Date of Award

January 2018

Document Type


Degree Name

Master of Science (MS)


Atmospheric Sciences

First Advisor

Aaron Kennedy


This thesis performs an intercomparison of reanalysis datasets with the goal of determining their respective proficiency in representing severe weather environments capable of producing phenomena such as strong wind, large hail, and tornadoes. A select reanalyses is then used to investigate the climatology and trends in pertinent severe weather parameters over a three-decade period from 1986-2015.

The intercomparison is performed by comparing a peer-reviewed dataset of Rapid Update Cycle 2 (RUC-2) proximity soundings to collocated soundings derived from the nearest grid point in six different modern reanalyses. These soundings are compared via various parameters related to severe weather such as: Convective available potential energy (CAPE), effective storm relative helicity (EFFSRH), and supercell composite parameter (SCP). Parameters are calculated using SHARPpy, which is an open source, peer reviewed python sounding analysis package modeled after the Storm Prediction Center’s (SPC) Sounding and Hodograph Analysis and Research Program (SHARP).

Representation of severe weather environments varies across the reanalyses and the presented results have ramifications for climatological studies that use these datasets. In particular, thermodynamic parameters such as CAPE show the widest range in variations, and this property feeds back to other parameters that incorporate thermodynamic information directly or indirectly through the effective layer. As a result, better segregation of soundings by storm type is found for fixed-layer shear parameters. Although no reanalysis can exactly reproduce the results of earlier RUC-2 studies, many of the reanalyses can broadly distinguish between environments that are significantly tornadic vs. nontornadic. Overall, the reanalyses found to have the most favorable error characteristics for severe weather environments are the North American Regional Reanalysis (NARR) and the Japanese 55-Year Reanalysis (JRA55).

Given the results of the first objective, NARR is used to understand the climatology and trends in severe weather parameters across the contiguous United States. A suite of severe weather parameters is calculated for the full domain of NARR by taking “pseudo-soundings” at each grid point. It is found that the spatial distribution of average severe weather climatologies are similar to prior studies but tend to have significantly larger magnitudes. It is also found that certain severe weather parameters may be increasing over select regions, while others have either a neutral trend or are decreasing over time.

The raw data used for this study, i.e. a suite of severe weather parameters for the full domain of NARR, will be made publicly available. This dataset is potentially useful to members of the climate science and atmospheric science communities. This is due in part to the large amount of computational resources and time that were required to produce this dataset.