The Advanced Himawari Imager (AHI) on-board Himawari-8, which was launched on 7 October 2014, is the first geostationary instrument housed with a solar diffuser to provide accurate onboard calibrated data for the visible and near-infrared (VNIR) bands. In this study, the Ray-matching and collocated Deep Convective Cloud (DCC) methods, both of which are based on incidently collocated homogeneous pairs between AHI and Suomi NPP (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS), are used to evaluate the calibration difference between these two instruments. While the Ray-matching method is used to examine the reflectance difference over the all-sky collocations with similar viewing and illumination geometries, the near lambertian collocated DCC pxiels are used to examine the difference for the median or high reflectance scenes. Strong linear relationships between AHI and VIIRS can be found at all the paired AHI and VIIRS bands. Results of both methods indicate that AHI radiometric calibration accuracy agrees well with VIIRS data within 5% for B1-4 and B6 at mid and high reflectance scenes, while AHI B5 is generally brighter than VIIRS by ~6%–8%. No apparent East-West viewing angle dependent calibration difference can be found at all the VNIR bands. Compared to the Ray-matching method, the collocated DCC method provides less uncertainty of inter-calibration results at near-infrared (NIR) bands. As AHI has similar optics and calibration designs to the GOES-R Advanced Baseline Imager (ABI), which is currently scheduled to launch in fall 2016, the on-orbit AHI data provides a unique opportunity to develop, test and examine the cal/val tools developed for ABI.

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.

Desert-based vicarious calibration plays an important role in generating long-term reliable satellite radiances for the visible and near-infrared channels of the Advanced Very High Resolution Radiometer (AVHRR). Lacking an onboard calibration device, the AVHRR relies on reflected radiances from a target site, for example, a large desert, to calibrate its solar reflective channels. While the radiometric characteristics of the desert may be assumed to be stable, the reflected radiances from the target can occasionally be affected by the presence of clouds, sand storms, vegetation, and wet surfaces. These contaminated pixels must be properly identified and removed to ensure calibration performance. This paper describes an algorithm for removing the contaminated pixels from AVHRR measurements taken over the Libyan Desert based on the characteristics of consistent normalized difference vegetation index (NDVI) land-cover stratification. An NDVI histogram-determined threshold is first applied to screen pixels contaminated with vegetation in each individual AVHRR observation. The resulting analyses show that the vegetation growth inside the desert target has a negligibly small impact on the AVHRR operational calibration results. Two criteria based on the maximum NDVI compositing technique are then employed to remove pixels contaminated with clouds, severe sand storms, and wet sand surfaces. Compared to other cloud-screening methods, this algorithm is capable of not only identifying high-reflectance clouds, but also removing the low reflectance of wet surfaces and the nearly indifferent reflectance of severe dust storms. The use of clear pixels appears to improve AVHRR calibration accuracy in the first 3–4 yr after satellite launch.

The Geostationary Operational Environmental Satellites (GOES) have been observing the Western Hemisphere since the late 1970s, providing valuable information for weather forecast and climate change studies. Due to the lack of an onboard calibration device for the visible channel, accurate reflectance of the visible channel data depends on vicarious calibration methods to provide postlaunch calibration coefficients to compensate for the degraded responsivity. In this study, the Sonoran Desert, which can be viewed by both GOES‐East and GOES‐West satellites, is used to intercalibrate the visible channels on board the three‐axis stabilized GOES satellite Imagers traceable to the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 (C6) calibration standard. It was found that when the anomalous reflectance in 2004 and 2005 are excluded, the Sonoran Desert is radiometrically, spatially, and spectrally stable at the GOES viewing geometries and thus can be considered as a pseudo‐invariant calibration site to develop long‐term GOES Imager visible data set. To characterize the desert target reflectance with the MODIS data, GOES observations over 1 year period are used to convert the MODIS reflectance to the GOES viewing and solar illumination geometries. The spectral band adjustment factor for each GOES Imager visible channel is generated with a set of clear‐sky Hyperion measurements. A trending algorithm, which consists of a polynomial function for the description of instrument degradation performance and two sine terms for the impacts of the seasonal variations of the solar zenith angle and atmospheric components, is applied to fit the time series of prelaunch calibrated reflectance. The combined calibration uncertainty of the desert calibration method is less than 4% at the Aqua MODIS C6 calibration standard. The difference of the postlaunch calibration coefficients between the desert calibration and the current GOES visible operational calibration methods is mainly within 5%.

The Advanced Very High Resolution Radiometers (AVHRRs) flown on the National Oceanic and Atmospheric Administration (NOAA) and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) polar satellites have been providing invaluable data for Earth system science and global change studies. However, the use of AVHRR solar reflectance products (e.g., normalized difference vegetation index (NDVI)) is constrained by calibration uncertainty, largely owing to the lack of an on-orbit calibration device for solar reflectance channels. Since the mid-1990s, NOAA has been applying operational calibration to these channels using the time series of top-of-atmosphere (TOA) measurements of the Libyan Desert site. However, the assumed radiometric stability of Libyan Desert TOA measurements can be disrupted by short-term variations in atmospheric components, especially for AVHRR channel 2, which has a spectral response function covering water vapor absorption lines in the near-infrared (NIR) spectra. This study aims to improve the calibration accuracy of AVHRR channel 2 data by applying water vapor correction over the homogeneous desert target surface using the water vapor content (W) derived from the linear relationships between the AVHRR split-window temperature difference (ΔT) and the Moderate Resolution Imaging Spectroradiometer (MODIS) NIR water vapor product. Results show that the linear ΔT–W relationship is affected by the temperature lapse rate at the satellite over-passing time and the spectral response functions of AVHRR split-window channels. Water vapor correction reduces the calibration uncertainty from 2.6%–3.5% to 1.7%–1.8%, and about 1.0% surface-based relative calibration accuracy is independent of the water vapor uncertainty introduced by the ΔT–W regression. Furthermore, the short-term variations in the channel 2 operational degradation rate are largely reduced after water vapor correction. Thus, implementing water vapor correction can improve the operational calibration accuracy for AVHRR channel 2.