A Deep-Learning-Based Microwave Radiative Transfer Emulator for Data Assimilation and Remote Sensing
In this article, we introduce a fully connected deep neural network algorithm to emulate the Community Cadiative Transfer Model (FCDN_CRTM) simulation of brightness temperatures (BTs) from the Advanced Technology Microwave Sounder (ATMS) channels for clear-sky cases over ocean surfaces. The FCDN_CRT...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9909997/ |
| _version_ | 1811250934543548416 |
|---|---|
| author | Xingming Liang Kevin Garrett Quanhua Liu Eric S. Maddy Kayo Ide Sid Boukabara |
| author_facet | Xingming Liang Kevin Garrett Quanhua Liu Eric S. Maddy Kayo Ide Sid Boukabara |
| author_sort | Xingming Liang |
| collection | DOAJ |
| description | In this article, we introduce a fully connected deep neural network algorithm to emulate the Community Cadiative Transfer Model (FCDN_CRTM) simulation of brightness temperatures (BTs) from the Advanced Technology Microwave Sounder (ATMS) channels for clear-sky cases over ocean surfaces. The FCDN_CRTM fine-tuned through three sensitivity experiments with respect to sample-size determination, model separation, and introduction of novel features toward improving the accuracy of the model. In addition to the BT simulation, we evaluated the Jacobians with respect to surface and atmospheric parameters. Atmosphere profiles from the European Centre for Medium-Range Weather Forecasts, sea surface temperature from the Canadian Meteorology Centre, and ATMS sensor data records were used as FCDN_CRTM inputs. In comparison to CRTM, the FCDN_CRTM minus CRTM mean biases were within several hundredths of a Kelvin (K), and the corresponding standard deviations (SDs) were between 0.05 and 0.15 K for all ATMS bands. The accuracies for both mean bias and SD were consistent throughout the evaluation period, which spanned approximately 1 year beyond the period of the FCDN_CRTM training dataset. Furthermore, the model Jacobians generally compared well with CRTM Jacobians in terms of surface temperature, wind speed, air temperature, and (log) water vapor. The performance of the FCDN_CRTM forward and Jacobian model indicate potential for use in data assimilation and physical retrieval systems, such as the NOAA operational Microwave Integrated Retrieval System. |
| first_indexed | 2024-04-12T16:12:13Z |
| format | Article |
| id | doaj.art-ccffd16add5d40bfbf5f40ac2d7f6394 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-04-12T16:12:13Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-ccffd16add5d40bfbf5f40ac2d7f63942022-12-22T03:25:52ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352022-01-01158819883310.1109/JSTARS.2022.32104919909997A Deep-Learning-Based Microwave Radiative Transfer Emulator for Data Assimilation and Remote SensingXingming Liang0https://orcid.org/0000-0001-5641-0509Kevin Garrett1Quanhua Liu2https://orcid.org/0000-0002-3616-351XEric S. Maddy3https://orcid.org/0000-0003-0453-6569Kayo Ide4https://orcid.org/0000-0001-5789-9326Sid Boukabara5https://orcid.org/0000-0002-1857-3806CISESS, University of Maryland, College Park, MD, USACenter for Satellite Applications and Research, National Environmental Satellite Data, and Information Service (NESDIS), NOAA, College Park, MD, USACenter for Satellite Applications and Research, National Environmental Satellite Data, and Information Service (NESDIS), NOAA, College Park, MD, USARiverside Technology Inc., College Park, MD, USAUniversity of Maryland, College Park, MD, USAThe Office of Systems Architecture and Advanced Planning, NESDIS, NOAA, College Park, MD, USAIn this article, we introduce a fully connected deep neural network algorithm to emulate the Community Cadiative Transfer Model (FCDN_CRTM) simulation of brightness temperatures (BTs) from the Advanced Technology Microwave Sounder (ATMS) channels for clear-sky cases over ocean surfaces. The FCDN_CRTM fine-tuned through three sensitivity experiments with respect to sample-size determination, model separation, and introduction of novel features toward improving the accuracy of the model. In addition to the BT simulation, we evaluated the Jacobians with respect to surface and atmospheric parameters. Atmosphere profiles from the European Centre for Medium-Range Weather Forecasts, sea surface temperature from the Canadian Meteorology Centre, and ATMS sensor data records were used as FCDN_CRTM inputs. In comparison to CRTM, the FCDN_CRTM minus CRTM mean biases were within several hundredths of a Kelvin (K), and the corresponding standard deviations (SDs) were between 0.05 and 0.15 K for all ATMS bands. The accuracies for both mean bias and SD were consistent throughout the evaluation period, which spanned approximately 1 year beyond the period of the FCDN_CRTM training dataset. Furthermore, the model Jacobians generally compared well with CRTM Jacobians in terms of surface temperature, wind speed, air temperature, and (log) water vapor. The performance of the FCDN_CRTM forward and Jacobian model indicate potential for use in data assimilation and physical retrieval systems, such as the NOAA operational Microwave Integrated Retrieval System.https://ieeexplore.ieee.org/document/9909997/Advanced Technology Microwave Sounder (ATMS)Community Radiative Transfer Model (CRTM)data assimilationdeep learning (DL)Jacobianremote sensing |
| spellingShingle | Xingming Liang Kevin Garrett Quanhua Liu Eric S. Maddy Kayo Ide Sid Boukabara A Deep-Learning-Based Microwave Radiative Transfer Emulator for Data Assimilation and Remote Sensing IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Advanced Technology Microwave Sounder (ATMS) Community Radiative Transfer Model (CRTM) data assimilation deep learning (DL) Jacobian remote sensing |
| title | A Deep-Learning-Based Microwave Radiative Transfer Emulator for Data Assimilation and Remote Sensing |
| title_full | A Deep-Learning-Based Microwave Radiative Transfer Emulator for Data Assimilation and Remote Sensing |
| title_fullStr | A Deep-Learning-Based Microwave Radiative Transfer Emulator for Data Assimilation and Remote Sensing |
| title_full_unstemmed | A Deep-Learning-Based Microwave Radiative Transfer Emulator for Data Assimilation and Remote Sensing |
| title_short | A Deep-Learning-Based Microwave Radiative Transfer Emulator for Data Assimilation and Remote Sensing |
| title_sort | deep learning based microwave radiative transfer emulator for data assimilation and remote sensing |
| topic | Advanced Technology Microwave Sounder (ATMS) Community Radiative Transfer Model (CRTM) data assimilation deep learning (DL) Jacobian remote sensing |
| url | https://ieeexplore.ieee.org/document/9909997/ |
| work_keys_str_mv | AT xingmingliang adeeplearningbasedmicrowaveradiativetransferemulatorfordataassimilationandremotesensing AT kevingarrett adeeplearningbasedmicrowaveradiativetransferemulatorfordataassimilationandremotesensing AT quanhualiu adeeplearningbasedmicrowaveradiativetransferemulatorfordataassimilationandremotesensing AT ericsmaddy adeeplearningbasedmicrowaveradiativetransferemulatorfordataassimilationandremotesensing AT kayoide adeeplearningbasedmicrowaveradiativetransferemulatorfordataassimilationandremotesensing AT sidboukabara adeeplearningbasedmicrowaveradiativetransferemulatorfordataassimilationandremotesensing AT xingmingliang deeplearningbasedmicrowaveradiativetransferemulatorfordataassimilationandremotesensing AT kevingarrett deeplearningbasedmicrowaveradiativetransferemulatorfordataassimilationandremotesensing AT quanhualiu deeplearningbasedmicrowaveradiativetransferemulatorfordataassimilationandremotesensing AT ericsmaddy deeplearningbasedmicrowaveradiativetransferemulatorfordataassimilationandremotesensing AT kayoide deeplearningbasedmicrowaveradiativetransferemulatorfordataassimilationandremotesensing AT sidboukabara deeplearningbasedmicrowaveradiativetransferemulatorfordataassimilationandremotesensing |