著者
岩渕 弘信 岡村 凜太郎 Sebastian Schmidt
出版者
日本地球惑星科学連合
雑誌
JpGU-AGU Joint Meeting 2017
巻号頁・発行日
2017-03-10

Estimation of cloud properties such as the cloud optical thickness and effective droplet radius is usually based on the independent pixel approximation (IPA) assuming a plane-parallel, homogeneous cloud for each pixel of a satellite image. Prior studies have pointed out that horizontal and vertical inhomogeneities produce significant errors in the retrieved cloud properties. The observed reflectance at each pixel is influenced by the spatial arrangement of cloud water in adjacent pixels, which necessitates the consideration of the adjacent cloud effects when estimating the cloud properties at a target pixel. We study the feasibility of a multi-spectral, multi-pixel approach to estimate the cloud optical thickness and effective droplet radius using a deep neural network (DNN), which is a kind of machine-learning technique and has capabilities of multi-variable estimation, automatic characterization of data, and non-linear approximation. A Monte Carlo three-dimensional radiative transfer model is used to simulate the reflectances with a resolution of 280 m for large eddy simulation cloud fields in cases of boundary layer clouds. Two retrieval methods are constructed: 1) DNN-2r that correct IPA retrievals using the reflectances (from 3D simulations) at 0.86 and 2.13 µm and 2) DNN-4w that uses the so-called convolution layer and directly retrieve cloud properties from the reflectances at 0.86, 1.64, 2.13 and 3.75 µm. Both DNNs efficiently derive the spatial distribution of cloud properties at about 6×6 pixels all at once from reflectances at multiple pixels. Both DNNs outperform the IPA-based retrieval in estimating cloud optical thickness and effective droplet radius more accurately. The DNN-4w can robustly estimate cloud properties even for optically thick clouds, and the use of a convolution layer in the DNN seems adequate to represent three-dimensional radiative transfer effects.
著者
齊藤 雅典 岩渕 弘信 Yang Ping Tang Guanglin King Michael Sekiguchi Miho
出版者
日本地球惑星科学連合
雑誌
JpGU-AGU Joint Meeting 2017
巻号頁・発行日
2017-03-10

Microphysical properties and ice particle morphology of cirrus clouds are important for estimating the radiative forcing associated with these clouds. Many satellite measurements allow us to estimate the cloud optical thickness (COT) and cloud-particle effective radius (CER) of cirrus clouds over the globe via multiple retrieval methods such as the bi-spectral method using visible and near-infrared cloud reflectivities, the split-window method using thermal infrared brightness temperatures and the unconstrained method using lidar signals. However, comparisons among these retrievals exhibit discrepancies in some cases due to particular error sources for each method. In addition, methods to infer ice particle morphology of clouds from satellite measurements are quite limited. To tackle these current problems, we develop an optimal estimation based algorithm to infer cirrus COT, CER, plate fraction including horizontally oriented plates (HOPs) and the degree of surface roughness from the Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) and the Infrared Imaging Radiometer (IIR) on the Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) platform. A simple but realistic ice particle model is used, and the bulk optical properties are computed using state-of-the-art light-scattering computational capabilities. A rigorous estimation of the uncertainties related to the surface properties, atmospheric gases and cloud heterogeneity is performed. A one-month global analysis for April 2007 with a focus on HOPs shows that the HOP fraction has significant temperature dependence and therefore latitudinal variation. Ice particles containing many HOPs have small lidar ratio due to strong backscattering. The lidar ratio of cirrus clouds has a negative correlation with the temperature where the cloud temperature is warmer than −40℃, for which the median HOP fraction is larger than 0.01%.
著者
岩渕 弘信
出版者
北海道大学低温科学研究所 = Institute of Low Temperature Science, Hokkaido University
雑誌
低温科学 (ISSN:18807593)
巻号頁・発行日
vol.72, pp.151-157, 2014

不均質な雲場における放射フラックスの空間分布は, 平行平板大気の放射フラックスの分布とは大きく異なる. 不均質雲場の放射の分布は, 雲が水平不均質であることによる影響と, 不均質媒体中での3次元放射伝達の影響を受ける. 本稿では, これらの効果について, これまでの研究からわかったことを概説する. 50~200kmの水平スケールの領域平均放射フラックスについては, 水平不均質性の効果を考えることで精度よく計算が可能となる. より小さい水平スケールでは放射の水平収束・発散が顕著になり, 局所的な放射フラックスは, 近傍の雲水の配置に強く影響されることが示される.Radiative flux distribution in inhomogeneous cloud field is largely different from that in the plane-parallel atmosphere. Horizontal inhomogeneity and three-dimensional radiative transfer in the inhomogeneous media affect radiation in inhomogeneous cloud field. In this review, current understandings about these effects are presented. Domain-average radiative fluxes over an area of 50-200 km horizontal scale can be accurately calculated by incorporating the effect of horizontal inhomogeneity in radiation scheme. On smaller horizontal scale, horizontal radiative convergence/divergence is apparent, and nearby spatial arrangement of cloud water should influence local radiative fluxes.
著者
岩渕 弘信
出版者
北海道大学低温科学研究所
雑誌
低温科学 (ISSN:18807593)
巻号頁・発行日
vol.72, pp.151-157, 2014-03-31

不均質な雲場における放射フラックスの空間分布は, 平行平板大気の放射フラックスの分布とは大きく異なる. 不均質雲場の放射の分布は, 雲が水平不均質であることによる影響と, 不均質媒体中での3次元放射伝達の影響を受ける. 本稿では, これらの効果について, これまでの研究からわかったことを概説する. 50~200kmの水平スケールの領域平均放射フラックスについては, 水平不均質性の効果を考えることで精度よく計算が可能となる. より小さい水平スケールでは放射の水平収束・発散が顕著になり, 局所的な放射フラックスは, 近傍の雲水の配置に強く影響されることが示される.
著者
岩渕 弘信 早坂 忠裕 岡本 創 片桐 秀一郎
出版者
東北大学
雑誌
基盤研究(B)
巻号頁・発行日
2013-04-01

地球の放射収支に重要な上層雲の変動実態を全球規模で把握するため,衛星データから上層雲の特性を推定する手法を開発し,雲量と光学特性,微物理特性の変動を解析した。熱帯域では光学的に厚い上層雲の分布は対流活動が活発な地域とよく対応していた。巻雲の分布域は対流雲の分布域よりも高緯度側に広がっており,対流活動の活発な地域の季節変動に対応して,巻雲も変動していた。上層雲の雲量と雲頂高度の経年変動は特にエルニーニョ南方振動と密接に結びついていることがわかった。また,静止気象測衛星ひまわり8号のデータを用いた雲解析アルゴリズムを開発し,雲の時間的な変化を詳細に捉えられるようになった。