Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

TRACKING THE GODZILLA DUST PLUME USING GOOGLE EARTH ENGINE PLATFORM

Domain:

environment and energyclimategeospatial

Record type:

paper
Creator:
A. Y.AZ. P.
Publisher:
Cop
Host:
Abstract. As part of Earth’s nutrient cycle, a layer of air travels every summer from Africa across the Atlantic Ocean. In June 2020, the thickest and densest dust plume traveled over 5000 miles along with the Saharan Air Layer (SAL) from Africa towards the USA and the Caribbean. Due to its gravity and impact, it was nicknamed “Godzilla”. While the cause of this event remains unclear, the advantage of using remote sensing applications to monitor aerosol concentrations and movement provides future opportunities to leverage machine learning technologies to build predictive models with the goal of early forecasting and public health interventions. The Sentinel-5P satellite instrument measures the air quality, ozone, and Ultraviolet (UV) radiation, and can be used for climate monitoring, and forecasting. Available on this platform is the UV Aerosol Index (AI) product, a qualitative index that indicates the presence of elevated layers of aerosols in the atmosphere. In this paper, we used Google Earth Engine to monitor the transatlantic movement of this historic dust plume across the Sahara Desert and estimate the aerosol concentrations throughout June 2020. The flexibility of the platform enabled us to generate time series maps to visualize the movement of the Godzilla dust storm from the Sahara Desert across the ocean. The results obtained are relevant for effective planning and interventions to ameliorate the health threats associated with the movement of the dust plume. The outcome is useful for defining the relationship between aerosol concentrations, human health, and aquatic life.

Visit

doi.org

Licenses

https://creativecommons.org/licenses/by/4.0/

Similar

Probabilistic Tracking of Annual Cropland Changes over Large, Complex Agricultural Landscapes Using Google Earth EngineDetection of Large-Scale Floods Using Google Earth Engine and Google ColabLeveraging the Google Earth Engine for Drought Assessment Using Global Soil Moisture DataVisualizing inconsistencies among global agricultural land cover products using Google Earth EngineCHIRPS Combined Precipitation Analysis in Google Earth EngineDigital Soil Mapping of Soil Organic Carbon in Namibia Using Google Earth Engine

Probabilistic Tracking of Annual Cropland Changes over Large, Complex Agricultural Landscapes Using Google Earth Engine

Cropland expansion is expected to increase across sub-Saharan African (SSA) countries in the next th

Detection of Large-Scale Floods Using Google Earth Engine and Google Colab

International audience This paper presents an operational approach for detecting floo

Leveraging the Google Earth Engine for Drought Assessment Using Global Soil Moisture Data

Soil moisture is considered to be a key variable to assess crop and drought conditions. However, rea

Visualizing inconsistencies among global agricultural land cover products using Google Earth Engine

Visualizing inconsistencies among global agricultural land cover products using Google Eart

CHIRPS Combined Precipitation Analysis in Google Earth Engine

This JavaScript code is meant to be run inside the Google Earth Engine web interface ( https://code.

Digital Soil Mapping of Soil Organic Carbon in Namibia Using Google Earth Engine

The Namibian Soil Profile Database contains 4960 entries, all samples with geographic coordinates. E