A Global Climatology of Reanalysis-Derived Parameters for Deep Moist Convection

Parameters that provide information of storm environments are typically produced from analysis of a single atmospheric sounding, including many produced by the Sounding/Hodograph Analysis and Research Program in Python (SHARPpy, Halpert et al., 2015.). These are typically limited to data derived from radiosonde launches, which are variable in time and space across the globe with many areas being completely data deficient.  Atmospheric reanalyses provide global coverage of the state of the atmosphere, but processing every model grid point from an atmospheric reanalysis is incredibly computationally expensive as each reanalysis time slice presents over 10,000 individual 1-D soundings.  Advances in parallel processing of data has led to formation of the first global climatology of 55 parameters used to diagnose deep moist convection. The parameters derived from the NCEP-DOE Reanalysis 2 at 6 hour spacing in time (00, 06, 12, 18 UTC) globally from 1979-2015.   Individual soundings from each point in the reanalysis data are input into SHARPpy to produce values CAPE, CIN, 0-6 km shear and LCL, among other parameters.  Precipitable water values from the SHARPpy-derived climatology are compared to the reanalysis data to determine the overall accuracy of this approach.  Proximity soundings from past tornado outbreaks in both Europe and the United States are compared to determine the relative frequency both spatially and temporally of these extreme values.  Global patterns of CAPE and shear are discussed, highlighting individual severe storm hotspots across the globe.