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Bruce B. Hicks
,
William J. Callahan
,
William R. Pendergrass III
,
Ronald J. Dobosy
, and
Elena Novakovskaia

Abstract

The utility of aggregating data from near-surface meteorological networks for initiating dispersion models is examined by using data from the “WeatherBug” network that is operated by Earth Networks, Inc. WeatherBug instruments are typically mounted 2–3 m above the eaves of buildings and thus are more representative of the immediate surroundings than of conditions over the broader area. This study focuses on subnetworks of WeatherBug sites that are within circles of varying radius about selected stations of the DCNet program. DCNet is a Washington, D.C., research program of the NOAA Air Resources Laboratory. The aggregation of data within varying-sized circles of 3–10-km radius yields average velocities and velocity-component standard deviations that are largely independent of the number of stations reporting—provided that number exceeds about 10. Given this finding, variances of wind components are aggregated from arrays of WeatherBug stations within a 5-km radius of selected central DCNet locations, with on average 11 WeatherBug stations per array. The total variance of wind components from the surface (WeatherBug) subnetworks is taken to be the sum of two parts: the temporal variance is the average of the conventional wind-component variances at each site and the spatial variance is based on the velocity-component averages of the individual sites. These two variances (and the standard deviations derived from them) are found to be similar. Moreover, the total wind-component variance is comparable to that observed at the DCNet reference stations. The near-surface rooftop wind velocities are about 35% of the magnitudes of the DCNet measurements. Limited additional data indicate that these results can be extended to New York City.

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Bruce B. Hicks
,
Elena Novakovskaia
,
Ronald J. Dobosy
,
William R. Pendergrass III
, and
William J. Callahan

Abstract

Data from six urban areas in a nationwide network of sites within the surface roughness layer are examined. It is found that the average velocity variances in time, derived by averaging the conventional variances from a network of n stations, are nearly equal to the velocity variances in space, derived as the variances among the n average velocities. This similarity is modified during sunlit hours, when convection appears to elevate the former. The data show little dependence of the ratio of these two variances on wind speed. It is concluded that the average state of the surface roughness layer in urban and suburban areas like those considered here tends toward an approximate equality of these two measures of variance, much as has been observed elsewhere for the case of forests.

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Svetla M. Hristova-Veleva
,
P. Peggy Li
,
Brian Knosp
,
Quoc Vu
,
F. Joseph Turk
,
William L. Poulsen
,
Ziad Haddad
,
Bjorn Lambrigtsen
,
Bryan W. Stiles
,
Tsae-Pyng Shen
,
Noppasin Niamsuwan
,
Simone Tanelli
,
Ousmane Sy
,
Eun-Kyoung Seo
,
Hui Su
,
Deborah G. Vane
,
Yi Chao
,
Philip S. Callahan
,
R. Scott Dunbar
,
Michael Montgomery
,
Mark Boothe
,
Vijay Tallapragada
,
Samuel Trahan
,
Anthony J. Wimmers
,
Robert Holz
,
Jeffrey S. Reid
,
Frank Marks
,
Tomislava Vukicevic
,
Saiprasanth Bhalachandran
,
Hua Leighton
,
Sundararaman Gopalakrishnan
,
Andres Navarro
, and
Francisco J. Tapiador

Abstract

Tropical cyclones (TCs) are among the most destructive natural phenomena with huge societal and economic impact. They form and evolve as the result of complex multiscale processes and nonlinear interactions. Even today the understanding and modeling of these processes is still lacking. A major goal of NASA is to bring the wealth of satellite and airborne observations to bear on addressing the unresolved scientific questions and improving our forecast models. Despite their significant amount, these observations are still underutilized in hurricane research and operations due to the complexity associated with finding and bringing together semicoincident and semicontemporaneous multiparameter data that are needed to describe the multiscale TC processes. Such data are traditionally archived in different formats, with different spatiotemporal resolution, across multiple databases, and hosted by various agencies. To address this shortcoming, NASA supported the development of the Jet Propulsion Laboratory (JPL) Tropical Cyclone Information System (TCIS)—a data analytic framework that integrates model forecasts with multiparameter satellite and airborne observations, providing interactive visualization and online analysis tools. TCIS supports interrogation of a large number of atmospheric and ocean variables, allowing for quick investigation of the structure of the tropical storms and their environments. This paper provides an overview of the TCIS’s components and features. It also summarizes recent pilot studies, providing examples of how the TCIS has inspired new research, helping to increase our understanding of TCs. The goal is to encourage more users to take full advantage of the novel capabilities. TCIS allows atmospheric scientists to focus on new ideas and concepts rather than painstakingly gathering data scattered over several agencies.

Free access
Svetla M. Hristova-Veleva
,
P. Peggy Li
,
Brian Knosp
,
Quoc Vu
,
F. Joseph Turk
,
William L. Poulsen
,
Ziad Haddad
,
Bjorn Lambrigtsen
,
Bryan W. Stiles
,
Tsae-Pyng Shen
,
Noppasin Niamsuwan
,
Simone Tanelli
,
Ousmane Sy
,
Eun-Kyoung Seo
,
Hui Su
,
Deborah G. Vane
,
Yi Chao
,
Philip S. Callahan
,
R. Scott Dunbar
,
Michael Montgomery
,
Mark Boothe
,
Vijay Tallapragada
,
Samuel Trahan
,
Anthony J. Wimmers
,
Robert Holz
,
Jeffrey S. Reid
,
Frank Marks
,
Tomislava Vukicevic
,
Saiprasanth Bhalachandran
,
Hua Leighton
,
Sundararaman Gopalakrishnan
,
Andres Navarro
, and
Francisco J. Tapiador
Full access