Coupling of the aerosols, moisture and winds in 4D-var data assimilation for Numerical Weather PredictionZaplotnik, Žiga (Avtor)
Žagar, Nedjeljka (Mentor)
wind tracing4D-Vartropical data assimilationadjoint adjustmenthumidity control variablenonlinearitymoisture observationsaerosolsThe increasing amount of remotely sensed data on atmospheric trace constituents has been provided by satellites in recent years as well as numerous vertical temperature and moisture profiles in form of radiances. This trend is going to continue with the launch of the Aeolus and EarthCARE satellites. In spite of significant improvements in atmospheric wind analyses expected from the Aeolus mission, especially in the tropics, there will remain a large gap between the number of available wind field and mass field observations. The initialization of wind field will remain strongly dependent on the quality of the background state and the modeling assumptions regarding the background-error covariances.
The thesis addresses the potential of the four-dimensional variational data assimilation (4D-Var) to retrieve the unobserved wind field from the observations of atmospheric tracers and the mass field (temperature, moisture) through the 4D-Var internal model dynamics and the multivariate relationships in the background-error term. These mass-field data provide the information on advection. The presence of discontinuous and nonlinear moist dynamics as well as numerous non-mass conserving aerosol processes make the wind tracing very difficult and susceptible to errors. On the other hand, moisture observations were shown to influence wind in both tropics and midlatitudes.
The problem of wind retrieval is studied using a novel intermediate-complexity 4D-Var data assimilation system which simulates nonlinear interactions between wind, temperature, moisture and aerosols. The description of moist processes includes a simple representation of condensation and the impact of released latent heat on dynamics. The prognostic equation for the total aerosol mixing ratio describes the dominant processes affecting the aerosol spatial distribution: advection and wet deposition by precipitation. The 4D-Var assimilation applies the incremental approach and uses a transformed relative humidity as control variable. In contrast to the model dynamical variables, which are analyzed in the multivariate fashion, moisture and aerosol data are assimilated univariately.
The observing system simulation experiments are performed for the tropics, where the lack of wind information is most critical. Results show that the wind tracing from both aerosol and moisture data in unsaturated atmosphere largely depends on the spatial density and accuracy of the observations as well as the frequency of observation update and assimilation window length. The first two are needed to describe the spatial gradients of tracer and the last two provide information about the advection. In the case with linear flow, the spatial density of observations is more important than their update frequency while the opposite holds in nonlinear flow. There, the accuracy of wind tracing depends on the level of nonlinearity.
In saturated atmosphere, combined assimilation of moisture and temperature data is shown to significantly improve wind analyses, as the intensity of the condensation process is susceptible to slightest changes in saturation humidity and thus temperature. The perfect-model 4D-Var with moisture observations can extract wind information even in the precipitating regions and strongly non-linear flow provided sufficient observations of humidity gradients.
Wind tracing from aerosol data in saturated atmosphere is more complex, as the dominant aerosol process becomes deposition. As a result, small prior errors in thermodynamic fields (humidity, temperature) can amplify in a positive feedback loop, ruining the wind analysis. The results suggest that the assimilation of aerosols (and tracers in general) with feedback on winds is beneficial if the local rate of unmodeled or unknown aerosol sources and sinks (e.g. unmodeled wet deposition) is lower than the local magnitude of the wind advection rate, or else the analysis is ruined.
Last, an ensemble of assimilation experiments provided a quantified estimation of the wind tracing potential for various modeling choices regarding the background-error covariance model, observation availability and accuracy, and assimilation settings.20182018-10-05 07:45:28Doktorsko delo/naloga104360VisID: 93171COBISS_ID: 372649sl