Dust model dichotomous performance analysis

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Text Data & NLP Dust-model-dichotomous-performance-analysis
Overview

Dust-model-dichotomous-performance-analysis

Using a collated dataset of >90,000 dust point source observations from 9 drylands studies from around the world to assess the performance of dust emission models. Here we use the albedo-based dust emission model developed by Chappell and Webb (2016) in Google Earth Engine. The outputs from each DPS location are standardised and analysed in Python to describe the dichotomous coincidence of observed dust emission with model simulation. Discussion topics are developed from the empirical cumulative distribution function (ECDF) of wind shear velocity conditions during observed dust emission days and all days. These ECDF are also produced in Python.

Link to Google Earth Engine code (Google user account and login required): https://code.earthengine.google.com/a48652d123f7c56dab0bcdf4a3f9abf5

Includes two folders:

  1. Dust Point Source (DPS) observation data, including co-ordinates, dates of observation, and 1-degree grid id
  2. Look-up-tables (LUT) required to run Python code
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