Date of Award
2025
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Atmospheric and Earth Science
Committee Chair
John Mecikalski
Committee Member
Pavlos Kollias
Committee Member
Kevin Knupp
Committee Member
Xuanli Li
Committee Member
Udaysankar Nair
Research Advisor
John Mecikalski
Subject(s)
Numerical weather forecasting, Convective clouds--Analysis, Convection (Meteorology)
Abstract
Cloud base height (CBH) and cloud base vertical velocity (CBVV) are essential parameters for understanding cloud formation, boundary layer evolution, and air exchange between the atmosphere and the boundary layer. CBH is linked to aviation safety and impacting the amount of solar radiation reaching the surface, while CBVV influences cloud development, condensation (droplet formation) and precipitation potential. High CBVV can erode capping inversions and trigger convection. These two parameters are the focus of this dissertation. CBH and maximum CBVV values were derived using a multi-linear regression equation (i.e., generalized linear models (GLM)) for CBH and a random forest model for CBVV, with inputs from the Visible Infrared Imaging Radiometer Suite and the High-Resolution Rapid Refresh numerical weather prediction (NWP) model. Observations from the Doppler lidar (DL) and Automated Surface Observing Systems (ASOS) at 10 stations at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) location provided ground truth. Case dates from 2018-2023 over 102 time periods consisted of shallow cumulus with buoyancy as the primary source of vertical motion. The models were tested over a broad region (2° by 2°), with smaller domains created to assess model performance at different resolutions. The CBH GLM model performed well across all domains, but the random forests CBVV model performance degraded as the domain size decreased due to the limited DL stations (1-3) as some of the higher values of CBVV skewed the results. CBH and CBVV data were subsequently incorporated into the Kain-Fritsch (KF) convective parameterization trigger function, which calculates the potential for deep convection in NWP grids. A temperature perturbation (δT) that is part of the trigger function was derived using both CBH and CBVV. δT values ranged from 1.5 to 7.8 K. Finally, Weather Research and Forecasting model simulations were conducted for two case dates, comparing observed and derived values of CBH, CBVV, and δT against a 3 km resolution simulation using the domain setup across the ARM SGP region. Results show that CBH is accurate (~ 200 m), but CBVV is underestimated by 2-3 ms-1, leading to a 3-5 K underestimation in δT in the KF scheme.
Recommended Citation
Haliczer, David T., "Synergizing ground, satellite and numerical weather prediction datasets to derive cloud base properties as used in the Kain-Fritsch convective parameterization" (2025). Dissertations. 442.
https://louis.uah.edu/uah-dissertations/442
