Create grid and era5 download
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era5.py
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era5.py
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"""Download and preprocess ERA5 data.
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Variables of Interest:
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- 2 metre temperature (t2m)
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- Total precipitation (tp)
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- Snow Fall (sf)
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- Snow cover (snowc)
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- Snow depth (sde)
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- Surface sensible heat flux (sshf)
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- Lake ice bottom temperature (lblt)
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Aggregations:
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- Summer / Winter 20-bin histogram?
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Spatial -> Enrich -> Temporal ?
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Author: Tobias Hölzer
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Date: 09. June 2025
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"""
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import time
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from pathlib import Path
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from typing import Literal
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import cyclopts
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import dask.distributed as dd
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import geopandas as gpd
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import odc.geo
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import odc.geo.xr
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import pandas as pd
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import shapely
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import shapely.ops
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import xarray as xr
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from numcodecs.zarr3 import Blosc
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from rich import pretty, print, traceback
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from shapely.geometry import LineString, Polygon
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traceback.install(show_locals=True)
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pretty.install()
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DATA_DIR = Path("data/era5")
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AGG_PATH = DATA_DIR / "era5_agg.zarr"
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ALIGNED_PATH = DATA_DIR / "era5_spatial_aligned.zarr"
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MONTHLY_PATH = DATA_DIR / "era5_monthly.zarr"
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YEARLY_PATH = DATA_DIR / "era5_yearly.zarr"
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min_lat = 50
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max_lat = 85
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min_time = "2022-01-01"
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max_time = "2024-12-31"
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subset = {"latitude": slice(max_lat, min_lat), "time": slice(min_time, max_time)}
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DATA_DIR = Path("/isipd/projects/p_aicore_pf/tohoel001/era5_thawing_data")
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today = time.strftime("%Y-%m-%d")
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# TODO: I think it would be better to aggregate via hours instead of days
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# Pipeline would be:
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# Download hourly data -> Spatially match hourly data ->
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# For {daily, monthly, yearly}:
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# Enrich -> Aggregate temporally
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def create_encoding(ds: xr.Dataset):
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# encoding = {var: {"compressors": BloscCodec(cname="zlib", clevel=9)} for var in ds.data_vars}
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encoding = {var: {"compressors": Blosc(cname="zstd", clevel=9)} for var in [*ds.data_vars, *ds.coords]}
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return encoding
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def download_daily_aggregated():
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era5 = xr.open_dataset(
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"https://data.earthdatahub.destine.eu/era5/reanalysis-era5-land-no-antartica-v0.zarr",
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storage_options={"client_kwargs": {"trust_env": True}},
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chunks={"latitude": 64 * 4, "longitude": 64 * 4},
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# chunks={},
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engine="zarr",
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).rename({"valid_time": "time"})
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era5 = era5.sel(**subset)
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era5_agg = xr.merge(
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[
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era5.t2m.resample(time="1D").max().rename("t2m_daily_max"),
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era5.t2m.resample(time="1D").min().rename("t2m_daily_min"),
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era5.tp.resample(time="1D").sum().rename("tp_daily_sum"),
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# era5.sf.resample(time="1D").sum().rename("sf_daily_sum"),
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# era5.snowc.resample(time="1D").mean().rename("snowc_daily_mean"),
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# era5.sde.resample(time="1D").mean().rename("sde_daily_mean"),
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# era5.sshf.resample(time="1D").sum().rename("sshf_daily_sum"),
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# era5.lblt.resample(time="1D").max().rename("lblt_daily_max"),
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]
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)
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# Rechunk if the first time chunk is not the same as the middle ones
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if era5_agg.chunksizes["time"][0] != era5_agg.chunksizes["time"][1]:
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era5_agg = era5_agg.chunk({"time": 120})
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# Assign attributes
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era5_agg["t2m_daily_max"].attrs = {"long_name": "Daily maximum 2 metre temperature", "units": "K"}
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era5_agg["t2m_daily_min"].attrs = {"long_name": "Daily minimum 2 metre temperature", "units": "K"}
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era5_agg["tp_daily_sum"].attrs = {"long_name": "Daily total precipitation", "units": "m"}
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# era5_agg["sf_daily_sum"].attrs = {"long_name": "Daily total snow fall", "units": "m"}
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# era5_agg["snowc_daily_mean"].attrs = {"long_name": "Daily mean snow cover", "units": "m"}
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# era5_agg["sde_daily_mean"].attrs = {"long_name": "Daily mean snow depth", "units": "m"}
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# era5_agg["sshf_daily_sum"].attrs = {"long_name": "Daily total surface sensible heat flux", "units": "J/m²"}
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# era5_agg["lblt_daily_max"].attrs = {"long_name": "Daily maximum lake ice bottom temperature", "units": "K"}
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era5_agg.to_zarr(AGG_PATH, mode="w", encoding=create_encoding(era5_agg), consolidated=False)
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def crosses_antimeridian(geom: Polygon) -> bool:
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coords = shapely.get_coordinates(geom)
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crosses_any_meridian = (coords[:, 0] > 0).any() and (coords[:, 0] < 0).any()
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return crosses_any_meridian and abs(coords[:, 0]).max() > 90
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def split_antimeridian_cell(geom: Polygon) -> list[Polygon]:
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# Assumes that it is a antimeridian hex
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coords = shapely.get_coordinates(geom)
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for i in range(coords.shape[0]):
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if coords[i, 0] < 0:
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coords[i, 0] += 360
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geom = Polygon(coords)
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antimeridian = LineString([[180, -90], [180, 90]])
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polys = shapely.ops.split(geom, antimeridian)
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return list(polys.geoms)
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def check_geobox(geobox):
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x, y = geobox.shape
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return x > 1 and y > 1
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def extract_cell_data(idx: int, geom: Polygon) -> xr.Dataset:
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era5_agg = xr.open_zarr(AGG_PATH)
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assert {"latitude", "longitude", "time"} == set(era5_agg.dims), (
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f"Expected dims ('latitude', 'longitude', 'time'), got {era5_agg.dims}"
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)
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# cell.geometry is a shapely Polygon
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if not crosses_antimeridian(geom):
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geoms = [geom]
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# Split geometry in case it crossed antimeridian
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else:
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geoms = split_antimeridian_cell(geom)
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cell_data = []
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for geom in geoms:
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geom = odc.geo.Geometry(geom, crs="epsg:4326")
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if not check_geobox(era5_agg.odc.geobox.enclosing(geom)):
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continue
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cell_data.append(era5_agg.odc.crop(geom).drop_vars("spatial_ref").mean(["latitude", "longitude"]))
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if len(cell_data) == 0:
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return None
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elif len(cell_data) == 1:
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return cell_data[0].expand_dims({"cell": [idx]}).chunk({"cell": 1})
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else:
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return xr.concat(cell_data, dim="part").mean("part").expand_dims({"cell": [idx]}).chunk({"cell": 1})
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def spatial_matching(grid: gpd.GeoDataFrame, n_workers: int = 10):
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with ThreadPoolExecutor(max_workers=n_workers) as executor:
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futures = {
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executor.submit(extract_cell_data, idx, row.geometry): idx
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for idx, row in grid.to_crs("epsg:4326").iterrows()
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}
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for future in as_completed(futures):
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idx = futures[future]
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try:
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data = future.result()
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data.to_zarr(ALIGNED_PATH, append_dim="cell", consolidated=False, encoding=create_encoding(data))
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except Exception as e:
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print(f"Error processing cell {idx}: {e}")
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def daily_enrich() -> xr.Dataset:
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era5 = xr.open_zarr(ALIGNED_PATH)
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assert {"cell", "time"} == set(era5.dims), f"Expected dims ('cell', 'time'), got {era5.dims}"
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# Formulas based on Groeke et. al. (2025) Stochastic Weather generation...
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era5["t2m_daily_avg"] = (era5.t2m_daily_max + era5.t2m_daily_min) / 2
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era5.t2m_daily_avg.attrs = {"long_name": "Daily average 2 metre temperature", "units": "K"}
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era5["t2m_daily_range"] = era5.t2m_daily_max - era5.t2m_daily_min
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era5.t2m_daily_range.attrs = {"long_name": "Daily range of 2 metre temperature", "units": "K"}
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era5["t2m_daily_skew"] = (era5.t2m_daily_avg - era5.t2m_daily_min) / era5.t2m_daily_range
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era5.t2m_daily_skew.attrs = {"long_name": "Daily skewness of 2 metre temperature"}
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era5["thawing_degree_days"] = (era5.t2m_daily_avg - 273.15).clip(min=0)
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era5.thawing_degree_days.attrs = {"long_name": "Thawing degree days", "units": "K"}
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era5["freezing_degree_days"] = (273.15 - era5.t2m_daily_avg).clip(min=0)
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era5.freezing_degree_days.attrs = {"long_name": "Freezing degree days", "units": "K"}
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era5["thawing_days"] = (era5.t2m_daily_avg > 273.15).astype(int)
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era5.thawing_days.attrs = {"long_name": "Thawing days"}
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era5["freezing_days"] = (era5.t2m_daily_avg < 273.15).astype(int)
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era5.freezing_days.attrs = {"long_name": "Freezing days"}
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era5["precipitation_occurrences"] = (era5.tp_daily_sum > 0).astype(int)
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era5.precipitation_occurrences.attrs = {"long_name": "Precipitation occurrences"}
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era5["snowfall_occurrences"] = (era5.sf_daily_sum > 0).astype(int)
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era5.snowfall_occurrences.attrs = {"long_name": "Snowfall occurrences"}
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era5["snow_isolation"] = era5.snowc_daily_mean * era5.sde_daily_mean
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era5.snow_isolation.attrs = {"long_name": "Snow isolation"}
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return era5
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def monthly_aggregate():
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era5 = daily_enrich()
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assert {"cell", "time"} == set(era5.dims), f"Expected dims ('cell', 'time'), got {era5.dims}"
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# Monthly aggregates
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monthly = xr.merge(
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[
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# Original variables
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era5.t2m_daily_min.resample(time="1M").min().rename("t2m_monthly_min"),
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era5.t2m_daily_max.resample(time="1M").max().rename("t2m_monthly_max"),
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era5.tp_daily_sum.resample(time="1M").sum().rename("tp_monthly_sum"),
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era5.sf_daily_sum.resample(time="1M").sum().rename("sf_monthly_sum"),
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era5.snowc_daily_mean.resample(time="1M").mean().rename("snowc_monthly_mean"),
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era5.sde_daily_mean.resample(time="1M").mean().rename("sde_monthly_mean"),
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era5.sshf_daily_sum.resample(time="1M").sum().rename("sshf_monthly_sum"),
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era5.lblt_daily_max.resample(time="1M").max().rename("lblt_monthly_max"),
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# Enriched variables
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era5.t2m_daily_avg.resample(time="1M").mean().rename("t2m_monthly_avg"),
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era5.t2m_daily_range.resample(time="1M").mean().rename("t2m_daily_range_monthly_avg"),
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era5.t2m_daily_skew.resample(time="1M").mean().rename("t2m_daily_skew_monthly_avg"),
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era5.thawing_degree_days.resample(time="1M").sum().rename("thawing_degree_days_monthly"),
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era5.freezing_degree_days.resample(time="1M").sum().rename("freezing_degree_days_monthly"),
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era5.thawing_days.resample(time="1M").sum().rename("thawing_days_monthly"),
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era5.freezing_days.resample(time="1M").sum().rename("freezing_days_monthly"),
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era5.precipitation_occurrences.resample(time="1M").sum().rename("precipitation_occurrences_monthly"),
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era5.snowfall_occurrences.resample(time="1M").sum().rename("snowfall_occurrences_monthly"),
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era5.snow_isolation.resample(time="1M").mean().rename("snow_isolation_monthly_mean"),
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]
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)
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monthly.to_zarr(MONTHLY_PATH, mode="w", encoding=create_encoding(monthly), consolidated=False)
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def yearly_aggregate():
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monthly = xr.open_zarr(MONTHLY_PATH)
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assert {"cell", "time"} == set(monthly.dims), f"Expected dims ('cell', 'time'), got {monthly.dims}"
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# Yearly aggregates (shifted by +10 months to start in Oktober, first and last years will be cropped)
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monthly_shifted = monthly.copy()
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monthly_shifted["time"] = monthly_shifted.get_index("time") + pd.DateOffset(months=10)
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incomplete_years = {monthly_shifted.time.dt.year.min().item(), monthly_shifted.time.dt.year.max().item()}
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monthly_shifted = monthly_shifted.sel(time=~monthly_shifted.time.dt.year.isin(incomplete_years))
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yearly = xr.merge(
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[
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# Original variables
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monthly_shifted.t2m_monthly_min.resample(time="1Y").min().rename("t2m_yearly_min"),
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monthly_shifted.t2m_monthly_max.resample(time="1Y").max().rename("t2m_yearly_max"),
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monthly_shifted.tp_monthly_sum.resample(time="1Y").sum().rename("tp_yearly_sum"),
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monthly_shifted.sf_monthly_sum.resample(time="1Y").sum().rename("sf_yearly_sum"),
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monthly_shifted.snowc_monthly_mean.resample(time="1Y").mean().rename("snowc_yearly_mean"),
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monthly_shifted.sde_monthly_mean.resample(time="1Y").mean().rename("sde_yearly_mean"),
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monthly_shifted.sshf_monthly_sum.resample(time="1Y").sum().rename("sshf_yearly_sum"),
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monthly_shifted.lblt_monthly_max.resample(time="1Y").max().rename("lblt_yearly_max"),
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# Enriched variables
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monthly_shifted.t2m_monthly_avg.resample(time="1Y").mean().rename("t2m_yearly_avg"),
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# TODO: Check if this is correct -> use daily / hourly data instead for range and skew?
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monthly_shifted.t2m_monthly_range.resample(time="1Y").mean().rename("t2m_daily_range_yearly_avg"),
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monthly_shifted.t2m_monthly_skew.resample(time="1Y").mean().rename("t2m_daily_skew_yearly_avg"),
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monthly_shifted.thawing_degree_days_monthly.resample(time="1Y").sum().rename("thawing_degree_days_yearly"),
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monthly_shifted.freezing_degree_days_monthly.resample(time="1Y")
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.sum()
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.rename("freezing_degree_days_yearly"),
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monthly_shifted.thawing_days_monthly.resample(time="1Y").sum().rename("thawing_days_yearly"),
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monthly_shifted.freezing_days_monthly.resample(time="1Y").sum().rename("freezing_days_yearly"),
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monthly_shifted.precipitation_occurrences_monthly.resample(time="1Y")
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.sum()
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.rename("precipitation_occurrences_yearly"),
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monthly_shifted.snowfall_occurrences_monthly.resample(time="1Y")
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.sum()
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.rename("snowfall_occurrences_yearly"),
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monthly_shifted.snow_isolation_monthly_mean.resample(time="1Y").mean().rename("snow_isolation_yearly_mean"),
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]
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)
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# Summer / Winter aggregates
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winter_months = [1, 2, 3, 4, 5, 6, 7] # These do NOT correspond to calendar months, but to the shifted months
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summer_months = [8, 9, 10, 11, 12]
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monthly_shifted_winter = monthly_shifted.sel(time=monthly_shifted.time.dt.month.isin(winter_months))
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monthly_shifted_summer = monthly_shifted.sel(time=monthly_shifted.time.dt.month.isin(summer_months))
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winter = xr.merge(
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[
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# Original variables
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monthly_shifted_winter.t2m_monthly_min.resample(time="1Y").min().rename("t2m_winter_min"),
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monthly_shifted_winter.t2m_monthly_max.resample(time="1Y").max().rename("t2m_winter_max"),
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monthly_shifted_winter.tp_monthly_sum.resample(time="1Y").sum().rename("tp_winter_sum"),
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monthly_shifted_winter.sf_monthly_sum.resample(time="1Y").sum().rename("sf_winter_sum"),
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monthly_shifted_winter.snowc_monthly_mean.resample(time="1Y").mean().rename("snowc_winter_mean"),
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monthly_shifted_winter.sde_monthly_mean.resample(time="1Y").mean().rename("sde_winter_mean"),
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monthly_shifted_winter.sshf_monthly_sum.resample(time="1Y").sum().rename("sshf_winter_sum"),
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monthly_shifted_winter.lblt_monthly_max.resample(time="1Y").max().rename("lblt_winter_max"),
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# Enriched variables
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monthly_shifted_winter.t2m_monthly_avg.resample(time="1Y").mean().rename("t2m_winter_avg"),
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# TODO: Check if this is correct -> use daily / hourly data instead for range and skew?
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monthly_shifted_winter.t2m_monthly_range.resample(time="1Y").mean().rename("t2m_daily_range_winter_avg"),
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monthly_shifted_winter.t2m_monthly_skew.resample(time="1Y").mean().rename("t2m_daily_skew_winter_avg"),
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monthly_shifted_winter.thawing_degree_days_monthly.resample(time="1Y")
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.sum()
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.rename("thawing_degree_days_winter"),
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monthly_shifted_winter.freezing_degree_days_monthly.resample(time="1Y")
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.sum()
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.rename("freezing_degree_days_winter"),
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monthly_shifted_winter.thawing_days_monthly.resample(time="1Y").sum().rename("thawing_days_winter"),
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monthly_shifted_winter.freezing_days_monthly.resample(time="1Y").sum().rename("freezing_days_winter"),
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monthly_shifted_winter.precipitation_occurrences_monthly.resample(time="1Y")
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.sum()
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.rename("precipitation_occurrences_winter"),
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monthly_shifted_winter.snowfall_occurrences_monthly.resample(time="1Y")
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.sum()
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.rename("snowfall_occurrences_winter"),
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monthly_shifted_winter.snow_isolation_monthly_mean.resample(time="1Y")
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.mean()
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.rename("snow_isolation_winter_mean"),
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]
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)
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summer = xr.merge(
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[
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# Original variables
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||||
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)
|
||||
Loading…
Add table
Add a link
Reference in a new issue