Abstract
Numerical weather prediction (NWP) systems at the convective scale are operated to gain reliable forecasts for diverse atmospheric variables at high spatial resolution. Especially for the prediction of small-scale weather phenomena such as deep convection including the associated precipitation patterns and wind gusts the high-resolution models provide additional benefit over coarser scale models. In this context the distribution of atmospheric humidity plays an important role, however conventional observations of atmospheric humidity are sparse in space and time. The present work aims at the assimilation of water vapour channel radiances of the satellite instrument SEVIRI in an operational framework based on a Local Ensemble Transform Kalman Filter (LETKF) and a convection permitting NWP model. This article describes all the essential elements for a successful incorporation of this kind of data into the system, from the application of a cloud filtering technique over bias correction and vertical localisation of the radiance observation. Data assimilation experiments over two four-week periods show a neutral to slightly positive impact of SEVIRI radiances on upper-air relative humidity and wind speed forecasts.
Highlights
Weather forecasts around the world are based on numerical weather prediction (NWP)
The present work introduces a novel combination of techniques which allows to exploit infrared water vapor channel satellite radiances at the convective scale in an operational Numerical weather prediction (NWP) setting using an ensemble Kalman filter
Short-range forecast models at the convective scale are prone to many small-scale nonlinear processes and feedback processes, which can lead to a negative impact of a sub-optimal setup when trying to assimilate formerly unused observations
Summary
Weather forecasts around the world are based on numerical weather prediction (NWP). National weather services operate different NWP systems with different data assimilation (DA) methods. Widespread DA techniques in operational frameworks are variational techniques, i.e., 3DVar and 4DVar (Rabier et al, 2000; Fischer et al, 2005) and, more recently, ensemble Kalman filters (Bonavita et al, 2010; Schraff et al, 2016) These techniques merge short-range forecasts from an atmospheric model (“first guess”) and a large number of different observations to gain an optimal initial state for the atmospheric state variables of the forecast model. The observations used are on the one hand in situ observations of pressure, temperature, horizontal wind or humidity measured close to the surface, e.g., by weather stations or buoys, or in the upper atmosphere by radiosondes and air planes. These observations have a specific spatial location.
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