CASPER West Evaporation Duct Height Inversion Using LATPROP-Radar

2018 ◽  
Author(s):  
Joshua Compalec ◽  
Caglar Yardim ◽  
Luyao Xu ◽  
Shanka Wijesundara ◽  
Joel Johnson ◽  
...  
Keyword(s):  
Evaporation Duct ◽  
Evaporation Duct Height

A Passive Technique to Monitor Evaporation Duct Height Using Coastal GNSS-R

2011 ◽  
Vol 8 (4) ◽  
pp. 587-591 ◽  
Author(s):  
Bo Wang ◽  
Zhen-Sen Wu ◽  
Zhen-Wei Zhao ◽  
Hong-Guang Wang
Keyword(s):  
Evaporation Duct ◽  
Passive Technique ◽  
Evaporation Duct Height

The short-term forecasting of evaporation duct height (EDH) based on ARIMA model

2016 ◽  
Vol 76 (23) ◽  
pp. 24903-24916 ◽  
Author(s):  
Shaobo Yang ◽  
Sihui Liu ◽  
Xingfei Li ◽  
Ying Zhong ◽  
Xin He ◽  
...  
Keyword(s):  
Arima Model ◽  
Short Term ◽  
Evaporation Duct ◽  
Short Term Forecasting ◽  
Evaporation Duct Height

scholarly journals Evaporation Duct Height Nowcasting in China’s Yellow Sea Based on Deep Learning

2021 ◽  
Vol 13 (8) ◽  
pp. 1577
Author(s):  
Jie Han ◽  
Jia-Ji Wu ◽  
Qing-Lin Zhu ◽  
Hong-Guang Wang ◽  
Yu-Feng Zhou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Time Resolution ◽  
Yellow Sea ◽  
Forecast Accuracy ◽  
The Yellow Sea ◽  
Evaporation Duct ◽  
Novel Approach ◽  
Deep Learning Network ◽  
Evaporation Duct Height ◽  
Working Parameters

The evaporation duct is a weather phenomenon that often occurs in marine environments and affects the operation of shipborne radar. The most important evaluation parameter is the evaporation duct height (EDH). Forecasting the EDH and adjusting the working parameters and modes of the radar system in advance can greatly improve radar performance. Traditionally, short-term forecast methods have been used to estimate the EDH, which are characterized by low time resolution and poor forecast accuracy. In this study, a novel approach for EDH nowcasting is proposed based on the deep learning network and EDH data measured in the Yellow Sea, China. The factors that affect nowcasting were analyzed. The time resolution and forecast time were 5 min and 0–2 h, respectively. The results show that our proposed method has a higher forecast accuracy than traditional time series forecasting methods and confirm its feasibility and effectiveness.

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