Inferring atmospheric dynamics from aerosol observations in 4D‐Var

Abstract

This article explores the potential of aerosol observations to provide wind information in four-dimensional variational data assimilation (4D-Var). It is shown that the relative horizontal gradients, crucial for wind extraction from tracers, are on average greater for the aerosol mixing ratio than for the specific humidity, observations of which are known to provide significant information on the wind field. The potential of aerosols to infer atmospheric dynamics is investigated in the Tropics, where the wind information is most critical. An intermediate-complexity incremental 4D-Var system, the Moist Atmosphere Dynamics Data Assimilation Model (MADDAM), has been developed, with a forecast model that simulates most dominant processes involving moisture, aerosols, and dynamics: nonlinear advection, condensation, and wet deposition. The results of 4D-Var experiments reveal a detrimental impact of saturation-related nonlinearities and aerosol wet deposition on the extraction of wind from aerosol data. Fraternal-twin experiments show about 30%smaller impact of aerosol data on the wind analysis compared with humidity data, mainly due to the greater aerosol observation error and suboptimal background-error covariance model. However, the assimilation of aerosol data together with temperature and humidity observations shows significant added value for wind analyses. K E Y W O R D Saerosols, background-error model, data assimilation, humidity, tracing, wind extraction, 4D-Var

Flow‐dependent wind extraction in strong‐constraint 4D‐Var

“…In this case, 4D-Var internal dynamics establishes locally highly convergent flow, which acts to compensate for tracer loss due to excessive wet deposition. Such spurious wind analyses have been observed in the operational 4D-Var (Dee, 2008) and have been explained in detail in Zaplotnik et al (2020). They can occur whenever there is a large mismatch between the truth and the model evolution, and the dynamics are not constrained by the direct observations.…”

“…The impact of simulated tracer observations on the wind and tracer analyses is evaluated using the case of a strongly nonlinear flow near saturation, shown in Figure 7a The background ensemble is generated by perturbing the control background field using the randomization method (Andersson et al, 2000). The method applies random normal perturbations on the complex elements of the control vector, which uses the implemented background-error variance spectrum (Zaplotnik et al, 2018(Zaplotnik et al, , 2020. In this way, 50 ensemble members are generated.…”

“…The same set of observations is used in all assimilation experiments. The rest of the model setup for the assimilation experiments follows Zaplotnik et al (2020).…”

“…How to include time‐ and space‐dependent parameter $$$ \gamma \left(x,y,t\right) $$$ into the strong‐constraint 4D‐Var with a 12‐hr assimilation window? Let us consider a 2D nonlinear advection of passive tracer $$$ c $$$ with wind $$$ \mathbf{v}=\left(u,v\right) $$$, that is, without sources, sinks and other physical processes (as in (Zaplotnik et al, 2020). The governing equations are as follows: $$$$ \frac{\partial \mathbf{v}}{\partial t}\kern0.5em =-\left(\mathbf{v}\cdotp \nabla \right)\mathbf{v}, $$$$ $$$$ \frac{\partial c}{\partial t}\kern0.5em =-\nabla \cdotp \left(\mathbf{v}c\right).$$…”

“…For example, the 4D-Var humidity-wind tracing appears to be less effective in the Tropics at around 400 hPa pressure level than in the midlatitudes, which leads to increased errors in the short-range forecasts of winds (Geer et al, 2014). While this impact may be an artefact of the own-analysis-based verification, the reasons for degraded skill may be physical as well: (1) the spatial variability of specific humidity in the Tropics is much lower than in the midlatitudes and the winds are slower on average, resulting in lower humidity advection (Zaplotnik et al, 2020), and (2) the magnitude of physical forcings, that is, the condensation rate and associated diabatic heating, are largest in the tropical midtroposphere. A high advection rate in comparison with the rate of physical forcing is essential for successful wind extraction, as we show in this article.…”