Precipitation Nowcasting from Geostationary Satellite: Neural Approaches Trained By Polar Orbiting and Ground-Based Data

被引：0
|
作者：
Rivolta, Giancarlo [1 ,2 ]
de Rosa, Michele [1 ]
Marzano, Frank Silvio [1 ,2 ]
机构：
[1] Univ Roma La Sapienza, Dipartimento Ingn Elettron, I-00184 Rome, Italy
[2] Univ Aquila, CETEMPS, I-67100 Laquila, Italy
来源：
RIVISTA ITALIANA DI TELERILEVAMENTO | 2010年 / 42卷 / 01期
关键词：
Nowcasting; Neural Networks; Precipitation; Geostationary satellite; Meteorological radar; RAINFALL ESTIMATION; PASSIVE MICROWAVE; NETWORKS; RETRIEVAL; FRAMEWORK; ENSEMBLE; RADAR;
D O I：
暂无
中图分类号：
TP7 [遥感技术];
学科分类号：
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要：
This work explores possible improvements of the Neural Combined Algorithm for Storm Tracking (NeuCAST) proposed in Marzano et al. [2007]. In its single channel version, developed for the Visible-Infrared Imager (VIRI) onboard Meteosat-7. this technique has been successfully applied to the rainfall field nowcast from thermal infrared (TIR) and microwave (MW) passive-sensor imagery aboard, respectively, Geostationary-Earth-Orbit and Low-Earth-Orbit satellites. The multi-channel NeuCAST methodology is here introduced,. It extends the single-channel NeuCAST technique to infrared (IR) multi-channel data available from Meteosat Second Generation (MSG) and MW data from ground based meteorological Radar.
引用
收藏
页码：91 / 115
页数：25
相关论文
共 50 条
  • [1] Cloud Cover and Precipitation Monitoring Based on Data from Polar Orbiting and Geostationary Satellites
    Volkova, E. V.
    Andreev, A. I.
    Kostornaya, A. A.
    [J]. RUSSIAN METEOROLOGY AND HYDROLOGY, 2021, 46 (12) : 830 - 838
  • [2] Cloud Cover and Precipitation Monitoring Based on Data from Polar Orbiting and Geostationary Satellites
    E. V. Volkova
    A. I. Andreev
    A. A. Kostornaya
    [J]. Russian Meteorology and Hydrology, 2021, 46 : 830 - 838
  • [3] Uncertainties in albedo derived from geostationary and polar orbiting satellite data
    van Leeuwen, WJD
    Roujean, JL
    Lacaze, R
    [J]. IGARSS 2001: SCANNING THE PRESENT AND RESOLVING THE FUTURE, VOLS 1-7, PROCEEDINGS, 2001, : 1823 - 1825
  • [4] Method for geostationary satellite thermal infrared data simulation from polar-orbiting MODIS sensors
    Qian, Yonggang
    Li, Kun
    Yao, Weiyuan
    Li, Wan
    Qiu, Shi
    Ma, Lingling
    Liang, Shi
    Yao, Guanglin
    [J]. OPTICS EXPRESS, 2021, 29 (26) : 43836 - 43851
  • [5] A Lookup-Table-Based Approach to Estimating Surface Solar Irradiance from Geostationary and Polar-Orbiting Satellite Data
    Zhang, Hailong
    Huang, Chong
    Yu, Shanshan
    Li, Li
    Xin, Xiaozhou
    Liu, Qinhuo
    [J]. REMOTE SENSING, 2018, 10 (03)
  • [6] A SIMPLE FUSION ALGORITHM OF POLAR-ORBITING AND GEOSTATIONARY SATELLITE DATA FOR THE ESTIMATION OF SURFACE SHORTWAVE FLUXES
    Chen, Ling
    Yan, Guangjian
    Ren, Huazhong
    Wang, Tianxing
    [J]. 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2016, : 2657 - 2660
  • [7] Development of a virtual active fire product for Africa through a synthesis of geostationary and polar orbiting satellite data
    Freeborn, Patrick H.
    Wooster, Martin J.
    Roberts, Gareth
    Malamud, Bruce D.
    Xu, Weidong
    [J]. REMOTE SENSING OF ENVIRONMENT, 2009, 113 (08) : 1700 - 1711
  • [8] Simultaneous satellite and ground-based observations of polar cap aurora
    Yahnin, AG
    Sergeev, VA
    [J]. THREE-DIMENSIONAL MAGNETOSPHERE, 1996, 18 (08): : 111 - 114
  • [9] Investigation and validation of a dust data fusion method based on monitoring data from geostationary and polar-orbiting satellites
    Cao, GuangZhen
    Zhang, Peng
    Hou, Peng
    Hu, Xiuqing
    Chen, Lin
    [J]. REMOTE SENSING OF THE ATMOSPHERE, CLOUDS, AND PRECIPITATION V, 2014, 9259
  • [10] Validation of satellite and ground-based estimates of precipitation over the Sahel
    Laurent, H
    Jobard, I
    Toma, A
    [J]. ATMOSPHERIC RESEARCH, 1998, 48 : 651 - 670
12345