[ACG53-04] An Enterprise Land Surface Temperature (LST) Algorithm for the GOES-R and
Himawari Satellites
キーワード：Land Surface Temperature, Geostationary Satellites, Algorithm
As one of the essential climate variables defined by the Global Climate Observing System of the
World Meteorological Organization, land surface temperature (LST) is a fundamental parameter
in the physics of land surface processes from regional to global scales. LST products have been
widely applied in and closely linked to radiative, latent and sensible heat fluxes at the surfaceatmosphere
interface. Satellite platforms provide an excellent opportunity of measuring LSTs continuously at such scales. Among satellites with different types of orbits, geostationary satellites uniquely provide LST measurements with very high temporal resolution, which are critical for many LST applications.
An enterprise LST algorithm has been developed and applied to the U.S. Geostationary
Environmental Observation Satellite (GOES) R series (GOES-R) at the U.S. National
Environmental Satellite Data Information Service (NESDIS), which currently consists of GOES-
16 and GOES-17. The algorithm is based on a traditional linear regression split-window LST
retrieval technique in the thermal infrared spectrum, with a mitigation package to reduce risks
from abnormal Focal Plane Module (FPM) performance found on the Advanced Baseline Imager
(ABI) onboard GOES-17. The enterprise algorithm was evaluated using in-situ measurements
and compared with the baseline algorithm that has been used for operational GOES-16 LST
production since it was launched. Polar-orbiting satellite data such as JPSS LST products have
also been used for the comparison analyses. Furthermore, the algorithm has been applied to
Advanced Himawari Imager (AHI) data to extend the spatial coverage of the geostationary
satellite LST product, since the AHI sensor is mostly similar to the ABI sensor. This presentation
will provide scientific details of the enterprise LST algorithm and its performance on GOES-16
and -17 satellite data, as well as some samples based on Himawari satellite data.
World Meteorological Organization, land surface temperature (LST) is a fundamental parameter
in the physics of land surface processes from regional to global scales. LST products have been
widely applied in and closely linked to radiative, latent and sensible heat fluxes at the surfaceatmosphere
interface. Satellite platforms provide an excellent opportunity of measuring LSTs continuously at such scales. Among satellites with different types of orbits, geostationary satellites uniquely provide LST measurements with very high temporal resolution, which are critical for many LST applications.
An enterprise LST algorithm has been developed and applied to the U.S. Geostationary
Environmental Observation Satellite (GOES) R series (GOES-R) at the U.S. National
Environmental Satellite Data Information Service (NESDIS), which currently consists of GOES-
16 and GOES-17. The algorithm is based on a traditional linear regression split-window LST
retrieval technique in the thermal infrared spectrum, with a mitigation package to reduce risks
from abnormal Focal Plane Module (FPM) performance found on the Advanced Baseline Imager
(ABI) onboard GOES-17. The enterprise algorithm was evaluated using in-situ measurements
and compared with the baseline algorithm that has been used for operational GOES-16 LST
production since it was launched. Polar-orbiting satellite data such as JPSS LST products have
also been used for the comparison analyses. Furthermore, the algorithm has been applied to
Advanced Himawari Imager (AHI) data to extend the spatial coverage of the geostationary
satellite LST product, since the AHI sensor is mostly similar to the ABI sensor. This presentation
will provide scientific details of the enterprise LST algorithm and its performance on GOES-16
and -17 satellite data, as well as some samples based on Himawari satellite data.