XCO2 diurnal cycles from machine learning

Creators: Marchetti, Calla^{1, 2, 3}; Laughner, Joshua³

1. University of California, Los Angeles
2. Princeton University
3. Jet Propulsion Lab

Description

This repo contains experimental code designed to test the ability of a machine learning model trained on [TCCON](https://tccondata.org/) data to reproduce diurnal cycles of XCO2 from more temporally sparse observations, such as those from satellites.

Files

Files (10.0 MB)

Name	Size	Actions
ml-xco2-diurnal-cycles.tgz md5:b8a9117bd97beb6ddb43abb6f001578c	10.0 MB	Download

Additional details

DOI

Resource type: Software
Publisher: CaltechDATA
Languages: English

Apache License 2.0

A permissive license whose main conditions require preservation of copyright and license notices. Contributors provide an express grant of patent rights. Licensed works, modifications, and larger works may be distributed under different terms and without source code. Read more

Created:: July 5, 2024
Modified:: July 5, 2024