Detection of defects in metallic materials using an improved post-processing method of pulsed thermography

2022 ◽  
Vol 151 ◽  
pp. 106927
Author(s):  
Pengfei Zhu ◽  
Dan Wu ◽  
Lei Qian
Keyword(s):  
Processing Method ◽  
Metallic Materials ◽  
Post Processing ◽  
Pulsed Thermography ◽  
Detection Of Defects

Post Processing Method for Multiple Spinecho Brain MR by grouping voxels by signal decay

1990 ◽  
Vol 25 (12) ◽  
pp. 1355
Author(s):  
S. S. Winkler ◽  
Y. H. Kao ◽  
J. A. Sorenson
Keyword(s):  
Processing Method ◽  
Post Processing ◽  
Signal Decay

Improved trend-aware post-processing of GCM seasonal precipitation forecasts

2021 ◽  
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Processing Method ◽  
Global Climate Models ◽  
Seasonal Precipitation ◽  
Seasonal Temperature ◽  
Post Processing ◽  
Climate Trends ◽  
Climate Forecasting ◽  
New Formulation

Abstract Climate trends have been observed over the recent decades in many parts of the world, but current global climate models (GCMs) for seasonal climate forecasting often fail to capture these trends. As a result, model forecasts may be biased above or below the trendline. In our previous research, we developed a trend-aware forecast post-processing method to overcome this problem. The method was demonstrated to be effective for embedding observed trends into seasonal temperature forecasts. In this study, we further develop the method for post-processing GCM seasonal precipitation forecasts. We introduce new formulation and evaluation features to cater for special characteristics of precipitation amounts, such as having a zero lower bound and highly positive skewness. We apply the improved method to calibrate ECMWF SEAS5 forecasts of seasonal precipitation for Australia. Our evaluation shows that the calibrated forecasts reproduce observed trends over the hindcast period of 36 years. In some regions where observed trends are statistically significant, forecast skill is greatly improved by embedding trends into the forecasts. In most regions, the calibrated forecasts outperform the raw forecasts in terms of bias, skill, and reliability. Wider applications of the new trend-aware post-processing method are expected to boost user confidence in seasonal precipitation forecasts.

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