Create grid and era5 download

create_grid.py

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+"""Create a global hexagonal grid using H3.
+
+Author: Tobias Hölzer
+Date: 09. June 2025
+"""
+
+from typing import Literal
+
+import cartopy.crs as ccrs
+import cartopy.feature as cfeature
+import cyclopts
+import geopandas as gpd
+import h3
+import matplotlib.path as mpath
+import matplotlib.pyplot as plt
+import numpy as np
+import xarray as xr
+import xdggs
+import xvec  # noqa: F401
+from rich import pretty, print, traceback
+from shapely.geometry import Polygon
+from shapely.ops import transform
+from stopuhr import stopwatch
+from xdggs.healpix import HealpixInfo
+
+traceback.install()
+pretty.install()
+
+
+@stopwatch("Create a global hex grid")
+def create_global_hex_grid(resolution):
+    """Create a global hexagonal grid using H3.
+
+    Args:
+        resolution (int): H3 resolution level (0-15)
+
+    Returns:
+        GeoDataFrame: Global hexagonal grid
+
+    """
+    # Generate hexagons
+    hex0_cells = h3.get_res0_cells()
+
+    if resolution > 0:
+        hex_cells = []
+        for hex0_cell in hex0_cells:
+            hex_cells.extend(h3.cell_to_children(hex0_cell, resolution))
+
+    else:
+        hex_cells = hex0_cells
+
+    # Initialize lists to store hex information
+    hex_list = []
+    hex_id_list = []
+    hex_area_list = []
+
+    # Convert each hex ID to a polygon
+    for hex_id in hex_cells:
+        boundary_coords = h3.cell_to_boundary(hex_id)
+        hex_polygon = Polygon(boundary_coords)
+        hex_polygon = transform(lambda x, y: (y, x), hex_polygon)  # Convert from (lat, lon) to (lon, lat)
+        hex_area = h3.cell_area(hex_id, unit="km^2")
+        hex_list.append(hex_polygon)
+        hex_id_list.append(hex_id)
+        hex_area_list.append(hex_area)
+
+    # Create GeoDataFrame
+    grid = gpd.GeoDataFrame({"cell_id": hex_id_list, "cell_area": hex_area_list, "geometry": hex_list}, crs="EPSG:4326")
+
+    return grid
+
+
+@stopwatch("Create a global HEALPix grid")
+def create_global_healpix_grid(level: int):
+    """Create a global HEALPix grid.
+
+    Args:
+        level (int): HEALPix level (0-12)
+
+    Returns:
+        GeoDataFrame: Global HEALPix grid
+
+    """
+    grid_info = HealpixInfo(level=level, indexing_scheme="nested")
+    healpix_ds = xr.Dataset(
+        coords={
+            "cell_ids": (
+                "cells",
+                np.arange(12 * 4**grid_info.level),
+                grid_info.to_dict(),
+            )
+        }
+    ).pipe(xdggs.decode)
+
+    cell_ids = healpix_ds.cell_ids.values
+    geometry = healpix_ds.dggs.cell_boundaries()
+
+    # Create GeoDataFrame
+    grid = gpd.GeoDataFrame({"cell_id": cell_ids, "geometry": geometry}, crs="EPSG:4326")
+
+    grid["cell_area"] = grid.to_crs("EPSG:3413").geometry.area / 1e6  # Convert to km^2
+
+    return grid
+
+
+@stopwatch("Filter grid")
+def filter_permafrost_grid(grid: gpd.GeoDataFrame):
+    """Filter an existing grid to permafrost extent & remove water.
+
+    Args:
+        grid (gpd.GeoDataFrame): Input grid
+
+    Returns:
+        gpd.GeoDataFrame: Filtered grid
+
+    """
+    # Filter for Permafrost region (> 50° latitude)
+    grid = grid[grid.geometry.bounds.miny > 50]
+    # Filter for Arctic Sea (<85° latitude)
+    grid = grid[grid.geometry.bounds.maxy < 85]
+
+    # Convert to arctic stereographic projection
+    grid = grid.to_crs("EPSG:3413")
+
+    # Filter out non-land areas (e.g., oceans)
+    water_mask = gpd.read_file("./data/simplified-water-polygons-split-3857/simplified_water_polygons.shp")
+    water_mask = water_mask.to_crs("EPSG:3413")
+
+    ov = gpd.overlay(grid, water_mask, how="intersection")
+    ov["area"] = ov.geometry.area / 1e6  # Convert to km^2
+    ov = ov.groupby("cell_id").agg({"area": "sum"})
+
+    grid["water_area"] = grid["cell_id"].map(ov.area).fillna(0)
+    grid["land_area"] = grid["cell_area"] - grid["water_area"]
+    grid["land_ratio"] = grid["land_area"] / grid["cell_area"]
+
+    # Filter for land areas (> 10% land)
+    grid = grid[grid["land_ratio"] > 0.1]
+
+    return grid
+
+
+def vizualize_grid(data: gpd.GeoDataFrame, grid: str, level: int) -> plt.Figure:
+    """Vizualize the grid on a polar stereographic map.
+
+    Args:
+        data (gpd.GeoDataFrame): The grid data to visualize.
+        grid (str): The type of grid (e.g., "hex" or "healpix").
+        level (int): The level of the grid.
+
+    Returns:
+        plt.Figure: The matplotlib figure object.
+
+    """
+    fig, ax = plt.subplots(1, 1, figsize=(10, 10), subplot_kw={"projection": ccrs.NorthPolarStereo()})
+    ax.set_extent([-180, 180, 50, 90], crs=ccrs.PlateCarree())
+
+    # Add features
+    ax.add_feature(cfeature.LAND, zorder=0, edgecolor="black", facecolor="white")
+    ax.add_feature(cfeature.OCEAN, zorder=0, facecolor="lightgrey")
+    ax.add_feature(cfeature.COASTLINE)
+    ax.add_feature(cfeature.BORDERS, linestyle=":")
+    ax.add_feature(cfeature.LAKES, alpha=0.5)
+    ax.add_feature(cfeature.RIVERS)
+
+    # Add gridlines
+    gl = ax.gridlines(draw_labels=True)
+    gl.top_labels = False
+    gl.right_labels = False
+
+    # Plot grid cells, coloring by 'cell_area'
+    data = data.to_crs("EPSG:4326")
+
+    is_anti_meridian = data.bounds.apply(lambda b: (b.maxx - b.minx) > 180, axis=1)
+    data = data[~is_anti_meridian]
+    data.plot(
+        ax=ax,
+        column="cell_area",
+        cmap="viridis",
+        legend=True,
+        transform=ccrs.PlateCarree(),
+        edgecolor="k",
+        linewidth=0.2,
+        aspect="equal",
+        alpha=0.5,
+    )
+
+    ax.set_title(f"{grid.capitalize()} grid ({level=})", fontsize=14)
+
+    # Compute a circle in axes coordinates, which we can use as a boundary
+    # for the map. We can pan/zoom as much as we like - the boundary will be
+    # permanently circular.
+    theta = np.linspace(0, 2 * np.pi, 100)
+    center, radius = [0.5, 0.5], 0.5
+    verts = np.vstack([np.sin(theta), np.cos(theta)]).T
+    circle = mpath.Path(verts * radius + center)
+
+    ax.set_boundary(circle, transform=ax.transAxes)
+
+    return fig
+
+
+def cli(grid: Literal["hex", "healpix"], level: int):
+    """CLI entry point."""
+    print(f"Creating {grid} grid at level {level}...")
+    if grid == "hex":
+        grid_gdf = create_global_hex_grid(level)
+    elif grid == "healpix":
+        grid_gdf = create_global_healpix_grid(level)
+    else:
+        print(f"Unknown grid type: {grid}")
+        return
+
+    grid_gdf = filter_permafrost_grid(grid_gdf)
+    print(f"Number of cells at level {level}: {len(grid_gdf)}")
+
+    if not len(grid_gdf):
+        print("No valid grid cells found.")
+        return
+
+    grid_gdf.to_parquet(f"./data/grids/permafrost_{grid}{level}_grid.parquet")
+    print(f"Saved to ./data/grids/permafrost_{grid}{level}_grid.parquet")
+
+    fig = vizualize_grid(grid_gdf, grid, level)
+    fig.savefig(f"./figures/permafrost_{grid}{level}_grid.png", dpi=300)
+    print(f"Saved figure to ./figures/permafrost_{grid}{level}_grid.png")
+    plt.close(fig)
+
+
+if __name__ == "__main__":
+    cyclopts.run(cli)

era5.py

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+"""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)
+
+Aggregations:
+- Summer / Winter 20-bin histogram?
+
+Spatial -> Enrich -> Temporal ?
+
+Author: Tobias Hölzer
+Date: 09. June 2025
+"""
+
+import time
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from pathlib import Path
+from typing import Literal
+
+import cyclopts
+import dask.distributed as dd
+import geopandas as gpd
+import odc.geo
+import odc.geo.xr
+import pandas as pd
+import shapely
+import shapely.ops
+import xarray as xr
+from numcodecs.zarr3 import Blosc
+from rich import pretty, print, traceback
+from shapely.geometry import LineString, Polygon
+
+traceback.install(show_locals=True)
+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"
+
+min_lat = 50
+max_lat = 85
+min_time = "2022-01-01"
+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")
+
+
+# TODO: I think it would be better to aggregate via hours instead of days
+# Pipeline would be:
+# Download hourly data -> Spatially match hourly data ->
+# For {daily, monthly, yearly}:
+#    Enrich -> Aggregate temporally
+
+
+def create_encoding(ds: xr.Dataset):
+    # 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():
+    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={},
+        engine="zarr",
+    ).rename({"valid_time": "time"})
+    era5 = era5.sel(**subset)
+
+    era5_agg = xr.merge(
+        [
+            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"),
+        ]
+    )
+
+    # 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.to_zarr(AGG_PATH, mode="w", encoding=create_encoding(era5_agg), consolidated=False)
+
+
+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]:
+    # Assumes that it is a antimeridian hex
+    coords = shapely.get_coordinates(geom)
+    for i in range(coords.shape[0]):
+        if coords[i, 0] < 0:
+            coords[i, 0] += 360
+    geom = Polygon(coords)
+    antimeridian = LineString([[180, -90], [180, 90]])
+    polys = shapely.ops.split(geom, antimeridian)
+    return list(polys.geoms)
+
+
+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}"
+    )
+    # cell.geometry is a shapely Polygon
+    if not crosses_antimeridian(geom):
+        geoms = [geom]
+    # Split geometry in case it crossed antimeridian
+    else:
+        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)):
+            continue
+        cell_data.append(era5_agg.odc.crop(geom).drop_vars("spatial_ref").mean(["latitude", "longitude"]))
+    if len(cell_data) == 0:
+        return None
+    elif len(cell_data) == 1:
+        return cell_data[0].expand_dims({"cell": [idx]}).chunk({"cell": 1})
+    else:
+        return xr.concat(cell_data, dim="part").mean("part").expand_dims({"cell": [idx]}).chunk({"cell": 1})
+
+
+def spatial_matching(grid: gpd.GeoDataFrame, n_workers: int = 10):
+    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):
+            idx = futures[future]
+            try:
+                data = future.result()
+                data.to_zarr(ALIGNED_PATH, append_dim="cell", consolidated=False, encoding=create_encoding(data))
+            except Exception as e:
+                print(f"Error processing cell {idx}: {e}")
+
+
+def daily_enrich() -> xr.Dataset:
+    era5 = xr.open_zarr(ALIGNED_PATH)
+    assert {"cell", "time"} == set(era5.dims), f"Expected dims ('cell', 'time'), got {era5.dims}"
+
+    # Formulas based on Groeke et. al. (2025) Stochastic Weather generation...
+    era5["t2m_daily_avg"] = (era5.t2m_daily_max + era5.t2m_daily_min) / 2
+    era5.t2m_daily_avg.attrs = {"long_name": "Daily average 2 metre temperature", "units": "K"}
+    era5["t2m_daily_range"] = era5.t2m_daily_max - era5.t2m_daily_min
+    era5.t2m_daily_range.attrs = {"long_name": "Daily range of 2 metre temperature", "units": "K"}
+    era5["t2m_daily_skew"] = (era5.t2m_daily_avg - era5.t2m_daily_min) / era5.t2m_daily_range
+    era5.t2m_daily_skew.attrs = {"long_name": "Daily skewness of 2 metre temperature"}
+
+    era5["thawing_degree_days"] = (era5.t2m_daily_avg - 273.15).clip(min=0)
+    era5.thawing_degree_days.attrs = {"long_name": "Thawing degree days", "units": "K"}
+    era5["freezing_degree_days"] = (273.15 - era5.t2m_daily_avg).clip(min=0)
+    era5.freezing_degree_days.attrs = {"long_name": "Freezing degree days", "units": "K"}
+
+    era5["thawing_days"] = (era5.t2m_daily_avg > 273.15).astype(int)
+    era5.thawing_days.attrs = {"long_name": "Thawing days"}
+    era5["freezing_days"] = (era5.t2m_daily_avg < 273.15).astype(int)
+    era5.freezing_days.attrs = {"long_name": "Freezing days"}
+
+    era5["precipitation_occurrences"] = (era5.tp_daily_sum > 0).astype(int)
+    era5.precipitation_occurrences.attrs = {"long_name": "Precipitation occurrences"}
+    era5["snowfall_occurrences"] = (era5.sf_daily_sum > 0).astype(int)
+    era5.snowfall_occurrences.attrs = {"long_name": "Snowfall occurrences"}
+
+    era5["snow_isolation"] = era5.snowc_daily_mean * era5.sde_daily_mean
+    era5.snow_isolation.attrs = {"long_name": "Snow isolation"}
+
+    return era5
+
+
+def monthly_aggregate():
+    era5 = daily_enrich()
+    assert {"cell", "time"} == set(era5.dims), f"Expected dims ('cell', 'time'), got {era5.dims}"
+
+    # Monthly aggregates
+    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"),
+            # 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"),
+        ]
+    )
+    monthly.to_zarr(MONTHLY_PATH, mode="w", encoding=create_encoding(monthly), consolidated=False)
+
+
+def yearly_aggregate():
+    monthly = xr.open_zarr(MONTHLY_PATH)
+    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)
+    monthly_shifted = monthly.copy()
+    monthly_shifted["time"] = monthly_shifted.get_index("time") + pd.DateOffset(months=10)
+    incomplete_years = {monthly_shifted.time.dt.year.min().item(), monthly_shifted.time.dt.year.max().item()}
+    monthly_shifted = monthly_shifted.sel(time=~monthly_shifted.time.dt.year.isin(incomplete_years))
+    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"),
+            # Enriched variables
+            monthly_shifted.t2m_monthly_avg.resample(time="1Y").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")
+            .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")
+            .sum()
+            .rename("precipitation_occurrences_yearly"),
+            monthly_shifted.snowfall_occurrences_monthly.resample(time="1Y")
+            .sum()
+            .rename("snowfall_occurrences_yearly"),
+            monthly_shifted.snow_isolation_monthly_mean.resample(time="1Y").mean().rename("snow_isolation_yearly_mean"),
+        ]
+    )
+    # Summer / Winter aggregates
+    winter_months = [1, 2, 3, 4, 5, 6, 7]  # These do NOT correspond to calendar months, but to the shifted months
+    summer_months = [8, 9, 10, 11, 12]
+    monthly_shifted_winter = monthly_shifted.sel(time=monthly_shifted.time.dt.month.isin(winter_months))
+    monthly_shifted_summer = monthly_shifted.sel(time=monthly_shifted.time.dt.month.isin(summer_months))
+
+    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"),
+            # Enriched variables
+            monthly_shifted_winter.t2m_monthly_avg.resample(time="1Y").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")
+            .sum()
+            .rename("thawing_degree_days_winter"),
+            monthly_shifted_winter.freezing_degree_days_monthly.resample(time="1Y")
+            .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")
+            .sum()
+            .rename("precipitation_occurrences_winter"),
+            monthly_shifted_winter.snowfall_occurrences_monthly.resample(time="1Y")
+            .sum()
+            .rename("snowfall_occurrences_winter"),
+            monthly_shifted_winter.snow_isolation_monthly_mean.resample(time="1Y")
+            .mean()
+            .rename("snow_isolation_winter_mean"),
+        ]
+    )
+
+    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"),
+            # Enriched variables
+            monthly_shifted_summer.t2m_monthly_avg.resample(time="1Y").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")
+            .sum()
+            .rename("thawing_degree_days_summer"),
+            monthly_shifted_summer.freezing_degree_days_summer.resample(time="1Y")
+            .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")
+            .sum()
+            .rename("precipitation_occurrences_summer"),
+            monthly_shifted_summer.snowfall_occurrences_summer.resample(time="1Y")
+            .sum()
+            .rename("snowfall_occurrences_summer"),
+            monthly_shifted_summer.snow_isolation_summer.resample(time="1Y")
+            .mean()
+            .rename("snow_isolation_summer_mean"),
+        ]
+    )
+
+    combined = xr.merge([yearly, summer, winter])
+    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.
+
+    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.
+
+    """
+    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.")
+
+
+if __name__ == "__main__":
+    cyclopts.run(cli)