Predictability of Stratospheric Sudden Warmings in the Beijing Climate Center Forecast System with Statistical Error Corrections

Journal of Geophysical Research Atmospheres ◽

10.1029/2019jd030900 ◽

2019 ◽

Cited By ~ 2

Author(s):

Jian Rao ◽

Rongcai Ren ◽

Haishan Chen ◽

Xiangwen Liu ◽

Yueyue Yu ◽

...

Keyword(s):

Statistical Error ◽

Forecast System ◽

Error Corrections ◽

Stratospheric Sudden Warmings

Download Full-text

An innovative LDV data processing method for statistical error corrections. Application to homogeneous and non-homogeneous seeding

Flow Measurement and Instrumentation ◽

10.1016/j.flowmeasinst.2018.02.011 ◽

2018 ◽

Vol 60 ◽

pp. 67-77 ◽

Cited By ~ 3

Author(s):

Romain Rodriguez ◽

Frédéric Murzyn ◽

Judicael Aubry ◽

Amine Mehel ◽

Frédérique Larrarte

Keyword(s):

Data Processing ◽

Statistical Error ◽

Processing Method ◽

Error Corrections ◽

Data Processing Method

Download Full-text

Background subtraction in STEM energy-loss mapping

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100112774 ◽

1984 ◽

Vol 42 ◽

pp. 568-569

Author(s):

R.D. Leapman ◽

K.E. Gorlen ◽

C.R. Swyt

Keyword(s):

Energy Loss ◽

Signal To Noise Ratio ◽

Statistical Error ◽

Scanning Transmission Electron Microscope ◽

Reference Image ◽

Inverse Power Law ◽

Transmission Electron ◽

Least Squares Fit ◽

Scanning Transmission ◽

Single Number

The determination of elemental distributions by electron energy loss spectroscopy necessitates removal of the non-characteristic spectral background from a core-edge at each point in the image. In the scanning transmission electron microscope this is made possible by computer controlled data acquisition. Data may be processed by fitting the pre-edge counts, at two or more channels, to an inverse power law, AE-r, where A and r are parameters and E is energy loss. Processing may be performed in real-time so a single number is saved at each pixel. Detailed analysis, shows that the largest contribution to noise comes from statistical error in the least squares fit to the background. If the background shape remains constant over the entire image, the signal-to-noise ratio can be improved by fitting only one parameter. Such an assumption is generally implicit in subtraction of the “reference image” in energy selected micrographs recorded in the CTEM with a Castaing-Henry spectrometer.

Download Full-text