MIIDAPS-AI: An Explainable Machine-Learning Algorithm for Infrared and Microwave Remote Sensing and Data Assimilation Preprocessing - Application to LEO and GEO Sensors
In this article, we leverage and apply state-of-the-art artificial intelligence (AI) techniques to satellite remote sensing of temperature, moisture, surface, and cloud parameters in all-weather, all-surface conditions, from both microwave and infrared sensors. The multi-instrument inversion and dat...
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ftdoajarticles:oai:doaj.org/article:1d57f45203a64ee0994fc39036136409 2023-05-15T18:18:46+02:00 MIIDAPS-AI: An Explainable Machine-Learning Algorithm for Infrared and Microwave Remote Sensing and Data Assimilation Preprocessing - Application to LEO and GEO Sensors Eric S. Maddy Sid A. Boukabara 2021-01-01T00:00:00Z https://doi.org/10.1109/JSTARS.2021.3104389 https://doaj.org/article/1d57f45203a64ee0994fc39036136409 EN eng IEEE https://ieeexplore.ieee.org/document/9512479/ https://doaj.org/toc/2151-1535 2151-1535 doi:10.1109/JSTARS.2021.3104389 https://doaj.org/article/1d57f45203a64ee0994fc39036136409 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 8566-8576 (2021) Artificial Intelligence (AI) atmosphere earth observing system machine learning neural networks remote sensing Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 article 2021 ftdoajarticles https://doi.org/10.1109/JSTARS.2021.3104389 2022-12-31T09:12:25Z In this article, we leverage and apply state-of-the-art artificial intelligence (AI) techniques to satellite remote sensing of temperature, moisture, surface, and cloud parameters in all-weather, all-surface conditions, from both microwave and infrared sensors. The multi-instrument inversion and data assimilation preprocessing system, artificial intelligence version, or MIIDAPS-AI for short, is valid for both polar and geostationary microwave and infrared sounders and imagers as well as for pairs of combined infrared and microwave sounders. The algorithm produces vertical profiles of temperature and moisture as well as surface temperature, surface emissivity, and cloud parameters. Additional products from hyperspectral infrared sensors include selected trace gases. From microwave sensors, additional products such as rainfall rate, first year/multiyear sea ice concentration, and soil moisture can be derived from primary products. The MIIDAPS-AI algorithm is highly efficient with no noticeable decrease in accuracy compared to traditional operational sounding algorithms. The automatically generated Jacobians from this deep-learning algorithm could provide an explainability mechanism to build trustworthiness in the algorithm, and to quantify uncertainties of the algorithm's outputs. The computation gain is estimated to be two orders of magnitude, which opens the door to either 1) process massively larger amounts of satellite data, or to 2) offer improvements in timeliness and significant saving in computing power (and therefore cost) if the same amount of data is processed. Here, we present an overview of the MIIDAPS-AI implementation, discuss its applicability to various sensors and provide an initial performance assessment for a select number of sensors and geophysical parameters. Article in Journal/Newspaper Sea ice Directory of Open Access Journals: DOAJ Articles IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14 8566 8576 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Artificial Intelligence (AI) atmosphere earth observing system machine learning neural networks remote sensing Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 |
spellingShingle |
Artificial Intelligence (AI) atmosphere earth observing system machine learning neural networks remote sensing Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 Eric S. Maddy Sid A. Boukabara MIIDAPS-AI: An Explainable Machine-Learning Algorithm for Infrared and Microwave Remote Sensing and Data Assimilation Preprocessing - Application to LEO and GEO Sensors |
topic_facet |
Artificial Intelligence (AI) atmosphere earth observing system machine learning neural networks remote sensing Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 |
description |
In this article, we leverage and apply state-of-the-art artificial intelligence (AI) techniques to satellite remote sensing of temperature, moisture, surface, and cloud parameters in all-weather, all-surface conditions, from both microwave and infrared sensors. The multi-instrument inversion and data assimilation preprocessing system, artificial intelligence version, or MIIDAPS-AI for short, is valid for both polar and geostationary microwave and infrared sounders and imagers as well as for pairs of combined infrared and microwave sounders. The algorithm produces vertical profiles of temperature and moisture as well as surface temperature, surface emissivity, and cloud parameters. Additional products from hyperspectral infrared sensors include selected trace gases. From microwave sensors, additional products such as rainfall rate, first year/multiyear sea ice concentration, and soil moisture can be derived from primary products. The MIIDAPS-AI algorithm is highly efficient with no noticeable decrease in accuracy compared to traditional operational sounding algorithms. The automatically generated Jacobians from this deep-learning algorithm could provide an explainability mechanism to build trustworthiness in the algorithm, and to quantify uncertainties of the algorithm's outputs. The computation gain is estimated to be two orders of magnitude, which opens the door to either 1) process massively larger amounts of satellite data, or to 2) offer improvements in timeliness and significant saving in computing power (and therefore cost) if the same amount of data is processed. Here, we present an overview of the MIIDAPS-AI implementation, discuss its applicability to various sensors and provide an initial performance assessment for a select number of sensors and geophysical parameters. |
format |
Article in Journal/Newspaper |
author |
Eric S. Maddy Sid A. Boukabara |
author_facet |
Eric S. Maddy Sid A. Boukabara |
author_sort |
Eric S. Maddy |
title |
MIIDAPS-AI: An Explainable Machine-Learning Algorithm for Infrared and Microwave Remote Sensing and Data Assimilation Preprocessing - Application to LEO and GEO Sensors |
title_short |
MIIDAPS-AI: An Explainable Machine-Learning Algorithm for Infrared and Microwave Remote Sensing and Data Assimilation Preprocessing - Application to LEO and GEO Sensors |
title_full |
MIIDAPS-AI: An Explainable Machine-Learning Algorithm for Infrared and Microwave Remote Sensing and Data Assimilation Preprocessing - Application to LEO and GEO Sensors |
title_fullStr |
MIIDAPS-AI: An Explainable Machine-Learning Algorithm for Infrared and Microwave Remote Sensing and Data Assimilation Preprocessing - Application to LEO and GEO Sensors |
title_full_unstemmed |
MIIDAPS-AI: An Explainable Machine-Learning Algorithm for Infrared and Microwave Remote Sensing and Data Assimilation Preprocessing - Application to LEO and GEO Sensors |
title_sort |
miidaps-ai: an explainable machine-learning algorithm for infrared and microwave remote sensing and data assimilation preprocessing - application to leo and geo sensors |
publisher |
IEEE |
publishDate |
2021 |
url |
https://doi.org/10.1109/JSTARS.2021.3104389 https://doaj.org/article/1d57f45203a64ee0994fc39036136409 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 8566-8576 (2021) |
op_relation |
https://ieeexplore.ieee.org/document/9512479/ https://doaj.org/toc/2151-1535 2151-1535 doi:10.1109/JSTARS.2021.3104389 https://doaj.org/article/1d57f45203a64ee0994fc39036136409 |
op_doi |
https://doi.org/10.1109/JSTARS.2021.3104389 |
container_title |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
container_volume |
14 |
container_start_page |
8566 |
op_container_end_page |
8576 |
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1766195463664435200 |