Standardised Precipitation Index - ERA5_QM SPI-2

FAIR Overall Score

Abstract

The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. SPI is used on different time scales (1, 2, 3, 6, 12 months). The value of the SPI index around 0 represents the normal expected conditions regarding the amount of precipitation in the selected time scale compared to the long-term average (1981-2020). Value 1 represents approximately one standard deviation of precipitation amount during wet conditions and -1 denotes about one standard deviation of precipitation amount during dry conditions. Drought is usually defined as period when SPI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

Keywords

SPI, STANDARDISED PRECIPITATION INDEX, PRECIPITATION ANOMALIES, ADO PROJECT, ADO

Legal constraints

CC-BY-4.0

FAIR Overall Score: 77%

Principle	Score	Earned	Level
Findable	57%	4 of 7	moderate
Accessible	100%	7 of 7	advanced
Interoperable	67%	4 of 6	moderate
Reusable	83%	5 of 6	moderate

Assessed 2026-07-22 (metrics 0.8)

Evaluated by F-UJI web service: Anusuriya Devaraju, & Robert Huber. (2020). F-UJI - An Automated FAIR Data Assessment Tool. Zenodo. https://doi.org/10.5281/zenodo.6361400

Snippet code

##### ----Explore and download STAC data with Python ----- #####

from pystac_client import Client

## Read the example catalog
URL = 'https://stac.eurac.edu/'
catalog = Client.open(URL)

## List the Collections in the given Catalog
stac_collections = list(catalog.get_collections())
print(f"Number of collections: {len(stac_collections)}")

## Print collection IDs
print("Collections IDs:")
for collection in stac_collections:
    print(f"- {collection.id}")
print("-------------------------")

## Retrieve a specific collection
collection = catalog.get_collection("ID_OfTheCollection")

## Search for items in the collection
collection_items = list(catalog.search(collections=['ID_OfTheCollection'], max_items=10).items())
print(collection_items)

## Retrieve a list of the first 10 items belonging to a specific collection
item = collection.get_item("NameOfTheItem")
#print(list(item.assets.items())[0:10])

##Print the band name and the href of a specific item retrieved from the previous list
print(item.assets["ID_OfTheCollection"].title)
print(item.assets["ID_OfTheCollection"].href)

## Print Item’s assets through the assets attribute, which is a dictionary
for asset_key in item.assets:
    asset = item.assets[asset_key]
    print("{}: {} ({})".format(asset_key, asset.href, asset.media_type))

## ------------- DONWLOAD COG files from a specific collection ------- ####
from pystac_client import Client
import requests

## Read the example catalog
URL = 'https://stac.eurac.edu/'
catalog = Client.open(URL)

## List the Collections in the given Catalog
stac_collections = list(catalog.get_collections())
# print(f"Number of collections: {len(stac_collections)}")

## Print collection IDs
print("Collections IDs:")
for collection in stac_collections:
	print(f"- {collection.id}")
print("-------------------------")

## Retrieve a specific collection
collection = catalog.get_collection("ID_OfTheCollection")

## Search for items in the collection
collection_items = list(catalog.search(collections=['ID_OfTheCollection'], max_items=10).items())
print(collection_items)

url_basepath = "https://eurac-eo.s3-eu-west-1.amazonaws.com/"
collection_url = url_basepath + "ID_OfTheCollection" + "/"
print(collection_url)
for item_id in collection_items:
	# item = item.assets[asset_key]
	print(item_id.id)
	cog_file = str(item_id.id + '.tif')
	url = collection_url + cog_file
	response = requests.get(url)
	with open(cog_file, "wb") as f:
		f.write(response.content)