Abstract
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.
Highlights
Data assimilation is a process by which observations are integrated into a model of the atmosphere thereby changing the model state and its associated forecast
The Canadian Meteorological Center (CMC) 3D-Var scheme developed for meteorology [56] and extended to include chemical variables [63] was further extended in this study to include cross-covariances between observed variables and between observed and unobserved variables using a balance operator
Before we discuss the balance operators, we should note two things: (1) The CMC 3D-Var system uses a non-separable error correlation model. This means that for each horizontal wavenumber there is a unique vertical correlation matrix, and this introduces a dependence between horizontal and vertical scales; and (2) it is usual in meteorological applications to perform the minimization on an analysis grid of lower resolution than the model grid (e.g., [56] and in 4D-Var incremental formulation [68]), as we argued in Part I, Section 6, the meteorological model and analysis increment, as well as the chemical forecast model and the chemical analysis increment should all be on the same grid, in order to avoid a loss of information
Summary
Data assimilation is a process by which observations are integrated into a model of the atmosphere thereby changing the model state and its associated forecast. Instruments on board these satellites typically perform measurements which are tangent to the atmosphere ( called limb soundings) and provide height-resolved retrievals of a number of chemical species as well as temperature Chemical transformations, especially those related to stratospheric ozone, have an impact on the temperature, while winds change the distribution of chemical tracers (i.e., long-lived species). Using an ensemble Kalman filter and an intermediate-complexity model, Milewski and Bourqui [48] demonstrated that information about the ozone-wind cross-covariance is essential in constraining dynamical fields when ozone only is assimilated They showed that a further reduction in error can be obtained with an ensemble Kalman smoother [49].
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