AbstractRadar reflectivity observations contain valuable information on precipitation and have been assimilated into numerical weather prediction models for improved microphysics initialization. However, low-reflectivity (or so-called “no-rain”) echoes have often been ignored or not effectively used in radar data assimilation schemes. In this paper, a scheme to assimilate no-rain radar observations is described with the framework of the Weather Research and Forecasting model’s three-dimensional variational data assimilation system (3DVar), and its impact on precipitation forecasts is demonstrated. The key feature of the scheme is a neighborhood-based approach to adjust water vapor when a grid point is deemed no-rain. The performance of the scheme is first examined using a severe convective case in the Colorado Rocky Mountain Front Range and then verified by running the 3DVar system in the same region, with and without the no-rain assimilation scheme for 68 days and three-hourly rapid update cycles. It is shown that the no-rain data assimilation method reduces the BIAS and false alarm ratio of precipitation over its counterpart without that assimilation. The no-rain assimilation also improved humidity, temperature and wind fields, with the largest error reduction in the water vapor field, both near the surface and at upper levels. It is also shown that the advantage of the scheme is in its ability to conserve total water content in cycled radar data assimilation, which cannot be achieved by assimilating only precipitation echoes.
Weather and Forecasting – American Meteorological Society
Published: Dec 1, 2017
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