Inferring surface and soil variables from assimilation of atmospheric boundary layer observations

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The evolution of the atmospheric boundary layer is strongly coupled via energy and moisture fluxes with the state of the surface and soil. This interaction is used in meteorological data assimilation to derive soil variables, especially soil moisture, from deviations of forecasts of screen-level atmospheric variables from the observations. This established approach, which is also used operationally by some weather services, will be implemented in the integrated data assimilation scheme of the Research Unit to quantify the information content of atmospheric boundary layer (ABL) observations. We will first consider screen-level temperature and humidity as ABL observations and add later boundary layer height and skin temperature.As a novel research approach, we will extend the assimilation of ABL observations in two main directions: First, model parameters, e.g. leaf area index, will be estimated additionally by the data assimilation to avoid a too strong attribution of forecast errors to state variables of the soil. Second, we will consider not only instantaneous differences of forecasts and observations as so-called innovations, but also take the temporal structure of forecast errors into account. This will account for the very different temporal scales at which surface and soil variables vary: Surface temperature, for example, changes within seconds, whereas soil moisture varies at scales of hours to months (depending on the depth), and parameters, like e.g. the heat conductivity of a dry soil, are constant in time. Accordingly, errors of these quantities result in characteristic atmospheric forecast errors, which we call “fingerprints”, since they allow identifying the error source.The virtual catchment is perfectly suited to determine these fingerprints: At selected grid points, a simplified one-dimensional land-atmosphere model will be used to perform ensemble simulations with systematic perturbations of the fingerprint variable and random perturbations of other model variables. We will identify which atmospheric quantities under which conditions and at which timescales are affected most by the systematic perturbations. As second step, an Ensemble Kalman Filter (EnKF) assimilation system will be installed for the one-dimensional model and expanded to allow for an assimilation of model parameters. Finally, the novel fingerprints will be implemented as observation operator in this framework. This first implementation will be further expanded to account for non-instantaneous observations. We will review existing concepts for this multi-timescale extension and identify the moist suitable approach. The implementation of this enhancement as well as the adaptation of the methodology, which will be developed first for single-column models, to the three-dimensional coupled model system is planned for the second project phase.