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Mesoscale Data Assimilation for Improving Quantitative Precipitation Forecasts

Title: Mesoscale Data Assimilation for Improving Quantitative Precipitation Forecasts.
Name(s): Peng, Shiqiu, author
Zou, Xiaolei, professor directing dissertation
Navon, I. M., outside committee member
O’Brien, James J., committee member
Ray, Peter S., committee member
Barcilon, Albert I., committee member
Department of Earth, Ocean and Atmospheric Sciences, degree granting department
Florida State University, degree granting institution
Type of Resource: text
Genre: Text
Issuance: monographic
Date Issued: 2004
Publisher: Florida State University
Place of Publication: Tallahassee, Florida
Physical Form: computer
online resource
Extent: 1 online resource
Language(s): English
Abstract/Description: NCEP multi-sensor hourly rainfall data and ground-baed GPS zenith total delay (ZTD) were used for data assimilation and evaluation of quantitative precipitation forecasts (QPFs) through three case studies. Improvements in QPFs were obtained through direct assimilation of these rainfall observations and ZTD data using 4-dimensional variational assimilation (4D-Var). Inclusion of the observed no-rain information was shown to be beneficial to QPFs. Although the assimilation of ZTD observations does not produce a rainfall distributionas close to the observations as does the assimilation of rainfall within the assimilation window, the improvement in the QPFs beyond the window from the ZTD experiment is comparable to that from the rainfall experiment. Assimilation of ZTD and rainfall observations modifies the thermodynamic structures of the atmosphere, favoring development of precipitation in the observed rainy areas. The horizontal and vertical wind velocities are also adjusted consistent with the precipitation process. Sensitivity studies indicated that the adjustments in the moisture and temperature fields resulting from precipitation assimilation played a more important role than those of other state variables for improving QPFs. Spectral analysis indicates that rainfall assimilation adjusts the model variables on smaller scales (25 to 50 km) while the ZTD assimilation adjusts the model variables mainly on larger scales (>50 km).50 km). A modified digital filter for intensifying mesoscale gravity wave signatures is developed and applied to a real case study of rainfall assimilation. The results show that the rainfall assimilation experiment with the modified digital filter produced further improvements in quantitative precipitation forecasts compared with the rainfall assimilation experiment with a regular digital filter. Spectral analysis confirms that the mesoscale gravity waves are intensified not only within the rainfall assimilation window during which the modified digital filter is applied, but also beyond the assimilation window. The gravity-wave-induced vertical motions along the direction of wave propagation are also intensified, resulting in a more realistic time evolution of the pecipitation pattern. It is also found that the assimilation of 6-h accumulated rainfall outperforms the assimilation of hourly rainfall within the same 6-h window.
Identifier: FSU_migr_etd-2038 (IID)
Submitted Note: A Dissertation Submitted to the Department of Meteorology in Partial FulfiLlment of the Requirements for the Degree of Doctor of Philosophy.
Degree Awarded: Summer Semester, 2004.
Date of Defense: July 8, 2004.
Keywords: Mesoscale Gravity Wave, Data Assimilation, Digital Filter, 4D-Var, QPF
Bibliography Note: Includes bibliographical references.
Advisory Committee: Xiaolei Zou, Professor Directing Dissertation; I. M. Navon, Outside Committee Member; James J. O’Brien, Committee Member; Peter S. Ray, Committee Member; Albert I. Barcilon, Committee Member.
Subject(s): Meteorology
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Owner Institution: FSU

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Peng, S. (2004). Mesoscale Data Assimilation for Improving Quantitative Precipitation Forecasts. Retrieved from