Cloud Type Classification (cldtype) Value-Added Product

Satellite remote sensing plays a pivotal role in characterizing hydrometeorological components including cloud types and their associated precipitation. The Cloud Profiling Radar (CPR) on the Polar Orbiting CloudSat satellite has provided a unique dataset to characterize cloud types. However, data from this nadir-looking radar offers limited capability for estimating precipitation because of the narrow satellite swath coverage and low temporal frequency. We use these high-quality observations to build a Deep Neural Network Cloud-Type Classification (DeepCTC) model to estimate cloud types from multispectral data from the Advanced Baseline Imager (ABI) onboard the GOES-16 platform. The DeepCTC model is trained and tested using coincident data from both CloudSat and ABI over the CONUS region. Evaluations of DeepCTC indicate that the model performs well for a variety of cloud types including Altostratus, Altocumulus, Cumulus, Nimbostratus, Deep Convective and High clouds. However, capturing low-level clouds remains a challenge for the model. Results from simulated GOES-16 ABI imageries of the Hurricane Harvey event show a large-scale perspective of the rapid and consistent cloud-type monitoring is possible using the DeepCTC model. Additionally, assessments using half-hourly Multi-Radar/Multi-Sensor (MRMS) precipitation rate data (for Hurricane Harvey as a case study) show the ability of DeepCTC in identifying rainy clouds, including Deep Convective and Nimbostratus and their precipitation potential. We also use DeepCTC to evaluate the performance of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) product over different cloud types with respect to MRMS referenced at a half-hourly time scale for July 2018. Our analysis suggests that DeepCTC provides supplementary insights into the variability of cloud types to diagnose the weakness and strength of near real-time GEO-based precipitation retrievals. With additional training and testing, we believe DeepCTC has the potential to augment the widely used PERSIANN-CCS algorithm for estimating precipitation.

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Cloud-type Classification of Ground-Based Images using Deep Learning

2019 17th International Conference on Emerging eLearning Technologies and Applications (ICETA) ◽

10.1109/iceta48886.2019.9040149 ◽

2019 ◽

Author(s):

Martin Sinko ◽

Patrik Kamencay ◽

Peter Sykora ◽

Miroslav Benco ◽

Robert Hudec

Keyword(s):

Deep Learning ◽

Cloud Type ◽

Type Classification

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Comparison of GOES Cloud Classification Algorithms Employing Explicit and Implicit Physics

Journal of Applied Meteorology and Climatology ◽

10.1175/2009jamc2103.1 ◽

2009 ◽

Vol 48 (7) ◽

pp. 1411-1421 ◽

Cited By ~ 16

Author(s):

Richard L. Bankert ◽

Cristian Mitrescu ◽

Steven D. Miller ◽

Robert H. Wade

Keyword(s):

Nearest Neighbor ◽

Training Data ◽

Cloud Type ◽

Multispectral Satellite Imagery ◽

Wide Range ◽

Physically Based ◽

Northeastern Pacific ◽

Characteristic Features ◽

One Year ◽

Type Classification

Abstract Cloud-type classification based on multispectral satellite imagery data has been widely researched and demonstrated to be useful for distinguishing a variety of classes using a wide range of methods. The research described here is a comparison of the classifier output from two very different algorithms applied to Geostationary Operational Environmental Satellite (GOES) data over the course of one year. The first algorithm employs spectral channel thresholding and additional physically based tests. The second algorithm was developed through a supervised learning method with characteristic features of expertly labeled image samples used as training data for a 1-nearest-neighbor classification. The latter’s ability to identify classes is also based in physics, but those relationships are embedded implicitly within the algorithm. A pixel-to-pixel comparison analysis was done for hourly daytime scenes within a region in the northeastern Pacific Ocean. Considerable agreement was found in this analysis, with many of the mismatches or disagreements providing insight to the strengths and limitations of each classifier. Depending upon user needs, a rule-based or other postprocessing system that combines the output from the two algorithms could provide the most reliable cloud-type classification.

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