
+        steps: 0/120/6
+      extra_dependencies:
+        - git+https://github.com/ecmwf/anemoi-inference@14189907b4f4e3b204b7994f828831b8aa51e9b6
+        - torch-geometric==2.6.1
+        - anemoi-graphs==0.5.2
+
+baselines:
+  - baseline:
+      baseline_id: COSMO-E
+      label: COSMO-E
+      root: /store_new/mch/msopr/ml/COSMO-E
+      steps: 0/120/6
+
+analysis:
+  label: COSMO KENDA
+  analysis_zarr: /scratch/mch/fzanetta/data/anemoi/datasets/mch-co2-an-archive-0p02-2015-2020-1h-v3-pl13.zarr
+
+locations:
+  output_root: output/
+  mlflow_uri:
+    - https://servicedepl.meteoswiss.ch/mlstore
+    - https://mlflow.ecmwf.int
+
+profile:
+  executor: slurm
+  global_resources:
+    gpus: 15
+  default_resources:
+    slurm_partition: "postproc"
+    cpus_per_task: 1
+    mem_mb_per_cpu: 1800
+    runtime: "1h"
+    gpus: 0
+  jobs: 50
+  batch_rules:
+    plot_forecast_frame: 32

pyproject.toml

@@ -18,6 +18,9 @@ dependencies = [
     "pydantic>=2.11.7",
     "toml>=0.10.2",
     "netcdf4>=1.7.2",
+    "cartopy",
+    "earthkit-plots",
+    "marimo>=0.16.5",
 ]
 
 [project.optional-dependencies]

resources/inference/configs/forecaster.yaml

@@ -7,17 +7,18 @@ allow_nans: true
 output:
   tee:
     outputs:
-    - extract_lam:
-        output:
-          assign_mask:
-            mask: "source0/trimedge_mask"
-            output:
-              grib:
-                path: grib/{dateTime}_{step:03}.grib
-                encoding:
-                  typeOfGeneratingProcess: 2
-                templates:
-                  samples: _resources/templates_index_cosmo.yaml
-    - printer
+      - extract_lam:
+          output:
+            assign_mask:
+              mask: "source0/trimedge_mask"
+              output:
+                grib:
+                  path: grib/{dateTime}_{step:03}.grib
+                  encoding:
+                    typeOfGeneratingProcess: 2
+                  templates:
+                    samples: _resources/templates_index_cosmo.yaml
+      - printer
+      - raw: raw/
 
 write_initial_state: true

resources/inference/configs/forecaster_no_trimedge_with_global.yaml

@@ -0,0 +1,28 @@
+input:
+  test:
+    use_original_paths: true
+
+allow_nans: true
+
+output:
+  tee:
+    outputs:
+      - extract_lam:
+          output:
+            grib:
+              path: grib/{dateTime}_{step:03}.grib
+              encoding:
+                typeOfGeneratingProcess: 2
+              templates:
+                samples: _resources/templates_index_cosmo.yaml
+      - grib:
+          path: grib/ifs-{dateTime}_{step:03}.grib
+          encoding:
+            typeOfGeneratingProcess: 2
+          templates:
+            samples: _resources/templates_index_ifs.yaml
+          post_processors:
+            - extract_slice: [919619, -1]
+            - assign_mask: "global/cutout_mask"
+
+write_initial_state: true

resources/inference/configs/interpolator_from_test_data.yaml

@@ -1,5 +1,4 @@
 runner: time_interpolator
-include_forcings: true
 
 input:
   test:

resources/inference/configs/interpolator_stretched_with_global.yaml

@@ -0,0 +1,116 @@
+runner: time_interpolator
+
+input:
+  cutout:
+    lam_0:
+      grib:
+        path: forecaster_grib/20*
+        pre_processors:
+          - extract_mask: "source0/trimedge_mask"
+        namer:
+          rules:
+            - - shortName: T
+              - t_{level}
+            - - shortName: U
+              - u_{level}
+            - - shortName: V
+              - v_{level}
+            - - shortName: W
+              - w_{level}
+            - - shortName: QV
+              - q_{level}
+            - - shortName: FI
+              - z_{level}
+            - - shortName: PMSL
+              - msl
+            - - shortName: FIS
+              - z
+            - - shortName: PS
+              - sp
+            - - shortName: T_2M
+              - 2t
+            - - shortName: TD_2M
+              - 2d
+            - - shortName: T_G
+              - skt
+            - - shortName: U_10M
+              - 10u
+            - - shortName: V_10M
+              - 10v
+            - - shortName: FR_LAND
+              - lsm
+            - - shortName: TOT_PREC
+              - tp
+    global:
+      grib:
+        path: forecaster_grib/ifs*
+        namer:
+          rules:
+            - - shortName: T
+              - t_{level}
+            - - shortName: U
+              - u_{level}
+            - - shortName: V
+              - v_{level}
+            - - shortName: W
+              - w_{level}
+            - - shortName: QV
+              - q_{level}
+            - - shortName: FI
+              - z_{level}
+            - - shortName: PMSL
+              - msl
+            - - shortName: FIS
+              - z
+            - - shortName: PS
+              - sp
+            - - shortName: T_2M
+              - 2t
+            - - shortName: TD_2M
+              - 2d
+            - - shortName: T_G
+              - skt
+            - - shortName: U_10M
+              - 10u
+            - - shortName: V_10M
+              - 10v
+            - - shortName: FR_LAND
+              - lsm
+            - - shortName: TOT_PREC
+              - tp
+
+constant_forcings:
+  test:
+    use_original_paths: true
+
+patch_metadata:
+  dataset:
+    constant_fields: [z, lsm]
+
+output:
+  tee:
+    outputs:
+      - extract_lam:
+          output:
+            assign_mask:
+              mask: "source0/trimedge_mask"
+              output:
+                grib:
+                  path: grib/{dateTime}_{step:03}.grib
+                  encoding:
+                    typeOfGeneratingProcess: 2
+                  templates:
+                    samples: _resources/templates_index_cosmo.yaml
+      - grib:
+          path: grib/ifs-{dateTime}_{step:03}.grib
+          encoding:
+            typeOfGeneratingProcess: 2
+          templates:
+            samples: _resources/templates_index_ifs.yaml
+          post_processors:
+            - extract_slice: [189699, -1]
+            - assign_mask: "global/cutout_mask"
+
+verbosity: 1
+allow_nans: true
+output_frequency: "1h"

resources/report/plotting/README

@@ -0,0 +1,2 @@
+# This is a copy of the ncl colortables developed in the cosmolib
+# SRC:  https://github.com/MeteoSwiss-APN/mch-ncl/tree/master/cosmolib/ct

resources/report/plotting/RH_6lev.ct

@@ -0,0 +1,11 @@
+; Type: COSMO Library Color Table
+; Description: Color table for plotting the relative humidity in % as in NinJo
+6
+30 45 60 75 90 95
+251 155  52
+253 206 102
+254 255 153
+206 254 154
+120 240 116
+ 55 202  51
+ 54 177  52

resources/report/plotting/t2m_29lev.ct

@@ -0,0 +1,34 @@
+; Type: COSMO Library Color Table
+; Description: Color table for 2m temperature as in model browser
+29
+-18.0 -16.0 -14.0 -12.0 -10.0 -8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0 18.0 20.0 22.0 24.0 26.0 28.0 30.0 32.0 34.0 36.0 38.0
+109 227 255
+175 240 255
+255 196 226
+255 153 204
+255   0 255
+128   0 128
+  0   0 128
+ 70  70 255
+ 51 102 255
+133 162 255
+255 255 255
+204 204 204
+179 179 179
+153 153 153
+ 96  96  96
+128 128   0
+  0  92   0
+  0 128   0
+ 51 153 102
+157 213   0
+212 255  91
+255 255   0
+255 184 112
+255 153   0
+255 102   0
+255   0   0
+188  75   0
+171   0  56
+128   0   0
+163 112 255

resources/report/plotting/uv_17lev.ct

@@ -0,0 +1,23 @@
+; Type: COSMO Library Color Table
+; Description: Wind speed representation in 17 levels. This color table
+;   is used in the model browser to plot wind at 10m, 850, 700 and 500hPa
+17
+4.0 6.0 10.0 14.0 18.0 22.0 26.0 30.0 35.0 40.0 45.0 50.0 60.0 70.0 80.0 90.0 100.0
+255 255 255
+239 244 209
+232 244 158
+170 206  99
+226 237  22
+255 237   0
+255 237 130
+244 209 127
+237 165  73
+229 140  61
+219 124  61
+239   7  61
+232  86 163
+155 112 168
+ 99 112 247
+127 150 255
+142 178 255
+181 201 255

src/evalml/config.py

@@ -248,6 +248,10 @@ class Profile(BaseModel):
     global_resources: GlobalResources
     default_resources: DefaultResources
     jobs: int = Field(..., ge=1, description="Maximum number of parallel jobs.")
+    batch_rules: Dict[str, int] = Field(
+        default_factory=dict,
+        description="Define batches of the same rule that shall be executed within one job submission.",
+    )
 
     def parsable(self) -> Dict[str, str]:
         """Convert the profile to a dictionary of command-line arguments."""
@@ -256,6 +260,15 @@ def parsable(self) -> Dict[str, str]:
         out += ["--resources"] + self.global_resources.parsable()
         out += ["--default-resources"] + self.default_resources.parsable()
         out += ["--jobs", str(self.jobs)]
+
+        # Add rule grouping options if specified
+        if self.batch_rules:
+            # Groups: rule=rule
+            groups = [f"{rule}={rule}" for rule in self.batch_rules.keys()]
+            # Group components: rule=<n>
+            components = [f"{rule}={n}" for rule, n in self.batch_rules.items()]
+            out += ["--groups"] + groups
+            out += ["--group-components"] + components
         return out