Search Results

You are looking at 1 - 5 of 5 items for

  • Author or Editor: Sean Arms x
  • Refine by Access: All Content x
Clear All Modify Search
David Bodine
,
Petra M. Klein
,
Sean C. Arms
, and
Alan Shapiro

Abstract

Temperature and wind data from a rural micronet and nearby site of the Oklahoma Mesonet are analyzed to study the frequency, strength, and formation processes of cold-pool events in a region with gentle terrain. Spatial analyses were performed for a 2-yr-long temperature record from 26 temperature/humidity surface stations, deployed across a 120 m × 320 m micronet located in a region of gently sloped terrain with maximum elevation changes of ∼25 m. Cold pools frequently formed at the base of a gentle slope in a small depression of only ∼6-m depth that is also sheltered by trees. The strength of each cold-pool event was classified according to a cold-pool index based on average nocturnal temperature perturbations within the cold-pool region. Wind data collected with sonic anemometers on a 15-m-tall tower at the micronet for a period of three months (spring 2005) suggest that flow sheltering by vegetation plays an important role in the cold-pool formation. The wind data also show signatures of katabatic flow for about 50% of the strong cold-pool events. However, a heat budget analysis for these nights suggested that the katabatic flows were associated with warm-air advection along the slope and that if katabatic jets had penetrated the cold pool, they would have produced substantial warming in the region of the cold pool. Since such warming was not observed, it is concluded that the katabatic jets did not actually penetrate the cold pool but likely flowed over it. An analysis of Richardson numbers demonstrates that cold-pool formation frequently occurs under strongly stable conditions that tend to suppress vertical turbulent mixing in the surface layer. Observations that significant temperature changes can occur even with elevation changes on the order of 6 m have important implications in agriculture as well as in data assimilation.

Full access
Sean Arms
,
Julien Chastang
,
Maxwell Grover
,
Jon Thielen
,
Matthew Wilson
, and
Douglas Dirks
Free access
Alan Shapiro
,
Petra M. Klein
,
Sean C. Arms
,
David Bodine
, and
Matthew Carney

The Lake Thunderbird Micronet is a dense network of environmental sensors and a meteorological tower situated on ~10 acres of rural land in central Oklahoma. The Micronet was established in the spring of 2002 as part of a grassroots effort by a team of faculty and researchers at the University of Oklahoma to provide unique training and research opportunities for undergraduate and graduate students in meteorology and related environmental sciences. The history and design of the Micronet and use of the Micronet in undergraduate and graduate student training and research are described. Examples of interesting phenomena sampled at the Micronet are also presented.

Full access
Brian J. Etherton
,
Sean C. Arms
,
Larry D. Oolman
,
Gary M. Lackmann
, and
Mohan K. Ramamurthy

No Abstract available.

Full access
Ryan M. May
,
Kevin H. Goebbert
,
Jonathan E. Thielen
,
John R. Leeman
,
M. Drew Camron
,
Zachary Bruick
,
Eric C. Bruning
,
Russell P. Manser
,
Sean C. Arms
, and
Patrick T. Marsh

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.

Full access