Estimating Infrared Radiometric Satellite Sea Surface Temperature Retrieval Cold Biases in the Tropics due to Unscreened Optically Thin Cirrus Clouds
Passive longwave infrared radiometric satellite–based retrievals of sea surface temperature (SST) at instrument nadir are investigated for cold bias caused by unscreened optically thin cirrus (OTC) clouds [cloud optical depth (COD) ≤ 0.3]. Level 2 nonlinear SST (NLSST) retrievals over tropical ocean...
| Main Authors: | , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
American Meteorological Society
2017
|
| Online Access: | http://hdl.handle.net/1721.1/111125 https://orcid.org/0000-0002-0467-3785 |
| _version_ | 1811097442723037184 |
|---|---|
| author | Marquis, Jared W. Campbell, James R. Cummings, James A. Westphal, Douglas L. Smith, Nathaniel J. Zhang, Jianglong Bogdanoff, Alec Setnor |
| author2 | Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences |
| author_facet | Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences Marquis, Jared W. Campbell, James R. Cummings, James A. Westphal, Douglas L. Smith, Nathaniel J. Zhang, Jianglong Bogdanoff, Alec Setnor |
| author_sort | Marquis, Jared W. |
| collection | MIT |
| description | Passive longwave infrared radiometric satellite–based retrievals of sea surface temperature (SST) at instrument nadir are investigated for cold bias caused by unscreened optically thin cirrus (OTC) clouds [cloud optical depth (COD) ≤ 0.3]. Level 2 nonlinear SST (NLSST) retrievals over tropical oceans (30°S–30°N) from Moderate Resolution Imaging Spectroradiometer (MODIS) radiances collected aboard the NASA Aqua satellite (Aqua-MODIS) are collocated with cloud profiles from the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. OTC clouds are present in approximately 25% of tropical quality-assured (QA) Aqua-MODIS Level 2 data, representing over 99% of all contaminating cirrus found. Cold-biased NLSST (MODIS, AVHRR, and VIIRS) and triple-window (AVHRR and VIIRS only) SST retrievals are modeled based on operational algorithms using radiative transfer model simulations conducted with a hypothetical 1.5-km-thick OTC cloud placed incrementally from 10.0 to 18.0 km above mean sea level for cloud optical depths between 0.0 and 0.3. Corresponding cold bias estimates for each sensor are estimated using relative Aqua-MODIS cloud contamination frequencies as a function of cloud-top height and COD (assuming they are consistent across each platform) integrated within each corresponding modeled cold bias matrix. NLSST relative OTC cold biases, for any single observation, range from 0.33° to 0.55°C for the three sensors, with an absolute (bulk mean) bias between 0.09° and 0.14°C. Triple-window retrievals are more resilient, ranging from 0.08° to 0.14°C relative and from 0.02° to 0.04°C absolute. Cold biases are constant across the Pacific and Indian Oceans. Absolute bias is lower over the Atlantic but relative bias is higher, indicating that this issue persists globally. |
| first_indexed | 2024-09-23T16:59:36Z |
| format | Article |
| id | mit-1721.1/111125 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T16:59:36Z |
| publishDate | 2017 |
| publisher | American Meteorological Society |
| record_format | dspace |
| spelling | mit-1721.1/1111252022-10-03T09:40:21Z Estimating Infrared Radiometric Satellite Sea Surface Temperature Retrieval Cold Biases in the Tropics due to Unscreened Optically Thin Cirrus Clouds Marquis, Jared W. Campbell, James R. Cummings, James A. Westphal, Douglas L. Smith, Nathaniel J. Zhang, Jianglong Bogdanoff, Alec Setnor Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences Woods Hole Oceanographic Institution Bogdanoff, Alec Setnor Passive longwave infrared radiometric satellite–based retrievals of sea surface temperature (SST) at instrument nadir are investigated for cold bias caused by unscreened optically thin cirrus (OTC) clouds [cloud optical depth (COD) ≤ 0.3]. Level 2 nonlinear SST (NLSST) retrievals over tropical oceans (30°S–30°N) from Moderate Resolution Imaging Spectroradiometer (MODIS) radiances collected aboard the NASA Aqua satellite (Aqua-MODIS) are collocated with cloud profiles from the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. OTC clouds are present in approximately 25% of tropical quality-assured (QA) Aqua-MODIS Level 2 data, representing over 99% of all contaminating cirrus found. Cold-biased NLSST (MODIS, AVHRR, and VIIRS) and triple-window (AVHRR and VIIRS only) SST retrievals are modeled based on operational algorithms using radiative transfer model simulations conducted with a hypothetical 1.5-km-thick OTC cloud placed incrementally from 10.0 to 18.0 km above mean sea level for cloud optical depths between 0.0 and 0.3. Corresponding cold bias estimates for each sensor are estimated using relative Aqua-MODIS cloud contamination frequencies as a function of cloud-top height and COD (assuming they are consistent across each platform) integrated within each corresponding modeled cold bias matrix. NLSST relative OTC cold biases, for any single observation, range from 0.33° to 0.55°C for the three sensors, with an absolute (bulk mean) bias between 0.09° and 0.14°C. Triple-window retrievals are more resilient, ranging from 0.08° to 0.14°C relative and from 0.02° to 0.04°C absolute. Cold biases are constant across the Pacific and Indian Oceans. Absolute bias is lower over the Atlantic but relative bias is higher, indicating that this issue persists globally. 2017-09-05T17:36:46Z 2017-09-05T17:36:46Z 2017-09-05 Article http://purl.org/eprint/type/JournalArticle 0739-0572 1520-0426 http://hdl.handle.net/1721.1/111125 Marquis, Jared W. et al. “Estimating Infrared Radiometric Satellite Sea Surface Temperature Retrieval Cold Biases in the Tropics Due to Unscreened Optically Thin Cirrus Clouds.” Journal of Atmospheric and Oceanic Technology 34, 2 (February 2017): 355–373 © 2017 American Meteorological Society https://orcid.org/0000-0002-0467-3785 en_US http://dx.doi.org/10.1175/JTECH-D-15-0226.1 Journal of Atmospheric and Oceanic Technology Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf American Meteorological Society American Meteorological Society |
| spellingShingle | Marquis, Jared W. Campbell, James R. Cummings, James A. Westphal, Douglas L. Smith, Nathaniel J. Zhang, Jianglong Bogdanoff, Alec Setnor Estimating Infrared Radiometric Satellite Sea Surface Temperature Retrieval Cold Biases in the Tropics due to Unscreened Optically Thin Cirrus Clouds |
| title | Estimating Infrared Radiometric Satellite Sea Surface Temperature Retrieval Cold Biases in the Tropics due to Unscreened Optically Thin Cirrus Clouds |
| title_full | Estimating Infrared Radiometric Satellite Sea Surface Temperature Retrieval Cold Biases in the Tropics due to Unscreened Optically Thin Cirrus Clouds |
| title_fullStr | Estimating Infrared Radiometric Satellite Sea Surface Temperature Retrieval Cold Biases in the Tropics due to Unscreened Optically Thin Cirrus Clouds |
| title_full_unstemmed | Estimating Infrared Radiometric Satellite Sea Surface Temperature Retrieval Cold Biases in the Tropics due to Unscreened Optically Thin Cirrus Clouds |
| title_short | Estimating Infrared Radiometric Satellite Sea Surface Temperature Retrieval Cold Biases in the Tropics due to Unscreened Optically Thin Cirrus Clouds |
| title_sort | estimating infrared radiometric satellite sea surface temperature retrieval cold biases in the tropics due to unscreened optically thin cirrus clouds |
| url | http://hdl.handle.net/1721.1/111125 https://orcid.org/0000-0002-0467-3785 |
| work_keys_str_mv | AT marquisjaredw estimatinginfraredradiometricsatelliteseasurfacetemperatureretrievalcoldbiasesinthetropicsduetounscreenedopticallythincirrusclouds AT campbelljamesr estimatinginfraredradiometricsatelliteseasurfacetemperatureretrievalcoldbiasesinthetropicsduetounscreenedopticallythincirrusclouds AT cummingsjamesa estimatinginfraredradiometricsatelliteseasurfacetemperatureretrievalcoldbiasesinthetropicsduetounscreenedopticallythincirrusclouds AT westphaldouglasl estimatinginfraredradiometricsatelliteseasurfacetemperatureretrievalcoldbiasesinthetropicsduetounscreenedopticallythincirrusclouds AT smithnathanielj estimatinginfraredradiometricsatelliteseasurfacetemperatureretrievalcoldbiasesinthetropicsduetounscreenedopticallythincirrusclouds AT zhangjianglong estimatinginfraredradiometricsatelliteseasurfacetemperatureretrievalcoldbiasesinthetropicsduetounscreenedopticallythincirrusclouds AT bogdanoffalecsetnor estimatinginfraredradiometricsatelliteseasurfacetemperatureretrievalcoldbiasesinthetropicsduetounscreenedopticallythincirrusclouds |