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The Edition 2 (Ed2) cloud property retrieval algorithm system was upgraded and applied to the MODerate-resolution Imaging Spectroradiometer (MODIS) data for the Clouds and the Earth’s Radiant Energy System (CERES) Edition 4 (Ed4) products. New calibrations for solar channels and the use of the 1.24- $\mu \text{m}$ channel for cloud optical depth (COD) over snow improve the daytime consistency between Terra and Aqua MODIS retrievals. Use of additional spectral channels and revised logic enhanced the cloud-top phase retrieval accuracy. A new ice crystal reflectance model and a CO2-channel algorithm retrieved higher ice clouds, while a new regional lapse rate technique produced more accurate water cloud heights than in Ed2. Ice cloud base heights are more accurate due to a new cloud thickness parameterization. Overall, CODs increased, especially over the polar (PO) regions. The mean particle sizes increased slightly for water clouds, but more so for ice clouds in the PO areas. New experimental parameters introduced in Ed4 are limited in utility, but will be revised for the next CERES edition. As part of the Ed4 retrieval evaluation, the average properties are compared with those from other algorithms and the differences between individual reference data and matched Ed4 retrievals are explored. Part II of this article provides a comprehensive, objective evaluation of selected parameters. More accurate interpretation of the CERES radiation measurements has resulted from the use of the Ed4 cloud properties.

The Moderate Resolution Imaging Spectroradiometer (MODIS) instruments onboard the Aqua and Terra satellites have been operated for nearly two decades, producing high-quality earth observation data sets suitable for a broad range of scientific studies regarding the earth’s land, ocean, and atmospheric processes. The high radiometric accuracy of MODIS reflective solar band (RSB) calibration has also served as benchmark measurements for on-orbit cross-calibration studies. As the two MODIS instruments have operated well beyond their design lifespan of six years, the measurements from the onboard calibrators alone become inadequate to characterize the sensor’s response at all scan angles, as evinced by long-term drifts observed at certain scan positions of the Aqua-MODIS 0.64- and 0.86- $\mu \text{m}$ bands in Collection 6 (C6) data set. The latest MODIS Level 1B C6.1 data set incorporates earth-view response trending from invariant desert sites as supplemental inputs to characterize the scan-angle calibration dependencies for all RSB. This article presents a deep convective cloud (DCC)-based calibration approach for an independent evaluation of the MODIS RSB response versus scan-angle (RVS) performance in C6.1. The long-term calibration stability and RVS differences in C6.1 have been significantly improved for Aqua-MODIS RSB. The observed RVS differences of more than 2% in Aqua-MODIS C6 bands 1 and 2 have been reduced to within 1% in C6.1. Some RSBs of Terra-MODIS have suffered temporal drifts up to ~2% and calibration shifts up to 3%, particularly around 2016 when the Terra satellite entered into safe mode. The DCC approach has been found very effective in tracking the on-orbit RVS changes over time.

The NASA Clouds and the Earth’s Radiant Energy System (CERES) project relies on top-of-atmosphere (TOA) broadband fluxes derived from geostationary (GEO) satellite imagery to account for the diurnal flux variations between the CERES observation intervals, and thereby produce a synoptic gridded (SYN1deg) product based on continuous temporal observations. Consistent broadband flux derivation depends on accurate radiative property measurements and cloud retrievals, which largely determine the radiance-to-flux conversion process. Therefore, it is important to ensure a high quality of cloud property input in order to maintain a reliable broadband flux record. In Edition 4 of the CERES SYN1deg product, a robust automated image anomaly detection algorithm based on inter-line and inter-pixel differences, spatial variance, and 2-D Fourier analysis has been successful in identifying imagery with linear artifacts, but the line-by-line inspection and cleaning process must still be performed by a human. Therefore, further automation of this quality assurance process is warranted, especially considering the excessive amount of additional cleaning necessitated by the GOES-17 Advance Baseline Imager (ABI) cooling system anomaly. As such, this article highlights advancement of the CERES GEO image artifact cleaning approach based on a convolutional neural network (CNN) for classification of bad scanlines. Once trained, the CNN approach is a computationally inexpensive means to ensure greater consistency in cloud retrievals, and therefore broadband flux derivation, based on GOES-17 measurements.

In satellite-based remote sensing applications, the conversion of the sensor recorded top-of-atmosphere reflectance to radiance, or vice-versa, is carried out using a reference spectral solar irradiance (SSI) dataset. The choice of reference SSI spectrum has consistently changed over the past four decades with the increasing availability of more accurate SSI measurements with greater spectral coverage. Considerable differences (up to 15% at certain wavelengths) exist between the numerous SSI spectra that are currently being used in satellite ground processing systems. The aim of this study is to quantify the absolute differences between the most commonly used SSI datasets and investigate their impact in satellite inter-calibration and environmental retrievals. It was noted that if analogous SNPP and NOAA-20 VIIRS channel reflectances were perfectly inter-calibrated, the derived channel radiances can still differ by up to 3% due to the utilization of differing SSI datasets by the two VIIRS instruments. This paper also highlights a TSIS-1 SIM-based Hybrid Solar Reference Spectrum (HSRS) with an unprecedented absolute accuracy of 0.3% between 460 and 2365 nm, and recommends that the remote sensing community use it as a common reference SSI in satellite retrievals.

Satellites routinely observe deep convective clouds across the world. The cirrus outflow from deep convection, commonly referred to as anvil cloud, has a ubiquitous appearance in visible and infrared (IR) wavelength imagery. Anvil clouds appear as broad areas of highly reflective and cold pixels relative to the darker and warmer clear sky background, often with embedded textured and colder pixels that indicate updrafts and gravity waves. These characteristics would suggest that creating automated anvil cloud detection products useful for weather forecasting and research should be straightforward, yet in practice such product development can be challenging. Some anvil detection methods have used reflectance or temperature thresholding, but anvil reflectance varies significantly throughout a day as a function of combined solar illumination and satellite viewing geometry, and anvil cloud top temperature varies as a function of convective equilibrium level and tropopause height. This paper highlights a technique for facilitating anvil cloud detection based on visible observations that relies on comparative analysis with expected cloud reflectance for a given set of angles, thereby addressing limitations of previous methods. A one-year database of anvil-identified pixels, as determined from IR observations, from several geostationary satellites was used to construct a bi-directional reflectance distribution function (BRDF) model to quantify typical anvil reflectance across almost all expected viewing, solar, and azimuth angle configurations, in addition to the reflectance uncertainty for each angular bin. Application of the BRDF model for cloud optical depth retrieval in deep convection is described as well.

Previous research has revealed inconsistencies between the Collection 5 (C5) calibrations of certain channels common to the Terra and Aqua MODerate-resolution Imaging Spectroradiometers (MODIS). To achieve consistency between the Terra and Aqua MODIS radiances used in the Clouds and the Earth’s Radiant Energy System (CERES) Edition 4 (Ed4) cloud property retrieval system, adjustments were developed and applied to the Terra C5 calibrations for channels 1–5, 7, 20, and 26. These calibration corrections were developed independently of those used for MODIS Collection 6 (C6) data, which became available after the CERES Ed4 processing had commenced. The comparisons demonstrate that the corrections applied to the Terra C5 data for CERES Edition 4 generally resulted in Terra-Aqua radiance consistency that is as good as or better than that of the C6 datasets. The C5 adjustments resulted in more consistent Aqua and Terra cloud property retrievals than seen in the previous CERES edition. Other calibration artifacts were found in one of the corrected channels and in some of the uncorrected thermal channels after Ed4 began. Where corrections were neither developed nor applied, some artifacts are likely to have been introduced into the Ed4 cloud property record. For example, the degradation in the Aqua MODIS 0.65-μm channel in both the C5 and C6 datasets affects trends in cloud optical depth retrievals. Thus, despite the much-improved consistency achieved for the Terra and Aqua datasets in Ed4, the CERES Ed4 cloud property datasets should be used cautiously for cloud trend studies because of those remaining calibration artifacts.

Intense tropopause-penetrating updrafts and gravity wave breaking generate cirrus plumes that reside above the primary anvil. These “above anvil cirrus plumes” (AACPs) exhibit unique temperature and reflectance patterns in satellite imagery, best recognized within 1-min “super rapid scan” observations. AACPs are often evident during severe weather outbreaks and, due to their importance, have been studied for 35+ years. Despite this research, there is uncertainty regarding why some storms produce AACPs but other nearby storms do not, exactly how severe are storms with AACPs, and how AACP identification can assist with severe weather warning. These uncertainties are addressed through analysis of severe weather reports, NOAA/National Weather Service (NWS) severe weather warnings, metrics of updraft cloud height, intensity, and rotation derived from Doppler radars, as well as ground-based total lightning observations for 4583 storms observed by GOES super rapid scanning, 405 of which produced an AACP. Datasets are accumulated throughout storm lifetimes through radar object tracking. It is found that 1) AACP storms generated 14 times the number of reports per storm compared to non-AACP storms; 2) AACPs appeared, on average, 31 min in advance of severe weather; 3) 73% of significant severe weather reports were produced by AACP storms; 4) AACP recognition can provide comparable warning lead time to that provided by a forecaster; and 5) the presence of an AACP can increase forecaster confidence that large hail will occur. Given that AACPs occur throughout the world, and most of the world is not observed by Doppler radar, AACP-based severe storm identification and warning would be extremely helpful for protecting lives and property.

Surface skin temperature (Ts) is an important parameter for characterizing the energy exchange at the ground/water–atmosphere interface. The Satellite ClOud and Radiation Property retrieval System (SatCORPS) employs a single-channel thermal-infrared (TIR) method to retrieve Ts over clear-sky land and ocean surfaces from data taken by geostationary Earth orbit (GEO) and low Earth orbit (LEO) satellite imagers. GEO satellites can provide somewhat continuous estimates of Ts over the diurnal cycle in non-polar regions, while polar Ts retrievals from LEO imagers, such as the Advanced Very High Resolution Radiometer (AVHRR), can complement the GEO measurements. The combined global coverage of remotely sensed Ts, along with accompanying cloud and surface radiation parameters, produced in near-realtime and from historical satellite data, should be beneficial for both weather and climate applications. For example, near-realtime hourly Ts observations can be assimilated in high-temporal-resolution numerical weather prediction models and historical observations can be used for validation or assimilation of climate models. Key drawbacks to the utility of TIR-derived Ts data include the limitation to clear-sky conditions, the reliance on a particular set of analyses/reanalyses necessary for atmospheric corrections, and the dependence on viewing and illumination angles. Therefore, Ts validation with established references is essential, as is proper evaluation of Ts sensitivity to atmospheric correction source.This article presents improvements on the NASA Langley GEO satellite and AVHRR TIR-based Ts product that is derived using a single-channel technique. The resulting clear-sky skin temperature values are validated with surface references and independent satellite products. Furthermore, an empirically adjusted theoretical model of satellite land surface temperature (LST) angular anisotropy is tested to improve satellite LST retrievals. Application of the anisotropic correction yields reduced mean bias and improved precision of GOES-13 LST relative to independent Moderate-resolution Imaging Spectroradiometer (MYD11_L2) LST and Atmospheric Radiation Measurement Program ground station measurements. It also significantly reduces inter-satellite differences between LSTs retrieved simultaneously from two different imagers. The implementation of these universal corrections into the SatCORPS product can yield significant improvement in near-global-scale, near-realtime, satellite-based LST measurements. The immediate availability and broad coverage of these skin temperature observations should prove valuable to modelers and climate researchers looking for improved forecasts and better understanding of the global climate model.

The Clouds and the Earth’s Radiant Energy System (CERES) project relies on geostationary imager–derived TOA broadband fluxes and cloud properties to account for the regional diurnal fluctuations between the Terra and Aqua CERES and MODIS measurements. The CERES project employs a ray-matching calibration algorithm in order to transfer the Aqua MODIS calibration to the geostationary (GEO) imagers, thereby allowing the derivation of consistent fluxes and cloud retrievals across the 16 GEO imagers utilized in the CERES record. The CERES Edition 4 processing scheme grants the opportunity to recalibrate the GEO record using an improved GEO/MODIS all-sky ocean ray-matching algorithm. Using a graduated angle matching method, which is most restrictive for anisotropic clear-sky ocean radiances and least restrictive for isotropic bright cloud radiances, reduces the bidirectional bias while preserving the dynamic range. Furthermore, SCIAMACHY hyperspectral radiances are used to account for both the solar incoming and Earth-reflected spectra in order to correct spectral band differences. As a result, the difference between the linear regression offset and the maintained GEO space count was reduced, and the calibration slopes computed from the linear fit and the regression through the space count agreed to within 0.4%. A deep convective cloud (DCC) ray-matching algorithm is also presented. The all-sky ocean and DCC ray-matching timeline gains are within 0.7% of one another. Because DCC are isotropic and the brightest, Earth targets with near-uniform visible spectra, the temporal standard error of GEO imager gains, are reduced by up to 60% from that of all-sky ocean targets.

The Earth-viewed images acquired by the space probe OSIRIS-REx during its Earth gravity assist flyby maneuver on 22 September 2017 provided an opportunity to radiometrically calibrate the onboard NavCam imagers. Spatially-, temporally-, and angularly-matched radiances from the Earth viewing GOES-15 and DSCOVR-EPIC imagers were used as references for deriving the calibration gain of the NavCam sensors. An optimized all-sky tropical ocean ray-matching (ATO-RM) calibration approach that accounts for the spectral band differences, navigation errors, and angular geometry differences between NavCam and the reference imagers is formulated in this paper. Prior to ray-matching, the GOES-15 and EPIC pixel level radiances were mapped into the NavCam field of view. The NavCam 1 ATO-RM gain is found to be 9.874 &times, 10&minus, 2 Wm&minus, 2sr&minus, 1&micro, m&minus, 1DN&minus, 1 with an uncertainty of 3.7%. The ATO-RM approach predicted an offset of 164, which is close to the true space DN of 170. The pre-launch NavCam 1 and 2 gains were compared with the ATO-RM gain and were found to be within 2.1% and 2.8%, respectively, suggesting that sensor performance is stable in space. The ATO-RM calibration was found to be consistent within 3.9% over a factor of &plusmn, 2 NavCam 2 exposure times. This approach can easily be adapted to inter-calibrate other space probe cameras given the current constellation of geostationary imagers.

The Clouds and the Earth's Radiant Energy System (CERES) project provides observed flux and cloud products for the climate community. To accomplish this goal, the CERES project must merge six CERES instruments, along with two MODIS sensors, two VIIRS sensors, and twenty GEO imagers in order to produce a climate-quality dataset. The input satellite sensors must be properly calibrated and known sensor anomalies must be removed before operational processing. The GEO imager radiances are radiometrically scaled to the Aqua-MODIS C6.1 calibration reference using all-sky tropical ocean ray-matched (ATO-RM) coincident radiance pairs. However, monthly-ATO-RM calibration analysis is inadequate for detecting sensor L1B radiance anomalies, which may span only a few days. The CERES monthly-ATO-RM calibration method was modified to increase the number of ray-matched pairs in order to apply the ATO-RM calibration method on a daily basis. The goal of the daily-ATO-RM calibration method is to detect slight L1B radiance anomalies by limiting the daily gain noise over the record. To test the daily-ATO-RM calibration method, the GOES-16 record, with known L1B radiance anomalies, was evaluated. The GOES-16 channel 2 (0.65 µm) daily-ATORM gain standard error is 1% and thereby allows for confident identification of days with calibration anomalies greater 3%, or three standard deviations (σ). The daily-ATO-RM calibration method detected the three known L1B calibration anomalies, however, there were two daily gains that exceeded three σ that were not associated L1B anomalies. Similarly, the daily-ATO-RM was able to identify the calibration discontinuity of NOAA20-VIIRS in the Land SIPS L1B V001 processing

Tropical deep convective clouds (DCC) are proven to be an excellent Earth invariant target for post-launch radiometric assessment of satellite imagers. The success of the DCC technique (DCCT) relies on a large ensemble of identified DCC pixels that are collectively analyzed as a stable reflectance reference. The near-Lambertian reflectance of DCC, as well as their high signal-to-noise ratio, location near the tropopause above most of the atmospheric water vapor and aerosol, and availability across the globe make them an ideal target for radiometric scaling of geostationary (GEO) and low-earth orbiting (LEO) satellites. The DCCT has been successfully applied to calibrate reflective solar bands (with wavelengths < 1 µm) of numerous GEO and LEO imagers. The DCC reflectivity in VIS-NIR is mainly a function of cloud optical depth, and the DCCT provides a stable monthly statistical mode that can be tracked over time for monitoring the radiometric stability of the sensor. However, at shortwave infrared (SWIR) wavelengths, the DCC reflectance is affected by both cloud particle size and cloud optical depth. The DCCT for SWIR bands is found to be most sensitive to the BRDF and brightness temperature, resulting in large seasonal cycles of DCC reflectivity that make the implementation of the DCCT more challenging. The key to improving the DCCT at SWIR wavelengths is proper characterization of the DCC reflectance as a function of viewing and solar angular conditions. This paper presents channel-specific seasonal BRDF models for SWIR bands based on five years of SNPP-VIIRS DCC measurements. The seasonal BRDFs are effective in reducing the temporal variability of the DCC time series by up to 65% when applied to both Aqua-MODIS and NPP-VIIRS SWIR bands. The use of seasonal BRDF models for radiometric stability assessment and absolute inter-calibration of the MODIS and SNPPVIIRS SWIR bands is discussed in the paper. In addition, the modification of the baseline DCCT for daily monitoring of the radiometric stability of the GEO imager L1B radiances is also illustrated. The DCCT is capable of detecting daily GOeS-16 L1B radiance anomalies with a magnitude greater than ±3% for bands 2 and 3, and ±4% shift for band 1 with 3σ significance.

Earth-viewed images acquired during a recent asteroid-intercept mission present a unique opportunity for radiometric calibration of visible imagers onboard a space exploration probe. Measurements from the CERES-consistent DSCOVR-EPIC imager act as a reference in providing spatially, temporally, and angularly matched radiance values for deriving OSIRIS-REx-NavCam sensor calibration gains. The calibration is accomplished using an optimized all-sky tropical ocean ray-matching technique, which employs complex pixel remapping, navigation correction, and angular geometry consideration. Of critical consideration in this specific inter-calibration event is the extreme difference in spectral response function (SRF) width between the NavCam and EPIC imagers, which could cause a rather large bias. The NASA-LaRC SCIAMACHY-based online spectral band adjustment factor (SBAF) calculation tool provides an empirical solution to such potential spectral-difference-induced biases through a high-spectral-resolution hyperspectral convolution approach. The adjustments produced from this tool can effectively reduce the calibration gain bias of NavCam2 by nearly 6%, thereby adjusting the NavCam2 sensor to within 3.2% of its pre-launch calibration. These results highlight the capability of the SBAF tool to account for exceptionally disparate SRFs.

Consistent cross-sensor Advanced Very High Resolution Radiometer (AVHRR) calibration coefficients are determined using desert, polar ice, and deep convective cloud (DCC) invariant Earth targets. The greatest AVHRR calibration challenge is the slow orbit degradation of the host satellite, which precesses toward a terminator orbit. This issue is solved by characterizing the invariant targets with NOAA-16 AVHRR observed radiances that have been referenced to the Aqua Moderate Resolution Imaging Spectrometer (MODIS) calibration using simultaneous nadir overpass (SNO) observations. Another benefit of the NOAA-16 invariant target–modeled reflectance method is that, because of the similarities among the AVHRR spectral response functions, a smaller spectral band adjustment factor is required than when establishing calibrations relative to a non-AVHRR reference instrument. The sensor- and band-specific calibration uncertainties, with respect to the calibration reference, are, on average, 2% and 3% for channels 1 and 2, respectively. The uncertainties are smaller for sensors that are in afternoon orbits, have longer records, and spend less time in terminator conditions.The multiple invariant targets referenced to Aqua MODIS (MITRAM) AVHRR calibration coefficients are evaluated for individual target consistency, compared against Aqua MODIS/AVHRR SNOs, and selected published calibration gains. The MITRAM and SNO relative calibration biases mostly agree to within 1% for channels 1 and 2, respectively. The individual invariant target and MITRAM sensor relative calibration biases are mostly consistent to within 1% and 2% for channels 1 and 2, respectively. The differences between the MITRAM and other published calibrations are mostly attributed to the reference instrument calibration differences.

The 35-yr NOAA Advanced Very High Resolution Radiometer (AVHRR) observation record offers an excellent opportunity to study decadal climate variability, provided that all participating AVHRR instruments are calibrated on a consistent radiometric scale. Because of the lack of onboard calibration systems, the solar imaging channels of the AVHRR must be vicariously calibrated using invariant Earth targets as a calibrated reference source. The greatest challenge in calibrating the AVHRR dataset is the orbit degradation of the NOAA satellites, which eventually drift into a terminator orbit several years after launch. Therefore, the invariant targets must be characterized over the full range of solar zenith angles (SZAs) sampled by the satellite instrument.This study outlines a multiple invariant Earth target calibration approach specifically designed to account for the degrading NOAA orbits. The desert, polar ice, and deep convective cloud (DCC) invariant targets are characterized over all observed SZAs using NOAA-16 AVHRR measurements, which are referenced to the Aqua MODIS Collection 6 calibration via direct transfer of the MODIS calibration to the NOAA-16 AVHRR instrument using simultaneous nadir overpass (SNO) observations over the North Pole. The multiple invariant target calibrations are combined using the inverse of their temporal variance to optimize the resulting calibration stability. The NOAA-18 AVHRR gains derived using the desert, polar ice, and DCC targets, as well as from SNO, were found consistent within 1%, thereby validating that the Aqua MODIS calibration is effectively transferred to the reference calibration targets. The companion paper, Part II, applies the methodology across the AVHRR record to derive the sensor-specific calibration coefficients.

Monitoring and adjusting calibrations of various satellite imagers is often exacerbated by differences in their spectral response functions (SRFs). To help account for spectral disparities among satellite imagers, a web-based spectral band difference correction calculator has been developed to characterize the relationship between a specified pair of satellite imager channels in the hyperspectral wavelength range of 240-1750 nm. These spectral band adjustment factors (SBAFs) are derived by convolving hyperspectral data from the SCIAMACHY instrument with the SRFs of a reference and target sensor. The SBAF tool can be used for all combinations of instrument/channel pairs over predefined Earth spectra, intercalibration domains, or user-defined spatial domains. Options are available to the user whereby SBAFs can be subsetted by time, angle, and/or precipitable water content. To evaluate the relative spectral calibration of SCIAMACHY, comparisons of SBAFs derived from SCIAMACHY, Hyperion, and Global Ozone Monitoring Experiment-2 (GOME-2) were performed. Using observations over the Libya 4 desert pseudoinvariant calibration site, it is shown that SCIAMACHY-based SBAFs are within 0.1%-0.3% of SBAFs derived from Hyperion or GOME-2. This result implies that spectral calibration differences, i.e., the calibration uncertainties of SCIAMACHY relative to other potential spectral sources, have a minor impact on the SBAF compared with the influence of effective parameter-based subsetting. The SCIAMACHY instrument is most suited for calculating the SBAFs, given its high spectral resolution, broad spectral range, and nearly continuous global availability. The calibration community will find this SBAF tool useful for mitigating the SRF differences that can complicate the comparison and intercalibration of visible and near-infrared sensors.

Prior evaluations of Visible Infrared Imaging Radiometer Suite (VIIRS) out-of-band (OOB) contribution to total signal revealed specification exceedance for multiple key solar reflective and infrared bands that are of interest to the passive remote-sensing community. These assessments are based on laboratory measurements, and although highly useful, do not necessarily translate to OOB contribution with consideration of true Earth-reflected or Earth-emitted spectra, especially given the significant spectral variation of Earth targets. That is, although the OOB contribution of VIIRS is well known, it is not a uniform quantity applicable across all scene types. As such, this article quantifies OOB contribution for multiple relative spectral response characterization versions across the S-NPP, NOAA-20, and JPSS-2 VIIRS sensors as a function of varied SCIAMACHY- and IASI-measured hyperspectral Earth-reflected and Earth-emitted scenes. For instance, this paper reveals measured radiance variations of nearly 2% for the S-NPP VIIRS M5 (~0.67 &mu, m) band, and up to 5.7% for certain VIIRS M9 (~1.38 &mu, m) and M13 (~4.06 &mu, m) bands that are owed solely to the truncation of OOB response for a set of spectrally distinct Earth scenes. If unmitigated, e.g., by only considering the published extended bandpass, such variations may directly translate to scene-dependent scaling discrepancies or subtle errors in vegetative index determinations. Therefore, knowledge of OOB effects is especially important for inter-calibration or environmental retrieval efforts that rely on specific or multiple categories of Earth scene spectra, and also to researchers whose products rely on the impacted channels. Additionally, instrument teams may find this evaluation method useful for pre-launch characterization of OOB contribution with specific Earth targets in mind rather than relying on general models.

The advanced baseline imager (ABI) instrument onboard Geostationary Operational Environmental Satellite (GOES)-16 is the first of National Oceanic and Atmospheric Administration (NOAA’s) new-generation geostationary earth orbiting (GEO) imagers that provides high-quality calibrated and geolocated Earth observations in six reflective solar bands (RSBs). The spectral similarity between the Visible Infrared Imaging Radiometer Suite (VIIRS) and ABI RSB offers an opportunity for deriving VIIRS-quality cloud retrievals from the ABI radiances. NASA’s Clouds and the Earth’s Radiant Energy System (CERES) project utilizes GEO imager (including ABI) radiances to retrieve clouds and derive broadband fluxes that are used to account for the regional diurnal flux variation between the CERES measurements and to convert the CERES observed radiances into fluxes. In order to derive a seamless cloud and flux datasets for CERES, it is important that the GEO, MODIS, and VIIRS imagers are all placed on the same radiometric scale. We describe an absolute radiometric intercomparison between the NOAA-20 VIIRS and GOES-16 ABI RSB using ray-matched radiance/reflectance pairs over all-sky tropical ocean scenes as well as a deep convective cloud invariant target calibration algorithm. Results indicate that the ABI and VIIRS RSB calibration are within 5%, except for the 0.47-μm band, for which the radiometric inconsistency is found to be ∼7 % . The GOES-16 radiometric scaling factors referenced to NOAA-20 VIIRS were computed from the two independent calibration methods to agree within 1% for ABI bands 1 to 4, and within 3% for bands 5 and 6. Results from this study were used to propose a future CERES GEO intercalibration algorithm referenced to NOAA-20 VIIRS, given the eventual demise of the Terra and Aqua satellites.

The CERES project utilizes geostationary-derived broadband measurements to infer the regional diurnal flux in between CERES instantaneous measurements to estimate the daily-averaged flux. The geostationary (GEO) imager radiances must be calibrated to the same reference to ensure spatial and temporal consistency of cloud properties and fluxes across the contiguous GEO domains. In order to place all of the GEO visible imagers on the same radiometric scale, the CERES project inter-calibrates the GEO imagers with Aqua-MODIS using multiple independent approaches. The primary inter-calibration approach relies on coincident, ray-matched GEO and MODIS radiance pairs over all-sky tropical ocean scenes (ATO). Another ray-matching approach was recently developed to take advantage of the visible spectral uniformity and near-Lambertian reflectance of deep convective clouds (DCC). The success of the DCC raymatching (DCC-RM) approach has been demonstrated by comparing the calibration with the ATO ray-matching (ATORM) approach for the 0.65-μm GEO and MODIS bands. Now that many of the recently launched GEO imagers have multiple reflective solar band channels, the DCCRM algorithm is being modified to inter-calibrate those channels as well, especially for the SWIR bands. The spectral uniformity of the DCC over the SWIR bands is not uniform, given that the ice particle absorption is a function of wavelength. New Spectral Band Adjustment Factor (SBAF) strategies will need to be developed. DCC-RM is also wellsuited to inter-calibrate historical near-broadband visible GEO imagers. DCC are spectrally flat across the visible spectrum, which reduces the SBAF uncertainty between two ray-matched sensors. Applying the DCC-RM technique on historical GEO imagers is challenging due to the coarser pixel and temporal resolution of the ISCCP B1U formatted dataset. The ATO-RM and DCC-RM calibration methods were applied to multiple visible bands on Himawari-8 using MODIS as the calibration reference. The Aqua-MODIS and Himawari-8 calibration difference was less than 0.4% for wavelengths less than 1 µm and for the Terra-MODIS 0.65-μm channel. Other channel combinations would need further examination to obtain consistent ATO and DCC gain results. The ATO-RM and DCC-RM calibration methods were also applied to GOES-8 in the ISCCP B1U format with NOAA-14 AVHRR as the calibration reference. The GOES-8 ATO and DCC calibration gain difference was within 0.15%. The agreement between ATO and DCC gains provides confidence in both methods.

With close to 40 years of satellite observations, from which, cloud, land-use, and aerosol parameters can be measured, inter-consistent calibrations are needed to normalize retrievals across satellite records. Various visible-sensor inter-calibration techniques have been developed that utilize radiometrically stable Earth targets, e.g., deep convective clouds and desert/polar ice pseudo-invariant calibration sites. Other equally effective, direct techniques for intercalibration between satellite imagers are simultaneous nadir overpass comparisons and ray-matched radiance pairs. Combining independent calibration results from such varied techniques yields robust calibration coefficients, and is a form of self-validation. One potential source of significant error when cross-calibrating satellite sensors, however, are the often small but substantial spectral discrepancies between comparable bands, which must be accounted for. As such, visible calibration methods rely on a Spectral Band Adjustment Factor (SBAF) to account for the spectral-responsefunction-induced radiance differences between analogous imagers. The SBAF is unique to each calibration method as it is a function of the Earth-reflected spectra. In recent years, NASA Langley pioneered the use of SCIAMACHY-, GOME-2-, and Hyperion-retrieved Earth spectra to compute SBAFs. By carefully selecting hyperspectral footprints that best represent the conditions inherent to an inter-calibration technique, the uncertainty in the SBAF is greatly reduced. NASA Langley initially provided the Global Space-based Inter-calibration System processing and research centers with online SBAF tools, with which users select conditions to best match their calibration criteria. This article highlights expanded SBAF tool capabilities for visible wavelengths, with emphasis on spectral range filtering for the purpose of separating scene conditions for the channel that the SBAF is needed based on the reflectance values of other bands. In other words, spectral filtering will enable better scene-type selection for bands where scene determination is difficult without information from other channels, which should prove valuable to users in the calibration community.

The radiometric resolution of a satellite sensor refers to the smallest increment in the spectral radiance that can be detected by the imaging sensor. The fewer bits that are used for signal discretization, the larger the quantization error in the measured radiance. In satellite inter-calibration, a difference in radiometric resolution between a reference and a target sensor can induce a calibration bias, if not properly accounted for. The effect is greater for satellites with a quadratic count response, such as the Geostationary Meteorological Satellite-5 (GMS-5) visible imager, where the quantization difference can introduce non-linearity in the inter-comparison datasets, thereby affecting the cross-calibration slope and offset. This paper describes a simulation approach to highlight the importance of considering the radiometric quantization in cross-calibration and presents a correction method for mitigating its impact. The method, when applied to the cross-calibration of GMS-5 and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) sensors, improved the absolute calibration accuracy of the GMS-5 imager. This was validated via radiometric inter-comparison of GMS-5 and Multifunction Transport Satellite-2 (MTSAT-2) imager top-of-atmosphere (TOA) measurements over deep convective clouds (DCC) and Badain Desert invariant targets. The radiometric bias between GMS-5 and MTSAT-2 was reduced from 1.9% to 0.5% for DCC, and from 7.7% to 2.3% for Badain using the proposed correction method.

The CERES measured shortwave and longwave fluxes rely on the cloud properties derived using the coincident observations from the accompanying high-resolution MODIS and VIIRS imagers. The calibration consistency is required between MODIS and VIIRS radiances to ensure that the CERES provided cloud property retrievals are temporally consistent. This paper presents multiple approaches of cross-calibrating the spectrally comparable reflective solar bands (RSB) of Aqua-MODIS and NPP-VIIRS, and estimates the radiometric biases for individual band pair. The inter-comparison is performed between the Aqua-MODIS collection 6.1 level 1B and NPP-VIIRS Land PEATE V1 datasets. Radiometric biases up to 3% were estimated between the MODIS and VIIRS radiances for visible bands.

The Moderate Resolution Imaging Spectroradiometer (MODIS) Calibration Team has recently released the Collection 6 (C6) radiances, which offer broad improvements over Collection 5 (C5). The recharacterization of the solar diffuser, lunar measurements, and scan mirror angle corrections removed most of the visible channel calibration drifts. The visible band calibration stability was validated over the Libyan Desert, Dome-C, and deep convective cloud (DCC) invariant Earth targets, for wavelengths less than 1 μm. The lifetime stability of Terra and Aqua C6 is both within 1%, whereas the Terra C5 degradation exceeded 2% for most visible bands. The MODIS lifetime radiance trends over the invariant targets are mostly within 1%; however, the band-specific target fluctuations are inconsistent, which suggests that the stability limits of the invariant targets have been reached. Based on Terra- and Aqua-MODIS nearly simultaneous nadir overpass (NSNO) radiance comparisons, the Terra and Aqua C6 calibration shows agreement within 1.2%, whereas the C5 calibration exceeds 2%. Because the MODIS instruments are alike, the same NSNOs are used to radiometrically scale the Terra radiances to Aqua. For most visible bands, the Terra-scaled and Aqua C6 radiances are consistent to within 0.5% over Dome-C, DCC, and for geostationary visible imagers having similar spectral response functions, which are used as transfer radiometers. For bands greater than 1 μm, only minor calibration adjustments were made, and the C6 calibration is stable within 1% based on Libya-4.

The multifunctional transport satellite (MTSAT)-1R imager was launched in 2005 and is operated by the Japan Meteorological Agency (JMA). A nonlinear behavior in the MTSAT-1R visible sensor response is observed when the instrument is intercalibrated with coincident moderate resolution imaging spectroradiometer (MODIS) ray-matched radiances. Analysis reveals that the nonlinear behavior is not a result of imager navigation, sensor spectral response difference, nor scan pattern. Examination of coincident MTSAT-1R and MTSAT-2 images reveals that MTSAT-1R dark ocean radiances are affected by neighboring bright clouds, whereas large regions of dark ocean radiances are not impacted. Although the IR and visible optical paths are shared, the MTSAT-1R brightness temperatures are not affected. A dust contaminant coating the mirror, which only affects certain wavelengths, may be one explanation. To address the nonlinearity, a pixel point spread function (PSF) correction algorithm is implemented, wherein most of the radiance contribution is from the pixel field of view itself, as well as including a small contribution from all pixels within a radii of several hundred kilometers. The application of the PSF-corrected ~80% of the affected pixel radiances. After application, a near linear response is observed between the coincident MTSAT-1R and Aqua-MODIS ray-matched radiances, and the intercept is now near the predicted space count of zero. The monthly calibration gain noise is reduced by one-third when compared with the non-PSF-corrected gains. The monthly gains are the most erratic during the first two years of operation, and the MTSAT-1R visible sensor is degrading at ~1.9 % decade.

© 2015 American Meteorological Society. In land data assimilation, bias in the observation-minus-forecast (O - F) residuals is typically removed from the observations prior to assimilation by rescaling the observations to have the same long-term mean (and higher-order moments) as the corresponding model forecasts. Such observation rescaling approaches require a long record of observed and forecast estimates and an assumption that the O - F residuals are stationary. A two-stage observation bias and state estimation filter is presented here, as an alternative to observation rescaling that does not require a long data record or assume stationary O - F residuals. The twostage filter removes dynamic (nonstationary) estimates of the seasonal-scale mean O - F difference from the assimilated observations, allowing the assimilation to correct the model for subseasonal-scale errors without adverse effects from observation biases. The two-stage filter is demonstrated by assimilating geostationary skin temperature T skin observations into the Catchment land surface model. Global maps of the estimated O - F biases are presented, and the two-stage filter is evaluated for one year over the Americas. The twostage filter effectively removed the T skin O - F mean differences, for example, the Geostationary Operational Environmental Satellite (GOES)-West O - F mean difference at 2100 UTC was reduced from 5.1K for a bias-blind assimilation to 0.3 K. Compared to independent in situ and remotely sensed T skin observations, the two-stage assimilation reduced the unbiased root-mean-square difference (ubRMSD) of the modeled T skin by 10% of the open-loop values. ispartof: Journal of Hydrometeorology vol:16 issue:1 pages:449-464 status: published

The new 3rd generation geostationary (GEO) imagers will have many of the same NPP-VIIRS imager spectral bands, thereby offering the opportunity to apply the VIIRS cloud, aerosol, and land use retrieval algorithms on the new GEO imager measurements. Climate quality retrievals require multi-channel calibrated radiances that are stable over time. The deep convective cloud calibration technique (DCCT) is a large ensemble statistical technique that assumes that the DCC reflectance is stable over time. Because DCC are found in sufficient numbers across all GEO domains, they provide a uniform calibration stability evaluation across the GEO constellation. The baseline DCCT has been successful in calibrating visible and near-infrared channels. However, for shortwave infrared (SWIR) channels the DCCT is not as effective to monitor radiometric stability. The DCCT was optimized as a function wavelength in this paper. For SWIR bands, the greatest reduction of the DCC response trend standard error was achieved through deseasonalization. This is effective because the DCC reflectance exhibits small regional seasonal cycles that can be characterized on a monthly basis. On the other hand, the inter-annually variability in DCC response was found to be extremely small. The Met-9 0.65-μm channel DCC response was found to have a 3% seasonal cycle. Deseasonalization reduced the trend standard error from 1% to 0.4%. For the NPP-VIIRS SWIR bands, deseasonalization reduced the trend standard error by more than half. All VIIRS SWIR band trend standard errors were less than 1%. The DCCT should be able to monitor the stability of all GEO imager solar reflective bands across the tropical domain with the same uniform accuracy.

Accurate characterization of the Earth’s radiant energy is critical for many climate monitoring and weather forecasting applications. For example, groups at the NASA Langley Research Center rely on stable visible- and infraredchannel calibrations in order to understand the temporal/spatial distribution of hazardous storms, as determined from an automated overshooting convective top detection algorithm. Therefore, in order to facilitate reliable, climate-quality retrievals, it is important that consistent calibration coefficients across satellite platforms are made available to the remote sensing community, and that calibration anomalies are recognized and mitigated. One such anomaly is the infrared imager brightness temperature (BT) drift that occurs for some Geostationary Earth Orbit satellite (GEOsat) instruments near local midnight. Currently the Global Space-Based Inter-Calibration System (GSICS) community uses the hyperspectral Infrared Atmospheric Sounding Interferometer (IASI) sensor as a common reference to uniformly calibrate GEOsat IR imagers. However, the combination of IASI, which has a 21:30 local equator crossing time (LECT), and hyperspectral Atmospheric Infrared Sounder (AIRS; 01:30 LECT) observations are unable to completely resolve the GEOsat midnight BT bias. The precessing orbit of the Tropical Rainfall Measuring Mission (TRMM) Visible and Infrared Scanner (VIRS), however, allows sampling of all local hours every 46 days. Thus, VIRS has the capability to quantify the BT midnight effect observed in concurrent GEOsat imagers. First, the VIRS IR measurements are evaluated for long-term temporal stability between 2002 and 2012 by inter-calibrating with Aqua-MODIS. Second, the VIRS IR measurements are assessed for diurnal stability by inter-calibrating with Meteosat-9 (Met-9), a spin-stabilized GEOsat imager that does not manifest any diurnal dependency. In this case, the Met-9 IR imager is first adjusted with the official GSICS calibration coefficients. Then VIRS is used as a diurnal calibration reference transfer to produce hourly corrections of GEOsat IR imager BT. For the 9 three-axis stabilized GEO imagers concurrent with VIRS, the midnight effect increased the BT on average by 0.5 K (11 μm) and 0.4 K (12 μm), with a peak at ~01:00 local time. As expected, the spin-stabilized GEOsats revealed a smaller diurnal temperature cycle (mostly < 0.2 K) with inconsistent peak hours.

The AVHRR satellite data spans nearly 40 years, and is critical for studying decadal climate change, provided that the multiple AVHRR sensors are calibrated on a consistent radiometric scale. Because the AVHRR instruments lack onboard calibration capability, the radiometric scaling of the AVHRR record is feasible only using vicarious approaches. This paper presents a consistent radiometric calibration of the AVHRR visible (VIS) and near-infrared (NIR) records based on ray-matching, invariant deserts, and deep convective clouds (DCC) techniques that are referenced to Aqua-MODIS collection-6 (C6) calibration.

The Deep Space Climate Observatory (DSCOVR) through the earth polychromatic imaging camera (EPIC) continuously observes the illuminated disk from the Lagrange-1 point. The EPIC sensor was designed to monitor the diurnal variation of ozone, clouds, aerosols, and vegetation, especially those features that benefit from observation near-backscatter conditions. The EPIC sensor does not contain any onboard calibration systems. This study describes the inter-calibration of EPIC channels 5 (0.44 &micro, m), 6 (0.55 &micro, m), 7 (0.68 &micro, m), and 10 (0.78 &micro, m) with respect to Aqua-MODIS and NPP-VIIRS. The calibration is transferred using coincident ray-matched reflectance pairs over all-sky tropical ocean (ATO) and deep convective cloud (DCC) targets. A robust and automated image-alignment technique based on feature matching was formulated to improve the navigation quality of the EPIC images. The EPIC V02 dataset exhibits improved navigation over V01. As the visible channels display similar spatial features, a single visible channel can be used to co-register the remaining visible bands. The VIIRS-referenced EPIC ATO and DCC ray-matched calibration coefficients are within 0.3%. The EPIC four-year calibration trends based on VIIRS are within 0.15%/year. The MODIS-based EPIC calibration coefficients were compared against the Geogdzhayev and Marshak 2018 published calibration coefficients and were found to be within 1.6%.

Spectral band differences between sensors can complicate the process of intercalibration of a visible sensor against a reference sensor. This can be best addressed by using a hyperspectral reference sensor whenever possible because they can be used to accurately mitigate the band differences. This paper demonstrates the feasibility of using operational Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) large-footprint hyperspectral radiances to calibrate geostationary Earth-observing (GEO) sensors. Near simultaneous nadir overpass measurements were used to compare the temporal calibration of SCIAMACHY with Aqua Moderate Resolution Imaging Spectroradiometer band radiances, which were found to be consistent to within 0.44% over seven years. An operational SCIAMACHY/GEO ray-matching technique was presented, along with enhancements to improve radiance pair sampling. These enhancements did not bias the underlying intercalibration and provided enough sampling to allow up to monthly monitoring of the GEO sensor degradation. The results of the SCIAMACHY/GEO intercalibration were compared with other operational four-year Meteosat-9 0.65-μm calibration coefficients and were found to be within 1% of the gain, and more importantly, it had one of the lowest temporal standard errors of all the methods. This is more than likely that the GEO spectral response function could be directly applied to the SCIAMACHY radiances, whereas the other operational methods inferred a spectral correction factor. This method allows the validation of the spectral corrections required by other methods.

Deep convective clouds (DCCs) are ideal visible calibration targets because they are bright nearly isotropic solar reflectors located over the tropics and they can be easily identified using a simple infrared threshold. Because all satellites view DCCs, DCCs provide the opportunity to uniformly monitor the stability of all operational sensors, both historical and present. A collective DCC anisotropically corrected radiance calibration approach is used to construct monthly probability distribution functions (PDFs) to monitor sensor stability. The DCC calibration targets were stable to within 0.5% and 0.3 % per decade when the selection criteria were optimized based on Aqua MODerate Resolution Imaging Spectroradiometer 0.65-μm -band radiances. The Tropical Western Pacific (TWP), African, and South American regions were identified as the dominant DCC domains. For the 0.65-μm band, the PDF mode statistic is preferable, providing 0.3% regional consistency and 1% temporal uncertainty over land regions. It was found that the DCC within the TWP had the lowest radiometric response and DCC over land did not necessarily have the highest radiometric response. For wavelengths greater than 1 μm, the mean statistic is preferred, and land regions provided a regional variability of 0.7 % with a temporal uncertainty of 1.1 % where the DCC land response was higher than the response over ocean. Unlike stratus and cirrus clouds, the DCC spectra were not affected by water vapor absorption.

Atmospheric models rely on high-accuracy, high-resolution initial radiometric and surface conditions for better short-term meteorological forecasts, as well as improved evaluation of global climate models. Remote sensing of the Earth’s energy budget, particularly with instruments flown on geostationary satellites, allows for near-real-time evaluation of cloud and surface radiation properties. The persistence and coverage of geostationary remote sensing instruments grant the frequent retrieval of near-instantaneous quasi-global skin temperature. Among other cloud and clear-sky retrieval parameters, NASA Langley provides a non-polar, high-resolution land and ocean skin temperature dataset for atmospheric modelers by applying an inverted correlated k-distribution method to clear-pixel values of top-of-atmosphere infrared temperature. The present paper shows that this method yields clear-sky skin temperature values that are, for the most part, within 2 K of measurements from ground-site instruments, like the Southern Great Plains Atmospheric Radiation Measurement (ARM) Infrared Thermometer and the National Climatic Data Center Apogee Precision Infrared Thermocouple Sensor. The level of accuracy relative to the ARM site is comparable to that of the Moderate-resolution Imaging Spectroradiometer (MODIS) with the benefit of an increased number of daily measurements without added bias or increased error. Additionally, matched comparisons of the high-resolution skin temperature product with MODIS land surface temperature reveal a level of accuracy well within 1 K for both day and night. This confidence will help in characterizing the diurnal and seasonal biases and root-mean-square differences between the retrievals and modeled values from the NASA Goddard Earth Observing System Version 5 (GEOS-5) in preparation for assimilation of the retrievals into GEOS-5. Modelers should find the immediate availability and broad coverage of these skin temperature observations valuable, which can lead to improved forecasting and more advanced global climate models.

The Deep Space Climate Observatory (DSCOVR), launched on 11 February 2015, is a satellite positioned near the Lagrange-1 (L1) point, carrying several instruments that monitor space weather, and Earth-view sensors designed for climate studies. The Earth Polychromatic Imaging Camera (EPIC) onboard DSCOVR continuously views the sun-illuminated portion of the Earth with spectral coverage in the UV, VIS, and NIR bands. Although the EPIC instrument does not have any onboard calibration abilities, its constant view of the sunlit Earth disk provides a unique opportunity for simultaneous viewing with several other satellite instruments. This arrangement allows the EPIC sensor to be inter-calibrated using other well-characterized satellite instrument reference standards. Two such instruments with onboard calibration are MODIS, flown on Aqua and Terra, and VIIRS, onboard Suomi-NPP. The MODIS and VIIRS reference calibrations will be transferred to the EPIC instrument using both all-sky ocean and deep convective clouds (DCC) ray-matched EPIC and MODIS/VIIRS radiance pairs. An automated navigation correction routine was developed to more accurately align the EPIC and MODIS/VIIRS granules. The automated navigation correction routine dramatically reduced the uncertainty of the resulting calibration gain based on the EPIC and MODIS/VIIRS radiance pairs. The SCIAMACHY-based spectral band adjustment factors (SBAF) applied to the MODIS/ VIIRS radiances were found to successfully adjust the reference radiances to the spectral response of the specific EPIC channel for over-lapping spectral channels. The SBAF was also found to be effective for the non-overlapping EPIC channel 10. Lastly, both ray-matching techniques found no discernable trends for EPIC channel 7 over the year of publically released EPIC data.

The absolute radiometric calibration of the reflective solar bands (RSBs) of Aqua- and Terra-MODIS is performed using on-board calibrators. A solar diffuser (SD) panel along with a solar diffuser stability monitor (SDSM) system, which tracks the performance of the SD over time, provides the absolute reference for calibrating the MODIS sensors. MODIS also views the moon and deep space through its space view (SV) port for lunar-based calibration and computing the zero input radiance, respectively. The MODIS instrument views the Earth's surface through a two-sided scan mirror, whose reflectance is a function of angle of incidence (AOI) and is described by response versus scan-angle (RVS). The RVS for both MODIS instruments was characterized prior to launch. MODIS also views the SD and the moon at two different assigned RVS positions. There is sufficient evidence that the RVS is changing on orbit over time and as a function of wavelength. The SD and lunar observation scans can only track the RVS variation at two RVS positions. Consequently, the MODIS Characterization Support Team (MCST) developed enhanced approaches that supplement the onboard calibrator measurements with responses from pseudo-invariant desert sites. This approach has been implemented in Level 1B (L1B) Collection 6 (C6) for selected short-wavelength bands. This paper presents an alternative approach of characterizing the mirror RVS to derive the time-dependent RVS correction factors for MODIS RSBs using tropical deep convective cloud (DCC) targets. An initial assessment of the DCC response from Aqua-MODIS band 1 C6 data indicates evidence of RVS artifacts, which are not uniform across the scans and are more prevalent in the left side Earth-view scans.

Surface skin temperature (Ts) is an important parameter for characterizing the energy exchange at the ground/water-atmosphere interface. The Satellite ClOud and Radiation Property retrieval System (SatCORPS) employs a single-channel thermal-infrared- (TIR-) method to retrieve Ts over clear-sky land and ocean surfaces from data taken by geostationary-Earth orbit (GEO) satellite and low-Earth orbit (LEO) satellite imagers. GEO satellites can provide somewhat continuous estimates of Ts over the diurnal cycle in non-polar regions, while polar Ts retrievals from LEO imagers, such as the Advanced Very High Resolution Radiometer (AVHRR) can complement the GEO measurements. The combined global coverage of remotely sensed Ts, along with accompanying cloud and surface radiation parameters, produced in near-real time and from historical satellite data, should be beneficial for both weather and climate applications. For example, near-real-time hourly Ts observations can be assimilated in high-temporal resolution numerical weather prediction models and historical observations can be used for validation or assimilation of climate models. Key drawbacks to the utility of TIR-derived Ts, data include the limitation to clear-sky conditions, the reliance on a particular set of analyses/reanalyses necessary for atmospheric corrections, and the dependence on viewing angle. Therefore, Ts validation with established references is essential, as is proper evaluation of Ts sensitivity to atmospheric correction source. This article presents improvements on the NASA Langley GEO satellite and AVHRR TIR-based Ts product, derived using a single-channel technique. The resulting clear-sky skin temperature values are validated with surface references and independent satellite products. Furthermore, an empirical means of correcting for the viewing-angle dependency of satellite land surface temperature (LST) is explained and validated. Application of a daytime nadir-normalization model yields improved accuracy and precision of GOES-13 LST relative to independent Moderate-resolution Imaging Spectroradiometer (MYD11_L2) LST and Atmospheric Radiation Measurement Program/NOAA ESRL Surface Radiation network ground stations. These corrections serve as a basis for a means to improve satellite-based LST accuracy, thereby leading to better monitoring and utilization of the data. The immediate availability and broad coverage of these skin temperature observations should prove valuable to modelers and climate researchers looking for improved forecasts and better understanding of the global climate model.

A project representing an effort to reprocess the NASA based solar resource data sets is reviewed. The effort represented a collaboration between NASA, NOAA, NREL and the SUNY-Albany and aimed to deliver a 10 km resolution, 3-hourly data set spanning from 1983 through near-present. Part of the project was to transition project capability to NREL for annual processing to extend data set. Due to delays in the key input project called ISCCP, we evaluate only Beta versions of this data set and also introduce the potential use of another NASA Langley based cloud data set for the CERES project. The CERES project uses these cloud properties to compute global top-of-atmosphere and surface fluxes at the 1x1 degree resolution. Here, we also briefly discuss these data sets in potential usage for solar resource benchmarking.

High-resolution spectra measured by the ENVISAT SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) are used to develop spectral correction factors for satellite imager solar channels to improve the transfer of calibrations from one imager to another. SCIAMACHY spectra averaged for various scene types demonstrate the dependence of reflectance on imager spectral response functions. Pseudo imager radiances were computed separately over land and water from SCIAMACHY pixel spectra taken over two tropical domains. Spectral correction factors were computed from these pseudo imager radiance pairs. Intercalibrations performed using matched 12th Geostationary Operational Environmental Satellite and Terra MODerate-resolution Imaging Spectroradiometer (MODIS) visible ( ~ 0.65 μm) channel data over the same domains yielded ocean and land calibration gain and offset differences of 4.5% and 41%, respectively. Applying the spectral correction factors reduces the gain and offset differences to 0.1% and 3.8%, respectively, for free linear regression. Forcing the regression to use the known offset count reduces the land-ocean radiance differences to 0.3% or less. Similar difference reductions were found for matched MODIS and Meteosat-8 Spinning Enhanced Visible and Infrared Imager channel 2 ( ~ 0.86 μm ). The results demonstrate that SCIAMACHY-based spectral corrections can be used to significantly improve the transfer of calibration between any pair of imagers measuring reflected solar radiances under similar viewing and illumination conditions.