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Lagrangian 4-Dimensional Variational Data Assimilation of Chemical Species

Dr. David Lary in a suit in front of computers, portrait. Professor of Physics in the Hanson Center for Space Sciences; Founding Director of MINTS (Multi-Use Multi-scale Integrated Interactive Intelligent Sensing and Simulation in Service of  Society); Center for Brain Health Investigator

Quarterly Journal of the Royal Meteorological Society

M. Fisher and D. J. Lary

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Overview

For the first time, the method of four-dimensional variational data assimilation is applied to the analysis of chemically active trace species. By combining observations with a numerical model to analyze simultaneously several species over a period of a few days, the analysis method is able to exploit information which is not available to conventional analysis techniques. Moreover, effective use can be made of asynoptic observations even for species which have strong diurnal cycles. Synoptic analyses are produced. A Lagrangian approach is adopted, allowing a separation of dynamics and chemistry which considerably reduces the computational expense of the method.
Differences between observed and analysed mixing ratios. (a) 03 (ppmv), @) daytime NO:! (ppbv), (c) night-time NOz(ppbv). Unshaded areas indicate differences in the range 10.5 units. Light, medium and dark shaded areas indicate differences in the ranges 11, f2 and more than f4 units, respectively. The locations of the observations used for the analysis are shown in the lower plots.

Differences between observed and analyzed mixing ratios. (a) 03 (ppmv), @) daytime NO:! (ppbv), (c) night-time NOz(ppbv). Unshaded areas indicate differences in the range 10.5 units. Light, medium and dark shaded areas indicate differences in the ranges 11, f2 and more than f4 units, respectively. The locations of the observations used for the analysis are shown in the lower plots.

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AUTHOR SPOTLIGHT

David Lary, PhD

BrainHealth Investigator Professor of Physics, Hanson Center for Space Sciences Founding Director, MINTS