diff --git a/.gitignore b/.gitignore
index 00f25b7..d670061 100755
--- a/.gitignore
+++ b/.gitignore
@@ -11,3 +11,6 @@ wheels/
 
 # Data
 data
+
+# Editors
+.vscode/
diff --git a/alphaearth.py b/alphaearth.py
index 1ebd4cf..0a4d29b 100644
--- a/alphaearth.py
+++ b/alphaearth.py
@@ -1,5 +1,6 @@
 """Extract satellite embeddings from Google Earth Engine and map them to a grid."""
 
+import os
 from pathlib import Path
 from typing import Literal
 
@@ -16,68 +17,69 @@ pretty.install()
 traceback.install()
 ee.Initialize(project="ee-tobias-hoelzer")
 
-DATA_DIR = Path("data")
+DATA_DIR = Path(os.environ.get("DATA_DIR", "data")) / "entropyc-rts"
 EMBEDDINGS_DIR = DATA_DIR / "embeddings"
 EMBEDDINGS_DIR.mkdir(parents=True, exist_ok=True)
 
 
-def cli(grid: Literal["hex", "healpix"], level: int, year: int):
+def cli(grid: Literal["hex", "healpix"], level: int, backup_intermediate: bool = False):
     """Extract satellite embeddings from Google Earth Engine and map them to a grid.
 
     Args:
         grid (Literal["hex", "healpix"]): The grid type to use.
         level (int): The grid level to use.
-        year (int): The year to extract embeddings for. Must be between 2017 and 2024.
+        backup_intermediate (bool, optional): Whether to backup intermediate results. Defaults to False.
 
     """
     gridname = f"permafrost_{grid}{level}"
     grid = gpd.read_parquet(DATA_DIR / f"grids/{gridname}_grid.parquet")
-    embedding_collection = ee.ImageCollection("GOOGLE/SATELLITE_EMBEDDING/V1/ANNUAL")
-    embedding_collection = embedding_collection.filterDate(f"{year}-01-01", f"{year}-12-31")
-    bands = [f"A{str(i).zfill(2)}" for i in range(64)]
 
-    def extract_embedding(feature):
-        # Filter collection by geometry
-        geom = feature.geometry()
-        embedding = embedding_collection.filterBounds(geom).mosaic()
-        # Get mean embedding value for the geometry
-        mean_dict = embedding.reduceRegion(
-            reducer=ee.Reducer.median(),
-            geometry=geom,
-        )
-        # Add mean embedding values as properties to the feature
-        return feature.set(mean_dict)
+    for year in track(range(2022, 2025), total=3, description="Processing years..."):
+        embedding_collection = ee.ImageCollection("GOOGLE/SATELLITE_EMBEDDING/V1/ANNUAL")
+        embedding_collection = embedding_collection.filterDate(f"{year}-01-01", f"{year}-12-31")
+        bands = [f"A{str(i).zfill(2)}" for i in range(64)]
 
-    # Process grid in batches of 100
-    batch_size = 100
-    all_results = []
-    n_batches = len(grid) // batch_size
-    for batch_num, batch_grid in track(
-        enumerate(np.array_split(grid, n_batches)),
-        description="Processing batches...",
-        total=n_batches,
-    ):
-        print(f"Processing batch with {len(batch_grid)} items")
+        def extract_embedding(feature):
+            # Filter collection by geometry
+            geom = feature.geometry()
+            embedding = embedding_collection.filterBounds(geom).mosaic()
+            # Get mean embedding value for the geometry
+            mean_dict = embedding.reduceRegion(
+                reducer=ee.Reducer.median(),
+                geometry=geom,
+            )
+            # Add mean embedding values as properties to the feature
+            return feature.set(mean_dict)
 
-        # Convert batch to EE FeatureCollection
-        eegrid_batch = ee.FeatureCollection(batch_grid.to_crs("epsg:4326").__geo_interface__)
+        # Process grid in batches of 100
+        batch_size = 100
+        all_results = []
+        n_batches = len(grid) // batch_size
+        for batch_num, batch_grid in track(
+            enumerate(np.array_split(grid, n_batches)),
+            description="Processing batches...",
+            total=n_batches,
+        ):
+            # Convert batch to EE FeatureCollection
+            eegrid_batch = ee.FeatureCollection(batch_grid.to_crs("epsg:4326").__geo_interface__)
 
-        # Apply embedding extraction to batch
-        eeegrid_batch = eegrid_batch.map(extract_embedding)
-        df_batch = geemap.ee_to_df(eeegrid_batch)
+            # Apply embedding extraction to batch
+            eeegrid_batch = eegrid_batch.map(extract_embedding)
+            df_batch = geemap.ee_to_df(eeegrid_batch)
 
-        # Save batch immediately to disk as backup
-        batch_filename = f"{gridname}_embeddings-{year}_batch{batch_num:06d}.parquet"
-        batch_result = batch_grid.merge(df_batch[[*bands, "cell_id"]], on="cell_id", how="left")
-        batch_result.to_parquet(EMBEDDINGS_DIR / f"{batch_filename}")
+            # Store batch results
+            all_results.append(df_batch)
 
-        # Store batch results
-        all_results.append(df_batch)
+            # Save batch immediately to disk as backup
+            if backup_intermediate:
+                batch_filename = f"{gridname}_embeddings-{year}_batch{batch_num:06d}.parquet"
+                batch_result = batch_grid.merge(df_batch[[*bands, "cell_id"]], on="cell_id", how="left")
+                batch_result.to_parquet(EMBEDDINGS_DIR / f"{batch_filename}")
 
-    # Combine all batch results
-    df = pd.concat(all_results, ignore_index=True)
-    embeddings_on_grid = grid.merge(df[[*bands, "cell_id"]], on="cell_id", how="left")
-    embeddings_on_grid.to_parquet(EMBEDDINGS_DIR / f"{gridname}_embeddings-{year}.parquet")
+        # Combine all batch results
+        df = pd.concat(all_results, ignore_index=True)
+        embeddings_on_grid = grid.merge(df[[*bands, "cell_id"]], on="cell_id", how="left")
+        embeddings_on_grid.to_parquet(EMBEDDINGS_DIR / f"{gridname}_embeddings-{year}.parquet")
 
 
 if __name__ == "__main__":
diff --git a/cds.py b/cds.py
index adad49f..4ed77b2 100644
--- a/cds.py
+++ b/cds.py
@@ -3,6 +3,7 @@
 Web platform: https://cds.climate.copernicus.eu
 """
 
+import os
 import re
 from pathlib import Path
 
@@ -13,6 +14,8 @@ from rich import pretty, print, traceback
 traceback.install()
 pretty.install()
 
+DATA_DIR = Path(os.environ.get("DATA_DIR", "data")) / "entropyc-rts"
+
 
 def hourly(years: str):
     """Download ERA5 data from the Copernicus Data Store.
@@ -28,7 +31,7 @@ def hourly(years: str):
     dataset = "reanalysis-era5-single-levels"
     client = cdsapi.Client(wait_until_complete=False)
 
-    outdir = Path("/isipd/projects/p_aicore_pf/tohoel001/era5-cds").resolve()
+    outdir = (DATA_DIR / "era5/cds").resolve()
     outdir.mkdir(parents=True, exist_ok=True)
 
     print(f"Downloading ERA5 data from {start_year} to {end_year}...")
diff --git a/era5.py b/era5.py
index d4b421e..c0deebc 100644
--- a/era5.py
+++ b/era5.py
@@ -1,23 +1,70 @@
 """Download and preprocess ERA5 data.
 
 Variables of Interest:
-- 2 metre temperature (t2m)
-- Total precipitation (tp)
-- Snow Fall (sf)
-- Snow cover (snowc)
-- Snow depth (sde)
-- Surface sensible heat flux (sshf)
-- Lake ice bottom temperature (lblt)
+- 2 metre temperature (t2m) [instant]
+- Total precipitation (tp) [accum]
+- Snow Fall (sf) [accum]
+- Snow cover (snowc) [instant]
+- Snow depth (sde) [instant]
+- Surface sensible heat flux (sshf) [accum]
+- Lake ice bottom temperature (lblt) [instant]
 
-Aggregations:
-- Summer / Winter 20-bin histogram?
+Daily Variables (downloaded from hourly data):
+- t2m_daily_max
+- t2m_daily_min
+- tp_daily_sum
+- sf_daily_sum
+- snowc_daily_mean
+- sde_daily_mean
+- sshf_daily_sum
+- lblt_daily_max
 
-Spatial -> Enrich -> Temporal ?
+Derived Daily Variables:
+- t2m_daily_avg
+- t2m_daily_range
+- t2m_daily_skew
+- thawing_degree_days
+- freezing_degree_days
+- thawing_days
+- freezing_days
+- precipitation_occurrences
+- snowfall_occurrences
+- snow_isolation (snowc * sde)
+
+Monthly Variables:
+- t2m_monthly_max
+- t2m_monthly_min
+- tp_monthly_sum
+- sf_monthly_sum
+- snowc_monthly_mean
+- sde_monthly_mean
+- sshf_monthly_sum
+- lblt_monthly_max
+- t2m_monthly_avg
+- t2m_monthly_range_avg
+- t2m_monthly_skew_avg
+- thawing_degree_days_monthly
+- freezing_degree_days_monthly
+- thawing_days_monthly
+- freezing_days_monthly
+- precipitation_occurrences_monthly TODO: Rename to precipitation_days_monthly?
+- snowfall_occurrences_monthly TODO: Rename to snowfall_days_monthly?
+- snow_isolation_monthly_mean
+
+Yearly Variables:
+- TODO
+
+# TODO Variables:
+- Day of first thaw (yearly)
+- Day of last thaw (yearly)
+- Thawing period length (yearly)
+- Freezing period length (yearly)
 
 Author: Tobias Hölzer
 Date: 09. June 2025
 """
 
+import os
 import time
 from concurrent.futures import ThreadPoolExecutor, as_completed
 from pathlib import Path
@@ -34,24 +81,29 @@ import shapely.ops
 import xarray as xr
 from numcodecs.zarr3 import Blosc
 from rich import pretty, print, traceback
+from rich.progress import track
 from shapely.geometry import LineString, Polygon
 
-traceback.install(show_locals=True)
+traceback.install(show_locals=True, suppress=[cyclopts, xr, pd])
 pretty.install()
 
-DATA_DIR = Path("data/era5")
-AGG_PATH = DATA_DIR / "era5_agg.zarr"
-ALIGNED_PATH = DATA_DIR / "era5_spatial_aligned.zarr"
-MONTHLY_PATH = DATA_DIR / "era5_monthly.zarr"
-YEARLY_PATH = DATA_DIR / "era5_yearly.zarr"
+cli = cyclopts.App()
+
+# TODO: Directly handle stuff on a grid level - this is more what the zarr access is indented to do
+
+DATA_DIR = Path(os.environ.get("DATA_DIR", "data")) / "entropyc-rts"
+# DATA_DIR = Path("data")
+ERA5_DIR = DATA_DIR / "era5"
+AGG_PATH = ERA5_DIR / "era5_agg.zarr"
+ALIGNED_PATH = ERA5_DIR / "era5_spatial_aligned.zarr"
+MONTHLY_PATH = ERA5_DIR / "era5_monthly.zarr"
+YEARLY_PATH = ERA5_DIR / "era5_yearly.zarr"
 
 min_lat = 50
-max_lat = 85
-min_time = "2022-01-01"
+max_lat = 83.7  # Ensures the right Chunks Size (90 - 64 / 10 + 0.1)
+min_time = "1990-01-01"
 max_time = "2024-12-31"
-subset = {"latitude": slice(max_lat, min_lat), "time": slice(min_time, max_time)}
 
-DATA_DIR = Path("/isipd/projects/p_aicore_pf/tohoel001/era5_thawing_data")
 
 today = time.strftime("%Y-%m-%d")
 
@@ -63,20 +115,67 @@ today = time.strftime("%Y-%m-%d")
 #    Enrich -> Aggregate temporally
 
 
+# TODO: Rethink aggregations by differentiating between "instant" and "accum" variables:
+# https://consensus.app/search/instantaneous-versus-accumulated-weather/JBaNbhc1R_-BwN5E9Un0Fw/
+
+# ================
+# === Download ===
+# ================
+
+
 def create_encoding(ds: xr.Dataset):
+    """Create compression encoding for zarr dataset storage.
+
+    Creates Blosc compression configuration for all data variables and coordinates
+    in the dataset using zstd compression with level 9.
+
+    Args:
+        ds (xr.Dataset): The xarray Dataset to create encoding for.
+
+    Returns:
+        dict: Encoding dictionary with compression settings for each variable.
+
+    """
     # encoding = {var: {"compressors": BloscCodec(cname="zlib", clevel=9)} for var in ds.data_vars}
     encoding = {var: {"compressors": Blosc(cname="zstd", clevel=9)} for var in [*ds.data_vars, *ds.coords]}
     return encoding
 
 
 def download_daily_aggregated():
+    """Download and aggregate ERA5 data to daily resolution.
+
+    Downloads ERA5 reanalysis data from the DESTINE Earth Data Hub and aggregates
+    it to daily resolution. Includes temperature extremes, precipitation, snow,
+    and surface heat flux variables.
+
+    The function downloads hourly data and creates daily aggregates:
+    - Temperature: daily min/max
+    - Precipitation and snowfall: daily totals
+    - Snow cover and depth: daily means
+    - Surface heat flux: daily totals
+    - Lake ice temperature: daily max
+
+    The aggregated data is saved to a zarr file with compression.
+    """
     era5 = xr.open_dataset(
         "https://data.earthdatahub.destine.eu/era5/reanalysis-era5-land-no-antartica-v0.zarr",
         storage_options={"client_kwargs": {"trust_env": True}},
-        chunks={"latitude": 64 * 4, "longitude": 64 * 4},
+        chunks={},
         # chunks={},
         engine="zarr",
     ).rename({"valid_time": "time"})
+    subset = {
+        "latitude": slice(max_lat, min_lat),
+    }
+
+    # Compute the clostest chunk-start to min_time, to avoid problems with cropped chunks at the start
+    tchunksize = era5.chunksizes["time"][0]
+    era5_chunk_starts = pd.date_range(era5.time.min().item(), era5.time.max().item(), freq=f"{tchunksize}h")
+    closest_chunk_start = era5_chunk_starts[
+        era5_chunk_starts.get_indexer([pd.to_datetime(min_time)], method="ffill")[0]
+    ]
+    subset["time"] = slice(str(closest_chunk_start), max_time)
+
     era5 = era5.sel(**subset)
 
     era5_agg = xr.merge(
@@ -84,38 +183,59 @@ def download_daily_aggregated():
             era5.t2m.resample(time="1D").max().rename("t2m_daily_max"),
             era5.t2m.resample(time="1D").min().rename("t2m_daily_min"),
             era5.tp.resample(time="1D").sum().rename("tp_daily_sum"),
-            # era5.sf.resample(time="1D").sum().rename("sf_daily_sum"),
-            # era5.snowc.resample(time="1D").mean().rename("snowc_daily_mean"),
-            # era5.sde.resample(time="1D").mean().rename("sde_daily_mean"),
-            # era5.sshf.resample(time="1D").sum().rename("sshf_daily_sum"),
-            # era5.lblt.resample(time="1D").max().rename("lblt_daily_max"),
+            era5.sf.resample(time="1D").sum().rename("sf_daily_sum"),
+            era5.snowc.resample(time="1D").mean().rename("snowc_daily_mean"),
+            era5.sde.resample(time="1D").mean().rename("sde_daily_mean"),
+            era5.sshf.resample(time="1D").sum().rename("sshf_daily_sum"),
+            era5.lblt.resample(time="1D").max().rename("lblt_daily_max"),
         ]
     )
 
-    # Rechunk if the first time chunk is not the same as the middle ones
-    if era5_agg.chunksizes["time"][0] != era5_agg.chunksizes["time"][1]:
-        era5_agg = era5_agg.chunk({"time": 120})
-
     # Assign attributes
     era5_agg["t2m_daily_max"].attrs = {"long_name": "Daily maximum 2 metre temperature", "units": "K"}
     era5_agg["t2m_daily_min"].attrs = {"long_name": "Daily minimum 2 metre temperature", "units": "K"}
     era5_agg["tp_daily_sum"].attrs = {"long_name": "Daily total precipitation", "units": "m"}
-    # era5_agg["sf_daily_sum"].attrs = {"long_name": "Daily total snow fall", "units": "m"}
-    # era5_agg["snowc_daily_mean"].attrs = {"long_name": "Daily mean snow cover", "units": "m"}
-    # era5_agg["sde_daily_mean"].attrs = {"long_name": "Daily mean snow depth", "units": "m"}
-    # era5_agg["sshf_daily_sum"].attrs = {"long_name": "Daily total surface sensible heat flux", "units": "J/m²"}
-    # era5_agg["lblt_daily_max"].attrs = {"long_name": "Daily maximum lake ice bottom temperature", "units": "K"}
+    era5_agg["sf_daily_sum"].attrs = {"long_name": "Daily total snow fall", "units": "m"}
+    era5_agg["snowc_daily_mean"].attrs = {"long_name": "Daily mean snow cover", "units": "m"}
+    era5_agg["sde_daily_mean"].attrs = {"long_name": "Daily mean snow depth", "units": "m"}
+    era5_agg["sshf_daily_sum"].attrs = {"long_name": "Daily total surface sensible heat flux", "units": "J/m²"}
+    era5_agg["lblt_daily_max"].attrs = {"long_name": "Daily maximum lake ice bottom temperature", "units": "K"}
 
+    era5_agg = era5_agg.odc.assign_crs("epsg:4326")
+    era5_agg = era5_agg.drop_vars(["surface", "number", "depthBelowLandLayer"])
     era5_agg.to_zarr(AGG_PATH, mode="w", encoding=create_encoding(era5_agg), consolidated=False)
 
 
-def crosses_antimeridian(geom: Polygon) -> bool:
+@cli.command
+def download():
+    """Download ERA5 data using Dask cluster for parallel processing.
+
+    Creates a local Dask cluster and downloads daily aggregated ERA5 data.
+    The cluster is configured with a single worker with 10 threads and 100GB
+    memory limit for optimal performance.
+    """
+    with (
+        dd.LocalCluster(n_workers=1, threads_per_worker=10, memory_limit="100GB") as cluster,
+        dd.Client(cluster) as client,
+    ):
+        print(client)
+        print(client.dashboard_link)
+        download_daily_aggregated()
+        print("Downloaded and aggregated ERA5 data.")
+
+
+# ===========================
+# === Spatial Aggregation ===
+# ===========================
+
+
+def _crosses_antimeridian(geom: Polygon) -> bool:
     coords = shapely.get_coordinates(geom)
     crosses_any_meridian = (coords[:, 0] > 0).any() and (coords[:, 0] < 0).any()
     return crosses_any_meridian and abs(coords[:, 0]).max() > 90
 
 
-def split_antimeridian_cell(geom: Polygon) -> list[Polygon]:
+def _split_antimeridian_cell(geom: Polygon) -> list[Polygon]:
     # Assumes that it is a antimeridian hex
     coords = shapely.get_coordinates(geom)
     for i in range(coords.shape[0]):
@@ -127,53 +247,134 @@ def split_antimeridian_cell(geom: Polygon) -> list[Polygon]:
     return list(polys.geoms)
 
 
-def check_geobox(geobox):
+def _check_geobox(geobox):
     x, y = geobox.shape
     return x > 1 and y > 1
 
 
 def extract_cell_data(idx: int, geom: Polygon) -> xr.Dataset:
-    era5_agg = xr.open_zarr(AGG_PATH)
-    assert {"latitude", "longitude", "time"} == set(era5_agg.dims), (
-        f"Expected dims ('latitude', 'longitude', 'time'), got {era5_agg.dims}"
+    """Extract ERA5 data for a specific grid cell geometry.
+
+    Extracts and spatially averages ERA5 data within the bounds of a grid cell.
+    Handles antimeridian-crossing cells by splitting them appropriately.
+    The extracted data is written to the aligned zarr file.
+
+    Args:
+        idx (int): Index of the grid cell.
+        geom (Polygon): Polygon geometry of the grid cell.
+
+    Returns:
+        xr.Dataset or bool: Returns True if successful, False if cell doesn't
+                          overlap with ERA5 data.
+
+    """
+    era5_agg = (
+        xr.open_zarr(AGG_PATH, consolidated=False)
+        .set_coords("spatial_ref")
+        .drop_vars(["surface", "number", "depthBelowLandLayer"])
     )
     # cell.geometry is a shapely Polygon
-    if not crosses_antimeridian(geom):
+    if not _crosses_antimeridian(geom):
         geoms = [geom]
     # Split geometry in case it crossed antimeridian
     else:
-        geoms = split_antimeridian_cell(geom)
+        geoms = _split_antimeridian_cell(geom)
     cell_data = []
     for geom in geoms:
         geom = odc.geo.Geometry(geom, crs="epsg:4326")
-        if not check_geobox(era5_agg.odc.geobox.enclosing(geom)):
+        if not _check_geobox(era5_agg.odc.geobox.enclosing(geom)):
             continue
         cell_data.append(era5_agg.odc.crop(geom).drop_vars("spatial_ref").mean(["latitude", "longitude"]))
     if len(cell_data) == 0:
-        return None
+        return False
     elif len(cell_data) == 1:
-        return cell_data[0].expand_dims({"cell": [idx]}).chunk({"cell": 1})
+        cell_data = cell_data[0]
     else:
-        return xr.concat(cell_data, dim="part").mean("part").expand_dims({"cell": [idx]}).chunk({"cell": 1})
+        cell_data = xr.concat(cell_data, dim="part").mean("part")
+    cell_data = cell_data.expand_dims({"cell": [idx]}).compute()
+    cell_data.to_zarr(ALIGNED_PATH, region="auto", consolidated=False)
+    return True
 
 
-def spatial_matching(grid: gpd.GeoDataFrame, n_workers: int = 10):
+@cli.command
+def spatial_agg(grid: Literal["hex", "healpix"], level: int, n_workers: int = 10):
+    """Perform spatial aggregation of ERA5 data to grid cells.
+
+    Loads a grid and spatially aggregates ERA5 data to each grid cell using
+    parallel processing. Creates an empty zarr file first, then fills it
+    with extracted data for each cell.
+
+    Args:
+        grid ("hex", "healpix"): Grid type.
+        level (int): Grid resolution level.
+        n_workers (int, optional): Number of parallel workers to use. Defaults to 10.
+
+    """
+    gridname = f"permafrost_{grid}{level}"
+    grid = gpd.read_parquet(DATA_DIR / f"grids/{gridname}_grid.parquet")
+    # Create an empty zarr array with the right dimensions
+    era5_agg = (
+        xr.open_zarr(AGG_PATH, consolidated=False)
+        .set_coords("spatial_ref")
+        .drop_vars(["surface", "number", "depthBelowLandLayer"])
+    )
+    assert {"latitude", "longitude", "time"} == set(era5_agg.dims), (
+        f"Expected dims ('latitude', 'longitude', 'time'), got {era5_agg.dims}"
+    )
+    assert era5_agg.odc.crs == "epsg:4326", f"Expected CRS 'epsg:4326', got {era5_agg.odc.crs}"
+    empty = (
+        xr.zeros_like(era5_agg.isel(latitude=0, longitude=0))
+        .expand_dims({"cell": [idx for idx, _ in grid.iterrows()]})
+        .chunk({"cell": 1, "time": len(era5_agg.time)})
+    )
+    empty.to_zarr(ALIGNED_PATH, mode="w", consolidated=False, encoding=create_encoding(empty))
+
+    print(f"Starting spatial matching of {len(grid)} cells with {n_workers} workers...")
+
+    # TODO: Maybe change to process pool executor?
     with ThreadPoolExecutor(max_workers=n_workers) as executor:
         futures = {
             executor.submit(extract_cell_data, idx, row.geometry): idx
             for idx, row in grid.to_crs("epsg:4326").iterrows()
         }
-        for future in as_completed(futures):
+        for future in track(as_completed(futures), total=len(futures), description="Processing cells"):
             idx = futures[future]
             try:
-                data = future.result()
-                data.to_zarr(ALIGNED_PATH, append_dim="cell", consolidated=False, encoding=create_encoding(data))
+                flag = future.result()
+                if flag:
+                    print(f"Successfully written cell {idx}")
+                else:
+                    print(f"Cell {idx} did not overlap with ERA5 data.")
             except Exception as e:
                 print(f"Error processing cell {idx}: {e}")
+                print(type(e))
+    print("Finished spatial matching.")
+
+
+# ============================
+# === Temporal Aggregation ===
+# ============================
 
 
 def daily_enrich() -> xr.Dataset:
-    era5 = xr.open_zarr(ALIGNED_PATH)
+    """Enrich daily ERA5 data with derived climate variables.
+
+    Loads spatially aligned ERA5 data and computes additional climate variables.
+    Creates derived variables including temperature statistics, degree days, and occurrence indicators.
+
+    Derived variables include:
+    - Daily average and range temperature
+    - Temperature skewness
+    - Thawing and freezing degree days
+    - Thawing and freezing day counts
+    - Precipitation and snowfall occurrences
+    - Snow isolation index
+
+    Returns:
+        xr.Dataset: Enriched dataset with original and derived variables.
+
+    """
+    era5 = xr.open_zarr(ALIGNED_PATH, consolidated=False).set_coords("spatial_ref")
     assert {"cell", "time"} == set(era5.dims), f"Expected dims ('cell', 'time'), got {era5.dims}"
 
     # Formulas based on Groeke et. al. (2025) Stochastic Weather generation...
@@ -206,6 +407,15 @@ def daily_enrich() -> xr.Dataset:
 
 
 def monthly_aggregate():
+    """Aggregate enriched daily ERA5 data to monthly resolution.
+
+    Takes the enriched daily ERA5 data and creates monthly aggregates using
+    appropriate statistical functions for each variable type. Temperature
+    variables use min/max/mean, accumulation variables use sums, and derived
+    variables use appropriate aggregations.
+
+    The aggregated monthly data is saved to a zarr file for further processing.
+    """
     era5 = daily_enrich()
     assert {"cell", "time"} == set(era5.dims), f"Expected dims ('cell', 'time'), got {era5.dims}"
 
@@ -213,32 +423,46 @@ def monthly_aggregate():
     monthly = xr.merge(
         [
             # Original variables
-            era5.t2m_daily_min.resample(time="1M").min().rename("t2m_monthly_min"),
-            era5.t2m_daily_max.resample(time="1M").max().rename("t2m_monthly_max"),
-            era5.tp_daily_sum.resample(time="1M").sum().rename("tp_monthly_sum"),
-            era5.sf_daily_sum.resample(time="1M").sum().rename("sf_monthly_sum"),
-            era5.snowc_daily_mean.resample(time="1M").mean().rename("snowc_monthly_mean"),
-            era5.sde_daily_mean.resample(time="1M").mean().rename("sde_monthly_mean"),
-            era5.sshf_daily_sum.resample(time="1M").sum().rename("sshf_monthly_sum"),
-            era5.lblt_daily_max.resample(time="1M").max().rename("lblt_monthly_max"),
+            era5.t2m_daily_min.resample(time="1ME").min().rename("t2m_monthly_min"),
+            era5.t2m_daily_max.resample(time="1ME").max().rename("t2m_monthly_max"),
+            era5.tp_daily_sum.resample(time="1ME").sum().rename("tp_monthly_sum"),
+            era5.sf_daily_sum.resample(time="1ME").sum().rename("sf_monthly_sum"),
+            era5.snowc_daily_mean.resample(time="1ME").mean().rename("snowc_monthly_mean"),
+            era5.sde_daily_mean.resample(time="1ME").mean().rename("sde_monthly_mean"),
+            era5.sshf_daily_sum.resample(time="1ME").sum().rename("sshf_monthly_sum"),
+            era5.lblt_daily_max.resample(time="1ME").max().rename("lblt_monthly_max"),
             # Enriched variables
-            era5.t2m_daily_avg.resample(time="1M").mean().rename("t2m_monthly_avg"),
-            era5.t2m_daily_range.resample(time="1M").mean().rename("t2m_daily_range_monthly_avg"),
-            era5.t2m_daily_skew.resample(time="1M").mean().rename("t2m_daily_skew_monthly_avg"),
-            era5.thawing_degree_days.resample(time="1M").sum().rename("thawing_degree_days_monthly"),
-            era5.freezing_degree_days.resample(time="1M").sum().rename("freezing_degree_days_monthly"),
-            era5.thawing_days.resample(time="1M").sum().rename("thawing_days_monthly"),
-            era5.freezing_days.resample(time="1M").sum().rename("freezing_days_monthly"),
-            era5.precipitation_occurrences.resample(time="1M").sum().rename("precipitation_occurrences_monthly"),
-            era5.snowfall_occurrences.resample(time="1M").sum().rename("snowfall_occurrences_monthly"),
-            era5.snow_isolation.resample(time="1M").mean().rename("snow_isolation_monthly_mean"),
+            era5.t2m_daily_avg.resample(time="1ME").mean().rename("t2m_monthly_avg"),
+            era5.t2m_daily_range.resample(time="1ME").mean().rename("t2m_monthly_range_avg"),
+            era5.t2m_daily_skew.resample(time="1ME").mean().rename("t2m_monthly_skew_avg"),
+            era5.thawing_degree_days.resample(time="1ME").sum().rename("thawing_degree_days_monthly"),
+            era5.freezing_degree_days.resample(time="1ME").sum().rename("freezing_degree_days_monthly"),
+            era5.thawing_days.resample(time="1ME").sum().rename("thawing_days_monthly"),
+            era5.freezing_days.resample(time="1ME").sum().rename("freezing_days_monthly"),
+            era5.precipitation_occurrences.resample(time="1ME").sum().rename("precipitation_occurrences_monthly"),
+            era5.snowfall_occurrences.resample(time="1ME").sum().rename("snowfall_occurrences_monthly"),
+            era5.snow_isolation.resample(time="1ME").mean().rename("snow_isolation_monthly_mean"),
         ]
     )
     monthly.to_zarr(MONTHLY_PATH, mode="w", encoding=create_encoding(monthly), consolidated=False)
 
 
 def yearly_aggregate():
-    monthly = xr.open_zarr(MONTHLY_PATH)
+    """Aggregate monthly ERA5 data to yearly resolution with seasonal splits.
+
+    Takes monthly aggregated data and creates yearly aggregates using a shifted
+    calendar (October to September) to better capture Arctic seasonal patterns.
+    Creates separate aggregates for full year, winter (Oct-Apr), and summer
+    (May-Sep) periods.
+
+    The first and last incomplete years are excluded from the analysis.
+    Winter months are defined as months 1-7 in the shifted calendar,
+    and summer months are 8-12.
+
+    The final dataset includes yearly, winter, and summer aggregates for all
+    climate variables, saved to a zarr file.
+    """
+    monthly = xr.open_zarr(MONTHLY_PATH, consolidated=False).set_coords("spatial_ref")
     assert {"cell", "time"} == set(monthly.dims), f"Expected dims ('cell', 'time'), got {monthly.dims}"
 
     # Yearly aggregates (shifted by +10 months to start in Oktober, first and last years will be cropped)
@@ -249,32 +473,34 @@ def yearly_aggregate():
     yearly = xr.merge(
         [
             # Original variables
-            monthly_shifted.t2m_monthly_min.resample(time="1Y").min().rename("t2m_yearly_min"),
-            monthly_shifted.t2m_monthly_max.resample(time="1Y").max().rename("t2m_yearly_max"),
-            monthly_shifted.tp_monthly_sum.resample(time="1Y").sum().rename("tp_yearly_sum"),
-            monthly_shifted.sf_monthly_sum.resample(time="1Y").sum().rename("sf_yearly_sum"),
-            monthly_shifted.snowc_monthly_mean.resample(time="1Y").mean().rename("snowc_yearly_mean"),
-            monthly_shifted.sde_monthly_mean.resample(time="1Y").mean().rename("sde_yearly_mean"),
-            monthly_shifted.sshf_monthly_sum.resample(time="1Y").sum().rename("sshf_yearly_sum"),
-            monthly_shifted.lblt_monthly_max.resample(time="1Y").max().rename("lblt_yearly_max"),
+            monthly_shifted.t2m_monthly_min.resample(time="1YE").min().rename("t2m_yearly_min"),
+            monthly_shifted.t2m_monthly_max.resample(time="1YE").max().rename("t2m_yearly_max"),
+            monthly_shifted.tp_monthly_sum.resample(time="1YE").sum().rename("tp_yearly_sum"),
+            monthly_shifted.sf_monthly_sum.resample(time="1YE").sum().rename("sf_yearly_sum"),
+            monthly_shifted.snowc_monthly_mean.resample(time="1YE").mean().rename("snowc_yearly_mean"),
+            monthly_shifted.sde_monthly_mean.resample(time="1YE").mean().rename("sde_yearly_mean"),
+            monthly_shifted.sshf_monthly_sum.resample(time="1YE").sum().rename("sshf_yearly_sum"),
+            monthly_shifted.lblt_monthly_max.resample(time="1YE").max().rename("lblt_yearly_max"),
             # Enriched variables
-            monthly_shifted.t2m_monthly_avg.resample(time="1Y").mean().rename("t2m_yearly_avg"),
+            monthly_shifted.t2m_monthly_avg.resample(time="1YE").mean().rename("t2m_yearly_avg"),
             # TODO: Check if this is correct -> use daily / hourly data instead for range and skew?
-            monthly_shifted.t2m_monthly_range.resample(time="1Y").mean().rename("t2m_daily_range_yearly_avg"),
-            monthly_shifted.t2m_monthly_skew.resample(time="1Y").mean().rename("t2m_daily_skew_yearly_avg"),
-            monthly_shifted.thawing_degree_days_monthly.resample(time="1Y").sum().rename("thawing_degree_days_yearly"),
-            monthly_shifted.freezing_degree_days_monthly.resample(time="1Y")
+            monthly_shifted.t2m_monthly_range_avg.resample(time="1YE").mean().rename("t2m_daily_range_yearly_avg"),
+            monthly_shifted.t2m_monthly_skew_avg.resample(time="1YE").mean().rename("t2m_daily_skew_yearly_avg"),
+            monthly_shifted.thawing_degree_days_monthly.resample(time="1YE").sum().rename("thawing_degree_days_yearly"),
+            monthly_shifted.freezing_degree_days_monthly.resample(time="1YE")
             .sum()
             .rename("freezing_degree_days_yearly"),
-            monthly_shifted.thawing_days_monthly.resample(time="1Y").sum().rename("thawing_days_yearly"),
-            monthly_shifted.freezing_days_monthly.resample(time="1Y").sum().rename("freezing_days_yearly"),
-            monthly_shifted.precipitation_occurrences_monthly.resample(time="1Y")
+            monthly_shifted.thawing_days_monthly.resample(time="1YE").sum().rename("thawing_days_yearly"),
+            monthly_shifted.freezing_days_monthly.resample(time="1YE").sum().rename("freezing_days_yearly"),
+            monthly_shifted.precipitation_occurrences_monthly.resample(time="1YE")
             .sum()
             .rename("precipitation_occurrences_yearly"),
-            monthly_shifted.snowfall_occurrences_monthly.resample(time="1Y")
+            monthly_shifted.snowfall_occurrences_monthly.resample(time="1YE")
             .sum()
             .rename("snowfall_occurrences_yearly"),
-            monthly_shifted.snow_isolation_monthly_mean.resample(time="1Y").mean().rename("snow_isolation_yearly_mean"),
+            monthly_shifted.snow_isolation_monthly_mean.resample(time="1YE")
+            .mean()
+            .rename("snow_isolation_yearly_mean"),
         ]
     )
     # Summer / Winter aggregates
@@ -286,34 +512,36 @@ def yearly_aggregate():
     winter = xr.merge(
         [
             # Original variables
-            monthly_shifted_winter.t2m_monthly_min.resample(time="1Y").min().rename("t2m_winter_min"),
-            monthly_shifted_winter.t2m_monthly_max.resample(time="1Y").max().rename("t2m_winter_max"),
-            monthly_shifted_winter.tp_monthly_sum.resample(time="1Y").sum().rename("tp_winter_sum"),
-            monthly_shifted_winter.sf_monthly_sum.resample(time="1Y").sum().rename("sf_winter_sum"),
-            monthly_shifted_winter.snowc_monthly_mean.resample(time="1Y").mean().rename("snowc_winter_mean"),
-            monthly_shifted_winter.sde_monthly_mean.resample(time="1Y").mean().rename("sde_winter_mean"),
-            monthly_shifted_winter.sshf_monthly_sum.resample(time="1Y").sum().rename("sshf_winter_sum"),
-            monthly_shifted_winter.lblt_monthly_max.resample(time="1Y").max().rename("lblt_winter_max"),
+            monthly_shifted_winter.t2m_monthly_min.resample(time="1YE").min().rename("t2m_winter_min"),
+            monthly_shifted_winter.t2m_monthly_max.resample(time="1YE").max().rename("t2m_winter_max"),
+            monthly_shifted_winter.tp_monthly_sum.resample(time="1YE").sum().rename("tp_winter_sum"),
+            monthly_shifted_winter.sf_monthly_sum.resample(time="1YE").sum().rename("sf_winter_sum"),
+            monthly_shifted_winter.snowc_monthly_mean.resample(time="1YE").mean().rename("snowc_winter_mean"),
+            monthly_shifted_winter.sde_monthly_mean.resample(time="1YE").mean().rename("sde_winter_mean"),
+            monthly_shifted_winter.sshf_monthly_sum.resample(time="1YE").sum().rename("sshf_winter_sum"),
+            monthly_shifted_winter.lblt_monthly_max.resample(time="1YE").max().rename("lblt_winter_max"),
             # Enriched variables
-            monthly_shifted_winter.t2m_monthly_avg.resample(time="1Y").mean().rename("t2m_winter_avg"),
+            monthly_shifted_winter.t2m_monthly_avg.resample(time="1YE").mean().rename("t2m_winter_avg"),
             # TODO: Check if this is correct -> use daily / hourly data instead for range and skew?
-            monthly_shifted_winter.t2m_monthly_range.resample(time="1Y").mean().rename("t2m_daily_range_winter_avg"),
-            monthly_shifted_winter.t2m_monthly_skew.resample(time="1Y").mean().rename("t2m_daily_skew_winter_avg"),
-            monthly_shifted_winter.thawing_degree_days_monthly.resample(time="1Y")
+            monthly_shifted_winter.t2m_monthly_range_avg.resample(time="1YE")
+            .mean()
+            .rename("t2m_daily_range_winter_avg"),
+            monthly_shifted_winter.t2m_monthly_skew_avg.resample(time="1YE").mean().rename("t2m_daily_skew_winter_avg"),
+            monthly_shifted_winter.thawing_degree_days_monthly.resample(time="1YE")
             .sum()
             .rename("thawing_degree_days_winter"),
-            monthly_shifted_winter.freezing_degree_days_monthly.resample(time="1Y")
+            monthly_shifted_winter.freezing_degree_days_monthly.resample(time="1YE")
             .sum()
             .rename("freezing_degree_days_winter"),
-            monthly_shifted_winter.thawing_days_monthly.resample(time="1Y").sum().rename("thawing_days_winter"),
-            monthly_shifted_winter.freezing_days_monthly.resample(time="1Y").sum().rename("freezing_days_winter"),
-            monthly_shifted_winter.precipitation_occurrences_monthly.resample(time="1Y")
+            monthly_shifted_winter.thawing_days_monthly.resample(time="1YE").sum().rename("thawing_days_winter"),
+            monthly_shifted_winter.freezing_days_monthly.resample(time="1YE").sum().rename("freezing_days_winter"),
+            monthly_shifted_winter.precipitation_occurrences_monthly.resample(time="1YE")
             .sum()
             .rename("precipitation_occurrences_winter"),
-            monthly_shifted_winter.snowfall_occurrences_monthly.resample(time="1Y")
+            monthly_shifted_winter.snowfall_occurrences_monthly.resample(time="1YE")
             .sum()
             .rename("snowfall_occurrences_winter"),
-            monthly_shifted_winter.snow_isolation_monthly_mean.resample(time="1Y")
+            monthly_shifted_winter.snow_isolation_monthly_mean.resample(time="1YE")
             .mean()
             .rename("snow_isolation_winter_mean"),
         ]
@@ -322,34 +550,36 @@ def yearly_aggregate():
     summer = xr.merge(
         [
             # Original variables
-            monthly_shifted_summer.t2m_monthly_min.resample(time="1Y").min().rename("t2m_summer_min"),
-            monthly_shifted_summer.t2m_monthly_max.resample(time="1Y").max().rename("t2m_summer_max"),
-            monthly_shifted_summer.tp_monthly_sum.resample(time="1Y").sum().rename("tp_summer_sum"),
-            monthly_shifted_summer.sf_monthly_sum.resample(time="1Y").sum().rename("sf_summer_sum"),
-            monthly_shifted_summer.snowc_monthly_mean.resample(time="1Y").mean().rename("snowc_summer_mean"),
-            monthly_shifted_summer.sde_monthly_mean.resample(time="1Y").mean().rename("sde_summer_mean"),
-            monthly_shifted_summer.sshf_monthly_sum.resample(time="1Y").sum().rename("sshf_summer_sum"),
-            monthly_shifted_summer.lblt_monthly_max.resample(time="1Y").max().rename("lblt_summer_max"),
+            monthly_shifted_summer.t2m_monthly_min.resample(time="1YE").min().rename("t2m_summer_min"),
+            monthly_shifted_summer.t2m_monthly_max.resample(time="1YE").max().rename("t2m_summer_max"),
+            monthly_shifted_summer.tp_monthly_sum.resample(time="1YE").sum().rename("tp_summer_sum"),
+            monthly_shifted_summer.sf_monthly_sum.resample(time="1YE").sum().rename("sf_summer_sum"),
+            monthly_shifted_summer.snowc_monthly_mean.resample(time="1YE").mean().rename("snowc_summer_mean"),
+            monthly_shifted_summer.sde_monthly_mean.resample(time="1YE").mean().rename("sde_summer_mean"),
+            monthly_shifted_summer.sshf_monthly_sum.resample(time="1YE").sum().rename("sshf_summer_sum"),
+            monthly_shifted_summer.lblt_monthly_max.resample(time="1YE").max().rename("lblt_summer_max"),
             # Enriched variables
-            monthly_shifted_summer.t2m_monthly_avg.resample(time="1Y").mean().rename("t2m_summer_avg"),
+            monthly_shifted_summer.t2m_monthly_avg.resample(time="1YE").mean().rename("t2m_summer_avg"),
             # TODO: Check if this is correct -> use daily / hourly data instead for range and skew?
-            monthly_shifted_summer.t2m_monthly_range.resample(time="1Y").mean().rename("t2m_daily_range_summer_avg"),
-            monthly_shifted_summer.t2m_monthly_skew.resample(time="1Y").mean().rename("t2m_daily_skew_summer_avg"),
-            monthly_shifted_summer.thawing_degree_days_summer.resample(time="1Y")
+            monthly_shifted_summer.t2m_monthly_range_avg.resample(time="1YE")
+            .mean()
+            .rename("t2m_daily_range_summer_avg"),
+            monthly_shifted_summer.t2m_monthly_skew_avg.resample(time="1YE").mean().rename("t2m_daily_skew_summer_avg"),
+            monthly_shifted_summer.thawing_degree_days_monthly.resample(time="1YE")
             .sum()
             .rename("thawing_degree_days_summer"),
-            monthly_shifted_summer.freezing_degree_days_summer.resample(time="1Y")
+            monthly_shifted_summer.freezing_degree_days_monthly.resample(time="1YE")
             .sum()
             .rename("freezing_degree_days_summer"),
-            monthly_shifted_summer.thawing_days_summer.resample(time="1Y").sum().rename("thawing_days_summer"),
-            monthly_shifted_summer.freezing_days_summer.resample(time="1Y").sum().rename("freezing_days_summer"),
-            monthly_shifted_summer.precipitation_occurrences_summer.resample(time="1Y")
+            monthly_shifted_summer.thawing_days_monthly.resample(time="1YE").sum().rename("thawing_days_summer"),
+            monthly_shifted_summer.freezing_days_monthly.resample(time="1YE").sum().rename("freezing_days_summer"),
+            monthly_shifted_summer.precipitation_occurrences_monthly.resample(time="1YE")
             .sum()
             .rename("precipitation_occurrences_summer"),
-            monthly_shifted_summer.snowfall_occurrences_summer.resample(time="1Y")
+            monthly_shifted_summer.snowfall_occurrences_monthly.resample(time="1YE")
             .sum()
             .rename("snowfall_occurrences_summer"),
-            monthly_shifted_summer.snow_isolation_summer.resample(time="1Y")
+            monthly_shifted_summer.snow_isolation_monthly_mean.resample(time="1YE")
             .mean()
             .rename("snow_isolation_summer_mean"),
         ]
@@ -359,32 +589,28 @@ def yearly_aggregate():
     combined.to_zarr(YEARLY_PATH, mode="w", encoding=create_encoding(combined), consolidated=False)
 
 
-def cli(grid: Literal["hex", "healpix"], level: int, download: bool = False, n_workers: int = 10):
-    """Run the CLI for ERA5 data processing.
+@cli.command
+def temporal_agg(n_workers: int = 10):
+    """Perform temporal aggregation of ERA5 data using Dask cluster.
+
+    Creates a Dask cluster and runs both monthly and yearly aggregation
+    functions to generate temporally aggregated climate datasets. The
+    processing uses parallel workers for efficient computation.
 
     Args:
-        grid (Literal["hex", "healpix"]): The grid type to use.
-        level (int): The processing level.
-        download (bool, optional): Whether to download data. Defaults to False.
-        n_workers (int, optional): Number of workers for parallel processing. Defaults to 10.
+        n_workers (int, optional): Number of Dask workers to use. Defaults to 10.
 
     """
-    cluster = dd.LocalCluster(n_workers=n_workers, threads_per_worker=4, memory_limit="20GB")
-    client = dd.Client(cluster)
-    print(client)
-    print(client.dashboard_link)
-
-    if download:
-        download_daily_aggregated()
-        print("Downloaded and aggregated ERA5 data.")
-
-    grid = gpd.read_parquet(DATA_DIR / f"grids/permafrost_{grid}{level}_grid.parquet")
-    spatial_matching(grid, n_workers=n_workers)
-    print("Spatially matched ERA5 data to grid.")
-    monthly_aggregate()
-    yearly_aggregate()
-    print("Enriched ERA5 data with additional features and aggregated it temporally.")
+    with (
+        dd.LocalCluster(n_workers=n_workers, threads_per_worker=20, memory_limit="10GB") as cluster,
+        dd.Client(cluster) as client,
+    ):
+        print(client)
+        print(client.dashboard_link)
+        monthly_aggregate()
+        yearly_aggregate()
+        print("Enriched ERA5 data with additional features and aggregated it temporally.")
 
 
 if __name__ == "__main__":
-    cyclopts.run(cli)
+    cli()
diff --git a/pyproject.toml b/pyproject.toml
index 2fde7b0..e74046b 100755
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -15,7 +15,7 @@ dependencies = [
     "distributed>=2025.5.1",
     "earthengine-api>=1.6.9",
     "eemont>=2025.7.1",
-    # "entropyc",
+    "entropyc",
     "flox>=0.10.4",
     "folium>=0.19.7",
     "geemap>=0.36.3",
@@ -37,12 +37,12 @@ dependencies = [
     "smart-geocubes[arcticdem,dask,stac,viz]>=0.0.9",
     "stopuhr>=0.0.10",
     "xanimate",
-    "xarray>=2025.4.0",
+    "xarray>=2025.9.0",
     "xdggs>=0.2.1",
     "xvec>=0.5.1",
-    "zarr[remote]>=3.0.8",
+    "zarr[remote]>=3.1.3",
 ]
 
 [tool.uv.sources]
-# entropyc = { git = "ssh://git@github.com/AlbertEMC2Stein/entropyc", branch = "refactor/tobi" }
+entropyc = { git = "ssh://git@github.com/AlbertEMC2Stein/entropyc", branch = "refactor/tobi" }
 xanimate = { git = "https://github.com/davbyr/xAnimate" }
diff --git a/uv.lock b/uv.lock
index cb33562..ec1a9a0 100755
--- a/uv.lock
+++ b/uv.lock
@@ -1075,6 +1075,7 @@ dependencies = [
     { name = "distributed" },
     { name = "earthengine-api" },
     { name = "eemont" },
+    { name = "entropyc" },
     { name = "flox" },
     { name = "folium" },
     { name = "geemap" },
@@ -1113,6 +1114,7 @@ requires-dist = [
     { name = "distributed", specifier = ">=2025.5.1" },
     { name = "earthengine-api", specifier = ">=1.6.9" },
     { name = "eemont", specifier = ">=2025.7.1" },
+    { name = "entropyc", git = "ssh://git@github.com/AlbertEMC2Stein/entropyc?branch=refactor%2Ftobi" },
     { name = "flox", specifier = ">=0.10.4" },
     { name = "folium", specifier = ">=0.19.7" },
     { name = "geemap", specifier = ">=0.36.3" },
@@ -1134,10 +1136,19 @@ requires-dist = [
     { name = "smart-geocubes", extras = ["arcticdem", "dask", "stac", "viz"], specifier = ">=0.0.9" },
     { name = "stopuhr", specifier = ">=0.0.10" },
     { name = "xanimate", git = "https://github.com/davbyr/xAnimate" },
-    { name = "xarray", specifier = ">=2025.4.0" },
+    { name = "xarray", specifier = ">=2025.9.0" },
     { name = "xdggs", specifier = ">=0.2.1" },
     { name = "xvec", specifier = ">=0.5.1" },
-    { name = "zarr", extras = ["remote"], specifier = ">=3.0.8" },
+    { name = "zarr", extras = ["remote"], specifier = ">=3.1.3" },
+]
+
+[[package]]
+name = "entropyc"
+version = "0.1.0"
+source = { git = "ssh://git@github.com/AlbertEMC2Stein/entropyc?branch=refactor%2Ftobi#22a191d194a76b6c182481acb2af1bde3f60b49e" }
+dependencies = [
+    { name = "numpy" },
+    { name = "scipy" },
 ]
 
 [[package]]