Remote Sensing of Cloud Properties from the Communication, Ocean and Meteorological Satellite (COMS) Imagery

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1 Remote Sensing of Cloud Properties from the Communication, Ocean and Meteorological Satellite (COMS) Imagery Choi, Yong-Sang, 1 Chang-Hoi Ho, 1 Myoung-Hwan Ahn, and Young-Mi Kim 1 1 School of Earth and Environmental Sciences, Seoul National University, Seoul 11-4 Korea Meteorological Research Institute, Korea Meteorological Administration, Seoul 16 Korea Abstract The present study documents optimal methods for the retrieval of cloud properties using channels (.6, 3., 6.,, and 1. μm) being adopted for many geostationary meteorological satellite observations. Those channels are also adopted for the Communication, Ocean and Meteorological Satellite (COMS) scheduled to be launched in 8. The cloud properties focused on are cloud thermodynamic phase, cloud optical thickness, effective particle radius, and cloud top properties with certain uncertainties due to the limited channels. Discrete ordinate radiative transfer models are simulated to build up the retrieval algorithm. The cloud observations derived from the Moderateresolution Imaging Spectroradiometer (MODIS) are compared with the results to assess the validity of the algorithm. The validation indicates that the additional use of a 6. μm band would be better in discriminating cloud ice phase. Also, cloud optical thickness and effective particle radius can be produced up to, respectively, 64 and 3 μm by functionally eliminating both ground-reflected and cloud- and ground-thermal radiation components at.6 and 3. μm. Cloud top temperature (pressure) at 3 K ( hpa) can be estimated by a simple μm method for opaque clouds, and by an infrared ratioing method using 6. and μm for semitransparent clouds. 1. INTRODUCTION Remote sensing of cloud properties has been largely studied centering on the applications of the spectral bands of onboard radiometers. In the past few years, cloud analysis techniques have been considerably improved with the advent of Moderate-resolution Imaging Spectroradiometer (MODIS) instruments. The MODIS provides information on a variety of cloud properties by using spectral radiances at 36 visible and infrared (IR) bands (King et al. 199). Recently, the Spinning Enhanced Visible and InfraRed Imager (SEVIRI), loaded onto the geostationary Meteosat-8, has also promoted enhanced cloud data that are evaluated with total of 1 spectral bands. Their cloud data include cloud top properties and cloud type over Africa and the European regions. Although MODIS provides advanced atmospheric information with a high spatial resolution of up to. km. km, the data are temporally limited in application for severe weather forecasting due to its being provided by polar orbiting platforms (i.e. the Terra and Aqua satellites). Korea has been using the geostationary observation data from the Japanese Multi-functional Transport Satellite (MTSAT-1R), which succeeded the Geostationary Meteorological Satellite (GMS) series covering the East Asia and the Western Pacific regions. However, its half-hourly data do not fulfill forecasters requirements fully, especially for the fast developing weather system such as the severe thunderstorm. Also, the information attainable from MTSAT-1R is still limited to the conventional parameters such as cloud amount, top pressure, surface temperature, etc. Therefore, both frequent observations in near realtime and diversely retrieved atmospheric products have become a key requirement, particularly to forecast severe weather events such as tropical cyclone and torrential downpours in and around the Korean peninsula. The launch of the first Korean geostationary satellite, the Communication, Ocean and Meteorological Satellite (COMS), is opportunely planned for 8. The COMS will carry a separate Imager and ocean color sensor for the meteorological and oceanography mission, respectively. Although the operation for

2 the COMS Imager is not fixed yet, it certainly will include a rapid scan mode which acquires data for a limited area with much higher sampling frequency, possibly 8 times per hour (Ahn et al. ). The COMS Imager measures radiances in bands centered at approximately.6, 3., 6.,, and 1. μm (see table 1). It intends to provide data with spatial resolutions of 1 and 4 km for visible and IR channels, respectively. The channels, in fact, only contain a narrow range of atmospheric information, because some cloud properties available in the MODIS and SEVIRI would not be distinguishable due to the limited number of bands. In particular, the absence of some essential channels, such as., 8. and 13.4 μm, limits the retrieval of cloud properties with high accuracy. The cloud analysis algorithm (hereafter CLA) optimized for the channels is nevertheless designed as a part of the meteorological data processing system for COMS. The CLA mainly derives cloud property parameters cloud phase, cloud type (not discussed in this paper), cloud optical thickness (τ c ), effective particle radius (r e ), and cloud top properties. Band Bandwidth, μm Used in cloud analysis 1..8 CT, COT/ER CT, COT/ER CP, CTTP CP, CT, COT/ER, CTTP CP, CT Table 1: COMS spectral band number and bandwidth. CP, COT/ER, CTTP stands for cloud phase, cloud optical thickness / effective particle radius, cloud top temperature and pressure, respectively.. DATA AND METHODOLOGY a. Data The present study uses kinds of MODIS data sets: level 1b calibrated radiance (MOD) and cloud product (MOD6). The MOD contains calibrated radiances located at all 36 MODIS channels (both visible and IR regions). The data in MOD have a 1 km 1 km nadir resolution. The MOD6 includes various cloud properties whose items cover all CLA products. The items in the MOD6 used in this study are cloud phase, τ c, r e and CTTP. The cloud phase in MOD6 is derived from the IR trispectral algorithm using 8.-, -, and 1.-μm at km km nadir resolution (refer to Baum et al. for the details). The total-column τ c and r e in MOD6 is determined by the combination of visible channels (.6,.8 or 1. μm) and a near-ir channel (.1 μm) at 1 km 1 km nadir resolution (refer to King et al. 199 for the details). The CTTP in MOD6 have the same resolution as cloud phase (i.e. km km). The CTTP is retrieved by a CO slicing method (also called radiance ratioing method) developed by Menzel et al. (1983). This method uses MODIS CO absorption channels within μm (i.e. MODIS bands 33, 34, 3 and 36). The 169 MODIS granules (-min data) were collected for the mid-latitudes and the tropics during the period 1-16 March. The radiances and angular parameters are used as inputs of the CLA. For this purpose, in this study we chose particularly.6- (MODIS band 1), 3.- (band ), 6.- (band ), - (band 31), and 1.-μm (band 3) radiances (or converted BTs at km km). The bands were chosen as they corresponded to COMS. The MODIS band filters are relatively finer than those of COMS. This difference of bandwidths between MODIS and COMS may affect any estimation of exact cloud properties. And yet, an advantage of using MODIS radiances is a capability to compare COMSderived cloud properties with MODIS-derived cloud data (MOD6) on the pixel scale. b. Radiative Transfer Model In order to design the CLA with the channels, this study uses the discrete ordinates radiative transfer (DISORT) models; Streamer (Key and Schweiger 1998) and Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) (Ricchiazzi et al. 1998). The spectral resolutions of the Streamer and the SBDART models are cm -1 bandwidth in both shortwave and longwave. A characteristic of the DISORT models is that the atmosphere is composed of a discrete number of adjacent homogeneous layers. The single-scattering albedo and optical thickness are constant within each layer but may vary from layer to layer (Baum et al. ). Although just one model should be dealt with for consistency of simulation, models are used for

3 different purposes due to their flexibility on cloud physical properties. In addition, the Streamer exhibits a number of problems to simulate radiance for near-ir bands ( μm) (Key ). The Streamer is thus employed for cloudy conditions with varying cloud phase and CTTP, while the SBDART is used for for τ c and r e. Both models are used to calculate the top-of-atmosphere (TOA) radiances expected for clear and cloudy conditions. As the exact response functions of the COMS Imager channels are unknown at this point, we use the MTSAT- values as surrogates. Although they are not the same as the COMS Imager, it is expected the difference would be very small because the specification and design of those two sensors are almost the same. Aerosol contributions are neglected in this study. c. Validation The results of the CLA are verified by using the MODIS data and the radiative transfer (RT) models. The validation of the CLA is carried out for the cloud properties: cloud phase, τ c, r e, and p c. Cloud phase is first obtained by the CLA and consists of the thresholding tests inputting MODIS BTs. The retrieved cloud phase is simply compared with that of MODIS. For τ c, r e, and p c, the simulation of the RT model is prior to comparison with those of MODIS. For this purpose, TOA radiances are calculated by the RT model for the response function and the wavelengths corresponding to MODIS (i.e. MODIS bands 1,,, 31, and 3). This is because MODIS radiances are used as input radiances for the CLA. 3. CLOUD THERMODYNAMIC PHASE Cloud phase has been retrieved based on the IR trispectral method using 8., and 1. μm, as described by Strabala et al. (1994). The IR trispectral method actually requires an 8.-µm band, which plays an essential role in discriminating cloud phase, in cooperation with a µm band. However, the 8. µm is not a component band in the most of geostationary meteorological satellite except the Meteosat Second Generation (MSG). The determination of cloud phase by applying 3. µm, in addition to and 1. µm, was noted early by Key and Intrieri () for the case of a nonexistent 8.-µm in Advanced Very High Resolution Radiometer (AVHRR) of National Oceanic and Atmospheric Administration (NOAA) satellites. However, their method was not sufficient because the 3. µm is affected by so many factors such as viewing/illumination geometry, surface reflectance and cloud and surface temperatures (Key and Intrieri ). The present study attempts to use a 6.-µm instead. The 6.-µm band is known to be sensitive to water vapor in the atmospheric layer between approximately and hpa, and the BT 6. has a lower value when high clouds exist in the layer (Ackerman et al. 1998). This implies that BT 6. can be used to obtain useful information on the ice/water phase confined to high clouds. 3 3 r e = 9 r e = 8. 1 r 3 8 e = 16 Water. 1 Ice r 1. e = Water - Ice BT (K) BT (K) Figure 1: The results of a RT model simulation for water and ice clouds with respect to BT and BTD 8. (a), BT and BT 6. (b). The results for water and ice clouds are plotted as filled circles and opened squares, respectively. The numbers indicate cloud optical thickness. r e stands for effective particle radius. BTD 8. (K) (a) BTD 8. vs. BT (b) BT 6. vs. BT BT 6. (K) Figure 1 shows the RT model Streamer calculation of BTD 8. and BT 6. at TOA for single-layer ice and water particle clouds. The spectral BTs in the ice cloud are simulated with spherical particles for ice crystals. The assumption that ice crystals behave as spheres may be flawed because the high ice clouds include ice crystals with many different shapes. However, it is known that scattering in the LW is secondary to absorption (Pavolonis and Heidinger 4). The calculation is carried out under

4 various τ c from to (numbers marked in the graphs) and r e with, 8, 16, and 3. Water (ice) clouds are assumed to have their top pressure of hpa (3 hpa), so that the simulation may represent minimum (maximum) values for water (ice) clouds. Here, the cloud water (ice) content is set to. (.) g m 3. In comparison to ice clouds, water clouds can have greater BTD 8. up to. regardless of their effective radius (figure 1a). This is consistent with the result of Baum et al. (). In figure 1b, water clouds have greater values of BT 6. than ice clouds. Namely, water clouds have BT 6. over 6 K, whereas ice clouds under 4 K. This difference, in relation to values of BT 6. between water and ice clouds, can be used to identify cloud phase. The actual relations between the cloud phase and BTD 8., BT, and BT 6. are examined by using the MODIS data collected in this study. The relation between MODIS cloud phase and BTD 8. (or BT ) is of course discrete because it turns out to be ice or water phase by those tests. Clouds identified as ice phase have greater BTD 8. (or lower BT ) values. This study also remarks on the relation between the MODIS cloud phase and BT 6., which is also obvious but not as distinct as that with BTD 8. (or BT ) (not shown in this manuscript). Ice clouds in MODIS tend to have low a BT 6. (under about K), while water clouds a high BT 6. (over about 34 K). Consequently, ice, water, and mixed clouds in MODIS appear to take similar values of BT 6. between 34 K and K. Based on the results of both the RT calculation and the examination of the MODIS data, the algorithm for cloud phase was recomposed. The algorithm consists of the phase criteria from ice to unknown phase. The BT and BTD 1. tests are applied from the IR trispectral method of the MODIS. The BT 6. test is combined with the BT (or BTD 1. ) test at each stage of phase decision. In detail, cloud pixels first pass the stage of ice phase decision. At this stage, the three tests judge whether the pixel is composed of ice particles or not. If the pixel is not identified as ice, it passes on to the next tests using BT and BT 6. for mixed phase. If the pixel does not satisfy the criteria of being mixed phase, it will go through to the next stage using BT and BT 6. for water phase. Finally, the pixel unclassified as any phase category will be assigned to unknown phase. MODIS COMS Clear Water Mixed Ice Uncertain Total Clear Water. 11. (19.)..1 (.3).9 (4.1) 1. (4.1) Mixed..3 (.).1 (.) 1. (8.4).8 (.1) 36.4 (1.) Ice.. (.3). (1.6) 1.6 (9.6). (1.) 1.6 (3.) Uncertain (3.9). 3.9 (.) 4.9 (.9).6 (9.3) Total Table : Comparison of cloud phase from the MODIS IR trispectral algorithm and from the algorithm for the COMS. The numbers (in parentheses) designate those from the algorithm from which BT 6. is excluded (included). An effect of missing an 8.-µm band in the IR trispectral method of the MODIS can be found through the comparison of MODIS cloud phase with that newly retrieved by a BT 8. -free algorithm (i.e. only using and 1. µm). Table shows that a large portion of ice clouds are not well distinguished by the BT 8. -free algorithm. MODIS ice phase takes 4.8% whereas that from the BT 8. -free algorithm does only 1.6%. Moreover, the MODIS ice phase is in less agreement with that from the BT 8. -free algorithm. Most of scenes identified as ice clouds in MODIS are distinguished as mixed phase in the BT 8. -free algorithm (table ). An effect of adding a 6.-µm band to the BT 8. -free algorithm is also examined in the same manner, and the results are presented in parentheses in table. One can notice that the MODIS ice phase pixels are easily detected in the BT 6. -employed algorithm (i.e. using 6., and 1. µm). Specifically, MODIS data on detection of ice pixels are in 9.6% agreement with that from the BT 6. - employed algorithm, which takes.% of the total MODIS ice phase. The total percentage of ice phase also increased up to 3.%. This is a large improvement when compared to the results from the previous BT 6. -unemployed algorithm. Those results are simply because some ice clouds may have a high BT and low BT 6.. Thus, the detection of ice phase using only BT and BT 1. can cause a serious problem in that a large portion of such ice clouds can be overlooked. In brief, when a 6.-μm band is involved to the algorithm, ice cloud retrieval is improved. A 6.-µm band can be a useful alternative in case of the non-existence of a 8.-µm band, but we note that it should not be the preference.

5 4. CLOUD OPTICAL THICKNESS (Τ c ) AND EFFECTIVE PARTICLE RADIUS (r e ) Since the determination of the scaled τ c using a nonabsorbing visible wavelength.6- µm band was introduced by King (198), the method has been operationally used for GMS- (Okada et al. ). τ c is solely retrieved by this method because the near IR channel is not available. Here, GMS- assumed the effective particle radius of all clouds to be μm. Later, the retrieval method for both τ c and r e (also called the sun reflection method) had developed by combining water-absorbing near IR wavelengths such as 1.6,., and 3. μm with the reflected radiance at.6 μm (Nakajima and King 199, Nakajima and Nakajima 199; hereafter NN). Unlike 1.6 and. μm, however, the radiance at 3. μm also contains large thermal components emitted from both the surface and the cloud top. The price of the removal of the thermal components is importing other variables such as ground temperature (T g ) and cloud top temperature (T c ), so that the accuracy of the products may decrease depending on these factors. For that reason, the algorithm of MODIS uses near-ir. μm, which is free of such components, together with visible.6 or.8 μm (King et al. 199). The sun reflection method using.6 and 3. µm, in fact, has already been discussed by NN for the AVHRR. The method accompanies an essential process to undertake decoupling undesirable radiation components: (1) ground-reflected radiation, () cloud and ground thermal radiation based on the radiative transfer theory for plane-parallel layers with an underlying Lambertian surface (refer to NN). The decoupled radiances for.6- and 3.-µm wavelengths are simply given as follows..6 = L obs L sr (1) L.6. 6 obs sr th L3. L3. L3. L3. = () where L obs is the satellite-received radiance, L sr the ground-reflected radiance, and L th the cloud and ground thermal radiance. The radiance is a function of τ c, r e, θ, θ, and φ. For their purpose, NN designed an iterative algorithm which starts from initial values such as τ c = 3, r e = µm, and Z = km, where Z is the cloud-top height. They used preprocessed data; cloud-reflected radiance and reflectivity (at.6 and 3. µm), and transmissivity (at.6, 3. and µm) (see NN for more details). Briefly, their algorithm compared model radiance with calculated radiance (observed radiance minus undesirable components), and it was iterated until exact values of τ c and r e were found. This method is certainly applicable to the COMS algorithm because it has all the channels needed. However, NN requires a little too many assumptions (e.g. Eq. (11) of NN, cloud types, a constant lapse rate, numerical uncertainty from other lookup tables. etc.). To overcome those limitations, we use following approaches. Firstly, the calculation of D with initial Z is not carried out. Subsequently, the calculation of t, T g, and T c is also passed over. Instead, observed radiances are explicitly decoupled from undesirable radiation components by the direct use of climatological A g and -µm radiance, respectively. For this process, we only need to use one lookup table, which contains angular variables (θ, θ, φ) and their corresponding radiances for a variety of τ c ( to 64) and r e ( to 3 µm). The lookup table is composed for A g = and 1 without thermal effects. Once the angular variables and A g are sr known, ground-reflected radiation at i channel ( ) can be calculated as [ Li ( Ag = 1 ) Li ( Ag = ] sr L Ag ) (3) i L i where spherical albedo is assumed to be relatively small, so that the radiance increase linearly as A g rises. Figs. and 3 of NN also imply a linear increase of radiance at both.6- and 3.-µm bands in Eq. (3). Cloud and ground thermal radiance at 3. μm is inferred by the following: L th obs obs a L b L c (4) obs where L. 8 is the -μm satellite-received radiance, and a, b, and c are regression coefficients. Finally, we remove undesirable components from the observed radiance by Eqs. (1) and () with the aid of Eqs. (3) and (4). Figures a and b respectively show the comparison of τ c and r e from the new algorithm with those of MODIS. τ c from the new algorithm is in fairly good agreement with MODIS τ c for optically thin clouds (τ c ~). For thick clouds, the τ c deviates even more from the linear relation (figure a). The mean rootmean-square errors of τ c for thin and thick clouds are 1.39 and.38, respectively. On the other hand, r e from the new algorithm is well-matched to MODIS r e for small particles (r e ~1 μm). For large particles, the deviation of r e is increased due to a similar reason to the case of τ c. The mean root-

6 mean-square errors in r e for small and large particles are.83 and 1.6 μm, respectively (figure b). (a) Cloud optical thickness (b) Effective particle radius MODIS τ MODIS r c e Figure : Comparison between MODIS-retrieved and COMS-retrieved τ c (a), and r e (b).. CLOUD TOP TEMPERATURE AND PRESSURE The IR-window channel estimate is the typical method in which BT is compared with a vertical temperature profile in the area of interest. It is assumed that the cloud is opaque and fills the field-ofview (FOV). This is inaccurate for semi-transparent cirrus and small-element cumulus clouds (Menzel et al. 1983). To alleviate this inaccuracy, a radiance rationing method has been developed and used for operational purposes (Menzel et al. 1983, etc.). Moreover, the window channel has also been involved in the radiance ratioing method together with the sounding channels (6.,.3 and 13.4 μm) to retrieve the p c of thin clouds for SEVIRI (Gléau ). The COMS Imager has actually limited channels for importing the radiance ratioing method, so that only the IR-window channel ( μm) and one sounding channel (6. μm) are available. Thus, it is necessary to evaluate this method with the available channels. All the equations for this method are the same as those driven in Menzel et al. (1983). We briefly show the relation between ratio ( G 6. ) and pc as follows. cld clr L L G 6. ( pc ) () cld clr L6. L6. where L clr and L cld are the radiances of clear-sky and cloudy-sky, respectively. We assumed here that cloud emissivities at channels are near unity. It should be noted that 6.-μm is a strong water vapor absorbing channel, so that its maximum value of weighting function is located at an altitude of around 4 hpa. Thus, G in Eq. () can be applied only to high clouds with pc 4 hpa Tropical Mid-latitude Polar p c (hpa) Figure 3: G 6. s for clouds with cloud top pressure of 4 hpa. See the manuscript for the details. Figure 3 shows the RT model Streamer simulation of for single-layer ice clouds located at 4 hpa. This is calculated under the conditions of θ = 3 for the tropical (solid), midlatitude.8 G 6.

7 (dashed), and polar (dotted) atmospheric profiles. The ice clouds are assumed to be composed of spherical particles. Each ratio is computed by the regression of 16 cases for various τ c (., 1,, and ) and r e (,,, and 13). Clouds at, 3 and 4 hpa have distinct ratios of 13, 18, and 3 (18, 3, and 8) for mid-latitudes (the tropics). It is obvious that the ratio increases depending on p c, except for the polar region. The satellite zenith angle θ is also an important component to be duly considered for the calculation of G 6. in Eq. (), while other angles such as θ and φ relative to the sun do not affect 6.-μm radiance. It has also been found that a greater value of the ratio is computed for larger values of θ for the same pc (not shown). The decision of appropriate clear-sky radiance is one of the common critical issues, as implied in Eq. (). The ratio from measured radiance must be well-matched with the pre-calculated ratio from the RT model in operational CTTP retrieval. If the ratio is calculated with improper clear-sky radiance for cloudy FOV, it leads to serious uncertainty in CTTP. COMS CLA takes up the method in which we find a maximum of clear-sky radiance between observed pixels adjacent to cloudy pixels in km km FOV and simulated by the RT model with numerically-predicted atmospheric profiles. 8 RTM result Observation p c (hpa) Figure 4: Relation of MODIS p c and G 6. calculated from MODIS observations for 1 UTC of 4 March over South-East Asia (13 34 N, E). The th, th, and 9 th percentiles are displayed as lines on the bars, and the th and 9 th percentiles as error bars. Figure 4 shows an example of an observable relation between G and MODIS pc. The is calculated under the condition of θ = 3 from MODIS observation for 1 UTC of 4 March, which FOV covered South-East Asia (13 34 N, E) (box plots in figure 4). In the calculation of G 6., the clear-sky radiance is chosen as the maximum value among clear-sky pixels in pixels, and the cloudy sky radiance as the mean value of 4 cloudy pixels in pixels. The Streamer calculation of G 6. is simulated with the tropical profile in the similar manner to figure 3 and shown together as a solid line in figure 4. The bias between MODIS observation and RT calculation generally results from the atmospheric profile assumed for a RT model. For the level of 3 4 hpa, the bias is even larger. This implies that water vapor contaminates 6.-μm radiance up to an altitude of 3 hpa, contrary to the RT calculation. The bias under 3 hpa is reduced in mid-latitude, but the gradient of the G with respect to pc is somewhat undersized (not shown) CONCLUDING REMARKS The first Korean geostationary satellite, COMS is scheduled to be launched in 8. One of the most important meteorological mission objective of COMS is to help better prediction of severe weather events. The COMS imager will have channels at.6, 3., 6.,, and 1. μm. This study suggests practical methods of retrieving cloud properties by fully utilizing the channels of the COMS. The major characteristics of the methods are summarized as follows. (1) A new algorithm applying 6. μm in addition to and 1. μm has shown the improved.8 6. G 6.

8 accuracy in the detection of ice phase from the available data. This approach works comparatively well even in the absence of the 8.-μm band that is essential for the retrieval of cloud phase in the MODIS IR trispectral algorithm. () The retrieval of τ c and r e using cloud-reflected.6- and 3.-μm radiances is achieved by the rapid removal of undesirable radiance components. These components are obtained from a lookup table composed of angular variables, climatological A g, and -μm radiance measured for a coincident pixel. The τ c (r e ) attained by this algorithm has shown the valid relation, better below (1 μm) than MODIS-retrieved τ c (r e ) in its validation analysis using the available data. (3) The IR-window estimate using BT is performed for the CTTP of optically thick clouds (τ c > ~). The radiance ratioing method using 6.- and -μm bands is introduced for optically thin high clouds ( 4 hpa). Contrary to the in situ methods using other sounding channels, it must consider two factors; θ and the atmospheric profile. REFERENCES Ackerman, S. A., Strabala, K. I., Menzel, W. P., Frey, R. A., Moeller, C. C. and Gumley, L. E., (1998) Discriminating clear-sky from clouds with MODIS. Journal of Geophysical Research, 3, pp Ahn, M. H., Seo, E. J., Chung, C. Y., Sohn, B. J., Suh, M. S. and Oh, M., () Introduction to the COMS meteorological data processing system, International Symposium on Remote Sensing, Jeju, Korea. Baum, B. A., Soulen, P. F., Strabala, K. I., King, M. D., Ackerman, S. A., Menzel, W. P. and Yang, P., () Remote sensing of cloud properties using MODIS airborne simulator imagery during SUCCESS,, Cloud thermodynamic phase. Journal of Geophysical Research,, pp Gléau, H. L., () Software user manual of the SAFNWC / MSG: Scientific part for the PGE1--3. SAFNWC software V1., pp 3 3. Key, J. R. and Schweiger, A., (1998) Tools for atmospheric radiative transfer: Streamer and FluxNet. Computers and Geosciences, 4, pp , and Intrieri, J. M., () Cloud particle phase determination with the AVHRR. Journal of Applied Meteorology, 39, pp , () Steamer version 3. user s guide. NOAA/NESDIS, Madison, Wisconsin. [online] stratus.ssec.wisc.edu. King, M. D., (198) Determination of the scaled optical thickness of clouds from reflected solar radiation measurements. Journal of Atmospheric Science, 44, pp , Tsay, S. C., Platnick, S. E., Wang, M. and Liou, K. N., (199) Cloud retrieval algorithms for MODIS: optical thickness, effective particle radius, and thermodynamic phase. in MODIS Algorithm Theoretical Basis Document, NASA. Menzel, W. P., Smith, W. L. and Stewart, T. R., (1983) Improved cloud motion wind vector and altitude assignment using VAS. Journal of Climate and Applied Meteorology,, pp Nakajima, T. Y. and King, M. D., (199) Determination of the optical thickness and effective particle radius of clouds from reflected solar radiation measurements, Part:Theory. Journal of the atmospheric sciences, 4, pp , and Nakajima, T., (199) Wide-area determination of cloud microphysical properties from NOAA AVHRR measurements for FIRE and ASTEX regions. Journal of Atmospheric Science,, pp Okada, I., Takamura, T., Kawamoto, K., Inoue, T., Takayabu, Y. and Kikuchi, T., () Cloud cover and optical thickness from GMS- image data. Proc. GAME AAN/Radiation Workshop, Phuket, 8 March, 1, pp Pavolonis, M. J. and Heidinger, A. K., (4) Daytime cloud overlap detection from AVHRR and VIIRS, Journal of Applied Meteorology, 43, pp 6 8. Ricchiazzi, P., Yang, S., Gautier, C. and Sowle, D., (1998) SBDART: A research and teaching software tool for plane-parallel radiative transfer in the earth s atmosphere. Bulletin of the American Meteorological Society, 9, pp Strabala, K. I., Ackerman, S. A. and Menzel, W. P., (1994) Cloud properties inferred from 8 1-μm data. Journal of Applied Meteorology, 33, pp 1 9.

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