i
Preliminary Inter-Comparison between AHI, VIIRS and MODIS Clear-Sky Ocean Radiances for Accurate SST Retrievals
-
2016
Source: Remote Sens. 8(3), 1-13
[PDF-3.43 MB]
Details:
-
Journal Title:Remote Sensing
-
Personal Author:
-
NOAA Program & Office:NESDIS (National Environmental Satellite, Data, and Information Service) ; GOES-R (Geostationary Operation Environmental Satellite-R Series) ; JPSS (Joint Polar Satellite System Program Office) ; STAR (Center for Satellite Applications and Research) ; CIRA (Cooperative Institute for Research in the Atmosphere)NESDIS (National Environmental Satellite, Data, and Information Service) ; GOES-R (Geostationary Operation Environmental Satellite-R Series) ; JPSS (Joint Polar Satellite System Program Office) ; STAR (Center for Satellite Applications and Research) ; CIRA (Cooperative Institute for Research in the Atmosphere) Less -
-
Description:Clear-sky brightness temperatures (BT) in five bands of the Advanced Himawari Imager (AHI; flown onboard Himawari-8 satellite) centered at 3.9, 8.6, 10.4, 11.2, and 12.3 µm (denoted by IR37, IR86, IR10, IR11, and IR12, respectively) are used in the NOAA Advanced Clear-Sky Processor for Oceans (ACSPO) sea surface temperature (SST) retrieval system. Here, AHI BTs are preliminarily evaluated for stability and consistency with the corresponding VIIRS and MODIS BTs, using the sensor observation minus model simulation (O-M) biases and corresponding double differences. The objective is to ensure accurate and consistent SST products from the polar and geo sensors, and to prepare for the launch of the GOES-R satellite in 2016. All five AHI SST bands are found to be largely in-family with their polar counterparts, but biased low relative to the VIIRS and MODIS (which, in turn, were found to be stable and consistent, except for Terra IR86, which is biased high by 1.5 K). The negative biases are larger in IR37 and IR12 (up to ~−0.5 K), followed by the three remaining longwave IR bands IR86, IR10, and IR11 (from −0.3 to −0.4 K). These negative biases may be in part due to the uncertainties in AHI calibration and characterization, although uncertainties in the coefficients of the Community Radiative Transfer Model (CRTM, used to generate the “M” term) may also contribute. Work is underway to add AHI analyses in the NOAA Monitoring of IR Clear-Sky Radiances over Oceans for SST (MICROS) system and improve AHI BTs by collaborating with the sensor calibration and CRTM teams. The Advanced Baseline Imager (ABI) analyses will be also added in MICROS when GOES-R is launched in late 2016 and the ABI IR data become available.
-
Source:Remote Sens. 8(3), 1-13
-
Document Type:
-
Rights Information:CC BY
-
Compliance:Library
-
Main Document Checksum:urn:sha256:a9e86edf4daaeb37a507f349d225eb468705804e1d9843bd6947275269083782
-
File Type:
Supporting Files
-
No Additional Files
More +
Related Documents
-
Personal Author:Becker, Elizabeth ;Forney, Karin...2016 | Remote Sens. 8(2), 1-26Description:Species distribution models are now widely used in conservation and management to predict suitable habitat for protected marine species. The primary s...2016 | Remote Sens. 8(1), 1-20Description:Moderate Resolution Imaging Spectroradiometers (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) radiometers, flown onboard Terra/Aqua and...Personal Author:Obata, Kenta ;Miura, Tomoaki...2016 | Remote Sens. 8(1), 1-17Description:In this study, the Visible Infrared Imaging Radiometer Suite (VIIRS) Enhanced Vegetation Index (EVI) was spectrally cross-calibrated with the Moderate...Personal Author:Heidinger, Andrew ;Foster, Michael...2016 | Remote Sens. 8(6), 1-18Description:An important component of the AVHRR PATMOS-x climate date record (CDR)—or any satellite cloud climatology—is the performance of its cloud detectio...Personal Author:Claverie, Martin ;Matthews, Jessica...2016 | Remote Sens. 8(3), 1-12Description:In-land surface models, which are used to evaluate the role of vegetation in the context of global climate change and variability, LAI and FAPAR play ...Personal Author:Ignatov, Alexander ;Zhou, Xinjia...2016 | Remote Sens. 8(4), 1-17Description:In response to its users’ needs, the National Oceanic and Atmospheric Administration (NOAA) initiated reanalysis (RAN) of the Advanced Very High Res...Algorithm Development of Temperature and Humidity Profile Retrievals for Long-Term HIRS ObservationsCitePersonal Author:Shi, Lei ;Matthews, Jessica...2016 | Remote Sens. 8(4), 1-17Description:A project for deriving temperature and humidity profiles from High-resolution Infrared Radiation Sounder (HIRS) observations is underway to build a lo...2016 | Remote Sens. 8(9), 1-17Description:In this study, six Arctic sea ice thickness products are compared: the AVHRR Polar Pathfinder-extended (APP-x), ICESat, CryoSat-2, SMOS, NASA IceBridg...Personal Author:Adler, Robert ;Sapiano, Mathew...2018 | Atmos., 9(4), 1-14Description:The new Version 2.3 of the Global Precipitation Climatology Project (GPCP) Monthly analysis is described in terms of changes made to improve the homog...Sensor Stability for SST (3S): Toward Improved Long-Term Characterization of AVHRR Thermal BandsCitePersonal Author:He, Kai ;Ignatov, Alexander...2016 | Remote Sens. 8(4), 1-21Description:Recently, the National Oceanic and Atmospheric Administration (NOAA) performed sea surface temperature (SST) reanalysis (RAN1) from seven AVHRR/3s onb...Personal Author:Koner, Prabhat K. ;Harris, Andy2016 | Remote Sens. 8(9), 1-17Description:Global sea-surface temperatures (SST) from MODIS measured brightness temperatures generated using the regression methods, have been available to users...Personal Author:O’Donnell, John P. R. ;Schalles, John F.2016 | Remote Sens. 8(6), 1-22Description:We examined the influence of abiotic drivers on inter-annual and phenological patterns of aboveground biomass for Marsh Cordgrass, Spartina alterniflo...Personal Author:Heron, Scott ;Johnston, Lyza...2016 | Remote Sens. 8(1), 1-16Description:Satellite monitoring of thermal stress on coral reefs has become an essential component of reef management practice around the world. A recent develop...Personal Author:Caldwell, Jamie ;Heron, Scott...2016 | Remote Sens. 8(2), 1-15Description:Predicting wildlife disease risk is essential for effective monitoring and management, especially for geographically expansive ecosystems such as cora...
More +
You May Also Like
Personal Author:
Wang, Zhipeng ;
Iturbide-Sanchez, Flavio
...
2022 | Remote Sens. 2022, 14(4), 876
Description:
Radiometric intercomparison between satellite remote sensing instruments has become an increasingly common practice to monitor the stability and even ...
2016 | Remote Sens. 8(1), 1-20
Description:
Moderate Resolution Imaging Spectroradiometers (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) radiometers, flown onboard Terra/Aqua and...
Checkout today's featured content at repository.library.noaa.gov