The Qinghai-Tibet Plateau, known for its high altitude, cold climate, and fragile ecosystem, presents unique challenges and opportunities for the implementation of an intelligent sponge urban system. The
Climate data for adaptation and vulnerability assessments — northwest (ClimAVA-NW) provides bias-corrected, downscaled daily climatic data at ~4km spatial resolution from 17 CMIP6 GCMs, three different
Sentinel-3 OLCI images processed with the Atmospheric Correction for Optical Water Types, A4O [Hieronymi et al. in prep & 2023], and the water algorithm OLCI Neural Network Swarm, ONNS [Hieronymi et al.,
The experiment conducted aimed to enhance the temporal resolution of climate projections for agricultural applications by using machine learning to downscale daily NEX-GDDP-CMIP6 climate data (https://doi.org/10.7917/OFSG3345)
Climate data for adaptation and vulnerability assessments — southwest (ClimAVA-SW) provides bias-corrected, downscaled daily climatic data at ~4km spatial resolution from 17 CMIP6 GCMs, three different
IceCloudNet is a novel method based on machine learning able to obtain high-
quality vertically resolved predictions for ice water content and ice crystal number concentration of clouds containing ice.
The objective of the project is to better understand what controls the size of intense storms, also known as deep convective systems. The larger the storm is the more it has consequences in terms of extreme