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Research Article

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

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Pages 1611-1627 | Received 03 Nov 2022, Accepted 22 Feb 2023, Published online: 22 Mar 2023

References

  • Acharya, Y. B., S. Sharma, and H. Chandra. 2004. “Signal Induced Noise in PMT Detection of Lidar Signals.” Measurement 35 (3): 269–276. doi:10.1016/j.measurement.2003.11.003.
  • Bardsley, J. M., A. Solonen, H. Haario, and M. Laine. 2014. “Randomize-Then-Optimize: A Method for Sampling from Posterior Distributions in Nonlinear Inverse Problems.” SIAM Journal on Scientific Computing 36 (4): A1895–1910. doi:10.1137/140964023.
  • Behrendt, A. 2005. “Temperature Measurements with Lidar.” In Lidar, 273–305. New York, NY: Springer.
  • Bishop, C. M. 1995. “Training with Noise is Equivalent to Tikhonov Regularization.” Neural Computation 7 (1): 108–116. doi:10.1162/neco.1995.7.1.108.
  • Blackwell, W. J. 2005. “A Neural-Network Technique for the Retrieval of Atmospheric Temperature and Moisture Profiles from High Spectral Resolution Sounding Data.” IEEE Transactions on Geoscience and Remote Sensing 43 (11): 2535–2546. doi:10.1109/TGRS.2005.855071.
  • Blundell, C., J. Cornebise, K. Kavukcuoglu, and D. Wierstra. 2015. “Weight Uncertainty in Neural Network.” In International Conference on Machine Learning, LILLE GRAND PALAIS, France, 1613–1622. PMLR.
  • Cai, X., Y. Bao, G. P. Petropoulos, F. Lu, Q. Lu, L. Zhu, and W. Ying. 2020. “Temperature and Humidity Profile Retrieval from FY4-GIIRS Hyperspectral Data Using Artificial Neural Networks.” Remote Sensing 12 (11): 1872. doi:10.3390/rs12111872.
  • Caruana, R. 1997. “Multitask Learning.” Machine Learning 28 (1): 41–75. doi:10.1023/A:1007379606734.
  • Cooney, J. 1972. “Measurement of Atmospheric Temperature Profiles by Raman Backscatter.” Journal of Applied Meteorology and Climatology 11 (1): 108–112. doi:10.1175/1520-0450(1972)011<0108:MOATPB>2.0.CO;2.
  • Cortesi, U., J. C. Lambert, C. De Clercq, G. Bianchini, T. Blumenstock, A. Bracher, E. Castelli, V. Catoire, K. V. Chance, and M. De Maziere 2007. Atmospheric Chemistry and Physics Vol. 7 (18): 4807–4867. Copernicus GmbH.
  • Del Frate, F., M. Iapaolo, S. Casadio, S. Godin-Beekmann, and M. Petitdidier. 2005. “Neural Networks for the Dimensionality Reduction of GOME Measurement Vector in the Estimation of Ozone Profiles.” Journal of Quantitative Spectroscopy & Radiative Transfer 92 (3): 275–291. doi:10.1016/j.jqsrt.2004.07.028.
  • Dinoev, T., V. Simeonov, Y. Arshinov, S. Bobrovnikov, P. Ristori, B. Calpini, M. Parlange, and H. Bergh. 2013. “Raman Lidar for Meteorological Observations, RALMO–Part 1: Instrument Description.” Atmospheric Measurement Techniques 6 (5): 1329–1346. doi:10.5194/amt-6-1329-2013.
  • Duc, N. T., S. Ryu, M. N. I. Qureshi, M. Choi, K. H. Lee, and B. Lee. 2020. “3D-Deep Learning Based Automatic Diagnosis of Alzheimer’s Disease with Joint MMSE Prediction Using Resting-State fMri.” Neuroinformatics 18 (1): 71–86. doi:10.1007/s12021-019-09419-w.
  • Farhani, G., R. J. Sica, and M. J. Daley. 2020. “Classification of Lidar Measurements Using Supervised and Unsupervised Machine Learning Methods.” Atmospheric Measurement Techniques Discussions 2020: 1–18.
  • Farhani, G., R. J. Sica, S. Godin-Beekmann, and A. Haefele. 2019. “Optimal Estimation Method Retrievals of Stratospheric Ozone Profiles from a DIAL Lidar.” Atmospheric Measurement Techniques 12 (4): 2097–2111. doi:10.5194/amt-12-2097-2019.
  • Gal, Y., and Z. Ghahramani. 2016. “Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning.” In international conference on machine learning, New York, USA, 1050–1059. PMLR.
  • Hervo, M., Y. Poltera, and A. Haefele. 2016. “An Empirical Method to Correct for Temperature-Dependent Variations in the Overlap Function of Chm15k Ceilometers.” Atmospheric Measurement Techniques 9 (7): 2947–2959. doi:10.5194/amt-9-2947-2016.
  • Kingma, D. P., and J. Ba. 2015. “Adam: A Method for Stochastic Optimization.” In International Conference on Learning Representations (ICLR), San Diego, CA, USA.
  • Korattikara, A., V. Rathod, K. Murphy, and M. Welling. 2015. “Bayesian Dark Knowledge.” arXiv preprint arXiv:1506.04416.
  • Lakshminarayanan, B., A. Pritzel, C. Blundell, and London DeepMind. 2016. “Simple and Scalable Predictive Uncertainty Estimation Using Deep Ensembles.” Stat 1050: 5.
  • Leblanc, T., R. J. Sica, J. A. van Gijsel, A. Haefele, G. Payen, and G. Liberti. 2016. “Proposed Standardized Definitions for Vertical Resolution and Uncertainty in the NDACC Lidar Ozone and Temperature Algorithms–Part 3: Temperature Uncertainty Budget.” Atmospheric Measurement Techniques 9 (8): 4079–4101. doi:10.5194/amt-9-4079-2016.
  • Liu, Z., W. Hunt, M. Vaughan, C. Hostetler, M. McGill, K. Powell, D. Winker, and Y. Hu. 2006. “Estimating Random Errors Due to Shot Noise in Backscatter Lidar Observations.” Applied Optics 45 (18): 4437–4447. doi:10.1364/AO.45.004437.
  • Mahagammulla Gamage, S., R. J. Sica, G. Martucci, and A. Haefele. 2019. “Retrieval of Temperature from a Multiple Channel Pure Rotational Raman Backscatter Lidar Using an Optimal Estimation Method.” Atmospheric Measurement Techniques 12 (11): 5801–5816. doi:10.5194/amt-12-5801-2019.
  • Martucci, G., F. Navas-Guzmán, L. Renaud, G. Romanens, S. M. Gamage, M. Hervo, P. Jeannet, and A. Haefele. 2021. “Validation of Pure Rotational Raman Temperature Data from the Raman Lidar for Meteorological Observations (RALMO) at Payerne.” Atmospheric Measurement Techniques 14 (2): 1333–1353. doi:10.5194/amt-14-1333-2021.
  • Milstein, A. B., and W. J. Blackwell. 2016. “Neural Network Temperature and Moisture Retrieval Algorithm Validation for AIRS/AMSU and CrIs/ATMS.” Journal of Geophysical Research Atmospheres 121 (4): 1414–1430. doi:10.1002/2015JD024008.
  • Müller, M. D., A. K. Kaifel, M. Weber, S. Tellmann, J. P. Burrows, and D. Loyola. 2003. “Ozone Profile Retrieval from Global Ozone Monitoring Experiment (GOME) Data Using a Neural Network Approach (Neural Network Ozone Retrieval System (NNORSY)).” Journal of Geophysical Research Atmospheres 108 (D16). doi:10.1029/2002JD002784.
  • Pearce, T., F. Leibfried, and A. Brintrup. 2020. “Uncertainty in Neural Networks: Approximately Bayesian Ensembling.” In International conference on artificial intelligence and statistics, Palermo, Italy, 234–244. PMLR.
  • Pettifer, R. E. W. 1975. “Signal Induced Noise in Lidar Experiments.” Journal of Atmospheric and Terrestrial Physics 37 (4): 669–673. doi:10.1016/0021-9169(75)90062-8.
  • Philipona, R., C. Mears, M. Fujiwara, P. Jeannet, P. Thorne, G. Bodeker, L. Haimberger, et al. 2018. “Radiosondes Show That After Decades of Cooling, the Lower Stratosphere is Now Warming.” Journal of Geophysical Research Atmospheres 123 (22): 12–509. doi:10.1029/2018JD028901.
  • Pierson, H. A., and M. S. Gashler. 2017. “Deep Learning in Robotics: A Review of Recent Research.” Advanced Robotics 31 (16): 821–835. doi:10.1080/01691864.2017.1365009.
  • Quinonero-Candela, J., C. E. Rasmussen, F. Sinz, O. Bousquet, and B. Schölkopf. 2005. “Evaluating Predictive Uncertainty Challenge.” In Machine Learning Challenges Workshop, 1–27. Berlin, Heidelberg: Springer.
  • Santer, B. D., K. E. Taylor, T. M. L. Wigley, T. C. Johns, P. D. Jones, D. J. Karoly, J. F. B. Mitchell, et al. 1996. “A Search for Human Influences on the Thermal Structure of the Atmosphere.” Nature 382 (6586): 39–46. doi:10.1038/382039a0.
  • Sengupta, U., M. Amos, J. S. Hosking, C. E. Rasmussen, M. Juniper, and P. J. Young. 2020. “Ensembling Geophysical Models with Bayesian Neural Networks.” arXiv preprint arXiv:2010.03561.
  • Sica, R. J., and A. Haefele. 2015. “Retrieval of Temperature from a Multiple-Channel Rayleigh-Scatter Lidar Using an Optimal Estimation Method.” Applied Optics 54 (8): 1872–1889. doi:10.1364/AO.54.001872.
  • Springenberg, J. T., A. Klein, S. Falkner, and F. Hutter. 2016. “Bayesian Optimization with Robust Bayesian Neural Networks.” In Proceedings of the 30th International Conference on Neural Information Processing Systems, Barcelona Spain, 4141–4149.
  • Sun, L., H. Gao, S. Pan, and J.X. Wang. 2020. “Surrogate Modeling for Fluid Flows Based on Physics-Constrained Deep Learning Without Simulation Data.” Computer Methods in Applied Mechanics and Engineering 361: 112732. doi:10.1016/j.cma.2019.112732.
  • Tett, S. F., J. F. Mitchell, D. E. Parker, and M. R. Allen. 1996. “Human Influence on the Atmospheric Vertical Temperature Structure: Detection and Observations.” Science 274 (5290): 1170–1173. doi:10.1126/science.274.5290.1170.
  • Theodoridis, S. 2015. Machine Learning: A Bayesian and Optimization Perspective. Academic press.
  • Thorne, P. W., J. R. Lanzante, T. C. Peterson, D. J. Seidel, and K. P. Shine. 2011. “Tropospheric Temperature Trends: History of an Ongoing Controversy.” Wiley Interdisciplinary Reviews: Climate Change 2 (1): 66–88. doi:10.1002/wcc.80.
  • Tiwari, M. K., and C. Chatterjee. 2010. “Uncertainty Assessment and Ensemble Flood Forecasting Using Bootstrap Based Artificial Neural Networks (BANNs).” Journal of Hydrology 382 (1–4): 20–33. doi:10.1016/j.jhydrol.2009.12.013.
  • Törn, A., M. M. Ali, and S. Viitanen. 1999. “Stochastic Global Optimization: Problem Classes and Solution Techniques.” Journal of Global Optimization 14 (4): 437–447. doi:10.1023/A:1008395408187.
  • Wandinger, U., and A. Ansmann. 2002. “Experimental Determination of the Lidar Overlap Profile with Raman Lidar.” Applied Optics 41 (3): 511–514. doi:10.1364/AO.41.000511.
  • Welling, M., and Y. W. Teh. 2011. “Bayesian Learning via Stochastic Gradient Langevin Dynamics.” In Proceedings of the 28th international conference on machine learning (ICML-11), Bellevue, Washington, USA, 681–688. Citeseer.
  • Yazdi, A., X. Lin, L. Yang, and F. Yan. 2020. “SEFEE: Lightweight Storage Error Forecasting in Large-Scale Enterprise Storage Systems.” In 2020 SC20: International Conference for High Performance Computing, Networking, Storage and Analysis (SC), Online Conference, United States, 894–907. IEEE Computer Society.
  • Zhang, Y., F. Yi, W. Kong, and Y. Yi. 2014. “Slope Characterization in Combining Analog and Photon Count Data from Atmospheric Lidar Measurements.” Applied Optics 53 (31): 7312–7320. doi:10.1364/AO.53.007312.
  • Zhigljavsky, A., and A. Zilinskas. 2007. Stochastic Global Optimization. Vol. 9. New York, NY: Springer Science & Business Media.

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