MetPy is an open-source, Python-based package for meteorology, providing domain specific functionality built extensively on top of the robust scientific Python software stack, which includes libraries like NumPy, SciPy, Matplotlib, and xarray. The goal of the project is to bring the weather analysis capabilities of GEMPAK (and similar software tools) into a modern computing paradigm. MetPy strives to employ best practices in its development, including software tests, continuous integration, and automated publishing of web-based documentation. As such, MetPy represents a sustainable, long-term project that fills a need for the meteorological community. MetPy’s development is substantially driven by its user community, both through feedback on a variety of open, public forums like Stack Overflow, and through code contributions facilitated by the GitHub collaborative software development platform. MetPy has recently seen the release of version 1.0, with robust functionality for analyzing and visualizing meteorological datasets. While previous versions of MetPy have already seen extensive use, the 1.0 release represents a significant milestone in terms of completeness and a commitment to long-term support for the programming interfaces. This article provides an overview of MetPy’s suite of capabilities, including its use of labeled arrays and physical unit information as its core data model, unit-aware calculations, cross-sections, Skew-T and GEMPAK-like plotting, station model plots, and support for parsing a variety of meteorological data formats. The general roadmap for future planned development for MetPy is also discussed.

Hailstorms in subtropical South America are known to be some of the most frequent anywhere in the world, causing significant damage to the local agricultural economy every year. Convection in this region tends to be orographically forced, with moisture supplied from the Amazon rain forest by the South American low-level jet. Previous climatologies of hailstorms in this region have been limited to localized and sparse observational networks. Because of the lack of sufficient ground-based radar coverage, objective radar-derived hail climatologies have also not been produced for this region. As a result, this study uses a 16-yr dataset of TRMM Precipitation Radar and Microwave Imager observations to identify possible hailstorms remotely, using 37-GHz brightness temperature as a hail proxy. By combining satellite instruments and ERA-Interim reanalysis data, this study produces the first objective study of hailstorms in this region. Hailstorms in subtropical South America have an extended diurnal cycle, often occurring in the overnight hours. In addition, they tend to be multicellular in nature, rather than discrete. High-probability hailstorms (≥50% probability of containing hail) tend to be deeper by 1–2 km and horizontally larger by greater than 15 000 km2 than storms having a low probability of containing hail (<25% probability of containing hail). Hailstorms are supported synoptically by strong upper- and lower-level jets, anomalously warm and moist low levels, and enhanced instability. The findings of this study will support the forecasting of these severe storms and mitigation of their damage within this region.

El Niño–Southern Oscillation (ENSO) is known to have teleconnections to atmospheric circulations and weather patterns around the world. Previous studies have examined connections between ENSO and rainfall in tropical South America, but little work has been done connecting ENSO phases with convection in subtropical South America. The Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) has provided novel observations of convection in this region, including that convection in the lee of the Andes Mountains is among the deepest and most intense in the world with frequent upscale growth into mesoscale convective systems. A 16-yr dataset from the TRMM PR is used to analyze deep and wide convection in combination with ERA-Interim reanalysis storm composites. Results from the study show that deep and wide convection occurs in all phases of ENSO, with only some modest variations in frequency between ENSO phases. However, the most statistically significant differences between ENSO phases occur in the three-dimensional storm structure. Deep and wide convection during El Niño tends to be taller and contain stronger convection, while La Niña storms contain stronger stratiform echoes. The synoptic and thermodynamic conditions supporting the deeper storms during El Niño is related to increased convective available potential energy, a strengthening of the South American low-level jet (SALLJ), and a stronger upper-level jet stream, often with the equatorward-entrance region of the jet stream directly over the convective storm locations. These enhanced synoptic and thermodynamic conditions provide insight into how the structure of some of the most intense convection on Earth varies with phases of ENSO.

The downwind shores of the Laurentian Great Lakes region often receive prolific amounts of lake-effect snowfall during the cold season (October–March). The location and intensity of this snowfall can be influenced by upper-tropospheric features such as short-wave troughs. A 7-yr cold-season climatology of 500-hPa short-wave troughs was developed for the Great Lakes region. A total of 607 short-wave troughs were identified, with an average of approximately 87 short waves per cold season. Five classes of short-wave troughs were identified on the basis of their movement through the Great Lakes region. This short-wave trough dataset was subsequently compared with the lake-effect cloud-band climatology created by N. F. Laird et al. in 2017 to determine how frequently short-wave troughs occurred concurrently with lake-effect cloud bands. Of the 607 short-wave troughs identified, 380 were concurrent with lake-effect clouds. Over 65% of these 380 short-wave troughs occurred with a lake-effect cloud band on at least four of the five Great Lakes. Short-wave troughs that rotated around the base of a long-wave trough were found to have the highest frequency of concurrence. In general, concurrence was most likely during the middle cold-season months. Further, Lake Michigan featured the highest number of concurrent events, and Lake Erie featured the fewest. It is evident that short-wave troughs are a ubiquitous feature near the Great Lakes during the cold season and have the potential to impart substantial impacts on lake-effect snowbands.

The National Weather Service (NWS) is charged with the responsibility of issuing severe weather warnings for the public whenever life and property may be in danger. During severe convective events, the NWS issues severe thunderstorm, tornado, and flash flood warnings. This study solely examines severe thunderstorm and tornado warnings conveying threats for wind, hail, and tornadoes.Since 1 October 2007, the NWS has issued storm-based warnings, which cover smaller areas than the previous county-based system. Situational awareness and appropriate staffing levels are necessary to make warning operations successful within a NWS Weather Forecast Office (WFO). If storm coverage and severity are great enough, warning outbreaks can occur in which an NWS WFO has an anomalously high number of warnings valid at the same time-covering large portions of their areas of responsibility. In the top cases, there have been ≥10 warnings in effect at the same time within a county warning area, and ≥30 across the country.A dichotomy exists between the environments that are associated with local and national tornado warning and severe thunderstorm warning outbreaks. Tornado warning outbreaks occur with high-end supercellular storm modes in high convective available potential energy (CAPE) and shear profiles. These events are often identified by the Storm Prediction Center as moderate or high risk with particularly dangerous situation tornado watches issued. Meanwhile, severe thunderstorm warning outbreaks transpire in mostly slight or enhanced risk areas with modest CAPE and low shear, which produce mainly pulse and linear thunderstorms. Verification statistics of these warnings indicate poorer performance compared to national averages-whether on local or national scales-with lower critical success index scores and higher false alarm ratios, although most events are warned during these outbreaks. ABSTRACT (Manuscript