Skip to main content

AgERA5 Climatic Regions Embeddings

Project description

AgERA5 Climatic Regions Embeddings

Introduction

Climatic regions embeddings V1 are 6 global rasters variables describing the global meteoreological conditions for the years 2018-2023. They are obtained by aggregating AgERA5 variables across single years and then reducing and normalizing the aggregated varibles to a set of 6 variables.

NOTE: 2023 needs to be recomputed. It is currently a copy of 2022.

Usage

Load for a lat lon point:

from climatic_regions import load_meteo_point

vals = load_meteo_point(lon, lat, year)

Load resampled array from bounds:

from climatic_regions import load_meteo_embeddings

arr = load_meteo_embeddings(bounds, epsg, year, resolution=10)

Covert lat, lon to x, y, z:

from climatic_regions import lat_lon_to_unit_sphere, load_xyz

x, y, z = lat_lon_to_unit_sphere(lat, lon)

xyz_arr = load_xyz(bounds, epsg, resolution=10)

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

climatic_regions-0.0.4-py3-none-any.whl (37.6 MB view details)

Uploaded Python 3

File details

Details for the file climatic_regions-0.0.4-py3-none-any.whl.

File metadata

File hashes

Hashes for climatic_regions-0.0.4-py3-none-any.whl
Algorithm Hash digest
SHA256 b48cac140060ec7105bf5bb653be9e80c616841f6523f1a66ec1fb68eabe5ce6
MD5 5ce333725d42c24526a7724fb03c4e49
BLAKE2b-256 0bff65e98bc3a55470906f45bf43b95f0d5af49f150e19fadc4b5e0e22bfddcf

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page