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Colorado State University

1. Bechini, Renzo. Microphysics and dynamics retrievals from dual-polarization radar for very short-term forecasting.

Degree: 2017, Colorado State University

Nowcasting is primarily a description of the near-future forecasted atmospheric state, relying heavily on observations. Besides routine meteorological observations (pressure, temperature, humidity, wind), dual-polarization weather radar provides a large amount of useful information due to the frequent-update (~5 min) and high-resolution (~500 m) three-dimensional sampling of the atmosphere. However, the atmospheric state variables are not readily invertible from radar remote observations, resulting in complexity in the numerical model data assimilation. This problem is normally dealt with by defining observation operators to simulate the radar variables from the model state vector. In this work the dual-polarization radar based retrievals are developed in order to demonstrate their potential for microphysics and dynamics retrievals. In particular the analysis of radar observations in convective storms and in stratiform ice clouds revealed that specific dual-polarization signatures can be successfully related to important dynamic properties such as vertical air motions, both in convective precipitation (strong updrafts, several m s-1) and in stratiform precipitation (large areas of weak updrafts, tenths of m s-1, associated with mid-tropospheric mesoscale forcing). Given the relevance of polarimetric signatures to dynamics retrievals, an improved hydrometeor classification method is developed based on a learn-from-data approach. In this technique, the traditional bin-based classification is replaced with a semi-supervised approach which combines cluster analysis, spatial contiguity, and statistical inference to assign the most likely class to a set of identified connected regions. The hydrometeor classification and relevant dual-polarization signatures establish a starting point to explore new means to improve the analysis of precipitation and near-surface winds, and their subsequent nowcasting. In particular the relevance of a well-known dual-polarization feature associated with deep convection (vertical columns of differential reflectivity) is illustrated by including the microphysics and dynamics-related information into a simple method for the analysis of surface winds. The goal of a physically consistent analysis is further pursued considering the Variational Doppler Radar Analysis System (VDRAS), an advanced four-dimensional data assimilation system based on a cloud-scale model, specifically designed for ingesting Doppler weather radar observations. The typical application using single-polarization observations from long-range S-band or C-band radars is here extended to high frequency (X-band), short range radars and dual-polarization observations. The combination of the hydrometeor classification and dual-polarization rainwater estimation allows to successfully assimilating the X-band observations, otherwise prone to relevant errors when using the reflectivity-based observation operator widely employed in numerical models. The feasibility of X-band data assimilation to contribute building a consistent analysis… Advisors/Committee Members: Chandrasekar, V. (advisor), Jayasumana, Anura (committee member), Mielke, Paul (committee member), Sun, Juanzhen (committee member).

Subjects/Keywords: microphysics; polarimetry; weather; nowcasting; dynamics; radar

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Bechini, R. (2017). Microphysics and dynamics retrievals from dual-polarization radar for very short-term forecasting. (Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/178810

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Bechini, Renzo. “Microphysics and dynamics retrievals from dual-polarization radar for very short-term forecasting.” 2017. Thesis, Colorado State University. Accessed August 23, 2017. http://hdl.handle.net/10217/178810.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Bechini, Renzo. “Microphysics and dynamics retrievals from dual-polarization radar for very short-term forecasting.” 2017. Web. 23 Aug 2017.

Vancouver:

Bechini R. Microphysics and dynamics retrievals from dual-polarization radar for very short-term forecasting. [Internet] [Thesis]. Colorado State University; 2017. [cited 2017 Aug 23]. Available from: http://hdl.handle.net/10217/178810.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bechini R. Microphysics and dynamics retrievals from dual-polarization radar for very short-term forecasting. [Thesis]. Colorado State University; 2017. Available from: http://hdl.handle.net/10217/178810

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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