A Bayesian neural network approach for tropospheric temperature retrievals from a lidar instrument-Reference-Cited by-同舟云学术

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A Bayesian neural network approach for tropospheric temperature retrievals from a lidar instrument

Published:2023-03-04 Issue:5 Volume:44 Page:1611-1627
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Farhani Ghazal¹,Martucci Giovanni²,Roberts Tyler³,Haefele Alexander²,Sica Robert J.³

Affiliation:

1. National Research Council Canada, Automotive and Surface Transportation Research Centre, London, Canada

2. Federal Office of Meteorology and Climatology, MeteoSwiss, Payerne, Switzerland

3. Department of Physics and Astronomy, The University of Western Ontario, London, Canada

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2023.2187723

Reference46 articles.

1. Signal induced noise in PMT detection of lidar signals

2. Randomize-Then-Optimize: A Method for Sampling from Posterior Distributions in Nonlinear Inverse Problems

3. Temperature Measurements with Lidar

4. Training with Noise is Equivalent to Tikhonov Regularization

5. A neural-network technique for the retrieval of atmospheric temperature and moisture profiles from high spectral resolution sounding data

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1. Orthogonal retrieval algorithm of atmospheric temperature profiles from pure rotational Raman lidar signals;Applied Optics;2024-02-02

2. Dynamic dielectric function and phonon self-energy from electrons strongly correlated with acoustic phonons in 2D Dirac crystals;Journal of Physics: Condensed Matter;2023-05-11

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