Synergistic Use of Spaceborne Active Sensors and Passive Multispectral Imagers for Investigating Cloud Evolution Processes

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1 Trans. JSASS Aerospace Tech. Japan Vol. 12, No. ists29, pp. Tn_19-Tn_24, 2014 Topics Synergistic Use of Spaceborne Active Sensors and Passive Multispectral Imagers for Investigating Cloud Evolution Processes By Takashi Y. NAKAJIMA 1), Takashi M. NAGAO 1), Husi LETU 1) and Hajime OKAMOTO 2) 1) Research and Information Center, Tokai University, Tokyo, Japan 2) Research Institute for Applied Mechanics, Kyushu University, Fukuoka, Japan (Received June 22nd, 2013) This paper examines the use of the spaceborne radar and imagers aboard CloudSat, Aqua, EarthCARE, GCOM-C, and third-generation geostationary satellites for investigating and revealing the cloud evolution processes for warm water clouds. These satellites either have been in orbit or are planned to be launched in the mid-2010s, and will contribute to observations of aerosols and clouds on Earth. Although most data analyses are performed using each satellite and sensor separately, combining the sensors offers the potential of new findings on the states and evolution processes of clouds, such as by obtaining a rough sketch of cloud vertical structure through the use of a microwave scanner and a visible-to-infrared imager, or by observing the cloud evolution transition through the use of a cloud profiling radar and a visible-to-infrared imager. This is the work we aim to perform over the next decade. This research will be conducted by using various kinds of satellites and sensors, radiative transfer theory, electromagnetic scattering theory, and modeling. This paper presents recent topics and strategies for synergistic use of spaceborne sensors with a review of important past studies. Key Words: Cloud Physics, Renewable Energy, EarthCARE, GCOM-C, Geostationary Satellite 1. Introduction Aerosols and clouds exert an important influence on the planet s water and energy balances and processes, and further understanding of the states and lifecycles of clouds including the processes of their evolution is required. Recently, cloud science has also been recognized as being important for studying renewable energy in terms of solar irradiance estimation. Active sensors, such as the CloudSat cloud profiling radar (CPR) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar, represent a new era in cloud and aerosol observations for revealing particle transitions from cloud condensation nuclei to rain droplets via cloud and drizzle particles. At present, synergistic use of multispectral wide-swath imagers and active sensors is effective for extending the observation areas on Earth. The Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) satellite, which will be equipped with active and passive sensors, is being planned through collaboration between the Japan Aerospace Exploration Agency (JAXA), the National Institute of Information and Communications Technology (NICT), and the European Space Agency (ESA) to follow on from CloudSat and CALIPSO. Moreover, third-generation geostationary weather satellites are expected to enter service in 2015 to observe global-scale aerosols and clouds at intervals of 10 min or less. The combined use of passive and active sensors on polar-orbiting satellites and sensors on geostationary satellites is then expected to reveal details of cloud evolution processes by exploiting the multispectral capabilities and vertical observation capabilities of the active and passive sensors. In this paper, we introduce recent progress in cloud observation from spaceborne sensors by showing multisensor views of the cloud droplet growth process as obtained from Advanced Microwave Scanning Radiometer (AMSR) / Advanced Earth Observing Satellite-II (ADEOS-II) plus Global Imager (GLI)/ADEOS-II, and CPR/CloudSat plus MODIS/Aqua. We also discuss our research strategy for using future missions including EarthCARE and Global Change Observation Mission Carbon Cycle (GCOM-C) satellites, and third-generation geostationary satellites such as Himawari. In particular, we focus on warm water-phase clouds that have spherical particle shape in this paper. Table 1. Expected sensors in this study. Orbit Polar-orbiting satellite Geostationary orbital satellite Active or Passive Active sensor Passive sensor Passive sensor Name of Sensors/Satellite CPR/CloudSat CPR/EarthCARE MODIS/Aqua SGLI/GCOM-C MSI/EarthCARE AMSR/Aqua AMSR2/GCOM-W AHI/Himawari ABI/GOES FCI/Meteosat Principle Millimeter radar Visible-to-infrared imager Microwave scanner Visible-to-infrared imager Outputs Radar Reflectivity Cloud, aerosol properties, Water vapor Cloud, aerosol properties etc. Cloud property etc. Liquid water path etc. Vertical profiling Horizontal structure Copyright 2014 by the Japan Society for Aeronautical and Space Sciences and ISTS. All rights reserved. Tn_19

2 Trans. JSASS Aerospace Tech. Japan Vol. 12, No. ists29 (2014) 2. Sensors and Satellites The sensors discussed or used in this paper are listed in Table. 1. Most of these are polar-orbiting satellites and measure clouds from relatively low altitudes of around 800 km. They cover the total expanse of Earth's surface almost every day. Active and passive sensors are available for observing aerosols, clouds, and so on. The Advanced Himawari Imager (AHI) is a third-generation imager that will be mounted on the Himawari satellite (a geostationary orbiting weather satellite) that is planned for launch in Japan fiscal year This satellite will be put into geostationary orbit at an altitude of approximately 35,800 km. It will be able to observe the full disk of Earth that is facing the satellite every 10 min or more frequently. AHI will be able to measure the horizontal states and physical properties of clouds and aerosols. 3. Sensor Combination and Outputs 3.1. Step 1: Obtain a rough sketch of vertical structure by using a passive microwave scanner and visible-to-infrared imager The vertical structure of the effective particle radii in a cloud can be obtained by using a passive microwave scanner and visible-to-infrared imager in combination. The algorithm was firstly developed by Masunaga et al. (2002a) 1) for obtaining the vertical structure of the effective cloud particle radii in warm water-phase clouds over low and middle latitude areas (35 N to 35 S) by using the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and the Visible-to-Infrared Scanner (VIRS) aboard the TRMM satellite. Nakajima et al. (2009) 3) applied this algorithm to the ADEOS-II (Midori-II) satellite sensors, AMSR and the GLI, and extended the analysis area to the range of 60 N to 60 S. The principle of the algorithm is simple. The effective particle radius obtained from the visible-to-infrared imager is the cloud droplet size near the cloud top because reflection of solar radiances occurs mainly near the cloud top. In contrast, the microwave scanner provides information about the cloud droplet size throughout the entire cloud layer because the microwave signals emitted from ocean surface penetrate the entire cloud layer. Combined use of visible-to-infrared and microwave scanners for observing clouds can thus provide significant information about the vertical profile of cloud properties. Let the effective particle radii obtained from the visible-to-infrared imager and microwave scanner be r e (Vis & Ir) and r e (Mic), respectively. Generally speaking, r e (Vis & Ir) represents the cloud droplet size at the cloud top and r e (Mic) is the average droplet size throughout the entire cloud layer. The ratio of r e (Mic) to r e (Vis & Ir) is therefore assumed to be an indicator of the vertical structure of the cloud droplet size. We define the effective particle radius ratio (ERR) as ERR r e (Mic)/r e (Vis & Ir) (1). A large value of ERR means that the cloud contains large particles in the lower part of the cloud. Such clouds are expected to be accompanied by precipitation (see Table 2). A small value of ERR, on the other hand, means that the particles in the cloud have an adiabatic structure in which small and large cloud particles are located in the lower and upper cloud layers, respectively. The clouds are assumed to be non-precipitable in such cases. The global distribution of ERR thus represents the local tendency for clouds to be non-precipitable or precipitable. Indeed, Masunaga et al. (2002b) 2) categorized clouds by ERR and r e (Vis & Ir) ; the certain r e (Vis & Ir) > 15 µm implies that the clouds are probably drizzling (Fig. 1.). This simple assessment method of clouds will be useful to interpret results obtained from a synergistic analysis of clouds using visible-to-infrared imager and cloud radar appeared in the section 3.2. Table 2. ERR and cloud precipitability. ERR Cloud state Typical area Large Precipitable Central Pacific Ocean Small Non- precipitable Intertropical convergence zone South Pacific convergence zone etc. East Asia California Peru Namibia Fig. 1. Categorizing clouds by ERR and r e (Vis & Ir). [Reproduced from Masunaga et al. (2002b) 3] ]. Figure 2 [reproduced from Fig. 6 in Nakajima et al. (2009) 3] ] shows the global distribution of ERR as obtained by combined use of GLI and AMSR. The data are the one-month mean for April Relatively large ERR appears over the central Pacific and the South Pacific convergence zone in this figure. These are known to be high-precipitation areas. The large value of ERR indicates that the column averaged effective particle radius r e (Mic) is larger than that near the cloud top r e (Vis & Ir). The results at low-to-mid latitudes are consistent with the results obtained from the VIRS-TMI analysis performed by Masunaga et al. (2002b) 2). The figure also shows that areas of small ERR are spread over the North Pacific Ocean, the North Atlantic Ocean, and Namibian, East Asian, Californian, and Peruvian regions. In these areas, the effective particle radius at the cloud top r e (Vis & Ir) is similar to or larger than that in the middle to bottom layers. etc. Tn_20

3 T.Y. NAKAJIMA et al.: Synergistic Use of Active and Passive Sensors for Investigating Cloud Evolution Processes for visible-to-infrared imaging and cloud profiling, respectively. They are in sun-synchronous polar orbit along the A-train path, and observe clouds from virtually the same locations at the same times. Fig. 2. ERR obtained by combined use of the GLI and AMSR aboard the ADEOS-II (Midori-II) satellite. Data are the one-month average of the values measured in April Only warm water-phase clouds were included. The areas that are observable by the ADEOS-II and TRMM satellites are ±60 and ±35 of latitude (indicated by dashed lines), respectively. [Reproduced from Fig. 6 in Nakajima et al. (2009), RSSJ 3].] The results obtained by combined use of a microwave scanner and visible-to-infrared imager as shown in this subsection give a rough sketch of the vertical structure of the cloud droplet size in clouds. However, the vertical structure of clouds needs to be known more precisely in order to better understand cloud evolution processes. Active remote sensing is expected to be another useful technique for investigating the vertical structure of cloud properties. In fact, CloudSat (Stephens et al. 2002) 4) which is part of the A-Train constellation of satellites, is equipped with CPR and is in operation today. EarthCARE (Kumagai et al. 2003) 5), which will be equipped with a CPR having Doppler capability, a lidar, an imager, and a broadband spectrometer is planned for launch around Table 3. Output from microwave scanner and visible-to-infrared imager. Sensor Microwave Visible-to-infrared imager Scanner Individual Column water Cloud fraction outputs vapor Cloud top temperature Sea surface Cloud optical thickness temperature Effective radius r e (Vis & Ir) Synergistic output Cloud liquid water path Effective radius r e (Mic) Effective radius ratio r e (Mic)/r e (Vis & Ir) 3.2. Step 2: Observe cloud evolution transitions statistically by using a CPR and visible-to-infrared imager For more precise observation of the cloud vertical structure, it is obvious that spaceborne CPR is a useful sensor. We combined the cloud properties obtained from passive imagers with radar reflectivities obtained from a CPR in order to improve our understanding of cloud evolution processes. This is a new technique for satellite data analysis for the next decade. Today, MODIS/Aqua and CPR/CloudSat can be used Developed by Nakajima et al. (2010) 6) and Suzuki et al. (2010) 7), a contoured frequency by optical depth diagram (CFODD) is a joint probability density function that is a new visualization method for CPR radar reflectivities. It is similar to the conventional contoured frequency by altitude diagram (CFAD), except that CFODD uses in-cloud-optical-depth (ICOD) instead of altitude as the vertical axis. Since ICOD represents the vertical structure of a cloud as a function of the cloud optical property, ICOD=0 always corresponds to the cloud top. CFODD requires total cloud optical thickness to be coincidentally retrieved by the passive visible-to-infrared imager, whereas CFAD can be generated from cloud radar data alone. In this regard, CFODD is considered to include significant information about cloud state and evolution processes. We used the 2B-TAU product of the CloudSat project (Polinsky 2008) 8) in the first version of CFODD to obtain the ICOD. However, the optical depth of each layer bin was dependent on the CPR reflectivities, Ze, in the 2B-TAU product, and thus the vertical and horizontal axes were not independent. In version 2, Suzuki et al. (2010) 7) introduced a method for cloud optical depth slicing based on an adiabatic condensation growth assumption. CFODD was originally developed for investigating the relationships between the radar reflectivities and optical characteristics of clouds. However, when we categorized CFODD by using the effective particle size of clouds as measured by the visible-to-infrared imager, it became clear that CFODD revealed the cloud evolution process. For example, Fig. 3 [Reproduced using data from Nakajima et al. (2010), JAS 6], shown in grayscale and rearranged data from Nakajima et al. (2012), SPIE 9) ] shows the CFODD generated from the combination of CPR/CloudSat and MODIS/Aqua. Again, the radar reflectivities were obtained from CPR, and total cloud optical thicknesses and effective particle radii (denoted by R21 in this figure) were obtained from MODIS (see Table 4). Each CFODD was generated by categorizing the R21 range, (a) 6 R21 8 µm, (b) 8 R21 10 µm, (c) 10 R21 12 µm, (d) 12 R21 14 µm, (e) 14 R21 16 µm, (f) 16 R21 18 µm, (g) 18 R21 20 µm, (h) 20 R21 25 µm, (i) 25 R21 30 µm. As can be seen in Figs. 3(a) to (d), large PDF quantities are obtained at smaller radar reflectivities (Ze) in the range of -20 to -30 dbze. As R21 increases, PDF starts shifting toward larger radar reflectivities corresponding to the drizzle particle mode (see Fig. 3(e)). This result is consistent with a result mentioned in the section 3.1; the certain r e (Vis & Ir) > 15 µm implies that the clouds are probably drizzling. Here, it is also interesting that the shift to drizzle mode takes place in the middle optical depths of the cloud. Once R21 exceeds 20 µm, PDF shifts toward larger radar reflectivities that correspond to the rain (precipitation) mode (see Fig. 3(h)). This continuous transition in the CFODD clearly shows the cloud growth Tn_21

4 Trans. JSASS Aerospace Tech. Japan Vol. 12, No. ists29 (2014) process. Table 4. Output from cloud radar and visible-to-infrared imager. Sensor Cloud radar Visible-to-infrared imager Individual outputs Radar reflectivity Ze as function of altitude Cloud optical thickness Effective radius r e (Vis & Ir) Synergistic output e.g., CFODD Fig. 3. CFODD classified by cloud effective radii (denoted R21 in this figure) with threshold R21 values of 6, 8, 10, 12, 16, 18, 20, 25, and 30 µm over an ocean area. Data are from July 2006 over the entire ocean area of Earth. [Reproduced using data from Nakajima et al. (2010) JAS 6], shown in grayscale and rearranged data from Nakajima et al. (2012), SPIE] 9].] bands compared with the first- and second-generation satellites. The number of bands is up to 16, which is similar to the latest visible-to-infrared imagers in sun-synchronous polar orbit. The idea of vertical slicing the cloud droplet size in clouds by using multiple spectra from passive visible-to-infrared imagers has been suggested by several papers (e.g., Platnick ), Chang and Li ), Nakajima et al. 2010a 12) ) and progress is being made on the algorithms. This is an idea that two or three near-infrared bands such as 1.6µm, 2.1µm, and 3.7µm wavelength will retrieve cloud droplet size that differ in character since the effective vertical weighting function for shorter wavelength extends deeper into the cloud than does the weighting function of the longer wavelength. Nakajima et al. 2010a 12) reported that the effective particle radii retrieved from MODIS 3.7-µm band are smaller than those retrieved from 1.6- and 2.1-µm bands in terms of one month average of the effective particle radii for global scale analysis. However, there are some issues to be solved, e.g. influences of the horizontal inhomogeneity of clouds to measured reflectance, for extracting the vertical structure of cloud droplet distribution from multi-wavelength retrievals. Therefore, more investigations are needed. Using third-generation satellite observations combined with knowledge obtained from sun-synchronous polar-orbiting satellite observations are therefore expected to contribute to the time series observation of cloud evolution processes. For this, combined use of next-generation CPR (e.g., CPR/EarthCARE), visible-to-infrared imagers (e.g., SGLI/GCOM-C) for more precise observation of clouds, and third-generation geostationary satellites (Himawari, GOES, Meteosat) is even more important. Table 5. CPR/EarthCARE specifications Step 3: Advance toward time series observation of cloud evolution processes by using polar-orbiting satellites together with geostationary satellites There are at least two paths toward the third step for observing cloud evolution processes from satellites. One is to add vertical motion information about cloud, drizzle, and rain particles by adding the capability for Doppler measurements from spaceborne CPR. JAXA, ESA, and NICT are developing the EarthCARE satellite with this aim. The other path is to take time series measurements of cloud evolution. Geostationary satellites are capable of high frequency observation of the globe for this purpose. Third-generation geostationary weather satellites such as Himawari (Japan), GOES (United States), and Meteosat (Europe) in particular will observe the globe every 10 min or even more frequently. Although time series observation of clouds that follows the cloud evolution process is a great idea, it is difficult to utilize active sensors and microwave scanners mounted on geostationary satellites because of the very large distance from the satellite to the target (approximately 35,800 km). However, one promising advance that is being made in third-generation geostationary satellites is the large number of multispectral Item Altitude Frequency Range resolution Height range Sensitivity Doppler accuracy Specification 450 km 94 GHz 500 m -0.5 to 20 km -35 dbz Approx. 1 m/s Tn_22

5 T.Y. NAKAJIMA et al.: Synergistic Use of Active and Passive Sensors for Investigating Cloud Evolution Processes Table 6. SGLI/GCOM-C bands. Band No. Wavelength (µm) Instantaneous field of view (km) VN VN VN VN VN VN VN VN VN VN VN P P SW SW SW SW T T Table 7. AHI/Himawari bands. Band No. Wavelength (µm) Instantaneous field of view (km) Table 8. Expected output from polar-orbiting satellites combined with third-generation geostationary satellites in the next decade (planned) Satellite Polar-orbiting satellite Third-generation Individual outputs Synergistic output (tentative) Horizontal and vertical structure of clouds Cloud evolution process 4. Discussion and Conclusion geostationary satellite Horizontal and partly vertical properties of clouds measured every 10 min or more frequently In this paper, we introduced the recent progress in cloud observations from satellite sensors and showed multisensor views of cloud vertical structure and droplet growth processes as obtained from AMSR/ADEOS-II plus GLI/ADEOS-II and CPR/CloudSat plus MODIS/Aqua, respectively. Advances in cloud science contribute to a better understanding of the planet s water and energy balances and processes via understanding the cloud lifecycle including cloud evolution. Cloud science has also been recognized as being important for studying renewable energy in terms of solar irradiance estimation. We are able to use both passive and active sensors aboard existing and/or future-launch satellites. As shown in this paper, some synergistic uses of two sensors provide novel viewgraphs of cloud characteristics, ERR and CFODD, that provide the vertical structure of cloud droplet sizes and cloud droplet evolution processes. We note that more investigation is required because cloud evolution proceeds faster than the once-daily observation frequency of most polar-orbiting satellites. Adding time series data about the vertical motion of droplet in clouds is expected to become an important observation target over the next decade. Doppler capabilities in next-generation CPR and the high-frequency observation from geostationary satellites are important. Simulation using models is also necessary if we are to take the next step. For instance, Nakajima et al. (2010) 12) used a simple two-layer cloud model to investigate and understand the cloud properties (e.g., effective particle radii) obtained 13) from satellite remote sensing. Nagao et al. (2013) investigated the influence of in-cloud vertical inhomogeneity on obtained cloud properties using a spectral-bin microphysics cloud model and one-dimensional remote sensing simulator. 14) Sato et al. (2012) examined CFODD using a three-dimensional spectral-bin microphysical model and remote sensing simulator and showed that the simulated CFODDs are characterized by distinctive patterns of radar reflectivities, similar to the patterns often observed by satellite remote sensing. Such scientific investigations test the consistency of observations with cloud modeling and are very important for understanding cloud state and cloud evolution processes in nature. Tn_23

6 Trans. JSASS Aerospace Tech. Japan Vol. 12, No. ists29 (2014) Acknowledgments This work was supported by the Japan Science and Technology Agency (JST), CREST/EMS/TEEDDA; EarthCARE; the GCOM-C Science Project of JAXA; and the Greenhouse Gases Observing Satellite Science Project of the National Institute of Environmental Studies, Tsukuba, Japan. This work was also supported by the Ministry of Education, Culture, Sports, Science and Technology, Japan, through a Grant-in-Aid for Scientific Research (B) ( ) and a Grant-in-Aid for Scientific Research (A) ( ). The authors are grateful to Dr. Kentaroh Suzuki at the Jet Propulsion Laboratory, California Institute of Technology for valuable discussions of cloud evolution process. References 1) Masunaga, H., T. Y. Nakajima, T. Nakajima, M. Kachi, R. Oki and S. Kuroda: Physical properties of maritime low clouds as retrieved by combined use of Tropical Rainfall Measurement Mission Microwave Imager and Visible/Infrared Scanner: Algorithm, Journal of Geophysical Research-Atmospheres, 107(D10) (2002a), Doi /2001jd ) Masunaga, H., T. Y. Nakajima, T. Nakajima, M. Kachi and K. Suzuki: Physical properties of maritime low clouds as retrieved by combined use of Tropical Rainfall Measuring Mission (TRMM) Microwave Imager and Visible/Infrared Scanner - 2. Climatology of warm clouds and rain, Journal of Geophysical Research-Atmospheres, 107(D19) (2002b), Doi /2001jd ) Nakajima, T. Y., H. Masunaga, T. Nakajima: Near-global scale retrievals of the cloud optical and microphysical properties from the Midori-II GLI and AMSR data, Journal of Remote Sensing Society of Japan, 29 (2009), pp ) Stephens, G.L., D. G. Vane, R. J. Boain, G. G. Mace, K. Sassen, Z. Wang, A. J. Illingworth, E. J. O Connor, W. B. Rossow, S. L. Durden, S. D. Miller, R. T. Austin, A. Benedetti, C. Mitrescu and the CloudSat Science Team: The CloudSat mission and the A-Train, Bull. Am. Met. Soc., 83 (2002), pp ) H. Kumagai, H. Kuroiwa, S. Kobayashi and T. Orikasa: Cloud profiling radar for EarthCARE mission, Proc. SPIE, 118 (2003), 4894, doi: / ) Nakajima, T. Y., K. Suzuki and G. L. Stephens: Droplet growth in warm water clouds observed by the A-Train. Part II: A Multi-sensor view. J. Atmos. Sci., 67 (2010), pp ) Suzuki, K., T. Y. Nakajima and G. L. Stephens: Particle growth and drop collection efficiency of warm clouds as inferred from joint CloudSat and MODIS observations, Journal of the Atmospheric Sciences, 67 (2010), ) Polinsky, I. N., A NASA Earth system science pathfinder mission.,", (2008), CIRA Colorado State University, Fort Collins. 9) Nakajima, T. Y., T. M. Magao, H. Letu, H. Ishida and K. Suzuki: On the cloud observations in JAXA's next coming satellite mission, SPIE Asia-Pacific Remote Sensing, Remote Sensing of the Atmosphere, Clouds, and Precipitation IV, Tadahiro Hayasaka; Kenji Nakamura; Eastwood Im, Editors, (2012), ) Platnick, S.: Vertical photon transport in cloud remote sensing problems. J. Geophys. Res., (2000), 105(D18), 22,919 22,935, doi: / 2000JD ) Chang, F. L. and Z. Li,: Estimating the vertical variation of cloud droplet effective radius using multispectral near infrared satellite measurements, J. Geophys. Res., 107(D15) (2002), 4257, doi: /2001jd ) Nakajima, T. Y., K. Suzuki, G. L. Stephens: Droplet growth in warm water clouds observed by the A-Train. Part I: Sensitivity analysis of the MODIS-derived cloud droplet size, J. Atmos. Sci., 67 (2010), pp , doi: /2009JAS ) Nagao, T. M., K. Suzuki and T. Y. Nakajima: Interpretation of multiwavelength-retrieved droplet effective radii for warm water clouds in terms of in-cloud vertical inhomogeneity by using spectral bin microphysics cloud model, J. Atmos. Sci., 70 (2013), pp ) Sato, Y., T. Y. Nakajima and T. Nakajima: Investigation of the vertical structure of warm cloud microphysical properties using the cloud evolution diagram, CFODD, simulated by three-dimensional spectral bin microphysical model, Journal of the Atmospheric Sciences, 69 (2012), pp Tn_24