IT is well known that clouds are strong modulators of

Transcription

1 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 56, NO. 9, SEPTEMBER Effects and Applications of Satellite Radiometer 2.25-μm Channel on Cloud Property Retrievals Jianjie Wang, Chao Liu,MinMin, Xiuqing Hu, Qifeng Lu, and Letu Husi Abstract Near-infrared (NIR) channels, such as the 1.6- and 2.13-µm channels of Moderate Resolution Imaging Spectroradiometer (MODIS), play an important role in inferring cloud properties because of their sensitivity to cloud amount and particle size. Instead of the 2.13-µm channel, which has shown great success on MODIS, the central wavelength of the Visible Infrared Imaging Radiometer Suite (VIIRS) is shifted to 2.25 µm. This paper investigates the influences of NIR channels (i.e., 2.13 and 2.25 µm) on cloud optical and microphysical property retrievals and reveals the potential applications of the 2.25-µm channel to cloud thermodynamic phase and multilayer cloud detections by combining with the 1.6-µm channel. Rigorous radiative transfer simulations are performed to provide theoretical reflectance at the channels of interest, and MODIS and VIIRS observations are used for case studies. Our results indicate a minor influence of the 2.25-µm channel on cloud optical depth and effective particle size retrievals. In combination with the 1.6-µm channel, the 2.25-µm channel provides additional information indicating cloud phases. However, the 1.6- and 2.13-µm channels do not show any sensitivity to cloud phase. Furthermore, by considering the infrared-based cloud phase results, the 1.6- and 2.25-µm channel combination becomes possible to infer multilayer clouds. Case studies based on simultaneous MODIS and VIIRS observations demonstrate the capability of the Manuscript received August 14, 2017; revised January 16, 2018; accepted February 27, Date of publication March 26, 2018; date of current version August 27, This work was supported in part by the National Natural Science Foundation of China under Grant , in part by the National High-Tech Research and Development Program (863 Program) under Grant 2015AA123704, in part by the Young Elite Scientists Sponsorship Program by CAST under Grant 2017QNRC001, in part by the Double Innovation Talent Program of Jiangsu Province under Grant R2015SCB03, and in part by the Open Project Fund of Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, NSMC/CMA. (Corresponding author: Chao Liu.) J. Wang is with the Collaborative Innovation Center on the Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol- Cloud-Precipitation of the China Meteorological Administration, School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing , China. C. Liu is with the Collaborative Innovation Center on the Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol- Cloud-Precipitation of the China Meteorological Administration, School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing , China, and also with the Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center, China Meteorological Administration, Beijing , China ( chao_liu@nuist.edu.cn). M. Min, X. Hu, and Q. Lu are with the Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center, China Meteorological Administration, Beijing , China. L. Husi is with the Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing , China. Color versions of one or more of the figures in this paper are available online at Digital Object Identifier /TGRS µm channel combination for determining cloud phase and multilayer clouds. Collocated satellite-based active lidar observations further validate these advantages of the 2.25-µm channel over the original 2.13-µm channel. Index Terms Cloud properties, cloud thermodynamic phase, multilayer cloud detection, near-infrared (NIR) channels. I. INTRODUCTION IT is well known that clouds are strong modulators of earth s radiation budget and play an important role in global weather and climate systems [1] [4]. By reflecting solar radiation and absorbing thermal infrared (IR) emission in the lower atmosphere and at the surface, clouds significantly influence the radiation budget of the earth atmosphere system and, thus, affect the atmospheric cooling/heating profile. Cloud properties and their spatiotemporal variations are crucial to the studies of global climate change [5]. Consequently, the knowledge of cloud optical, microphysical, and physical properties is crucial for improving our understanding of earth s weather and climate systems. The latest technological developments of meteorological satellites make it possible to understand and determine cloud properties over increasingly larger spatial and temporal scales. Various remote sensing algorithms for cloud detection and property retrievals (e.g., cloud top, microphysical, and optical properties) have been developed, and a comprehensive set of cloud products, which includes cloud masks, cloud top properties, cloud thermodynamic phases, cloud optical and microphysical parameters (optical thickness, effective particle radius and liquid/ice water path), and multilayer cloud detection, has been archived [6], [7]. Among various satellite instruments, the Moderate Resolution Imaging Spectroradiometer (MODIS), onboard NASA s Earth Observing System Terra and Aqua platforms, is one of the most popular and successful polar-orbiting radiometers for cloud property retrievals, and the features of this instrument have been introduced in a number of earlier studies [8] [11]. MODIS has 36 channels, including 29 channels with 1-km spatial resolution, five channels with 0.5-km spatial resolution, and two channels with 0.25-km spatial resolution. The spectrum of the channels is distributed between 0.4 and 14 μm [6]. For cloud thermodynamic phase retrievals, the MODIS operational algorithm uses brightness temperature differences between thermal IR channels (7.3/11, 8.5/11, and 11/12 μm) [6], [8], [12] [14]. The MODIS operational algorithm for multilayer clouds uses the 0.94-μm water IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See for more information.

2 5208 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 56, NO. 9, SEPTEMBER 2018 vapor channel together with the CO 2 channels (13.3, 13.6, 13.8, and 14.2 μm) [15]. Because both MODIS retrievals for cloud top properties and optical thickness assume a single-layer homogeneous cloud [16], there are inevitable ambiguities when multilayer clouds exist [17], [18]. Consequently, the knowledge of overlapping clouds is critical for quality control in satellite cloud classification schemes and cloud property retrievals [15], [19] [22]. An example of those important works done in this regard is provided by Sourdeval et al. [19], in which five channels were used to retrieve cloud properties in the presence of multilayer cloud, and presented an example using Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). Their study shows the limitations of MODIS retrievals in the presence of multilayer clouds [20]. For cloud optical and microphysical property retrievals, the operational MODIS product applies a bispectral method, i.e., using a cloud nonabsorptive channel (e.g., 0.66, 0.87, or 1.24 μm) and a cloud absorptive channel (e.g., 1.6, 2.13, or 3.75 μm) under daytime conditions [6], [23]. The MODIS bispectral algorithm based on the and 2.13-μm channels has shown great success in cloud property retrievals because of the high sensitivity of these channels to cloud amount and particle size. Thus, the corresponding cloud product has been widely accepted and used to improve our understanding of clouds [6]. Consequently, most aforementioned channels have been established as key channels for satellite spectroradiometers, and been incorporated in the design of most current onboard instruments. After over 15 years of service, MODIS is far beyond its designed lifetime [24]. The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-Orbiting Partnership (NPP) satellite has been designed to be the new generation imager for operational polar-orbiting environmental imaging observation systems [25], [26]. VIIRS is expected to continue the role of MODIS in providing data for accurate determination of cloud and aerosol properties, ocean color, and sea and land surface temperature. VIIRS has 21 channels, including 16 moderate-resolution channels with 0.75-km spatial resolution and five high-resolution channels with km resolution. VIIRS succeeds some of the key MODIS channels, whereas there is a minor but noticeable change in the spectral location of a near-ir (NIR) channel. The cloud absorptive channel of Terra/Aqua MODIS has its central wavelength located at approximately 2.13 μm; this is shifted to a central wavelength of 2.25 μm on VIIRS. Furthermore, VIIRS lacks the key water vapor (0.94 μm) and CO 2 (13.3, 13.6, 13.8, and 14.2 μm) channels that are used on MODIS for cloud property retrievals (e.g., cloud height, cloud mask, and multilayer cloud). There is significant concern regarding the impact of missing or modified VIIRS channels on cloud property retrievals, and will the new instrument be able to continue the success of MODIS [27]? Meanwhile, many countries and organizations intend to launch their satellites for weather and climate observations, and the satellite radiometer channels must be designed in advance. For example, China has plans to develop and launch 14 operational meteorological satellites for weather and climate observations before 2025, and most of them will include a radiometer. This paper investigates the influences of the choice of satellite radiometer NIR absorptive channel (i.e., and 2.25-μm channels) on cloud property retrievals, and reveals the new opportunities provided by the combination of the 1.6- and 2.25-μm channels. Forward radiation transfer (RT) simulations are performed to provide theoretical reflectance at the channels of interest, and MODIS and VIIRS observations are used for case studies. The discussion focuses on cloud thermodynamic, optical, and microphysical properties, as well as multilayer cloud detection. The remainder of this paper is organized as follows. The theoretical basis and details of data used in this study are provided in Section II. Section III quantifies the sensitivities of the NIR channels to cloud amount (i.e., optical thickness) and particle size retrievals. Section IV provides the principles and examples of cloud thermodynamic phase retrieval related to the VIIRS 1.6- and 2.25-μm channels. Section V introduces a new method for multilayer cloud retrieval that also uses the μm channel combination. Section VI provides a summary and states the conclusions of this paper. II. NUMERICAL MODEL AND SATELLITE DATA In order to infer cloud properties from satellite imager observations, an accurate forward RT model is invaluable for the simulation of reflectances or brightness temperatures under given atmospheric and cloud conditions. Many rigorous RT schemes such as the line-by-line radiative transfer model [28], [29], adding-doubling algorithm [30], [31], and discrete ordinate radiative transfer (DISORT) method [32], [33] have been developed and applied under different circumstances. These rigorous RT models have to be implemented independently for large numbers of simulations at each wavelength because of the significant spectral variations exhibited by molecular absorption, and then to be used to conduct spectral integrations over a given radiometer channel. However, performing hundreds or even thousands of monochromatic simulations requires considerable time, especially for satellite applications. Consequently, various fast RT models have been developed for satellite applications, e.g., the correlated-k distribution (CKD) [34], [35], principal-component-based radiative transfer model [36], and others based on precalculated databases [37], [38]. The design of the CKD method is intended to minimize the computational effort when determining transmissivities associated with atmospheric gas absorption by reducing the number of individual RT simulations within a spectral channel. This paper focuses on the effects of clouds and pays less attention to gas absorption. Thus, a combination of the DISORT and CKD approaches is used to efficiently account for the satellite-observed reflectance. Because of the weak gas absorption in the considered NIR channels, the CKD method introduces little error to the RT calculations. Specifically, the gas absorption optical thickness, i.e., less than 0.05 in the channels of interest, is much smaller than the cloud optical thickness (COT), and the relative errors introduced by the CKD are less than 0.1% [35]. Meanwhile, the exact

3 WANG et al.: EFFECTS AND APPLICATIONS OF SATELLITE RADIOMETER 5209 Fig. 1. Bulk SSA for (a) water and (b) ice clouds as a function of effective particle radius. (c) Imaginary part of refractive indices (Im) for water and ice, and the SRF of VIIRS 1.6- and 2.25-μm channels and the MODIS 2.13-μm channel. (d) Asymmetry factor (g) of water and ice clouds at the 0.87-μm channel. DISORT method exhibits accurate performance for multiple scattering among clouds. First, channel-averaged bulk-scattering properties for both liquid and ice clouds are required by the DISORT method to determine the absorption/scattering of the cloud layers. To obtain bulk-scattering properties, scattering properties are calculated at discrete sizes and wavelengths, and averaged over the assumed particle size distribution and spectral response function (SRF) for each channel. The calculation of channel-averaged bulk-scattering properties used in this paper follows [39]. Water clouds are spherical, and the corresponding scattering properties can be obtained using the Lorenz Mie theory. The bulk-scattering properties are calculated for effective radii ranging from 4 to 40 μm. For ice clouds in the form of complex nonspherical crystals, the particle habit model is critical, because particle optical properties that are fundamental in cloud property retrievals are significantly influenced by particle habits. Yang et al. [40] developed a powerful database for ice cloud single-scattering properties, which includes the scattering properties of ice particles with nine crystal habits and three surface conditions in the spectral range of μm. This paper uses singlescattering properties from the database, and considers the ice crystals to be hexagonal aggregates composed of eight solid columns with severe surface roughness, which is used for MODIS collection 6 cloud property retrievals [11], [14], [41]. The effective radii range from 5 to 90 μm for ice clouds. Because the scattering properties of clouds at the relevant wavelengths are less sensitive to the shape of particle size distribution [42], classic gamma size distributions with an effective variance of 0.1 are used for both water and ice clouds [43]. Fig. 1 compares the optical properties of water and ice clouds at the NIR channels considered in this paper. The top panels show the single-scattering albedos (SSAs) of water and ice clouds at three channels (SSA 1.6, SSA 2.13, and SSA 2.25 ), and the channel SRFs and imaginary (Im) parts of water and ice refractive indices are shown in Fig. 1(c). The refractive index data for water and ice are obtained from [44] and [45], respectively. The SSA presents cloud absorptivity (when extinction efficiencies are close), and directly reveals the observed reflectance from the corresponding channels (referred to as Ref 1.6,Ref 2.13,andRef 2.25 ). For water clouds, SSA 1.6 > SSA 2.25 > SSA 2.13, and the differences between SSA 2.25 and SSA 2.13 are minor. However, the SSAs for ice clouds show a different order, i.e., SSA 2.25 > SSA 1.6 > SSA In other words, in contrast to water droplets, ice clouds are less absorptive at the 2.25-μm channel than at the 1.6-μm channel; this is an important feature for cloud phase and multilayer detection. Fig. 1(d) shows the asymmetry factors (g) of water and ice clouds at the 0.87-μm channel as a function of particle effective radius. The value of g is largely insensitive to particle size, especially for ice clouds. This property is mainly due to the constant particle habit at different sizes [40], [41], [46]. With the SSA at the nonabsorptive channel being 1, a sizeindependent g means that the observed reflectance is less sensitive to cloud particle size. With the channel-averaged bulk-scattering properties obtained, it becomes straightforward to simulate the satellite-observed reflectance at the particular channels. The CKD models for MODIS 0.87-, 1.6-, and 2.13-μm channels and VIIRS 0.87-, 1.6-, and 2.25-μm channels are built following [35]. The 128-stream DISORT simulations are performed using the implementation developed by Stamnes et al. [32] and Thomas and Stamnes [33]. A standard U.S. atmospheric profile with a single cloud layer (ice or water) and a lambertian surface is used for the simulation. In addition to the numerical modeling, we also use MODIS and VIIRS observations for case studies to investigate the performances of the and 2.25-μm channels in cloud property retrieval. The satellite lidar, CALIOP onboard the Cloud- Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), is used for validation [47]. Table I lists all the data of the three instruments used for two case studies. The simultaneous collocated reflectances as well as level 2 cloud products are used. We used two NIR channel (1.6 and 2.25 μm) reflectance values as well as geolocation data from NPP/VIIRS sensor data records (Level 1b). The operational MODIS collection 6 cloud product (i.e., MYD06_L2) provides cloud thermodynamic phase and multilayer cloud flag, and the 1-km resolution geolocation is obtained from the MYD03 data set. As an active lidar, CALIOP can provide unique information on the vertical distribution of clouds and the cloud ice/water phase; thus, it can function as a benchmark for evaluating MODIS and VIIRS retrieval results [48], [49]. The CALIPSO/CALIOP Level 2 vertical feature mask product provides region and cloud type classifications. Two Aqua granules with almost simultaneous observations from the three instruments are considered for case studies. The CALIPSO is approximately s behind the Aqua/MODIS. For both cases, collocated NPP/VIIRS observations at similar locations have time differences of <7 min from Aqua observations.

4 5210 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 56, NO. 9, SEPTEMBER 2018 TABLE I SATELLITE INSTRUMENTS AND DATA Fig. 2. Reflectance LUTs for (a) and (b) water clouds and (c) and (d) ice clouds used for the bispectral algorithm at two different solar-viewing geometries. The same cloud nonabsorbing channel (0.87 μm) and absorbing channels (2.13 and 2.25 μm) are used. Red curves are the LUTs for and 2.25-μm channels, and blue curves are those for and 2.13-μm channels. Solid and dashed curves are isolines of specified CER or COT, respectively. III. CLOUD OPTICAL THICKNESS AND EFFECTIVE PARTICLE SIZE RETRIEVAL COT and cloud effective radius (CER) are primary cloud optical and microphysical parameters, and are essential to cloud radiative properties [6], [23]. One of the most popular COT and CER retrievals based on reflected solar radiation is known as the bispectral algorithm [23]. The approach uses a nonabsorptive channel and a moderately absorptive channel. The former is mainly a function of COT, whereas the latter is sensitive to both COT and CER. The method has been widely adopted for operational retrievals of satellite radiometers [6], [11]. The operational MODIS cloud products use the and 2.13-μm channels as the nonabsorptive and absorptive channels, respectively [6]. As mentioned above, the 0.87-μm channel on VIIRS has only slight SRF differences from that of MODIS, whereas the central wavelength of the VIIRS NIR absorptive channel is shifted to 2.25 μm [27]. This section investigates the sensitivity of the and 2.25-μm channels to COT and CER retrievals using theoretical RT simulations. Fig. 2 illustrates examples of reflectance lookup tables (LUTs) for the bispectral algorithm, and results for a pair of MODIS channels (referred to as μm) are compared with those for the corresponding VIIRS channels (referred to as μm). Results for both water (top) and ice (bottom) clouds at two different solarsatellite geometries are illustrated, and the solar zenith angles (θ 0 ), satellite view zenith angles (θ), and relative azimuth angles (ϕ) are listed in each panel. Because of the similar absorptivity of water at the and 2.25-μm channels, the reflectance LUTs of the μm channels and μm channels are almost coincident for water clouds, and only slight differences are noticed for the solid isolines with constant CERs. This means that the shift of the NIR cloud absorptive channel will have little effect on COT and CER retrievals for water clouds. However, the differences between the two sets of LUTs are much more significant for ice clouds. The reflectances at the 2.25-μm channel are systematically larger than those at the 2.13-μm channel for the same CER. As clouds become optically thick, the difference between the and 2.13-μm channel reflectances at a fixed CER reaches more than This can be interpreted by differences in ice SSA and Im at the and 2.25-μm channels, as analyzed in Section II. Due to the weaker absorption and larger reflectance at the 2.25-μm channel, a greater range in ice water paths may be achieved by applying the channel. The difference indicates that the 2.25-μm channel will affect ice CER retrievals to some degree. However, it is the sensitivity of reflectance to the CER, which determines the performance of the spectral channel in cloud property retrievals, not the absolute reflectance. It is noted in Fig. 2 that, based on the ice cloud model considered in this paper, reflectance at the 0.87-μm channel is less sensitive to CER than the other channels. As a result, the COT can be inferred using the single 0.87-μm channel, and the or 2.25-μm channel can be used to retrieve CER at a given COT. We consider only the influence of the and 2.25-μm channels on CER retrieval. To evaluate the performance of the two channels, we introduce a parameter to quantify the different sensitivities of the two channels to the CER, i.e., reflectance difference ratio (α). First, we calculate the reflectance differences (Ref) at a given COT with different CERs as Ref τ,re(i) = Ref τ,re(i+1) Ref τ,re(i) (1) where τ and re are COT and CER, respectively. Here, Ref can be understood as a direct indictor of channel sensitivity

5 WANG et al.: EFFECTS AND APPLICATIONS OF SATELLITE RADIOMETER 5211 Fig. 3. (a) Water and (b) ice reflectance difference ratio (α) of the to 2.13-μm channel. to CER. Then, we define parameter α as the ratio between the two reflectance differences α = Ref(2.25) Ref(2.13). (2) As a result, α > 1 means that the 2.25-μm channel reflectance is more sensitive than the 2.13-μm channel to CER at the COT and CER interval at which α is calculated. A value of α<1 means that the 2.25-μm channel reflectance is less sensitive than the 2.13-μm channel to CER. Fig. 3 illustrates α values for water and ice clouds at the solar-satellite geometry used for Fig. 2. For water clouds, the α values are close to 1 throughout the entire (COT and CER) domain. This can be explained easily by the closeness of the LUTs of the μm channels and μm channels shown in Fig. 2. Thus, there is little influence on COT and CER retrievals. However, the reflectance difference ratio α deviates from 1 for ice clouds, as shown in Fig. 3(b). For optically thin clouds with relatively small ice crystals (COT < 10 and CER < 20 μm), α might be as small as 0.5, whereas its value is mostly >1 for optically thick clouds. Because the bispectral method is less robust for optically thin clouds, the COT and CER retrievals based on the μm channel reflectance LUTs might outperform those based on the μm channels. IV. CLOUD THERMODYNAMIC PHASE RETRIEVAL Retrievals to infer cloud thermodynamic phases normally take advantage of differences in the microphysical and optical properties between water and ice [12], [13]. For example, the MODIS IR operational cloud phase algorithm uses three pairs of spectral channels, i.e., 7.3/11, 8.5/11, and 11/12 μm, and a method based on a combination of NIR and IR tests is also widely applied [6], [7], [11]. As discussed in Section II, the 1.6- and 2.25-μm channels also show clear differences in the optical properties between water and ice clouds, Fig. 4. Reflectances for ice and water clouds at a pair of NIR channels. (a) 1.6- and 2.25-μm channels. (b) 1.6- and 2.13-μm channels. The results are simulated at six sets of solar-satellite geometries, and cloud layers with large ranges of COT and CER are considered. Red and blue dots are for ice and water cloud layers, respectively. i.e., SSA 1.6 > SSA 2.25 for water clouds but SSA 1.6 < SSA 2.25 for ice clouds, whereas there is no such difference between the 1.6- and 2.13-μm channels. This means that water clouds at the 2.25-μm channel are more absorptive compared with the 1.6-μm channel, and ice cloud absorption at the 2.25-μm channel is weaker than that at the 1.6-μm channel. This will further influence the satellite-observed reflectance values at the two channels, making it possible to develop a new method to derive cloud thermodynamic phases using VIIRS instrument based on the two NIR channels (1.6 and 2.25 μm). Similar features of the 1.6- and 2.25-μm channels have been used by Miller et al. [50] to detect liquid-top mixed-phase clouds. Furthermore, the Plankton, Aerosol, Cloud, ocean Ecosystem, a future NASA mission planned for launch in the early 2020s, will include both and 2.25-μm channels, because a combination of the two channels can provide more information on cloud thermodynamic phase [51]. Fig. 4 compares the reflectances at the (top) and 2.13-μm (bottom) channels with those at the 1.6-μm channel. Each dot in Fig. 4 corresponds to the RT simulation of a particular cloud layer with a given COT and CER at a given set of solar-satellite geometries. As expected, for the μm channel combination, most red dots (ice clouds) are above the 1:1 line, whereas those for water clouds lie beneath the 1:1 line. Meanwhile, for the pair of 1.6- and μm channels, reflectances for both water and ice clouds are mixed together, and located beneath the 1:1 line. Fig. 4(a) indicates that the cloud phase could be determined simply by comparing the VIIRS reflectances at the 1.6- and 2.25-μm channels. It should be noted that some red dots in Fig. 4(a) have values of Ref 2.25 < Ref 1.6, which correspond to optically thin ice clouds with COT < 3, or thick ice clouds with CER < 10 μm. However, it is worth testing the practical per-

6 5212 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 56, NO. 9, SEPTEMBER 2018 Fig. 5. Aqua/MODIS granules over (a) western Atlantic Ocean (Case 1) at 16:40 UTC on October 20, 2016 and (b) southern Indian Ocean (Case 2) at 08:25 UTC on September 2, Fig. 7. Cloud thermodynamic phase retrieval results of Case 1. (a) Operational CALIOP cloud phase product. (b) Blue asterisks indicate the MODIS cloud phase product and red dots represent VIIRS μm channel results. Fig. 6. Cloud thermodynamic phase of (a) and (c) MODIS IR operational product and (b) and (d) VIIRS μm channel retrieval results. (a) and (b) correspond to Case 1 and (c) and (d) correspond to Case 2. The yellow solid lines indicate the associated CALIPSO/CALIOP orbit tracks. formance of this feature on cloud phase detection by combining Ref2.25 and Ref1.6 of VIIRS cloud pixels. Again, MODIS 1.6- and 2.13-μm channels fundamentally do not show such sensitivities. Two case studies are performed to illustrate the potential of the method for cloud phase retrievals. Fig. 5 shows RGB images of Aqua/MODIS observations for the two granules. Both granules are over the ocean with collocated and simultaneous VIIRS observations available. The left panel shows a granule located in the western Atlantic Ocean with most areas covered by extensive marine cirrus and stratocumulus clouds. The right panel is a granule of the southern Indian Ocean with >90% cloud pixels. Fig. 6 compares the cloud phase results inferred by the VIIRS μm channel (right) with the MODIS operational product (left). A collocated MODIS cloud mask is used to pick cloud pixels, and the ice or water phase is retrieved by direct comparison of VIIRS reflectances at the 1.6- and 2.25-μm channels. As discussed above, if Ref2.25 > Ref1.6, the cloud pixel is considered as ice phase, and pixels with Ref2.25 < Ref1.6 are defined as water clouds. The yellow solid lines represent the CALIPSO orbit tracks. The MODIS cloud phase product also includes flags for mixedphase clouds, although both cases show only a few such pixels. For VIIRS, we can define cloud pixels only as ice or water. The two products show similar results. However, compared with the MODIS operational product, the VIIRS μm channel retrieval misclassifies large regions of cirrus edges as water clouds. One reason is that optically thin ice clouds or thick ice clouds with small CERs have Ref2.25 < Ref1.6, as shown by the red dots beneath the 1:1 line in Fig. 4(a). This systemic error greatly limits the application of such cloud phase retrieval algorithm. The second reason is related to appearance of multilayer clouds, which will be illustrated in the following paragraph by comparing with CALIOP results. To further validate the results, Fig. 7 compares the MODIS IR cloud phase product and the VIIRS μm channel retrieval results with the most reliable results produced by CALIOP. CALIOP shows that ice clouds occur mainly in two regions with cloud top heights at approximately 9 10 km, and cloud bottom heights between 6 and 8 km. There are three regions marked by orange boxes with ice clouds above water clouds. For the radiometer results, the MODIS IR products are more consistent with the CALIOP cloud phase, and the multilayer clouds are mostly detected as ice clouds. For the regions in the two green boxes, i.e., single-layer clouds, the VIIRS μm channel algorithm also performs well by producing results similar to MODIS and CALIOP. However, the algorithm fails in the regions of the orange boxes, where multilayer clouds occur (ice clouds above water clouds). Most of the pixels in those areas are detected as ice by the MODIS IR algorithm, but identified as water by the VIIRS μm channel algorithm.

7 WANG et al.: EFFECTS AND APPLICATIONS OF SATELLITE RADIOMETER 5213 To conclude, although the VIIRS μm channel algorithm shows reasonable results for cloud phase retrieval, its limitations are obvious. First, this method can be applied only during the daytime. Second, both the numerical simulation and the case studies indicate that it misclassifies ice clouds as water clouds for optically thin clouds or thick clouds with small particle sizes. Moreover, the case studies show that the results for pixels with multilayer clouds are relatively poor, i.e., misclassifying multilayer clouds with ice tops as water clouds. However, considering the opposite results provided by the MODIS IR cloud phase product and the new VIIRS μm channel algorithm over multilayer clouds, it appears that the two channels could be extended to multilayer cloud detection. Fig. 8. clouds. Flowchart for determining the presence of single-layer and multilayer V. MULTILAYER CLOUD DETECTION The MODIS operational multilayer cloud detection algorithm uses the 0.94-μm channel and the CO 2 channels (13.3, 13.6, 13.8, and 14.2 μm) to obtain above-cloud water vapor, respectively, and, due to the different sensitivities of the two algorithms to different clouds, the differences between the results as well as several other tests can indicate the existence of multilayer clouds [15]. Unlike MODIS, VIIRS lacks the key water vapor and CO 2 channels used by the MODIS multilayer cloud detection algorithm. The cloud phase results outlined in Section IV, especially those shown in Fig. 7, show an interesting feature, i.e., completely opposite results given by the combination of the 1.6- and 2.25-μm channels and the MODIS IR cloud phase product. Actually, this could be applied to multilayer cloud retrieval, because most opposite results occur at pixels with multiplayer clouds. This can be understood by considering the characteristics of the two algorithms. First, water clouds reflect more at the 1.6-μm channel than at the 2.25-μm channel, whereas ice clouds show the opposite results. However, larger reflectance at the 1.6-μm channel due to water clouds may be still larger after passing a thin ice cloud layer, and, thus, multilayer clouds with ice clouds above water clouds are mostly detected as water clouds by the VIIRS μm channel comparison. Second, those multilayer clouds are detected as ice clouds by the MODIS IR cloud phase algorithm, because the IR channels are more sensitive to cloud top properties. As a result, pixels determined as ice by the IR algorithm but as water by the μm channel comparison can be defined as multilayer clouds consisting of ice clouds above water ones. Fig. 8 illustrates a flowchart developed for multilayer cloud detection. If a VIIRS pixel has Ref 2.25 < Ref 1.6 and is indicated as ice by the collocated MODIS IR phase product, the pixel is considered as a multilayer cloud. Meanwhile, a pixel is defined as single-layer clouds if Ref 2.25 > Ref 1.6 (i.e., single-layer ice cloud) or if the collocated MODIS shows a water phase (i.e., single-layer water cloud). The multilayer cloud results based on the μm channels, as well as the MODIS operational product, are illustrated in Fig. 9, in which only the results for Case 2 are shown. Fig. 9(a) shows the methodology (or logic) in determining MODIS multilayer cloud flag. Values of 0 indicate Fig. 9. (a) MODIS multilayer cloud product. (b) Multilayer cloud results of the MODIS IR algorithm combined with the VIIRS μm channel combination. The red solid lines indicate the associated CALIPSO/CALIOP orbit tracks. The MODIS multilayer cloud product is obtained from MYD06 level-2 files developed by the NASA MODIS science team [14]. clear sky, and values of 1 indicate single-layer clouds. Values of 2 9 indicate various successful multilayer cloud tests with larger values indicating higher confidence in multilayer cloud detection. Fig. 9(b) shows the multilayer cloud results based on the VIIRS μm channel combination and the collocated MODIS IR phase product. The retrieval captures the multilayer cloud region indicated by the MODIS product with confidence levels >7, and correctly classifies single-layer water clouds. Compared with the MODIS multilayer cloud product, the most significant differences occur in the regions of the red box, where the MODIS product predicts probable multilayer clouds with flag values of 2 7. Most pixels in the red box are indicated as single-layer ice clouds by the μm channel retrieval. Fig. 10(a) shows the results of the CALIOP cloud phase product. It shows a typical case of mixed single-layer and multilayer clouds, which exhibit ice cloud layers above water clouds. Fig. 10(b) compares the multilayer cloud results based on radiometer observations. In the green box area, CALIOP distinguishes the clouds as multilayer clouds, and both MODIS and our algorithms are consistent with CALIOP results. In the orange box area, CALIOP shows clouds as single-layer clouds, and our method is more consistent than MODIS with CALIOP results. Overall, the new algorithm provides reasonably reliable results for multilayer cloud detection. However, in the

8 5214 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 56, NO. 9, SEPTEMBER 2018 Fig. 10. (a) Operational CALIOP cloud phase product. (b) Blue asterisks represent the MODIS multilayer cloud product and the red dots represent VIIRS results based on the μm channel and the collocated MODIS IR phase product. purple box, both methods failed for multilayer clouds with optically thin ice clouds on the top, because the MODIS IR algorithm detects them as water clouds, which are directly detected as single-water clouds in our multilayer algorithm. This indicates the limitation of the algorithm, which is highly sensitive to the performance of the IR cloud phase results. However, this may be solved by using a cloud phase algorithm that can better detect thin ice clouds. The results for the other MODIS granule considered in the case studies are similar to those in Figs. 9 and 10 and, thus, are not discussed here. VI. CONCLUSION MODIS and VIIRS exhibit a minor difference in the central wavelength of their NIR cloud absorptive channels (2.13 and 2.25 μm, respectively). This paper reveals the influences of this difference on cloud property retrievals, and investigates the potential applications of the 2.25-μm channel by combining it with the 1.6-μm channel. The impacts of the difference on cloud optical, microphysical, and thermodynamic properties, and multilayer cloud detection are investigated. RT simulations based on the DISORT and CKD methods are performed to provide theoretical reflectances of the channels of interest. Our results indicate that the difference between the and 2.25-μm channels in terms of cloud optical depth and effective particle size retrievals is minor. However, a combination of the 1.6- and 2.25-μm channels can provide new methods to infer cloud phases and multilayer clouds. This is because water and ice clouds show different characteristics at the 1.6- and 2.25-μm channels, whereas the differences do not exist for the MODIS 1.6- and 2.13-μm channels. Ice clouds reflect more at the 2.25-μm channel, whereas water clouds have higher reflectances at the 1.6-μm channel, which may pass through thin ice clouds for multilayer detection. Two cases based on simultaneous MODIS and VIIRS observations demonstrate the potential of the combination of the 1.6- and 2.25-μm channels for cloud phase and multilayer cloud detection. With collocated CALIOP data used for validation, the VIIRS μm channel combination is shown to provide reasonable results in terms of cloud phase, whereas an inevitable weakness is observed for optically thin ice clouds or thick ones with small particle sizes. However, the two channels show great potential for multilayer cloud retrievals. Thus, a 2.25-μm channel, rather than a 2.13-μm channel, should be designed in future radiometers if a 1.6-μm channel is included. Furthermore, besides NPP/VIIRS, the Advanced Himawari Imager onboard the Himawari-8 and the Advanced Geostationary Radiation Imager onboard the FengYun-4A geostationary satellite both include the 1.6- and 2.25-μm channels, providing opportunity and enhancing the need for further development of retrieval algorithms based on these channels. In future research, we plan to present a more quantitative and systematic study for the development of a multilayer cloud product. ACKNOWLEDGMENT The authors would like to thank Dr. Z. Zhang from University of Maryland, Baltimore County, for constructive discussions and three anonymous reviewers for their helpful comments and suggestions. 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Atmos., vol. 122, no. 15, pp , Jianjie Wang received the B.S. degree in atmospheric sounding from the Nanjing University of Information Science and Technology, Nanjing, China, in 2016, where he is currently pursuing the master s degree. His research interests include remote sensing and polarization properties of clouds.

10 5216 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 56, NO. 9, SEPTEMBER 2018 Chao Liu received the B.S. degree in physics from Tongji University, Shanghai, China, in 2009, and the Ph.D. degree in atmospheric science from Texas A&M University, College Station, TX, USA, in He is currently a Professor with the School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, China. His research interests include light scattering modeling of atmospheric particles, optical properties of aerosols and ice clouds, radiative transfer, and remote sensing of clouds. Qifeng Lu received the B.S. degree in hydrology from Xinjiang University, Urumuqi, China, in 1999, and the Ph.D. degree in meteorology from the Nanjing University of Information Science and Technology, Nanjing, China, in He is involved in simulating, characterizing, and assimilating the FengYun satellite data into the NWP model. His work contributes to the achievements of operational assimilation of FY-3 data in CMA, ECMWF, and UKMO NWP model system. Min Min received the B.S. degree in applied meteorology from the Nanjing University of Information Science and Technology, Nanjing, China, in 2005, and the Ph.D. degree in atmospheric physics and environment from the Chinese Academy of Sciences, Beijing, China, in From 2013 to 2014, he was a Visiting Research Assistant with the Department of Physics, University of Maryland Baltimore County, Baltimore, MD, USA. He is currently an Associate Professor with the National Satellite and Meteorological Center, China Meteorological Administration, Beijing. His research interests include the calibration of FengYun satellite instruments, the cloud mask and optical property algorithms of the FengYun satellite imager, and the atmospheric radiative transfer model. Xiuqing Hu received the B.S. degree in atmospheric science from Nanjing University, Nanjing, China, in 1996, the M.S. degree in cartography and geographical information systems from Beijing Normal University, Beijing, China, in 2004, and the Ph.D. degree in quantitative remote sensing science from the Institute of Remote Sensing Application, Chinese Academy of Sciences, Beijing, in He is currently the Deputy Chief Scientist of the ground segment system for FengYun-3 meteorological series satellite and the Team Leader of satellite calibration/validation of the National Satellite Meteorological Center, China Meteorological Administration (CMA), Beijing. Since 2010, he has been a Professor of engineering, and since 2015, he has been among the selected Technological Leading Talent of CMA. He worked as an Instrument Scientist for FY-3 s MERSI and as the Chief Engineer of the data preprocessing system in FY-3 and the calibration and validation system in FY-4. His research interests include calibration and validation of optical and infrared sensors, the retrieval algorithm of aerosol/dust and water vapor, and the fundamental climate data records from environment satellite. Letu Husi received the B.S. and M.S. degrees in geography from Inner Mongolia Normal University, Inner Mongolia, China, in 1999 and 2002, respectively, and the Ph.D. degree in geosciences and remote sensing from Chiba University, Chiba, Japan, in He is currently a Professor with the Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China. His research interests include light scattering by ice particles and retrieval of cloud properties from passive and active sensors.