This PDF file contains the front matter associated with SPIE Proceedings Volume 8523, including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

To contribute to solving earth and environmental issues, particularly climate change mitigation and adaptation, Japan Aerospace Exploration Agency (JAXA) has developed and operated several types of earth observation remote sensing satellites starting with the Marine Observation Satellite-1 (MOS-1) in 1987. At the 2002 World Summit on Sustainable Development, the GEO (Group on Earth Observation) was proposed and established by the G8 (Group of Eight) leading industrialized countries. The GEO is constructing a Global Earth Observation System of Systems (GEOSS) on the basis of a 10-Year Implementation Plan for the period of 2005 to 2015. The Plan defines a vision statement for GEOSS, its purpose, scope, expected benefits, and the nine “Societal Benefit Areas” of disasters, health, energy, climate, water, weather, ecosystems, agriculture, and biodiversity. JAXA’s earth observation satellite program is expected to develop GEOSS, particularly the areas of climate, water, and disaster. This paper describes the outline of JAXA’s earth observation program including operating satellites [Greenhouse gas Observing SATellite (GOSAT), Tropical Rainfall Measurement Mission (TRMM), and Global Change Observation Mission-Water 1 (GCOM-W1)] as well as new generation satellites [Advanced Land Observing Satellite (ALOS)- 2/3, GCOM-C, Global Precipitation Measurement (GPM), Earth Cloud, Aerosol, and Radiation Explorer (EarthCARE) and GOSAT-2].

Global Precipitation Measurement (GPM) is poised to be the next generation precipitation monitoring system from space after the Tropical rainfall measurement (TRMM) mission. The GPM mission is centered on the deployment of a core observatory satellite with an active dual-frequency radar DPR, operating at Ku- and Ka- band. Two independent observations from DPR provide the possibility to retrieve two independent parameters from gamma drop size distribution (DSD), namely median volume diameter (D₀) and scaled intercept (N_W), at each resolution volume. Dual-frequency method proposed for the DPR radar can be formulated in terms of integral equations and the two DSD parameters D₀ and N_W can be estimated at each bin based on the assumed microphysical models of hydrometeors. One known error in the dual frequency retrievals is the dual-valued problem when retrieving D₀ from DFR for rain. Rose and Chandrasekar (2006)[1], remedied the bi-valued problem by assuming a linear model with height for D₀ as well as N_W (in log scale) in rain. The algorithm with the linear assumption was evaluated by Le et al. (2009) [2] based on the whole vertical profile including rain, melting ice, and ice region through a hybrid method. The hybrid method combines forward retrieval by Meneghini et al (1997) [3] in frozen and melting region and the linear assumption in rain region. The retrieval process uses recursive procedure to optimize DSD parameters at the bottom of rain by constructing the cost function along the vertical profile. This retrieval algorithm is applied to tropical storm Earl, a category 4 hurricane captured by APR-2 precipitation radar during the Genesis and Rapid Intensification Processes (GRIP) campaign in 2010.

In this presentation we will discuss the performance of classification and retrieval algorithms for spaceborne cloud and precipitation radars such as the Global Precipitation Measurement mission [1] Dual-frequency Precipitation Radar (GPM/DPR) [2], and notional radar for the Aerosol/Clouds/Ecosystem (ACE) [1] mission and related concepts. Spaceborne radar measurements are simulated either from Airborne Precipitation Radar 2^nd Generation (APR-2, [3]) observations, or from atmospheric model outputs via instrument simulators contained in the NASA Earth Observing Systems Simulators Suite (NEOS³). Both methods account for the three dimensional nature of the scattering field at resolutions smaller than that of the spaceborne radar under consideration. We will focus on the impact of nonhomogeneities of the field of hydrometeors within the beam. We will discuss also the performance of methods to identify and mitigate such conditions, and the resulting improvements in retrieval accuracy. The classification and retrieval algorithms analyzed in this study are those derived from APR-2’s Suite of Processing and Retrieval Algorithms (ASPRA); here generalized to operate on an arbitrary set of radar configuration parameters to study the expected performance of spaceborne cloud and precipitation radars. The presentation will highlight which findings extend to other algorithm families and which ones do not.

Current version of the over-land Global Satellite Mapping of Precipitation (GSMaP) algorithm for microwave sounder tends to underestimate rain areas because of missing warm rain due to scattering-based algorithm only applied over land. Therefore we develop a new rain/no-rain classification (RNC) method using channels such as 89, 150, 186 and 190 GHz to detect the warm rain. In order to estimate the performance of the revised RNC method, the AMSU-PR matched-up cases are used. The result shows that the shallow precipitation over land, which is missed by the original RNC method, is detected by the revised RNC method.

Rain retrieval algorithms from satellite-borne microwave radiometers (MWR) utilize lookup tables (LUTs) related between MWR brightness temperatures (Tbs) and rain intensity and databases about precipitation characteristics. Since LUT is generated to simulate Tbs from vertical rain profiles through a radiative transfer model, the accuracy of estimation in precipitation amount depends on the input vertical rain profiles. Some previous studies reported that underestimation of precipitation occurred for generated or reinforced rain systems by orography and over high elevation area. In order to improve the underestimation, orographic precipitation identification was applied to the Global Satellite Mapping of Precipitation (GSMaP) algorithm. Upward wind by topography and moisture convergence at near the surface calculated by a re-analysis data and a digital elevation map were utilized to identify areas in orographic precipitation, and a new LUT based on a warm rain case was constructed and applied to the GSMaP algorithm. In addition to the case, we examined representative vertical profiles in precipitation for above mentioned precipitation characteristics. Compared to the standard GSMaP product, clear improvement can be found for a orographic precipitation case affected by a typhoon in Taiwan.

Early warning systems can predict rainfall-induced landslides by comparing rainfall data with landslide rainfall thresholds. These systems are based on empirical rainfall thresholds defined using rain gauges data. Despite quantitative satellite rainfall estimates are currently available, limited research has compared satellite estimates and rain gauge measurements for the forecasting of possible landslide occurrence. In this work, we validate satellite estimates obtained for Italy by the NASA Tropical Rainfall Measuring Mission (TRMM) against rainfall measurements from the Italian rain gauge network (< 1950 rain gauges), in the period from 1 September 2009 to 31 August 2010. Using cumulative rainfall measurements/estimates, we: (i) evaluate the correlation between the rain gauge measurements and the satellite estimates in different morpho-climatological domains, (ii) analyse the distributions of the ground-based measurements and the satellite estimates using different statistical approaches, and (iii) compare rainfall events derived automatically from satellite and rain gauge rainfall series. We observe differences between satellite estimates and rain gauge measurements in different morpho-climatological domains. The differences are larger in mountain areas, and collectively reveal a complex relationship between the ground-based measurements and the satellite estimates. We find that a power law correlation model is appropriate to describe the relation between the two rainfall data series. We conclude that specific rainfall thresholds must be defined to exploit satellite rainfall estimates in existing landslide early warning systems.

Separation between rain echo and surface clutter is essential in the radar observation of rain from space. However, the separation between these becomes very difficult in the radar observation over high mountain areas where the range profile of surface clutter takes a complicated shape. It is expected that this separation problem would be solved by the use of a reliable, high resolution digital elevation model. This paper examines the feasibility of using a high resolution ASTER GDEM for the separation of rain echo and surface clutter. Using the height information of ASTER GDEM, a simulation of the range profile of the surface clutter is made by assuming triangle surface elements and Lambert's law. The simulation results are compared with some TRMM PR data. The comparison shows that the simulation produces a reasonable result in the nadir direction and, to some extent, at the antenna scan edges. Tough the occurrence is very small, there seems to be some areas where the accuracy of ASTER GDEM is not good.

A dual Ka-band radar system is developed by the Japan Aerospace Exploration Agency (JAXA) for the GPM DPR algorithm development. The dual Ka-radar system which consists of two identical Ka-band radars can measure both the specific attenuation and the equivalent radar reflectivity at Ka-band. Those parameters are important particularly for snow measurement. Using the dual Ka-radar system along with other instruments, such as a polarimetric precipitation radar, a windprofiler radar, ground-based rain measurement systems, the uncertainties of the parameters in the DPR algorithm can be reduced. The verification of improvement of rain retrieval with the DPR algorithm is also included as an objective. Observations using the dual Ka-radar system were performed in Okinawa Island, in Tsukuba, over the slope of Mt. Fuji, and in Nagaoka, Japan. In Okinawa Island, the performance of the measurement has been confirmed by rain observation. In Tsukuba, one radar was directed in vertical and the other was in slant direction. By this configuration, total attenuation in the melting layer was estimated. The objective of the Mt. Fuji experiment was to observe the melting layer. In Nagaoka, a lot of wet snow fell, and much data on the snow have been obtained. The main results are measured k-Ze relationships. For the rain, reasonable k-Ze relationship has been obtained. The feasibility of total attenuation in melting layer has been studied. Different k-Ze relationships have been obtained in snow observations.

Flooding in general, especially the urban flash flooding is one of the most destructive nature hazards. Rainfall estimates from radar network are often used as input to various hydrological models for further flood warning and mitigations. The X-band dual-polarization radar network developed by the United States National Science Foundation Engineering Research Center (NSF-ERC) for Collaborative Adaptive Sensing of the Atmosphere (CASA) has shown great improvement to radar based Quantitative Precipitation Estimation (QPE), through many years of experimental validation studies. QPE and rainfall nowcasting are important goals of CASA X-band dual-polarization radar networks. This paper presents an overview of CASA QPE and nowcasting methodology. In addition, 20 rainfall events collected from the Oklahoma test best during the past 3 years are used to evaluate the networked radar rainfall products. Cross validation with a gauge network using these 20 events’ data shows that the estimates of instantaneous rain rate, 5-minute,10- minute, and hourly rainfall have normalized standard error of about 47.57%, 40.03%, 34.61% and 24.78% , respectively, whereas a low bias of about -3.83%, -2.83%,-2.77% and -3.45% respectively. These evaluation results demonstrate great improvement compared to the current state-of-the-art. The paper also deals with the potential role of these highresolution rainfall products for flash floods warning and mitigation.

The Ku-/Ka-band, Doppler, scanning, polarimetric airborne radar, known as the Airborne Dual-Frequency Precipitation Radar (APR-2) has been collecting data since 2001 in support of many spaceborne instruments and missions aiming at the observation of clouds and precipitation (e.g., TRMM, AMSR-E, GPM, CloudSat, ACE). The APR-2 suite of processing and retrieval algorithms (ASPRA) produces Level 1 (L1) products, microphysical classification and retrievals, and wind intensity estimates. ASPRA was also generalized to operate on an arbitrary set of radar configuration parameters to study the expected performance of multi-frequency spaceborne cloud and precipitation radars such as the GPM DPR (Global Precipitation Measurement mission, Dual-Frequency Precipitation Radar) and a notional radar for the Aerosol/Clouds/Ecosystem (ACE) mission. In this paper we illustrate the unique dataset collected during the Global Precipitation Measurement Cold-season Precipitation Experiment (GCPEx, US/Canada Jan/Feb 2012). We will focus on the significance of these observations for the development of algorithms for GPM and ACE, with particular attention to classification and retrievals of frozen and mixed phase hydrometeors.

The Global Change Observation Mission (GCOMC)/Second Generation Global Imager (SGLI) is an passive optical radiometer for monitoring climate change, which is scheduled to launch in around 2014 by the Japan Aerospace eXploration Agency (JAXA). The SGLI sensor is an optical sensor capable of multi-channel observation at wavelengths from near-UV to thermal infrared. The SGLI consists of two radiometer instruments, the Visible and Near Infrared Radiometer (VNR) and the Infrared Scanner (IRS). SGLI-VNR is capable of observing polarized and non-polarized radiance. In the GCOMC satellite mission, cloud properties such as the cloud optical thickness, the effective particle radii, and the cloud top temperature will be retrieved from SGLI-VNR data. The International Satellite Cloud Climatology Project (ISCCP) cloud product will be produce and cloud inhomogeneity of the warm water cloud will be discussed. This is one of the new sciences of the GCOM-C satellite mission in terms of cloud sciences. Furthermore, ice crystal scattering database will be developed for ice cloud remote sensing.

Basic system design of dual-frequency (13.6/35.5 GHz) Precipitation SAR (PSAR) is described, which is based on the orbit parameters of the GPM core satellite. The designed PSAR requires the along-track antenna size of about 4 m at both frequencies which are about twofold (13.6 GHz) and four-fold increase (35.5 GHz) compared with the DPR antennas. Instead of this, along-track resolution could be drastically improved to about 0.7 km in comparison with that of GPM-DPR (5 km). It also has reasonable number of independent samples for incoherent averaging (50~70), and swath width (~180 km) with the cross-track resolution of 2.5 km. Effects of apparent beam shift and the beam smearing caused by the spread of raindrop terminal velocity spectrum, inherent problems in PSAR, are quantitatively studied using a large number of disdrometer samples. The along-track beam shift of rain echo could also be used to estimate path-averaged raindrop fall velocity. Finally various issues in the system development and usefulness of the PSAR are discussed.

Numerous space missions carrying a radar are presently envisioned, particularly to study tropical rain systems. Among those missions, BOITATA is a joint effort between Brazil (INPE/AEB) and France (CNES). The goal is to embark a Doppler radar with scanning possibilities onboard a low-orbiting satellite. This instrument should be implemented in addition to a Passive Microwave Radiometer (PMR) between 19 and 183 GHz, an improved ScaraB-like broadband radiometer, a mm/submm PMR and a lightning detection instrument. This package would be meant to document the feedback of the ice microphysics on the rain systems life cycle and on their heat and radiative budgets. Since the microphysics and the water and energy budgets are strongly driven by the dynamics, the addition of a Doppler radar with scanning possibilities could provide precious information (3D wind and rain fields). It would allow us to build a large statistics of such critical information over the entire tropics and for all the stages of development of the convection. This information could be used to better understand the tropical convection and to improve convection parameterization relevant for cloud and climate models and associated applications such as now-casting and risk prevention. The present work focuses on the feasibility to retrieve 3D winds in precipitating areas from such a radar. A simulator of some parts of the spaceborne radar is developed to estimate the precision on the retrieved wind field depending on the scanning strategies and instrumental parameters and to determine the best sampling parameters.

NASA’s Moderate Resolution Imaging Spectro-radiometer (MODIS) sensors have been observing the Earth from polar orbit, from Terra since early 2000 and from Aqua since mid 2002. MODIS is uniquely suited for characterization of aerosols, combining broad swath size, multi-band spectral coverage and moderately high spatial resolution imaging. By using MODIS data, many algorithms have showed excellent competence at the aerosol distribution and properties retrieval. However, in China, many regions are not satisfied with the dark density pixel condition. In this paper, aerosol optical depth (AOD) datasets (China Collection 1.1) at 1 km resolutions have been derived from the MODIS data using the Synergetic Retrieval of Aerosol Properties (SRAP) method over mainland China for the period from August 2002 to now, comprising AODs at 470, 550, and 660 nm. We compared the China Collection 1.1 AOD datasets for 2010 with AERONET data. From those 2460 collocations, representing mutually cloud-free conditions, we find that 62% of China Collection 1.1 AOD values comparing with AERONET-observed values within an expected error envelop of 20% and 55% within an expected error envelop of 15%. Compared with MODIS Level 2 aerosol products, China Collection 1.1 AOD datasets have a more complete coverage with fewer data gaps over the study region.

Southeast Asia is one of the biggest regions of biomass burning with forest fires and slash-and-burn farming. From the fire events, a large amount of air pollutants are emitted such as carbon monoxide (CO), nitrogen oxide (NO_x) and aerosol (black carbon; BC). Biomass burning generally causes not only local, but also transboundary air pollution, and influences the atmospheric environment in the world accordingly. However, impact of air pollutants’ emissions from large-scale fire in Southeast Asia is not well investigated compared to other regions such as South America and Africa. In this study, characteristics of the atmospheric environment were investigated with correlative analyses among several satellite data (MOPITT, OMI, and MODIS) and emission inventory (GFEDv3) in Southeast Asia from October 2004 to June 2008 on a monthly basis. As a result, it is suggested that the transboundary air pollution from the biomass burning regions occurred over Southeast Asia, which caused specifically higher air pollutants’ concentration at Hanoi, Vietnam in spring dry season.

A yellow dust event with moderate strength was observed on 9 April 2012 at Sendai in North part of Japan. Backward trajectory calculations with NOAA HYSPLIT showed the complex flow of aerosols into the North Japan. The sharp edge of this dust cloud was recognized by the data taken at several observatories, and the dust cloud conducted by low pressure system had heterogeneous structure, therefore very complicated interaction among aerosols may occur. Mie scattering lidar data was used to reproduce the radiative effect caused by this yellow dust event at Sendai with radiative transfer model. The results estimated every 15 minutes of radiative forcing at the top of the atmosphere and at the bottom of the atmosphere. The results show the slight warming effects < 6.5 W/m² during night time both at the top and the bottom of the atmosphere, and during day time the large cooling effects < 150 W/m² at the bottom and < 60W/m² at the top of the atmosphere.

The Arctic region is especially sensitive to climate change; meanwhile atmospheric aerosol is one of the largest uncertainties geophysical factors in climate modeling, calling for aerosol database in Arctic regions with sufficient temporal and spatial coverage. Satellite remote sensing is the best approach to obtain the aerosol information over the Arctic region, for which appropriate aerosol models are required. In this study, five distinctive aerosol models are classified using cluster analysis from Level 1.5 data collected in Aerosol Robotic Network (AERONET) sites. More than 14,000 cases are collected over 17 AERONET sites in Arctic region from 1995 to 2012. For each case, 23 parameters, representing either optical properties or size distribution patterns are input into cluster analysis after abnormal records and outliers are discarded and data of different attributes are standardized. Averaged properties in each cluster are obtained then and we extensively study the absorptive, scattering, and size distributive characteristics along with the temporal and spatial distributions for each model. Aerosol optical properties are carried out for each model using Second Simulation of a Satellite Signal in the Solar Spectrum - Vector (6SV) code and we conclude that our models are representative of the major aerosol properties in the Arctic region and can be utilized in the retrieval algorithms designed for this area.

The integrated precipitable water vapor (IPW) is characterized by strong spatial and temporal variability, especially over tropical regions where the troposhere is not purely in hydrostatic equilibrium (convection). As an evidence, the survey of water vapor distibution as permanently as possible is an important issue and should serve as inputs for tropical climate modelling. In this paper, we present an estimation of the IPV from ground­ ba,.sed GPS receivers, which we compare to radiosondes and microwave radiometer. The data used here were collected in the vicinity of French Polynesia University site, during eight years from 2001 to 2008. In addition, we also include the IPW calculated using Era-Interim reanalyses (ECMWF). The main purpose of this paper is to highlight precision, qualities and limitations of each method available on the Island of Tahiti. During wet periods, the radiosondes vertical profiles of water vapor show an efficient mixing of water vapor between the the boundary layer (below trade winds inversion at Tahiti) and the free troposphere. Thus the rainy event detection allows to better constrain the validity range of a model of the vertical distribution of water vapor, which is based on a pseudo-adiabatic saturated evolution of the temperature.

The use of JAXA’s next generation satellites, the EarthCARE and the GCOM-C, for observing overall cloud systems on the Earth is discussed. The satellites will be launched in the middle of 2010-era and contribute for observing aerosols and clouds in terms of climate change, environment, weather forecasting, and cloud revolution process study. This paper describes the role of such satellites and how to use the observing data showing concepts and some sample viewgraphs. Synergistic use of sensors is a key of the study. Visible to infrared bands are used for cloudy and clear discriminating from passively obtained satellite images. Cloud properties such as the cloud optical thickness, the effective particle radii, and the cloud top temperature will be retrieved from visible to infrared wavelengths of imagers. Additionally, we are going to combine cloud properties obtained from passive imagers and radar reflectivities obtained from an active radar in order to improve our understanding of cloud evolution process. This is one of the new techniques of satellite data analysis in terms of cloud sciences in the next decade. Since the climate change and cloud process study have mutual beneficial relationship, a multispectral wide-swath imagers like the GCOM-C SGLI and a comprehensive observation package of cloud and aerosol like the EarthCARE are both necessary.

The Cloud Profiling Radar (CPR) on EarthCARE satellite is the first spaceborne cloud profiling Doppler radar using Wband frequency in order to measure vertical velocity of clouds and rain. The EarthCARE/CPR has -35dBZ in sensitivity after 10km integration and less than 1 m/s in Doppler velocity measurement error. Because satellite velocity and beam width spread Doppler spectrum and coherency is low, the measurement error of Doppler velocity is increased. EarthCARE/CPR is the first Doppler radar, so we need to make simulation data for the algorithm development, but the simulation itself is difficult in order to take into account these effects. The current method is calculated 2-dimentional integration within illuminated area by antenna beam and hit by hit for all pulses, then it takes many computation times. We developed the new simple calculation method, which is calculated using integrated antenna pattern, then the computation time is decreased significantly. This paper is reported the comparison for, both methods.

This paper mainly discussed how to retrieve cloud liquid water over land using sensitivity of ΔTb_18.7 / ΔTb_36.5 (ratio of polarization differences of AMSR-E brightness temperature at frequency 18.7GHz and 36.5GHz) to cloud liquid water. The surface emissivity parameter Δ_18.7/Δε_36.5 is first estimated using AMSR-E brightness temperature, MODIS water vapor product, and 1-Dimension Microwave Radiative Transfer Model (1DMWRTM). And then, cloud liquid water over land is retrieved using AMSR-E brightness temperature, surface temperature, surface emissivity parameter Δε_18.7/Δε_36.5 and cloud top height with the help of a look up table built by 1DMWRTM. The retrieved cloud liquid water of this algorithm is validated by observation data from ground based ARM site. According to the validation, the correlation of the two is about 0.65, and the RMSE is about 0.14mm.

The classification of precipitating cloud systems over Thailand was attempted by using radar reflectivity and Multifunctional Transport Satellites (MTSAT) infrared brightness temperature (TBB) data. The proposed method can classify the convective rain (CR) area, stratiform rain (SR) area and non-precipitation area such as cumulus and cirrus cloud by applying an integrating analysis of rain gauge, ground-based radar and geostationary satellite data. Since the present study focuses on precipitation, the classified results of precipitation area are used to estimate quantitative precipitation amount. To merge different rainfall products, the bias between the products should be removed. The bias correction method is used to estimate spatially varying multiplicative biases in hourly radar and satellite rainfall using a gauge and radar rainfall product, respectively. An extreme rain event was selected to obtain the multiplicative bias correction and to merge data set. Correlation coefficient (CC), root mean square error (RMSE) and mean bias are used to evaluate the performance of bias correction method. The combined radar-MTSAT method is a simple and useful method. This method has been successfully applied to merge radar and gauge rainfall for hydrological purpose.

Ground-based, low-cost, uncooled infrared imagers are specially calibrated and deployed for long-term measurements of spatial and temporal cloud statistics. Measurements of cloud optical depth are shown for thin clouds, and validated with a dual-polarization cloud lidar. Good comparisons are achieved for thin clouds having 550-nm optical depth of 3 or less.

The present understanding of cirrus microphysical property climatology is limited, which is an important key for better understanding the earth radiation budget and climate. An algorithm using three infrared window bands of Moderate Resolution Imaging Spectroradiometer (MODIS) (bands 29, 31 and 32) has been developed for retrieval of cirrus radiative and microphysical properties. We have developed a semi-analytical formula of the brightness temperature, which represents the dependence of the infrared signal on atmospheric and surface parameters. The accuracy of the approximation is about 0.33 K in band 29 and 0.17 K in bands 31 and 32, with significant correlations between the errors in each band. The solution for the inverse problem is from an optimal estimation based on the maximum a posteori, where prior information, measurement noise and modeling error are taken into account. As known from previous studies, the infrared method is sensitive to the surface temperature and cloud top temperature, which should be given with high accuracy. Sensitivity tests for the brightness temperature and retrieval error analysis showed that compared to the twoband split-window method, the three-band retrieval is capable of reducing the retrieval errors in optical thickness (τ) for optically thin cirrus (τ< 1) and in effective particle radius (r_e) for very small particle sizes (r_e < 5 μm). In general cases, the three-band retrieval is better to stably obtain the cirrus cloud properties with higher accuracy.

The AVI method can detect the dust and sandstorms (DSS) in satellite images both at daytime and night. The aerosol vapor index (AVI) is defined as AVI=T12-T11, where T12 and T11 are the brightness temperatures at 12μm and 11μm wave lengths, respectively. The fault of AVI method is to mistake thick clouds for DSS rarely. Iino et al. (2002, 2004) proposed the composite color images for discriminating DSS from clouds in daytime NOAA-AVHRR images. In this paper, Terra/Aqua-MODIS data are used. First, it is explained that usual clouds bring the effect of AVI<0, and the clouds with very large optical thickness and very large particle size may bring the effect of AVI<0, by using the BTD vs. T11 charts of Inoue (2006), where BTD=-AVI. Examples of the cloud images of AVI<0 are shown and interpreted using the AVI vs. T11 scatter chart. Next, the views of objects (DSS, usual ice-cloud, usual water-cloud, ice-cloud with large optical thickness, water-cloud with large optical thickness, snow field and ice, land, sea) in the single-band images (bands 1, 3, 4, 6, 7 and T11(band31), T12(band32)) and the band-difference images (band1-band3, band4-band3, band6- band1, band7-band1, AVI) are examined. The good composite color images which can discriminate DSS from clouds etc. are {R,G,B=AVI, band7-band1, T11} and {R,G,B=AVI, band4-band3, T11} for daytime images, and {R,G,B=T11, AVI, none} for nighttime images.

Aerosol retrieval algorithm of EarthCARE/MSI is divided into two parts. One is ocean algorithm, and the other is land algorithm. To retrieval over ocean, two-channel method of band1 and band2 is used. To retrieve aerosol over land, we need to estimate ground reflectance. One idea to estimate the ground reflectance is to make MSI's original albedo product by choosing minimum reflectance data of MSI. But, the swath of MSI is as narrow as 150km. It is difficult to gather enough radiance data to make ground reflectance. Another idea is to extrapolate ground reflectance on larger wavelength to the ground reflectance on visible wavelength. We did example analysis with several methods and coefficients. Minimum reflectance method of 0.38 μm and that of 0.68 μm shows close results. On the other hand, Modified Kaufman method shows larger aerosol optical thickness pattern than others.

For most satellite aerosol retrieval algorithms even for multi-angle instrument, the simple forward model (FM) based on Lambertian surface assumption is employed to simulate top of the atmosphere (TOA) spectral reflectance, which does not fully consider the surface bi-directional reflectance functions (BRDF) effect. The approximating forward model largely simplifies the radiative transfer model, reduces the size of the look-up tables, and creates faster algorithm. At the same time, it creates systematic biases in the aerosol optical depth (AOD) retrieval. AOD product from the Moderate Resolution Imaging Spectro-radiometer (MODIS) data based on the dark target algorithm is considered as one of accurate satellite aerosol products at present. Though it performs well at a global scale, uncertainties are still found on regional in a lot of studies. The Lambertian surface assumpiton employed in the retrieving algorithm may be one of the uncertain factors. In this study, we first use radiative transfer simulations over dark target to assess the uncertainty to what extent is introduced from the Lambertian surface assumption. The result shows that the uncertainties of AOD retrieval could reach up to ±0.3. Then the Lambertian FM (L_FM) and the BRDF FM (BRDF_FM) are respectively employed in AOD retrieval using dark target algorithm from MODARNSS (MODIS/Terra and MODIS/Aqua Atmosphere Aeronet Subsetting Product) data over Beijing AERONET site. The validation shows that accuracy in AOD retrieval has been improved by employing the BRDF_FM accounting for the surface BRDF effect, the regression slope of scatter plots with retrieved AOD against AEROENET AOD increases from 0.7163 (for L_FM) to 0.7776 (for BRDF_FM) and the intercept decreases from 0.0778 (for L_FM) to 0.0627 (for BRDF_FM).

The International Space Station/Japanese Exposure Module (ISS/JEM) borne instrument, the Superconducting Submillimeter-Wave Limb-Emission Sounder (SMILES), has successfully measured chemical species in the middle atmosphere between October 2009 and April 2010. We focus on inorganic chlorine species measured inside the late spring Antarctic vortex, when hydrogen chloride (HCl) was a main component of the total inorganic chlorine (Cly). Comparisons with other satellite instruments, the Aura Microwave Limb Sounder (MLS) and Atmospheric Chemistry Experiment Fourier transform spectrometer (ACE-FTS), are also presented to show the SMILES HCl and chlorine monoxide (ClO) data quality.

It is known that local spatially and temporally resolved measurements of atmospheric aerosols in Asian urban city are meaningful since the aerosol distribution in East-Asia is complicated due to the increasing emissions of anthropogenic aerosols in association with economic growth and natural dust significantly varies with the seasons. In this work, we intend to show the spatial and temporal variation of atmospheric aerosols in East Asia, especially around AERONET/Osaka site and Dragon-Asia period, named "DRAGON-Osaka". AERONET (Aerosol Robotics Network) Osaka site was established in 2002 in the campus of Kinki University. Nowadays, LIDAR, PM_2.5 / ₁₀ measurements and others are available. The site data are useful for algorithm development of aerosol retrieval over busy city. However, human activities in this region also emit the huge amount of pollutions, thus it is needed to investigate the local distribution of aerosols in this region. In March 2012, to obtain maximum efficiency of DRAGON-Osaka, several Cimels are deployed at more sites as soon as possible. In order to investigate change of aerosol properties, PM-individual analysis is made with scanning electron microscope (SEM) coupled with energy dispersive X-ray analyzer (EDX). A componential analysis presents temporal variation of aerosol properties.

It is known that the aerosol distribution in Asia is complicated due to the increasing emissions of anthropogenic aerosols in association with economic growth and natural dust significantly varied with the seasons. Therefore it is clear that local spatially and temporally resolved measurements of atmospheric aerosols in Asian urban city are necessary. Since Osaka, Kobe, Kyoto, and Nara are located in very close each others (all cities are included in around 70×70 km² area). The population of the region is around 13 millions including neighbor prefectures, accordingly air quality in this region is slightly bad in comparison with the remote area. Furthermore, in recent years, Asian dusts and anthropogenic small particles some times transported from China and cover these cities throughout year. DRAGON (Distributed Regional Aerosol Gridded Observation Network) is a project of dense sun/sky radiometer network in the urban area. The DRAGON-West Japan field campaign was performed over Osaka and neighbor cities with 7 AERONET instruments from March to end of May in 2012. As results, DRAGON measurements indicate small differences among the values of AOT over Osaka region.

The Aihui-Tengchong Line or the internationally known “Hu Line” divides China into the east and west parts, based on differences in China's population, geography, climate and economy, all of which are closely associated with the aerosols over China. By using the aerosol optical depth (AOD) data of MODIS during years 2000-2010, the geographical and climatological distributions of aerosols over China are presented, and the ‘Hu Line” is found also to describe a geographic division of aerosols over China: on the east part, the monthly AOD varies from the peak (<0.5) during March and June to the low of around 0.3 in November and December with an annual mean of about 0.45, mostly contributed by anthropogenic aerosols from the human activities; on the west part, the AOD is dominated by the naturally emitted aerosols with an annual mean of 0.25 changing between the high (about 0.3) in the period of April to July and the low (<0.2) from October to January. The positive and negative trends in annual AOD over 2000-2010 are respectively found in the regions on the east and west. Asian monsoon has a notable impact on the interannual variability of aerosols over the east region by modulating the atmospheric transport and precipitation washout. The interannual aerosol variations in the west are strongly influenced by dust emission sources in the deserts. The dust weather processes control the natural dust emissions. The maximal AOD of 0.3 in the west China could be brought by the frequent dust storm events.

Low clouds are widespread over the North Pacific Ocean during summer. Past ship observations, which were carried out in the western region of the North Pacific Ocean, suggested that low clouds (stratus and fog) are likely to occur when sea surface temperature (SST) is lower than surface air temperature (SAT). In this study, we investigated the SST-SAT relationship and microphysical properties of low clouds for the first step of understanding the mechanism of cloud occurrence, maintenance and disappearance by using MODIS satellite observations, JAMSTEC ship observations and MERRA reanalysis data. We divided the North Pacific into four regions according to meteorological condition and made basic statistical analysis about cloud properties in each region by using monthly mean data for July 2011. The statistical analysis indicates that in the central region of the North Pacific where SST-SAT value is negative and the difference is the largest, cloud effective particle radius (r_e) is larger than those in other regions. We also used ship observation data and simultaneous satellite observation data to examine the relationship between SST-SAT and cloud microphysical properties in detail. This analysis indicates that r_e in the positive SST-SAT area is larger than that in the negative SSTSAT area. This feature is opposite to the monthly mean results. It suggests that other factors such as humidity and aerosols as well as SST-SAT have to be taken into account, although the SST-SAT relationship can be one of the important factors determining cloud microphysical properties in the summer North Pacific region.

Downward longwave radiation is a key process to understand the climate change, energy budget, and water cycle at the earth’s surface. Cloud is a dominant factor to determine the intensity of longwave radiation. It is widely known that cloud cover and cloud base height (CBH) have strong effects on the downward longwave radiation, however there are not so many studies regarding the quantitative evaluation of relationship between cloud properties and downward longwave radiation. The intent of the present study is to quantify the impact of cloud property on the downward longwave irradiance (DLI). We used the data obtained with CGR-4 pyrgeometer at Tateno, Japan for the period from January 2002 to December 2011. Cloud radiative contribution fraction (CRC) is evaluated with a ratio of the difference of DLI between observation under cloudy sky without precipitation and calculation assumed clear-sky condition to the observed DLI. The difference between calculation and observation is -4.60±3.00 W/m², and the calculation method reproduced to observation. Cloud is classified into three types by CBH, low (CBH<2000 m), middle (2000≥CBH<5000 m), and high (CBH≥5000 m). In the results, CRC is almost proportional and inverse proportional to cloud cover (CC) and CBH in the average, respectively. However, CRC for low cloud shows proportion to CBH because existence of low altitude cloud is related to large precipitable water (PW).

Aspect sensitivity of clear-air turbulence was examined with multiple-receiver coherent radar imaging (CRI) of VHF atmospheric radar. The study was carried out by means of aspect angle, estimated from two CRI parameters: direction of arrival of echo center from oblique radar beam and brightness distribution width from vertical radar beam. The brightness distribution was retrieved by the Capon method. Modification of brightness value has been made with a suitable radar beam-weighting function before estimating the two CRI parameters. The radar beam-weighting function used for correction is a Gaussian form, and its standard deviation (beam width) varies with off-beam direction angle and is also adaptive to signal-to-noise ratio of echoes. The use of adaptable beam width can avoid over-modifying the brightness values at the edges of the imaged map, yielding a more reliable estimate of aspect angle. The CRI-estimated aspect angle was compared with that obtained from comparison of echo powers of different oblique radar beams. The statistical features of aspect angles obtained from the two approaches are consistent.

We have been developing a data-set of global land surface microwave emissivity calculated from 9-channel Bright­ ness Temperatures (TBs) from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and atmospheric profile data from Japanese 25-year Reanalysis Project (JRA-25). The surface emissivity is derived using the non-scattering radiative transfer equation for regions identified as no-rain by TRMM Precipitation Radar (PR). An Empirical Orthogonal Function (EOF) analysis has been applied to this emissivity data-set. Emissivities at high frequencies, difficult to estimate due to high sensitivity to clouds and water vapor, are estimated from lower frequencies by using the principal components. Contributions from EOF1 to EOF4 are dominant and with the others being less than 1 %. Therefore, 5 high-frequency emissivities can be estimated from the other 4 emissivities at lower frequencies with 4 principal components. For example, when 37 GHz Horizontal emissivity on June 1998 is estimated from 4 channels of 10 and 19 GHz, correlation coefficient with the original estimate is 0.93 and the result of linear fitting shows an inclination of 0.97 and a cutoff of 0.02 for global data. This estimation method is applied for each area, each land surface condition (surface type and soil wetness) and so on, in search of optimal performance of the algorithm. The advantage of using the EOF analysis as described above is to minimize the cloud contamination at high frequency TB. A cloud-clearing method is also explored to improve the reliability of the EOFs.

Formosat-2 image is a kind of high-spatial-resolution (2 meters GSD) remote sensing satellite data, which includes one panchromatic band and four multispectral bands (Blue, Green, Red, near-infrared). An essential sector in the daily processing of received Formosat-2 image is to estimate the cloud statistic of image using Automatic Cloud Coverage Assessment (ACCA) algorithm. The information of cloud statistic of image is subsequently recorded as an important metadata for image product catalog. In this paper, we propose an ACCA method with two consecutive stages: preprocessing and post-processing analysis. For pre-processing analysis, the un-supervised K-means classification, Sobel’s method, thresholding method, non-cloudy pixels reexamination, and cross-band filter method are implemented in sequence for cloud statistic determination. For post-processing analysis, Box-Counting fractal method is implemented. In other words, the cloud statistic is firstly determined via pre-processing analysis, the correctness of cloud statistic of image of different spectral band is eventually cross-examined qualitatively and quantitatively via post-processing analysis. The selection of an appropriate thresholding method is very critical to the result of ACCA method. Therefore, in this work, We firstly conduct a series of experiments of the clustering-based and spatial thresholding methods that include Otsu’s, Local Entropy(LE), Joint Entropy(JE), Global Entropy(GE), and Global Relative Entropy(GRE) method, for performance comparison. The result shows that Otsu’s and GE methods both perform better than others for Formosat-2 image. Additionally, our proposed ACCA method by selecting Otsu’s method as the threshoding method has successfully extracted the cloudy pixels of Formosat-2 image for accurate cloud statistic estimation.