• Berk, A., , Anderson G. P. , , Acharya P. K. , , Chetwynd J. H. , , Bernstein L. S. , , Shettle E. P. , , Matthew M. W. , , and Adler-Golden S. M. , 2001: MODTRAN4 version 2 user’s manual. Air Force Research Laboratory, 98 pp.

    • Search Google Scholar
    • Export Citation
  • Blumstein, D., and Coauthors, 2004: IASI instrument: Technical overview and measured performances. Infrared Spaceborne Remote Sensing XII, M. Strojnik, Ed., International Society for Optical Engineering (SPIE Proceedings, Vol. 5543), 196–207.

    • Search Google Scholar
    • Export Citation
  • Blumstein, D., , Tournier B. , , Cayla F. R. , , Phulpin T. , , Fjortoft R. , , Buil C. , , and Ponce G. , 2007: In-flight performance of the infrared atmospheric sounding interferometer (IASI) on MetOp-A. Atmospheric and Environmental Remote Sensing Data Processing and Utilization III: Readiness for GEOSS, M. D. Goldberg et al., Eds., International Society for Optical Engineering (SPIE Proceedings, Vol. 6684), 66840H, doi:10.1117/12.734162.

    • Search Google Scholar
    • Export Citation
  • Bréon, F-M., , Jackson D. L. , , and Bates J. J. , 2000: Calibration of the Meteosat water vapor channel using collocated NOAA/HIRS 12 measurements. J. Geophys. Res., 105 , 1192511933.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cao, C., , Xu H. , , Sullivan J. , , McMillin L. , , Ciren P. , , and Hou Y-T. , 2005: Intersatellite radiance biases for the High-Resolution Infrared Radiation Sounders (HIRS) on board NOAA-15, -16, and -17 from simultaneous nadir observations. J. Atmos. Oceanic Technol., 22 , 381395.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clough, S. A., , Kneizys F. X. , , Rothman L. S. , , and Gallery W. O. , 1981: Atmospheric spectral transmittance and radiance: FASCOD1B. Atmospheric transmission, International Society for Optical Engineering (SPIE Proceedings, Vol. 277), 152–166.

    • Search Google Scholar
    • Export Citation
  • Desbiens, R., , Genest J. , , and Tremblay P. , 2002: Radiometry in line-shape modeling of Fourier-transform spectrometers. Appl. Opt., 41 , 14241432.

  • Garand, L., , and Hallé J. , 1997: Assimilation of clear- and cloudy-sky upper-tropospheric humidity estimates using GOES-8 and GOES-9 data. J. Atmos. Oceanic Technol., 14 , 10361054.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gunshor, M. M., , Schmit T. J. , , and Menzel W. P. , 2004: Intercalibration of the infrared window and water vapor channels on operational geostationary environmental satellites using a single polar-orbiting satellite. J. Atmos. Oceanic Technol., 21 , 6168.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gunshor, M. M., , Schmit T. J. , , Menzel W. P. , , and Tobin D. C. , 2009: Intercalibration of broadband geostationary imagers using AIRS. J. Atmos. Oceanic Technol., 26 , 746758.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Held, I. M., , and Soden B. J. , 2000: Water vapor feedback and global warming. Annu. Rev. Energy Environ., 25 , 441475.

  • Jedlovec, G. J., , Lerner J. A. , , and Atkinson R. J. , 2000: A satellite-derived upper-tropospheric water vapor transport index for climate studies. J. Appl. Meteor., 39 , 1541.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Joyce, R., , Janowiak J. , , and Huffman G. , 2001: Latitudinally and seasonally dependent zenith-angle corrections for geostationary satellite IR brightness temperatures. J. Appl. Meteor., 40 , 689703.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knapp, K., 2008: Calibration assessment of ISCCP geostationary infrared observations using HIRS. J. Atmos. Oceanic Technol., 25 , 183195.

  • Larar, A. M., and Coauthors, 2008: The Joint Airborne IASI Validation Experiment (JAIVEx) and select contributions from NAST-I. Preprints, Fourth Symp. on Future National Operational Environmental Satellites, New Orleans, LA, Amer. Meteor. Soc., P1.17.

    • Search Google Scholar
    • Export Citation
  • Menzel, W. P., , and Purdom J. F. , 1994: Introducing GOES-I: The first of a new generation of geostationary operational environmental satellites. Bull. Amer. Meteor. Soc., 75 , 757781.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schmit, T. J., , Prins E. M. , , Schreiner A. J. , , and Gurka J. J. , 2001: Introducing the GOES-M imager. Natl. Wea. Dig., 25 , 2837.

  • Soden, B. J., , and Bretherton F. P. , 1993: Upper tropospheric humidity from the GOES 6.7 μm channel: Method and climatology for July 1987. J. Geophys. Res., 98 , 1666916688.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sohn, B. J., , Schmetz J. , , Tjemkes S. , , Koenig M. , , Lutz H. , , Arriaga A. , , and Chung E. S. , 2000: Intercalibration of the Meteosat-7 water vapor channel with SSM/T-2. J. Geophys. Res., 105 , 1567315680.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sohn, B. J., , Schmetz J. , , and Chung E-S. , 2008: Moistening processes in the tropical upper troposphere observed from Meteosat measurements. J. Geophys. Res., 113 , D13109. doi:10.1029/2007JD009527.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Space Systems Loral, 1996: GOES I-M DataBook. Space Systems Loral Tech. Rep. DRL-101-08, 196 pp.

  • Strow, L. L., , Hannon S. , , Weiler M. , , Overoye K. , , Gaiser S. L. , , and Aumann H. H. , 2003: Prelaunch spectral calibration of the Atmospheric Infrared Sounder (AIRS). IEEE Trans. Geosci. Remote Sens., 41 , 274286.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Su, H., , Read W. G. , , Jiang J. H. , , Waters J. W. , , Wu D. L. , , and Fetzer E. J. , 2006: Enhanced positive water vapor feedback associated with tropical deep convection: New evidence from Aura MLS. Geophys. Res. Lett., 33 , L05709. doi:10.1029/2005GL025505.

    • Search Google Scholar
    • Export Citation
  • Tian, B., , Soden B. J. , , and Wu X. , 2004: Diurnal cycle of convection, clouds, and water vapor in the tropical upper troposphere: Satellites versus a general circulation model. J. Geophys. Res., 109 , D10101. doi:10.1029/2003JD004117.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tobin, D. C., , Revercomb H. E. , , Moeller C. C. , , and Pagano T. , 2006: Use of Atmospheric Infrared Sounder high–spectral resolution spectra to assess the calibration of Moderate resolution Imaging Spectroradiometer on EOS Aqua. J. Geophys. Res., 111 , D09S05. doi:10.1029/2005JD006095.

    • Search Google Scholar
    • Export Citation
  • Tobin, D. C., and Coauthors, 2007: Radiometric and spectral validation of Infrared Atmospheric Sounding Interferometer (IASI) observations. 2007 Conf. on Characterization and Radiometric Calibration for Remote Sensing, Logan, UT Space Dynamics Laboratory.

    • Search Google Scholar
    • Export Citation
  • Tournier, B., , Blumstein D. , , Cayla F-R. , , and Chalon G. , 2002: IASI level 0 and 1 processing algorithms description. Proc. Twelfth Int. TOVS Study Conf., Victoria, Australia, International TOVS Working Group, 12 pp. [Available online at http://cimss.ssec.wisc.edu/itwg/itsc/itsc12/presentations/6d5_B.Tournier.doc].

    • Search Google Scholar
    • Export Citation
  • Wang, L., , and Cao C. , 2008: On-orbit calibration assessment of AVHRR longwave channels on MetOp-A using IASI. IEEE Trans. Geosci. Remote Sens., 46 , 40054013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, L., , Cao C. , , and Ciren P. , 2007: Assessing NOAA-16 HIRS radiance accuracy using simultaneous nadir overpass observations from AIRS. J. Atmos. Oceanic Technol., 24 , 15461561.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weinreb, M. P., , Jamison M. , , Fulton N. , , Chen Y. , , Johnson J. X. , , Bremer J. , , Smith C. , , and Baucom J. , 1997: Operational calibration of Geostationary Operational Environmental Satellite-8 and -9 imagers and sounders. Appl. Opt., 36 , 68956904.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weng, F., 2007: Advances in radiative transfer modeling in support of satellite data assimilation. J. Atmos. Sci., 64 , 37993807.

  • Wimmers, A. J., , and Moody J. L. , 2001: A fixed-layer estimation of upper tropospheric specific humidity from the GOES water vapor channel: Parameterization and validation of the altered brightness temperature product. J. Geophys. Res., 106 , 1711517132.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • View in gallery

    (top) IASI and (bottom) AIRS BT spectra calculated from the community radiative transfer model (CRTM; Weng 2007) overlaid with the water vapor channel SRFs from the MetOp-A HIRS and the GOES-11 and GOES-12 imagers. Note that the IASI BT spectrum is displaced by 80 K in order to separate it from the AIRS spectrum for comparison.

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    An example of a water vapor composite image from GOES-11 and GOES-12 imagers observed at 1200 and 1145 UTC 1 Dec 2007, respectively. The BT values are shown with a linear grayscale from 200 (white) to 280 K (black).

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    GOES-11 water vapor images overlaid with the approximate IASI FOR (black circle) and pixels (small white circles) used in the spatial collocation. The plus symbol indicates the GOES-11 SSP, and the GOES BT image is displayed with a grayscale from 220 (white) to 260 K (black).

  • View in gallery

    BT difference for the GOES-11 and GOES-12 imager water vapor channels varying with satellite zenith angle, calculated for the standard tropical atmosphere profile using MODTRAN.

  • View in gallery

    IASI–GOES BT difference for GOES-11 water vapor channel as a function of the scene uniformity factor, which is defined as the ratio of the standard deviation to the mean of the GOES collocated pixels’ radiances. The vertical line indicates the value 0.01.

  • View in gallery

    IASI–GOES BT difference for the GOES-11 imager water vapor channel vs the scan-time difference. The vertical line indicates the value of 10 min.

  • View in gallery

    Time series of the IASI–GOES BT difference for the GOES-11 water vapor channel, where open circles indicate the day observations, and the triangles denote the night observations.

  • View in gallery

    (left) Scatterplot of the IASI–GOES BT difference vs the GOES scene temperature, and (right) the histogram of the BT differences for the GOES-11 imager water vapor channel. The dotted line indicates the zero line, and the dashed–dotted line shows the mean value.

  • View in gallery

    Scatterplot of the IASI–GOES radiance difference vs the GOES radiance for the GOES-11 imager water vapor channel (a) using the original SRF and (b) using the shifted SRF. The dotted line indicates the zero line, and the dashed–dotted line shows the mean value.

  • View in gallery

    As in Fig. 7, but for the GOES-12 imager water vapor channel. The dashed line indicates the date of 2 Jul 2007, when the decontamination procedure was performed for the GOES-12 imager.

  • View in gallery

    As in Fig. 8, but for the GOES-12 imager water vapor channel. The symbols with blue color indicate the data before the decontamination procedure.

  • View in gallery

    Scatterplot of the GOES-11 and GOES-12 BT for water vapor channels convolved from the IASI spectra, including the datasets coincidental with (a) GOES-11 and (b) GOES-12. The linear regression (solid line) coefficients are summarized in Table 2.

  • View in gallery

    Scatterplot of the GOES-12 and GOES-11 water vapor channel BT difference varying with the GOES-11 BT convolved from the IASI spectra, including the datasets coincidental with (a) GOES-11 and (b) GOES-12. The linear regression (solid line) is derived from Fig. 12.

  • View in gallery

    As in Fig. 2, but after the GOES-11 BT correction to match GOES-12.

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Intercalibration of GOES-11 and GOES-12 Water Vapor Channels with MetOp IASI Hyperspectral Measurements

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  • 1 Perot Systems Government Services, Fairfax, Virginia
  • | 2 NOAA/NESDIS/STAR, Camp Springs, Maryland
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Abstract

The calibrated radiances from geostationary water vapor channels play an important role for weather forecasting, data assimilation, and climate studies. Therefore, better understanding the data quality for radiance measurements and independently assessing their onboard calibrations become increasingly more important. In this study, the Infrared Atmospheric Sounding Interferometer (IASI) hyperspectral measurements on the polar-orbiting Meteorological Operation-A (MetOp-A) satellite are used to assess the calibration accuracy of water vapor channels on the Geostationary Operational Environmental Satellite-11 (GOES-11) and GOES-12 imagers with one year of data. The near-simultaneous nadir observations with homogeneous scenes from IASI and GOES imagers are spatially collocated. The IASI spectra are convolved with the GOES imager spectral response functions (SRFs) to compare with GOES imager observations. Assuming that IASI is well calibrated and can be used as an on-orbit radiometric reference standard, then the GOES imager water vapor channels have an overall relative calibration bias to IASI of better than 0.3 K (with a standard deviation of ∼0.2 K) at the brightness temperature (BT) range of 240–260 K, which meets the design specification (1.0-K calibration accuracy for infrared channels). This study further demonstrates the technique of using hyperspectral radiance measurements in a polar-orbiting satellite to accurately assess broadband radiometer calibration of the GOES imager, which also provides an effective way for monitoring sensor performance over time. In addition, the potential of using the intercalibration results to integrate and merge data from different observing systems involving both IASI and different GOES imagers to create consistent, seamless global products is explored. The method presented here can potentially be applied to other instruments on both polar-orbiting and geostationary satellites for generating long-term time series.

Corresponding author address: Dr. Likun Wang, 5200 Auth Rd., Rm. 810, Camp Springs, MD 20746. Email: likun.wang@noaa.gov

Abstract

The calibrated radiances from geostationary water vapor channels play an important role for weather forecasting, data assimilation, and climate studies. Therefore, better understanding the data quality for radiance measurements and independently assessing their onboard calibrations become increasingly more important. In this study, the Infrared Atmospheric Sounding Interferometer (IASI) hyperspectral measurements on the polar-orbiting Meteorological Operation-A (MetOp-A) satellite are used to assess the calibration accuracy of water vapor channels on the Geostationary Operational Environmental Satellite-11 (GOES-11) and GOES-12 imagers with one year of data. The near-simultaneous nadir observations with homogeneous scenes from IASI and GOES imagers are spatially collocated. The IASI spectra are convolved with the GOES imager spectral response functions (SRFs) to compare with GOES imager observations. Assuming that IASI is well calibrated and can be used as an on-orbit radiometric reference standard, then the GOES imager water vapor channels have an overall relative calibration bias to IASI of better than 0.3 K (with a standard deviation of ∼0.2 K) at the brightness temperature (BT) range of 240–260 K, which meets the design specification (1.0-K calibration accuracy for infrared channels). This study further demonstrates the technique of using hyperspectral radiance measurements in a polar-orbiting satellite to accurately assess broadband radiometer calibration of the GOES imager, which also provides an effective way for monitoring sensor performance over time. In addition, the potential of using the intercalibration results to integrate and merge data from different observing systems involving both IASI and different GOES imagers to create consistent, seamless global products is explored. The method presented here can potentially be applied to other instruments on both polar-orbiting and geostationary satellites for generating long-term time series.

Corresponding author address: Dr. Likun Wang, 5200 Auth Rd., Rm. 810, Camp Springs, MD 20746. Email: likun.wang@noaa.gov

1. Introduction

The upper-tropospheric humidity (UTH) fields, which are defined as the water vapor amount between about 600 and 200 hPa, have a significant effect on outgoing longwave radiation and, consequently, influence the earth’s climate system (Held and Soden 2000, and references therein). The calculation of UTH from satellite radiance measurements—especially radiance measurements from geostationary satellites with the capability to observe the time variability of UTH with high resolution—has proven useful not only for monitoring changes in water vapor in the upper troposphere on a global or regional scale, but also for validating climate models (e.g., Soden and Bretherton 1993; Jedlovec et al. 2000; Wimmers and Moody 2001; Tian et al. 2004; Su et al. 2006; Sohn et al. 2008). In addition, the calibrated radiance product from geostationary water vapor channels is one of the important inputs for data assimilation for numerical weather predication models (e.g., Garand and Hallé 1997). Therefore, better understanding of the data quality of radiance measurements and independently assessing their onboard calibration become increasingly more important.

A common method to assess geostationary satellite measurements is to compare measured radiances with a well-calibrated instrument on a low orbiting satellite. For example, Bréon et al. (2000) used observations from the High Resolution Infrared (IR) Radiation Sounder (HIRS) water vapor channels (channel 12) and Sohn et al. (2000) explored the observations by the Special Sensor Microwave T2 sounder to derive calibration coefficients for the Meteorological Satellite-5 (Meteosat-5) and Meteosat-7 Meteosat Visible and Infrared Radiation Imager (MVIRI) water vapor channels that do not have onboard calibration. Gunshor et al. (2004) used the observations from HIRS and Advanced Very High Resolution Radiometer (AVHRR) on polar-orbiting satellites to evaluate the measurements from geostationary imagers. To study diurnal cycle of water vapor in the tropical upper troposphere, Tian et al. (2004) intercalibrated the geostationary satellite measurements and then directly corrected geostationary satellite water vapor measurements based on HIRS on the National Oceanic and Atmospheric Administration-14 (NOAA-14) as a common reference standard. Recently, as an independent calibration assessment, Knapp (2008) compared the International Satellite Cloud Climatology Project (ISCCP) calibration of geostationary observations to the observations from HIRS channel 8 in the infrared window. Using a calibrated microwave instrument to intercalibrate thermal IR channels involves radiative transfer calculations in both the infrared and microwave domains. The radiative transfer calculations have uncertainties, and the footprint size of microwave instrument is also much larger than that of IR instruments. Both can introduce comparison uncertainties. The intercalibration method used for collocating HIRS observations, however, often suffers from the differences in the spectral response functions (SRFs) between HIRS and geostationary IR instruments (see water vapor channel SRFs in Fig. 1), which cannot be resolved accurately.

Recent studies indicate that well-calibrated hyperspectral radiances can effectively evaluate the onboard calibration accuracy of broad- or narrowband IR instruments sharing the same spectral regions (Tobin et al. 2006; Wang et al. 2007; Wang and Cao 2008; Gunshor et al. 2009). Gunshor et al. (2009) intercompared the high–spectral resolution Atmospheric Infrared Sounder (AIRS) on Aqua with geostationary imagers. However, AIRS has spectral gaps by design [see the AIRS brightness temperature (BT) spectrum in Fig. 1]. For the channels on geostationary imagers where AIRS has spectral gaps, especially the water vapor channels, the corresponding gap-filling technique has to be developed to account for the missing values, which unavoidably results in comparison uncertainties (Gunshor et al. 2009).

In this study, we use the Infrared Atmospheric Sounding Interferometer (IASI) hyperspectral measurements on the polar-orbiting Meteorological Operation-A (MetOp-A) satellite to intercalibrate water vapor channels on the Geostationary Operational Environmental Satellite-11 (GOES-11) and GOES-12 imagers with one year of data. In particular, the near-simultaneous nadir observations with homogeneous scenes from IASI and GOES imagers are spatially collocated. The IASI spectra are convolved with the GOES imager SRFs to compare with GOES imager observations. The primary purpose of this study is to independently assess onboard calibration of GOES imager water vapor channels and to examine its instrument calibration uncertainties compared with IASI.

It has been suggested that intercalibration can also help to optimally integrate and merge data from different observing systems to create consistent, seamless global products (e.g., Tian et al. 2004; Knapp 2008). However, a discrepancy often arises in creating a global composite water vapor image by merging the observations from different geostationary satellites, even though both satellites observe the earth simultaneously. Figure 2 shows an example of a water vapor composite image, which was created by merging the measurements on 1 December 2007 from the GOES-11 and GOES-12 imager water vapor channels (with 15-min observational time difference). Apparently, the GOES-12 imager is darker (warmer) than that of GOES-11 (indicated by the black arrow). Our analysis shows that this discrepancy is mainly due to the spectral response difference because the GOES-12 water vapor channel with a broader SRF (see Fig. 1) observes deeper into the atmosphere (or lower altitude of the atmosphere; Schmit et al. 2001), though the different view geometries and instrument calibration may also have some contributions. It is believed that IASI, when used as a transfer radiometer, can help resolve these discrepancies and create the link between the GOES-11 and GOES-12 water vapor channels, which is a secondary goal of this study.

This paper is organized as follows: Section 2 describes the method of using the hyperspectral IASI measurements to intercalibrate the water vapor channels of the GOES-11 and GOES-12 imagers. Section 3 presents the intercalibration results. The potential method for resolving the observational discrepancies between the GOES-11 and GOES-12 water vapor channels is discussed in section 4. The paper concludes with section 5.

2. Method

a. Instruments

The GOES satellite imager is a five-channel (one visible and four infrared channels) imaging radiometer designed to sense spectral radiances reflected and emitted from the earth and the atmosphere. During normal operation, GOES-11 and GOES-12 are positioned at 135° and 75°W, respectively. The GOES-12 imager has a new channel at 13.3 μm, replacing the 12.0-μm channel on GOES-11 (Schmit et al. 2001). The water vapor channel on the GOES-11 imager employs a square detector with an instantaneous geometric field of view (IGFOV) of 224 μrad corresponding to a square pixel with 8.0 km per side at the suborbital point (Space Systems Loral 1996). Because of the combination of scan rate (20° s−1) and detector sample rate (5460 samples per second for the IR channels), each sample step corresponds to an angle of 64 μrad for the IR channels along a scan line. In other words, the GOES imager oversamples infrared IGFOVs 4 and 8 km along a scan line by factors of 1.75 and 3.5, respectively (Menzel and Purdom 1994). As a result, for the water vapor channel, the GOES-11 imager gives a derived sampled subpoint resolution (SSR) of 2.3 km in the east–west direction, still with 8 km in the north–south direction. For the GOES-12 imager, the water vapor channel uses two 4-km detectors, and its spectral response is shifted slightly and broadened, giving it a nominal center wavelength near 6.5 μm (compared to 6.7 μm for GOES-11; see their SRFs in Fig. 1). Consequently, its IGFOV has a resolution of 4 km × 4 km at the subsatellite point (SSP), resulting in a derived SSR of 2.3 km × 4 km with an oversampling factor of 1.75. During the instrument operation, the infrared channels are frequently calibrated based on observations of space and an onboard blackbody (Menzel and Purdom 1994; Space Systems Loral 1996; Weinreb et al. 1997). The GOES imager data were archived as the Man-computer Interactive Data Access System (McIDAS) format. The GOES-11 data for the water vapor channel have been processed to a new scan-line resolution of 4 km—the same as that of GOES-12.

IASI, successfully launched on MetOp-A in October 2006, is the first operational interferometer in space. It measures radiation emitted from the surface and atmosphere in the 645–2760 cm−1 (i.e., 3.6–15.5 μm) spectrum with high spectral resolution (i.e., 8461 spectral channels with a spectral sampling interval of 0.25 cm−1). MetOp-A was placed in a sun-synchronous polar orbit at 820 km with the local equator crossing time of 2130 UTC in ascending node. The IASI observations are obtained by a step scanning mirror covering a ±47.85° range in 30 steps, with 3.3° for each step (normal mode). At each step, the field of regard (FOR) includes 2 × 2 instantaneous FOV (IFOV), each with 1.25° (pixel resolution of 12 km at nadir) positioned in the cross-track and along-track directions located at ±0.825° (see Fig. 3). The IASI calibration relies on the measurement of cold and warm reference targets once every scan line (i.e., deep space and onboard blackbody; Blumstein et al. 2004).

Focusing on the spectral and radiometric accuracy of the IASI radiance measurements, the Joint Airborne IASI Validation Experiment (JAIVEx) has been carried out from 14 April to 4 May 2007 by a joint team from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) and National Aeronautics and Space Administration (NASA; Larar et al. 2008). Preliminary comparison of the measurements between IASI and the aircraft sensor indicates that IASI has operated well within specifications, maintaining superb spectral and radiometric calibration accuracy (Tobin et al. 2007). Blumstein et al. (2007) reported the intercalibration results between IASI and AIRS at the orbital crossing point of MetOp-A and Aqua occurring at high latitude for cold scenes by comparing the broadband pseudochannels generated by averaging the IASI and AIRS individual channels in selected spectral regions. The difference between IASI and AIRS is less than 0.1 K for the nine pseudochannels from 600 to 1500 cm−1. In-flight performance of IASI on MetOp-A also has been fully characterized by the IASI Technical Expertise Center in the Centre National d’Études Spatiales (CNES), and the results suggest that the IASI spectral and radiometer calibration was very stable during the time period from January 2007 to July 2007. Because of its high spectral resolution nature and high-quality measurements, the IASI hyperspectral measurements can serve as a reference for the measured radiances for broad- or narrowband instruments to accurately evaluate their onboard calibration.

b. Data processing

Four major steps are used in this study for the intercalibration of the GOES imager water vapor channel using the IASI hyperspectral radiance: 1) spectral convolution, 2) data collocation for near-simultaneous nadir observations, 3) spatial collocation, and 4) statistical calculations for comparison. These steps are described in this section.

The objective of spectral convolution is to integrate the high-resolution IASI spectrum to match the broadband GOES imager SRF and make it comparable with the GOES imager observations. Given the IASI hyperspectral radiance R(ν) at each wavenumber ν with 8461 samples, it can be convolved with the GOES imager SRF S(ν) to generate the IASI-convolved GOES imager water vapor channel radiance L as
i1520-0426-26-9-1843-e1
where ν1 and ν2 are bandpass limits (GOES imager SRFs are available online at http://cimss.ssec.wisc.edu/goes/calibration/).

To reduce the uncertainties caused by the difference of observational time and view geometries as well as scene homogeneity, we only focus on near-simultaneous nadir observations from IASI and GOES imagers with a uniform scene in this study. Table 1 summarizes the specific constrains for data collection. As a polar-orbiting satellite, MetOp-A passes the satellite subpoints of GOES-11 (0°, 135°W) and GOES-12 (0°, 75°W) at 0630 or 1830 UTC and 0230 or 1430 UTC, respectively, a few times each month. When this happens, IASI and the GOES imagers from two satellites view the earth and the atmosphere at nadir at the same place nearly simultaneously. During the data processing, both IASI and GOES imager data are restricted to the measurements with satellite view zenith angle less than 10° for nadir viewing.

Data collocation involves mapping the high spatial resolution GOES imager pixels (4-km pixel resolution at nadir) to the lower spatial resolution IASI pixels (12-km resolution at nadir) and making them spatially suitable for comparison. Ideally, the IASI point spread function should be used for the pixel collocation. To simplify the implementation, a simple method was used for data collocation. Every IASI FOR has an instantaneous angular FOV of 3.3° × 3.3°, which is equivalent to an atmospheric cell of about 50 km × 50 km at nadir. As shown in Fig. 3, assuming a circular FOR of 3.3° yields a geolocated nadir FOR of 50.0 km in diameter, each of which includes 2 × 2 pixels with an IFOV of 12 km at nadir. The GOES pixels are collected within the corresponding IASI FOR assumed to be a 50.0-km-diameter circle. The mean radiance of these GOES imager pixels are then computed to compare with the mean radiance value of four IASI pixels in each corresponding IASI FOR. The scene uniformity factor—defined as the ratio of the standard deviation to the mean of the GOES radiance within each IASI FOR—is computed and retained for selection of spatially uniform scenes. The comparison is only performed for the nadir GOES and IASI pixels (both sensors’ satellite zenith angle less than 10°) when the following criteria are met: 1) the observational time difference is less than 10 min (simultaneity), 2) the satellite zenith angle difference is less than 1° (view geometry), and 3) the scene homogeneity factor is less than 0.01 (scene homogeneity). The comparison is carried out at the IASI FOR level rather than the pixel level because it accumulates enough GOES imager pixels (more than 200 pixels) for each IASI FOR and hence produces robust statistics for comparison.

c. Uncertainty analysis

The intercalibration uncertainties are generally related to the sensor view geometry, spatial collocation, spectral convolution, and observational time differences. The uncertainties caused by the sensor view geometry are minimal because this study is limited to the nadir observations with small satellite zenith angle difference. Figure 4 presents the BT difference for the GOES-11 and GOES-12 imagers water vapor channel varying with satellite zenith angle calculated for the standard tropical atmosphere by the Moderate Spectral Resolution Atmospheric Transmittance (MODTRAN; Berk et al. 2001). It shows that, when both satellite zenith angles are limited to <10°, a 1° difference in satellite zenith angle produces a BT difference of less than 0.02 K.

The scene nonuniformity also can cause intercalibration uncertainties. First, the IASI level 1c spectrum is an apodized and spectrally calibrated spectrum. The spectral calibration is only assumed for homogeneous scenes in the FOV. The presence of nonhomogeneous scenes in the IASI FOV introduces a spectral distortion because of the effect of nonhomogeneous scenes on the instrument spectral response function (ISRF; Desbiens et al. 2002; Tournier et al. 2002). However, after convolving the IASI observations over the GOES SRFs, the differences because of this effect can be reduced to a negligible level. Second, uniform scene within the collocation is desirable to compensate for minor violations of collocation and concurrence as well as to reduce the uncertainties resulting from navigation. Figure 5 shows the IASI–GOES BT difference for GOES-11 water vapor channel as a function of the scene uniformity factor (see the definition above). With a larger value of the scene uniformity factor, the spread of the BT difference becomes larger or vice versa. The values of the scene uniformity factor provide a practical way to constrain scene homogeneity to reduce the comparison uncertainties, especially when a smaller value is chosen. In this study, we choose the value of 0.01 (or equivalent to a signal-to-noise ratio of 100) to select spatially homogeneous scenes.

It is known that the IASI spectrum is not exactly monochromatic but has finite spectral sampling resolution of 0.25 cm−1, which can lead to some errors in spectral convolution. To address this issue, simulated clear-sky monochromatic and IASI spectrum using the Line-by-Line Radiative Transfer Model (LBLRTM; Clough et al. 1981) are generated. It was found that the convolution error resulting from the IASI spectral resolution effect (being not monochromatic) is less than 0.02% when using IASI to simulate the broad- or narrowband instruments.

The observational time difference of IASI and GOES imagers can also contribute some uncertainties. Instead of continuously scanning the earth, each GOES imager has its own routine scan schedule during operations. In other words, each GOES imager observes the portion of the earth at different times. For example, when MetOp-A passes the GOES SSPs, the available GOES observations closest to the IASI observations are the Northern Hemisphere scan for GOES-11 and extended Northern Hemisphere scan for GOES-12 (the scan schedule for the GOES-11 and GOES-12 imagers are available online at http://www.oso.noaa.gov/goes/schd-sector/index.htm). As a result, there are more samples for GOES-12 than GOES-11. Figure 6 presents the IASI–GOES BT difference for the GOES-11 imager water vapor channel versus the scan time difference. However, tightening time collocation criteria reduces both the standard deviation of the IASI–GOES BT difference and the number of samples. In this study, the 10-min observational time difference is chosen as a compromise.

Based on the above discussion, the intercalibration uncertainties related to the sensor view geometry, spatial collocation, spectral convolution, and satellite observational time are reduced by limiting the comparisons to the observations that meet the data processing criteria. We thus believe that the comparison results in this study are mainly related to the instruments themselves instead of the method used in this study.

3. Results

Using the method discussed previously, we processed the IASI and GOES imagers water vapor channel data from March 2007 to May 2008 and found a total of 255 and 459 comparison pairs for GOES-11 and GOES-12 imagers with IASI, respectively. Because the MetOp-A IASI level 1 product was declared operational on 26 July 2007, the comparison dataset also includes the IASI preoperational products.

a. GOES-11

Figure 7 shows the time series of the IASI–GOES BT difference for the GOES-11 water vapor channel, in which the different symbols indicate the day (open circles) and night (triangles) observations. Statistically, the water vapor channel for the GOES-11 imager observed a slightly warmer BT than IASI at a range of 240–260 K and the mean BT difference is −0.271 K, with a standard deviation of 0.170 K. A slight seasonal variation of the BT difference can be found in Fig. 7, with a minimum peak around July. It is reasonable to believe that these seasonal variations are related to the scene temperature; thus, the IASI–GOES BT difference versus the GOES-observed scene temperature is examined, which is given in Fig. 8. As indicated by the linear regression line, the scene temperature–dependent BT difference can be clearly seen: the higher the observed scene temperature, the larger the negative BT difference.

Recent studies by Tobin et al. (2006) and Wang et al. (2007) showed that the SRF uncertainties can cause the scene temperature–dependent bias. According to the previous study on HIRS and AIRS, the instrument SRF can shift as the operating condition changes, whereas its shapes are relatively stable (Strow et al. 2003; Cao et al. 2005). The GOES imager SRFs are determined based on prelaunch measurements at ambient temperatures and extrapolated to operating temperatures, which can introduce SRF uncertainties. To investigate this effect, we shifted the water vapor channel’s SRF on the GOES-11 imager with 1.2 cm−1 toward smaller wavenumber and then reconvolved the shifted SRF with the IASI hyperspectral measurements to compare with the GOES observations. Shown in Fig. 9, we replotted Fig. 8 in the radiance domain. The new IASI-convolved radiances based on a shifted SRF are also given in Fig. 9. It can be seen that, with a shifted SRF, the mean radiance difference is reduced to near zero, but the scene radiance dependence is slightly changed (the slope of regression line changes to −0.025 from −0.033). In other words, this scene temperature–dependent BT difference cannot be totally explained by the spectral uncertainties, though it does change the overall BT difference. Physically, this result should come as no surprise. Compared to the sharp slope of CO2 absorption regions, the slope in the water vapor absorption region is not very steep (see Fig. 1). The shift of the GOES imager water vapor channel SRF toward smaller wavenumbers causes its weighting function to move to a lower altitude (closer to the surface) but does not significantly change the scene temperature dependency. Therefore, we believe that other factors, such as detector nonlinearity, may contribute to this scene temperature–dependent bias.

The nonlinearities in sensor response, which primarily affect the long- and midwave channels, are well known to cause temperature-dependent biases. A nonlinear correction has been performed during the GOES imager calibration (Weinreb et al. 1997). The correction coefficients, however, were determined based on prelaunch measurements from the instrument vendor (Weinreb et al. 1997). We suspect that the nonlinearity corrections may not be fully adequate for instrument on-orbit operations, resulting in the temperature-dependent bias with a range of 0.1–0.2 K. However, this requires further investigation.

b. GOES-12

The time series of the IASI–GOES BT difference for GOES-12 water vapor channel is shown in Fig. 10. Also, Fig. 11 provides the IASI–GOES BT difference as a function of the scene temperature and its histogram. The most remarkable feature of Fig. 10 is that the IASI–GOES BT difference has a sudden jump after 2 July 2007 (indicated by the dashed line), where the mean BT difference jumped from −0.302 to 0.202 K (with standard deviations of 0.137 and 0.140 K, respectively). This is because a GOES-12 imager “decontamination procedure” was performed from 2 to 4 July 2007, where certain internal components (e.g., the cooler radiator) were warmed up in an attempt to drive off contaminants (such as gaseous and particulate contamination) that had been accumulating for several years. IASI successfully tracked the calibration bias changes resulting from the decontamination procedure. In particular, after the decontamination, the GOES-12 imager water vapor channel observed 0.5 K colder BT. This result agrees well with the analysis by Gunshor et al. (2009), who used AIRS to intercalibrate the GOES-12 imager. However, further investigation is still needed to understand the physical reason behind this phenomenon. Note that, in contrast to the case of GOES-11, the BT difference for the GOES-12 water vapor channel does not show an apparent seasonal variation or may be overshadowed by the instrument sudden change.

A careful look at the BT difference results for both GOES-11 and GOES-12 (Figs. 8, 11) reveal slight differences for day and night observations on the order of ∼0.1 K, which is more obvious for GOES-12. In other words, for the same BT, the GOES imager water vapor channel observed a little warmer BT during nighttime. In his paper on GOES imager calibration, Weinreb et al. (1997) indicated that the calibration slopes of the GOES imagers experienced a diurnal cycle, which was due to the variations in the background radiation on the detectors. We suspect that this may be related to the day–night difference here.

In general, with the assumption that IASI is a well-calibrated instrument and can be used as a stable reference standard, the GOES imager water vapor channels have a calibration accuracy relative to IASI of better than 0.3 K in the BT range of 240–260 K (with a standard deviation of less than 0.2 K), which meets the design specification (1.0-K calibration accuracy; Space Systems Loral 1996).

c. Spectral response difference

Shown in Fig. 1, the spectral response functions of GOES-11 and GOES-12 water vapor channels are not independent but are overlapped. It implies that their observed BTs are not independent but must be highly correlated. In the above section, we have evaluated the onboard calibration accuracy of the GOES-11 and GOES-12 water vapor channels. The results indicate that the GOES imager water vapor channels have a calibration accuracy relative to IASI of better than 0.3 K in the BT range of 240–260 K. In other words, the IASI-simulated channels can well represent the GOES imager observations. It is encouraging enough to merit using a wide variety of atmosphere spectra observed by IASI to simulate the GOES-11 and GOES-12 observations. We can examine how they are correlated and further evaluate the SRF difference–caused bias between GOES-11 and GOES-12. This method allows us to find the relationship that links the GOES-11 and GOES-12 water vapor channels, which can be applied to correct the GOES-11 observations to match GOES-12 or vice versa to seamlessly merge their observations.

We believe that the method based on the IASI spectra has some advantages compared to the one that directly compares the collocated GOES-11 and GOES-12 observations because the later method is confined to the limited samples from the satellite overlapped region and will not work for satellites that do not overlap (e.g., GOES-11 and Meteosat-9). It is also possible to use radiative transfer models to simulate the SRF-induced biases, which, however, has limitations because the models cannot realistically simulate a large variability of the atmosphere globally (e.g., when clouds and precipitation exist).

The 1-yr IASI orbits that underpass the GOES-11 and GOES-12 SSPs have been selected but confined to a region from 70°S to 70°N. Only two IASI FORs at nadir, with eight pixels for each scan line, are used. In total, we found 12 654 and 12 651 samples for the observations passing over GOES-11 and GOES-12, respectively. The IASI spectra are first convolved with the GOES-11 and GOES-12 water vapor SRFs to produce the IASI-convolved GOES imager radiances. The resultant radiance value is then converted to brightness temperature using the inverse Planck function and band correction coefficients. Given in Fig. 12, scatterplots of GOES-11 versus GOES-12 BT for two datasets suggest that they are highly correlated, which is what we expect. Therefore, the following linear regression model is used to establish the correlation between the GOES-11 and GOES-12 water vapor channels:
i1520-0426-26-9-1843-e2
where a and b are the slope and intercept. The statistical results are summarized in Table 2. The correlation coefficients and 1-sigma uncertainty estimates for the slope and intercept are the numerical measures of goodness of fit represented by the model. The 1-sigma uncertainty estimates for slope indicate that the estimated slope value is accurate to the fourth digit after the decimal point. The last column, the standard deviation of the residual, is a measure of how well the model predicts the exact GOES-12 water vapor channel BT given the GOES-11 water vapor channel BT. In other words, it is expected to have a 0.7-K (∼0.3%) uncertainty when one predicts the GOES-12 BT using the GOES-11 BT in the temperature range of 200–265 K. The original ∼3.0-K BT difference between GOES-11 and GOES-12 water vapor channels resulting from the spectral response difference can be resolved with 0.7-K uncertainty based on the linear relationship shown above.

Figure 13 shows the BT difference between the GOES-12 and GOES-11 imager water vapor as a function of the GOES-11 BT. The BT difference resulting from the SRF difference is highly dependent on the observed scene temperature, but the relationship is nonlinear. It is acknowledged that the linear regression line is only good for modeling the trend of their relationship but cannot fully characterize it. This implies that the SRF difference–caused bias for the GOES imager water vapor channels is more complex, which is related to the water vapor amount and, to a lesser extent, to the temperature of a broad layer from approximately 200–600 hPa as well as cloud converge and properties.

Further study is required to fully understand the observational difference caused by the spectral response difference. However, the linear model proposed in this paper is good enough for a first order of magnitude correction.

4. Merging data from GOES-11 and GOES-12

In this section, we show how the intercalibration results can be used to resolve the observational discrepancy between the GOES-11 and GOES-12 water vapor channels. There are three steps involved: 1) limb correction, 2) tying GOES measurements to IASI, and 3) resolving the SRF difference–induced biases.

The brightness temperature measured by the GOES imager (or any other instrument) is affected by the view angle associated with the given field of view (i.e., the measured brightness temperature changes as a function of view angle). The magnitude of this effect varies with wavelength and the atmosphere that the field of view covers. This effect can be demonstrated in Fig. 4, calculated from MODTRAN for the GOES-11 and GOES-12 water vapor channels for the standard tropical atmosphere. To eliminate this effect, a limb correction procedure must be performed to normalize the GOES off-nadir observations to a fixed view angle (Joyce et al. 2001). Although there are different limb correction algorithms, in this study a simple method is used based on the scan angle effect calculated in Fig. 4 to adjust the off-nadir observations to nadirlike ones.

The second step is to tie the GOES measurements to the IASI radiances, which is assumed as the absolute “truth.” Because each instrument has its own onboard calibration, the calibration difference can introduce discrepancies in the merged observations. The correction value can be obtained from the intercalibration results shown in Figs. 8 and 11, as discussed in the previous section. It should be noted that only the GOES-12 observations after the decontamination in Fig. 11 are used for the correction coefficients.

In the third step, the measurement difference caused by the spectral response difference is resolved. The linear relationship proposed in Fig. 12a is used to convert the GOES-11 water vapor channel measurements into the corresponding GOES-12 ones (or the other way around). In these three steps, IASI acts as a transfer radiometer and links the water vapor channels for GOES-11 and GOES-12 imagers. Figure 14 shows the image after the GOES-11 measurements were converted to match GOES-12, where the discrepancy between the two images is mostly removed (indicated by the black arrow).

5. Conclusions

Because the calibrated radiance product from GOES imager water vapor channels plays an important role for weather forecasting, data assimilation, and climate studies, better understanding of the data quality of radiance measurements and independently assessing its onboard calibration become increasingly needed. In this study, we use the IASI hyperspectral measurements on the polar-orbiting MetOp-A satellite to assess the calibration accuracy of water vapor channels on the GOES-11 and GOES-12 imagers with one year of match-up data. The near-simultaneous nadir observations with homogeneous scenes from IASI and GOES imagers are spatially collocated. The IASI spectra are convolved with the GOES imager SRFs to compare with GOES imager observations. The following conclusions can be made based on our analysis:

  1. assuming that IASI is well calibrated and can be used as a radiometric reference standard, then the GOES imager water vapors has estimated calibration accuracy of better than 0.3 K (with a standard deviation of ∼0.2 K) in the BT range of 240–260 K relative to IASI, which meets the GOES imager design specification (1.0-K calibration accuracy for infrared channels);
  2. IASI was able to detect the calibration bias changes during the decontamination procedure on the GOES-12 imager. After the decontamination, the observed BT changed by 0.5 K;
  3. a slight seasonal variation of the IASI-minus-GOES BT difference for the GOES-11 water vapor channel is observed, which appears to be related to the scene temperature, and we believe that this scene temperature–dependent bias is caused by the combination of detector nonlinearity and spectral uncertainties;
  4. a slight difference for day and night observations has been found on the order of ∼0.1 K for both GOES-11 and GOES-12 water vapor channels; and
  5. the BT difference caused by the spectral difference of the GOES-11 and GOES-12 water vapor channels is investigated, and it is found that the BT difference (ranging from 0.1 to 5.0 K) caused by the SRF difference is highly dependent on the observed scene temperature but with a nonlinear relationship.

The potential use of the intercalibration results to optimally integrate and merge data from GOES-11 and GOES-12 imager water vapor channels to create consistent, seamless global products is explored. An example of creating a water vapor composite image from the GOES-11 and GOES-12 to resolve their observational discrepancy is presented step by step. It demonstrates the possibility of using high spectral resolution radiance measurements to accurately resolve the observational difference caused by the spectral response difference for broad- and narrowband radiometers. Future study will extend this method to the water vapor channels on other geostationary imagers as well as HIRS toward creating consistent, seamless global products or time series.

In closing, we would like to point out that this study is only limited to the water vapor channel and it does not take into account off-nadir observations. In the future, we will extend this study to other channels and also include off-nadir observations, which will provide a more thorough evaluation of the geostationary imager calibration.

Acknowledgments

The authors wish to thank Drs. Quanhua Liu and Jerry Sullivan for their critical review and judicious comments. We also would like to thank Xianqian (Fred) Wu, William Smith, Michael Weinreb, Fangfang Yu, and Yaping Li for helpful discussion and advice for data processing. Dr. Yong Chen provided the CRTM, IASI, and AIRS spectra. This work is partially funded by the NOAA/NESDIS/Center for Satellite Applications and Research (STAR) and the GOES-R program office. The manuscript contents are solely the opinions of the authors and do not constitute a statement of policy, decision, or position on behalf of NOAA or the U.S. government.

REFERENCES

  • Berk, A., , Anderson G. P. , , Acharya P. K. , , Chetwynd J. H. , , Bernstein L. S. , , Shettle E. P. , , Matthew M. W. , , and Adler-Golden S. M. , 2001: MODTRAN4 version 2 user’s manual. Air Force Research Laboratory, 98 pp.

    • Search Google Scholar
    • Export Citation
  • Blumstein, D., and Coauthors, 2004: IASI instrument: Technical overview and measured performances. Infrared Spaceborne Remote Sensing XII, M. Strojnik, Ed., International Society for Optical Engineering (SPIE Proceedings, Vol. 5543), 196–207.

    • Search Google Scholar
    • Export Citation
  • Blumstein, D., , Tournier B. , , Cayla F. R. , , Phulpin T. , , Fjortoft R. , , Buil C. , , and Ponce G. , 2007: In-flight performance of the infrared atmospheric sounding interferometer (IASI) on MetOp-A. Atmospheric and Environmental Remote Sensing Data Processing and Utilization III: Readiness for GEOSS, M. D. Goldberg et al., Eds., International Society for Optical Engineering (SPIE Proceedings, Vol. 6684), 66840H, doi:10.1117/12.734162.

    • Search Google Scholar
    • Export Citation
  • Bréon, F-M., , Jackson D. L. , , and Bates J. J. , 2000: Calibration of the Meteosat water vapor channel using collocated NOAA/HIRS 12 measurements. J. Geophys. Res., 105 , 1192511933.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cao, C., , Xu H. , , Sullivan J. , , McMillin L. , , Ciren P. , , and Hou Y-T. , 2005: Intersatellite radiance biases for the High-Resolution Infrared Radiation Sounders (HIRS) on board NOAA-15, -16, and -17 from simultaneous nadir observations. J. Atmos. Oceanic Technol., 22 , 381395.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clough, S. A., , Kneizys F. X. , , Rothman L. S. , , and Gallery W. O. , 1981: Atmospheric spectral transmittance and radiance: FASCOD1B. Atmospheric transmission, International Society for Optical Engineering (SPIE Proceedings, Vol. 277), 152–166.

    • Search Google Scholar
    • Export Citation
  • Desbiens, R., , Genest J. , , and Tremblay P. , 2002: Radiometry in line-shape modeling of Fourier-transform spectrometers. Appl. Opt., 41 , 14241432.

  • Garand, L., , and Hallé J. , 1997: Assimilation of clear- and cloudy-sky upper-tropospheric humidity estimates using GOES-8 and GOES-9 data. J. Atmos. Oceanic Technol., 14 , 10361054.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gunshor, M. M., , Schmit T. J. , , and Menzel W. P. , 2004: Intercalibration of the infrared window and water vapor channels on operational geostationary environmental satellites using a single polar-orbiting satellite. J. Atmos. Oceanic Technol., 21 , 6168.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gunshor, M. M., , Schmit T. J. , , Menzel W. P. , , and Tobin D. C. , 2009: Intercalibration of broadband geostationary imagers using AIRS. J. Atmos. Oceanic Technol., 26 , 746758.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Held, I. M., , and Soden B. J. , 2000: Water vapor feedback and global warming. Annu. Rev. Energy Environ., 25 , 441475.

  • Jedlovec, G. J., , Lerner J. A. , , and Atkinson R. J. , 2000: A satellite-derived upper-tropospheric water vapor transport index for climate studies. J. Appl. Meteor., 39 , 1541.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Joyce, R., , Janowiak J. , , and Huffman G. , 2001: Latitudinally and seasonally dependent zenith-angle corrections for geostationary satellite IR brightness temperatures. J. Appl. Meteor., 40 , 689703.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knapp, K., 2008: Calibration assessment of ISCCP geostationary infrared observations using HIRS. J. Atmos. Oceanic Technol., 25 , 183195.

  • Larar, A. M., and Coauthors, 2008: The Joint Airborne IASI Validation Experiment (JAIVEx) and select contributions from NAST-I. Preprints, Fourth Symp. on Future National Operational Environmental Satellites, New Orleans, LA, Amer. Meteor. Soc., P1.17.

    • Search Google Scholar
    • Export Citation
  • Menzel, W. P., , and Purdom J. F. , 1994: Introducing GOES-I: The first of a new generation of geostationary operational environmental satellites. Bull. Amer. Meteor. Soc., 75 , 757781.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schmit, T. J., , Prins E. M. , , Schreiner A. J. , , and Gurka J. J. , 2001: Introducing the GOES-M imager. Natl. Wea. Dig., 25 , 2837.

  • Soden, B. J., , and Bretherton F. P. , 1993: Upper tropospheric humidity from the GOES 6.7 μm channel: Method and climatology for July 1987. J. Geophys. Res., 98 , 1666916688.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sohn, B. J., , Schmetz J. , , Tjemkes S. , , Koenig M. , , Lutz H. , , Arriaga A. , , and Chung E. S. , 2000: Intercalibration of the Meteosat-7 water vapor channel with SSM/T-2. J. Geophys. Res., 105 , 1567315680.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sohn, B. J., , Schmetz J. , , and Chung E-S. , 2008: Moistening processes in the tropical upper troposphere observed from Meteosat measurements. J. Geophys. Res., 113 , D13109. doi:10.1029/2007JD009527.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Space Systems Loral, 1996: GOES I-M DataBook. Space Systems Loral Tech. Rep. DRL-101-08, 196 pp.

  • Strow, L. L., , Hannon S. , , Weiler M. , , Overoye K. , , Gaiser S. L. , , and Aumann H. H. , 2003: Prelaunch spectral calibration of the Atmospheric Infrared Sounder (AIRS). IEEE Trans. Geosci. Remote Sens., 41 , 274286.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Su, H., , Read W. G. , , Jiang J. H. , , Waters J. W. , , Wu D. L. , , and Fetzer E. J. , 2006: Enhanced positive water vapor feedback associated with tropical deep convection: New evidence from Aura MLS. Geophys. Res. Lett., 33 , L05709. doi:10.1029/2005GL025505.

    • Search Google Scholar
    • Export Citation
  • Tian, B., , Soden B. J. , , and Wu X. , 2004: Diurnal cycle of convection, clouds, and water vapor in the tropical upper troposphere: Satellites versus a general circulation model. J. Geophys. Res., 109 , D10101. doi:10.1029/2003JD004117.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tobin, D. C., , Revercomb H. E. , , Moeller C. C. , , and Pagano T. , 2006: Use of Atmospheric Infrared Sounder high–spectral resolution spectra to assess the calibration of Moderate resolution Imaging Spectroradiometer on EOS Aqua. J. Geophys. Res., 111 , D09S05. doi:10.1029/2005JD006095.

    • Search Google Scholar
    • Export Citation
  • Tobin, D. C., and Coauthors, 2007: Radiometric and spectral validation of Infrared Atmospheric Sounding Interferometer (IASI) observations. 2007 Conf. on Characterization and Radiometric Calibration for Remote Sensing, Logan, UT Space Dynamics Laboratory.

    • Search Google Scholar
    • Export Citation
  • Tournier, B., , Blumstein D. , , Cayla F-R. , , and Chalon G. , 2002: IASI level 0 and 1 processing algorithms description. Proc. Twelfth Int. TOVS Study Conf., Victoria, Australia, International TOVS Working Group, 12 pp. [Available online at http://cimss.ssec.wisc.edu/itwg/itsc/itsc12/presentations/6d5_B.Tournier.doc].

    • Search Google Scholar
    • Export Citation
  • Wang, L., , and Cao C. , 2008: On-orbit calibration assessment of AVHRR longwave channels on MetOp-A using IASI. IEEE Trans. Geosci. Remote Sens., 46 , 40054013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, L., , Cao C. , , and Ciren P. , 2007: Assessing NOAA-16 HIRS radiance accuracy using simultaneous nadir overpass observations from AIRS. J. Atmos. Oceanic Technol., 24 , 15461561.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weinreb, M. P., , Jamison M. , , Fulton N. , , Chen Y. , , Johnson J. X. , , Bremer J. , , Smith C. , , and Baucom J. , 1997: Operational calibration of Geostationary Operational Environmental Satellite-8 and -9 imagers and sounders. Appl. Opt., 36 , 68956904.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weng, F., 2007: Advances in radiative transfer modeling in support of satellite data assimilation. J. Atmos. Sci., 64 , 37993807.

  • Wimmers, A. J., , and Moody J. L. , 2001: A fixed-layer estimation of upper tropospheric specific humidity from the GOES water vapor channel: Parameterization and validation of the altered brightness temperature product. J. Geophys. Res., 106 , 1711517132.

    • Crossref
    • Search Google Scholar
    • Export Citation

Fig. 1.
Fig. 1.

(top) IASI and (bottom) AIRS BT spectra calculated from the community radiative transfer model (CRTM; Weng 2007) overlaid with the water vapor channel SRFs from the MetOp-A HIRS and the GOES-11 and GOES-12 imagers. Note that the IASI BT spectrum is displaced by 80 K in order to separate it from the AIRS spectrum for comparison.

Citation: Journal of Atmospheric and Oceanic Technology 26, 9; 10.1175/2009JTECHA1233.1

Fig. 2.
Fig. 2.

An example of a water vapor composite image from GOES-11 and GOES-12 imagers observed at 1200 and 1145 UTC 1 Dec 2007, respectively. The BT values are shown with a linear grayscale from 200 (white) to 280 K (black).

Citation: Journal of Atmospheric and Oceanic Technology 26, 9; 10.1175/2009JTECHA1233.1

Fig. 3.
Fig. 3.

GOES-11 water vapor images overlaid with the approximate IASI FOR (black circle) and pixels (small white circles) used in the spatial collocation. The plus symbol indicates the GOES-11 SSP, and the GOES BT image is displayed with a grayscale from 220 (white) to 260 K (black).

Citation: Journal of Atmospheric and Oceanic Technology 26, 9; 10.1175/2009JTECHA1233.1

Fig. 4.
Fig. 4.

BT difference for the GOES-11 and GOES-12 imager water vapor channels varying with satellite zenith angle, calculated for the standard tropical atmosphere profile using MODTRAN.

Citation: Journal of Atmospheric and Oceanic Technology 26, 9; 10.1175/2009JTECHA1233.1

Fig. 5.
Fig. 5.

IASI–GOES BT difference for GOES-11 water vapor channel as a function of the scene uniformity factor, which is defined as the ratio of the standard deviation to the mean of the GOES collocated pixels’ radiances. The vertical line indicates the value 0.01.

Citation: Journal of Atmospheric and Oceanic Technology 26, 9; 10.1175/2009JTECHA1233.1

Fig. 6.
Fig. 6.

IASI–GOES BT difference for the GOES-11 imager water vapor channel vs the scan-time difference. The vertical line indicates the value of 10 min.

Citation: Journal of Atmospheric and Oceanic Technology 26, 9; 10.1175/2009JTECHA1233.1

Fig. 7.
Fig. 7.

Time series of the IASI–GOES BT difference for the GOES-11 water vapor channel, where open circles indicate the day observations, and the triangles denote the night observations.

Citation: Journal of Atmospheric and Oceanic Technology 26, 9; 10.1175/2009JTECHA1233.1

Fig. 8.
Fig. 8.

(left) Scatterplot of the IASI–GOES BT difference vs the GOES scene temperature, and (right) the histogram of the BT differences for the GOES-11 imager water vapor channel. The dotted line indicates the zero line, and the dashed–dotted line shows the mean value.

Citation: Journal of Atmospheric and Oceanic Technology 26, 9; 10.1175/2009JTECHA1233.1

Fig. 9.
Fig. 9.

Scatterplot of the IASI–GOES radiance difference vs the GOES radiance for the GOES-11 imager water vapor channel (a) using the original SRF and (b) using the shifted SRF. The dotted line indicates the zero line, and the dashed–dotted line shows the mean value.

Citation: Journal of Atmospheric and Oceanic Technology 26, 9; 10.1175/2009JTECHA1233.1

Fig. 10.
Fig. 10.

As in Fig. 7, but for the GOES-12 imager water vapor channel. The dashed line indicates the date of 2 Jul 2007, when the decontamination procedure was performed for the GOES-12 imager.

Citation: Journal of Atmospheric and Oceanic Technology 26, 9; 10.1175/2009JTECHA1233.1

Fig. 11.
Fig. 11.

As in Fig. 8, but for the GOES-12 imager water vapor channel. The symbols with blue color indicate the data before the decontamination procedure.

Citation: Journal of Atmospheric and Oceanic Technology 26, 9; 10.1175/2009JTECHA1233.1

Fig. 12.
Fig. 12.

Scatterplot of the GOES-11 and GOES-12 BT for water vapor channels convolved from the IASI spectra, including the datasets coincidental with (a) GOES-11 and (b) GOES-12. The linear regression (solid line) coefficients are summarized in Table 2.

Citation: Journal of Atmospheric and Oceanic Technology 26, 9; 10.1175/2009JTECHA1233.1

Fig. 13.
Fig. 13.

Scatterplot of the GOES-12 and GOES-11 water vapor channel BT difference varying with the GOES-11 BT convolved from the IASI spectra, including the datasets coincidental with (a) GOES-11 and (b) GOES-12. The linear regression (solid line) is derived from Fig. 12.

Citation: Journal of Atmospheric and Oceanic Technology 26, 9; 10.1175/2009JTECHA1233.1

Fig. 14.
Fig. 14.

As in Fig. 2, but after the GOES-11 BT correction to match GOES-12.

Citation: Journal of Atmospheric and Oceanic Technology 26, 9; 10.1175/2009JTECHA1233.1

Table 1.

Data processing constraints.

Table 1.
Table 2.

Linear regression coefficients for GOES-11 and GOES-12 water vapor channel BT, where N is the sample size, a is the intercept of the regression and its 1-sigma uncertainty estimates, b is the slope of the regression and its 1-sigma uncertainty estimates, R is the correlation coefficient, and ɛ is the standard deviation of the residual.

Table 2.
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