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Development of a Cloud-Top Height Estimation Method by Geostationary Satellite Split-Window Measurements Trained with CloudSat Data ATSUSHI HAMADA Research Institute for Humanity and Nature, Kyoto, Japan
NORIYUKI NISHI Division of Earth and Planetary Sciences, Graduate School of Science, Kyoto University, Kyoto, Japan (Manuscript received 19 May 2009, in final form 30 March 2010) ABSTRACT Lookup tables for estimating the cloud-top height and visible optical thickness of upper-tropospheric clouds by the infrared brightness temperature TB at 10.8 mm (T11) and its difference from TB at 12 mm (DT11–12) measured by a geostationary satellite are presented. These lookup tables were constructed by regressing the cloud radar measurements by the CloudSat satellite over the infrared measurements by the Japanese geostationary multifunctional transport satellite MTSAT-1R. Standard deviations of measurements around the estimates were also displayed as an indicator of the ambiguity in the estimates. For the upper-tropospheric clouds with T11 , 240 K, the standard deviations of the height estimations were less than 1 km. The dependences of the estimates of cloud-top height at each point in T112DT11–12 space on latitude, season, satellite zenith angle, day–night, and land–sea differences were examined. It was shown that these dependences were considered uniform in the tropics except for the region with large satellite zenith angle. The presented lookup tables can provide hourly estimates of cloud-top height and optical thickness at a specified location and are fairly useful in comparing them with ground-based observations such as vertical profiles of humidity and/or wind.
1. Introduction Cloud-top height is one of the crucial cloud parameters that provide information on the vertical structure of cloud water content. In field campaigns aiming mesoscale cloud systems, the cloud-top heights are needed to compare between the vertical profiles of ground-based measurements such as wind and humidity and those of cloud condensates (e.g., Nishi et al. 2007). Cloud-top heights are also required in studies that describe the temporal evolution of cloud parameters for long-lived cirrus clouds (e.g., Mace et al. 2006). For such purposes, measurements from a geostationary satellite that observes a wide area in a short time interval are useful to estimate the cloud-top height. Estimation by geostationary satellite measurements is quite useful to assess the behavior of cloud clusters with the scale of a few hundreds
Corresponding author address: Atsushi Hamada, Research Institute for Humanity and Nature, 457-4 Kamigamo-Motoyama, Kita, Kyoto 603-8047, Japan. E-mail:
[email protected] DOI: 10.1175/2010JAMC2287.1 Ó 2010 American Meteorological Society
of kilometers that are numerically simulated with global cloud resolving model (e.g., Inoue et al. 2008). Hamada et al. (2008; hereinafter referred to as H08) showed the lookup table for estimating the cloud-top height by the two brightness temperatures TB at infrared split-window wavelengths measured by the fifth Geostationary Meteorological Satellite (GMS-5). Their lookup table was constructed by regressing the cloud-top heights that were determined from shipborne cloud radar measurements in terms of two TB values. The presented lookup table, however, was limited to the nonrainy clouds, since ground-based cloud radar is inadequate to determine the cloud-top heights of rainy clouds because of heavy signal attenuation by precipitation particles (Lhermitte 1990). In addition, their analysis period and domain were insufficient to assess the dependencies of the season and geographical area on the estimated cloudtop height. The CloudSat satellite (Stephens et al. 2002) launched on April 2006 carries a cloud radar and provides the information on the cloud-top height without concern for the existence of precipitation. However, the CloudSat
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swath is narrow (;1.4 km) and observations are conducted only 2 times per day (0130 and 1330 local time). Therefore, for the purpose of monitoring a specific region or cloud system in a short time interval of ;1 h, indirect methods that use the measurements by geostationary satellite are required to estimate the cloud parameters. There have been several studies on the estimation of cloud parameters using a number of satellite measurements at near-infrared and/or infrared wavelengths, such as the CO2 slicing method (Menzel et al. 2008). Some of those have been applied to some geostationary satellites such as Meteosat Second Generation (Thies et al. 2008). Measurements by the Japanese geostationary Multifunctional Transport Satellite (MTSAT)-1R are important for examining the convective activities over the Maritime Continent that influence the climate on various temporal and spatial scales through pronounced cloud activities. However, MTSAT-1R unfortunately does not have the number of channels required to use these infrared multichannel methods. Estimation methods that use only infrared measurements and are applicable to MTSAT-1R are limited to such as the split-window method (Inoue 1985, 1987), which uses two measurements at infrared split-window wavelengths (;11 and ;12 mm), and the H2O-intercept method (Szejwach 1982), which uses two measurements at water vapor (;6.8 mm) and infrared wavelengths (;11 mm). In this study, we construct the lookup tables for estimating cloud-top height and visible optical thickness based on the split-window method, which is less affected by the variation of water vapor content in the atmosphere. It is also important to estimate the cloud microphysics such as the effective radius and phase of cloud. Although these characteristics could be inferred indirectly by, for example, estimating beta ratio (Inoue 1985; Heidinger and Pavolonis 2009), we chose the cloud-top height and visible optical thickness as the prior parameters since only two independent measurements are used in the split-window method. The split-window method uses two brightness temperatures at infrared window wavelengths, 10.8 and 12 mm (hereinafter referred to as T11 and T12, respectively). There are two predominant advantages of using T11 and T12: to be applicable to almost all the recent geostationary satellites as well as MTSAT-1R and to be available during both daytime and nighttime. At split-window wavelengths, the radiative transfer equation for the ground surface and a single-layer ‘‘cloud’’ without geometrical thickness can be simplified as follows (hereinafter referred to as the simplistic model): 5e I obs i
t i in Ii
1 (1
e
ti
)I cld i , (i 5 1, 2),
(1)
where Iiobs, Iiin, and Iicld, are the radiance received by the ith channel of the satellite, the radiance entering the
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cloud base from the ground through the lower atmosphere, and the radiance emitted by the cloud that is assumed to be equal to the Planck blackbody radiance at the cloud temperature. Here t i is the optical thickness of the cloud at the ith channel. The extinction coefficients for water at 12 mm are larger than those at 10.8 mm in all of the solid, liquid, and gaseous states. Therefore, the difference between the two brightness temperatures, DT11–12 5 T11 2 T12, usually takes a positive value related to cloud parameters in the simplistic model, and the cloud temperature and optical thickness each can be described as the function of T11 and DT11–12 (e.g., Cooper et al. 2003). The corresponding cloud-top height is estimated by matching the cloud temperature to the vertical temperature profile of local atmospheric sounding or objective analysis. Based on these facts, a number of methods for the estimation of cloud-top height and optical thickness have been proposed (e.g., Katagiri and Nakajima 2004; Minnis et al. 1998). However, validation using observational data is not sufficient, mainly because of the difficulty in direct observation of nonrainy clouds such as cirrus. In the case of simplistic model-based estimation of the cloud-top height and optical thickness with two splitwindow TB measurements, the rest of the cloud parameters, such as cloud coverage and shape and size distribution of cloud ice, which are related to optical thickness, remain a priori parameters. It is essential to adequately determine such parameters for estimating the cloud-top height and optical thickness accurately, since the uncertainties in the cloud-top height and optical thickness expected from models that are based on the simplistic model are mainly caused by these a priori parameters (Stephens and Kummerow 2007). However, it is difficult to determine these parameters since they are highly variable in every cloud and hard to obtain by direct measurements over large areas and long periods. In this study, the observation-based lookup tables are constructed by statistical method where the 2-yr measurements by the cloud radar on board the CloudSat satellite are regressed over the split-window measurements by the geostationary satellite MTSAT-1R. The paper is organized in the following manner. Section 2 describes the data used. The results are described in section 3. Discussions are presented in section 4, and conclusions are given in section 5.
2. Data We used two TB measurements by channel 1 and 2 of MTSAT-1R. Spectral response function for each channel is centered on 10.8 and 12.0 mm in the infrared window region, respectively. The horizontal resolution of the original data is ;4 km at the nadir (08, 1408E). We
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used the hourly data gridded on 0.058 in both longitude and latitude. The brightness temperature resolution is ;0.1 K at TB 5 300 K, and it is slightly coarser at lower temperatures, ;0.3 K at TB 5 210 K. Hereinafter, the brightness temperatures at channel 1 and 2 are described as T11 and T12, respectively. We also defined the difference between T11 and T12 as DT11–12 5 T11 2 T12. Cloudtop height and visible optical thickness will be estimated as the function of T11 and DT11–12. To determine the cloud types, cloud-top height, and visible optical thickness, 94-GHz cloud profiling radar (hereinafter referred to as cloud radar) measurements by the CloudSat satellite (Stephens et al. 2002), which was launched in April 2006, were used. Cloud radar has high sensitivity to the cloud particles, although the radar signal is heavily attenuated by the precipitation particles. Observations are made only just under the satellite with the footprint of 1.4 km 3 2.5 km (cross- and alongtrack) and the vertical resolution of ;240 m. Since CloudSat is sun-synchronous orbital satellite, the observation times are fixed at 0130 and 1330 local time. In this study, we used the data provided by Colorado State University (http://www.cloudsat.cira.colostate.edu/). Vertical profiles of radar reflectivity and cloud mask, and land–sea flag in 2B-GEOPROF data—cloud scenario data that include the information on cloud type and precipitation flag in 2B-CLDCLASS data—were used to estimate cloud type and cloud-top height. Optical depth data in 2B-TAU data were also used to estimate visible optical thickness. Note that the 2B-TAU optical depth data were retrieved using not only CloudSat radar measurements but also some auxiliary data from the constellation measurements by the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua satellite and the global objective analysis by European Centre for Medium-Range Weather Forecasts (ECMWF; Polonsky et al. 2008). We used a 2-yr CloudSat dataset from the beginning of July 2006 to the end of June 2008. The analysis domain was between 908E and 1708W and between 458S and 458N. Within this analysis period and domain, we selected CloudSat measurements where the difference of the observation times between CloudSat and MTSAT-1R was within 60 s. If more than one CloudSat observation corresponded to an MTSAT-1R observation, temporal averaging was applied to CloudSat locations and observations. Corresponding values of T11 and DT11–12 measured by MTSAT-1R were obtained by linear spatial interpolation, using four grids encompassing the CloudSat location. We obtained 259 617 samples with paired CloudSat and MTSAT-1R measurements within this analysis period and domain. Nine types of clouds (cirrus, altostratus, altocumulus, stratus, stratocumulus, cumulus, nimbostratus, deep
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convection, and no cloud) were stored in 2B-CLDCLASS cloud scenario data. This dataset also has four types of precipitation (liquid and solid precipitation, drizzle, and no precipitation). The cloud scenario was determined using horizontal and vertical distribution of cloud radar reflectivity (Sassen and Wang 2008; Wang and Sassen 2007). We classified the samples into the following four categories (hereinafter referred to as cloud types) by using these cloud scenario and precipitation flag data: (i) rainy clouds (R type) are samples that have any cloud and at least five consecutive vertical bins with nondrizzling precipitation, (ii) nonrainy high clouds (H-NR type) are samples that are not R type and have cirrus, altostratus, altocumulus, nimbostratus, and/or deep convection, (iii) nonrainy low clouds (L-NR type) are samples that are not the above two types and have stratus, stratocumulus, and/or cumulus, and (iv) clear sky (C type) are samples with no cloud. There were 14 515, 79 361, 53 506, and 112 435 samples for R, H-NR, L-NR, and C type, respectively. In this study, R type was defined simply as whether the cloud precipitates, being different from H08 where it was defined as whether the precipitation reached the ground. For each sample, cloud layers were defined by using the cloud mask value. The cloud layer was defined as the echo layer with at least three consecutive vertical bins (;720 m) where cloud mask values were higher than 20 and the boundaries of the echo layer were at least two bins (;480 m) away from those of neighboring echo layers. Hereinafter, the echo-top height and visible optical thickness of the uppermost echo layer are referred to as the cloud-top height and optical thickness, respectively. We did not use the constellate lidar measurements from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO; Winker et al. 2007) satellite, since the main subject of this study is the estimation of top height and visible optical thickness of mesoscale convective cloud systems, which consist of deep convective clouds surrounded by large nimbostratus and nonprecipitating dense cirriform cloud area (e.g., Houze et al. 1980). Because of the higher sensitivity to small cloud particles than cloud radar, lidar measurements are often used to determine the cloudtop height. The echo-top height of the lidar is expected to be more close to the ‘‘true’’ cloud-top height, which is determined to be the top boundary of cloud water content, than that of cloud radar. While the lidar can detect the thin cirrus clouds associated with anvils (Garrett et al. 2004), at the same time it also detects the optically thin clouds with smaller ice crystals (;10 mm; e.g., McFarquhar et al. 2000; Iwasaki et al. 2004) that are not
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related with convective systems and exist in the tropical tropopause layer with large horizontal extent. In terms of estimating the cloud-top heights of the high clouds that originate from convective activity, such thin cirrus should be precluded. It is, however, difficult to completely determine whether these thin cirrus clouds are associated with convective systems. In addition, CALIPSO lidar measurements could be affected by signal noise effects in the daytime (Sassen et al. 2008). It should be described in detail for the correspondence between the cloud-top height defined in this study and the ‘‘true’’ one determined as the boundary of cloud water content. Cloud mask values that determine the cloud-top heights of samples were determined by statistically processing the along-track vertical section of the returned power of cloud radar (Marchand et al. 2008). The obtained values correspond to the reliability of the detection of the spatially continuous hydrometeor field. The cloud mask values take from 0 to 40, and increasing values indicate reduced probability of false detection. In this study, the cloud radar echo with cloud mask value greater than 20 is considered as a significant hydrometeor echo. This threshold indicates the false detection rate of ;5% for hydrometeors whose radar reflectivity is below the sensitivity limit (;230 dBZe). The cloud-top height that is defined as the echo-top height of cloud radar corresponds approximately to the height where the lidar optical depth accumulated from the top reaches from ;0.15 to ;0.25 (McGill et al. 2004). We examined the CloudSat and CALIPSO measurements for some cases and compared the differences between the echo-top heights of radar and lidar. The differences were found to be a few hundred meters but sometimes up to ;1 km even for nimbostratus and deep convective clouds. Therefore, in strict terms, the estimates of cloud-top height in this study should be considered as the echo-top height measured by cloud radar rather than the true cloud-top height.
3. Methods and results a. Distribution and occurrence rate of each cloud type in T112DT11–12 space The bivariate probability density function (PDF) in T112DT11–12 space for each cloud type was computed and used to evaluate the estimated values of cloud-top height and optical thickness. As will be described in detail, the lookup table for estimating cloud-top height mainly depends on the latitude and satellite zenith angle of MTSAT-1R. We therefore show the results for the region between 1208 and 1608E and between 158S and 158N (hereinafter referred to as the nadir region) to reduce the dependences of the latitude and satellite zenith angle. This longitudinal section corresponds to the satellite
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zenith angle of MTSAT-1R less than ;308. In the nadir region, there were 3000, 14 245, 4197, and 11 708 samples for R, H-NR, L-NR, and C type, respectively. The PDFs were computed by the nonparametric kernel smoothing method adopted in H08, using a one-dimensional Epanechnikov kernel in both the T11 and DT11–12 dimensions (Silverman 1986): N
1 w Nhx hy i51 i ! x Xi y Yi K wi 5 K hx hy 8
