A scheme to assimilate "no-rain" observations from Doppler radar

A scheme to assimilate "no-rain" observations from Doppler radar 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Weather and Forecasting American Meteorological Society

A scheme to assimilate "no-rain" observations from Doppler radar

Loading next page...
 
/lp/ams/a-scheme-to-assimilate-no-rain-observations-from-doppler-radar-PkxNrdDK5J
Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0434
D.O.I.
10.1175/WAF-D-17-0108.1
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Weather and ForecastingAmerican Meteorological Society

Published: Dec 1, 2017

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

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

Explore the DeepDyve Library

Unlimited reading

Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.

Stay up to date

Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.

Organize your research

It’s easy to organize your research with our built-in tools.

Your journals are on DeepDyve

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.

See the journals in your area

Monthly Plan

  • Read unlimited articles
  • Personalized recommendations
  • No expiration
  • Print 20 pages per month
  • 20% off on PDF purchases
  • Organize your research
  • Get updates on your journals and topic searches

$49/month

Start Free Trial

14-day Free Trial

Best Deal — 39% off

Annual Plan

  • All the features of the Professional Plan, but for 39% off!
  • Billed annually
  • No expiration
  • For the normal price of 10 articles elsewhere, you get one full year of unlimited access to articles.

$588

$360/year

billed annually
Start Free Trial

14-day Free Trial