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Abstract Downbursts are rapidly evolving meteorological phenomena with numerous vertically oriented precursor signatures, and the temporal resolution and vertical sampling of the current NEXRAD system are too coarse to observe their evolution and precursor signatures properly. A future all-digital polarimetric phased-array weather radar (PAR) should be able to improve both temporal resolution and spatial sampling of the atmosphere to provide better observations of rapidly evolving hazards such as downbursts. Previous work has been focused on understanding the trade-offs associated with using various scanning techniques on stationary PARs; however, a rotating, polarimetric PAR (RPAR) is a more feasible and cost-effective candidate. Thus, understanding the trade-offs associated with using various scanning techniques on an RPAR is vital in learning how to best observe downbursts with such a system. This work develops a framework for analyzing the trade-offs associated with different scanning strategies in the observation of downbursts and their precursor signatures. A proof-of-concept analysis—which uses a Cloud Model 1 (CM1)-simulated downburst-producing thunderstorm—is also performed with both conventional and imaging scanning strategies in an adaptive scanning framework to show the potential value and feasibility of the framework. Preliminary results from the proof-of-concept analysis indicate that there is indeed a limit to the benefits of imaging as an update time speedup method. As imaging is used to achieve larger speedup factors, corresponding data degradation begins to hinder the observations of various precursor signatures.
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The Future of Earth Imaging
Abstract Imaging of Earth’s interior has led to a large number of successful discoveries of plausible structures and associated geophysical processes. However, due to the limitations of geophysical data, Earth imaging has many trade-offs between the underlying features, and most approaches apply smoothing to reduce the effect of such trade-offs. Unfortunately, this smoothing often results in blurry images that are not clear enough either to infer the geologic processes of interest or to make quantitative inferences about the various geologic properties. Here, we first summarize some of the basic issues that make Earth imaging so difficult and explain how Earth imagers must choose between more open-ended discovery-oriented goals and more specific, scientific-inference-oriented goals. We discuss how the choice of the optimal imaging framework depends crucially on the desired goal, and particularly on whether plausible discovery or inference is the desired outcome. We argue that as Earth imaging has become more mature, sufficiently many plausible structures have been imaged that it is becoming more crucial for Earth imaging to serve the inference goal and would benefit from an inference-oriented imaging framework, despite the additional challenges in posing imaging problems in this manner. Examples of inference-oriented imaging frameworks are provided and contrasted with discovery-oriented frameworks. We discuss how the success of the various frameworks depends critically on the data quality and suggest that a careful balance must be struck between the ambition of the imager and the reality of the data. If Earth imaging is to move beyond presenting qualitatively plausible structures, it should move toward making quantitative estimates of the underlying geologic processes inferred through a self-consistent framework.
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- Award ID(s):
- 2011079
- PAR ID:
- 10529109
- Publisher / Repository:
- Seismological Society of America
- Date Published:
- Journal Name:
- Seismological Research Letters
- Volume:
- 94
- Issue:
- 5
- ISSN:
- 0895-0695
- Page Range / eLocation ID:
- 2119 to 2128
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1785/0220230125
