scholarly journals Analysis of Eruption Cloud with Geostationary Meteorological Satellite Imagery (HIMAWARI).

2002 ◽  
Vol 111 (3) ◽  
pp. 374-394 ◽  
Author(s):  
Yoshihiro SAWADA
Keyword(s):  
Satellite Imagery ◽  
Eruption Cloud ◽  
Geostationary Meteorological Satellite ◽  
Meteorological Satellite

scholarly journals Estimating Sunshine Duration Using Hourly Total Cloud Amount Data from a Geostationary Meteorological Satellite

Atmosphere ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 26
Author(s):  
Weiwei Zhu ◽  
Bingfang Wu ◽  
Nana Yan ◽  
Zonghan Ma ◽  
Linjiang Wang ◽  
...  
Keyword(s):  
Sunshine Duration ◽  
Regional Scale ◽  
Cloud Amount ◽  
Important Indicator ◽  
Study Region ◽  
Total Cloud Amount ◽  
Cloud Coverage ◽  
Ground Observation ◽  
Geostationary Meteorological Satellite ◽  
Meteorological Satellite

Sunshine duration is an important indicator of the amount of solar radiation received in a region and an important input parameter for the study of atmospheric energy balance, climate change, ecosystem evolution, and social sustainability. Currently, extrapolation and interpolation of data from meteorological stations are the most common methods used to calculate sunshine duration on a regional scale. However, it is difficult to obtain high precision sunshine duration in areas lacking ground observation or where sunshine duration is highly heterogeneous on the ground. In this paper, a new method is proposed to estimate sunshine duration with hourly total cloud amount (CTA) data from sunrise to sunset derived from the Fengyun-2G geostationary meteorological satellite (FY-2G). This method constructs a new index known as daytime mean total cloud coverage amount and provides quadratic equations relating daytime mean total cloud coverage amount to relative sunshine duration in different seasons. The method was validated with ground observation data for 2016 from 18 meteorological stations in the Three-River Headwaters Region of Qinghai Province, China. For individual stations, the coefficient of determination (R2) between estimated and measured sunshine was at least 0.894, the RMSE (root mean square error) was 0.977 h/day or less, the MAE (mean absolute error) was 0.824 h/day or less, the RE (relative error) was 0.150 or lower, and the value of d was 0.963 or greater, which validated that the proposed method can effectively predict daily sunshine duration. These equations can also provide higher precision estimates of regional-scale sunshine duration. This was demonstrated by comparing, for the entire study region, the spatial distribution of sunshine duration estimated from season-based equations with results from three different interpolation methods based on ground observations. Overall, the study confirms that total cloud amount measures from a geostationary satellite can be used to successfully estimate sunshine duration.

A Deep Learning Network for Cloud-to-Ground Lightning Nowcasting with Multisource Data

2020 ◽  
Vol 37 (5) ◽  
pp. 927-942
Author(s):  
Kanghui Zhou ◽  
Yongguang Zheng ◽  
Wansheng Dong ◽  
Tingbo Wang
Keyword(s):  
Deep Learning ◽  
Weather Radar ◽  
Probability Of Detection ◽  
Learning Network ◽  
Source Data ◽  
Geostationary Meteorological Satellite ◽  
Lightning Initiation ◽  
Deep Learning Network ◽  
Meteorological Satellite ◽  
Cg Lightning

AbstractPrecise and timely lightning nowcasting is still a great challenge for meteorologists. In this study, a new semantic segmentation deep learning network for cloud-to-ground (CG) lightning nowcasting, named LightningNet, has been developed. This network is based on multisource observation data, including data from a geostationary meteorological satellite, Doppler weather radar network, and CG lightning location system. LightningNet, with an encoder–decoder architecture, consists of 20 three-dimensional convolutional layers, pooling and upsampling layers, normalization layers, and a softmax classifier. The central–eastern and southern China was selected as the study area, with considerations given to the topography and spatial coverage of the weather radar and lightning observation networks. Brightness temperatures (TB) of six infrared bands from the Himawari-8 satellite, composite reflectivity mosaic, and CG lightning densities were used as the predictors because of their close relationships with lightning activity. The multisource data were first interpolated into a uniform spatial/temporal resolution of 0.05° × 0.05°/10 min, and then training and test datasets were constructed, respectively. LightningNet was trained to extract the features of lightning initiation, development, and dissipation. The evaluation results demonstrated that LightningNet was able to achieve good performance of 0–1-h lightning nowcasts using the multisource data. The probability of detection, the false alarm ratio, the area under relative operating characteristic curve, and the threat score (TS) of LightningNet with all three types of data reached 0.633, 0.386, 0.931, and 0.453, respectively. Because geostationary meteorological satellite and radar both possess the capability of capturing lightning initiation (LI) features, LightningNet also showed good performance in LI nowcasting. When all three types of data were used, more than 50% LI was predicted accurately and the TS exceeded 0.36. LightningNet’s nowcast performance using triple-source data was clearly superior to that using only single-source or dual-source data, and these findings indicate that LightningNet has good capability of combining multisource data effectively to produce more reliable lightning nowcasts.

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