Editorial Type:
Article
Article Type:
Research Article

Variational Assimilation of Cloud Liquid/Ice Water Path and Its Impact on NWP

Yaodeng Chen * Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China

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Hongli Wang Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado
Global Systems Division, NOAA/Earth System Research Laboratory, Boulder, Colorado

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Jinzhong Min * Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China

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Xiang-Yu Huang Centre for Climate Research Singapore, Meteorological Service Singapore, Singapore

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Patrick Minnis NASA Langley Research Center, Hampton, Virginia

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Ruizhi Zhang * Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China

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Julie Haggerty ** National Center for Atmospheric Research, Boulder, Colorado

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Rabindra Palikonda Science Systems and Applications, Inc., Hampton, Virginia

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Print Publication:
01 Aug 2015
DOI:
https://doi.org/10.1175/JAMC-D-14-0243.1
Page(s):
1809–1825
Restricted access

Abstract

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 path 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 and 150 hPa after 5 cycles (15 h). It is also shown that assimilating cloud liquid/ice water paths significantly reduces forecast errors in temperature and wind at model levels between 300 and 150 hPa. 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.

Denotes Open Access content.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author address: Dr. Hongli Wang, Global Systems Division, NOAA/Earth System Research Laboratory, 325 Broadway, Boulder, CO 80305. E-mail: hongli.wang@noaa.gov

Abstract

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 path 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 and 150 hPa after 5 cycles (15 h). It is also shown that assimilating cloud liquid/ice water paths significantly reduces forecast errors in temperature and wind at model levels between 300 and 150 hPa. 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.

Denotes Open Access content.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author address: Dr. Hongli Wang, Global Systems Division, NOAA/Earth System Research Laboratory, 325 Broadway, Boulder, CO 80305. E-mail: hongli.wang@noaa.gov
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Journal of Applied Meteorology and Climatology

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