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

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Daytime Global Cloud Typing from AVHRR and VIIRS: Algorithm Description, Validation, and Comparisons 804 JOURNAL OF APPLIED METEOROLOGY VOLUME 44 Daytime Global Cloud 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 Clouds 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 Unauthenticated | Downloaded 09/28/21 06:45 AM UTC JAM2236 JUNE 2005 P A V O L O N I S E T A L . 805 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 radar 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.
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