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Variational Assimilation of Cloud Liquid/ice Water Path and Its Impact On NWP


Analysis of the cloud components in numerical weather prediction models using advanced data assimilation techniques has been a prime topic in recent years. In this research, the variational data assimilation (DA) system for the Weather Research and Forecasting (WRF) model (WRFDA) is further developed to assimilate satellite cloud products that will produce the cloud liquid water and ice water analysis. Observation operators for the cloud liquid water path and cloud ice water path are developed and incorporated into the WRFDA system. The updated system is tested by assimilating cloud liquid water path and cloud ice water path observations from Global Geostationary Gridded Cloud Products at NASA. To assess the impact of cloud liquid/ice water paths data assimilation on short-term regional numerical weather prediction (NWP), 3-hourly cycling data assimilation and forecast experiments with and without the use of the cloud liquid/ice water paths are conducted. It is shown that assimilating cloud liquid/ice water paths increases the accuracy of temperature, humidity, and wind analyses at model levels between 300 hPa and 150 hPa after 5 cycles (15 hours). It is also shown that the assimilating cloud liquid/ice water paths significantly reduces forecast errors in temperature and wind at model levels between 300hPa and 150hPa. The precipitation forecast skills are improved as well. One reason that leads to the improved analysis and forecast is that the 3-hourly rapid update cycle carries over the impact of cloud information from the previous cycles spun-up by the WRF model.

Article / Publication Data
Available Metadata
Accepted On
April 17, 2015
Fiscal Year
Peer Reviewed
Publication Name
Journal of Applied Meteorology and Climatology
Published On
August 01, 2015
Publisher Name
American Meteorological Society
Print Volume
Print Number
Page Range
Submitted On
September 18, 2014


Authors who have authored or contributed to this publication.

  • Yaodeng Chen - lead Other
  • Hongli Wang - second Gsl
    Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder
    NOAA/Global Systems Laboratory