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Coupled Stratospheric Chemistry–Meteorology Data Assimilation. Part II: Weak and Strong Coupling

1
Air Quality Research Division, Environment and Climate Change Canada, 2121 Transcanada Highway, Dorval, QC H9P 1J3, Canada
2
Département des Sciences de la Terre et de L’atmosphère, Université du Québec à Montréal, Montréal, QC H2L 2C4, Canada
3
Meteorological Research Division, Environment and Climate Change Canada; Dorval, QC H9P 1J3, Canada
4
Royal Belgian Institute for Space Aeronomy, BIRA-IASB, 1180 Brussels, Belgium
*
Author to whom correspondence should be addressed.
Atmosphere 2019, 10(12), 798; https://doi.org/10.3390/atmos10120798
Received: 19 October 2019 / Revised: 3 December 2019 / Accepted: 5 December 2019 / Published: 9 December 2019
(This article belongs to the Special Issue Air Quality Prediction)
We examine data assimilation coupling between meteorology and chemistry in the stratosphere from both weak and strong coupling strategies. The study was performed with the Canadian operational weather prediction Global Environmental Multiscale (GEM) model coupled online with the photochemical stratospheric chemistry model developed at the Belgian Institute for Space Aeronomy, described in Part I. Here, the Canadian Meteorological Centre’s operational variational assimilation system was extended to include errors of chemical variables and cross-covariances between meteorological and chemical variables in a 3D-Var configuration, and we added the adjoint of tracer advection in the 4D-Var configuration. Our results show that the assimilation of limb sounding observations from the MIPAS instrument on board Envisat can be used to anchor the AMSU-A radiance bias correction scheme. Additionally, the added value of limb sounding temperature observations on meteorology and transport is shown to be significant. Weak coupling data assimilation with ozone–radiation interaction is shown to give comparable results on meteorology whether a simplified linearized or comprehensive ozone chemistry scheme is used. Strong coupling data assimilation, using static error cross-covariances between ozone and temperature in a 3D-Var context, produced inconclusive results with the approximations we used. We have also conducted the assimilation of long-lived species observations using 4D-Var to infer winds. Our results showed the added value of assimilating several long-lived species, and an improvement in the zonal wind in the Tropics within the troposphere and lower stratosphere. 4D-Var assimilation also induced a correction of zonal wind in the surf zone and a temperature bias in the lower tropical stratosphere. View Full-Text
Keywords: coupled chemistry-meteorology data assimilation; weak and strong data assimilation coupling; Canadian Quick Covariance method (CQC); assimilation of MIPAS temperature observations; ozone-temperature cross-covariance; tracer-wind 4D-Var assimilation coupled chemistry-meteorology data assimilation; weak and strong data assimilation coupling; Canadian Quick Covariance method (CQC); assimilation of MIPAS temperature observations; ozone-temperature cross-covariance; tracer-wind 4D-Var assimilation
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Ménard, R.; Gauthier, P.; Rochon, Y.; Robichaud, A.; de Grandpré, J.; Yang, Y.; Charrette, C.; Chabrillat, S. Coupled Stratospheric Chemistry–Meteorology Data Assimilation. Part II: Weak and Strong Coupling. Atmosphere 2019, 10, 798.

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