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Wenhui Wang and Changyong Cao

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The Visible and Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar-Orbiting Partnership satellite brings new opportunities for improving scientists’ understanding of deep convective cloud (DCC) radiometry with multiple bands in the visible (VIS), near-infrared (NIR), and longwave infrared (LWIR) spectrum. This paper investigated the radiometric sensitivity of DCC reflectance to spatial resolution, brightness temperature of the LWIR band centered at ~11 μm (TB11), TB11 calibration bias, and cluster size using VIIRS VIS (M5), NIR (M7 and I2), and LWIR (M15 and I5) observations at 375- and 750-m spatial resolutions. The mean and mode of the monthly probability distribution functions of DCC reflectance are used as two important indices in using DCC for calibration, and the results show that the onboard radiometric calibration of M5, M7, and I2 are stable during May 2013–April 2014 despite severe instrument responsivity degradations. The standard deviations of the mean and mode of monthly DCC reflectance are 0.5% and 0.2%, respectively, for all bands. It was found that a TB11 calibration bias on the order of 0.5 K has minimal impact on monthly DCC reflectance, especially when the mode method is used. The mean and mode of VIS and NIR DCC reflectance are functions of spatial resolution, TB11 threshold, and DCC cluster size in all seasons. However, the mode of DCC reflectance is more stable than the mean in terms of all three factors. Therefore, the mode is more suitable as an indicator of calibration stability for individual VIS and NIR bands.

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Sirish Uprety and Changyong Cao

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An atmospheric CO2 increase has become a progressively important global concern in recent past decades. Since the 1950s, the Keeling curve has documented the atmospheric CO2 increase as well as seasonal variations, which also intrigued scientists to develop new methods for global CO2 measurements from satellites. One of the dedicated satellite missions is the CO2 measurement in the 1.6-μm shortwave infrared spectra by the Greenhouse Gases Observing Satellite (GOSAT) Thermal and Near Infrared Sensor for Carbon Observations–Fourier Transform Spectrometer (TANSO-FTS) instrument. While this spectral region has unique advantages in detecting lower-trophosphere CO2, there are many challenges because it relies on accurate measurements of reflected solar radiance from Earth’s surface. Therefore, the calibration of the TANSO-FTS CO2 has a direct impact on the CO2 retrievals and its long-term trends. Coincidently, the Suomi-NPP Visible Infrared Imaging Radiometer Suite (VIIRS) 1.6-μm band spectrally overlaps with the TANSO-FTS CO2 band, and both satellites are in orbit with periodical simultaneous nadir overpass measurements. This study performs an intercomparison of VIIRS and the TANSO-FTS CO2 band in an effort to evaluate and improve the radiometric consistency. Understanding the differences provides feedback on how well the GOSAT TANSO-FTS is performing over time, which is critical to ensure a well-calibrated, stable, and bias-free CO2 product.

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Lei Shi, John J. Bates, and Changyong Cao

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Measurements from the simultaneous nadir overpass (SNO) observations of the High Resolution Infrared Radiation Sounder (HIRS) are examined. The SNOs are the measurements taken at the orbital intersections of each pair of satellites viewing the same Earth target within a few seconds at high latitudes. The dataset includes satellites from NOAA-6 through NOAA-17 from 1981 to 2004. The authors found that for many channels, intersatellite biases vary significantly with respect to scene radiances. For a number of these channels, the change of the intersatellite bias within a channel can be larger than 1 mW (m2 sr cm−1)−1, which is approximately 1 K in brightness temperature, across the channel scene radiance ranges. Many of the channels with large variations of intersatellite biases are the tropospheric sounding channels centered along the sharp slope of the transmission line. These channels are particularly sensitive to the difference in spectral response functions from satellite to satellite. This radiance-dependency feature of the biases is an important factor to consider when performing intersatellite calibrations.

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Likun Wang, Changyong Cao, and Pubu Ciren

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The High-Resolution Infrared Radiation Sounder (HIRS) has been carried on NOAA satellites for more than two decades, and the HIRS data have been widely used for geophysical retrievals, climate studies, and radiance assimilation for numerical weather prediction models. However, given the legacy of the filter-wheel radiometer originally designed in the 1970s, the HIRS measurement accuracy is neither well documented nor well understood, despite the importance of this information for data users, instrument manufacturers, and calibration scientists. The advent of hyperspectral sounders, such as the Atmospheric Infrared Sounder (AIRS), and intersatellite calibration techniques makes it possible to independently assess the accuracy of the HIRS radiances. This study independently assesses the data quality and calibration accuracy of HIRS by comparing the radiances between HIRS on NOAA-16 and AIRS on Aqua with simultaneous nadir overpass (SNO) observations for the year 2004. The results suggest that the HIRS radiometric bias relative to the AIRS-convolved HIRS radiance is on the order of ∼0.5 K, except channel 16, which has a bias of 0.8 K. For all eight spectrally overlapped channels, the observations by HIRS are warmer than the corresponding AIRS-convolved HIRS channel. Other than channel 16, the biases are temperature dependent. The root causes of the bias can be traced to a combination of the HIRS blackbody emissivity, nonlinearity, and spectral uncertainties. This study further demonstrates the utility of high-spectral-resolution radiance measurements for high-accuracy assessments of broadband radiometer calibration with the SNO observations.

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Robert A. Iacovazzi Jr. and Changyong Cao

Abstract

Systematic biases between brightness temperature (Tb) measurements made from concurrently operational Advanced Microwave Sounding Unit-A (AMSU-A) instruments can introduce errors into weather and climate applications. For this reason, in this study the ability of the simultaneous nadir overpass (SNO) method to estimate relative Tb biases between operational Earth Observing System (EOS) Aqua and Polar-orbiting Operational Environmental Satellites (POES) NOAA-15, NOAA-16, and NOAA-18 AMSU-A instruments is evaluated.

From an analysis of SNO events occurring from 21 May 2005 to 31 July 2006, AMSU-A SNO-ensemble mean Tb biases could not be statistically determined for window channels, while significant bias detection to within about 0.02 K is accomplished in some low-noise sounding channels. These results are shown to be a consequence of the decrease of the earth-scene Tb variability with increasing atmospheric zenith opacity, which is a function of microwave frequency. Examination of SNO-ensemble mean Tb biases for two independent AMSU-A instrument components—AMSU-A1–1 and AMSU-A1–2—exposed a significant cold (warm) bias on the order of 0.4 K (0.2 K) in the AMSU-A1–1 unit on board the NOAA-18 (Aqua) satellite. This analysis also revealed on average a significant cold bias on the order of 0.1 K in the NOAA-16 AMSU-A1–2 component. Furthermore, the individual SNO mean Tb biases were often found to be a function of the SNO earth-scene average Tb, which is a manifestation of instrument calibration errors. On the other hand, it was found that determining the root cause of such errors is inhibited by the lack of postlaunch quality control of the AMSU-A calibration-related hardware.

Based on the results of this study, a need to reduce impacts of surface emissivity and temperature inhomogeneities on the SNO method in microwave radiometer window channels becomes evident. In addition, the unparalleled ability of the SNO method to isolate and quantify intersatellite, instrument-related Tb biases is demonstrated in most sounding channels, which is necessary to improve weather and climate applications.

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Robbie Iacovazzi, Quanhua “Mark” Liu, and Changyong Cao
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Changyong Cao, Michael Weinreb, and Hui Xu

Abstract

A method for accurately predicting simultaneous nadir overpasses (SNOs) among different sun-synchronous polar-orbiting meteorological satellites is presented for intersatellite radiometer calibration. At each SNO, the radiometers on the two satellites view the earth and its atmosphere at nadir within a few seconds of each other, providing an ideal scenario for the intercalibration of radiometers. The basic mechanism and frequency of occurrences of such events are analyzed. Prediction using the Simplified General Perturbations No. 4 (SGP4), an orbital perturbation model, is presented, and examples of SNOs among the NOAA-16, NOAA-17, Terra, and Aqua satellites are provided. Intersatellite calibration using this approach has the potential for achieving the calibration consistency and traceability required for long-term climate studies.

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Changyong Cao, Kenneth Jarva, and Pubu Ciren

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Radiance data from the High-Resolution Infrared Radiation Sounder (HIRS) have been used routinely in both direct radiance assimilation for numerical weather prediction and climate change detection studies. The operational HIRS calibration algorithm is critical for producing accurate radiance to meet the user’s needs, and it has significant impacts on products at all levels. Since the HIRS does not calibrate every scan line, the calibration coefficients between calibration cycles have to be interpolated based on a number of assumptions. In the more than 25-yr history of operational HIRS calibration, several interpolation methods have been used and, unfortunately, depending on which method is used, these algorithms can produce HIRS level 1b radiance data with significant differences. By analyzing the relationship between the instrument self-emission and gain change during filter temperature fluctuations, in this paper a significant flaw in the previous operational calibration algorithm (version 3) is identified. This caused calibration errors greater than 0.5 K and periodically degraded the HIRS radiance data quality of NOAA-15, -16, and -17 between 1998 and 2005. A new HIRS calibration algorithm (version 4) is introduced to improve the calibration accuracy, along with better indicators for instrument noise in the level 1b data. The new algorithm has been validated in parallel tests before it became operational at NOAA/National Environmental Satellite Data and Information Service (NESDIS). Test results show that significant improvements in calibration accuracy can be achieved especially for NOAA-15/HIRS. Several areas of further calibration improvements are also identified. The new algorithm has been used for all operational satellites at NOAA/NESDIS since 28 April 2005.

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Quanhua Liu, Changyong Cao, and Fuzhong Weng

Abstract

The Visible Infrared Imaging Radiometer Suite (VIIRS) thermal emissive band (TEB) M12 images centered at 3.7 μm were analyzed and unexpected striping was found. The striping was seen from ascending orbit (daytime) over uniform oceans and has a magnitude of ±0.5 K aligned with the VIIRS 16 detectors in a track direction of 12 km. From the ocean surface, reflected solar radiation can significantly increase the M12 radiance under certain geometric conditions in which bidirectional reflectance distribution function (BRDF) becomes important. Using the Community Radiative Transfer Model (CRTM), developed at the U.S. Joint Center for Satellite Data Assimilation (JCSDA), M12 band image striping over a uniform ocean was found that was caused by the difference of sensor azimuthal angles among detectors and the contamination of solar radiation. By analyzing the VIIRS M10 and M11 bands, which are two reflective bands, similar striping images over the uniform oceans were found. The M10 and M11 radiance/reflectance can be used to determine the BRDF effect on the thermal emissive band M12, and eventually be used to remove the solar radiation contamination from the M12 band. This study demonstrated that the M12 image striping is a real instrument artifact. Whether to remove the striping or to utilize the striping information fully depends on the application.

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Likun Wang, Changyong Cao, and Mitch Goldberg

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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.

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