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
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.
All for just $49/month
Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.
Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.
It’s easy to organize your research with our built-in tools.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera