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20 September 2020 Convective boundary-layer height estimation from combined radar and Doppler lidar observations in VORTEX-SE
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Conference Poster
The Verification of the Origins of Rotation in Tornadoes Experiment Southeast (VORTEX-SE) provides a wealth of long-duration, high-resolution, vertically pointing observations from active and passive ground-based remote sensing systems enabling characterization of the Atmospheric Boundary Layer (ABL) development over distinct regions that are well known for their relatively high tornado frequency. Application of the Extended Kalman Filter (EKF) to BL height estimation in the convective regime (CBLH) of the diurnal cycle from S-band radar reflectivity observations1 has shown to yield accurate results under simple CBL conditions. In this work, we revisit the radar-EKF technique and investigate its main limitations. For example, during daytime clear-sky conditions such as those prevailing in the BL morning transition, weak turbulence leads to very low reflectivity returns, limiting application of this technique. Additionally, turbulent mixing layers capped with a residual layer, and/or multi-layer scenarios can lead the filter to lose track of the BL signature over time. Doppler Wind Lidar (DWL) observations of the vertical wind velocity variance2 provide complementary CBLH estimates to those of the radar-EKF combination, providing potential to disambiguate more complex convective cases. DWL estimates are, however, strongly influenced by the variance threshold selected. The complementarity of radar and DWL for CBLH estimation is studied in reference to radiosoundings.
Conference Presentation
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Joan Villalonga, Susan L. Beveridge, Marcos P. Araujo da Silva, Robin L. Tanamachi, Francesc Rocadenbosch, David D. Turner, and Stephen J. Frasier "Convective boundary-layer height estimation from combined radar and Doppler lidar observations in VORTEX-SE", Proc. SPIE 11531, Remote Sensing of Clouds and the Atmosphere XXV, 115310X (20 September 2020);

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