This PDF file contains the front matter associated with SPIE Proceedings Volume 7152, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

The spatial distributions and temporal variations of the cloud properties, optical thickness (τ_c) and effective particle radius (r_e) are important observation targets to understand the role of clouds in the radiation budget estimations, especially in the cloud-aerosol interaction studies, because they will have information of cloud growth at the certain area. Thus, the wide-area and high-temporal observations of the cloud properties are necessary. We used MODIS 5-km sub-sampling radiance subsets (MOD02SSH) for the global scale retrievals of clouds. The MOD02SSH conserves scene texture and has moderately reduced data volume of 1/25 from original size of MODIS scene, so that they will be suitable for the more precise estimations of τ_c and r_e over synoptic to global scale. We retrieved τ_c and r_e from one-month MOD02SSH over the global region in July 2006 by using a cloud retrieval algorithm of CAPCOM developed by Nakajima and Nakajima (1995) with an extension by Kawamoto et al. (2001). In the obtained τ_c versus r_e scatter plots at every 5 x 5 degrees grid boxes, we found typical features of the τ_c versus r_e scatter patterns in the middle part of the Pacific and Indian oceans, Eastern Europe and Asia, and the west coast of the North and South America. Such patterns will be explained by the spectral microphysics cloud model developed by Suzuki et al. (2006), as the cloud properties under the pristine, turbid, and mixture aerosol environments. The aerosol transport model SPRINTARS developed by Takemura et al. (2005) simulated that the Eastern Europe and Asia covered by dense aerosols in the period.

The capabilities for observing clouds, precipitation and processes connected to condensed water in the atmosphere by satellites that presently orbit the Earth is unprecedented in the history of spaceborne Earth observations. The so-called A-Train of satellites (Stephens et al., 2002), in particular, represents entirely new observations of key cloud and precipitation processes in a way more advanced that ever before. These new observations represent a unique source of information for evaluating the moist physics parameterizations in models with benefits that are expected to lead to greatly improved and more realistic representations of these important atmospheric processes. This paper outlines a collection of the new findings from the A-Train observing system.

EarthCARE is ESA's Clouds Aerosols and Radiation Explorer. It is a joint mission in collaboration with JAXA. The satellite will carry a payload of four instruments: an atmospheric lidar (ATLID), a cloud profiling radar (CPR) provided by JAXA, a multi spectral imager (MSI) and a broad-band radiometer (BBR). The four instruments will operate synergistically with the aim of providing a better understanding of clouds and aerosols and their impact on Earth's climatology. An important feature of the mission is that its instruments will make, for the first time, near simultaneous measurements of the same cloud/aerosol scene, The presentation will describe the satellite, its mission and in particular, the design and specifications of the payload.

This paper introduces the present status of TRMM PR vesrion6 standard algorithms and proposes the possible improvements of them in version 7. The present PR standard algorithm system is composed of 1B21, 1C21, 2A21, 2A23, 2A25, 3A25 and 3A26 algorithms. These algorithms are used to analyze more than ten-year TRMM PR data. The algorithm 1B21 calculates PR received power, and 1C21 calculates Z value without rain attenuation correction. The algorithm 2A21 calculates surface reference sigma-zero values and estimates the path-integrated attenuation(PIA) by rain. The algorithm 2A23 detects bright band and classifies the rain type into the stratiform type, convective type and others. The algorithm 2A25 estimates rain rate profiles and Z profiles with rain attenuation correction for each radar beam. The algorithm 3A25 gives monthly statistical values of level 2 products. The algorithm 3A26 calculates monthly averaged rain rates of 5 degree by 5 degree boxes by applying the multiple threshold statistical method.

The rain/no-rain threshold value of cloud liquid water (CLW) is important for the microwave precipitation retrieval algorithms. In our previous study, we proposed a parameterization of rain/no-rain threshold value of CLW as a function of storm height for Global Satellite Mapping of Precipitation (GSMaP) algorithm. In this study, we determine rain/norain threshold value of CLW using CloudSat precipitation product and the cloud liquid water derived from Aqua/AMSRE. The threshold values of CLW from CloudSat precipitation product are lower than 0.5 kg m^-2 for GSMaP over all regions. The threshold value of CLW is found at its peak in the Tropics and decreases poleward. The threshold value of cloud liquid water contents computed from threshold value of CLW divided by the zonal mean storm height from PR3A25 is employed on the parameterization of threshold value of CLW. The result shows that GSMaP with new parameterization can detect the shallow rain observed by CloudSat.

Impacts of additional predictors on inverting atmospheric infrared radiance for temperature and humidity profiles are investigated using Atmospheric Infrared Sounder (AIRS) real measurements and empirical orthogonal function expansion method (EOF). These predictors are microwave channels, latitude, topography, surface altitude, surface temperature, and surface air pressure. The results suggest that microwave channels can remarkably help the improvement of the accuracy of retrieved profiles at lower troposphere (below 800hPa) and have little effect on that above 800hPa. With dataset classified by latitude, better retrievals are obtained. The root mean square errors (RMSE) of retrieved temperature at complicated terrain are significantly greater than that at plat area. For humidity retrievals it was found that RMSE exhibit weak sensitivity to topography. By combined use of infrared measurements and additional predictors, great improvements have achieved in the retrieval of atmospheric temperature and humidity profiles at lower troposphere.

Hyperspectral infrared atmospheric sounders (e.g. the Atmospheric Infrared Sounder (AIRS) on Aqua and the Infrared Atmospheric Sounding Interferometer (IASI) on MetOp) provide highly accurate temperature and water vapor profiles in the lower to upper troposphere. These systems are vital operational components of our National Weather Prediction system and the AIRS has demonstrated over 6 hrs of forecast improvement on the 5 day operational forecast¹. Despite the success in the mid troposphere to lower stratosphere, a reduction in sensitivity and accuracy has been seen in these systems in the boundary layer over land. In this paper we demonstrate the potential improvement associated with higher spatial resolution (1km vs currently 13.5 km) on the accuracy of boundary layer products with an added consequence of higher yield of cloud free scenes. This latter feature is related to the number of samples that can be assimilated and has also shown to have a significant impact on improving forecast accuracy. We also present a set of frequencies and resolutions that will improve vertical resolution of temperature and water vapor and trace gas species throughout the atmosphere. Development of an Advanced Low Earth Orbit (LEO) Sounder (ALS) with these improvements will improve weather forecast at the regional scale and of tropical storms and hurricanes. Improvements are also expected in the accuracy of the water vapor and cloud properties products, enhancing process studies and providing a better match to the resolution of future climate models. The improvements of technology required for the ALS are consistent with the current state of technology as demonstrated in NASA Instrument Incubator Program and NOAA's Hyperspectral Environmental Suite (HES) formulation phase development programs.

Sea fog is considered as a cloud cling the surface and reducing visibility to 1km or less in this paper. For fog or cloud, simulating results show that the reflected radiance in the visible spectral region depends mainly on the fog/cloud optical thickness and the reflected radiance in near-infrared spectral region depends mainly on the fog/cloud particle effective radius. Owing to this, by combining the visible and the near-infrared reflected solar radiation and using the lookup table about the reflectance function for various values of optical thickness and effective particle radius created by the Streamer radiative transfer model, a retrieval algorithm was developed to determine the optical thickness and the effective particle radius of sea fog simultaneously from NOAA17 AVHRR3 data. According to the definition of visibility and liquid water content, the relations and formulas of fog properties (optical thickness, particle effective radius, liquid water content and visibility) are described. Lastly, the other two properties, liquid water content and visibility on known sea fog regions are also estimated. It is those four sea fog properties retrieved from satellite observations that can offer the condition for analyzing and forecasting the time of fog clearance in future.

An algorithm for atmospheric correction of space based data given by AVNIR-2 sensor mounted on the land observing satellite ALOS is described here. Our procedure is based on the multiple scattering calculations in an Earth atmospheresurface system. The atmospheric constituents are roughly divided into gas molecules and aerosols. It is well known that the aerosol characteristics vary with time and place. This fact indicates that an aerosol retrieval problem should be solved before atmospheric correction of satellite data. Accordingly the atmospheric correction is treated step by step in this work. At the first step, atmospheric correction by gas molecules alone, what one calls Rayleigh correction, is considered. Then Rayleigh correction including the Earth surface height is dealt with. Finally atmospheric correction by both of gas molecules and aerosols is taken into account. In this phase, the aerosol properties are retrieved from the ground-based AERONET (Aerosol Robotics Network) data. The processed images after atmospheric correction according to the above three steps are compared with one another. It is shown that our atmospheric correction presents the improved satellite images.

The AErosol RObotic NETwork(AERONET) provides the necessary ground-based observation to validate and assess the applicability of the latest version MODIS Collection 005 aerosol optical thickness (AOT) products over the China sea for the first time. Based on the two kinds of data, the method of validation was discussed over the China Sea. The validation results show that the MODIS Collection 005 AOT has a good relationship with AERONET AOT and their correlation coefficient is larger than 0.9 over the China Sea; With testing different validation methods, we find the window size of MODIS data can influence the validation result, and the 30kmX30km window size is the best over the China sea. Finally, the comparison of MODIS- and AERONET-derived AOT separately over different regions of the China Sea suggests that MODIS Collection 005 AOT correspond with the error standard of NASA and most of its error can be controlled under ±0.05 ±0.05τ over the China Sea, so MODIS Collection 005 AOT is creditable over the China Sea for science research.

Concentration of suspended particulate matter less than 2.5 μm (PM_2.5) is a representative parameter of air quality. Simultaneous measurements of PM_2.5 and the column aerosol optical thickness (AOT) have been performed at a NASA/AERONET station, Higashi-Osaka, Japan since March 2004. They successfully provide a linear correlation between PM_2.5 and AOT. A Mie scattering lidar instrument was deployed at the same observational site in April, 2008. It provides us with the attenuated backscattering coefficients of aerosols at wavelengths of 0.532 and 1.064 μm, which indicate the vertical distribution of aerosols. This work intends to improve the correlation between AOT and PM_2.5 by using the measurements of lidar.

We measured clouds and precipitation by a combined use of a radar and a passive solar/infrared radiometer onboard the Tropical Rainfall Measuring Mission (TRMM) satellite and examined how precipitation characteristics are linked to cloud properties. The radar is used to measure precipitation. Optical thickness (τ) and effective radius (r_e) of convective clouds whose top is composed of ice particles were estimated from radiances at two wavelengths measured with the radiometer. Particularly, the sensitivities of retrieved cloud parameters to the assumption of ice crystal shape models (spherical or nonspherical) were examined. Assumption of spherical model results in larger τ comparing with that from nonspherical model. The re retrieved from liquid water spherical model is significantly larger than that with hexagonal columns and ice sphere models. The influence of ice particle shape (hexagonal columns and spheres) on the retrieved re is noted by several microns. The use of nonspherical model is essential for retrieving τ and r_e of convective clouds whose top is composed of ice particles by using visible and near-infrared channels of satellite measurements.

Planet Boundary Layer sulfur dioxide (PBL-SO₂) derived from Ozone Monitoring Instrument (OMI) are compared with in-situ measurements from Differential Optical Absorption Spectroscopy (DOAS) and gas analyzer observations at three sites in Beijing (Jan-Dec, 2007) and Hebei province (Jan-May, 2007). We use an Air Mass Factor (AMF) lookup table, which was calculated via Linearized Discrete Ordinate Radiative Transfer (LIDORT) model, to convert OMI PBL-SO₂ slant column density to vertical column density. Co-locate Lidar (UV) aerosol extinction profiles are used to correct the effect of aerosol. Results show that, AMF decreases less than 3% with the increasing solar zenith angle from 0° to 45°, AMF is more sensitive to surface albedo and the viewing zenith angle. AMF reduces by 6% with the increasing Ozone density from 275DU to 325DU. Normally, absorption aerosol reduces AMF and scattering aerosol increases AMF, aerosol profiles are critical to AMF estimation. Under very clear conditions, from winter to later spring, OMI observed SO₂ values are underestimated by 3.6ppbv to 20ppbv, but in reasonable agreement with in-situ measurements. Because of the effects of Sub-pixel cloud contamination, long slant path (higher solar zenith angles or viewing zenith angles), differences in aerosol types and large Aerosol Optical Depth (AOD), direct comparisons between the OMI retrieval and the in situ measurements show that the correlation is low and the differences vary with months, while averaging over half a month can significantly reduces the bias.

In recent years, the number of days which dust and sandstorms (DSS) events were observed is increasing in Japan, Korea, China and Mongolia. The Aerosol Vapor Index (AVI) method is a DSS detection method. The AVI is defined as AVI=Tb32-Tb31 for MODIS data of Terra and Aqua satellites, where Tb31 is the brightness temperature of band 31 (10.780-11.280μm) and Tb32 is that of band 32 (11.770-12.270μm). The MODIS mosaic images of true-color, AVI and thermal images are made for the detection of DSS from Taklamakan Desert to Japan. The detection of DSS is possible both at daytime and night, because the AVI method is used. The density of DSS is classified into six levels from 0 (DSS none) to 5 (DSS strong) according to the AVI values. The DSS phenomena during 6-11 April 2006 are analyzed by using the mosaic images of Terra-MODIS. The number of pixels, which is approximately equal to the area of square kilometers, at each level of DSS density is measured. The AVI value at sea is about 0.2-2.3K lower than that at land, because of the influence of water vapor. In daytime, the generation place of DSS hidden under the cloud can be estimated by comparing AVI image with true-color and thermal images.

Inter-comparison of various satellite data is performed for the purpose of validation of aerosol type classification algorithm from satellite remote sensing, so called, MODIS-OMI algorithm (MOA hereafter). Infrared Optical Depth Index (IODI), correlation coefficient between carbon monoxide (CO) column density and black carbon (BC) aerosol optical thickness (AOT), and aerosol types from 4-channel algorithm and CALIOP measurements are used to validate dust, BC, and aerosol type from MOA, respectively. The agreement of dust pixels between IODI and MOA ranges 0.1 to 0.6 with respect to AOT constraint, and it is inferred that IODI is less sensitive to optically thin dust layer. Increase of the correlation coefficient between AOT and CO column density when BC pixels are taken into account supports the performance of MOA to detect BC aerosol. The agreement of aerosol types from MOA and 4CA showed reasonable consistency, and the difference can be described by different absorptivity test and retrieval accuracy of AE. Intercomparison of aerosol types between MOA and CALIOP measurements represented reasonable consistency when AOT greater than 0.5, and height dependence of MOA is inferred from consistency analysis with respect to aerosol layer height from CALIOP measurements. Inter-comparisons among different satellite data showed feasible future for validating aerosol type classification algorithm from satellite remote sensing.

We are presenting the results of the studies related to propagation of ultrashort optical pulse through the turbulent atmosphere. The correlation of the atmospheric turbulence with the propagation delay fluctuation was measured. The entirely different approach in comparison to adaptive optics was developed to describe the effect. The experiments described enabled us for the first time to determine the L₀ parameter on the basis of direct measurement. The recent achievements in the field of pulsed lasers, fast optical detectors and timing systems enable us to resolve the effects of propagation differences monitoring on the level of units of picosecond propagation time. Three independent types of path configurations have been studied: horizontal path, slant path at elevation 10 - 80 degrees to a flying target and slant path from ground to space. Additionally, new techniques of optical receivers signal processing give a way to distinguish the atmospheric fluctuations contribution from the energy dependent detection delay effects.