A prediction method based on Kalman filter using mean value of time series data derived from the other source is proposed. As an example of the proposed method, prediction of missing ASTER/VNIR data based on Kalman filter using simultaneously acquired MODIS data as a mean value of time series data in revision of filter status is attempted together with a comparative study of prediction errors for both conventional Kalman filter and the proposed modified Kalman filter which utilizes mean value of time series data derived from the other sources. Experimental data shows that 4 to 111% of prediction error reduction can be achieved by the proposed modified Kalman filter in comparison to the conventional Kalman filter. It is found that the reduction rate depends on the mean value accuracy of time series data derived from the other data sources.
Lidar observations have been making at Moshiri (44.4°N), Tsukuba (36.1°N) and Lauder (45.0°S) for validation of the GOSAT (IBUKI) products. The lidar data at Ryori (39.0°N) and Kochi (33.6°N) are also used for the GOSAT validation. Thin aerosol layers were observed at an altitude of 20-21km over Japan on 25 June 2009. These aerosol layers are thought to be originated from the Mt. Sarychev (48.08°N, 153.23°E) volcanic eruption on 12 June 2009. About one month after the eruption, the stratospheric aerosols increased several times compared with the background level around 1996 at Tsukuba. The impact of this aerosol layer on the GOSAT product of CO2 column amount is estimated not to be always negligible. For an optical depth of 0.02∼0.037 of stratospheric aerosols, CO2 column amount will have a negative bias of 0.3∼0.5% for a surface albedo of 0.1 if we disregard them.
PAR (Photosynthetic Active Radiation) is one of the important environmental factors to understand the plant activities. However, the incident PAR on ground surface is affected by the weather especially caused by cloud existence. In this study, we focus on the sky conditions formed by cloud existence and its effects of incident PAR on the ground surfaces. The purpose of this study is to estimate the direct and diffuse PAR by considering the effects of sky conditions change in the short time period. For this purpose, this paper describes the method how to discriminate and classify sky conditions by the ground based observation using whole-sky camera and the properties of direct, diffuse and global PARs shown at various sky conditions. As the result, 14 sky conditions were classified by sun appearance/hiding, cloud cover and sky brightness using whole-sky images taken around noon time. We could clarify the properties of incident PAR at each sky conditions. Also, we tried to estimate the global and diffuse PAR from the relationship between the observed PAR and sky conditions. It was confirmed that the possibility of incident PAR estimation using whole-sky image.
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