With a variety of programming languages and data formats available, widespread adoption of computing standards by the atmospheric science community is often difficult to achieve. The Sounding and Hodograph Analysis and Research Program in Python (SHARPpy) is an open-source, cross-platform, upper-air sounding analysis and visualization package. SHARPpy is based on the National Oceanic and Atmospheric Administration/Storm Prediction Center’s (NOAA/SPC) in-house analysis package, SHARP, and is the result of a collaborative effort between forecasters at the SPC and students at the University of Oklahoma’s School of Meteorology. The major aim of SHARPpy is to provide a consistent framework for sounding analysis that is available to all. Nearly all routines are written to be as consistent as possible with the methods researched, tested, and developed in the SPC, which sets this package apart from other sounding analysis tools. SHARPpy was initially demonstrated and released to the atmospheric community at the American Meteorological Society (AMS) Annual Meeting in 2012, and an updated and greatly expanded version was released at the AMS Annual Meeting in 2015. Since this release, SHARPpy has been adopted by a variety of operational and research meteorologists across the world. In addition, SHARPpy’s open-source nature enables collaborations between other developers, resulting in major additions to the program.

The nocturnal stable boundary layer (SBL) can generally be classified into the weakly stable boundary layer (wSBL) and very stable boundary layer (vSBL). Within the wSBL, turbulence is relatively continuous, whereas in the vSBL, turbulence is intermittent and not well characterized. Differentiating characteristics of each type of SBL are still unknown. Herein, thermodynamic and kinematic data collected by a suite of instruments in north central Oklahoma in autumn 2012 are analyzed to better understand both SBL regimes and their differentiating characteristics. Many low-level jets were observed during the experiment, as it took place near a climatological maximum. A threshold wind speed, above which bulk shear-generated turbulence develops, is found to exist up to 300 m. The threshold wind speed must also be exceeded at lower heights (down to the surface) in order for strong turbulence to develop. Composite profiles, which are normalized using low-level jet scaling, of potential temperature, wind speed, vertical velocity variance, and the third-order moment of vertical velocity (w 3 ) are produced for weak and moderate/strong turbulence regimes, which exhibit features of the vSBL and wSBL, respectively. Within the wSBL, turbulence is generated at the surface and transported upward. In the vSBL, values of vertical velocity variance are small throughout the entire boundary layer, likely due to the fact that a strong surface inversion typically forms after sunset. The temperature profile tends to be approximately isothermal in the lowest portions of the wSBL, and it did not substantially change over the night. Within both types of SBL, stability in the residual layer tends to increase as the night progresses. It is thought that this stability increase is due to differential warm air advection, which frequently occurs in the southern Great Plains when southerly low-level jets and a typical north-south temperature gradient are present. Differential radiative flux divergence also contributes to this increase in stability.

Although current upper-air observing systems provide an impressive array of observations, many are deficient in observing the temporal evolution of the boundary layer thermodynamic profile. Ground-based remote sensing instruments such as the multichannel microwave radiometer (MWR) and Atmospheric Emitted Radiance Interferometer (AERI) are able to provide profiles of temperature and water vapor through the boundary layer at 5-min resolution or better. Previous work compared these instruments through optimal-estimation retrievals on simulated clear-sky spectra to evaluate the retrieval accuracy and information content of each instrument. In this study, this method is duplicated using real observations from collocated MWR and AERI instruments from a field campaign in southwestern Germany. When compared with radiosondes, this study confirms the previous results that AERI retrievals are more accurate than MWR retrievals in clear-sky and below-cloud-base profiling. These results demonstrate that the AERI has nearly 2 times as much information as the MWR.