isd-fetch 0.1.4

A Python package for fetching ISD Lite data

PyISD: A Python Package for NOAA's ISD Lite Dataset

PyISD is a Python package designed for loading and processing NOAA's ISD Lite dataset. The dataset, as described by NOAA, is a streamlined version of the full Integrated Surface Database (ISD). It includes eight common surface parameters in a fixed-width format, free of duplicate values, sub-hourly data, and complicated flags, making it suitable for general research and scientific purposes. For more details, visit the official ISD homepage.

Installation

pip install isd-fetch

Features

• Load and query the ISD Lite dataset with ease.
• Retrieve and process metadata for stations worldwide.
• Filter data based on spatial and temporal constraints.

Example Usage

1. Importing and Loading Metadata

You can start by importing the IsdLite module, fetching metadata for weather stations worldwide and displaying a sample of the station metadata:

from pyisd import IsdLite

CRS = 4326

module = IsdLite(crs=CRS, verbose=True)
module.raw_metadata.sample(5)

The output displays station metadata including station name, latitude, longitude, elevation, and the period of available records:

         USAF   WBAN         STATION NAME CTRY   ST  ...      BEGIN        END       x       y                geometry
5416   172650  99999             ADIYAMAN   TU  NaN  ... 2007-01-27 2024-11-17  38.283  37.750    POINT (38.283 37.75)
1362   032130  99999             ESKMEALS   UK  NaN  ... 1973-01-02 1997-12-26  -3.400  54.317     POINT (-3.4 54.317)
4729   153150  99999              SEMENIC   RO  NaN  ... 1973-07-21 2024-11-17  22.050  45.183    POINT (22.05 45.183)
28589  999999  13855  TULLAHOMA AEDC SITE   US   TN  ... 1963-01-01 1969-08-01 -86.233  35.383  POINT (-86.233 35.383)
6422   268530  99999             BEREZINO   BO  NaN  ... 1960-04-01 2024-11-17  28.983  53.833   POINT (28.983 53.833)

The available stations locations can be visualized :

2. Fetching and Visualizing Data

To retrieve data, you can specify the time period and spatial constraints. Here, we fetch temperature data (temp) for the bounding box around Paris between January 1, 2023, and November 20, 2024:

from pyisd.misc import get_box

geometry = get_box(place='Paris', width=1., crs=CRS)

data = module.get_data(start=20230101, end=20241120, geometry=geometry, organize_by='field')
data.keys()
# dict_keys(['temp', 'dewtemp', 'pressure', 'winddirection', 'windspeed', 'skycoverage', 'precipitation-1h', 'precipitation-6h'])

data['temp'].plot(figsize=(10, 4), legend=False, c='grey', lw=0.6)

Flexibility of geometry

The geometry parameter is highly flexible and can be set in different ways:

1. Bounding Box: Use the get_box() function as shown above to define a simple rectangular bounding box around a location.
2. Custom Geometries: You can pass any shapely.geometry object (e.g., Polygon, MultiPolygon) or a geopandas GeoDataFrame to define more specific regions of interest.
3. None: If geometry is set to None, the function retrieves data for all available stations globally.
   ⚠️ Note: Setting geometry=None is not advised unless strictly necessary, as the download time and data size can be extremely large.

By carefully specifying geometry, you can focus on the data most relevant to your study while avoiding unnecessarily large downloads.