COVID-19 lockdowns and air quality: Evidence from grey spatiotemporal forecasts
This paper proposes a novel grey spatiotemporal model and quantitatively analyzes the
spillover and momentum effects of the COVID-19 lockdown policy on the concentration of …
spillover and momentum effects of the COVID-19 lockdown policy on the concentration of …
Spatiotemporal patterns of COVID-19 impact on human activities and environment in mainland China using nighttime light and air quality data
The sudden outbreak of the COVID-19 pandemic has brought drastic changes to people's
daily lives, work, and the surrounding environment. Investigations into these changes are …
daily lives, work, and the surrounding environment. Investigations into these changes are …
[HTML][HTML] Applying machine learning methods to detect convection using Geostationary Operational Environmental Satellite-16 (GOES-16) advanced baseline imager …
An ability to accurately detect convective regions is essential for initializing models for short-
term precipitation forecasts. Radar data are commonly used to detect convection, but radars …
term precipitation forecasts. Radar data are commonly used to detect convection, but radars …
Automatic detection and classification of low-level orographic precipitation processes from space-borne radars using machine learning
Ground-clutter is a significant cause of missed-detection and underestimation of
precipitation in complex terrain from space-based radars such as the Global Precipitation …
precipitation in complex terrain from space-based radars such as the Global Precipitation …
An environmental data collection for COVID-19 pandemic research
The COVID-19 viral disease surfaced at the end of 2019 and quickly spread across the
globe. To rapidly respond to this pandemic and offer data support for various communities …
globe. To rapidly respond to this pandemic and offer data support for various communities …
[HTML][HTML] Annotated Dataset for Training Cloud Segmentation Neural Networks Using High-Resolution Satellite Remote Sensing Imagery
M He, J Zhang, Y He, X Zuo, Z Gao - Remote Sensing, 2024 - mdpi.com
The integration of satellite data with deep learning has revolutionized various tasks in
remote sensing, including classification, object detection, and semantic segmentation. Cloud …
remote sensing, including classification, object detection, and semantic segmentation. Cloud …
A method for retrieving cloud-top height based on a machine learning model using the Himawari-8 combined with near infrared data
Y Dong, X Sun, Q Li - Remote Sensing, 2022 - mdpi.com
Different cloud-top heights (CTHs) have different degrees of atmospheric heating, which is
an important factor for weather forecasting and aviation safety. AHIs (Advanced Himawari …
an important factor for weather forecasting and aviation safety. AHIs (Advanced Himawari …
Adopting GPU computing to support DL-based Earth science applications
With the advancement of Artificial Intelligence (AI) technologies and accumulation of big
Earth data, Deep Learning (DL) has become an important method to discover patterns and …
Earth data, Deep Learning (DL) has become an important method to discover patterns and …
[HTML][HTML] Thunderstorm cloud-type classification from space-based lightning imagers
Thunderstorm Cloud-Type Classification from Space-Based Lightning Imagers in: Monthly
Weather Review Volume 148 Issue 5 (2020) Jump to Content Logo Logo Logo Logo Logo Logo …
Weather Review Volume 148 Issue 5 (2020) Jump to Content Logo Logo Logo Logo Logo Logo …
Hyperspectral infrared sounder cloud detection using deep neural network model
Detection of cloud contaminated field of views (FOV) from satellite hyperspectral infrared
sounders is essential for numerical weather prediction. A new cloud detection model is …
sounders is essential for numerical weather prediction. A new cloud detection model is …