Near-surface air temperature dataset for the Qinghai-Tibet Plateau (2019) derived from thermal infrared remote sensing and elevation-constrained modeling
This dataset provides high-resolution (30 m) spatialized near-surface air temperature products for the Qinghai-Tibet Plateau, updated using thermal infrared remote sensing data from Landsat 8 (L8) and
Satellite remote sensing enables global monitoring of water quality in freshwater and marine ecosystems. However, consistent data quality is a challenge due to variations in the performance of used algorithms
This experiment contains 30-meter resolution near-surface air temperature datasets for four glacier regions (Guliya, Aru, Naimona’nyi, Dunde) on the Qinghai-Tibet Plateau, derived by integrating in situ
The HadEX-CAM dataset contains four land-based extreme indices (TX90p, TN90p, TX10p, TN10p) for the European region. The original dataset (containing missing values) has been created by the MetOffice by
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
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.
45-year hindcast dataset (1979 to 2024) created from Google Deepmind's Graphcast-operational model. Developed by researchers at The University of Texas at Austin, this dataset provides daily 15‑day deterministic
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