5.2 Near-continuous Profiling of Atmospheric Stability During Severe Weather Events

Wednesday, 13 January 2016: 10:45 AM
Room 345 ( New Orleans Ernest N. Morial Convention Center)
Timothy J. Wagner, CIMSS/Univ. of Wisconsin, Madison, WI; and W. G. Blumberg

The ability of an environment to support deep, moist convection strongly depends on the thermodynamic and kinematic instability of that environment. To quantify the instability (or lack thereof) convective indices have been developed. These simple parameters, like Convective Available Potential Energy (CAPE) and Lifted Index (LI), require observations from multiple levels to be computed, and so they are frequently calculated from radiosonde launches. However, the infrequent launch schedule precludes the tracking of short-term stability trends, limiting their utility for operational forecasting and nowcasting.

The Atmospheric Emitted Radiance Interferometer (AERI) is a ground-based infrared interferometer capable of retrieving thermodynamic profiles at a temporal resolution substantially better than that of the radiosonde network. Previous work showed that AERI retrievals are adept of providing a near-continuous assessment of atmospheric instability. However, these analyses were done with a previous generation of retrieval algorithm that was only capable of retrieving profiles in cloud-free environments. A new retrieval for AERI, called AERI Optimal Estimation (AERIoe), can retrieve profiles in both clear and cloudy conditions as long as there is no precipitation. This results in a much more robust and continuous observation that can be used to track local changes in stability on the order of minutes.

For several years in the late 1990s/early 2000s, a network of five AERIs was deployed in Oklahoma and Kansas, a spatial and temporal domain that enveloped over 40 tornadic events. Time series of convective indices for specific events as well as aggregate trends for tornadic and nontornadic events will be shown, demonstrating the utility of an operational network of AERIs for forecasting and nowcasting needs.

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