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Remote Sensing
Volume 15
Issue 7
10.3390/rs15071798
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Article

A Cloud Detection Neural Network Approach for the Next Generation Microwave Sounder Aboard EPS MetOp-SG A1

by
Salvatore Larosa
1,*,
Domenico Cimini
1,2,
Donatello Gallucci
1,
Francesco Di Paola
1,
Saverio Teodosio Nilo
1,
Elisabetta Ricciardelli
1,
Ermann Ripepi
1 and
Filomena Romano
1
1
Institute of Methodologies for Environmental Analysis, National Research Council (IMAA/CNR), 85100 Potenza, Italy
2
Center of Excellence Telesensing of Environment and Model Prediction of Severe Events (CETEMPS), University of L’Aquila, 67100 L’Aquila, Italy
*
Author to whom correspondence should be addressed.
Remote Sens. 2023, 15(7), 1798; https://doi.org/10.3390/rs15071798
Submission received: 19 February 2023 / Revised: 23 March 2023 / Accepted: 26 March 2023 / Published: 28 March 2023
(This article belongs to the Section Atmospheric Remote Sensing)
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Abstract

This work presents an algorithm based on a neural network (NN) for cloud detection to detect clouds and their thermodynamic phase using spectral observations from spaceborne microwave radiometers. A standalone cloud detection algorithm over the ocean and land has been developed to distinguish clear sky versus ice and liquid clouds from microwave sounder (MWS) observations. The MWS instrument—scheduled to be onboard the first satellite of the Eumetsat Polar System Second-Generation (EPS-SG) series, MetOp-SG A1—has a direct inheritance from advanced microwave sounding unit A (AMSU-A) and the microwave humidity sounder (MHS) microwave instruments. Real observations from the MWS sensor are not currently available as its launch is foreseen in 2024. Thus, a simulated dataset of atmospheric states and associated MWS synthetic observations have been produced through radiative transfer calculations with ERA5 real atmospheric profiles and surface conditions. The developed algorithm has been validated using spectral observations from the AMSU-A and MHS sounders. While ERA5 atmospheric profiles serve as references for the model development and its validation, observations from AVHRR cloud mask products provide references for the AMSU-A/MHS model evaluation. The results clearly show the NN algorithm’s high skills to detect clear, ice and liquid cloud conditions against a benchmark. In terms of overall accuracy, the NN model features 92% (88%) on the ocean and 87% (85%) on land, for the MWS (AMSU-A/MHS)-simulated dataset, respectively.
Keywords: neural network; microwave; cloud detection; MWS; AMSU-A; MHS neural network; microwave; cloud detection; MWS; AMSU-A; MHS
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MDPI and ACS Style

Larosa, S.; Cimini, D.; Gallucci, D.; Di Paola, F.; Nilo, S.T.; Ricciardelli, E.; Ripepi, E.; Romano, F. A Cloud Detection Neural Network Approach for the Next Generation Microwave Sounder Aboard EPS MetOp-SG A1. Remote Sens. 2023, 15, 1798. https://doi.org/10.3390/rs15071798

AMA Style

Larosa S, Cimini D, Gallucci D, Di Paola F, Nilo ST, Ricciardelli E, Ripepi E, Romano F. A Cloud Detection Neural Network Approach for the Next Generation Microwave Sounder Aboard EPS MetOp-SG A1. Remote Sensing. 2023; 15(7):1798. https://doi.org/10.3390/rs15071798

Chicago/Turabian Style

Larosa, Salvatore, Domenico Cimini, Donatello Gallucci, Francesco Di Paola, Saverio Teodosio Nilo, Elisabetta Ricciardelli, Ermann Ripepi, and Filomena Romano. 2023. "A Cloud Detection Neural Network Approach for the Next Generation Microwave Sounder Aboard EPS MetOp-SG A1" Remote Sensing 15, no. 7: 1798. https://doi.org/10.3390/rs15071798

APA Style

Larosa, S., Cimini, D., Gallucci, D., Di Paola, F., Nilo, S. T., Ricciardelli, E., Ripepi, E., & Romano, F. (2023). A Cloud Detection Neural Network Approach for the Next Generation Microwave Sounder Aboard EPS MetOp-SG A1. Remote Sensing, 15(7), 1798. https://doi.org/10.3390/rs15071798

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