Decomposition of Seasonality and Long-term Trend in Seismological Data: A Bayesian Modelling of Earthquake Detection Capability

2014 ◽  
Vol 56 (3) ◽  
pp. 201-215 ◽  
Author(s):  
Takaki Iwata
Keyword(s):  
Bayesian Modelling ◽  
Detection Capability ◽  
Term Trend ◽  
Earthquake Detection ◽  
Seismological Data ◽  
Long Term Trend

NAEP Trends: Main NAEP vs. Long-Term Trend

2010 ◽  
Author(s):  
Albert E. Beaton ◽  
James R. Chromy
Keyword(s):  
Term Trend ◽  
Long Term Trend

scholarly journals A Potential Risk Index Dataset for Landfalling Tropical Cyclones over the Chinese Mainland (PRITC dataset V1.0)

2021 ◽  
Vol 38 (10) ◽  
pp. 1791-1802
Author(s):  
Peiyan Chen ◽  
Hui Yu ◽  
Kevin K. W. Cheung ◽  
Jiajie Xin ◽  
Yi Lu
Keyword(s):  
Tropical Cyclones ◽  
Potential Risk ◽  
Risk Index ◽  
Chinese Mainland ◽  
Current Version ◽  
Term Trend ◽  
The Chinese Mainland ◽  
Landfalling Tropical Cyclones ◽  
Long Term Trend

AbstractA dataset entitled “A potential risk index dataset for landfalling tropical cyclones over the Chinese mainland” (PRITC dataset V1.0) is described in this paper, as are some basic statistical analyses. Estimating the severity of the impacts of tropical cyclones (TCs) that make landfall on the Chinese mainland based on observations from 1401 meteorological stations was proposed in a previous study, including an index combining TC-induced precipitation and wind (IPWT) and further information, such as the corresponding category level (CAT_IPWT), an index of TC-induced wind (IWT), and an index of TC-induced precipitation (IPT). The current version of the dataset includes TCs that made landfall from 1949–2018; the dataset will be extended each year. Long-term trend analyses demonstrate that the severity of the TC impacts on the Chinese mainland have increased, as embodied by the annual mean IPWT values, and increases in TCinduced precipitation are the main contributor to this increase. TC Winnie (1997) and TC Bilis (2006) were the two TCs with the highest IPWT and IPT values, respectively. The PRITC V1.0 dataset was developed based on the China Meteorological Administration’s tropical cyclone database and can serve as a bridge between TC hazards and their social and economic impacts.

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