J8J.4 Innovations in monitoring and nowcasting orographic precipitation by radar

Friday, 28 October 2005: 9:15 AM
Alvarado ABCD (Hotel Albuquerque at Old Town)
U. Germann, MeteoSwiss, Locarno-Monti, Switzerland; and G. Galli, A. M. Hering, K. Friedrich, P. Tabary, and C. N. James

This paper presents recent progress and innovations for monitoring and nowcasting orographic precipitation by radar in the Swiss Alps.

Recent modifications in radar precipitation estimation over Switzerland resulted in a large reduction of bias, scatter and the number of false alarms. An objective large-scale verification based on 9 years of radar and gauge measurements over all of Switzerland shows the relative contribution of the individual steps of error correction.

Over mountains the use of polarimetric measurements is expected to be more difficult than in flat terrain for at least two reasons: First, because of beam shielding by mountains a large part of radar measurements are made in the ice phase or a mixture of ice and water. Second, the specific differential phase, which is a key property for polarimetric rainfall retrieval, is highly sensitive to clutter signals. Raw measurements from a dual-polarisation radar recently installed by Meteo-France near to Paris will be used to simulate the potential usefulness of polarimetric measurements in a mountainous region.

Short-term prediction of thunderstorms and the onset, location and let-up of heavy precipitation over mountains requires detailed knowledge of the mechanisms of orographic precipitation. Results from the Mesoscale Alpine Programme (MAP) Field Experiment show high sensitivity of orographic rainfall to upstream static stability and the wind field, in particular the strength and direction of the low-level inflow. Based on these results and experience from 40 years of radar usage in the Swiss Alps heuristic systems for probabilistic short-term prediction of orographic precipitation and thunderstorms are currently being developed. These heuristic techniques are optimised for a specific application, trained on a large set of observations from the past, and can be run operationally with a high update rate of a few minutes.

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