Machine-Learned Cloud Classes From Satellite Data for Process-Oriented Climate Model Evaluation

@article{Kaps2022MachineLearnedCC,
  title={Machine-Learned Cloud Classes From Satellite Data for Process-Oriented Climate Model Evaluation},
  author={Andreas Kaps and Axel Lauer and Gustau Camps-Valls and Pierre Gentine and Luis G'omez-Chova and Veronika Eyring},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  year={2022},
  volume={61},
  pages={1-15}
}
  • A. KapsA. Lauer V. Eyring
  • Published 2 May 2022
  • Environmental Science, Computer Science
  • IEEE Transactions on Geoscience and Remote Sensing
Clouds play a key role in regulating climate change but are difficult to simulate within Earth system models (ESMs). Improving the representation of clouds is one of the key tasks toward more robust climate change projections. This study introduces a new machine-learning-based framework relying on satellite observations to improve understanding of the representation of clouds and their relevant processes in climate models. The proposed method is capable of assigning distributions of established… 

Assessment of CMIP6 Cloud Fraction and Comparison with Satellite Observations

The seasonal and regional variations of cloud fractions are compared across two generations of global climate model ensembles, specifically, the Coupled Model Intercomparison Project‐5 (CMIP5) and

Cumulo: A Dataset for Learning Cloud Classes

Cumulo, a benchmark dataset for training and evaluating global cloud classification models, is introduced, which consists of one year of 1km resolution MODIS hyperspectral imagery merged with pixel-width 'tracks' of CloudSat cloud labels.

Quantifying Progress Across Different CMIP Phases With the ESMValTool

More than 40 model groups worldwide are participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6), providing a new and rich source of information to better understand past, present,

Response to Referee 1 on manuscript ‘Machine learning of cloud types shows higher climate sensitivity is associated with lower cloud biases’

Thank you for your insightful comments. Please find below our response. In the following text, the original comments are in bold , followed by our response. We do not provide a document marking

EVALUATION OF CLOUDS, RADIATION, AND PRECIPITATION IN CMIP6 MODELS USING GLOBAL WEATHER STATES DERIVED FROM ISCCP-H CLOUD PROPERTY DATA

A clustering methodology is applied to cloud optical depth cloud top pressure (TAU-PC) histograms from the new, 1-degree resolution, ISCCP-H dataset, to derive an updated global Weather State (WS)

Observational constraints on low cloud feedback reduce uncertainty of climate sensitivity

Marine low clouds strongly cool the planet. How this cooling effect will respond to climate change is a leading source of uncertainty in climate sensitivity, the planetary warming resulting from CO2

Applying machine learning methods to detect convection using Geostationary Operational Environmental Satellite-16 (GOES-16) advanced baseline imager (ABI) data

A neural network model with convolutional layers is proposed to identify convection from the high-temporal resolution GOES-16 data, and has correctly learned features of convective clouds and resulted in a reasonably low false alarm ratio (FAR) and high probability of detection (POD).

Data-Driven Cloud Clustering via a Rotationally Invariant Autoencoder

An automated rotation-invariant cloud clustering (RICC) method that leverages deep learning autoencoder technology to organize cloud imagery within large datasets in an unsupervised fashion, free from assumptions about predefined classes is described.

Leveraging spatial textures, through machine learning, to identify aerosols and distinct cloud types from multispectral observations

Improvements in the discrimination between cloud and severe aerosols and an expanded capability to classify cloud types are demonstrated using a combination of machine learning techniques combined with a new methodology to create a human-labeled database of cloud and aerosol types.

Earth System Model Evaluation Tool (ESMValTool) v2.0 – diagnostics for extreme events, regional and impact evaluation and analysis of Earth system models in CMIP

This paper complements a series of now four publications that document the release of the Earth System Model Evaluation Tool (ESMValTool) v2.0. It describes new diagnostics on the hydrological cycle,
...