804 JOURNAL OF APPLIED METEOROLOGY VOLUME 44

Daytime Global Typing from AVHRR and VIIRS: Algorithm Description, Validation, and Comparisons

MICHAEL J. PAVOLONIS Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison, Madison, Wisconsin

ANDREW K. HEIDINGER NOAA/NESDIS Office of Research and Applications, Madison, Wisconsin

TANEIL UTTAL Environmental Technology Laboratory, NOAA, Boulder, Colorado

(Manuscript received 11 June 2004, in final form 30 November 2004)

ABSTRACT

Three multispectral algorithms for determining the cloud type of previously identified cloudy pixels during the daytime, using satellite imager data, are presented. Two algorithms were developed for use with 0.65-, 1.6-/3.75-, 10.8-, and 12.0-␮m data from the Advanced Very High Resolution Radiometer (AVHRR) on board the National Oceanic and Atmospheric Administration (NOAA) operational polar-orbiting sat- ellites. The AVHRR algorithms are identical except for the near-infrared data that are used. One algorithm uses AVHRR channel 3a (1.6 ␮m) reflectances, and the other uses AVHRR channel 3b (3.75 ␮m) reflec- tance estimates. Both of these algorithms are necessary because the AVHRRsonNOAA-15 through NOAA-17 have the capability to transmit either channel 3a or 3b data during the day, whereas all of the other AVHRRs on NOAA-7 through NOAA-14 can only transmit channel 3b data. The two AVHRR cloud-typing schemes are used operationally in NOAA’s extended from AVHRR (CLAVR)-x processing system. The third algorithm utilizes additional spectral bands in the 1.38- and 8.5-␮m regions of the spectrum that are available on the Moderate Resolution Imaging Spectroradiometer (MODIS) and will be available on the Visible–Infrared Imaging Radiometer Suite (VIIRS). The VIIRS will eventually replace the AVHRR on board the National Polar-Orbiting Operational Environmental Satellite System (NPOESS), which is currently scheduled to be launched in 2009. Five cloud-type categories are employed: warm liquid water, supercooled water–mixed phase, opaque ice, nonopaque high ice (cirrus), and cloud overlap (multiple cloud layers). Each algorithm was qualitatively evaluated through scene analysis and then validated against inferences of cloud type that were derived from ground-based observations of clouds at the three primary Atmospheric Radiation Measurement (ARM) Program sites to help to assess the potential continuity of a combined AVHRR channel 3a–AVHRR channel 3b–VIIRS cloud-type cli- matology. In this paper, “validation” is strictly defined as comparisons with ground-based estimates that are completely independent of the satellite retrievals. It was determined that the two AVHRR algorithms produce nearly identical results except for certain thin clouds and cloud edges. The AVHRR 3a algorithm tends to incorrectly classify the thin edges of some low- and midlevel clouds as cirrus and opaque ice more often than the AVHRR 3b algorithm. The additional techniques implemented in the VIIRS algorithm result in a significant improvement in the identification of cirrus clouds, cloud overlap, and overall phase iden- tification of thin clouds, as compared with the capabilities of the AVHRR algorithms presented in this paper.

Corresponding author address: Michael Pavolonis, 1225 West Dayton St., Madison, WI 53706. E-mail: [email protected]

© 2005 American Meteorological Society

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1. Introduction presented. The categories include warm liquid water clouds, supercooled–mixed-phase clouds, opaque ice The earth’s energy budget is greatly influenced by clouds/deep convection, nonopaque high ice clouds clouds. Clouds significantly impact radiative heating (e.g., cirrus), and cloud overlap (e.g., multiple cloud rates, latent heating rates, and moisture transport. The layers). The warm liquid water cloud category includes microphysical properties, spatial coverage, and location clouds that are composed of liquid water droplets that of clouds dictate the effect of clouds on the earth– have a temperature greater than 273.16 K (given by the atmosphere system. For instance, Pavolonis and Key measured 11-␮m brightness temperature). The second (2003) demonstrated that the radiative effect that class accounts for clouds that are either composed en- clouds have on the surface varied significantly with tirely of supercooled water droplets or both ice and cloud thermodynamic phase, cloud-top height, and supercooled water (given by the measured 11-␮m cloud optical thickness. Chen et al. (2000) showed that brightness temperature). Opaque ice clouds are taken the microphysical properties and vertical location of to be nontransmissive clouds (clouds with a visible op- clouds, which can be characterized by using cloud-type tical depth of about 5 or greater) that are either entirely categories, influence the earth radiation budget just as composed of ice crystals or opaque clouds that have much as cloud amount. In addition, it is also important glaciated tops that are consistent with deep convection. to have the ability to detect multiple cloud layers be- The fourth cloud type consists of high ice clouds that cause atmospheric heating/cooling rates are affected by are transmissive. Most cirrus clouds fall into this cat- the vertical distribution of clouds (Liang and Wang egory. Last, the cloud-overlap category is used to iden- 1997). Furthermore, surface observations have shown tify situations in which more than one cloud layer is that multilayered cloud systems occur in most parts of present. The cloud-overlap detection methods are the world (Warren et al. 1985), especially in the Tropics unique to these algorithms and have already been de- and in association with midlatitude cyclones (Hahn et tailed in Pavolonis and Heidinger (2004, hereinafter re- al. 1982, 1984; Tian and Curry 1989). ferred to as PH04). A comparison with cloud It is useful for cloud type, including the identification measurements is also presented in PH04. of multiple cloud layers, to be determined by using im- Each algorithm utilizes threshold values that are ap- aging satellite instruments, which can provide data at a plied to a single satellite pixel at a time. Two of the much higher spatial resolution than surface observa- algorithms presented in this study were designed to be tions. This is especially true over oceans. Also, cloud used with AVHRR data, and the other algorithm was optical depth, cloud particle size, and cloud-top tem- developed for use with Visible/Infrared Imaging Radi- perature satellite retrievals are often dependent on ometer Suite (VIIRS) data. VIIRS is the 22-channel cloud-type/phase information. Several previous studies instrument (sixteen 750-m resolution channels and six have focused on classifying cloudy satellite imager pix- 375-m high-resolution channels) that will eventually re- els. For instance, Baum et al. (1997) applied a “fuzzy place the operational AVHRR on board the National logic” approach to classifying global Advanced Very Polar-orbiting Operational Environmental Satellite High Resolution (AVHRR) data. Further work with System (NPOESS), which is currently scheduled for AVHRR data includes a neural network cloud classifi- launch in 2009. The first VIIRS, which will be nonop- cation system used by Tag et al. (2000), and Hutchinson erational, is scheduled to be launched as part of the (1999) and Key and Intrieri (2000) demonstrated the NPOESS Preparatory Project (NPP) in 2006. The utility of near-infrared reflectances in determining VIIRS channels are a subset of the channels available cloud-top phase. Strabala et al. (1994) and Baum et al. on the 36-band MODIS instrument. The VIIRS will (2000) developed techniques for separating water have a 8.5-␮m band, which, in combination with the clouds from ice clouds using data from the Moderate 11-␮m band, has been shown to be very useful for re- Resolution Imaging Spectroradiometer (MODIS) Air- trieving cloud-top phase, and a 1.38-␮m band, which borne Simulator (MAS). Li et al. (2003) utilized a maxi- can be used to identify high clouds. Neither of these mum-likelihood classification method with MODIS bands is available on the five-channel AVHRR, which measurements. A bispectral grouping approach, using has the following bands: 0.63, 0.86, 1.6/3.75, 10.8, and the 1.63- and 11-␮m MAS bands for detecting cloud 12.0 ␮m. The five-channel AVHRR has been on board overlap, was demonstrated by Baum and Spinhirne the National Oceanic and Atmospheric Administration (2000). (NOAA) operational polar-orbiting satellites since In this paper, three separate globally applicable al- 1981. All AVHRR channels are available on MODIS gorithms for classifying cloudy satellite pixels, during and will be available on VIIRS. Both of the AVHRR daytime, into the cloud-type categories listed below are algorithms are necessary because the AVHRRs that

Unauthenticated | Downloaded 09/28/21 06:45 AM UTC 806 JOURNAL OF APPLIED METEOROLOGY VOLUME 44 have been flown after NOAA-14 can transmit either 2. Models channel 3a (1.6 ␮m) or 3b (3.75 ␮m) data during the day, while all AVHRRs that have been flown on Two radiative transfer models were used to develop NOAA-14 and earlier only had the capability to mea- the theoretical basis for each algorithm that is pre- sure and transmit channel 3b data. Future AVHRRs sented in this study. The first model, Streamer (Key and will continue to be able to switch between channels 3a Schweiger 1998), was used to develop one of the cloud- and 3b. These two AVHRR cloud-typing schemes are overlap detection techniques and to simulate data that used operationally in NOAA’s extended Clouds from were used to create infrared-only tests. Liquid water AVHRR (CLAVR)-x processing system. cloud droplets were taken to be spherical and a Mie The development of all three algorithms is critical in scattering regime was assumed. In the infrared, ice par- that the AVHRR algorithms can be used to process ticles were taken to be spherical and Mie calculations over 20 yr of data for long-term climate studies and the were performed. Ice crystals may take on a number of VIIRS algorithm represents the capabilities for at least different shapes, and so the assumption that ice crystals the following 20 yr. Comparison of the results from that are in the infrared behave as spheres may be these three algorithms will provide preliminary guid- flawed (Takano and Liou 1989; Schmidt et al. 1995), ance on the potential continuity of a combined but scattering in the longwave is secondary to absorp- AVHRR 3b–AVHRR 3a–VIIRS cloud-type climatol- tion. ogy. Unlike a cloud optical depth or cloud-top tempera- Data for all other algorithm tests were simulated us- ture climatology, a cloud-type climatology can provide ing a model that employs a standard adding/doubling information on multilayered clouds. A cloud-type cli- approach to solve the radiative transfer equation with matology also allows for a more direct assessment of delta-M scaling of the phase function (Wiscombe 1977). cloud characteristics than cloud optical depth and A correlated-k approach is used to model gaseous ab- cloud-top temperature because cloud-type or phase in- sorption by H2O, CO2,O3, CO, CH4,O2,N2O, and formation is often used in those retrievals. other trace gases (Bennartz and Fischer 2000; Kratz The following questions will be addressed in this pa- 1995). Both water and ice particles were taken to be per. Will both AVHRR algorithms produce similar re- spheres at all wavelengths, and Mie scattering was as- sults? How much of an improvement can be made by sumed. The spectral bands that are available are the utilizing the 1.38- and 8.5-␮m bands that are available same as those associated with the MODIS instrument. on MODIS and will be available on VIIRS, but not This model is also described in PH04. Streamer was AVHRR? Will there be a large discontinuity between originally used to simulate all spectral channels, but AVHRR and VIIRS cloud-type climatologies? All because the Streamer bandwidths were determined to three algorithms will be described in detail, and theo- be too broad to simulate data in the 1.38- and 1.65-␮m retical data will be presented from radiative transfer regions of the spectrum, all near-infrared (NIR) data models that were used to help derive various thresholds were resimulated using this model. However, there was utilized in each algorithm. Each algorithm has been no reason to resimulate the data that were used in in- applied to three vastly different scenes and the results frared-only tests because the Streamer bandwidths for are examined and compared. Because MODIS offers the infrared (IR) channels of interest are similar to the channels with a similar spectral and spatial resolution as corresponding MODIS bands. that of AVHRR/VIIRS, MODIS data, with a spatial The cloud effective particle radius was set to 10 ␮m resolution of 1 km, are used in this study exclusively, for all water droplets and 30 ␮m for all ice crystals. and all of the spectral wavelengths given from this point These are the same values that were used in the Inter- forward will represent MODIS band central wave- national Satellite Cloud Climatology Project (ISCCP) lengths. Using MODIS data also allows for time-and- dataset processing (Rossow et al. 1996). The cloud liq- space coincident comparisons of the three algorithms. uid/ice water content was set to 0.2 g mϪ3 for water In addition, each algorithm will be validated against clouds and 0.07 g mϪ3 for ice clouds, though these val- inferences of cloud type based on measurements made ues should have little impact because the visible cloud by ground-based instruments at the Atmospheric Ra- optical depth is being specified directly. All algorithm diation Measurement (ARM) Program sites in the threshold values and/or functions were developed ini- tropical western Pacific (TWP), southern Great Plains tially from theoretical data and then adjusted, if (SGP), and North Slope of Alaska (NSA). In this pa- needed, based on the analysis of many AVHRR or per, “validation” is strictly defined as comparisons with MODIS scenes for a large variety of conditions. Thus, ground-based estimates that are completely indepen- even though ice particles were taken to be spherical in dent of the satellite retrievals. the NIR and IR, cloud particle size was fixed, and only

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totally cloudy scenes were simulated (e.g., cloud edges plane-parallel theory for a single-layer water cloud with were not simulated) by the radiative transfer models; a similar reflectance. The detection of cloud overlap in the thresholds were adjusted, based on actual satellite the AVHRR algorithm is fundamentally a detection of data, to help account for these and other factors. In this deviation from plane-parallel behavior. Model summary, simulations were used to define the expected simulations were performed using Streamer to deter- behavior and shape of the threshold functions, and mine appropriate 11–12-␮m brightness temperature comparisons with multispectral imagery were used to difference as a function of R[0.65] for the detection of adjust the final threshold curves/values. cloud overlap. The viewing and solar zenith angles are also taken into account, and a few other constraints are 3. Calculation of 3.75-␮m-reflectance estimate applied to the algorithm. For details concerning those constraints, refer to PH04. Further, poleward of 60° ␮ During daytime, the radiance at 3.75 m has both near-infrared-reflectance minimum and maximum significant solar and thermal components. To obtain an thresholds are applied in an effort to reduce the cloud- ␮ estimate of the 3.75- m reflectance due to the solar overlap false-alarm rate over snow and ice. When component, the contribution to the total radiance from AVHRR channel 3a (3b) is available, the 1.65 (3.75)- thermal emission must approximated and removed. As ␮m reflectance must be greater than 20% (6%) and less in Key and Intrieri (2000) and Heidinger et al. (2004), than 40% (18%) in order to apply the SWBTD test. ␮ the 3.75- m-reflectance estimate [R(3.75)] is calculated The lower bound is used to help prevent single-layer as shown in (1): cirrus clouds over a snow surface from being classified L͑3.75͒ Ϫ B͓T͑11͔͒ as cloud overlap, and the upper bound is to prevent R͑3.75͒ ϭ , ͑1͒ Lou Ϫ B͓T͑11͔͒ single-layer water clouds in the presence of a dry mid- and upper atmosphere from being identified as overlap. ␮ where L(3.75) is the observed 3.75- m radiance, Note that when snow is present at lower latitudes, the ␮ B[T(11)] is the Planck function radiance at 3.75 m that risk for the false detection of cloud overlap will be ␮ is calculated using the observed 11- m brightness tem- greater with this test. perature, Lo is the solar constant for the 3.75-␮m band (adjusted for earth–sun distance), and u is the cosine of b. VIIRS the solar zenith angle. The VIIRS cloud-overlap algorithm is also described in detail in PH04, so only a brief synopsis is given here. 4. Cloud-overlap detection The VIIRS algorithm includes all of the tests that are associated with the AVHRR cloud-overlap detection a. AVHRR algorithm and an additional group of tests that incor- The presence of cloud overlap is determined using porate NIR data. If a given pixel passes either group of the AVHRR algorithm that is described in detail in tests, then it is assumed that cloud overlap is present. PH04. Only a summary is given here. The AVHRR The following NIR spectral properties are exploited in cloud-overlap detection algorithm utilizes the 0.65-␮m the VIIRS algorithm. In the 1.65-␮m region of the spec- reflectance [R(0.65)] and brightness temperatures from trum, ice particles absorb radiation much more strongly the infrared window region of the spectrum (11 and 12 than water particles (Pilewskie and Twomey 1987). ␮m). The physical basis of this algorithm is that for a Thus, the radiation that is reflected back to the satellite single-layer cloud, the 0.65-␮m reflectance and the 11– at 1.65 ␮m will be greater for an optically thick water 12-␮m brightness temperature difference [split-window cloud than for an optically thick ice cloud. Further, in brightness temperature difference (SWBTD)] should the 1.38-␮m region, water vapor is a strong absorber of behave as predicted by plane-parallel radiative transfer radiation, so the radiation that is detected by a satellite simulations. In general, as a single-layer cloud becomes at this wavelength will mainly be from the upper tro- optically thick, its reflectance increases, and its posphere, unless the atmosphere is very dry. Because of SWBTD decreases (Inoue 1985). In the case of a semi- this fact, the 1.38-␮m band is very effective at detecting transparent cirrus cloud overlying a lower water cloud, cirrus clouds (Gao et al. 1993). If both the 1.65- the vertical separation has little effect on its reflectance, [R(1.65)] and the 1.38-␮m reflectance [R(1.38)] are but a large effect on the SWBTD. Given a sufficient greater than some specified thresholds, there is a good temperature difference between the cirrus and the possibility that both a high ice cloud and a lower water lower water cloud, the difference in transmission cloud are present in a given satellite field of view. The through the cirrus cloud at 11 and 12 ␮m will result in model that is built to simulate MODIS data was used to a SWBTD that is much larger than that predicted by determine the threshold values used in the NIR test.

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Thresholds also vary with viewing and illumination ge- ometry. A few other constraints are applied to the al- gorithm, which are described in PH04. Also, only the near-infrared reflectance test is used poleward of 50°.

5. Cirrus detection a. AVHRR The absorption of radiation by water vapor and ice crystals in semitransparent cirrus clouds is greater at 12 than at 11 ␮m (Inoue 1985). Cirrus clouds are mainly present in the upper troposphere, above which water vapor amounts are relatively small. Thus, for semitrans- parent ice clouds (e.g., cirrus), the difference between the brightness temperature at 11 and at 12 ␮m should mainly be because of the difference in absorption by the cloud particles at the two wavelengths. Semitrans- parent clouds that are lower in the troposphere should FIG. 1. Calculations of 11-␮m brightness temperature and the have a smaller SWBTD because more absorption by 11–12-␮m brightness temperature difference for a viewing zenith water vapor will occur at both 11 and at 12 ␮m because angle of 11.12° for water and ice particles of various cloud optical of the greater pathlength through the atmosphere. If a depths (CODs). The thick line represents the threshold function semitransparent ice cloud that is located in the upper used to identify cirrus clouds. troposphere is present in a given satellite pixel, the SWBTD should be greater than that for a clear sky or possible (ice clouds with an optical depth between 2 most lower clouds. These relationships were also de- and 5 are also of interest, but are not shown in Fig. 1 for scribed in Inoue (1987) and Saunders and Kriebel clarity). Of course, there is some ambiguity in the re- (1988). However, the SWBTD may also be large when gion that is close to the threshold function, but the viewing the edges of lower clouds or optically thin threshold function was created so that most of the am- lower clouds when the atmosphere above is sufficiently biguous results (liquid water versus ice) resided below dry. the threshold function. Because of this, some non- Streamer was used to perform a variety of calcula- opaque ice clouds will be missed using this technique. tions for single-layer ice and water clouds with various When this test is applied to satellite data, the viewing visible optical depths and the 1761 different atmo- zenith angle is checked and the appropriate threshold spheric profiles contained in the Television and Infra- function is used for a given satellite pixel. If the ob- red Observation Satellite (TIROS) initial guess atmo- served SWBTD is greater than the threshold deter- spheres (TIGR-2) database (Moine et al. 1987). Model mined for a given BT(11), then this test is passed. The simulations were performed for seven different satel- model results indicate that this test is best suited for lite-viewing zenith angles. Figure 1 shows the simulated identifying ice clouds with a visible optical depth be- SWBTD as a function of 11-␮m brightness temperature tween 1 and 2. However, as Fig. 1 shows, a thin water [BT(11)] for a viewing angle of 11°. For clarity, only cloud can exceed the cirrus threshold value, so for day- simulations for about 120 of the 1761 profiles used are time applications a NIR reflectance threshold is also shown. The threshold function that is used for this par- applied to aid in preventing cloud edges and thin low- ticular viewing zenith angle is also shown. Threshold and midlevel water clouds from being classified as cir- functions were initially determined by fitting a fourth- rus clouds, because clouds that contain water droplets degree polynomial to model output in such a manner will generally have a higher R(1.65) or R(3.75) than that it visually provided the optimal boundary between clouds that only contain ice. Thus, some of the squares ice clouds with a visible optical depth of approximately that reside above the threshold function shown in Fig. 1 5 or less, and all liquid water clouds (thick liquid water will be filtered out. It is also possible that some thin clouds are not shown because the SWBTD will often be cirrus clouds may be missed because of this extra con- Յ0 for such clouds) and ice clouds with a visible optical straint, but the analysis of many scenes reveals that the depth greater than about 5. Thus, the algorithm was main effect will be to decrease the number of noncirrus designed to isolate the clouds, indicated by the dia- clouds that are classified as cirrus. Table 1 shows the mond and asterisk symbols shown in Fig. 1, as best as surface-type-dependent near-infrared thresholds that

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TABLE 1. Near-infrared reflectance thresholds used in conjunc- being misclassified as nonopaque ice clouds. The tion with the split-window brightness temperature test to detect R(3.75) supplemental thresholds used are listed in ␮ ␮ cirrus clouds. The 1.65- m reflectance [R(1.65)] or the 3.75- m Table 1. reflectance [R(3.75)], depending on which is used, must be less than the threshold values given in this table. A second method that is applied independently of the test described above utilizes the 1.38-/0.65-␮m re- R(1.65) R(3.75) flectance ratio [RAT(1.38, 0.65)], based on the work of Surface type threshold threshold Roskovensky and Liou (2003). The use of this ratio Water/snow/ice 0.25 (25%) 0.15 (15%) helps to lessen the effects of water vapor that are asso- Vegetated land 0.30 (30%) 0.15 (15%) ciated with detecting cloud in the mid- and upper tro- Nonvegetated land (desert) 0.55 (55%) 0.40 (40%) posphere solely using the 1.38-␮m reflectance and pro- vides for a much greater sensitivity to cirrus with an optical depth less than 1, as Roskovensky and Liou have been adopted based on the analysis of many demonstrated. If RAT(1.38, 0.65) is greater than the MODIS and AVHRR scenes. Last, cirrus detection threshold value shown in Fig. 2 and the 0.65-␮m reflec- with this test will not work very well in colder (drier) tance is less than 0.40 (40%), cirrus is said to be present. atmospheres because the model results indicate that the The 0.40 visible reflectance threshold was selected be- SWBTD for an ice cloud with an optical depth of 1 in a cause only ice clouds with an optical depth of about 5.0 cold (dry) environment will have roughly the same or less are of interest. In Fig. 2, RAT(1.38, 0.65) is SWBTD as an ice cloud of optical depth 5 that is shown as a function of scattering angle (⌰), defined as present in a much warmer (more moist) overall envi- ⌰ ϭ cosϪ1͑cos␪ cos␪ ϩ sin␪ sin␪ cos␾͒, ͑2͒ ronment. A summary of this test is given in Table 2. sun sat sun sat ␪ ␪ The coefficients that are used to define the SWBTD where sun is the solar zenith angle, sat is the satellite ␾ threshold functions can be found online (http:// zenith angle, and is the relative azimuth angle. By this cimss.ssec.wisc.edu/viirs/). definition, angles less (greater) than 90° represent backward (forward) scattering. Because RAT(1.38, 0.65) is not a strong function of scattering angle, a con- b. VIIRS stant threshold value was adapted. Over snow and ice In the first VIIRS cirrus detection method, the surfaces this test will not be effective at identifying cir- R(1.38) and R(3.75) values are used to supplement the rus clouds because RAT(1.38, 0.65) will be greatly re- SWBTD test that is described in section 4a. As dis- duced because of the increase in R(0.65) that is caused cussed earlier, R(1.38) will most often only exceed a by the bright surface. The sensitivity of this test will also certain threshold value when high cloud is present in a be slightly decreased over bright desert surfaces, but given satellite pixel. The threshold value chosen is 0.025 cirrus of optical depth 1 or greater are still readily iden- (2.5%). Also, the presence of ice cloud particles will act tified. Also, the SWBTD test should still be effective to reduce R(3.75). Here, R(3.75) is used instead of over bright surfaces. Over most other land surfaces, the R(1.65) because R(3.75) model simulations indicate RAT(1.38, 0.65) test is effective at identifying cirrus that R(3.75) is not as dependent on surface type. Both with an optical depth of 0.5 or greater. A summary of R(1.38) and R(3.75) can then be applied to the SWBTD these cirrus tests is given in Table 2. It should be noted that is used in the AVHRR algorithm as extra con- that because near-infrared and visible channels are straints in order to help to prevent certain midlevel used in all of the AVHRR and VIIRS cirrus tests, thin cloud features and low- and midlevel cloud edges from cirrus clouds, especially those with an optical depth that

TABLE 2. A summary of the AVHRR 3a, AVHRR 3b, and VIIRS cirrus identification methods is presented. In this table, SWBTD is the 11–12-␮m brightness temperature difference; R(1.65), R(3.75), R(1.38), and R(0.65) represent the 1.65-, 3.75-, 1.38-, and 0.65-␮m reflectances, respectively. RAT(1.38, 0.65) is the 1.38-/0.65-␮m-reflectance ratio and BTD (8.5, 11) is the 8.5–11-␮m brightness tem- perature difference.

Algorithm Logic If a given set of tests is found to be true for a given algorithm, then cirrus cloud may be present. AVHRR 3a SWBTD Ͼ dynamic threshold and R(1.65 ␮m) Ͻ threshold (see Table 1) AVHRR 3b SWBTD Ͼ dynamic threshold and R(3.75 ␮m) Ͻ threshold (see Table 1) VIIRS SWBTD Ͼ dynamic threshold and R(3.75) Ͻ threshold (see Table 1) and R(1.38) Ͼ 0.025 (2.5%), or RAT(1.38, 0.65) Ͼ 0.17 and R(0.65) Ͻ 0.40 (40%), or BT(11) Ͼ 273.16 K and BTD(8.5, 11) Ͼ dynamic threshold and R(1.38) Ͼ 0.01 (1%) and R(3.75) Ͻ threshold (see Table 1)

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strated this ability by using reflectances in the 1.65-␮m region of the spectrum. Key and Intrieri (2000) used AVHRR channel 3b (3.75 ␮m) reflectances to deter- mine cloud phase in the Arctic. The 1.65- and 3.75-␮m reflectances were modeled for single-layer water and ice clouds of various optical depths ranging from 0.1 to 20.0 for a variety of viewing and illumination angles and surface types. Results for a vegetated (grass) surface are shown for the 1.65- and 3.75-␮m bands in Figs. 3a and 3b, respectively, as a function of scattering angle. Model results indicate that the distinct separation be- tween water and ice does not vary very much as a func- tion of scattering angle for either R(1.65) or R(3.75), and so constant threshold values are used for two dif- ferent surfaces: water/snow/ice, and all other land sur- faces. These values are shown in Table 3. A water (ice) cloud is deemed to be present if the observed NIR FIG. 2. Calculations of 1.38-/0.65-␮m reflectance ratio as a func- tion of scattering angle for water and ice particles of various reflectance is greater (less) than the threshold value. As CODs over a water surface. The thick line represents the thresh- with most methods, NIR reflectance thresholds will old value used to help to identify cirrus clouds. This threshold is have a limited utility for clouds that have a visible op- not set lower to avoid confusing midlevel cloud and cirrus. tical depth that is less than about 1. NIR reflectances are also very sensitive to cloud particle size (Lee et al. 1997), meaning that ice clouds that are composed of is less than unity, may be classified as water cloud in very small particles may have a similar NIR reflectance regions of strong sun glint. An additional multispectral as that of a water cloud that is composed of large water cirrus detection test is described in section 6b. droplets. b. VIIRS NIR–IR test 6. General cloud phase discrimination A combined NIR–IR technique is used in a series of a. AVHRR NIR test cloud phase–determining spectral tests that are de- During the day, NIR reflectances can be effectively signed to be used with VIIRS. As Strabala et al. (1994) utilized to infer cloud phase, especially for optically and Baum et al. (2000) showed, 8.5–11-␮m brightness thick clouds. Pilewskie and Twomey (1987) demon- temperature differences could be used to effectively

FIG. 3. Calculations of (a) 1.65-␮m reflectance and (b) the estimated 3.75-␮m reflectance as a function of scattering angle for water and ice cloud of various optical depths, ranging from 0.1 to 20.0, over a vegetated grass surface. The thick line represents the threshold value used to help to separate water and ice clouds.

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TABLE 3. Near-infrared reflectance thresholds used to distin- from tropical (warm, with total column precipitable wa- guish water and ice clouds. The 1.65-␮m reflectance [R(1.65)] or ter in excess of 6 cm) to polar (cold, with total column ␮ the 3.75- m reflectance [R(3.75)], depending on which is used, precipitable water less than 0.1 cm) in nature. The must be less (greater) than the threshold values given in this table for ice (water) clouds. cloud-top pressure of all ice (water) clouds was fixed at 300 (700) hPa. Cloud-top heights and temperature will Surface type R(1.65) threshold R(3.75) threshold vary simply because many different atmospheric pro- Water/snow/ice 0.25 (25%) 0.06 (6%) files were used. Threshold functions for seven different All other surfaces 0.32 (32%) 0.06 (6%) viewing angles were created in the same manner as the SWBTD cirrus-detection threshold functions. Ob- served values of BTD(8.5, 11) that are greater (less) separate water cloud from ice cloud. The 8.5–11-␮m than the threshold value for a given BT(11) and viewing difference [BTD(8.5, 11)] will be larger when mainly zenith angle imply that an ice (water) cloud is present. ice, rather than water, is present at cloud top. The The coefficients that are used to define the threshold physical reason for this behavior can be elucidated functions used in this bispectral infrared test can be upon through examination of the imaginary index of found online (http://cimss.ssec.wisc.edu/viirs/). NIR/visible reflectance thresholds are used as addi- refraction (mi), which is a direct indicator of absorp- tion/emission strength for a given size and shape distri- tional constraints to the bispectral IR algorithm that is ␮ discussed above to aid in further distinguishing cloud bution of cloud particles. Near 8.5 m, mi is roughly the ␮ phase. The following additional constraints are em- same for water and ice particles, whereas at 11 m, mi is larger for ice particles than for water particles. ployed: 1) ice clouds must have a 1.65-/0.65-␮m reflec- Hence, BTD(8.5, 11) will be larger for an ice cloud than tance ratio less than 1.0, 2) opaque ice clouds must have for a water cloud if both clouds have the same tempera- a BT(11) Ͻ 263.16 K, and 3) if BT(11) Ͼ 273.16 K, the ture. Figure 4 shows BTD(8.5, 11) as a function of cirrus cloud type can be retrieved if BTD(8.5, 11) Ͼ BT(11) for a viewing angle of about 11°. The threshold dynamic threshold and R(1.38) Ͼ 0.01 (1%) and function that is used to separate water from ice cloud is R(3.75) Ͻ threshold value shown in Table 1. also shown on the figure. The threshold function was chosen based on the modeling of single-layer water and 7. Algorithm logic ice clouds of various optical depths ranging from 0.1 to a. AVHRR 20. Calculations were performed for over 100 atmo- spheric profiles using Streamer. The profiles ranged Figure 5 shows the complete AVHRR algorithm de- cision tree. Each pixel is initially assigned to a series of spectral tests that are based solely on BT(11). From a physical perspective, this approach helps to take into account the dependence of cloud-top phase on cloud- top temperature. We check for the presence of cloud overlap unless BT(11) of the pixel is greater than 270 K. If BT(11) Ͻ 233.16 K—the maximum temperature for which homogeneous freezing occurs—and the cloud- overlap test is failed, the pixel is tested for the presence of nonopaque ice clouds using the AVHRR cirrus de- tection test. If the cirrus test is failed, the pixel is clas- sified as an opaque ice cloud; otherwise, it is classified as a nonopaque ice cloud. However, once a given pixel passes the cloud-overlap test, no other tests are per- formed and the final classification for that given pixel is cloud overlap. If 233.16 K Ͻ BT(11) Յ 253.16 K, and the cloud- overlap test is failed, the cirrus test is performed. If the cirrus test is failed, the NIR reflectance test (test 1 in ␮ FIG. 4. Calculations of 11- m brightness temperature and the Fig. 5) is utilized. A pixel that has an NIR reflectance 8.5–11-␮m brightness temperature difference for a viewing zenith angle of 11.12° for water and ice cloud of various optical depths that is greater than the threshold value is typed as being ranging from 0.1 to 20.0. The thick line represents the threshold supercooled water–mixed phase; otherwise, the opaque function used to separate water and ice clouds. ice cloud type is assigned to the pixel.

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FIG. 5. A flowchart representing the cloud-typing algorithms presented in this study. BT(11 ␮m) symbolizes the 11-␮m brightness temperature, and IR and/or NIR represents the infrared and near-infrared cloud phase tests. Only the NIR test is used in the AVHRR algorithms, and a combination IR and NIR test is used in the VIIRS algorithm. The split-window cloud overlap test is only applied when BT(11 ␮m) Ͻ 270 K (AVHRR and VIIRS algorithms) and the near-infrared test when BT(11 ␮m) Ͻ 280 K (VIIRS algorithm only).

If the 253.16 K Ͻ BT(11) Յ 273.16 K, and the cloud- IR–NIR–visible test is utilized (as described in section overlap test is failed, the cirrus test is performed. If the 6b) instead of the basic NIR test that is associated with cirrus test is failed, the NIR reflectance test is utilized the AVHRR algorithm. (test 2 in Fig. 5). A pixel that has an NIR reflectance less than the threshold value and a BT(11) Ͻ 263.16 K is typed as being an opaque ice cloud; otherwise, the 8. Algorithm performance supercooled water/mixed-phase cloud type is assigned Three total scenes, collected over the Tropics, mid- to the pixel. latitude, and high-latitude regions, are used to roughly If BT(11) Ͼ 273.16 K, the melting point of pure wa- qualitatively assess the global applicability of each al- ter, the cirrus detection test is simply applied. If it is gorithm. All of the data shown were taken by the passed, then the pixel is classified as nonopaque ice MODIS instrument on board the Terra spacecraft, and cloud, otherwise, it is a warm liquid water cloud type. the MODIS cloud mask product (Ackerman et al. 1998) was used to identify cloudy pixels. Only pixels b. VIIRS that were determined to be fully obscured by cloud The VIIRS algorithm logic is analogous to the (e.g., MODIS cloud mask ϭ 0) were processed by the AVHRR algorithm logic and is also outlined in Fig. 5. cloud-typing algorithms. Although, if any clear pixels The series of spectral tests that is applied to each pixel were processed because of errors in the cloud mask, is determined by BT(11). The SWBTD overlap test is they would be mostly classified as warm liquid water or only applied when BT(11) Ͻ 270 K, whereas the NIR supercooled water–mixed-phase cloud, depending on cloud-overlap test is applied when BT(11) Ͻ 280 K. BT(11), over most surfaces, and opaque ice cloud over The VIIRS algorithm structure is the same as the cold snow-covered surfaces. The tropical scene is AVHRR algorithm structure, except the expanded mainly located over the Indian Ocean near the north- cloud-overlap and cirrus algorithms are used and an west coast of Australia, extending north through Indo-

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FIG.6.Terra’s MODIS images for 4 Apr 2003 at 0250–0300 UTC: (a) 0.65-␮m reflectance, (b) 11-␮m brightness temperature, (c) 1.65-␮m reflectance, and (d) 8.5–11-␮m brightness temperature difference over the tropical Indian Ocean.

nesia. The data are from 0250–0300 UTC 4 April 2003. can be used to help to infer cloud optical thickness and The midlatitude scene is located over the central cloud height as well as cloud overlap. United States and the data are from 1715–1725 UTC 6 April 2003. The high-latitude scene covers much of a. Tropical scene Alaska and northwestern Canada, and portions of the In the tropical scene (Figs. 6a–6d), low, middle, high, Arctic Ocean at 2130 UTC 7 May 2000. and deeply convective cloud systems can be readily Four different spectral 1-km images are used to identified. There are also regions where multiple cloud qualitatively analyze the retrieved cloud types in each layers are present, especially in association with Tropi- scene. The 0.65-␮m reflectance (MODIS band 1), the cal Cyclone Inigo in the right half of the image (this is 1.65-␮m reflectance (MODIS band 6), the inverted 11- even more readily apparent in multispectral RGB im- ␮m brightness temperature (MODIS band 31), and the ages). The cloud-typing results are shown in Figs. 7a– 8.5–11-␮m brightness temperature difference (MODIS 7c, and the fraction of each cloud type, based only on bands 29–31) are displayed in four separate images. As cloudy pixels, is given in Table 4. The results from the discussed in sections 6a and 6b, the R(1.65) and two AVHRR algorithms (Figs. 7a and 7b) are similar, BTD(8.5, 11) images are useful for discerning water except, at times, for very thin cirrus. A visual analysis of clouds from ice clouds. The R(0.65) and BT(11) images this scene indicates that the AVHRR 3a algorithm bet-

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FIG. 7. Cloud-typing results using MODIS data at 0250–0300 UTC 4 Apr 2003 for (a) the AVHRR channel 3a algorithm, (b) the AVHRR channel 3b algorithm, and (c) the VIIRS algorithm. ter detects cirrus than the AVHRR 3b algorithm does. be cirrus in this scene, as compared with the 38.50% However, analysis of scenes where more midlevel cloud that is predicted for the AVHRR 3b algorithm. The is present (not shown) shows that the AVHRR 3a al- VIIRS algorithm, however, is clearly much more effec- gorithm often results in thin midlevel clouds or the edge tive at finding thin cirrus clouds (46.45%). This is due to of midlevel cloud to be classified as cirrus, whereas the the 1.38-/0.65-␮m ratio test, which is much more sensi- AVHRR 3b algorithm does not result in such a large tive to high, thin cloud than the SWBTD cirrus detec- misclassification. The near-infrared (1.65 or 3.75 ␮m) tion algorithm. The thin cirrus that the VIIRS algo- thresholds that are used in the SWBTD cirrus detection rithm detects are generally classified as water clouds by algorithm were set to produce optimal results (mini- the AVHRR algorithm, which is clearly reflected in the mize misclassification and maximize the number of cor- statistics. In addition, about 3 percentage units (PU) rect retrievals), based on model output and the analysis more cloud overlap is also detected with the VIIRS of many scenes for each algorithm. The AVHRR 3a algorithm. The amount of cloud overlap that is detected algorithm detects about 39.40% of the cloudy pixels to by each AVHRR algorithm is the same because the

TABLE 4. Cloud-type statistics for the MODIS scene shown in Fig. 6. The percentage of cloudy pixels that were flagged as each type is shown.

Supercooled Nonopaque ice Multilayered Algorithm Water (%) water–mixed (%) Opaque ice (%) (cirrus) (%) clouds (%) AVHRR 3a 35.40 0.79 9.44 39.40 14.98 AVHRR 3b 36.05 1.02 9.45 38.50 14.98 VIIRS 25.19 1.19 9.12 46.45 18.06

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FIG. 8. The same as Fig. 6, but at 1715–1725 UTC 6 Apr 2003 over the continental United States. algorithms are identical at low latitudes. It is important results from the AVHRR 3a, AVHRR 3b, and VIIRS to note that the determination of cloud phase for opti- algorithms are shown in Figs. 9a–c, respectively, and cally thick, nonmultilayered cloud systems is nearly the Table 5. Once again, all three algorithms produce same for all three algorithms. Thus, at low latitudes, the roughly the same results for optically thick nonover- additional spectral information used in the VIIRS al- lapped clouds. Further, the only noticeable differences gorithm will likely be most beneficial when viewing op- between the two AVHRR algorithms occur near cloud tically thin clouds or multilayered cloud systems. In ad- edges and for thin cirrus, but, in general, the results are dition, the greatest differences between the two very similar. The AVHRR algorithms seem to perform AVHRR algorithms will occur near the cloud edges. well when optically thick clouds are present; however, very thin cirrus clouds are quite often misclassified as supercooled water–mixed phase or water. This problem b. Midlatitude scene is largely eliminated in the VIIRS algorithm, with ap- As can be seen in Figs. 8a–d, this scene is rather proximately 3 PU more cirrus detected despite the fact complex and is located mostly over land surface. Low that some of the cirrus that are identified by the and high clouds, many of which overlap, are present. AVHRR algorithms correspond to cloud overlap in the Some of the clouds with high tops are quite thick, while VIIRS results. Also, in this scene, much more cloud semitransparent cirrus cloud is also present in the upper overlap is detected by the VIIRS algorithm (28.92% as Midwest and Great Lakes regions. The cloud-typing compared with 19.83%) because of the NIR reflectance

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FIG. 9. Same as Fig. 7, but at 1715–1725 UTC 6 Apr 2003. test that is used in addition to the SWBTD test. As Also, this scene is challenging because much of the pointed out in PH04, the NIR reflectance test is more cloud that is present is semitransparent; even surface effective than the SWBTD test at identifying cloud features such as leads in the ice pack are still apparent overlap when the top cloud layer is more optically in the 0.65-␮m image in some of the cloudy regions thick, as is the case in this scene (see Figs. 8a and 8b). north of 72°N. The cloud-typing results are shown in Figs. 11a–c and in Table 6. Both AVHRR algorithms, c. High-latitude scene but especially the AVHRR 3a algorithm, perform This scene contains single-layer supercooled water– rather poorly near the edge of the thin cloud north of mixed-phase cloud and ice cloud with some overlap 72°N. The R(1.65) and BTD(8.5, 11) images indicate between 140° and 160°W south of 72°N (Figs. 10a–d). that this cloud is mostly composed of water droplets

TABLE 5. Cloud-type statistics for the MODIS scene shown in Fig. 8. The percentage of cloudy pixels that were flagged as each type is shown.

Supercooled Nonopaque ice Multilayered Algorithm Water (%) water–mixed (%) Opaque ice (%) (cirrus) (%) clouds (%) AVHRR 3a 13.23 19.36 33.69 13.88 19.83 AVHRR 3b 13.28 19.59 33.93 13.38 19.83 VIIRS 13.03 15.38 25.97 16.70 28.92

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FIG. 10. The same as Figs. 6 and 8, but at 2130 UTC 7 May 2000 over Alaska. that are likely supercooled because BT(11) is less than more effective for discriminating cloud phase than the 273.16 K in this region. Much of the cloud edge is mis- 1.65-␮m reflectance in this scene. It was found that takenly labeled as cloud overlap and opaque ice. The R(1.65) was very small (Ͻ20%) for the edge of the SWBTD cloud-overlap test does not perform as well in mixed-phase cloud that was referenced earlier and yet the high latitudes because of the bright snow surface, quite large (Ͼ30%) for some regions of ice cloud; but and, because the atmosphere is generally quite dry, thin R(3.75) did not exhibit this sort of large variation. Thus, lower-level clouds are often detected using the SWBTD it is difficult to type this scene using a constant thresh- cloud-overlap test if no low-level temperature inversion olding approach with R(1.65). is present. Also, clouds that are mostly composed of At high latitudes, only the NIR reflectance test is water droplets, if optically thin, can be mistaken for ice used to detect cloud overlap in the VIIRS algorithm, so cloud when a snow or ice surface is present beneath the the occurrence of false cloud-overlap detection is much cloud. This is because the NIR reflectance that is used less. It is also important to point out that much of the to help determine cloud phase will be more influenced thin ice cloud over the snow/ice surface in this scene by the dark surface (dark at NIR wavelengths) than by would be identified as opaque ice, not cirrus, by each of the thin cloud, whereas BTD(8.5, 11) seems to provide the algorithms. This is because the 1.38-/0.65-␮m ratio a better ice/water phase sensitivity for this situation; test is not very effective over surfaces that are bright at hence, the results are much improved for the VIIRS visible wavelengths, and the SWBTD cirrus test is less algorithm. The 3.75-␮m reflectance is significantly effective because of the more complicated atmospheric

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FIG. 11. The same as Figs. 7 and 9, but at 2130 UTC 7 May 2000. structure characteristic of the high latitudes. For in- more of a consistency check that is based on ground- stance, persistent near-surface temperature inversions based estimates that are completely independent of the are very common (Liu and Key 2003). These tempera- satellite retrievals that are presented in this paper. Nev- ture inversions will often cause the SWBTD to be ertheless, in an effort to, at least roughly, quantitatively closer to zero, or at times, negative. Cirrus detection evaluate the performance and global applicability of will then often be limited at high latitudes; however, the each algorithm, data from various instruments at the cloud phase appears to be generally accurate in this Department of Energy (DOE) ARM TWP, SGP, and scene, with the VIIRS algorithm being most effective NSA sites (Ackerman and Stokes 2003) were used to and the AVHRR3b algorithm being slightly better than infer cloud-top temperature or cloud type. Compari- the AVHRR 3a algorithm. sons were then made to cloud-type retrievals from each algorithm using Terra’s MODIS level 1b–calibrated ra- 9. Validation diance data. At the TWP (Manus and Nauru) and SGP Though the work described here is termed “valida- (Central Facility) sites, cloud-top temperature was es- tion,” we acknowledge that this is not a comparison timated from millimeter cloud radar–derived cloud-top with direct observations of cloud phase/cloud type, but heights (Clothiaux et al. 2000) and atmospheric pro-

TABLE 6. Cloud-type statistics for the MODIS scene shown in Fig. 10. The percentage of cloudy pixels that were flagged as each type is shown.

Supercooled Nonopaque ice Multilayered Algorithm Water (%) water–mixed (%) Opaque ice (%) (cirrus) (%) clouds (%) AVHRR 3a 3.46 53.28 16.20 14.55 12.51 AVHRR 3b 3.50 58.22 15.43 13.01 9.84 VIIRS 3.65 64.30 13.90 12.39 5.76

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Fig 11 live 4/C JUNE 2005 P A V O L O N I S E T A L . 819 files. The atmospheric profiles for the TWP sites were layers, which precludes the use of an IR radiometer. To taken from rawinsonde data, and profiles that were de- approximate a combined optical depth for the total rived from integrated ground-based remote sensors cloud column, separate ice/liquid optical depths are cal- (Han and Westwater 1995; Turner et al. 1996) were culated as a function of IWP/LWP and ice crystal/ used at the SGP site. Rawinsonde profiles were not droplet size, respectively. The ice and liquid optical used at the SGP site because they were often not avail- depths are then summed. These optical depths are only able near the time of the Terra overpass. approximate because of uncertainties in the input val- At the NSA (Barrow, Alaska) site, an additional ues (40%–70%), but serve to separate optically thick cloud product is available for comparison. The National from optically thin clouds sufficiently for this study. Oceanic and Atmospheric Administration (NOAA) It should be noted that the millimeter cloud radar Environmental Technology Laboratory (ETL) has clas- may sometimes fail to detect some thin clouds and sified 6 yr of data (1998–2003) into cloud phase catego- some high clouds, but, in general, should be a good ries, including liquid water, ice, mixed phase, and vari- indicator of vertical cloud location. Once again, this is ous categories. The classifications are not a comparison with direct observations of cloud done subjectively with inputs from cloud radar, micro- phase/cloud type, but more of a consistency check that wave radiometer, IR radiometer, and rawinsondes. is based on observations that are completely indepen- Classifications are derived for 2D time–height cloud dent of the satellite retrievals presented in this paper. scenes from these zenith-pointing instruments, and it is This is especially true for the TWP and SGP analysis, possible to have multiple classifications for different because for cloud-top temperatures between 233.16 cloud layers (i.e., liquid water boundary layer clouds and 273.16 K, liquid water and/or ice may be present; with overlying all-ice cirrus cloud) and/or regions of a however, the TWP and SGP comparisons should be a single cloud layer (ice in upper regions and be- good indicator of how well cirrus and thin water clouds low cloud base). are classified. The primary input used to determine if there is liquid Because both cloud-overlap detection algorithms water in the atmospheric column is the microwave ra- have already been validated in PH04, only cases in diometer, which utilizes a retrieval technique (Frisch et which a single cloud layer is indicated by millimeter al. 1998) that is applied to 23.8- and 31.5-GHz channels cloud radar within a 30-min interval centered on the to determine integrated liquid water path (LWP). If no satellite overpass time are considered at the SGP and liquid is present, the cloud(s) is (are) classified as all ice. NSA sites. All scenes chosen were picked in an auto- If liquid water is detected, the cloud layers are analyzed mated fashion using software that examined the cloud with respect to the rawinsonde profile of temperature, radar data; the only requirements that were imposed as well as signatures in the radar Doppler velocities and were that a single-layer cloud be present during the velocity spectral widths to determine which regions of entire 30-min interval (e.g., no overlap and no clear the cloud scene are all liquid and which are mixed breaks), the solar zenith angle was Ͻ88°, and the view- phase. ing angle was Ͻ55°. Thus, no attempt was made to Once cloud phase classifications have been estab- select scenes based on human examination of the con- lished, a number of different cloud microphysical re- ditions near each site. For the TWP site, single-layer ice trieval techniques are run for the appropriate ice (Ma- clouds were not common during the time period stud- trosov 1999; Matrosov et al. 2002) and liquid (Frisch et ied (2000–02). Cirrus clouds were almost always located al. 1995, 1998, 2002) clouds regions. The retrievals are above lower cloud layers, and so multilayered cloudy either radar-only or radar–radiometer algorithms, de- situations were used in the analysis as long as the height pending on the cloud type and availability of data. Ice of the top cloud layer did not vary by more than 1 km retrievals are performed in the regions that are classi- during the 30-min time interval that was considered. fied as mixed phase because the radar is primarily sen- Because it was shown in PH04 that most pixels were sitive to the large ice crystals as opposed to the smaller indeed classified as cloud overlap when the radar indi- liquid droplets. One of the by-products of the retrieved cated multiple cloud layers for these scenes, the cloud- cloud microphysics is total cloud LWP (redundant with overlap detection algorithms were turned off and only the LWP from the microwave radiometer) and ice wa- single-layer cloud types were considered. This sort of ter path (IWP). analysis is also useful to determine if the top cloud layer Calculations of total optical depth are not straight- is typed correctly when lower cloud layers are present, forward because of the frequent occurrence of multiple should cloud overlap not be detected by the algorithms. cloud layers (often ice, liquid, and mixed phase com- At the TWP, SGP, and NSA sites, 35, 43, and 78 bined), and a prevalence of radiometrically thick cloud scenes were examined, respectively. The scenes encom-

Unauthenticated | Downloaded 09/28/21 06:45 AM UTC 820 JOURNAL OF APPLIED METEOROLOGY VOLUME 44 passed several seasons from 2000 to 2002 for all three dividing by the total number of cloudy nonoverlapped sites. Cloudy MODIS pixels (as given by the opera- pixels that were summed in the same manner. As indi- tional MODIS cloud mask) that were within 15 km of cated by the cloud-top temperature estimates, only one the ground-based instrumentation location were used midlevel cloud scene was found in those searched, with to calculate an area-weighted cloud phase. The 30-min the rest of the scenes either having high or boundary time interval and 15-km radius were selected to roughly layer clouds. The VIIRS algorithm, on average, detects account for cloud movement, as in PH04. Pixels that slightly more cirrus (ϳ1 PU more than the AVHRR 3a were flagged as having multilayered clouds were also algorithm and ϳ2 PU more than the AVHRR 3b algo- excluded from this analysis so that statistics were de- rithm) when the radar indicates a high cloud layer. Al- rived using the exact same pixels for each algorithm, most all of the high cloud scenes examined were mul- although cloud overlap was not found to be the domi- tilayered situations (this is why nearly all of the ice nant cloud type for any of the SGP or NSA scenes. The clouds have a visible reflectance greater than 20%), but area-weighted cloud phase (AWP) was determined as none of the algorithms retrieve anything other than cir- follows: rus as the dominant cloud type. This may be an indica- ϭ ͑ ϫ ϩ ϫ tion that when a high cloud overlaps lower cloud layers AWP 0.0 Nwater 0.5 Nsupercooledրmixed and cloud overlap is not detected, the top cloud layer ϩ ϫ ͒ր ͑ ͒ 1.0 Nice Nsingle, 3 will, for the most part, be typed correctly. The two where Nwater is the number of warm water–type pixels, AVHRR algorithms produce similar results for each Nsupercooled/mixed is the number of supercooled water– scene. Boundary layer clouds seem to be typed well by mixed-phase–type pixels, Nice is the number of opaque each algorithm, but each algorithm detects mostly high ice or nonopaque ice–type pixels, and Nsingle is the total cloud when the cloud radar only indicated midlevel number of nonoverlapped (based on algorithm results) cloud. However, because of horizontal inhomogeneity cloudy pixels. A value of 0.0 indicates that every single- in cloud fields, it is possible that multiple cloud types layer cloudy pixel was typed as being a water cloud; are present within 15 km of each ARM site. conversely, 1.0 indicates that every single-layer cloudy Analogous to Figs. 12a–c and Table 7, Figs. 13a–c pixel was typed as being an ice cloud. Because any and Table 8 show the results from the ARM SGP site. value between 0.0 and 1.0 may indicate that at least two As the scene analysis in section 8 indicated, the different cloud types were found, the dominant cloud AVHRR 3a algorithm has a tendency to detect more type (e.g., the statistical mode of the cloud type) is cirrus than the AVHRR 3b algorithm, both correctly shown as part of the results. Thus, an area-weighted and incorrectly. When a supercooled water–mixed- cloud phase of 0.6, where supercooled water–mixed phase cloud is present at the SGP site, more pixels tend phase is the dominant cloud type, indicates that the to be classified as cirrus (see Table 8) in the AVHRR majority of pixels not typed as being supercooled wa- 3a algorithm. All three algorithms are unable to differ- ter–mixed phase were typed as being ice. entiate optically thin supercooled water–mixed clouds In Figs. 12a–c the results from the TWP site are from water clouds when BT(11) Ͼ 273.16 K. This is a shown. The average cloud-top temperature for the en- known weakness associated with each algorithm. As tire 30-min interval is plotted against the AWP. The expected, the VIIRS algorithm is best at detecting op- dominant cloud type for each case, denoted by the dif- tically thin ice clouds (ϳ13 PU more than either ferent colors and cases in which only an optically thin AVHRR algorithm), although there are still a few cases mid- or high cloud was likely present, is symbolized by in which optically thin cirrus (indicated by the triangle a triangle and all other cases by an asterisk. Optically symbol in Fig. 13) are being misclassified as water thin cloud cases are defined as R(0.65) Ͻ 0.20 (20%) clouds. Because model results reveal that the 1.38-/0.65- Ϫ Ͼ ␮ and BT(11) Tcld 10 K, where R(0.65) and BT(11) m-reflectance ratio test should be effective at unam- of the MODIS pixel nearest the respective ARM site biguously detecting high clouds with a visible optical are used and Tcld is the estimated average cloud-top depth of about 0.5 or greater, the cirrus present in these temperature at the site, although none of the ice scenes cases must be very tenuous; this is apparent in the that are examined at TWP meet these requirements. R(0.65) and BT(11) data near the SGP site. The sensi- The total fraction, expressed as a percentage, of water, tivity of the 1.38-/0.65-␮m-reflectance ratio test can be mixed phase, and ice clouds that is retrieved as a func- adjusted to detect such thin high clouds, but many tion of cloud-top temperature and the algorithms are midlevel clouds would then be misclassified as cirrus. given in Table 7. Each percentage is derived by sum- For the pixel closest to the ARM site, R(0.65) was less ming all of the pixels of a given type for each case than 40% for all of the cases in which cirrus is the shown in Fig. 12 and then summing over all cases and dominant cloud type, and so it appears as though no

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FIG. 12. Radar/atmospheric profile–derived cloud-top temperatures plotted vs the area-weighted cloud phase retrieved using MODIS data over the ARM TWP site. Water clouds are given a weight of 0.0, supercooled water–mixed-phase clouds are 0.5, and ice clouds are 1.0. Only cloud types for cloudy pixels that were within 15 km of the TWP site were used to determine the area-weighted cloud phase. Both single and multilayered clouds were considered, but the cloud-overlap detection algorithms were not used. The dominant cloud type (the statis- tical mode) is also indicated by the different colors. The 0.65-␮m reflectance and the average estimated cloud-top temperature at the ARM site minus the 11-␮m brightness temperature of the MODIS pixel closest to the ARM site are used to help to identify cases in which very thin cloud may be present. A total of 35 MODIS scenes were analyzed. optically thick ice clouds were mislabeled as cirrus by ice cloud was present for the majority of the 78 scenes any of the algorithms. Water clouds appear to be that were used in this analysis. However, of the 18 wa- handled well by each algorithm. ter scenes, the dominant retrieved phase is supercooled As stated earlier, the validation effort at the NSA site water–mixed phase the majority of the time, regardless is aimed at comparing the satellite-derived cloud phase of the algorithm. This result occurs simply because with ETL’s multi-instrument retrievals, which will serve BT(11) Ͻ 273.16 K for many of the MODIS pixels as “truth.” Figures 14a–c show the 30-min-averaged within 15 km of the NSA site. With respect to mixed- shortwave optical depth, which was described earlier in phase clouds, the VIIRS algorithm, with one exception, this section, plotted against AWP. In Figs. 14a–c, the always retrieves supercooled water–mixed phase as the ETL cloud classification is given by the different sym- dominant cloud type, but the AVHRR 3a (AVHRR bols and, as before, the dominant satellite-retrieved 3b) algorithm classifies 9 (5) scenes as being ice cloud cloud type is depicted by the different colors. Table 9 is dominated. This even occurs with some clouds that are analogous to Tables 7 and 8. According to the ETL estimated to have an optical thickness greater than 10. cloud classification, either a mixed-phase cloud or an The tendency observed here was also evident in the

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TABLE 7. The total fraction, expressed as a percentage, of water, mixed-phase, and ice clouds retrieved at the TWP site as a function of the cloud-top temperature (Tcld) and algorithm. Each percentage is derived by summing all of the pixels of a given type for each case shown in Fig. 12 and then summing over all cases and dividing by the total number of cloudy nonoverlapped pixels that were summed in the same manner. Here, N is the number of cases corresponding to each cloud-top temperature category.

Յ Ͻ Յ Ͼ Tcld 243.16 K (%), 243.16 K Tcld 273.16 K (%), Tcld 273.16 K (%), Algorithm (cloud type) N ϭ 19 N ϭ 1 N ϭ 15 AVHRR 3a (water) 3.97 2.49 99.28 AVHRR 3b (water) 4.28 3.81 99.28 VIIRS (water) 1.66 12.27 98.84 AVHRR 3a (supercooled water–mixed) 0.71 7.63 0.09 AVHRR 3b (supercooled water–mixed) 1.53 16.09 0.09 VIIRS (supercooled water–mixed) 1.95 31.68 0.09 AVHRR 3a (ice) 95.33 89.88 0.63 AVHRR 3b (ice) 94.20 80.10 0.63 VIIRS (ice) 96.39 56.05 1.08

scene described in section 8c. For ice clouds with an retrieve almost 83% of the pixels used in these com- estimated optical depth less than 1, both AVHRR al- parisons to contain ice cloud when the ETL retrieval gorithms classify more pixels as containing ice cloud indicates an ice cloud, whereas this occurs for about than the VIIRS algorithm. The AVHRR algorithms 77% of the pixels when the VIIRS algorithm is used.

FIG. 13. The same as Fig. 12, but for the ARM southern Great Plains site. In addition, only single-layer cloudy cases were considered. A total of 43 MODIS scenes were analyzed.

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TABLE 8. The same as Table 7, but for the SGP site.

Յ Ͻ Յ Ͼ Tcld 243.16 K (%), 243.16 K Tcld 273.16 K (%), Tcld 273.16 K (%), Algorithm (cloud type) N ϭ 23 N ϭ 9 N ϭ 11 AVHRR 3a (water) 28.02 9.66 76.13 AVHRR 3b (water) 28.73 15.54 76.78 VIIRS (water) 22.67 16.65 76.16 AVHRR 3a (supercooled water–mixed) 9.25 67.94 22.74 AVHRR 3b (supercooled water–mixed) 9.38 80.28 23.03 VIIRS (supercooled water–mixed) 1.91 80.61 22.74 AVHRR 3a (ice) 62.74 22.40 1.14 AVHRR 3b (ice) 61.90 4.18 0.20 VIIRS (ice) 75.42 2.74 1.10

This is the opposite of the results seen at the TWP and processing system. The third algorithm utilizes addi- SGP sites. This could occur because over snow/ice/ tional spectral bands that are available on MODIS and water surfaces, which are dark in the NIR, the NIR will be available on the 22-channel VIIRS, which will reflectance test used in the AVHRR algorithms will be replace the AVHRR on board the NPOESS platform. naturally biased toward less reflective ice clouds when The AVHRR algorithms utilize data in the 0.65-, 1.65-, clouds that are very optically thin are present. In other or 3.75- (depending on which is available), 11-, and words, if clear-sky snow-covered pixels were processed, 12-␮m regions of the spectrum. In addition to the bands they would always be typed as opaque ice by the NIR just listed, the VIIRS algorithm incorporates additional reflectance test if BT(11) Ͻ 263.16 K. Conversely, information from the 1.38- and 8.5-␮m regions. BTD(8.5, 11) tends to be biased toward retrieving su- The analysis of three 1-km MODIS scenes revealed percooled water cloud when a very thin cloud of any that the VIIRS algorithm will be much more effective phase is present. These relationships may account for at identifying thin cirrus clouds over nonsnow/ice sur- the greater fraction of ice cloud retrieved by the faces, especially those with a visible optical depth less AVHRR algorithms for optically thin cloudy situations. than 1, than the AVHRR algorithms. This is accom- These differences may also be partly due to errors in plished through the added use of a 1.38-/0.65-␮m- the ETL cloud classification. None of the algorithms reflectance ratio test in the VIIRS algorithm. The are able to effectively separate cirrus clouds from VIIRS algorithm also has the capability to identify opaque ice clouds. For the reasons discussed in section cloud overlap for a greater variety of multiple cloud 8c, the methods used for cirrus detection often do not layer situations. It should also be noted that the actual perform well at high latitudes. VIIRS “1.38-␮m band” will have a bandwidth that is about 20-nm narrower than the MODIS 1.38-␮m band. 10. Conclusions This difference will result in a slightly greater sensitivity to water vapor absorption, which may improve the Three algorithms for determining cloud type during VIIRS cirrus and cloud-overlap detection results at the daytime with satellite imager data were described. high-latitudes compared to MODIS. The phase of Five cloud-type categories are considered: water cloud, single-layer clouds was also shown to be more accu- supercooled water–mixed-phase cloud, opaque ice rately determined using the 8.5–11-␮m brightness tem- cloud, nonopaque high ice cloud (e.g., cirrus), and perature difference in the VIIRS algorithm than the cloud overlap (e.g., multiple cloud layers). Two algo- near-infrared reflectances used in the AVHRR algo- rithms were developed for use with data from the rithms. This is mainly because the surface albedo at AVHRR. The AVHRR algorithms are identical except near-infrared wavelengths can vary considerably with for the near-infrared data that are used. One algorithm surface type and the 8.5–11-␮m brightness temperature uses AVHRR channel 3a (1.6 ␮m) reflectances and the difference will not vary much with respect to surface other uses AVHRR channel 3b (3.75 ␮m) reflectance type. It was also determined that the two AVHRR al- estimates. Both of these algorithms are necessary be- gorithms produce nearly identical results except for cause channel 3a was not available until NOAA-15, and certain thin clouds and cloud edges. The AVHRR 3a it is not possible to transmit data from both channels algorithm tends to misclassify the thin edges of some simultaneously. The two AVHRR cloud-typing low- and midlevel clouds as cirrus and opaque ice more schemes are used operationally in NOAA’s CLAVR-x often than the AVHRR 3b algorithm. Thin super-

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FIG. 14. Radar–IR radiometer–microwave radiometer-derived shortwave cloud optical depth plotted vs the area-weighted cloud phase retrieved using MODIS data over the ARM NSA site Water clouds are given a weight of 0.0, supercooled water–mixed-phase clouds are 0.5, and ice clouds are 1.0. Only cloud types for cloudy pixels that were within 15 km of the NSA site were used to determine the area-weighted cloud phase. Only single-layered cloud cases were considered. The dominant cloud type (the statistical mode) is also indicated by the different colors. The various symbols are used to identify the cloud phase given by NOAA’s ETL cloud classification product, which is derived using cloud radar, microwave radiometer, and rawinsonde data. A total of 78 MODIS scenes were analyzed. cooled water–mixed-phase clouds will be generally to lay the groundwork for future cloud-type studies in- classified as warm liquid water clouds by all three al- volving AVHRR and VIIRS (MODIS) data. A future gorithms. At high latitudes, cirrus clouds are often clas- study will include a global analysis of several days of sified as opaque ice clouds because of inherent weak- MODIS data to learn more about algorithm impact for nesses in the cirrus detection algorithms. A comparison a larger variety of conditions, and to assess the impact with inferences of cloud type based on measurements on global cloud-type statistics. Comparisons with the made by ground-based instruments at the Atmospheric MODIS operational cloud phase algorithm (Platnick et Radiation Program sites in the tropical western Pacific, al. 2003) will also be performed in future studies. southern Great Plains, and North Slope of Alaska sup- Last, the algorithms described in this manuscript are ports these conclusions. Overall, the techniques imple- relatively simple to implement, require no auxiliary mented in the VIIRS algorithm result in a significant data, and may be applicable to current and future sen- improvement over the capabilities of the AVHRR al- sors other than the AVHRR and VIIRS (MODIS). gorithms. Only very small adjustments to the algorithms may be The work that is presented in this paper was needed necessary to account for small differences in spectral

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TABLE 9. The same as Tables 7 and 8, but for the NSA site, and the ETL-derived cloud phase is substituted for cloud-top temperature.

ETL type ϭ ice (%), ETL type ϭ mixed phase (%), ETL type ϭ water (%), Algorithm (cloud type) N ϭ 22 N ϭ 38 N ϭ 18 AVHRR 3a (water) 0.17 0.13 15.49 AVHRR 3b (water) 0.17 0.13 15.49 VIIRS (water) 0.17 0.13 15.49 AVHRR 3a (supercooled water–mixed) 16.86 79.34 78.22 AVHRR 3b (supercooled water–mixed) 16.96 84.40 82.71 VIIRS (supercooled water–mixed) 23.37 98.41 84.51 AVHRR 3a (ice) 82.98 20.53 6.29 AVHRR 3b (ice) 82.87 15.47 1.79 VIIRS (ice) 76.46 1.45 0.00

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