Abstract
Aiming at the Four-Dimensional Variation source term inversion algorithm proposed earlier, the observation error regularization factor is introduced to improve the prediction accuracy of the diffusion model, and an improved Four-Dimensional Variation source term inversion algorithm with observation error regularization (OER-4DVAR STI model) is formed. Firstly, by constructing the inversion process and basic model of OER-4DVAR STI model, its basic principle and logical structure are studied. Secondly, the observation error regularization factor estimation method based on Bayesian optimization is proposed, and the error factor is separated and optimized by two parameters: error statistical time and deviation degree. Finally, the scientific, feasible and advanced nature of the OER-4DVAR STI model are verified by numerical simulation and tracer test data. The experimental results show that OER-4DVAR STI model can better reverse calculate the hazard source term information under the conditions of high atmospheric stability and flat underlying surface. Compared with the previous inversion algorithm, the source intensity estimation accuracy of OER-4DVAR STI model is improved by about 46.97%, and the source location estimation accuracy is improved by about 26.72%.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
