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Abstract 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 road map for future planned development for MetPy is also discussed. 

Abstract 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.