ACG09-P01

Room:Convention Hall

Time:May 27 18:15-19:30

Cloud-top Height Esimation Method by Geostationary Satellite Split-Window Measurements Trained with CALIPSO dataNISHI, Noriyuki1∗ ; HAMADA, Atsushi2 ; HIROSE, Hitoshi3
1Science Faculty, Fukuoka University, 2 AORI, University of Tokyo, 3 CEReS, Chiba University

We released a database of cloud top height and visible optical thickness (CTOP) with one-hour resolution over the tropical western Pacific and Maritime Continent, by using infrared split-window data of the geostationary satellites (MTSAT)(http://database.rish.kyoto-u.ac.jp/arch/ctop/). We made lookup tables for estimating cloud top height only with geostationary infrared observations by comparing them with the direct cloud observation by CloudSat (Hamada and Nishi, 2010, JAMC).
We picked out the same-time observations by MTSAT and CloudSat and regressed the cloud top height observation of CloudSatback onto 11 micro m brightness temperature (Tb) and the difference between the 11 micro m Tb and 12 micro m Tb of MTSAT.
The database contains digital data and quick look images from Jul 2005 to real time and the area in 85E-155W (MTSAT2) and20S-20N.
Though the CTOP dataset is particularly useful for the upper tropospheric clouds, it has one serious problem. The cloud radar
onboard CloudSat cannot well detect the optically thin cirrus clouds composed of small ice crystals and misses a certain part ofcirriform clouds in the upper troposphere. In order to overcome this weakness, we are now making next version of the CTOP
by using the lidar data (CALIOP) onboard CALIPSO satellite. One problem on the use of lidar observation is that they observe
very thin cirrus formed around the tropopause. The main purpose of CTOP dataset is to provide the top height of clouds thatoriginate from cloud clusters including cumulonimbus and nimbostratus, not of in-situ cirrus clouds formed near the tropopause.
To exclude the very thin tropopause cirrus, we define cloud-top height of CALIOP observation as the height at which the opticaldepth accumulated from the cloud top is 0.2, instead of the CALIOP cloud top itself. With this criterion we can succeed inestimating the top height of cirruiform clouds, but it has another problem for thick clouds like cumulonimbus. For such clouds,
the height of accumulated optical depth 0.2 is considerably lower than the real cloud top, possibly due to rather small numberof large cloud particles near the top. Therefore, the estimation using CloudSat data is closer to the real top for the thick clouds,
while that using CALIOP data is closer for cirriform clouds. So we are now making a lookup table with using both CloudSat andCALIPSO data to estimate cloud-top heights both for thick and thin clouds seamlessly.

Keywords: cloud top, geostationary satellite, split window, cirrus, tropical meteorology

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Room:Convention Hall

Time:May 27 18:15-19:30

Improvement of GSMaP with multi-channel geostationary meteorological satellite observation for oceanic precipitation
HIROSE, Hitoshi1∗ ; HIGUCHI, Atsushi1 ; MEGA, Tomoaki2 ; TOMOO, Ushio2 ; YAMAMOTO, Munehisa K.3 ;SHIGE, Shoichi3 ; HAMADA, Atsushi41

CEReS, Chiba University, 2 Department of Engineering, Osaka University, 3 Department of Science, Kyoto University, 4 AORI,The University of Tokyo

The Global Satellite Mapping of Precipitation (GSMaP) produces accurate precipitation data with high time and spatial resolution (per 1hour, 0.1 degree) by utilizing the satellite microwave radiometer. At the time and place which all microwave radiometer
satellites are not available, the GSMaP estimates where the precipitation area observed before that time will moves by using acloud moving vector retrieved from the infrared brightness temperature (IR Tb) observed by the geostationary meteorologicalsatellite (GMS). However this method has some possibility of mistaking a destination of precipitating cloud with vertical shearof environmental wind, and uses only IR1 channel of the GMS observation to calculate the cloud moving vector. Therefore, we
made a new data which can estimate precipitation probability globally with high temporal and special resolution by using IR1and water vapor (WV) channel of GMS observation, called precipitation potential map (PPM), and then improved the accuracyof GSMaP precipitation areas by utilizing the PPM (JpGU meeting, 2014).
Since it is difficult to distinguish small precipitating cumulus from non-precipitating stratus only with cloud top height information obtained from IR1 and WV channels, the past PPM has low accuracy of estimating precipitation area for low cloud.
Therefore this study first tried to improve the accuracy of PPM for low cloud by adding multi-channel information obtainedfrom Meteosat Second Generation (MSG2). The utilization of GMS multi-channel observation is important from the point ofview of preparing next-generation GMS, Himawari-8, Himawari-9, GOES-R series, and Meteosat Third Generation. Next weincluded the modified PPM into GSMaP precipitation detection algorithm to improve GSMaP precipitation area and precipitation
intensity product, and investigated the accuracy of modified GSMaP precipitation product by comparing them with precipitation
radar (PR) of Tropical Rainfall Measurement Mission (TRMM) as it is truth. We will intend to explain the result of case studyof tracking precipitation system over the ocean under vertical wind shear with modified and non-modified GSMaP. In these areasand conditions, we can expect that the GSMaP estimates the precipitation area more accurately by utilizing the potential map. Inthese circumstances, we can expect that the GSMaP precipitation estimation becomes more accurate by utilizing the PPM.
We used five geostationary satellites, MSG2, METEOSAT, GOES-West, GOES-East, and MTSAT-1R. All geostationary satellite data is released from the Center for Environmental Remote Sensing, Chiba University (CEReS). Global Satellite Mapping ofPrecipitation (GSMaP) MVK and GSMaP NRT (v6.000.0) was used as satellite observation of precipitation with the microwavesensor. The GSMaP products are produced by Earth Observation Research Center (EORC) in Japan Aerospace Exploration
Agency (JAXA). We used near surface rain observed by the precipitation radar of the Tropical Rainfall Measurement Mission(TRMM PR; 2A25, V7) as truth.
Keywords: geostationary meteorological satellite, precipitation, GSMaP

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ACG09-P03

Room:Convention Hall

Time:May 27 18:15-19:30

A greenhouse gas retrieval algorithm for GOSAT TANSO-FTS SWIR using polarization
information
KIKUCHI, Nobuhiro1∗ ; YOSHIDA, Yukio1 ; UCHINO, Osamu1 ; MORINO, Isamu1 ; YOKOTA, Tatsuya1
1

National Institute for Environmental Studies

TANSO FTS is a Fourier transform spectrometer onboard the Greenhouse Gases Observing Satellite (GOSAT), which is inorbit after the launch in January 2009. TANSO-FTS measures two orthogonal polarizations of solar backscattered spectra at threenarrow bands in the short wave infrared (SWIR). It is expected that by using the polarization information, undesirable effects ofcloud and aerosols on greenhouse gas retrievals are corrected more effectively, and the accuracy of the retrievals is improved. So
far, no retrieval algorithm has been realized which uses polarization information of TANSO-FTS. In this study, for the first time,
we present retrieval results of column-averaged concentrations of carbon dioxide (XCO2 ) from polarized TANSO-FTS SWIRspectra.
Accurate radiometric calibration of the two polarized spectra is one of the most crucial factors for successful retrievals fromTANSO-FTS spectra. With a simulation study conducted under the idealized situation that there is no calibration error in calibration coefficients of TANSO-FTS, we showed that the polarization information increases the information content of the aerosols
and reduces retrieval errors in XCO2 . In fact, degradation of the sensor is not avoidable. Unless the calibration coefficientsare evaluated after the launch with sufficient accuracy, polarization information of TANSO-FTS will not improve the XCO2
retrievals.
In this study, retrievals of XCO2 were compared using several calibration coefficients evaluated in different methods. The figure shows XCO2 retrievals from TANSO-FTS measurements over land in June 2011. Blue dots in the figure are results obtainedwith calibration coefficients evaluated from vicarious calibration reported by Kuze et al. (TGRS, 2014). We also tried to evaluate
calibration coefficients from the solar calibration data by analyzing polarization properties of the solar diffuser panel. Retrievalsof XCO2 with the solar diffuser calibration are plotted by red dots in the same figure. Comparing these results obtained with twodifferent calibration coefficients, we found that the values of XCO2 with the vicarious calibration tend to scatter toward a low
concentration in the Sahara desert. On the other hand, this tendency is hardly seen in XCO2 retrievals with the solar diffuser calibration. Also, some high XCO2 regions are observed in South America and Southern Africa in the retrievals with the vicarious
calibration, which are not seen in the retrievals with the solar diffuser calibration.
Our result indicates that the calibration coefficients make a marked difference in retrieved XCO2 if the polarization information
is used. We plan to analyze more observations, and to try another calibration technique, such as ocean glint observations.

Keywords: satellite observation, carbon dioxide, GOSAT1/1

ACG09-P04

Room:Convention Hall

Time:May 27 18:15-19:30

Evaluation of Satellite-Borne Radar, Lidar, and Imager Algorithm for Retrieval of Cloud
Microphysical Properties
HAGIHARA, Yuichiro1∗ ; OKAMOTO, Hajime1
1

Research Institute for Applied Mechanics, Kyushu University, Fukuoka, JapanWe developed algorithm for the retrieval of effective radius (Ref f ) and cloud water content (CWC) of clouds by using collocated CloudSat 94 GHz cloud radar, CALIPSO lidar and MODIS imager. Main aim of the study is to evaluate uncertainties of thealgorithm. The radar and lidar retrieval algorithm was initially developed by Okamoto et al., (2010) for ice cloud region detectedby radar and lidar. And Sato and Okamoto (2011) extended the range of applicability to the ice regions detected radar or lidar.
Then Okamoto et al., (2014) further extended the algorithm by introducing optical thickness (τ vis ) information from MODIS
that can be applicable to both ice and water clouds, and rainy regions detected radar or lidar. Here RL and RLI denote radar orlidar algorithm and radar/lidar with τ vis algorithm from imager (RLI). Major source of uncertainties in the RL is the treatmentof radar only detected clouds and precipitation where lidar signal is totally attenuated and we introduced empirical formula inradar-only region derived from ground-based Doppler cloud radar observations.
In this presentation, cloud microphysics of convective clouds was analysed in September 10, 2006 over the Pacific Ocean. We
examined the vertical distribution of Ref f , CWC, τ vis as well as cloud water path (CWP). By using τ vis as a constraint, Ref f is~ 50 µm smaller than RL results and ~ 300 µm smaller than RL results in the water cloud region below ~ 5 km. Responding
to this trend, IWC was 0.5 g/m3 larger and LWC was 0.01 g/m3 larger compare to the RL ones. We also compared τ vis and CWP
between from MODIS, RL, and RLI.
Instead of retrieved τ vis , MODIS reflectance was also combined with RL and we examined the uncertainties in the both versions of RLI algorithms, due to the possible variability of ice particle shape and orientations.
In 2017, the joint European and Japanese satellite mission EarthCARE, which will carry a Doppler cloud radar, high-spectral
resolution lidar, multi-spectral imager, and broadband radiometer, is scheduled to launch. We discuss how to use Doppler information to reduce the retrieval errors. The algorithm described above will be adapted to the standard algorithm.
Keywords: synergy, radar, lidar, imager, cloud microphysics

1/1ACG09-P05

Room:Convention Hall

Time:May 27 18:15-19:30

Current status and development of Cloud analysis algorithms based on EarthCARE/MSI
observation
TAKAGI, Seiko1∗ ; NAGAO, Takashi2 ; ISHIDA, Haruma3 ; HUSI, Letu1 ; NAKAJIMA, Takashi11Tokai University, Research and Information Center, 2 JAXA/EORC, 3 Yamaguchi University

Clouds and aerosols are the major uncertainty in the understanding of the Earth’s climate system. An improvement of understanding and better modeling of the relationship of clouds, aerosols and radiation are therefore prominent part in climate research
and weather prediction. It is important to obtain the global data of clouds and aerosols occurrence, structure and physical properties that are derived from measurements of solar and thermal radiation.
EarthCARE (Earth Clouds, Aerosols and Radiation Explorer) is one of the future earth observation mission of ESA and JAXA.
This mission aims at understanding of the role that clouds and aerosols play in reflecting incident solar radiation back into space
and trapping infrared radiation emitted from Earth’s surface. These observations are needed to improve the precision of climate
variability prediction.
The mission will achieve the objectives by measuring the vertical structure and horizontal distribution of clouds and aerosols
globally. The satellite will carry four instruments for observations of clouds and aerosols; Atmospheric Lidar (ATLID), Cloud
Profiling Rader (CPR), Multi-Spectral Imager (MSI) and Broad-Band Radiometer (BBR). MSI provides across-track information on clouds and aerosols with channels in the visible, near infrared, shortwave and thermal infrared. Two products based on
CLAUDIA [Ishida and Nakajima, 2009] and CAPCOM [Nakajima and Nakajima, 1995; Kawamoto et al., 2001] are advancedin this study. In this presentation, current status and development of algorithms will be introduced.
Keywords: EarthCARE, MSI, cloud, aerosol

1/1ACG09-P06Room:Convention Hall

Time:May 27 18:15-19:30

Early phase retrieval of aerosol optical characteristics by Himawari-8
FUKUDA, Satoru1∗ ; OKI, Riko1
1

Japan Aerospace Exploration Agency / Earth Ovservation Research Center

Himawari-8 is a geostationary meteorological satellite launched in October 2014 by. It equips an imager, called Advanced
Himawari Imager (AHI). Himawari-8/AHI is a latest imager as a geostationary satellite. For example, AHI has 16 bands fromvisible (0.47um) to thermal infrared (13.3um) Moreover, AHI can observe East Asia, South-East Asia, Oceania, and West Pacificarea as often as 10 min. The resolutions of sensors are as fine as 0.5km for 0.64um, 1.0km for 0.47um, 0.51um, and 0.86um, and2.0km for 1.6um to 13.3um. These high resolution and high frequent observation are very unique for geostationary satellite. We
are selected as ”Himawari-8’s data quality evaluator” by JMA, and now JAXA is receiving Himawari-8’s data.
In this study, we have retrieved aerosol characteristics from Himwari-8’s data. As a package of aerosol retrieval, we used
REAP (Higurashi and Nakajima, 1999), and retrieved aerosol optical thickness and Angstrom Exponent over the ocean. We needto assume some of the parameters of aerosol in the satellite remote sensing when we make Look Up Table. We have assumed
bimodal-lognormal distribution as a size distribution. Mode radii and standard deviations are cited by Fukuda et al (2013)’svalue. mode radius for coarse mode is 3.86um and that for fine mode is 0.148um. Standard deviation for coarse mode is 2.0 andthat for fine mode is 1.56. Complex refractive indexes are calculated from AERONET (Aerosol Robotic Network) observations.
We used AERONET data in Anmyon (126.330E, 36.539N), Baengnyeong (124.630E, 37.966N), Fukuoka (130.475E, 33.524N),
Gangneung WNU (128.867E, 37.771N), Shirahama (135.357E, 33.697N), and Yonsei University (126.935E, 37.564N), and we
calculated the average value; 1.51 + i∗0.0226 for 0.64um and 1.53 + i∗0.0233 for 0.86um. As a threshold of cloudiness, weapplied CLAUDIA’s threshold test concept. (Ishida et al., 2009) However, some of the thresholds are tuned for Himawari-8. We
will also compare our result with ground truth obtained by AERONET.

Keywords: Himawari-8, aerosol

1/1ACG09-P07

Room:Convention Hall

Time:May 27 18:15-19:30

Cloud detection algorithm using TIR spectra for improving gas retrievals from GOSAT
data
SOMEYA, Yu1∗ ; IMASU, Ryoichi1 ; SHIOMI, Kei2 ; SAITOH, Naoko3
1

Atmosphere and Ocean Research Institute, the University of Tokyo, 2 Japan Aerospace Exploration Agency, 3 Center of Environmental Remote Sensing, Chiba University
Thermal And Near-infrared Sensor for Observation (TANSO) onboard Greenhouse gases Observing SATellite (GOSAT) consists of Fourier Transform Spectrometer (FTS) and Cloud and Aerosol Imager (CAI). Greenhouse gas concentrations are retrieved
from the shortwave infrared (SWIR) bands and the thermal infrared (TIR) for scenes judged to be cloud and aerosol free throughthe cloud screening procedure with CAI observations. However, CAI does not operate during nighttime although TIR data canbe obtained. Moreover, it has no sensitivity for cloud and aerosol heights which must be measured to decrease the currently reported gas concentration biases. Therefore, we developed an algorithm to detect optically thin clouds and dust aerosols with theirheights from TIR data. The algorithm used in this study was based on a cirrus detection technique called CO2 slicing method,
modified as described below. The weighting functions which represent sensitivity profiles were calculated at each channel in
the TIR band of GOSAT. The channels were reconstructed as sets of several spectral channels for each height level based on thepeak heights of the weighting functions. Subsequently, the channel combinations were optimized based on simulation studies forseveral temperature profile patterns for each latitude and temperature at 500 hPa. The observed data were analyzed using these
optimized channels. Global tropospheric cloud amounts and cloud properties such as cloud top heights and optical thickness
were validated using CALIPSO data.
Monthly mean cloud amounts from GOSAT data were compared with those from CALIPSO. Results show some differencesof cloud amounts and heights between GOSAT and CALIPSO, which might be caused by surface temperature biases, the difference of sensitivity of sensors, the inverse layer, and marine stratocumulus clouds. However, the horizontal distributions ofclouds derived using the slicing method resembled those obtained from CALIPSO and it was revealed that the slicing method
algorithm has high sensitivity compared with TIR threshold cloud screening which is currently adopted as the cloud screening
method for GOSAT data in nighttime. Cloud properties were also compared for coincident observation between GOSAT andCALISPO and the results showed that the accuracy of cloud detection is improved drastically by the new approach presented inthis study. Clouds with optical thickness less than 0.1 are detectable using this method. Based on these results, the slicing method
algorithm developed for this study seems to be useful for cloud screening. It is expected to improve the accuracy of greenhousegas observations.

Keywords: satellite remote sensing, greenhouse gas, cirrus

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