ArticleScientific data2025
All-day cloud property and occurrence probability dataset based on satellite remote sensing data.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The cloud property database and different type cloud occurrence probability datasets are helpful for meteorological research and application, as well as climate comprehension. Building on the foundation of CldNet with all-day cloud type recognition capability, CldNet Version 2.0 (CldNetV2) is proposed. This enhanced version leverages transfer learning and model parameter sharing techniques to not only classify cloud types but also predict additional cloud properties. Datasets with multiple cloud properties obtained by CldNetV2 make up for the lack of current Himawari cloud product at nighttime. Meanwhile, the dataset of different cloud type occurrence probabilities is statistically obtained on three time scales including annual, seasonal, and monthly, and more importantly, the dataset distinguishes between all day, daytime, and nighttime. In addition, the reliability of our cloud product is independently validated by the cloud properties from CALIPSO trajectories, ERA5 cloud cover fraction and the visualization of cloud property distribution and typhoon eye track during typhoons. Further more, the all-day cloud property and occurrence probability dataset for meteorological environment assessment has been publicly released.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.