MIDAS Client

Features

• MIDAS tables – supports all hourly / daily rain, temperature, weather, wind, radiation & soil temp tables (RH, RD, TD, WH, WD, WM, RY, SH).

• Helper functions – download_station_year() for a single station/year, download_locations() to bulk-grab multiple nearest stations for many locations, and download_by_counties() for downloading groups of counties.

• Cache Location – current config defaults to data/raw/weather/ (edit in settings).

• Cache Format - customizable cache format; supports csv, parquet, json. Defaults to csv.

• Config: JSON – tweak dataset version, default columns, cache directory, etc. in settings.json.

• CEDA auth – automatically gets a bearer token using your CEDA_USER and CEDA_PASS env vars.

Finding a station ID (src_id)

The Met Office assigns every MIDAS station a five-digit source-ID (e.g. 03743).
You can look it up in three quick ways:

1. CEDA MIDAS Station Search (recommended)
   • Open https://archive.ceda.ac.uk/tools/midas_stations – no login required.
   • In “Search for station name” type part of the name (e.g. Oxford) and press Search.
     The first column of the results table is the src_id.
   • Use the check-box “Show only stations with MIDAS Open data” or restrict the year range / current stations only options to narrow results.
   • If you do not know the station name, switch to the postcode or county tabs on the same page.
2. Interactive maps
   • CEDA also publishes KML layers for Google Earth – each marker popup shows the src_id. Download the layers from the same tools section.

Quick start

pip install uk-midas-client

from midas_client import (
    download_station_year,
    download_locations,
    download_by_counties,
)

Set your CEDA credentials using either username/password:

export CEDA_USER="me@example.com"
export CEDA_PASS="••••••••"

and/or use a token:

export CEDA_TOKEN="••••••••..."

Fetch a single station-year

df = download_station_year(
    table="TD",
    station_id="03743",
    year=2020,
)
print(df.head())

Bulk Download Nearest Stations

Given a DataFrame containing observation locations with associated latitude and longitude coordinates, the algorithm:

1. Identifies the nearest MIDAS stations:

   • Finds the k-nearest MIDAS stations for each observation location that support a specified observation type (e.g., Rain Hourly — RH).

2. Downloads datasets:

   • Attempts to download datasets for each station-year combination, prioritizing nearest stations.

3. Fallback mechanism:

   • If a dataset for the closest station-year combination is unavailable, the algorithm sequentially attempts downloads from the next nearest stations until either:

     • A valid dataset is successfully retrieved, or
     • All k nearest stations have been attempted unsuccessfully.

Caching and Output Structure

The resulting datasets are stored in a specified cache directory (cache_dir), following the naming convention:

{obs}_{year}.{fmt}

Additionally, the process generates a JSON mapping file (station_map.json) within cache_dir. This file maps each observation location's input loc_id to the corresponding downloaded station identifiers.

Example Usage

import pandas as pd

locs = pd.DataFrame({
    "loc_id": ["here"],
    "lat": [51.5],
    "long": [-0.1],
})

station_map = download_locations(
    locs,
    locations=locs,
    years=range(2021, 2022),
    tables={"TD": ["max_air_temp", "min_air_temp"]},
)

results = download_by_counties(
    counties={"Hampshire": []},
   tables={"TD": ["max_air_temp", "min_air_temp"]},

Status

This project is currently in a pre-1.0 prototype stage and may change without notice.

License

Released under the MIT License. You are free to use, modify and distribute this software.