Nobuyuki Kikuchi

Absorption of solar radiation by stratocumulus clouds: Aircraft measurements and theoretical calculations

Abstract Aircraft observations of shortwave radiative properties of stratocumulus clouds were carried out over the western North Pacific Ocean during January 1991. Two aircraft were equipped with a pair of pyranometers and near-infrared pyranometers. Downward and upward shortwave fluxes above and below the cloud were synchronously measured by two aircraft. The cloud radiative properties, especially the absorptance obtained from measurements, were compared with those calculated. Aircraft measurements and Monte Carlo calculations showed that spatial inhomogeneities of clouds cause horizontal radiative convergence and divergence, and that vertical radiative convergence-that is, absorptance with a usual definition-apparently becomes extremely large or negative. The apparent absorptance could be corrected by a method that evaluates the true absorption from the difference between the apparent visible and near-infrared absorptions. The corrected absorptance agreed well with the theoretical absorptance calculated...

Comparison of Cloud-Screening Methods Applied to GOSAT Near-Infrared Spectra

Several existing and proposed satellite remote sensing instruments are designed to derive concentrations of trace gases, such as carbon dioxide (CO2) and methane (CH4), from measured spectra of reflected sunlight in absorption bands of the gases. Generally, these analyses require that the scenes be free of cloud and aerosol, necessitating robust screening algorithms. In this work, two cloud-screening algorithms are compared. One applies threshold tests, similar to those used by the MODerate resolution Imaging Spectrometer (MODIS), to visible and infrared reflectances measured by the Cloud and Aerosol Imager aboard the Greenhouse gases Observing SATellite (GOSAT). The second is a fast retrieval algorithm that operates on high-resolution spectra in the oxygen A-band measured by the Fourier Transform Spectrometer on GOSAT. Near-simultaneous cloud observations from the MODIS Aqua satellite are used for comparison. Results are expressed in terms of agreement and disagreement in the identification of clear and cloudy scenes for land and non-sun glint viewing over water. The accuracy, defined to be the fraction of scenes that are classified the same, is approximately 80% for both algorithms over land when comparing with MODIS. The accuracy rises to approximately 90% over ocean. Persistent difficulties with identifying cirrus clouds are shown to yield a large fraction of the disagreement with MODIS.

Investigation of GOSAT TANSO-CAI Cloud Screening Ability through an Intersatellite Comparison

AbstractIn this work, the Greenhouse Gases Observing Satellite (GOSAT) Thermal and Near-infrared Sensor for Carbon Observation–Cloud and Aerosol Imager (TANSO-CAI) cloud screening results, which are necessary for the retrieval of carbon dioxide (CO2) and methane (CH4) gas amounts from GOSAT TANSO–Fourier Transform Spectrometer (FTS) observations, are compared with results from Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS) in four seasons. A large number of pixels, acquired from both satellites with nearly coincident locations and times, are extracted for statistical comparisons. The same cloud screening algorithm was applied to both satellite datasets to focus on the performance of the individual satellite sensors, without concern for differences in algorithms. The comparisons suggest that CAI is capable of discriminating between clear and cloudy areas over water without sun glint and also may be capable of identifying thin cirrus clouds, which are generally difficult to detect without therma...

GOSAT higher level product status 1.5 year after the launch

After the 1.5 year operation of GOSAT (Greenhouse gases Observing SATellite), NIES GOSAT DHF (GOSAT Data Handling Facility of National Institute for Environmental Studies) has been producing CAI Level 1B and 1B+, FTS/CAI Level 2, and FTS/CAI Level 3 products, receiving FTS Level 1A/1B and CAI Level 1A data from JAXA (FTS: Fourier Transform Spectrometer; CAI: Cloud and Aerosol Imager; JAXA: Japan Aerospace Exploration Agency). In addition to the higher level data processing, GOSAT DHF has the following additional roles: 1) Data archive and distribution, and 2) Observational request collection from users and their submission to JAXA. After calibration and preliminary validation, the processed data are distributed to RA users at the first stage (RA users: researchers engaging in Research Announcement). The processed data are validated and then they are distributed to General Users (GU). All the distribution is carried out through the GOSAT User Interface Gateway (GUIG). As of August 2010, the total number of user registration exceeds 900, and a large number of products are distributed both to the RA users and GU. At this moment, validation indicates that the FTS SWIR L2 CO2 and CH4 data show slightly lower values than validation results. Therefore, further improvement of the algorithm is planned as a next version of the FTS L2 products. FTS TIR data processing is still on the way.

The possibility of SGLI/GCOM-C for global environment change monitoring

The Third Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) concluded that many collectiveobservations gave a aspect of a global warming and other changes in the climate system. It is very important to understand thisprocess accurately, and to construct the model by whom an environmental change is accurately forecast. Future earthobservation using satellite data should monitor global climate change, and should contribute to social benefits. Especially, human activities has given the big impacts to earth environment. This is a very complex affair, and nature itself also impacts the clouds,namely the seasonal variations. JAXA (former NASDA) has the plan of the Global Change Observation Mission (GCOM) formonitoring of global environmental change. SGLI (Second Generation GLI) onboard GCOM-C (Climate) satellite, which is one of this mission, is an optical sensor from Near-UV to TIR. SGLI can provide the various high accuracy products of aerosol, cloud information, various biophysical parameters (Biomass, Land Cover, Albedo, NPP, Water Stressed Vegetation, LST, etc.), coastal information (CDOM, SS, PAR, CHL, SST, etc.), and cryospheric information (Albedo, Snow/Ice Cover, NDII, Sea ice type, Snow Grain Size, NDSI, Snow Surface Temperature, etc.). This paper shows the introduction of the unique aspects and characteristics of the next generation satellite sensor, SGLI/GCOM-C, and shows the preliminary research for this sensor.

Update of the GOSAT higher level products 2.5 years after the launch

During the 2.5 year operation of GOSAT (Greenhouse gases Observing SATellite), NIES GOSAT DHF (GOSAT Data Handling Facility of National Institute for Environmental Studies) has been producing CAI Level 1B and 1B+, FTS Level 2 SWIR (column amount of CO2 and CH4), CAI Level 2 (cloud flag ), FTS Level 3 products (global map of XCO2, XCH4,) and CAI Level 3 (global radiance and global reflectance), receiving FTS Level 1A/1B, and CAI Level 1A data from JAXA (FTS: Fourier Transform Spectrometer; CAI: Cloud and Aerosol Imager, JAXA: Japan Aerospace eXploration Agency). In addition, FTS Level 2 TIR are released to RA users. FTS Level 3 TIR, and Level 4A and 4B products (emission/absorption and 3D global distribution of CO2) will be released in the near future.. Since the FTS Level 2 SWIR data have been accumulated more than 2.5 years, global trends can be observed, while the validation results show negative bias of 2 to 3 % for CO2 and negative bias of 1 to 2 % for CH4. A comparison with the results by different algorithms is also conducted. All the mentioned data products are distributed through a website, the GOSAT User Interface Gateway (GUIG), and users of the products include the researchers responded to the three Research Announcements (RA) and general users (GU). Number of the RA users exceeds 100 and that for GU exceeds 1100.

Validation of Two MODIS Aerosols Algorithms with SKYNET and Prospects for Future Climate Satellites Such as the GCOM-C/SGLI

Potential improvements of aerosols algorithms for future climate-oriented satellites such as the coming Global Change Observation Mission Climate/Second generation Global Imager (GCOM-C/SGLI) are discussed based on a validation study of three years’ (2008–2010) daily aerosols properties, that is, the aerosol optical thickness (AOT) and the Angstrom exponent (AE) retrieved from two MODIS algorithms. The ground-truth data used for this validation study are aerosols measurements from 3 SKYNET ground sites. The results obtained show a good agreement between the ground-truth data AOT and that of one of the satellites’ algorithms, then a systematic overestimation (around 0.2) by the other satellites’ algorithm. The examination of the AE shows a clear underestimation (by around 0.2–0.3) by both satellites’ algorithms. The uncertainties explaining these ground-satellites’ algorithms discrepancies are examined: the cloud contamination affects differently the aerosols properties (AOT and AE) of both satellites’ algorithms due to the retrieval scale differences between these algorithms. The deviation of the real part of the refractive index values assumed by the satellites’ algorithms from that of the ground tends to decrease the accuracy of the AOT of both satellites’ algorithms. The asymmetry factor (AF) of the ground tends to increase the AE ground-satellites discrepancies as well.

Retrieval of precipitable water using ADEOS-II/GLI near infrared data

Retrieval of vertically integrated water vapor amount (precipitable water) is proposed using near infrared channels of Global Imager onboard Advanced Earth Observing Satellite-II (GLI/ADEOS-II). The principle of retrieval algorithm is based upon that adopted with Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Earth Observing System (EOS) satellite series. Simulations were carried out with GLI Signal Simulator (GSS) to calculate the radiance ratio between water vapor absorbing bands and non-absorbing bands. As a result, it is found that for the case of high spectral reflectance background (a bright target) such as the land surface, the calibration curves are sensitive to the precipitable water variation. It turns out that aerosol loading has little influence on the retrieval over a bright target for the aerosol optical thickness less than about 1.0 at 500 nm wavelength. A preliminary analysis of GLI data was also carried out and the retrieved result is discussed. It is also anticipated that simultaneous retrieval of the water vapor amount using GLI data along with other channels will lead to improved accuracy of the determination of surface geophysical properties, such as vegetation, ocean color, and snow and ice, through the better atmospheric correction.

Cloud sciences using satellite remote sensing, cloud growth model, and radiative transfer.

In recent years, it has been revealed that the cloud microphysical properties such as cloud particle radii obtained from satellite remote sensing were of apparent values. A combined use of passive and active sensor has gradually revealed about what we observed using passive imager thorough the vertical information of clouds obtained from active sensors. For understanding the process of cloud growth in nature, model that simulates cloud droplet growth is also needed. Observation results obtained from the satellite remote sensing are used for validating model such as cloud resolving model and spectral-bin microphysics cloud model. Vice-versa, models are used for understanding the process that are hidden in satellite-remote sensing results. We are aiming consistent understanding of clouds with observation and modeling. In this paper, we will introduce a preliminary result of multi-sensor view of warm water clouds and we will review our research strategy of cloud sciences, using satellite remote sensing, the cloud growth model, and the radiative transfer.

GOSAT higher level product status more than 1.5 years after the launch and planned improvement

After the 1.5 year operation of GOSAT (Greenhouse gases Observing SATellite), NIES GOSAT DHF (GOSAT Data Handling Facility of National Institute for Environmental Studies) has been producing CAI Level 1B and 1B+, FTS/CAI Level 2 and FTS/CAI Level 3 products, receiving FTS Level 1A/1B, and CAI Level 1A data from JAXA ( FTS: Fourier Transform Spectrometer; CAI: Cloud and Aerosol Imager; JAXA: Japan Aerospace Exploration Agency). In addition to the higher level data processing, GOSAT DHF has several additional roles 1) observation request collection from users and their submission to JAXA 2) Data archive and 3) Data Distribution. After the calibration and preliminary validation, processed data are distributed to RA researchers at first stage. Then, after the validation, they are distributed to General users. All the distribution is through GOSAT User Interface Gateway (GUIG). As of May of 2010, total number of user registration exceeds 800, and a large number of products are distributed both to the RA researchers and to General Users. At this moment, validation indicates that the FTS L2 SWIR CO2 and CH4 data show slightly lower values than validation results. In addition, very high XCO2 values, which seemed caused by aerosol, appeared on some desert area. But, further improvement of the algorithm was conducted as version 01.XX of FTS L2 products. Preliminary FTS TIR L2 processing is being conducted and TIR processing is starting. In addition to the L2 products, some Level 3 products are going to be released: FTS L3 global distribution of CO2 and CH4, and CAI L3 global radiance distribution.