スロット カジノ

Hydrological Research Letters
Online ISSN : 1882-3416
ISSN-L : 1882-3416
Current issue
Displaying 1-2 of 2 articles from this issue
  • Yoshikazu Kitano, Masamichi Ohba, Naohiro Soda, Yasuo Hattori, Tsuyosh ...
    2023 Volume 17 Issue 4 Pages 69-76
    Published: 2023
    Released on J-STAGE: November 03, 2023
    DOI
    JOURNAL OPEN ACCESS
    Supplementary material

    The estimation of extreme wind speeds, their directional variation, and potential future changes is essential for wind-resistant design and is possible using climate models. Accurate evaluations of local topographic winds such as downslope windstorms and gap winds require high-resolution calculation and many ensemble years. However, few climate databases satisfy both requirements and none have been validated for extreme wind speeds.

    We assessed directional extreme wind speeds using a massive high-resolution ensemble climate dataset (d4PDF-5km-DS) for Hokkaido Island, Japan. Despite some issues due to limited reproducibility of spring extratropical cyclones, downslope windstorms caused by large mountains are reproduced well, indicating decreasing wind speed under predicted future climate. The findings suggest future climatic conditions may influence extreme topographic wind speeds.

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  • Kansei Fujimoto, Taichi Tebakari
    2023 Volume 17 Issue 4 Pages 77-84
    Published: 2023
    Released on J-STAGE: November 29, 2023
    DOI
    JOURNAL OPEN ACCESS
    Supplementary material

    Satellite products are expected to play important roles in water-related management and public welfare, particularly in developing countries. Higher-resolution precipitation products are required to cope with increasingly severe water-related disasters. In this study, we propose a new satellite precipitation estimation algorithm based on deep learning that uses data from multiple satellite infrared (IR) bands and geographic information (e.g. elevation, latitude, and longitude) as input. For the deep learning model component, we use various image segmentation models, including U-Net, PSPNet, and DeepLabv3+. Cosine similarity and correlation coefficients for precipitation rate were used to determine the IR bands of the input data; five bands were used as IR. Four input datasets were constructed: IR alone; IR and elevation data; IR and latitude/longitude; and IR, elevation data, and latitude/longitude. When PSPNet or DeepLabv3+ was used as the deep learning model, and elevation and latitude/longitude were added to IR as input data, the mean square error and fraction skill score showed improved accuracy over GSMaP_MVKv7 and PERSIANN-CCS; precipitation overestimation was eliminated. These results indicate that deep learning models can be used to estimate precipitation from satellite IR observations with high resolution and accuracy.

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