IEEE Transactions on Geoscience and Remote Sensing, 2000
This paper describes the satellite data processing and services that constitute current functiona... more 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.
IEEE Transactions on Geoscience and Remote Sensing, 2000
Satellite-derived aerosol data sets, such as those provided by NASA's Moderate Resolution Imaging... more Satellite-derived aerosol data sets, such as those provided by NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) instruments, are greatly improving our understanding of global aerosol optical depth (AOD). Yet, there are sampling issues. MODIS' specific orbital geometry, convolved with the need to avoid bright surfaces (glint, desert, clouds, etc.), means that AOD can be under-or over-sampled in places. When deriving downstream products, such as daily or monthly gridded AOD, one must consider the spatial and temporal density of the measurements relative to the gradients of the true AOD. Additionally, retrieval confidence criteria should be considered. Averaged products are highly dependent on choices made for data aggregation and weighting, and sampling errors can be further propagated when deriving regional or global "mean" AOD. Different choices for aggregation and weighting result in estimates of regional and global means varying by 30% or more. The impacts of a particular averaging algorithm vary by region and surface type and can be shown to represent different tolerance for clouds and retrieval confidence.
The advent of well-calibrated and well-characterized visible sensors, such as the MODerate resolu... more The advent of well-calibrated and well-characterized visible sensors, such as the MODerate resolution Imaging Spectroradiometer (MODIS), has allowed the opportunity to cross-calibrate other contemporary, un-calibrated visible sensors. Most of the operational geostationary-Earth-orbit satellite (GEOsat) visible sensors do not have a direct means of on-orbit calibration, and the cross-calibration of MODIS with GEOsats is plagued by the differences in the sensor spectral response functions (SRFs). Spectral band adjustment factors (SBAFs) are needed to correct for the solar flux and inter-band gaseous absorption discrepancies that are caused by SRF differences, which are sometimes significant. In addressing this problem, this manuscript describes a spectral band correction technique that employs Envisat SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) hyper-spectral radiances to derive pseudo-radiance, or equivalent-radiance, values for the MODIS and GEOsat sensors over the calibration targets, which include a desert, deep convective clouds, and a MODIS-with-GEOsat ray-matching ocean domain. The regressions from these equivalent-radiance comparisons constitute the necessary adjustment factor. The regressions of MODIS and GEOsat pseudo-radiance values are well-behaved, with small standard error and offsets, for spectral bands that are similar. When comparing narrowband to broadband, however, the correction difference between deep convective and maritime stratus clouds can be as large as 6%. New scene-selection criteria are investigated to derive spectral band adjustment factors that are dependent on the calibration-target domains, which reduces this uncertainty. Application of these SBAFs, which are validated for accuracy using ray-matched SCIAMACHY and GEOsat radiances, is shown to bring independently derived absolute calibrations to within 1% agreement, or better, with one-another. These spectral band adjustment factors are critical for obtaining accurate and consistent absolute calibration among multiple independent and scene-dependent inter-calibration techniques given that the variation of the SBAFs as a function of scene type can be close to 8% for a narrowband-to-broadband correction.
Earth Observing Missions and Sensors: Development, Implementation, and Characterization, 2010
There are over 25 years of historical satellite data available for climate analysis. The historic... more 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
ABSTRACT The latest CERES FM-5 instrument launched onboard the S-NPP spacecraft will use the VIIR... more ABSTRACT 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.
ABSTRACT The CERES observed EBAF 12-year TOA flux dataset are used to monitor the Earth's... more ABSTRACT The CERES observed EBAF 12-year TOA flux dataset are used to monitor the Earth's climate and in validating climate models. The associated cloud properties are retrieved from a fusion of MODIS and geostationary imagers. The quality of these cloud properties relies on the absolute calibration of these imagers. The geostationary imager radiances are calibrated against the Aqua-MODIS calibration reference in order to retrieve uniform cloud properties both spatially and temporally. Any calibration discontinuity or artifact may be interpreted as a climate trend. CERES record will be extended using CERES fluxes onboard NPP. CERES will need to tie the VIIRS with the Aqua-MODIS calibration in order to provide a seamless record of cloud and flux properties. This paper will present initial assessment results for interconsistency between VIIRS and Aqua-MODIS calibration of matching visible channels. Empirically derived exoatmospheric VIIRS radiance models for deep convective clouds and deserts invariant targets are used to assess the initial onboard calibration of VIIRS. The VIIRS models are based on characterizing these invariant targets with Aqua-MODIS as an absolute calibration reference in order to tie the VIIRS calibration to Aqua-MODIS. Correction for spectral band differences in the VIIRS and MODIS channels is performed using the SCIAMACHY hyperspectral data.
Journal of Quantitative Spectroscopy and Radiative Transfer, 1993
... (1) for n =0.476. 388 PRASAD VAxANAsiand ARuN GOPALAN 0.1731 cm' atm', resp... more ... (1) for n =0.476. 388 PRASAD VAxANAsiand ARuN GOPALAN 0.1731 cm' atm', respectively. The experimental errors in the data of Ref. ... 8. G. Baldacchini, S. Marchetti, V. Montelatici, G. Buffa, and O. Tarrini, J. Chem. Phys. 76, 5271 (1982). 9. JS Margolis and RL Poynter, Appl. ...
IEEE Transactions on Geoscience and Remote Sensing, 2000
This paper describes the satellite data processing and services that constitute current functiona... more 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.
IEEE Transactions on Geoscience and Remote Sensing, 2000
Satellite-derived aerosol data sets, such as those provided by NASA's Moderate Resolution Imaging... more Satellite-derived aerosol data sets, such as those provided by NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) instruments, are greatly improving our understanding of global aerosol optical depth (AOD). Yet, there are sampling issues. MODIS' specific orbital geometry, convolved with the need to avoid bright surfaces (glint, desert, clouds, etc.), means that AOD can be under-or over-sampled in places. When deriving downstream products, such as daily or monthly gridded AOD, one must consider the spatial and temporal density of the measurements relative to the gradients of the true AOD. Additionally, retrieval confidence criteria should be considered. Averaged products are highly dependent on choices made for data aggregation and weighting, and sampling errors can be further propagated when deriving regional or global "mean" AOD. Different choices for aggregation and weighting result in estimates of regional and global means varying by 30% or more. The impacts of a particular averaging algorithm vary by region and surface type and can be shown to represent different tolerance for clouds and retrieval confidence.
The advent of well-calibrated and well-characterized visible sensors, such as the MODerate resolu... more The advent of well-calibrated and well-characterized visible sensors, such as the MODerate resolution Imaging Spectroradiometer (MODIS), has allowed the opportunity to cross-calibrate other contemporary, un-calibrated visible sensors. Most of the operational geostationary-Earth-orbit satellite (GEOsat) visible sensors do not have a direct means of on-orbit calibration, and the cross-calibration of MODIS with GEOsats is plagued by the differences in the sensor spectral response functions (SRFs). Spectral band adjustment factors (SBAFs) are needed to correct for the solar flux and inter-band gaseous absorption discrepancies that are caused by SRF differences, which are sometimes significant. In addressing this problem, this manuscript describes a spectral band correction technique that employs Envisat SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) hyper-spectral radiances to derive pseudo-radiance, or equivalent-radiance, values for the MODIS and GEOsat sensors over the calibration targets, which include a desert, deep convective clouds, and a MODIS-with-GEOsat ray-matching ocean domain. The regressions from these equivalent-radiance comparisons constitute the necessary adjustment factor. The regressions of MODIS and GEOsat pseudo-radiance values are well-behaved, with small standard error and offsets, for spectral bands that are similar. When comparing narrowband to broadband, however, the correction difference between deep convective and maritime stratus clouds can be as large as 6%. New scene-selection criteria are investigated to derive spectral band adjustment factors that are dependent on the calibration-target domains, which reduces this uncertainty. Application of these SBAFs, which are validated for accuracy using ray-matched SCIAMACHY and GEOsat radiances, is shown to bring independently derived absolute calibrations to within 1% agreement, or better, with one-another. These spectral band adjustment factors are critical for obtaining accurate and consistent absolute calibration among multiple independent and scene-dependent inter-calibration techniques given that the variation of the SBAFs as a function of scene type can be close to 8% for a narrowband-to-broadband correction.
Earth Observing Missions and Sensors: Development, Implementation, and Characterization, 2010
There are over 25 years of historical satellite data available for climate analysis. The historic... more 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
ABSTRACT The latest CERES FM-5 instrument launched onboard the S-NPP spacecraft will use the VIIR... more ABSTRACT 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.
ABSTRACT The CERES observed EBAF 12-year TOA flux dataset are used to monitor the Earth's... more ABSTRACT The CERES observed EBAF 12-year TOA flux dataset are used to monitor the Earth's climate and in validating climate models. The associated cloud properties are retrieved from a fusion of MODIS and geostationary imagers. The quality of these cloud properties relies on the absolute calibration of these imagers. The geostationary imager radiances are calibrated against the Aqua-MODIS calibration reference in order to retrieve uniform cloud properties both spatially and temporally. Any calibration discontinuity or artifact may be interpreted as a climate trend. CERES record will be extended using CERES fluxes onboard NPP. CERES will need to tie the VIIRS with the Aqua-MODIS calibration in order to provide a seamless record of cloud and flux properties. This paper will present initial assessment results for interconsistency between VIIRS and Aqua-MODIS calibration of matching visible channels. Empirically derived exoatmospheric VIIRS radiance models for deep convective clouds and deserts invariant targets are used to assess the initial onboard calibration of VIIRS. The VIIRS models are based on characterizing these invariant targets with Aqua-MODIS as an absolute calibration reference in order to tie the VIIRS calibration to Aqua-MODIS. Correction for spectral band differences in the VIIRS and MODIS channels is performed using the SCIAMACHY hyperspectral data.
Journal of Quantitative Spectroscopy and Radiative Transfer, 1993
... (1) for n =0.476. 388 PRASAD VAxANAsiand ARuN GOPALAN 0.1731 cm' atm', resp... more ... (1) for n =0.476. 388 PRASAD VAxANAsiand ARuN GOPALAN 0.1731 cm' atm', respectively. The experimental errors in the data of Ref. ... 8. G. Baldacchini, S. Marchetti, V. Montelatici, G. Buffa, and O. Tarrini, J. Chem. Phys. 76, 5271 (1982). 9. JS Margolis and RL Poynter, Appl. ...
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Papers by Arun Gopalan