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All-day cloud property and occurrence probability dataset based on satellite remote sensing data.

Longfeng Nie1,2,3, Yuntian Chen4,5, Dongxiao Zhang6,7,8,9

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CldNet Version 2.0 (CldNetV2) enhances cloud classification and property prediction, providing crucial all-day datasets for meteorological research. This advancement improves climate comprehension and fills nighttime data gaps.

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Area of Science:

  • Meteorology and Climate Science
  • Remote Sensing
  • Artificial Intelligence in Earth Observation

Background:

  • Accurate cloud property datasets are vital for meteorological research, climate studies, and applications.
  • Existing satellite cloud products often lack comprehensive nighttime data and diverse cloud property information.
  • CldNet Version 2.0 builds upon previous work, extending cloud recognition capabilities to a full day-night cycle.

Purpose of the Study:

  • To introduce CldNet Version 2.0 (CldNetV2) for enhanced cloud type classification and property prediction.
  • To generate comprehensive, all-day datasets of cloud properties and occurrence probabilities.
  • To address limitations in current Himawari cloud products, particularly for nighttime conditions.

Main Methods:

  • Leveraging transfer learning and model parameter sharing techniques from the foundational CldNet.
  • Developing a deep learning model for classifying cloud types and predicting multiple cloud properties.
  • Statistically analyzing cloud type occurrence probabilities across annual, seasonal, and monthly time scales, distinguishing between all day, daytime, and nighttime.

Main Results:

  • CldNetV2 successfully classifies cloud types and predicts additional cloud properties, creating valuable nighttime datasets.
  • Generated datasets include cloud type occurrence probabilities across various time scales and conditions (all day, daytime, nighttime).
  • Independent validation using CALIPSO, ERA5, and visualization confirms the reliability of the CldNetV2 cloud products.

Conclusions:

  • CldNetV2 significantly advances the capability for all-day cloud property and occurrence probability assessment.
  • The publicly released dataset provides a valuable resource for meteorological environment assessment and climate research.
  • This work contributes to improved understanding and modeling of atmospheric processes through comprehensive cloud data.