From a40138515abb8b07bd1628f9053f7224881cd7e7 Mon Sep 17 00:00:00 2001
From: Luca Lanzilao <llanzila@balfrin-ln004.cscs.ch>
Date: Fri, 18 Sep 2026 18:20:29 +0200
Subject: [PATCH 1/7] support verification of surface variables added with
 post-processors

---
 config/varda-single-1.0.yaml                  | 48 ++++++++--------
 ...oral-downscaler-global_trimedge_multi.yaml | 30 +++++++++-
 ...m-temporal-downscaler-ich1-oper-patch.yaml | 29 ++++++++++
 .../templates/templates_index_icon.yaml       |  2 +-
 src/data_input/__init__.py                    | 47 ++++++++++++----
 src/evalml/config.py                          |  8 ++-
 src/plotting/colormap_defaults.py             | 49 +++++++++++++++--
 src/verification/__init__.py                  | 30 ++++++++++
 tests/unit/test_data_input.py                 | 18 +++++-
 tests/unit/test_jretrieve.py                  | 37 +++++++++++++
 tests/unit/test_verification.py               | 55 ++++++++++++++++++-
 workflow/rules/inference.smk                  | 32 +++++++----
 workflow/scripts/plot_forecast_frame.py       | 19 +++----
 workflow/tools/config.schema.json             |  2 +-
 14 files changed, 332 insertions(+), 74 deletions(-)

diff --git a/config/varda-single-1.0.yaml b/config/varda-single-1.0.yaml
index c5875cc7..8d09539e 100644
--- a/config/varda-single-1.0.yaml
+++ b/config/varda-single-1.0.yaml
@@ -5,26 +5,32 @@ description: |
 config_label: varda-single-1.0
 
 dates:
-  start: 2025-03-01T00:00
-  end: 2025-03-03T00:00
-  frequency: 24h
+  # start: 2025-01-01T06:00
+  # end: 2025-12-31T06:00
+  # frequency: 294h
+  - 2025-01-01T06:00
+  - 2025-01-02T12:00
+  - 2025-01-03T18:00
 
 runs:
   - temporal_downscaler:
       checkpoint: https://service.meteoswiss.ch/mlstore#/models/sruc-m-2-interpolator/versions/3
       label: Varda-single-1.0
-      steps: 0/120/1
+      steps: 0/12/1
       config: resources/inference/configs/sgm-temporal-downscaler-global_trimedge_multi.yaml
       extra_requirements:
         - anemoi-datasets==0.5.35
-        # NOTE this pins the merge commit that introduced 'copy-prognostic-from-forecaster'
-        # TODO pin a release tag once the plugins are versioned. Do not pin the main branch to avoid stale code (changes do to main to not change the evalml hash)
-        - git+https://github.com/MeteoSwiss/anemoi-plugins-meteoswiss.git@b4835d76346e1e5ec0181884520da00e317fbfbc
-        # - anemoi-inference==0.11.0
+        - -e /scratch/mch/llanzila/postprocessor/anemoi-plugins-meteoswiss
+        - git+https://github.com/ecmwf/anemoi-inference@88c1ec13632c570f246cc2580ce324e45f36a03b
       forecaster:
         checkpoint: https://service.meteoswiss.ch/mlstore#/models/sruc-m-1-forecaster/versions/4
         config: resources/inference/configs/sgm-multidataset-forecaster-global-ich1-oper.yaml
-        steps: 0/120/6
+        steps: 0/12/6
+        extra_requirements:
+          - anemoi-datasets==0.5.35
+          - -e /scratch/mch/llanzila/postprocessor/anemoi-plugins-meteoswiss
+          - git+https://github.com/ecmwf/anemoi-inference@88c1ec13632c570f246cc2580ce324e45f36a03b
+  
   - baseline:
       label: INCA
       root: /store_new/mch/msclim/INCA
@@ -41,20 +47,19 @@ runs:
 truth:
   label: SwissMetNet
   root: jretrievedwh:1,2
-  # To verify against SwissMetNet observations from the DWH via jretrievedwh,
-  # set instead (requires jretrievedwh.py on $PATH and $OPR_HOME set):
-  # Other selectors: root: jretrievedwh:locations=ARO,KLO,LUG
-  #                  root: jretrievedwh:bbox=45.8,47.8,5.9,10.5
-  #                  append ;stage=devt to target a non-prod DWH stage
 
 experiment:
   params:
     - T_2M
     - TD_2M
+    - U_10M
+    - V_10M
     - SP_10M
+    - DD_10M
+    - RELHUM_2M
     - TOT_PREC1
-    - TOT_PREC6
     - PMSL
+    - PS
   stratification:
     regions:
       - icon
@@ -66,11 +71,7 @@ experiment:
     root: /store_new/mch/msopr/ml/regions/Prognoseregionen_LV95_20220517
   thresholds:
     TOT_PREC1:
-      gt: [0.0, 0.1, 1, 5, 10]
-    TOT_PREC6:
-      gt: [0.0, 0.1, 5, 10, 20, 50]
-    SP_10M:
-      gt: [2.5, 5.0, 10.0]
+      gt: [0.0, 1, 5]
     T_2M:
       lt: [273.15]
       gt: [288.15, 298.15]
@@ -80,7 +81,7 @@ experiment:
       # - init_hour
       - season
   scorecards:
-    enabled: true
+    enabled: false
     sections:
       nowcasting:
         baseline: INCA
@@ -90,7 +91,7 @@ experiment:
           - "U_10M:RMSE,R2,ETS"
           - "V_10M:RMSE,R2,ETS"
           - "T_2M:RMSE,R2,ETS"
-          - "TOT_PREC1:RMSE,R2,ETS"
+          - "TOT_PREC:RMSE,R2,ETS"
       short_range:
         baseline: ICON-CH1-CTRL
         lead_times: "6/33/6"
@@ -116,6 +117,7 @@ showcase:
   params:
     - T_2M
     - SP_10M
+    - DD_10M
     - TOT_PREC1
   meteograms:
     enabled: false
@@ -143,5 +145,5 @@ profile:
     mem_mb_per_cpu: 1800
     runtime: "1h"
     gpus: 0
-    slurm_account: s83
+    slurm_account: go41
   jobs: 50
diff --git a/resources/inference/configs/sgm-temporal-downscaler-global_trimedge_multi.yaml b/resources/inference/configs/sgm-temporal-downscaler-global_trimedge_multi.yaml
index 0311e949..b145408a 100644
--- a/resources/inference/configs/sgm-temporal-downscaler-global_trimedge_multi.yaml
+++ b/resources/inference/configs/sgm-temporal-downscaler-global_trimedge_multi.yaml
@@ -57,6 +57,16 @@ constant_forcings:
 
 patch_metadata: resources/sgm-temporal-downscaler-ich1-oper-patch.yaml
 
+# The checkpoint never predicts 10-metre wind speed/direction or 2-metre
+# relative humidity, so anemoi-inference has no metadata of its own for them.
+typed_variables:
+  SP_10M:
+    mars: {param: SP_10M, levtype: sfc}
+  DD_10M:
+    mars: {param: DD_10M, levtype: sfc}
+  RELHUM_2M:
+    mars: {param: RELHUM_2M, levtype: sfc}
+
 post_processors:
   - accumulate_from_start_of_forecast: # accumulate tp from start of forecast
       accumulations:
@@ -79,15 +89,19 @@ output:
           - extract_mask: # removes global points
               mask: "lam_0/cutout_mask"
               as_slice: true
-          # here, the trimedge mask can be specified when available
-          # at overlap steps (multiples of the forecaster stride) replace the
-          # re-predicted prognostics with the forecaster's boundary values
           - forward_transform_filter:
               copy-prognostic-from-forecaster:
                 forecaster_path: forecaster/20*
                 common_leadtime: 6h
                 params_to_keep: [tp] # NOTE, if more diagnostics are added, update accordingly.
                 namer: *namer
+          - forward_transform_filter:
+              surface-diagnostics:
+                u_component: U_10M
+                v_component: V_10M
+                temperature: T_2M
+                dewpoint: TD_2M
+                variables: [SP_10M, DD_10M, RELHUM_2M]
 
     - grib:
         path: grib/ifs-{dateTime}_{step:03}.grib
@@ -108,6 +122,13 @@ output:
                 common_leadtime: 6h
                 params_to_keep: [tp] # NOTE, if more diagnostics are added, update accordingly.
                 namer: *namer
+          - forward_transform_filter:
+              surface-diagnostics:
+                u_component: U_10M
+                v_component: V_10M
+                temperature: T_2M
+                dewpoint: TD_2M
+                variables: [SP_10M, DD_10M, RELHUM_2M]
         modifiers:
           - patches:
             - variable:
@@ -132,6 +153,9 @@ output:
                 TOT_PREC: {"param": 228, "shortName": "tp"}
                 tp: {"param": 228, "shortName": "tp"}
                 z: {"param": 129, "shortName": "z"}
+                SP_10M: {"param": 207, "shortName": "10si"}
+                DD_10M: {"param": 260260, "shortName": "10wdir"}
+                RELHUM_2M: {"param": 260242, "shortName": "2r"}
                 "^q_(\\d+)$": {"param": 133, "shortName": "q"}
                 "^t_(\\d+)$": {"param": 130, "shortName": "t"}
                 "^u_(\\d+)$": {"param": 131, "shortName": "u"}
diff --git a/resources/inference/metadata/sgm-temporal-downscaler-ich1-oper-patch.yaml b/resources/inference/metadata/sgm-temporal-downscaler-ich1-oper-patch.yaml
index a27f3ff9..0542f65a 100644
--- a/resources/inference/metadata/sgm-temporal-downscaler-ich1-oper-patch.yaml
+++ b/resources/inference/metadata/sgm-temporal-downscaler-ich1-oper-patch.yaml
@@ -49,6 +49,35 @@ dataset:
           param: T_2M
           step: 12
           time: 0
+      DD_10M:
+        # See the SP_10M comment below
+          date: 20050101
+          levtype: sfc
+          param: DD_10M
+          step: 12
+          time: 0
+      RELHUM_2M:
+        # See the SP_10M comment below
+        mars:
+          date: 20050101
+          levtype: sfc
+          param: RELHUM_2M
+          step: 12
+          time: 0
+      SP_10M:
+        # Not predicted by the checkpoint -- created mid-pipeline by the
+        # surface-diagnostics post-processor filter. Every
+        # field a forward_transform_filter chain touches (including ones a
+        # prior filter just created) must be a known variable here: anemoi-inference's
+        # TransformFilter.process() calls wrap_state(state, metadata.typed_variables)
+        # before EVERY filter in the chain, and that lookup is a hard KeyError
+        # for any field name not in this variables_metadata map
+        mars:
+          date: 20050101
+          levtype: sfc
+          param: SP_10M
+          step: 12
+          time: 0
       cos_julian_day:
         computed_forcing: true
         constant_in_time: false
diff --git a/resources/inference/templates/templates_index_icon.yaml b/resources/inference/templates/templates_index_icon.yaml
index b8861d22..4027a71e 100644
--- a/resources/inference/templates/templates_index_icon.yaml
+++ b/resources/inference/templates/templates_index_icon.yaml
@@ -3,7 +3,7 @@
 - - {levtype: pl, param: [T, QV, U, V, OMEGA, FI]}
   - resources/icon-ch1-typeOfLevel=isobaricInhPa.grib
 
-- - {levtype: sfc, param: [T_2M, TD_2M, U_10M, V_10M]}
+- - {levtype: sfc, param: [T_2M, TD_2M, U_10M, V_10M, SP_10M, DD_10M, RELHUM_2M]}
   - resources/icon-ch1-typeOfLevel=heightAboveGround.grib
 
 - - {levtype: sfc, param: [FR_LAND, PS, FIS, T_G, SKT, SSO_SIGMA,SSO_STDH]}
diff --git a/src/data_input/__init__.py b/src/data_input/__init__.py
index 23e52dd9..9d1981df 100644
--- a/src/data_input/__init__.py
+++ b/src/data_input/__init__.py
@@ -54,7 +54,7 @@
 # Spatial/multi-field derivations: output param → required input params.
 DERIVED_PARAMS: dict[str, tuple[str, ...]] = {
     "SP_10M": ("U_10M", "V_10M"),
-    "SP": ("U", "V"),
+    "DD_10M": ("U_10M", "V_10M"),
 }
 
 
@@ -88,12 +88,15 @@ def compute_derived(ds: xr.Dataset, param: str) -> xr.DataArray:
             "name": "10m wind speed",
         }
         return da
-    if param == "SP":
-        da = (ds["U"] ** 2 + ds["V"] ** 2) ** 0.5
+    if param == "DD_10M":
+        # Meteorological convention: direction the wind is blowing FROM,
+        # clockwise from North. Matches the U/V <-> DD/FF convention used in
+        # load_obs_data_from_jretrieve (U = -FF*sin(DD), V = -FF*cos(DD)).
+        da = np.mod(np.degrees(np.arctan2(-ds["U_10M"], -ds["V_10M"])), 360.0)
         da.attrs["parameter"] = {
-            "shortName": "SP",
-            "units": "m/s",
-            "name": "Wind speed",
+            "shortName": "DD_10M",
+            "units": "degrees",
+            "name": "10m wind direction",
         }
         return da
     raise ValueError(f"No recipe for derived variable '{param}'")
@@ -698,6 +701,7 @@ def load_obs_data_from_jretrieve(
         "SP_10M": "fkl010z0",
         "DD_10M": "dkl010z0",
         "VMAX_10M": "fkl010z1",
+        "RELHUM_2M": "ure200h0",
     }
     DWH_WIND_SPEED = "fkl010z0"
     DWH_WIND_DIR = "dkl010z0"
@@ -771,7 +775,7 @@ def load_truth_data(
     """Load truth data from an analysis Zarr dataset or DWH observations via jretrieve.
 
     Handles derived and aggregated params transparently (same contract as
-    load_forecast_data): SP_10M is computed from U_10M/V_10M; TOT_PREC6 is
+    load_forecast_data): SP_10M/DD_10M are computed from U_10M/V_10M; TOT_PREC6 is
     disaggregated from cumulative TOT_PREC; plain TOT_PREC is returned as-is.
     Returns a dataset with 'time' dimension (valid datetimes).
 
@@ -1334,7 +1338,13 @@ def load_forecast_data(
     """Load forecast data from GRIB files or an ICON archive.
 
     Handles derived and aggregated params transparently:
-    - ``SP_10M`` is computed from ``U_10M`` / ``V_10M``
+    - For the ML inference GRIB output: ``SP_10M``/``DD_10M``/``RELHUM_2M`` are
+      expected to be produced directly by the anemoi-inference
+      surface-diagnostics post-processor filter and are
+      loaded as native fields, not recomputed here.
+    - For baselines (ICON archive, INCA): these archives have no such
+      filters, so ``SP_10M``/``DD_10M`` are computed from ``U_10M``/``V_10M``
+      (or, for INCA, read from its native FF/DD product) as before.
     - ``TOT_PREC6`` is disaggregated from the cumulative ``TOT_PREC`` field
     - Plain ``TOT_PREC`` is returned as cumulative-from-start without disaggregation
 
@@ -1345,13 +1355,22 @@ def load_forecast_data(
     3. Otherwise → ICON operational archive (via :func:`_load_icon_baseline_from_grib`)
     """
     root = Path(root)
-    load_params = get_base_params(params)
     load_steps = get_steps(steps, params)
     if any(root.glob("*.grib")):
         LOG.info("Loading forecasts from GRIB files...")
+        # ML inference output: request params as-is (only stripping
+        # aggregation suffixes, e.g. TOT_PREC6 -> TOT_PREC) so fields the
+        # anemoi-inference filters already produced (SP_10M, DD_10M,
+        # RELHUM_2M) are read natively instead of being recomputed from
+        # their components. _disaggregated_and_derived_params below only
+        # falls back to compute_derived for a DERIVED_PARAMS entry that
+        # isn't already present in the loaded dataset.
+        ml_load_params = list(
+            dict.fromkeys(parse_aggregated_param(p)[0] for p in params)
+        )
         ds = _load_forecast_data_from_grib(
             files=_collect_ml_grib_files(root, load_steps),
-            params=load_params,
+            params=ml_load_params,
         )
         # Try to derive elevation from surface geopotential (FIS/z at step 0)
         # before falling back to ICON grid constants lookup.
@@ -1367,7 +1386,8 @@ def load_forecast_data(
             ds = _try_assign_elevation(ds)
     elif "INCA" in root.parts:
         LOG.info("Loading INCA baseline from NetCDF files...")
-        # INCA provides wind speed natively (FF), so don't expand SP_10M → U_10M/V_10M.
+        # INCA provides wind speed/direction natively (FF/DD), so don't expand
+        # SP_10M/DD_10M → U_10M/V_10M.
         # Only expand aggregated params (e.g. TOT_PREC6 → TOT_PREC).
         inca_load_params = list(
             dict.fromkeys(parse_aggregated_param(p)[0] for p in params)
@@ -1377,7 +1397,10 @@ def load_forecast_data(
         )
     else:
         LOG.info("Loading baseline forecasts from ICON GRIB archive...")
+        # ICON's own archive has no post-processor filters, so SP_10M/DD_10M
+        # are expanded to U_10M/V_10M here and computed by
+        # _disaggregated_and_derived_params below, same as before.
         ds = _load_icon_baseline_from_grib(
-            root, reftime, load_steps, load_params, member=member
+            root, reftime, load_steps, get_base_params(params), member=member
         )
     return _disaggregated_and_derived_params(ds, steps, params)
diff --git a/src/evalml/config.py b/src/evalml/config.py
index 3980046a..3f6ffcd5 100644
--- a/src/evalml/config.py
+++ b/src/evalml/config.py
@@ -234,8 +234,12 @@ class ScoreMapsConfig(BaseModel):
         default=["T_2M"],
         description=(
             "List of parameters to plot. Supported values: T_2M, TD_2M, U_10M, V_10M, "
-            "PS, PMSL, SP_10M (derived from U_10M/V_10M), TOT_PREC1, TOT_PREC6, TOT_PREC24 "
-            "(period-accumulated precipitation, period encoded in the name)."
+            "PS, PMSL, SP_10M, DD_10M (read natively from ML-inference GRIB output, "
+            "or derived from U_10M/V_10M for other sources), RELHUM_2M (read natively "
+            "from ML-inference GRIB output, or derived from T_2M/TD_2M for the "
+            "SwissMetNet/DWH truth; not currently available for ICON/INCA baselines), "
+            "TOT_PREC1, TOT_PREC6, TOT_PREC24 (period-accumulated precipitation, "
+            "period encoded in the name)."
         ),
     )
     leadtimes: List[int] = Field(
diff --git a/src/plotting/colormap_defaults.py b/src/plotting/colormap_defaults.py
index f411bbb9..16f43e69 100644
--- a/src/plotting/colormap_defaults.py
+++ b/src/plotting/colormap_defaults.py
@@ -23,6 +23,18 @@ def _fallback():
     "levels": [0, 50, 100, 150, 200, 250, 300, 350],
 }
 _SCORE_REDS_PRECIP = {"cmap": plt.get_cmap("Reds", 6), "levels": [0, 1, 1.5, 2, 3, 4]}
+# Wind direction errors are circular (0-360°); a plain difference can be off
+# by up to 360° even though the true angular error never exceeds 180°. Scores
+# computed upstream aren't wraparound-aware, so treat DD_10M error magnitudes
+# with that caveat in mind. Levels scaled to a plausible degrees-of-error range.
+_SCORE_REDS_DEG = {
+    "cmap": plt.get_cmap("Reds", 6),
+    "levels": [0, 15, 30, 45, 60, 90, 180],
+}
+_SCORE_REDS_RH = {
+    "cmap": plt.get_cmap("Reds", 6),
+    "levels": [0, 5, 10, 15, 20, 30, 50],
+}
 
 
 def _precip_score_map(accum_h: int) -> dict:
@@ -46,12 +58,6 @@ def _precip_bias_map(accum_h: int) -> dict:
 
 
 _CMAP_DEFAULTS = {
-    "SP": {
-        "cmap": plt.get_cmap("coolwarm", 11),
-        "vmin": 800 * 100,
-        "vmax": 1100 * 100,
-        "extend": "both",
-    },
     "TD_2M": load_ncl_colormap("t2m_29lev.ct") | {"extend": "both"},
     "T_2M": load_ncl_colormap("t2m_29lev.ct") | {"units": "degC", "extend": "both"},
     "V_10M": load_ncl_colormap("modified_uv_17lev.ct")
@@ -60,6 +66,22 @@ def _precip_bias_map(accum_h: int) -> dict:
     | {"units": "m/s", "extend": "both"},
     "SP_10M": load_ncl_colormap("modified_uv_17lev.ct")
     | {"units": "m/s", "extend": "max"},
+    # Cyclic quantity (0-360°, wraps N=0=360): use a cyclic colormap so the
+    # boundary color matches on both ends, unlike a sequential map.
+    "DD_10M": {
+        "cmap": plt.get_cmap("twilight", 16),
+        "vmin": 0,
+        "vmax": 360,
+        "units": "degrees",
+        "extend": "neither",
+    },
+    "RELHUM_2M": {
+        "cmap": plt.get_cmap("viridis", 10),
+        "vmin": 0,
+        "vmax": 100,
+        "units": "%",
+        "extend": "neither",
+    },
     "T_850": {
         "cmap": plt.get_cmap("inferno", 11),
         "vmin": 220,
@@ -204,6 +226,8 @@ def _precip_bias_map(accum_h: int) -> dict:
     "U_10M.score.map": _SCORE_REDS | {"units": "m/s"},
     "V_10M.score.map": _SCORE_REDS | {"units": "m/s"},
     "SP_10M.score.map": _SCORE_REDS | {"units": "m/s"},
+    "DD_10M.score.map": _SCORE_REDS_DEG | {"units": "degrees"},
+    "RELHUM_2M.score.map": _SCORE_REDS_RH | {"units": "%"},
     "TD_2M.score.map": _SCORE_REDS | {"units": "°C"},
     "T_2M.score.map": _SCORE_REDS | {"units": "°C"},
     "PMSL.score.map": _SCORE_REDS_PA | {"units": "Pa"},
@@ -230,6 +254,19 @@ def _precip_bias_map(accum_h: int) -> dict:
         "levels": np.arange(start=-2.25, stop=2.26, step=0.5),
     }
     | {"units": "m/s"},
+    # Caveat: a plain signed difference wraps at the 0/360° boundary (e.g. 5°
+    # vs 355° is a 10° error, not 350°), so BIAS near due-north can look
+    # spuriously large. Same limitation as DD_10M.score.map above.
+    "DD_10M.BIAS.map": {
+        "cmap": plt.get_cmap("RdBu_r", 9),
+        "levels": np.arange(start=-90, stop=91, step=20),
+    }
+    | {"units": "degrees"},
+    "RELHUM_2M.BIAS.map": {
+        "cmap": plt.get_cmap("RdBu_r", 9),
+        "levels": np.arange(start=-45, stop=46, step=10),
+    }
+    | {"units": "%"},
     "TD_2M.BIAS.map": {
         "cmap": plt.get_cmap("RdBu_r", 11),
         "levels": np.arange(start=-2.75, stop=2.76, step=0.5),
diff --git a/src/verification/__init__.py b/src/verification/__init__.py
index 9ab3777d..d372cf55 100644
--- a/src/verification/__init__.py
+++ b/src/verification/__init__.py
@@ -22,6 +22,28 @@
 _T_LAPSE_RATE = 0.0065  # K/m — ICAO standard atmosphere
 _LAPSE_RATE_PARAMS: dict[str, float] = {"T_2M": _T_LAPSE_RATE}
 
+# Directional/circular params (degrees, period 360): a plain fcst - obs
+# difference can be off by up to a full period even though the true angular
+# error never exceeds period / 2 (e.g. fcst=355°, obs=5° is a 10° error, not
+# -350°). Listed params get their `obs` aliased onto the branch nearest
+# `fcst` before BIAS/MSE/MAE/CORR are computed — see _circular_align.
+_CIRCULAR_PARAMS: frozenset[str] = frozenset({"DD_10M"})
+_CIRCULAR_PERIOD = 360.0
+
+
+def _circular_align(
+    fcst: xr.DataArray, obs: xr.DataArray, period: float = _CIRCULAR_PERIOD
+) -> xr.DataArray:
+    """Shift `obs` by a multiple of `period` so that `fcst - obs` is the
+    minimal signed circular difference in (-period/2, period/2].
+
+    This makes a plain (non-circular) difference/score computed on
+    (fcst, aligned_obs) circular-correct, without needing a dedicated
+    circular-statistics implementation for BIAS/MSE/MAE/CORR.
+    """
+    diff = fcst - obs
+    return obs + period * np.round(diff / period)
+
 
 def apply_lapse_rate_correction_inplace(
     fcst: xr.Dataset,
@@ -226,6 +248,7 @@ def _compute_scores(
     suffix="",
     source="",
     thresholds: dict[str, list[float]] | None = None,
+    circular: bool = False,
 ) -> xr.Dataset:
     """
     Compute basic verification metrics between two xarray DataArrays (fcst and obs).
@@ -233,7 +256,13 @@ def _compute_scores(
     Computation of scores for continuous and categorical forecasts are supported.
     Categorical forecasts are specified via a dict mapping operator keys (gt, ge, lt, le, eq, ne)
     to lists of threshold values (e.g. {"gt": [10.0], "lt": [0.0]}).
+
+    If `circular` is True, `obs` is aliased onto the branch nearest `fcst`
+    (see _circular_align) before BIAS/MSE/MAE/CORR are computed, so a
+    directional quantity's 0/360° wraparound doesn't inflate its error.
     """
+    if circular:
+        obs = _circular_align(fcst, obs)
     LOG.info(f"Compute scores for {prefix} {suffix}")
     result = xr.Dataset(
         {
@@ -415,6 +444,7 @@ def verify(
             source=fcst_label,
             dim=dim,
             thresholds=thresholds,
+            circular=param in _CIRCULAR_PARAMS,
         )
         fcst_stats = _compute_statistics(
             fcst_param,
diff --git a/tests/unit/test_data_input.py b/tests/unit/test_data_input.py
index f28ab351..a0b725fe 100644
--- a/tests/unit/test_data_input.py
+++ b/tests/unit/test_data_input.py
@@ -57,6 +57,10 @@ def test_expand_derived():
     assert set(get_base_params(["SP_10M"])) == {"U_10M", "V_10M"}
 
 
+def test_expand_derived_dd10m():
+    assert set(get_base_params(["DD_10M"])) == {"U_10M", "V_10M"}
+
+
 def test_expand_mixed():
     assert set(get_base_params(["T_2M", "SP_10M", "TOT_PREC6"])) == {
         "T_2M",
@@ -104,9 +108,21 @@ def test_compute_derived_sp10m():
     np.testing.assert_allclose(compute_derived(ds, "SP_10M").values, [5.0])
 
 
+def test_compute_derived_dd10m():
+    # Northerly wind (blowing from N): velocity points south, U=0, V<0 -> DD=0
+    # Easterly wind (blowing from E): velocity points west, U<0, V=0 -> DD=90
+    ds = xr.Dataset(
+        {
+            "U_10M": xr.DataArray([0.0, -5.0]),
+            "V_10M": xr.DataArray([-5.0, 0.0]),
+        }
+    )
+    np.testing.assert_allclose(compute_derived(ds, "DD_10M").values, [0.0, 90.0])
+
+
 def test_compute_derived_unknown_raises():
     with pytest.raises(ValueError, match="No recipe"):
-        compute_derived(xr.Dataset(), "DD_10M")
+        compute_derived(xr.Dataset(), "UNKNOWN_PARAM")
 
 
 # ---------------------------------------------------------------------------
diff --git a/tests/unit/test_jretrieve.py b/tests/unit/test_jretrieve.py
index 7539dd0c..ef049edb 100644
--- a/tests/unit/test_jretrieve.py
+++ b/tests/unit/test_jretrieve.py
@@ -269,6 +269,43 @@ def test_load_obs_data_from_jretrieve(monkeypatch):
     np.testing.assert_allclose(ds["elevation"].values, [1878.0])
 
 
+def test_load_obs_data_from_jretrieve_relhum(monkeypatch):
+    meta = pd.DataFrame(
+        {
+            "station": [1],
+            "op_since": [19800101000000],
+            "op_till": [""],
+            "parameter": ["ure200h0"],
+            "latitude": [46.79],
+            "longitude": [9.68],
+            "elev": [1878.0],
+            "stn_name": ["Arosa"],
+            "nat_abbr": ["ARO"],
+        }
+    )
+    data = pd.DataFrame(
+        {
+            "station": [1, 1],
+            "termin": [20250115000000, 20250115010000],
+            "ure200h0": [55.0, 60.0],  # %
+        }
+    )
+    monkeypatch.setattr(jr, "check_prerequisites", lambda *a, **k: None)
+    monkeypatch.setattr(jr, "fetch_meta", lambda **kw: meta)
+    monkeypatch.setattr(jr, "fetch_data", lambda **kw: data)
+
+    ds = data_input.load_obs_data_from_jretrieve(
+        "jretrievedwh:locations=ARO",
+        datetime(2025, 1, 15, 0, 0),
+        [0, 1],
+        ["RELHUM_2M"],
+    )
+
+    assert set(ds.data_vars) == {"RELHUM_2M"}
+    # Fetched directly from the DWH sensor code, no T/TD derivation.
+    np.testing.assert_allclose(ds["RELHUM_2M"].sel(values="ARO").values, [55.0, 60.0])
+
+
 def test_load_truth_data_forwards_root(monkeypatch):
     seen = {}
 
diff --git a/tests/unit/test_verification.py b/tests/unit/test_verification.py
index 96399004..36174749 100644
--- a/tests/unit/test_verification.py
+++ b/tests/unit/test_verification.py
@@ -8,7 +8,12 @@
 sys.path.insert(0, str(Path(__file__).parents[2] / "workflow" / "scripts"))
 from verification_aggregation import aggregate_results
 
-from verification import decode_metric, apply_lapse_rate_correction_inplace
+from verification import (
+    decode_metric,
+    apply_lapse_rate_correction_inplace,
+    _circular_align,
+    _compute_scores,
+)
 
 
 @pytest.mark.parametrize(
@@ -166,3 +171,51 @@ def test_lapse_rate_correction_only_requested_params(make_lapse_rate_datasets):
     apply_lapse_rate_correction_inplace(fcst, obs, ["T_2M"])
     np.testing.assert_allclose(fcst["T_2M"].values, 280.0 - 0.0065 * 500.0, atol=1e-4)
     np.testing.assert_array_equal(fcst["TD_2M"].values, 270.0)
+
+
+# ---------------------------------------------------------------------------
+# circular (directional) parameter handling, e.g. DD_10M
+# ---------------------------------------------------------------------------
+
+
+def test_circular_align_wraps_across_north():
+    # fcst=355°, obs=5°: true angular error is 10°, not 350°.
+    fcst = xr.DataArray([355.0])
+    obs = xr.DataArray([5.0])
+    aligned = _circular_align(fcst, obs)
+    np.testing.assert_allclose((fcst - aligned).values, [-10.0])
+
+
+def test_circular_align_symmetric_case():
+    # fcst=5°, obs=355°: same 10° error, opposite sign.
+    fcst = xr.DataArray([5.0])
+    obs = xr.DataArray([355.0])
+    aligned = _circular_align(fcst, obs)
+    np.testing.assert_allclose((fcst - aligned).values, [10.0])
+
+
+def test_circular_align_no_wraparound_unaffected():
+    # Well away from the 0/360° boundary, alignment is a no-op.
+    fcst = xr.DataArray([100.0])
+    obs = xr.DataArray([90.0])
+    aligned = _circular_align(fcst, obs)
+    np.testing.assert_allclose(aligned.values, obs.values)
+
+
+def test_compute_scores_circular_bias_across_north():
+    fcst = xr.DataArray([355.0], dims="values")
+    obs = xr.DataArray([5.0], dims="values")
+    result = _compute_scores(fcst, obs, dim=["values"], prefix="DD_10M.", circular=True)
+    np.testing.assert_allclose(result["DD_10M.BIAS"].values, -10.0)
+    np.testing.assert_allclose(result["DD_10M.MAE"].values, 10.0)
+
+
+def test_compute_scores_noncircular_bias_across_north_is_wrong():
+    # Without the circular fix, the same inputs measure a 350° error instead
+    # of the true 10° — this is the behaviour being corrected.
+    fcst = xr.DataArray([355.0], dims="values")
+    obs = xr.DataArray([5.0], dims="values")
+    result = _compute_scores(
+        fcst, obs, dim=["values"], prefix="DD_10M.", circular=False
+    )
+    np.testing.assert_allclose(result["DD_10M.BIAS"].values, 350.0)
diff --git a/workflow/rules/inference.smk b/workflow/rules/inference.smk
index 1a83b57c..8ce977b5 100644
--- a/workflow/rules/inference.smk
+++ b/workflow/rules/inference.smk
@@ -284,6 +284,11 @@ rule inference_execute:
         disable_local_definitions=lambda wc: RUN_CONFIGS[wc.run_id].get(
             "disable_local_eccodes_definitions", False
         ),
+        account_flag=lambda wc: (
+            f"--account={config['profile']['default_resources']['slurm_account']}"
+            if config["profile"]["default_resources"].get("slurm_account")
+            else ""
+        ),
     # fmt: off
     shell:
         """
@@ -293,7 +298,7 @@ rule inference_execute:
             cd {params.workdir}
 
             _run_inference() {{
-                local VENV=$1
+                local VENV=/user-environment
                 source "$VENV/bin/activate"
 
                 if [ "{params.disable_local_definitions}" = "False" ]; then
@@ -307,20 +312,23 @@ rule inference_execute:
                     CMD_ARGS+=(runner.parallel.cluster=slurm)
                 fi
 
-                srun \
-                    --unbuffered \
-                    --partition={resources.slurm_partition} \
-                    --cpus-per-task={resources.cpus_per_task} \
-                    --mem-per-cpu={resources.mem_mb_per_cpu} \
-                    --time={resources.runtime} \
-                    --gres={resources.gres} \
-                    --ntasks={resources.ntasks} \
-                    anemoi-inference run config.yaml "${{CMD_ARGS[@]}}"
+                anemoi-inference run config.yaml "${{CMD_ARGS[@]}}"
             }}
             export -f _run_inference
 
-            squashfs-mount {params.env_path}:/user-environment -- bash -c '_run_inference /user-environment'
+            srun \
+                --job-name=anemoi-inference \
+                --uenv={params.env_path}:/user-environment \
+                --unbuffered \
+                {params.account_flag} \
+                --partition={resources.slurm_partition} \
+                --cpus-per-task={resources.cpus_per_task} \
+                --mem-per-cpu={resources.mem_mb_per_cpu} \
+                --time={resources.runtime} \
+                --gres={resources.gres} \
+                --ntasks={resources.ntasks} \
+                bash -c '_run_inference'
         ) >{log} 2>&1
         touch {output.okfile}
         """
-# fmt: on
+# fmt: on
\ No newline at end of file
diff --git a/workflow/scripts/plot_forecast_frame.py b/workflow/scripts/plot_forecast_frame.py
index ee8e564e..fe208584 100644
--- a/workflow/scripts/plot_forecast_frame.py
+++ b/workflow/scripts/plot_forecast_frame.py
@@ -5,7 +5,6 @@
 
 import cartopy.crs as ccrs
 from earthkit.meteo.utils.convert import kelvin_to_celsius
-import earthkit.meteo.wind as ekm_wind
 import earthkit.plots as ekp
 from matplotlib.colors import Colormap
 import numpy as np
@@ -75,17 +74,18 @@ def get_style(param, units_override=None, accu=1):
 def preprocess_field(param: str, state: dict):
     """
     - Temperatures: K -> °C
-    - Wind speed: sqrt(u^2 + v^2)
     - Precipitation: m -> mm
+
+    Note: SP_10M, DD_10M, and RELHUM_2M are NOT computed here -- they're
+    produced by the anemoi-inference surface-diagnostics
+    post-processor filter and read directly from the GRIB via the
+    fallthrough below, same as any other plain field.
+
     Returns: (field_array, units_override or None)
     """
     fields = state["fields"]
     if param in ("T_2M", "TD_2M", "T", "TD"):
         return kelvin_to_celsius(fields[param]), "°C"
-    if param == "SP_10M":
-        return ekm_wind.speed(fields["U_10M"], fields["V_10M"]), "m/s"
-    if param == "SP":
-        return ekm_wind.speed(fields["U"], fields["V"]), "m/s"
     if param == "TOT_PREC":
         return np.maximum(fields[param], 0), "mm"
     if param in ("CLCT", "CLCL"):
@@ -136,12 +136,7 @@ def main():
         list(regions.keys()),
     )
 
-    if param == "SP_10M":
-        paramlist = ["U_10M", "V_10M"]
-    elif param == "SP":
-        paramlist = ["U", "V"]
-    else:
-        paramlist = [param]
+    paramlist = [param]
 
     # Load grib once — shared across all region plots
     # TODO: fix file pattern & globbing
diff --git a/workflow/tools/config.schema.json b/workflow/tools/config.schema.json
index ea2b84ba..d51ddd1d 100644
--- a/workflow/tools/config.schema.json
+++ b/workflow/tools/config.schema.json
@@ -736,7 +736,7 @@
           "default": [
             "T_2M"
           ],
-          "description": "List of parameters to plot. Supported values: T_2M, TD_2M, U_10M, V_10M, PS, PMSL, SP_10M (derived from U_10M/V_10M), TOT_PREC1, TOT_PREC6, TOT_PREC24 (period-accumulated precipitation, period encoded in the name).",
+          "description": "List of parameters to plot. Supported values: T_2M, TD_2M, U_10M, V_10M, PS, PMSL, SP_10M, DD_10M (read natively from ML-inference GRIB output, or derived from U_10M/V_10M for other sources), RELHUM_2M (read natively from ML-inference GRIB output, or derived from T_2M/TD_2M for the SwissMetNet/DWH truth; not currently available for ICON/INCA baselines), TOT_PREC1, TOT_PREC6, TOT_PREC24 (period-accumulated precipitation, period encoded in the name).",
           "items": {
             "type": "string"
           },

From 45839ba2c58fd64ed5531d3d8ecb83288e8ba3c5 Mon Sep 17 00:00:00 2001
From: Luca Lanzilao <llanzila@balfrin-ln004.cscs.ch>
Date: Fri, 18 Sep 2026 19:54:47 +0200
Subject: [PATCH 2/7] update configs

---
 config/varda-single-1.0.yaml      | 10 +++++-----
 src/evalml/config.py              | 21 ++++++++++++++++-----
 workflow/tools/config.schema.json | 13 +++++++++++++
 3 files changed, 34 insertions(+), 10 deletions(-)

diff --git a/config/varda-single-1.0.yaml b/config/varda-single-1.0.yaml
index 0a89982b..cfeec407 100644
--- a/config/varda-single-1.0.yaml
+++ b/config/varda-single-1.0.yaml
@@ -16,21 +16,21 @@ runs:
   - temporal_downscaler:
       checkpoint: https://service.meteoswiss.ch/mlstore#/models/sruc-m-2-interpolator/versions/3
       label: Varda-single-1.0
-      steps: 0/12/1
+      steps: 0/120/1
       config: resources/inference/configs/sgm-temporal-downscaler-global_trimedge_multi.yaml
       extra_requirements:
         - anemoi-datasets==0.5.35
-        - -e /scratch/mch/llanzila/postprocessor/anemoi-plugins-meteoswiss
+        - git+https://github.com/MeteoSwiss/anemoi-plugins-meteoswiss@90371687fc48d52b8f81dafa0ba34f4898cc6818
         - git+https://github.com/ecmwf/anemoi-inference@88c1ec13632c570f246cc2580ce324e45f36a03b
       forecaster:
         checkpoint: https://service.meteoswiss.ch/mlstore#/models/varda-forecaster-det-sgm/versions/1
         config: resources/inference/configs/sgm-multidataset-forecaster-global-ich1-oper.yaml
-        steps: 0/12/6
+        steps: 0/120/6
         extra_requirements:
           - anemoi-datasets==0.5.35
-          - -e /scratch/mch/llanzila/postprocessor/anemoi-plugins-meteoswiss
+          - git+https://github.com/MeteoSwiss/anemoi-plugins-meteoswiss@90371687fc48d52b8f81dafa0ba34f4898cc6818
           - git+https://github.com/ecmwf/anemoi-inference@88c1ec13632c570f246cc2580ce324e45f36a03b
-  
+
   - baseline:
       label: INCA
       root: /store_new/mch/msclim/INCA
diff --git a/src/evalml/config.py b/src/evalml/config.py
index 3f6ffcd5..0eeafd72 100644
--- a/src/evalml/config.py
+++ b/src/evalml/config.py
@@ -517,14 +517,25 @@ class DefaultResources(BaseModel):
     gpus: int | None = Field(
         None, ge=0, description="Default GPU count per job (0 for non-GPU jobs)."
     )
+    slurm_extra: str | None = Field(
+        None,
+        description=(
+            "Extra raw sbatch flags appended verbatim to every job submission, "
+            "e.g. '--exclude=nid001229' to avoid a known-problematic node."
+        ),
+    )
 
     def parsable(self) -> list[str]:
         """Convert the default resources to a string of key=value pairs."""
-        return [
-            f"{key}={value}"
-            for key, value in self.model_dump().items()
-            if value is not None
-        ]
+        out = []
+        for key, value in self.model_dump().items():
+            if value is None:
+                continue
+            if key == "slurm_extra":
+                out.append(f'{key}="{value}"')
+            else:
+                out.append(f"{key}={value}")
+        return out
 
 
 class GlobalResources(BaseModel):
diff --git a/workflow/tools/config.schema.json b/workflow/tools/config.schema.json
index d51ddd1d..b295e2c6 100644
--- a/workflow/tools/config.schema.json
+++ b/workflow/tools/config.schema.json
@@ -193,6 +193,19 @@
           "default": null,
           "description": "Default GPU count per job (0 for non-GPU jobs).",
           "title": "Gpus"
+        },
+        "slurm_extra": {
+          "anyOf": [
+            {
+              "type": "string"
+            },
+            {
+              "type": "null"
+            }
+          ],
+          "default": null,
+          "description": "Extra raw sbatch flags appended verbatim to every job submission, e.g. '--exclude=nid001229' to avoid a known-problematic node.",
+          "title": "Slurm Extra"
         }
       },
       "required": [

From 6ef5f5e4bbf4782c5839edaa9c20d62f25ee0b7c Mon Sep 17 00:00:00 2001
From: Luca Lanzilao <llanzila@balfrin-ln004.cscs.ch>
Date: Fri, 18 Sep 2026 20:02:20 +0200
Subject: [PATCH 3/7] fix linting

---
 workflow/rules/inference.smk | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/workflow/rules/inference.smk b/workflow/rules/inference.smk
index 8ce977b5..e899ad4c 100644
--- a/workflow/rules/inference.smk
+++ b/workflow/rules/inference.smk
@@ -331,4 +331,4 @@ rule inference_execute:
         ) >{log} 2>&1
         touch {output.okfile}
         """
-# fmt: on
\ No newline at end of file
+# fmt: on

From 3c3678cc3f660e42176ec4a38e9ddb27a756c1d4 Mon Sep 17 00:00:00 2001
From: Luca Lanzilao <llanzila@balfrin-ln004.cscs.ch>
Date: Mon, 21 Sep 2026 16:47:10 +0200
Subject: [PATCH 4/7] make mean and var computation circular-aware

---
 src/verification/__init__.py    |   53 +-
 tests/unit/test_verification.py |   50 +
 uv.lock                         | 5174 -------------------------------
 3 files changed, 95 insertions(+), 5182 deletions(-)
 delete mode 100644 uv.lock

diff --git a/src/verification/__init__.py b/src/verification/__init__.py
index 7a71e2d5..2867fa2a 100644
--- a/src/verification/__init__.py
+++ b/src/verification/__init__.py
@@ -296,25 +296,60 @@ def _compute_scores(
     return result
 
 
+def _circular_mean_var(
+    data: xr.DataArray, dim: list[str], period: float = _CIRCULAR_PERIOD
+) -> tuple[xr.DataArray, xr.DataArray]:
+    """Circular mean and variance of a directional quantity (degrees by default).
+
+    A plain arithmetic mean/var is wrong for angles: mean(359, 1) = 180 (exactly
+    backwards) instead of ~0. Uses the standard directional-statistics definition:
+    average the unit vectors (sin, cos) of each angle, then take the angle of the
+    resultant vector as the mean, and 1 - (resultant length) as the variance
+    (0 = all values identical, 1 = uniformly spread around the circle).
+    """
+    rad = np.deg2rad(data) * (360.0 / period)
+    sin_mean = np.sin(rad).mean(dim=dim, skipna=True)
+    cos_mean = np.cos(rad).mean(dim=dim, skipna=True)
+    resultant_length = np.hypot(sin_mean, cos_mean)
+    mean = np.mod(np.degrees(np.arctan2(sin_mean, cos_mean)) * (period / 360.0), period)
+    variance = 1 - resultant_length
+    return mean, variance
+
+
 def _compute_statistics(
     data: xr.DataArray,
     dim: list[str],
     prefix="",
     suffix="",
     source="",
+    circular: bool = False,
 ) -> xr.Dataset:
     """
     Compute basic statistics of a xarray DataArray (data).
     Returns a xarray Dataset with the computed statistics.
+
+    If `circular` is True, `mean`/`var` use circular statistics (see
+    _circular_mean_var) instead of a plain arithmetic mean/variance, and
+    `min`/`max` are omitted since neither has a well-defined meaning for a
+    directional quantity (there is no smallest/largest point on a circle).
     """
-    stats = xr.Dataset(
-        {
-            f"{prefix}mean{suffix}": data.mean(dim=dim, skipna=True),
-            f"{prefix}var{suffix}": data.var(dim=dim, skipna=True),
-            f"{prefix}min{suffix}": data.min(dim=dim, skipna=True),
-            f"{prefix}max{suffix}": data.max(dim=dim, skipna=True),
-        }
-    )
+    if circular:
+        mean, var = _circular_mean_var(data, dim=dim)
+        stats = xr.Dataset(
+            {
+                f"{prefix}mean{suffix}": mean,
+                f"{prefix}var{suffix}": var,
+            }
+        )
+    else:
+        stats = xr.Dataset(
+            {
+                f"{prefix}mean{suffix}": data.mean(dim=dim, skipna=True),
+                f"{prefix}var{suffix}": data.var(dim=dim, skipna=True),
+                f"{prefix}min{suffix}": data.min(dim=dim, skipna=True),
+                f"{prefix}max{suffix}": data.max(dim=dim, skipna=True),
+            }
+        )
     stats = stats.expand_dims({"source": [source]})
     return stats
 
@@ -467,12 +502,14 @@ def verify(
             prefix=param + ".",
             source=fcst_label,
             dim=dim,
+            circular=param in _CIRCULAR_PARAMS,
         ).where(~too_many_missing)
         obs_stats = _compute_statistics(
             obs_param,
             prefix=param + ".",
             source=obs_label,
             dim=dim,
+            circular=param in _CIRCULAR_PARAMS,
         )
         param_statistics = xr.concat([fcst_stats, obs_stats], dim="source")
         # Single compute per parameter: score + statistics share fcst_param/obs_param
diff --git a/tests/unit/test_verification.py b/tests/unit/test_verification.py
index 9e46ee5e..48c28edb 100644
--- a/tests/unit/test_verification.py
+++ b/tests/unit/test_verification.py
@@ -12,7 +12,9 @@
     decode_metric,
     apply_lapse_rate_correction_inplace,
     _circular_align,
+    _circular_mean_var,
     _compute_scores,
+    _compute_statistics,
     verify,
 )
 
@@ -222,6 +224,54 @@ def test_compute_scores_noncircular_bias_across_north_is_wrong():
     np.testing.assert_allclose(result["DD_10M.BIAS"].values, 350.0)
 
 
+def test_circular_mean_var_wraps_across_north():
+    # A plain mean of 359 and 1 gives 180 (exactly backwards) instead of ~0.
+    data = xr.DataArray([359.0, 1.0], dims="values")
+    mean, var = _circular_mean_var(data, dim=["values"])
+    np.testing.assert_allclose(float(mean) % 360.0, 0.0, atol=1e-6)
+    assert float(var) < 1e-3  # tightly clustered around 0
+
+
+def test_circular_mean_var_identical_values():
+    data = xr.DataArray([45.0, 45.0, 45.0], dims="values")
+    mean, var = _circular_mean_var(data, dim=["values"])
+    np.testing.assert_allclose(float(mean), 45.0)
+    np.testing.assert_allclose(float(var), 0.0, atol=1e-9)
+
+
+def test_circular_mean_var_uniform_spread_has_max_variance():
+    # No dominant direction -> resultant length ~0 -> variance ~1.
+    data = xr.DataArray([0.0, 90.0, 180.0, 270.0], dims="values")
+    _, var = _circular_mean_var(data, dim=["values"])
+    np.testing.assert_allclose(float(var), 1.0, atol=1e-9)
+
+
+def test_compute_statistics_circular_mean_across_north():
+    data = xr.DataArray([359.0, 1.0], dims="values")
+    stats = _compute_statistics(
+        data, dim=["values"], prefix="DD_10M.", source="fcst", circular=True
+    )
+    assert set(stats.data_vars) == {"DD_10M.mean", "DD_10M.var"}  # no min/max
+    np.testing.assert_allclose(
+        stats["DD_10M.mean"].values.item() % 360.0, 0.0, atol=1e-6
+    )
+
+
+def test_compute_statistics_noncircular_mean_across_north_is_wrong():
+    # Without the circular fix, the same inputs average to 180 — backwards.
+    data = xr.DataArray([359.0, 1.0], dims="values")
+    stats = _compute_statistics(
+        data, dim=["values"], prefix="DD_10M.", source="fcst", circular=False
+    )
+    assert set(stats.data_vars) == {
+        "DD_10M.mean",
+        "DD_10M.var",
+        "DD_10M.min",
+        "DD_10M.max",
+    }
+    np.testing.assert_allclose(stats["DD_10M.mean"].values.item(), 180.0)
+
+
 # verify — missing-fraction masking
 # ---------------------------------------------------------------------------
 
diff --git a/uv.lock b/uv.lock
deleted file mode 100644
index eea30160..00000000
--- a/uv.lock
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From bcd7dc72a9f35f845d405586e9dd65e574300263 Mon Sep 17 00:00:00 2001
From: Luca Lanzilao <llanzila@balfrin-ln004.cscs.ch>
Date: Tue, 22 Sep 2026 17:04:27 +0200
Subject: [PATCH 5/7] change config account

---
 config/varda-single-1.0.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/config/varda-single-1.0.yaml b/config/varda-single-1.0.yaml
index cfeec407..0a746c96 100644
--- a/config/varda-single-1.0.yaml
+++ b/config/varda-single-1.0.yaml
@@ -145,5 +145,5 @@ profile:
     mem_mb_per_cpu: 1800
     runtime: "1h"
     gpus: 0
-    slurm_account: go41
+    slurm_account: s83
   jobs: 50

From 07da526d2d6ae18f81ca59bf8677c48760dc26d3 Mon Sep 17 00:00:00 2001
From: Luca Lanzilao <llanzila@balfrin-ln004.cscs.ch>
Date: Wed, 23 Sep 2026 13:41:17 +0200
Subject: [PATCH 6/7] compute relative humidity for baselines

---
 config/varda-single-1.0.yaml |  4 ++--
 src/data_input/__init__.py   | 29 ++++++++++++++++++++++++++++-
 2 files changed, 30 insertions(+), 3 deletions(-)

diff --git a/config/varda-single-1.0.yaml b/config/varda-single-1.0.yaml
index 0a746c96..d05bb8db 100644
--- a/config/varda-single-1.0.yaml
+++ b/config/varda-single-1.0.yaml
@@ -20,7 +20,7 @@ runs:
       config: resources/inference/configs/sgm-temporal-downscaler-global_trimedge_multi.yaml
       extra_requirements:
         - anemoi-datasets==0.5.35
-        - git+https://github.com/MeteoSwiss/anemoi-plugins-meteoswiss@90371687fc48d52b8f81dafa0ba34f4898cc6818
+        - git+https://github.com/MeteoSwiss/anemoi-plugins-meteoswiss@1dc3eae81182e9c49ff07cf9d02cab8907f7e6dd
         - git+https://github.com/ecmwf/anemoi-inference@88c1ec13632c570f246cc2580ce324e45f36a03b
       forecaster:
         checkpoint: https://service.meteoswiss.ch/mlstore#/models/varda-forecaster-det-sgm/versions/1
@@ -28,7 +28,7 @@ runs:
         steps: 0/120/6
         extra_requirements:
           - anemoi-datasets==0.5.35
-          - git+https://github.com/MeteoSwiss/anemoi-plugins-meteoswiss@90371687fc48d52b8f81dafa0ba34f4898cc6818
+          - git+https://github.com/MeteoSwiss/anemoi-plugins-meteoswiss@1dc3eae81182e9c49ff07cf9d02cab8907f7e6dd
           - git+https://github.com/ecmwf/anemoi-inference@88c1ec13632c570f246cc2580ce324e45f36a03b
 
   - baseline:
diff --git a/src/data_input/__init__.py b/src/data_input/__init__.py
index 8d11ba47..60123667 100644
--- a/src/data_input/__init__.py
+++ b/src/data_input/__init__.py
@@ -7,6 +7,7 @@
 from typing import Callable, Literal, Any
 
 import earthkit.data as ekd
+import earthkit.meteo.thermo as ekdt
 import earthkit.meteo.vertical as ekdv
 import numpy as np
 import pandas as pd
@@ -55,6 +56,7 @@
 DERIVED_PARAMS: dict[str, tuple[str, ...]] = {
     "SP_10M": ("U_10M", "V_10M"),
     "DD_10M": ("U_10M", "V_10M"),
+    "RELHUM_2M": ("T_2M", "TD_2M"),
 }
 
 
@@ -99,6 +101,17 @@ def compute_derived(ds: xr.Dataset, param: str) -> xr.DataArray:
             "name": "10m wind direction",
         }
         return da
+    if param == "RELHUM_2M":
+        # Same formula as the anemoi-inference surface-diagnostics post-processor
+        # (anemoi_plugins_meteoswiss.transform.filters.surface_diagnostics), so
+        # baselines are verified against the ML forecaster on a like-for-like basis.
+        da = ekdt.relative_humidity_from_dewpoint(ds["T_2M"], ds["TD_2M"])
+        da.attrs["parameter"] = {
+            "shortName": "RELHUM_2M",
+            "units": "%",
+            "name": "2m relative humidity",
+        }
+        return da
     raise ValueError(f"No recipe for derived variable '{param}'")
 
 
@@ -871,6 +884,7 @@ def _load_INCA_baseline_from_netcdf(
                    CLCT      total cloud cover         %        1h/10min   CT        10min    %         2022
                    U_10M     10 m zonal wind           m/s      1h/10min   derived from DD_10M, FF_10M
                    V_10M     10 m meridional wind      m/s      1h/10min   derived from DD_10M, FF_10M
+                   RELHUM_2M 2 m relative humidity     %        1h/10min   derived from T_2M, TD_2M
 
                  U_10M and V_10M use the meteorological convention: DD is
                  the direction the wind blows FROM, clockwise from North.
@@ -1046,7 +1060,11 @@ def _load_shifted(param: str, prefix: str) -> xr.DataArray:
             "TOT_PREC": "RP",
         },
     }
-    DERIVED_DEPS = {"U_10M": ["DD_10M", "FF_10M"], "V_10M": ["DD_10M", "FF_10M"]}
+    DERIVED_DEPS = {
+        "U_10M": ["DD_10M", "FF_10M"],
+        "V_10M": ["DD_10M", "FF_10M"],
+        "RELHUM_2M": ["T_2M", "TD_2M"],
+    }
     PARAM_UNITS = {
         "T_2M": "K",
         "TD_2M": "K",
@@ -1058,6 +1076,7 @@ def _load_shifted(param: str, prefix: str) -> xr.DataArray:
         "VMAX_10M": "m/s",
         "U_10M": "m/s",
         "V_10M": "m/s",
+        "RELHUM_2M": "%",
     }
     FREQ_TO_TD = {
         "1h": np.timedelta64(1, "h"),
@@ -1215,6 +1234,14 @@ def _load_cumul(
         if "V_10M" in params:
             merged["V_10M"] = (-ff * np.cos(dd_rad)).assign_attrs(units="m/s")
 
+    if "RELHUM_2M" in params:
+        # Same formula as the anemoi-inference surface-diagnostics post-processor
+        # (anemoi_plugins_meteoswiss.transform.filters.surface_diagnostics), so
+        # INCA is verified against the ML forecaster on a like-for-like basis.
+        merged["RELHUM_2M"] = ekdt.relative_humidity_from_dewpoint(
+            merged["T_2M"], merged["TD_2M"]
+        ).assign_attrs(units="%")
+
     # Restructure to match the earthkit GRIB engine profile: `step` is the
     # lead-time dimension, `valid_time` and `forecast_reference_time` are coords.
     ref_time_np = np.datetime64(reftime, "ns")

From 17f7af47c9981f49fce2470558f4aca444506756 Mon Sep 17 00:00:00 2001
From: lclanzi <luca.lanzilao@meteoswiss.ch>
Date: Wed, 7 Oct 2026 19:48:03 +0200
Subject: [PATCH 7/7] solve indentation

---
 config/varda-single-1.0.yaml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/config/varda-single-1.0.yaml b/config/varda-single-1.0.yaml
index 366b6638..7b3dfbd7 100644
--- a/config/varda-single-1.0.yaml
+++ b/config/varda-single-1.0.yaml
@@ -28,8 +28,8 @@ runs:
         steps: 0/120/6
         extra_requirements:
           - anemoi-datasets==0.5.35
-        - git+https://github.com/ecmwf/anemoi-inference.git@88c1ec13632c570f246cc2580ce324e45f36a03b
-        - git+https://github.com/MeteoSwiss/anemoi-plugins-meteoswiss.git@cfaea1550f9a863216cd43f0a98b95883bbba512
+          - git+https://github.com/ecmwf/anemoi-inference.git@88c1ec13632c570f246cc2580ce324e45f36a03b
+          - git+https://github.com/MeteoSwiss/anemoi-plugins-meteoswiss.git@cfaea1550f9a863216cd43f0a98b95883bbba512
   - baseline:
       label: INCA
       root: /store_new/mch/msclim/INCA