Arun Gopalan

The Characterization of Deep Convective Clouds as an Invariant Calibration Target and as a Visible Calibration Technique

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.

The Radiometric Stability and Scaling of Collection 6 Terra- and Aqua-MODIS VIS, NIR, and SWIR Spectral 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.

Initial Stability Assessment of S-NPP VIIRS Reflective Solar Band Calibration Using Invariant Desert and Deep Convective Cloud Targets

The latest CERES FM-5 instrument launched onboard the S-NPP spacecraft will use the VIIRS visible radiances from the NASA Land Product Evaluation and Analysis Tool Elements (PEATE) product for retrieving the cloud properties associated with its TOA flux measurement. In order for CERES to provide climate quality TOA flux datasets, the retrieved cloud properties must be consistent throughout the record, which is dependent on the calibration stability of the VIIRS imager. This paper assesses the NASA calibration stability of the VIIRS reflective solar bands using the Libya-4 desert and deep convective clouds (DCC). The invariant targets are first evaluated for temporal natural variability. It is found for visible (VIS) bands that DCC targets have half of the variability of Libya-4. For the shortwave infrared (SWIR) bands, the desert has less variability. The brief VIIRS record and target variability inhibits high confidence in identifying any trends that are less than ±0.6%/yr for most VIS bands, and ±2.5%/yr for SWIR bands. None of the observed invariant target reflective solar band trends exceeded these trend thresholds. Initial assessment results show that the VIIRS data have been consistently calibrated and that the VIIRS instrument stability is similar to or better than the MODIS instrument.

A Web-Based Tool for Calculating Spectral Band Difference Adjustment Factors Derived From SCIAMACHY Hyperspectral Data

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.

The characterization of deep convective cloud albedo as a calibration target using MODIS reflectances

There are over 25 years of historical satellite data available for climate analysis. The historical satellite data needs to be properly calibrated, especially in the visible, for sensors with no onboard calibration. Accurate vicarious calibration of historical satellites relies on invariant targets, such as the moon, Dome C, and deserts. Deep convective clouds (DCC) also show promise of being a stable or predictable target viewable by all satellites, since they behave as solar diffusers. However DCC have not been well characterized for calibration. Ten years of well-calibrated MODIS radiances are now available. DCC can easily be identified using IR thresholds, where the IR calibration can be traced to the onboard blackbodies. The natural variability of the DCC radiance will be analyzed geographically, seasonally, and for differences of convection initiated over land and ocean. Functionality between particle size and ozone absorption with DCC albedo will be examined theoretically. Although DCC clouds are nearly Lambertian, the angular distribution of reflectances will be sampled and compared with theoretical models. Both Aqua and Terra MODIS DCC angular models were compared for consistency. The DCC method was able to identify two calibration coefficient discontinuities in the Terra-MODIS Collection 5 10-year record and validated the calibration stability of MODIS to within 0.1% per decade. The DCC method needs to take into account the functionality of the 0.65μm DCC radiance with the 11μm brightness temperature threshold and the DCC 0.65μm radiance difference observed over the tropical western pacific and the afternoon generated DCC over land. Both of these cases cause a bias on the order of 5%. These improvements are the first steps towards successful use of DCC as an absolute calibration target.

Desert-Based Absolute Calibration of Successive Geostationary Visible Sensors Using a Daily Exoatmospheric Radiance Model

A desert daily exoatmospheric radiance model (DERM) based on a well-calibrated (reference) geostationary Earth orbit (GEO) satellite visible sensor can be used to transfer the calibration to a (target) GEO sensor located at the same equatorial longitude location. The DERM is based on the reference GEO daily radiances observed over a single pseudoinvariant calibration site (PICS) being that the daily angular conditions are repeated annually for any historical or successive colocated GEO. The GEO-specific PICSs used in the study are first inspected using the well-calibrated Aqua-MODerate Resolution Imaging Spectroradiometer (MODIS) exoatmospheric reflectances for stability. The Libyan Desert site was found to be stable within 1 % over ten years. The average clear-sky daily local-noon interannual variability based on Meteosat-9 0.65- μm top-of-atmosphere radiances over the Libyan Desert is 0.74 %, which implies that the combined surface and atmospheric column is invariant. A spectral band adjustment factor, based on Scanning Imaging Absorption Spectrometer for Atmospheric Cartography spectral radiances, is used to account for sensor spectral response function (SRF) differences between the reference and target GEO. The GEO reference calibration was based on the GEO/Aqua-MODIS ray-matched radiance intercalibration transfer technique. The reference Meteosat-9 DERM and ray-matched calibration consistency was within 0.4 % and 1.9 % for Meteosat-8 and Meteosat-7, respectively. Similarly, GOES-10 and GOES-15 were calibrated based on the GOES-11 DERM using the Sonoran Desert and were found to have a consistency within 1 % and 3 %, respectively.

A-Train data depot - bringing Atmospheric measurements together

This paper describes the satellite data processing and services that constitute current functionalities of the A-Train Data Depot. We first provide a brief introduction to the original geometrical intricacies of the platforms and instruments of the A-Train constellation and then proceed with a description of our A-Train collocation-processing algorithm that provides subsets that facilitate synergistic use of the various instruments. Finally, we present some sample image products from our web-based Giovanni tool which allows users to display, compare, and download coregistered A-Train-related data.

A Consistent AVHRR Visible Calibration Record Based on Multiple Methods Applicable for the NOAA Degrading Orbits. Part I: Methodology

AbstractThe 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 SZA...

The Intercalibration of Geostationary Visible Imagers Using Operational Hyperspectral SCIAMACHY Radiances

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.

Study of Data-Merging and Interpolation Methods for Use in an Interactive Online Analysis System: MODIS Terra and Aqua Daily Aerosol Case

Data merging with interpolation is a method of combining near-coincident satellite observations to provide complete global or regional maps for comparison with models and ground station observations. We investigate various methods and limitations of data merging (or data fusion), with and without interpolation, as a first step toward merging data sets archived in the National Aeronautics and Space Administration Goddard Earth Sciences Data and Information Services Center and made public through the Goddard Interactive Online Visualization and ANalysis Infrastructure (Giovanni) data portals. As a prototype for the data-merging algorithm, this paper uses daily global observations of aerosol optical thickness (AOT), as measured by the MODerate resolution Imaging Spectroradiometer onboard the Terra and Aqua satellites. The goal is to develop a very fast and accurate online method of data merging for implementation into Giovanni. We demonstrate three different methods for pure merging (without interpolation): simple arithmetic averaging (SAA), maximum likelihood estimate (MLE), and weighting by pixel counts. All three methods are roughly comparable, with the MLE (SAA) being slightly preferable when validating with respect to the AOT standard deviations (AOT means). To evaluate the merged product, we introduce two confidence functions, which characterize the percentage of the merged AOT pixels as a function of the relative deviation of the merged AOT from the initial Terra and Aqua AOTs. Eight combinations of merging-interpolation are applied to scenes with regular and irregular data gap patterns. Our results show that the merging-interpolation procedure can produce complete spatial fields with acceptable errors.