Integrating Reanalysis and Satellite Cloud Information to Estimate Surface Downward Long-Wave Radiation
The estimation of downward long-wave radiation (DLR) at the surface is very important for the understanding of the Earth’s radiative budget with implications in surface–atmosphere exchanges, climate variability, and global warming. Theoretical radiative transfer and observationally based studies ide...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-04-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/7/1704 |
| _version_ | 1827622584662884352 |
|---|---|
| author | Francis M. Lopes Emanuel Dutra Isabel F. Trigo |
| author_facet | Francis M. Lopes Emanuel Dutra Isabel F. Trigo |
| author_sort | Francis M. Lopes |
| collection | DOAJ |
| description | The estimation of downward long-wave radiation (DLR) at the surface is very important for the understanding of the Earth’s radiative budget with implications in surface–atmosphere exchanges, climate variability, and global warming. Theoretical radiative transfer and observationally based studies identify the crucial role of clouds in modulating the temporal and spatial variability of DLR. In this study, a new machine learning algorithm that uses multivariate adaptive regression splines (MARS) and the combination of near-surface meteorological data with satellite cloud information is proposed. The new algorithm is compared with the current operational formulation used by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Land Surface Analysis (LSA-SAF). Both algorithms use near-surface temperature and dewpoint temperature along with total column water vapor from the latest European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis ERA5 and satellite cloud information from the Meteosat Second Generation. The algorithms are trained and validated using both ECMWF-ERA5 and DLR acquired from 23 ground stations as part of the Baseline Surface Radiation Network (BSRN) and the Atmospheric Radiation Measurement (ARM) user facility. Results show that the MARS algorithm generally improves DLR estimation in comparison with other model estimates, particularly when trained with observations. When considering all the validation data, root mean square errors (RMSEs) of 18.76, 23.55, and 22.08 W·m<sup>−2</sup> are obtained for MARS, operational LSA-SAF, and ERA5, respectively. The added value of using the satellite cloud information is accessed by comparing with estimates driven by ERA5 total cloud cover, showing an increase of 17% of the RMSE. The consistency of MARS estimate is also tested against an independent dataset of 52 ground stations (from FLUXNET2015), further supporting the good performance of the proposed model. |
| first_indexed | 2024-03-09T11:28:32Z |
| format | Article |
| id | doaj.art-7a0892716bac400faca3a42ed49b3b46 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T11:28:32Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-7a0892716bac400faca3a42ed49b3b462023-11-30T23:57:45ZengMDPI AGRemote Sensing2072-42922022-04-01147170410.3390/rs14071704Integrating Reanalysis and Satellite Cloud Information to Estimate Surface Downward Long-Wave RadiationFrancis M. Lopes0Emanuel Dutra1Isabel F. Trigo2Instituto Dom Luiz (IDL), Faculty of Sciences, University of Lisbon, Campo Grande, 1749-016 Lisbon, PortugalInstituto Dom Luiz (IDL), Faculty of Sciences, University of Lisbon, Campo Grande, 1749-016 Lisbon, PortugalInstituto Dom Luiz (IDL), Faculty of Sciences, University of Lisbon, Campo Grande, 1749-016 Lisbon, PortugalThe estimation of downward long-wave radiation (DLR) at the surface is very important for the understanding of the Earth’s radiative budget with implications in surface–atmosphere exchanges, climate variability, and global warming. Theoretical radiative transfer and observationally based studies identify the crucial role of clouds in modulating the temporal and spatial variability of DLR. In this study, a new machine learning algorithm that uses multivariate adaptive regression splines (MARS) and the combination of near-surface meteorological data with satellite cloud information is proposed. The new algorithm is compared with the current operational formulation used by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Land Surface Analysis (LSA-SAF). Both algorithms use near-surface temperature and dewpoint temperature along with total column water vapor from the latest European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis ERA5 and satellite cloud information from the Meteosat Second Generation. The algorithms are trained and validated using both ECMWF-ERA5 and DLR acquired from 23 ground stations as part of the Baseline Surface Radiation Network (BSRN) and the Atmospheric Radiation Measurement (ARM) user facility. Results show that the MARS algorithm generally improves DLR estimation in comparison with other model estimates, particularly when trained with observations. When considering all the validation data, root mean square errors (RMSEs) of 18.76, 23.55, and 22.08 W·m<sup>−2</sup> are obtained for MARS, operational LSA-SAF, and ERA5, respectively. The added value of using the satellite cloud information is accessed by comparing with estimates driven by ERA5 total cloud cover, showing an increase of 17% of the RMSE. The consistency of MARS estimate is also tested against an independent dataset of 52 ground stations (from FLUXNET2015), further supporting the good performance of the proposed model.https://www.mdpi.com/2072-4292/14/7/1704downward surface long-wave radiationmachine learningmultivariate adaptive regression splinesEUMETSAT LSA-SAFECMWF-ERA5 |
| spellingShingle | Francis M. Lopes Emanuel Dutra Isabel F. Trigo Integrating Reanalysis and Satellite Cloud Information to Estimate Surface Downward Long-Wave Radiation Remote Sensing downward surface long-wave radiation machine learning multivariate adaptive regression splines EUMETSAT LSA-SAF ECMWF-ERA5 |
| title | Integrating Reanalysis and Satellite Cloud Information to Estimate Surface Downward Long-Wave Radiation |
| title_full | Integrating Reanalysis and Satellite Cloud Information to Estimate Surface Downward Long-Wave Radiation |
| title_fullStr | Integrating Reanalysis and Satellite Cloud Information to Estimate Surface Downward Long-Wave Radiation |
| title_full_unstemmed | Integrating Reanalysis and Satellite Cloud Information to Estimate Surface Downward Long-Wave Radiation |
| title_short | Integrating Reanalysis and Satellite Cloud Information to Estimate Surface Downward Long-Wave Radiation |
| title_sort | integrating reanalysis and satellite cloud information to estimate surface downward long wave radiation |
| topic | downward surface long-wave radiation machine learning multivariate adaptive regression splines EUMETSAT LSA-SAF ECMWF-ERA5 |
| url | https://www.mdpi.com/2072-4292/14/7/1704 |
| work_keys_str_mv | AT francismlopes integratingreanalysisandsatellitecloudinformationtoestimatesurfacedownwardlongwaveradiation AT emanueldutra integratingreanalysisandsatellitecloudinformationtoestimatesurfacedownwardlongwaveradiation AT isabelftrigo integratingreanalysisandsatellitecloudinformationtoestimatesurfacedownwardlongwaveradiation |