Günther Haase

Dealiasing of Doppler Radar Velocities Using a Torus Mapping

A novel dealiasing algorithm for Doppler radar velocity data has been developed at the Swedish Meteorological and Hydrological Institute (SMHI). Unlike most other methods, it does not need independent wind information from other instruments (e.g., nearby radiosonde or wind profiler) or numerical weather prediction (NWP) models. The innovation of the new technique is that it maps the measurements onto the surface of a torus. Dealiased volume radar data can be used in variational assimilation schemes for NWP models through the generation of so-called superobservations. Their use is expected to improve with the introduction of the proposed dealiasing method.

Doppler radar radial winds in HIRLAM. Part II: optimizing the super-observation processing

Abstract Doppler radar radial wind observations are modelled in numerical weather prediction (NWP) within observation errors which consist of instrumental, modelling and representativeness errors. The systematic and random modelling errors can be reduced through a careful design of the observation operator (Part I). The impact of the random instrumental and representativeness errors can be decreased by optimizing the processing of the so-called super-observations (spatial averages of raw measurements; Part II). The super-observation processing is experimentally optimized in this article by determining the optimal resolution for the super-observations for differentNWPmodel resolutions. A 1-month experiment with the HIRLAM data assimilation and forecasting system is used for radial wind data monitoring and for generating observation minus background (OmB) differences. The OmB statistics indicate that the super-observation processing reduces the standard deviation of the radial wind speedOmBdifference, while themean vectorwindOmBdifference tends to increase. The optimal parameter settings correspond at a measurement range of 50 km (100 km) to an averaging area of 1.7 km2 (7.3 km2). In conclusion, an accurate and computationally feasible observation operator for the Doppler radar radial wind observations is developed (Part I) and a super-observation processing system is optimized (Part II).

Assimilation of Radar Data in Numerical Weather Prediction (NWP) Models

Radar data have exciting potential for improving forecasts from operational numerical weather prediction (NWP) models. This potential, already partially realised, arises from a combination of developments. NWP models of the European National Meteorological Services (NMS) are now running routinely at the 10 km grid scale and in a few years will be moving to resolutions of the order of 2 km. Such high resolution models require correspondingly high resolution wind and moisture data for initialisation, which radar networks are well placed to deliver. Secondly, NWP data assimilation techniques have advanced considerably in the 1990s, with the arrival of techniques capable of extracting information from time sequences of observations only indirectly related to model prognostic variables. The first decade of the twenty-first century is likely to see further improvements in computing power, microphysical parametrisation and assimilation methods which will enable better exploitation of the information available from weather radars. Thirdly, developments in radar networking and processing around Europe are beginning to reach a maturity which makes feasible the routine operational delivery of quality controlled radar information of an accuracy sufficient for worthwhile NWP assimilation.