Intercalibration of Broadband Geostationary Imagers Using AIRS

746 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 26

MATHEW M. GUNSHOR Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison, Madison, Wisconsin

TIMOTHY J. SCHMIT NOAA/NESDIS, Center for Satellite Applications and Research, Advanced Satellite Products Branch, Madison, Wisconsin

W. PAUL MENZEL AND DAVID C. TOBIN Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison, Madison, Wisconsin

(Manuscript received 14 April 2008, in ﬁnal form 19 August 2008)

ABSTRACT

Geostationary simultaneous nadir observations (GSNOs) are collected for Earth Observing System (EOS) Atmospheric Infrared Sounder (AIRS) on board Aqua and a global array of geostationary imagers. The imagers compared in this study are on (Geostationary Operational Environmental Satellites) GOES-10, GOES-11, GOES-12, (Meteorological Satellites) Meteosat-8, Meteosat-9, Multifunctional Transport Satellite-IR (MTSAT-IR), and Fenguyun-2C (FY-2C). It has been shown that a single polar-orbiting satellite can be used to intercalibrate any number of geostationary imagers. Using a high-spectral-resolution infrared sensor, in this case AIRS, brings this method closer to an absolute reckoning of imager calibration accuracy based on laboratory measurements of the instrument’s spectral response. An intercalibration method is presented here, including a method of compensating for AIRS’ spectral gaps, along with results for approximately 22 months of comparisons. The method appears to work very well for most bands, but there are still unre- solved issues with bands that are not spectrally covered well by AIRS (such as the water vapor bands and the 8.7-mm band on Meteosat). To the ﬁrst approximation, most of the bands on the world’s geostationary imagers are reasonably well calibrated—that is, they compare to within 1 K of a standard reference (AIRS). The next step in the evolution of geostationary intercalibration is to use Infrared Atmospheric Sounding Interferometer (IASI) data. IASI is a high-spectral-resolution instrument similar to AIRS but without sig- niﬁcant spectral gaps.

1. Introduction same target, which provides valuable information for a wide range of satellite activities. Intercalibration of satellite sensors is needed to im- There are numerous quantitative products, many pro- prove the use of space-based global observations for duced operationally, which are made at least in part weather, climate, and environmental applications. using either polar or geostationary satellite radiances. There is a desire to use satellites, traditionally used to Application areas include weather, climate, hazards, enhance weather forecasting, for global climate moni- land, ocean, and the cryosphere. Absolute validation is toring and other applications related to the environ- difﬁcult, because a method to measure earth-emitted ment. Satellites are also playing a larger role in nu- radiances precisely from a broadband instrument does merical weather prediction (NWP;Chahine et al. 2006). not exist. Differences in satellite view angle, scan time, The purpose of intercalibration is to quantitatively re- ﬁeld of view (FOV) size and shape, and navigation ac- late the radiances from different sensors viewing the curacy all contribute to noncalibration differences between measured radiances. In addition, broadband imagers have different spectral responses, differing even Corresponding author address: Mathew M. Gunshor, CIMSS, between detectors for the same band on the same in- 1225 W. Dayton St., Madison, WI 53706. strument. Past studies intercalibrating two broadband E-mail: [email protected] instruments required accounting for spectral response

DOI: 10.1175/2008JTECHA1155.1

Ó 2009 American Meteorological Society Unauthenticated | Downloaded 09/26/21 08:04 PM UTC APRIL 2009 G U N S H O R E T A L . 747 function (SRF) differences with the use of forward TABLE 1. Nominal subpoints of the geostationary imagers in this model calculations (Gunshor et al. 2004; Tjemkes et al. study over the equator. Meteosat-8 and Meteosat-9 are remapped 8 8 2001). The present study is able to avoid that issue by to 0 , but Meteosat-8 is located at approximately 3.5 W. Nominal FOV sizes at nadir of the infrared bands are included. GOES using high-spectral-resolution infrared data to simulate imager oversamples 4-km FOVs in the east–west by 1.7 km, with the spectral response of the broadband imagers. the exception of 8-km FOVs, the GOES-10 and GOES-11 water An international effort is being undertaken (Gold- vapor band, and the GOES-12 13.3-mm bands. Meteostat-8 and berg 2007) to intercalibrate the world’s environmental Meteostat-9 have been remapped to 3 km. MTSAT-1R samples at 2 satellites: Global Space-based Intercalibration System km but the data have been remapped to 4 km. (GSICS). One of the goals of this undertaking is ‘‘to Satellite Subpoint Data provider Nominal FOV size (km) improve the use of space-based global observations for GOES-10 608W USA 4 / 8 weather, climate, and environmental applications through GOES-11 1358W USA 4 / 8 operational intercalibration of the space component of GOES-12 758W USA 4 / 8 the World Weather Watch’s (WWW) Global Observing Meteostat-8 08 EUMETSAT 3 System (GOS) and Global Earth Observing System of Meteostat-9 08 EUMETSAT 3 MTSAT-1R 1408E Japan 4 Systems (GEOSS)’’ [available online at http://www.star. FY-2C 1058E China 5 nesdis.noaa.gov/smcd/spb/calibration/icvs/GSICS/index. php]. Intercalibration of broadband geostationary imagers with a high-spectral-resolution instrument such as shortwave spectral regions. This has been duplicated Atmospheric Infrared Sounder (AIRS) represents a with other high-spectral-resolution data, such as Infra- major step in reaching the goals set forth by GEOSS and red Atmospheric Sounding Interferometer (IASI) and the World Meteorological Organization (WMO). U.S. National Polar-orbiting Operational Environmen- Geostationary simultaneous nadir observations tal Satellite System (NPOESS) Atmospheric Sounder (GSNOs) are collected for Earth Observing System Testbed—Interferometer (NAST-I), demonstrating the (EOS) AIRS on board Aqua and a global array of ge- high quality of AIRS data that is being compared with ostationary imagers. The imagers compared in this the many geostationary imager data. In early in-ﬂight study are on (Geostationary Operational Environmen- measurements, IASI compared favorably with AIRS, to tal Satellites) GOES-10, GOES-11, and GOES-12 from within 0.1 K in most bands (Blumstein et al. 2007). the United States; (Meteorological Satellites) Meteosat- 8 and Meteosat-9 from the European Organisation for 2. Method the Exploitation of Meteorological Satellites (EUMET- SAT); Multifunction Transport Satellite-IR (MTSAT-1R) The technique for intercalibration was developed at from Japan; and Fengyun-2C (FY-2C)fromChina the Cooperative Institute for Meteorological Satellite (Table 1). This is an expansion upon Gunshor et al. Studies (CIMSS) during the past decade (Schmit and (2007). It has been shown that a single polar-orbiting, Herman 1992; Wanzong et al. 1998; Gunshor et al. or low earth orbiting (LEO), satellite can be used 2004). The following paragraphs describe the process of to intercalibrate any number of geostationary (GEO) obtaining and processing a single intercomparison. imagers (Gunshor et al. 2004). Using a high-spectral- There are four steps to satellite intercalibration: data resolution IR sensor—in this case, AIRS—with abso- collection, data transformations, study area selection, lute calibration to within 0.2 K in most bands (Tobin and calculations. The intercalibration method used et al. 2006b) reﬁnes the ‘‘LEO–GEO’’ method, which is here is similar to the one described in Gunshor et al. closer to an absolute estimate of imager calibration (2004). The fundamental steps of that method are still accuracy. used, though altered to accommodate high-spectral- This method of comparison with AIRS data—as the resolution AIRS data in use as the polar-orbiting in- standard implies—is that it is pertinent to know how strument. well the individual AIRS spectra are calibrated. This a. Data collection has been researched by a number of investigators for many types of atmospheric conditions. For example, Collocation of the polar orbiter and geostationary Tobin et al. (2006b) have compared AIRS data to time data in space and time is required. GSNOs are used to coincident Scanning High-Resolution Interferometer minimize satellite zenith angle differences between the Sounder (S-HIS) aircraft data at comparable spectral instruments and should employ a minimal temporal and spatial resolutions and found very good agreement. offset. Data are selected within 6108 (latitude and The ‘‘double observation minus calculation difference’’ longitude) of the geostationary subsatellite point, and was smaller than 0.2 K for the longwave, midwave, and AIRS radiometer scan angles must be 6 108 to minimize

Unauthenticated | Downloaded 09/26/21 08:04 PM UTC 748 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 26 satellite zenith angle differences. This encompasses the (GVAR) data stream by Weinreb et al. (1997), and a 18 central AIRS FOVs. To maximize scene consistency similar process is followed for the other instrument data between the two instruments, ideally GEO data are streams. Typically, users obtain data as radiances, and it collected within 615 min of LEO overpass time at the is necessary that all calculations performed on the data geostationary subsatellite point. For imagers that only concerning spatial smoothing or averaging are com- provide equatorial data once an hour, this requirement puted on the radiances or scaled radiances, before of the GSNO must be waived. The nature of the sun- converting to equivalent brightness temperatures. synchronous orbit typically employed for low earth AIRS is known to have some poorly performing (or orbiting platforms, such as Aqua, is that at a single lo- bad) channels. There is a consistent set of bad channels as cation on Earth, there are essentially only two overpass well as channels that can be bad from granule to granule. times—a ‘‘day’’ and a ‘‘night’’ pass from which the There is a test using the appropriate AIRS channel overpass time does not significantly vary. Thus, a dis- properties file that does a reasonable job eliminating bad advantage of this technique is that if the GEO does not channels. Additionally, there are other simple data sample often enough, it may not be possible to obtain quality checks to be sure no channels are used that have data within 615 min of the overpass. The worse-case radiances at or below 0 or radiances unreasonably high. scenario is a range within plus or minus half the number For a single AIRS granule, the same channel subset is of minutes of the GEO’s sampling rate. For instance, if a used for every field of view. This is not a consistent GEO collects data once an hour, a LEO pass within the channel subset for all cases that form the overall averages GSNO range will always be within at least 30 min of the seen later in the results. Also, AIRS has spectral gaps GEO data. Sampling time differences are at least par- that are part of the instrument design. This means that tially mitigated by spatial smoothing; thus, it is more not every GEO band is sufficiently covered spectrally by important to collect comparisons than it is to adhere AIRS. The subject of spectral gaps will be covered in within strict limits to define a GSNO. section 3. The most difficult part of comparing two broadband AIRS data are convolved with GEO spectral re- instruments is accounting for differences in their spec- sponse functions (SRFs) and, in this manner, are be- tral responses. In Gunshor et al. (2004), this was done lieved to be an accurate simulation of the geostationary with a forward model calculation on an atmospheric instrument. The term ‘‘convolved’’ is widely used in this profile generated from a global numerical model. The application and will be used throughout this paper, results were then somewhat vulnerable to the accuracy though it may be more accurate to describe the process of the model and to assumptions regarding the surface mathematically as a weighted average: emissivity. That method also requires an additional data Ðy2 collection step. This step has been removed in the R(y)S(y)dy technique as it applies to AIRS, which is an advantage y1 CR 5 , (1) the AIRS method has over the prior method. Ðy2 S(y)dy b. Data transformations y1 There are a number of data transformations necessary where CR is the convolved radiance, R is the AIRS before the comparison can occur. Data, in the form of radiance at wavenumber y, and y1 and y2 are the lower radiances, from each satellite are smoothed to an ef- and upper wavenumber limits, respectively, of the GEO fective 100-km resolution to mitigate the effects of dif- SRF S. In this way, we calculate an average AIRS ra- ferent FOV sizes and sampling densities, and AIRS diance, weighted by the geostationary imager’s SRF, for samples at an effective 13-km FOV. Nominal nadir each AIRS FOV. In theory, and if the GEO SRF is viewing FOV sizes for the GEOs are in Table 1. Image accurate, this convolved radiance would be the same as smoothing consists of applying a running average; the what the geostationary imager would given the same resultant image has the same number of pixels as the instrument characteristics as AIRS (FOV shape, FOV original; however, each pixel is the average of the ap- size, altitude, angle, etc.). proximate 100 km 3 100 km surrounding it. Another c. Study area option would be to resample or average the data to a 100 km 3 100 km grid, thereby simulating instruments The study area is located between 108 latitude north with 100-km footprints. and south of the equator and 108 longitude east and west Scaled radiances, commonly referred to as counts, are of the geostationary subsatellite point. These are the converted to radiances with a linear conversion; this outer limits of a potential study area, however, and not processisdescribedfortheGOESvariableformat the specific study area for a given case, that varies

Unauthenticated | Downloaded 09/26/21 08:04 PM UTC APRIL 2009 G U N S H O R E T A L . 749 depending on the orbital characteristics of AIRS and the AIRS spectral gaps, channel centers are calculated the overlapping spatial coverage of the two instruments. by using a mean of the widths between channel centers The study area for an individual case is a narrow strip of on either side of the gap and applying that across the data 18 AIRS FOV wide that fits within those criteria gap from the longwave side. This was done for any in- and is less than 6108 scan angle for AIRS (with a mean dividual gap greater than 5 wavenumbers. scan angle of 08). The GEO view angles vary between 08 The U.S. Standard Atmosphere, 1976 (USSA) calcu- and approximately 6158, and the mean view angle lated spectrum is convolved with Gaussian mock AIRS varies by case between approximately 28 and 108.A SRFs with channel centers ranging from 649.6 to single intercalibration case will consist of a subset of one 2999.25 mm. This simulates an AIRS-like view of the AIRS granule and a subset of one geostationary image calculated spectrum, with imaginary AIRS channels in corresponding to one scan time. the AIRS spectral gaps and extending into the shortwave region of the spectrum. These convolved radi- d. Calculations ances are then converted to brightness temperatures as AIRS data, specifically radiances or scaled radiances, monochromatic radiances. Since radiances are wave- are convolved with the GEO spectral response. This is a length dependent, the gaps are filled using brightness common technique for simulating broadband radiances temperatures. from a high-spectral-resolution instrument, such as For each AIRS FOV, prior to spatial smoothing, the AIRS (Tobin et al. 2006a). The mean radiance is com- gaps are filled across the spectrum. For each gap greater puted by averaging the spatially smoothed data within than 5 wavenumbers, the temperatures at the endpoints the study area for both instruments (AIRS data are of the gap are compared to the temperatures at the same convolved with GEO SRF before being spatially wavelengths for the theoretical spectrum described smoothed.). The mean measured radiance difference above. The theoretical data are adjusted at the gap between GEO and AIRS is attributed to calibration endpoints to fit the measured AIRS spectrum. Then the differences between the two instruments. These radi- points in the gap are filled with temperatures using a ances are converted to brightness temperature using the weighted average between the two endpoints, weighted inverse Planck equation, using band correction coeffi- by distance across the gap. For instance, at the midpoint cients for the specific GEO band of interest. The results of the gap, half of the temperature difference at one section shows the mean intercalibration difference for endpoint is added to half of the temperature difference multiple such cases, presented as brightness tempera- at the other endpoint and that sum total is added to (or ture differences. subtracted from, as the case may be) the theoretical spectrum at that point. The data used to fill the gaps are called the ‘‘adjusted U.S. Standard Atmosphere,’’ or 3. Spectral gaps in AIRS ‘‘adjusted USSA,’’ and are converted back into radi- Accounting for spectral gaps in AIRS is an important ances (again, monochromatically) for the rest of the issue. In Tobin et al. (2006a), a method was presented calculations. An example of the finished product can be where a ‘‘convolution error’’ was calculated for several seen in Fig. 1. synthetic atmospheric spectra, and it appears this is a This method appears to be effective, though there is reasonable fix. It is important to consider other possible room for improvement in its application. Since the methods of dealing with this issue, since other high- measured atmosphere varies from the U.S. Standard spectral-resolution instruments in the future will also Atmosphere in temperature or moisture content, have spectral gaps, such as the Cross-track Infrared adjusting the AIRS spectra in the manner described Sounder (CrIS), which will fly on the NPOESS space- does not always accurately take these changes into craft. A method is presented here of filling the spectral consideration. It has been shown in preliminary results gaps with generic spectral information from a calculated that using a cloudy spectrum in cloudy scenes may work atmosphere that has been adjusted to fit each AIRS better than the USSA (Y. Tahara 2008, personal com- FOV. This spectrum was originally calculated at 0.1 munication). Also, the method may be less useful if one cm21 resolution using the line-by-line radiative transfer endpoint, or both, of a spectral gap falls on an atmo- model (LBLRTM) developed at Atmospheric and En- spheric absorption line. vironmental Research (AER) Inc. (Clough and Iacono 1995) and must be convolved with AIRS SRFs to match 4. Error analysis AIRS channel centers. This is done by calculating Gaussian SRFs for AIRS bands at AIRS channel cen- The known sources of error in intercalibration are ters (taken from the AIRS channel properties file). In caused by temporal collocation differences, spatial

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FIG. 1. Sample tropical spectrum (black) with spectral gaps filled with the adjusted USSA (gray). collocation differences (partially due to spatial resolu- used for all cases in all bands. A test was performed by tion differences), and geometric alignment differences convolving the USSA spectrum with the mock SRFs (satellite zenith angle and azimuth angle differences). (only where AIRS channels exist) and the real SRFs, The errors that are specific to this type of intercalibra- thus two simulated atmospheres were created. Both of tion include spectral convolution errors, errors in the these were then converted to brightness temperature. measurement of the spectral response of various GEO There are differences for some AIRS channels: the bands, and errors associated with the method used to fill maximum being 4.6 K but the mean is 0.02 K, with a AIRS spectral gaps. The biggest errors are caused by standard deviation of 0.6 K (Fig. 2). For some individual the AIRS spectral gaps and errors in GEO spectral re- AIRS channels, the difference between real and mock sponse. The temporal collocation differences can also SRFs is significant. However, when convolving these have a measurable effect on the results. These error two simulations of the USSA with GOES and Meteosat- sources may interact unpredictably for some scene 8 SRFs, those differences disappear. For the purposes of types. It is believed that spatial collocation differences intercalibrating a broadband sensor, such as the GOES and geometric alignment differences are minimal—- imager or Meteosat Spinning Enhanced Visible and since in the method presented here, a 100 km 3 100 km Infrared Imager (SEVIRI) on Meteosat-8 and Meteosat- spatial smoothing is applied to each FOV and because a 9, the mock SRFs are an adequate approximation. Even fairly large area of collocation is used in the compari- in bands where the differences between mock and real sons instead of a pixel-to-pixel-type approach. are relatively large, such as in the 13-mm region (Fig. 2), the results upon convolving with GOES-12 or Meteosat- a. Spectral convolution and spectral gap-filling error 8 SRFs (Table 2) show there is no measurable differ- analysis ence. The only broadband channel where there appears Using the spectrum calculated from the USSA at 0.1 to be a difference is in Meteosat-8 band 7 (8.7 mm), cm21 resolution, it was determined that Gaussian (mock) where the SRF is almost entirely inside an AIRS spec- AIRS SRFs can be used in place of the real ones. It is tral gap. Table 2 includes a column where the original preferable to use mock SRFs for consistency. Since USSA spectrum, at 0.1 cm21 spectral resolution, was mock SRFs will be used in the spectral gaps, it is im- convolved with the GOES-12 and Meteosat-8 bands and portant to know that Gaussian approximations are ef- a brightness temperature was calculated. This shows fective and that a consistent set of mock SRFs can be what sort of error the AIRS spectral gaps introduce for

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FIG.2.GOES-12 imager (green) and Meteosat-8 (blue) SRFs shown with the brightness

temperature difference DTbb of AIRS real SRFs convolved with the USSA spectrum subtracted from AIRS mock SRFs convolved with the USSA spectrum for each AIRS channel. (right to left) GOES bands are 2, 3, 4, and 6 and Meteostat-8 bands are 4–11. these bands—if one does not try to account for it. For For this study comparing AIRS to GEOs in an equa- GOES-12 and Meteosat-8 bands that do not have sig- torial environment, only the atmospheres taken from nificant AIRS spectral gaps, nearly identical brightness the tropics are pertinent; however, results from other temperatures were calculated as those for the mock and atmospheres are discussed as well, since GSNO is not real AIRS SRFs. Such bands include bands 4 and 6 of the only method by which intercalibration can be done. the GOES-12 imager and bands 6, 9, 10, and 11 of For those considering simultaneous nadir observations Meteosat-8. For this table, convolutions were not done (SNOs between two LEOs) in colder atmospheres, when the SRF exceeded the endpoints of the spectrum, these other results may be of interest. The first of the 32 as is the case for the shortwave window on both GOES- is the U.S. Standard Atmosphere, 1976. Atmospheres 25 12 and Meteosat-8. and 27–32 are essentially tropical atmospheres, within An analysis performed on a set of 32 atmospheres 108 latitude of the equator. Without going into much used originally at CIMSS for radiative transfer model- detail, most of the other atmospheres are from rela- ing studies in relation to Nimbus-5 sounder data (Smith tively colder locations (Table 3). et al. 1974), denoted as the CIMSS-32, shows how well Using the same method described in the methods this method works on calculated atmospheres that have section for preparing the U.S. Standard Atmosphere to been converted to high spectral resolution (at 0.1 cm21). fill the spectral gaps, each of the CIMSS-32 calculated

TABLE 2. The USSA radiance spectrum was convolved (5) with mock (Gaussian) AIRS SRFs, which introduce AIRS spectral gaps to the USSA, and then convolved with (top) GOES-12 imager and (bottom) Meteosat-8 SRFs and converted to Tbb (USSA 5 w/mock AIRS SRFs Tbb). The same was done with a set of actual AIRS SRFs (USSA 5 w/real AIRS SRFS Tbb). The original USSA radiance spectrum was also convolved with the GOES-12 imager (top) and Meteosat-8 (bottom) SRFs and converted to Tbb (original USSA). The differences in the ﬁnal column show why it is necessary to account for AIRS spectral gaps in some GEO channels. Channels in bold type are channels for which it appears the spectral gaps are most manageable.

Band / USSA 5 w/mock AIRS USSA 5 w/real AIRS Original USSA USSA 5 w/mock AIRS

wavelength (mm) SRFs Tbb (K) (gaps) SRFS Tbb (K) (gaps) (without gaps) SRFs—original USSA (K) GOES-12 imager 3 6.5 244.73 244.74 242.31 2.42 4 10.7 288.78 288.78 288.71 0.07 6 13.3 270.41 270.41 270.41 0.00 Meteosat-8 5 6.2 239.74 239.76 235.99 3.75 6 7.3 255.42 255.43 255.42 0.00 7 8.7 284.87 285.23 286.50 21.63 8 9.7 262.62 262.57 262.34 0.28 9 10.8 288.76 288.76 288.69 0.07 10 12.0 287.82 287.82 287.82 0.00 11 13.4 268.72 268.72 268.56 0.16

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TABLE 3. The location and dates of CIMSS-32. The first at- tested in such bands as the IR window (IRW), for ex- mosphere listed is really the USSA, and thus the location and date ample (Fig. 3). In the water vapor region, where AIRS are not literal. Total precipitable water (TPW) is included. spectral gaps comprise a significant portion of the GEO Atmosphere No. Lat Lon Date TPW (mm) SRF, the results are mixed. In the tropical atmospheres, 1 45.008N 90.008W 1 Apr 1976 18.85 for GOES-12 imager band 3, there are differences up to 2 80.628N 58.058E 4 Jan 1966 0.88 0.3 K (Fig. 3). While this is a nontrivial difference, it is 3 80.628N 58.058E 17 Jan 1966 1.07 deemed acceptable considering the alternative of not 4 80.628N 58.058E 9 Feb 1966 0.75 accounting for the gaps at all introduces errors on the 5 80.628N 58.058E 22 Nov 1967 5.07 order of several degrees in this band. It is clear though, 6 80.628N 58.058E 5 Feb 1968 2.41 7 80.628N 58.058E 19 Feb 1968 3.31 from Fig. 3, that the U.S. Standard Atmosphere is not 8 57.358N 7.378W 21 Dec 1967 19.15 the appropriate atmosphere to use to fill the gaps of all 9 57.358N 7.378W 23 Dec 1967 18.37 of the CIMSS-32. For this study where the comparisons 10 57.358N 7.378W 19 Dec 1967 11.00 are done at the equator, it will suffice. For studies with 11 63.978N 145.708W 15 Dec 1966 5.00 SNOs in other latitude regions, such as Wang et al. 12 80.628N 58.058E 2 Jan 1966 0.39 13 58.758N 94.078W 4 Jan 1966 1.27 (2007), this may be a serious consideration. 14 58.758N 94.078W 19 Jan 1967 0.80 The Meteosat-8 shortwave band (band 4) is less 15 76.528N 68.758W 14 May 1968 3.64 straightforward to analyze. In this band, the shortwave 16 77.858S 166.678E 9 Jan 1963 1.17 gap is not so much a gap as it is an extension of the 17 32.388N 106.488W 26 Feb 1967 24.96 AIRS spectrum. Since this band extends in spectral 18 76.528N 68.758W 30 Jun 1968 11.77 19 32.388N 106.488W 9 Oct 1968 28.87 coverage beyond the shortest-wave AIRS band, an as- 20 34.128N 119.128W 31 Jul 1968 60.31 sumption has been made about the shortwave to extend 21 34.128N 119.128W 18 Jul 1966 27.19 the spectrum. The assumption is that the slope in the 22 34.128N 119.128W 25 Jul 1967 31.51 spectrum from the last AIRS channel, near 2665.2 cm21, 23 34.128N 119.128W 30 Jul 1966 59.92 out to approximately 2999.5 cm21 remains constant in 24 17.128N 61.788W 9 Feb 1966 45.95 25 9.338N 79.988W 20 Dec 1968 62.37 brightness temperature space. It is likely that shortwave 26 37.858N 75.488W 16 Aug 1966 60.64 window channel intercalibration is only useful at night, 27 9.338N 79.988W 23 Nov 1968 68.91 when there is no solar-reflected component to obscure 28 7.978S 14.408W 23 Jul 1968 41.50 the measurements. With these calculated spectra, we can 29 7.978S 14.408W 22 Jun 1967 40.21 see how well the method might work at night. The re- 30 9.338N 79.988W 26 Jul 1967 78.89 31 9.338N 79.988W 14 Jun 1967 74.21 sults are encouraging (Fig. 3) and suggest that the 32 9.338N 79.988W 9 Aug 1967 73.72 method does a more than adequate job extending the shortwave side of the spectrum beyond AIRS spectral coverage. spectra was convolved with the mock AIRS SRFs to The test showed that there are relatively large errors produce spectra that looked like AIRS with AIRS associated in all 32 atmospheres using this method for channel centers. However, the spectral gaps were left in. Meteosat-8 band 5 (approximately 0.5 K in the tropical Then the gap-filling method was used to fill the gaps of atmospheres) and band 7 (greater than 1 K in most of these AIRS-like CIMSS-32 with the adjusted USSA. the tropical atmospheres), though it was better in the These gap-filled AIRS-like CIMSS-32 spectra were then tropical atmospheres than the others. For all of the convolved with GOES-12 and Meteosat-8 SRFs and a other bands—besides the GOES-12 water vapor, as subsequent brightness temperature was calculated. mentioned already—the errors associated with this These brightness temperatures were then compared to method are very small, less than 0.1 K for all atmos- the brightness temperatures calculated by convolving pheres in all the other bands. GOES-12 and Meteosat-8 bands with the original b. Time dependency of matches CIMSS-32 prior to adding AIRS gaps. The GOES-12 and Meteosat-8 bands are close enough spectrally to the When collecting data for this study, the initial crite- other geostationary imager bands that the results in this rion for matching AIRS with a geostationary imager is test with these two can be used as a proxy for the others. based on the nominal image time. There is room for The results of this test are encouraging where there error here related to the difference in time when the two are not significant AIRS spectral gaps. Small gaps pose instruments actually scanned the lines used in the no problem whatsoever to this gap-filling method, and comparison. This time difference for individual cases is the U.S. Standard Atmosphere is a good enough proxy somewhat correlated to the temperature difference for atmosphere to fill those gaps for all 32 atmospheres those cases. By comparing the actual scan times at the

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FIG. 3. The CIMSS-32, convolved with mock AIRS SRFs and then gap ﬁlled with the adjusted USSA, were then convolved with GEO SRFs and differenced with the CIMSS-32 convolved with the GEO SRFs. The GOES-12 IRW (circles), GOES-12 water vapor band (squares), and the Meteosat-8 shortwave window (asterisks) are shown. The differences are less than 0.1 K for all 32 atmospheres for all the bands except for the water vapor bands and the Meteosat 8.7-mm band (not shown).

GOES-12 subpoint for AIRS to GOES-12, it was dis- cases with brightness temperature differences greater covered that the actual range in differences in the scan than 60.8 K are removed. Because the mean difference times between the two instruments ranged from 8 s to between GOES-12 and AIRS in this band is very small, approximately 24.5 min. Table 4 examines the GOES- the time difference between comparisons is a likely 12 11-mm results more closely, by limiting the compar- explanation for any of the individual cases that had isons to only those that were within 15, 10, and 5 min. very-high brightness temperature differences, which All of these steps show an improvement in the mean included one case with a difference of more than 3 K. By temperature difference and standard deviation over the limiting the comparisons to a 5-min time difference for results without sorting for time, though there is little the other bands on GOES-12 yields interesting results difference between the 15- and 10-min increments. (Table 5). The 3.9- and 13.3-mm band mean and stan- There are 174 infrared window band comparisons for dard deviation also improve, but this not true for the GOES-12 but limiting the comparisons to an actual 15- 6.7-mm water vapor band. min time difference reduces the number of comparisons n to 91 or 52% of the original sample size; limiting the comparisons to a 10-min time difference reduces n to 73 TABLE 4. GOES-12 IRW band comparisons to AIRS spanning 8 or 42% of the original sample size, and limiting the Jan 2006 to 5 Oct 2007. Comparisons limiting the scan time dif- comparisons to a 5-min time difference reduces n to 46 ference between GOES and AIRS yielded improved results compared to allowing all of the cases, which spanned in time dif- or 26% of the original sample size. Reducing the sample ference from approximately 8 s to 24.5 min. size greatly may not be ideal in all situations, but limiting the comparisons to only those with relatively small D Tbb (K) Std dev (K) N time differences provides a more accurate assessment of All cases 20.08 0.68 174 the calibration of the instrument. By looking at com- 15-min limit 20.02 0.46 91 parisons with only a 5-min or smaller time difference 10-min limit 0.01 0.47 73 between AIRS and GOES-12, all of the 11-mm band 5-min limit 0.02 0.35 46

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TABLE 5. GOES-12 comparisons to AIRS spanning 8 Jan 2006 to 5 Oct 2007 for all bands. Comparisons limited to a 5-min scan time difference between GOES and AIRS yielded improved results over allowing all of the cases, which spanned in time difference from approximately 8 s to 24.5 min.

3.9 6.7 11.2 13.3 mm mm mm mm

Original dataset D Tbb (K) 20.71 0.26 20.08 21.66 Std dev (K) 0.78 0.45 0.68 0.73 N 151 175 174 173

5-min limit applied D Tbb (K) 0.02 0.21 0.02 21.54 Std dev (K) 0.23 0.53 0.35 0.55 N 40 46 46 46 c. GEO spectral response uncertainty errors FIG.4.GOES-13 imager band 6 (13.3 mm) SRF (blue) and the In Tobin et al. (2006a), it was shown that most of the shifted SRF (green) shifted 24.7 cm21 (to approximately 13.4 difference between AIRS and a broadband imager mm), with a representative brightness temperature Tbb spectrum in [Moderate Resolution Imaging Spectroradiometer the background (red). By shifting the spectral response this (MODIS)] can be removed by shifting the SRF of the amount, the bias, or mean Tbb difference for all 19 cases, becomes 0.0 K (rounded from 0.01 K) with a std dev of 0.7 K. MODIS band without otherwise changing its shape. A similar exercise was done after the GOES-13 science test (Hillger and Schmit 2007). differences in comparisons to AIRS is uncertainty in the In 19 cases compared to AIRS, the 13.3-mmbandon GEO SRF. This is not an error in this method. This is GOES-13 had a 22.4-K mean brightness temperature a calibration error caused by the various methods em- difference and a standard deviation of 0.6 K. By shifting ployed in laboratory measurements of detector spectral the GOES-13 SRF 24.7 cm21 (Fig. 4), the mean bright- response and also possibly by on-orbit contamination ness temperature difference was reduced to 0 K, with a due to the build up of ice on the detectors, for example. standard deviation of 0.7 K. The value of 24.7 cm21 was d. Scene uniformity arrived at empirically using an iterative method. As shown in the results section below, there appear to be calibration A measure of scene uniformity can be determined by problems with this band on GOES-12 and both Meteosats. the standard deviation about the mean of the radiances ITT Industries, the manufacturers of the GOES im- in the comparison area. The geostationary radiances agers, re-evaluated the 13.3-mm band SRFs. Updated were used for this purpose because of their finer spatial SRFs were made available for that band. The new SRFs resolution over AIRS. Comparing GOES-12 scene uni- for that band have a modified shape that effectively shif- formity to the GEO–AIRS brightness temperature dif- ted the central wavenumber by approximately 21cm21. ference showed there was not a statistically significant ITT concluded that the spectral response for this band correlation between the two for any bands. The results could be shifted by 24.7 cm21 without being implausibly are believed to be independent of scene uniformity. beyond the estimated instrument spectral uncertainty. The National Oceanic and Atmospheric Administra- 5. Results tion’s (NOAA) calibration experts continued to investi- gate this matter, with hopes of coming to a resolution With the knowledge that the method works very well prior to GOES-13 becoming operational. They have for GOES bands 2, 4, and 6 and reasonably well for found that shifting the SRF eliminated the scene tem- band 3 and for Meteosat bands other than 5 and 7, the perature bias, and that the bias is most likely not due to Tables 6a–f are presented. Results are not shown for detector nonlinearity or other factors (X. Wu 2008, per- Meteosat bands 5 (6.2 mm) or 7 (8.7 mm), because the sonal communication). This type of exercise could be gap filling method does not work well enough for those done with all of the world’s geostationary imagers, bands. The results calculated for the 6.2-mm band were though that falls outside the scope of this paper, which is particularly poor (mean brightness temperature differ- to demonstrate how to measure these types of errors that ences approximately 21.4 K) and would unfairly char- exist in the data with the official or operational spectral acterize the instrument’s performance in that band. response. It appears, though, that the major reason for Surprisingly, the results calculated for the 8.7-mm band

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TABLE 6. Here, DT (GEO–AIRS) is the mean Tbb difference over N cases collected from early January 2006 through early October 2007. Std dev is the dev about the mean. The min and max mean scene Tbb (Tbb-Min and Tbb-Max, respectively) (for all FOVs in a single comparison) found in those N cases. (f) The water vapor band results, but those results reﬂect less conﬁdence in the method, based on the tests referenced in Fig. 3.

(a) Shortwave window band (;3.7 to ;3.9 mm) results (nighttime only).

GOES-10 GOES-11 GOES-12 Meteosat-8 Meteosat-9 MTSAT-IR FY-2C

DTbb (K) 0.5 20.1 0.0 0.0 20.1 20.1 0.8 Std dev (K) 0.2 0.1 0.43 0.2 0.2 0.9 1.4 N 29 30 28 40 6 76 104

Tbb-Min (K) 271.9 284.4 265.6 269.2 274.1 271.1 279.8 Tbb-Max (K) 302.2 296.6 301.8 301.0 297.5 303.9 305.4

(b) Ozone band (9.7 mm) results.

Meteosat-8 Meteosat-9

DTbb (K) 0.0 20.1 Std dev (K) 0.2 0.3 N 158 56

Tbb-Min (K) 237.7 256.7 Tbb-Max (K) 273.7 273.4

GOES-10 GOES-11 GOES-12 Meteosat-8 Meteosat-9 MTSAT-IR FY-2C

DTbb (K) 20.3 20.7 20.1 0.4 0.4 20.4 20.7 Std dev (K) 0.5 0.3 0.7 0.3 0.4 0.7 1.7 N 87 43 174 158 56 154 105

Tbb-Min (K) 255.3 263.4 239.6 239.3 269.6 213.5 241.4 Tbb-Max (K) 295.5 294.1 298.4 297.9 293.9 294.5 293.8

(d) ‘‘Dirty’’ window band (;12 mm) results.

GOES-10 GOES-11 Meteosat-8 Meteosat-9 MTSAT-IR FY-2C

DTbb (K) 0.0 20.3 0.6 0.5 0.0 20.3 Std dev (K) 0.6 0.3 0.3 0.4 0.8 1.8 N 79 43 158 56 155 105

Tbb-Min (K) 251.1 259.5 235.9 265.1 211.8 239.2 Tbb-Max (K) 293.3 292.5 296.4 291.8 291.7 291.5

(e) Carbon dioxide absorption band (;13.3 mm) results.

GOES-12 Meteosat-8 Meteosat-9

DTbb (K) 21.7 0.6 21.0 Std dev (K) 0.7 0.3 0.3 N 173 158 56

Tbb_Min (K) 232.6 228.6 249.8 Tbb-Max (K) 273.5 275.5 271.3

(f) Water vapor band (;6.7 to 7.3 mm) results.

GOES-10 GOES-11 GOES-12 Meteosat-8 Meteosat-9 MTSAT-IR FY-2C

DTbb (K) 0.5 0.0 0.3 0.5 0.3 0.2 22.1 Std dev (K) 0.2 0.1 0.5 0.2 0.2 0.3 2.9 N 76 43 175 158 56 155 105

Tbb-Min (K) 231.0 233.2 229.1 229.2 246.3 213.0 226.3 Tbb-Max (K) 256.3 259.5 256.3 267.2 265.9 248.8 254.3

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TABLE 7. Results showing the effects of daytime vs nighttime comparisons in the shortwave window bands. FY-2C is not included because no FY-2C daytime cases were used in this study.

GOES-10 GOES-11 GOES-12 Meteosat-8 Meteosat-9 MTSAT-IR

Day DTbb (K) 20.4 20.5 20.9 20.5 20.5 20.5 Std dev (K) 0.5 N/A 0.7 0.6 0.3 0.6 N 35 1 123 93 35 5

Night DTbb (K) 0.5 0.1 0.0 0.0 20.1 20.1 Std dev (K) 0.2 0.0 0.4 0.2 0.2 0.8 N 29 30 28 40 6 76 were very good (mean brightness temperature differ- duced by direct solar irradiations to the telescope tube ences approximately 0.2 K), but they may not neces- (Guo et al. 2005). Light from the telescope mounts affects sarily be indicative of a well-calibrated channel. Most of the imagery between approximately 1500 and 2000 UTC. that band is not covered spectrally by AIRS. The results The effect is most dramatic in the visible and shortwave in Table 6f, the water vapor bands, are shown here with bands, but it affects the quality of all bands during those less confidence than those of the other bands. times. One of the AIRS overpass times at the FY-2C The results cover cases collected from January 2006 subpoint is between 1800 and 1900 UTC and that data through early October 2007, though not all of the in- cannot be used. Fortunately, nighttime shortwave band struments were operational during that entire period. cases can be used. Only the nighttime cases are used for GOES-11 cases only cover June 2006 through October the other bands in the results shown here as well to 2007. Meteosat-8 cases run from January 2006 to 10 avoid the contaminated times. By eliminating the con- April 2007. Meteosat-9 cases run from 12 April 2007 to taminated cases, the mean temperature differences were October 2007. The results for the shortwave window not always moved closer to 0 K, though the standard bands are nighttime cases only. deviations were reduced in each of the bands: from 3.7 to 2.9 K for the water vapor band; from 2.7 to 1.7 K a. Diurnal signature in the shortwave window bands in the IR window; and from 3.7 to 1.8 K in the ‘‘dirty’’ The shortwave window band is susceptible to solar window band. reflection, and this has an effect on the results (Table 7). d. GOES-12 decontamination Therefore, the results in Table 6a included only nighttime cases. There are two overpass times for Aqua at a The GOES-12 imager went through a decontamina- given point on the equator: one is during daylight hours tion (a heating of certain internal optical components) and the other is during nighttime hours. The results that lasted from just after 1200 UTC on 2 July to just generally improve when only using nighttime cases in after 1200 UTC on 4 July 2007. There was a case col- the shortwave band, as both the mean temperature lected on 1 July 2007 and the next one was 8 July 2007, difference and standard deviation move closer to 0 K. bracketing the decontamination period. The 6.7-mm band differences change sharply between these two days b. MTSAT’s shortwave window band (Fig. 5). The mean brightness temperature difference Puschell et al. (2006) discussed problems with before this period and the mean after that period are MTSAT’s shortwave window band. There is crosstalk roughly the same but opposite in sign (0.4 before, 20.4 between the water vapor band and the shortwave band. K after). The sudden change in the results of the com- Two corrections were implemented: one in mid-March parisons between AIRS and GOES-12 in this region of of 2006 and the other in mid-July of 2006. Prior to the the spectrum can be attributed to the decontamination first correction being implemented, mean temperature (Table 8). This is perhaps a result of ice deposition on differences between AIRS and MTSAT in this region the detectors, which would have the effect of altering were slightly worse than 9 K (MTSAT’s colder than the detector’s spectral response. AIRS). For the results shown here, only cases after 20 July 2006 are used for that band and the mean tem- 6. Summary perature difference is improved to 20.1 K, with a standard deviation of 0.9 K. To the first approximation, most of the bands on the world’s geostationary imagers are reasonably well c. FY-2C’s midnight problem calibrated—that is, they compare to within 1 K of a FY-2C has an instrument problem that affects cali- standard reference (AIRS). Using AIRS as a common bration around satellite midnight. Stray light is intro- reference, typically, the GOES series of satellites are

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FIG. 5. Time series of GOES-12 water vapor band differences with AIRS, illustrating the effect of decontamination. similar to each other in most bands; the Meteosat Sec- to fulfill an operational role. The other unfortunate ond Generation series of instruments are also similar to finding of these results is that FY-2C is not as well each other, as are MTSAT-1R and FY-2C. The most calibrated as the other geostationary imagers. In these glaring exception to this is in the 13.3-mm band. In that comparisons to AIRS, FY-2C has larger mean bright- band, the Meteosat series are the most different from ness temperature differences and larger standard devi- AIRS and from each other. The GOES-12 imager is still ations than any of the other instruments. nearly 1-K different from AIRS in that band, even after The method of filling AIRS spectral gaps with the decontamination. Preliminary results, not shown, with adjusted U.S. Standard Atmosphere spectrum works the GOES-13 imager suggest that it, too, is different well for most bands for comparisons to AIRS near the from both AIRS and GOES-12 in the 13.3-mm band equator. The exception to this is when the AIRS spec- (Hillger and Schmit 2007). This highlights a funda- tral gaps fall too heavily on GEO SRFs, such as for the mental truth about intercalibration, which is that the water vapor bands and the 8.7-mm band on Meteosat. most important time to perform intercalibration is fol- For the other GEO bands, the method appears to work lowing launch, before a satellite becomes operational. well. For the water vapor bands, the results shown here This can potentially reveal problems that are not found are not meant to be accurate assessments of the cali- during engineering postlaunch tests and those problems bration of those instruments. Results were not even can often be addressed before the instrument is expected generated for the 8.7-mm band as a result that uncertainty.

TABLE 8. Results before and after GOES-12 decontamination event (2–4 Jul 2007).

3.9 mm 6.7 mm 11.2 mm 13.3 mm

Prior to decontamination DTbb (K) 20.7 0.4 0.0 21.8 Std dev (K) 0.8 0.3 0.7 0.8 N 125 148 148 147

Post decontamination DTbb (K) 20.6 20.4 20.2 20.9 Std dev (K) 0.7 0.3 0.5 0.5 N 26 27 27 27

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In future comparisons, the standard tropical atmos- Goldberg, M. D., 2007: Global space-based inter-calibration system phere will be tested more thoroughly for use in the gap (GSICS). Atmospheric and Environmental Remote Sensing filling, since these comparisons are done in tropical re- Data Processing and Utilization III: Readiness for GEOSS,M. D. Goldberg et al., Eds., International Society for Optical gions. Preliminary tests suggest it will not affect the Engineering (SPIE Proceedings, Vol. 6684), DOI:10.1117/ results in most bands but could improve the results in 12.735246. the water vapor bands by approximately 0.1 K. Gunshor,M.M.,T.J.Schmit,andW.P.Menzel,2004:Intercali- The next step in the evolution of GEO intercalibration bration of the infrared window and water vapor channels on is to use IASI data. IASI is part of the Meterological operational geostationary environmental satellites using a single polar-orbiting satellite. J. Atmos. Oceanic Technol., 21, 61–68. Operation-A (MetOp-A) satellite package operated by ——, ——, ——, and D. Tobin, 2007: Intercalibrating geosta- the European Organisation for the Exploitation of Me- tionary imagers via polar orbiting high spectral resolution teorological Satellites (EUMETSAT). IASI is a high- data. Atmospheric and Environmental Remote Sensing Data spectral-resolution instrument similar to AIRS but Processing and Utilization III: Readiness for GEOSS,M.D. without significant spectral gaps. Efforts are already un- Goldberg et al., Eds., International Society for Optical En- gineering (SPIE Proceedings, Vol. 6684), DOI:10.1117/ derway at EUMETSAT, and will begin soon at CIMSS 12.735855. and elsewhere, to use IASI to intercalibrate the world’s Guo, Q., J. Xu, and W. Zhang, 2005: Stray light modelling and geostationary imagers. Preliminary results are being cir- analysis for the FY-2 meteorological satellite. Int. J. Remote culated among the World Meteorological Organization’s Sens., 26, 2817–2830. (WMO) Global Space-based Intercalibration System Hillger, D. W., and T. J. Schmit, 2007: The GOES-13 science test: Imager and sounder radiance and product validations. NOAA (GSICS) Research Working Group members. Tech. Rep. NESDIS 125, 75 pp. Puschell, J. J., and Coauthors, 2006: In-flight performance of the Acknowledgments. The authors thank Tim Hewison Japanese Advanced Meteorological Imager. Earth Observing (EUMETSAT), Marianne Koenig (EUMETSAT), Fred Systems XI, J. J. Butler, Ed., International Society for Optical Wu (NOAA), and the rest of the GSICS community for Engineering (SPIE Proceedings, Vol. 6296), DOI:10.1117/ their input, advice, questions, and collaboration. Jim 12.683505. Nelson (CIMSS) is thanked for his support on software Schmit, T. J., and L. D. Herman, 1992: Comparison of multi- spectral data: GOES-7 (VAS) to NOAA-11 (HIRS). Pre- and hardware issues. Hal Woolf (CIMSS) is thanked for prints, Sixth Conf. on Satellite Meteorology and Oceanogra- providing the CIMSS-32 atmospheres, spectral response phy, Atlanta, GA, Amer. Meteor. Soc., 258–259. functions for many of the instruments, and for his edi- Smith, W. L., H. M. Woolf, P. G. Abel, C. M. Hayden, M. Chalfant, torial comments. The SSEC Data Center is thanked for and N. Grody, 1974: NIMBUS-5 Sounder Data Processing providing easily accessible, real-time satellite data in System. Part I: Measurement characteristics and data reduc- tion procedures. NOAA Tech. Memo. National Environ- McIDAS format. Walter Wolf (NOAA) is thanked for mental Satellite Service (NESS) 57, 99 pp. providing AIRS data in near-real time. The GIMPAP Tjemkes, S. A., M. Ko¨ nig, H.-J. Lutz, L. van de Berg, and J. project at NOAA is to be thanked for their financial Schmetz, 2001: Calibration of Meteosat water vapor channel support of this project. This research was performed observations with independent satellite observations. J. Ge- primarily under NOAA Grant NA06NES4400002. The ophys. Res., 106, 5199–5209. views, opinions, and findings contained in this report are Tobin, D. C., H. E. Revercomb, C. C. Moeller, and T. S. Pagano, 2006a: Use of Atmospheric Infrared Sounder high-spectral those of the author(s) and should not be construed as an resolution spectra to assess the calibration to Moderate res- official NOAA or U.S. government position, policy, or olution Imaging Spectroradiometer on EOS Aqua. J. Geo- decision. phys. Res., 111, D09S05, doi:10.1029/2005JD006095. ——, and Coauthors, 2006b: Radiometric and spectral validation of Atmospheric Infrared Sounder observations with the air- REFERENCES craft-based Scanning High-Resolution Interferometer Sounder. Blumstein, D., B. Tournier, F. R. Cayla, T. Phulpin, R. Fjortoft, C. J. Geophys. Res., 111, D09S02, doi:10.1029/2005JD006094. Buel, and G. 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