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Jebb Q. Stewart, C. David Whiteman, W. James Steenburgh, and Xindi Bian

This paper investigates the diurnal evolution of thermally driven plain–mountain winds, up- and down-valley winds, up- and downslope winds, and land–lake breezes for summer fair weather conditions in four regions of the Intermountain West where dense wind networks have been operated. Because of the diverse topography in these regions, the results are expected to be broadly representative of thermally driven wind climates in the Intermountain West. The regions include the Wasatch Front Valleys of northern Utah, the Snake River Plain of Idaho, the southern Nevada basin and range province, and central Arizona. The analysis examines wind characteristics, including the regularity of the winds and interactions of the four types of thermally driven winds, using meteorological data from the University of Utah's MesoWest network.

In general, on fair weather days, winds in all four regions exhibit a consistent direction from day to day at a given hour. A measure of this wind consistency is defined. The nighttime hours exhibit a generally higher consistency than the daytime hours. Lower consistency during the day–night and night–day transition periods reflects day-to-day variations in the timing of wind system reversals. Thermally driven circulations are similar in the four regions, but the Wasatch Front Valleys are influenced by lake breezes from the adjacent Great Salt Lake, the Snake River Plain is influenced by along-plain circulations and localized outflow from the Central Idaho Mountains, and winds in both southern Nevada and central Arizona are influenced by plain–mountain circulations associated with regional-scale contrasts in elevation and surface heating.

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Sid-Ahmed Boukabara, Vladimir Krasnopolsky, Jebb Q. Stewart, Eric S. Maddy, Narges Shahroudi, and Ross N. Hoffman

Abstract

Artificial intelligence (AI) techniques have had significant recent successes in multiple fields. These fields and the fields of satellite remote sensing and NWP share the same fundamental underlying needs, including signal and image processing, quality control mechanisms, pattern recognition, data fusion, forward and inverse problems, and prediction. Thus, modern AI in general and machine learning (ML) in particular can be positively disruptive and transformational change agents in the fields of satellite remote sensing and NWP by augmenting, and in some cases replacing, elements of the traditional remote sensing, assimilation, and modeling tools. And change is needed to meet the increasing challenges of Big Data, advanced models and applications, and user demands. Future developments, for example, SmallSats and the Internet of Things, will continue the explosion of new environmental data. ML models are highly efficient and in some cases more accurate because of their flexibility to accommodate nonlinearity and/or non-Gaussianity. With that efficiency, ML can help to address the demands put on environmental products for higher accuracy, for higher resolution—spatial, temporal, and vertical, for enhanced conventional medium-range forecasts, for outlooks and predictions on subseasonal to seasonal time scales, and for improvements in the process of issuing advisories and warnings. Using examples from satellite remote sensing and NWP, it is illustrated how ML can accelerate the pace of improvement in environmental data exploitation and weather prediction—first, by complementing existing systems, and second, where appropriate, as an alternative to some components of the NWP processing chain from observations to forecasts.

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Sid-Ahmed Boukabara, Vladimir Krasnopolsky, Jebb Q. Stewart, Eric Maddy, Narges Shahroudi, and Ross N. Hoffman
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Sid-Ahmed Boukabara, Vladimir Krasnopolsky, Stephen G. Penny, Jebb Q. Stewart, Amy McGovern, David Hall, John E. Ten Hoeve, Jason Hickey, Hung-Lung Allen Huang, John K. Williams, Kayo Ide, Philippe Tissot, Sue Ellen Haupt, Kenneth S. Casey, Nikunj Oza, Alan J. Geer, Eric S. Maddy, and Ross N. Hoffman

Capsule Summary

Current research applying artificial intelligence to the Earth and environmental sciences is progressing quickly, with emerging developments in terms of efficiency, accuracy, and discovery.

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