Development of a high spatiotemporal resolution cloud-type classification approach using Himawari-8 and CloudSat

Zhang, Chengwei; Zhuge, Xiaoyong; Yu, Fan

doi:10.1080/01431161.2019.1594438

Cited by 19 publications

(12 citation statements)

References 43 publications

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“…By exploiting this a priori knowledge, cloud processes can be highlighted in further evaluation. Our approach extends the recent development of machine learning based cloud classification methods for satellite data [10], [11], [12], [13], [14], [15] to climate models. Machine learning-based cloud classification is not a new idea (see [16]), but has only recently become feasible for large-scale applications due to the increase in available computing power and the different available methods have distinct properties.…”

Section: Introductionmentioning

confidence: 96%

“…Supervised classification relies on the assumption that the assigned classes fit the purpose, whereas the user has limited control over the makeup of the classes in unsupervised methods. Therefore, supervised methods allow for interpreting the final results without additional analysis steps but require a set of labeled data [11], [14]. If the goal is to find classes that are as distinct as possible, or if no previously labeled data are available, unsupervised methods are preferable [12], [13].…”

Section: Introductionmentioning

confidence: 99%

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

Kaps

Lauer

Camps‐Valls

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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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 cloud types to coarse data. It facilitates a more objective evaluation of clouds in ESMs and improves the consistency of cloud process analysis. The method is built on satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument labeled by deep neural networks with cloud types defined by the World Meteorological Organization (WMO), using cloud-type labels from CloudSat as ground truth. The method is applicable to datasets with information about physical cloud variables comparable to MODIS satellite data and at sufficiently high temporal resolution. We apply the method to alternative satellite data from the Cloud_cci project (ESA Climate Change Initiative), coarse-grained to typical resolutions of climate models. The resulting cloud-type distributions are physically consistent and the horizontal resolutions typical of ESMs are sufficient to apply our method. We recommend outputting crucial variables required by our method for future ESM data evaluation. This will enable the use of labeled satellite data for a more systematic evaluation of clouds in climate models.

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Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

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

Kaps

Lauer

Camps‐Valls

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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“…A combination of geostationary and polar orbit satellite data can contribute to obtaining more structural and physical characteristics of clouds. Zhang et al [15] used cloud scenario products from CloudSat and developed a procedure for achieving high-spatiotemporal-resolution cloud type classifications for multi-spectral Himawari-8 datasets using a maximum likelihood estimation (MLE) and a random forest classification. Liu et al [16] accessed cloud retrievals from Himawari-8 and compared them against those obtained from CloudSat and CALIPSO, finding that the retrieved cloud-top altitudes and the optical thicknesses and effective particle radii of the clouds are consistent with passive Moderate Resolution Imaging Spectroradiometer (MODIS) data.…”

Section: Introductionmentioning

confidence: 99%

All-Day Cloud Classification via a Random Forest Algorithm Based on Satellite Data from CloudSat and Himawari-8

Wang,

Hu,

Ding

et al. 2023

Atmosphere

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It remains challenging to accurately classify complicated clouds owing to the various types of clouds and their distribution on multiple layers. In this paper, multi-band radiation information from the geostationary satellite Himawari-8 and the cloud classification product of the polar orbit satellite CloudSat from June to September 2018 are investigated. Based on sample sets matched by two types of satellite data, a random forest (RF) algorithm was applied to train a model, and a retrieval method was developed for cloud classification. With the use of this method, the sample sets were inverted and classified as clear sky, low clouds, middle clouds, thin cirrus, thick cirrus, multi-layer clouds and deep convection (cumulonimbus) clouds. The results indicate that the average accuracy for all cloud types during the day is 88.4%, and misclassifications mainly occur between low and middle clouds, thick cirrus clouds and cumulonimbus clouds. The average accuracy is 79.1% at night, with more misclassifications occurring between middle clouds, multi-layer clouds and cumulonimbus clouds. Moreover, Typhoon Muifa from 2022 was selected as a sample case, and the cloud type (CLT) product of an FY-4A satellite was used to examine the classification method. In the cloud system of Typhoon Muifa, a cumulonimbus area classified using the method corresponded well with a mesoscale convective system (MCS). Compared to the FY-4A CLT product, the classifications of ice-type (thick cirrus) and multi-layer clouds are effective, and the location, shape and size of these two varieties of cloud are similar.

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“…The temporal and spatial distributions of various cloud types could vary with climate and weather conditions. Cumulonimbus clouds, for example, are associated with atmospheric instability, turbulence, and thunderstorms (Zhang et al, 2019). Hence, understanding clouds is of great importance for multiple applications related to hydrological cycle, climate, and atmosphere.…”

Section: Introductionmentioning

confidence: 99%

Ground‐Based Cloud Classification Using Task‐Based Graph Convolutional Network

Liu

Zhong

et al. 2020

Geophysical Research Letters

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Clouds play a significant role in weather forecasts, water cycle, and climate system. However, existing methods ignore the relations of ground‐based cloud images. In this letter, we propose a novel method named task‐based graph convolutional network (TGCN) for ground‐based cloud classification, which takes image relations into consideration. To this end, we construct the graph using convolutional neural network‐based features of ground‐based cloud images which are learned in a supervised manner, and incorporate the graph computation into TGCN. Given that existing ground‐based cloud databases are with limited labeled training images and categorized according to different classification criteria, we release the largest ground‐based remote sensing cloud database (GRSCD) to provide a comparative study for different methods and to further improve the study of regional sky conditions. The experimental results on GRSCD manifest the effectiveness of TGCN for ground‐based cloud classification.

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Development of a high spatiotemporal resolution cloud-type classification approach using Himawari-8 and CloudSat

Cited by 19 publications

References 43 publications

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

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

All-Day Cloud Classification via a Random Forest Algorithm Based on Satellite Data from CloudSat and Himawari-8

Ground‐Based Cloud Classification Using Task‐Based Graph Convolutional Network

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