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To study the urban heat island and other local climatic processes in Madison, Wisconsin, in March 2012, 135 HOBO U23 Pro v2 temperature/relative humidity sensors in RS1 solar shields (Onset Computing) were attached to streetlight and utility poles in and around Madison, Wisconsin. Additional locations were added in 2012 and 2013 for a total of 150 locations. The sensors were installed at a height of 3.5 meters, and they automatically record instantaneous temperature and relative humidity every 15 minutes. This dataset includes all temperature/humidity measurements and a separate file with the coordinates of each measurement location. 

The urban heat island (UHI) effect, the phenomenon by which cities are warmer than rural surroundings, is increasingly important in a rapidly urbanizing and warming world, but fine-scale differences in temperature within cities are difficult to observe accurately. Networks of air temperature (Tair) sensors rarely offer the spatial density needed to capture neighborhood-level disparities in warming, while satellite measures of land surface temperature (LST) do not reflect the air temperatures that people physically experience. This analysis combines both Tair measurements recorded by a spatially-dense stationary sensor network in Dane County, Wisconsin, and remotely-sensed measurements of LST over the same area—to improve the use and interpretation of LST in UHI studies. The data analyzed span three summer months (June, July, and August) and eight years (2012–2019). Overall, Tair and LST displayed greater agreement in spatial distribution than in magnitude. The relationship between day of the year and correlation was fit to a parabolic curve (R2 = 0.76, p = 0.0002) that peaked in late July. The seasonal evolution in the relationship between Tair and LST, along with particularly high variability in LST across agricultural land cover suggest that plant phenology contributes to a seasonally varying relationship between Tair and LST measurements of the UHI. 

The Formosa array, with 137 broadband seismometers and ∼5 km station spacing, was deployed recently in Northern Taiwan. Here by using eight months of continuous ambient noise records, we construct the first high‐resolution three‐dimensional (3‐D) shear wave velocity model of the crust in the area. We first calculate multi‐component cross‐correlations to extract robust Rayleigh wave signals. We then determine phase velocity maps between 3 and 10 s periods using Eikonal tomography and measure Rayleigh wave ellipticity at each station location between 2 and 13 s periods. For each location, we jointly invert the two types of Rayleigh wave measurements with a Bayesian‐based inversion method for a one‐dimensional shear wave velocity model. All piecewise continuous one‐dimensional models are then used to construct the final 3‐D model. Our 3‐D model reveals upper crustal structures that correlate well with surface geological features. Near the surface, the model delineates the low‐velocity Taipei and Ilan Basins from the adjacent fast‐velocity mountainous areas, with basin geometries consistent with the results of previous geophysical exploration and geological studies. At a greater depth, low velocity anomalies are observed associated with the Linkou Tableland, Tatun Volcano Group, and a possible dyke intrusion beneath the Southern Ilan Basin. The model also provides new geometrical constraints on the major active fault systems in the area, which are important to understand the basin formation, orogeny dynamics, and regional seismic hazard. The new 3‐D shear wave velocity model allows a comprehensive investigation of shallow geologic structures in the Northern Taiwan.

 

Near‐surface seismic velocity structure plays a critical role in ground motion amplification during large earthquakes. In particular, the local Vp/Vs ratio strongly influences the amplitude of Rayleigh waves. Previous studies have separately imaged 3D seismic velocity and Vp/Vs ratio at seismogenic depth, but lack regional coverage and/or fail to constrain the shallowest structure. Here, we combine three datasets with complementary sensitivity in a Bayesian joint inversion for shallow crustal shear velocity and near‐surface Vp/Vs ratio across Southern California. Receiver functions–including with an apparent delayed initial peak in sedimentary basins, and long considered a nuisance in receiver function imaging studies–highly correlate with short‐period Rayleigh wave ellipticity measurements and require the inclusion of a Vp/Vs parameter. The updated model includes near‐surface low shear velocity more in line with geotechnical layer estimates, and generally lower than expected Vp/Vs outside the basins suggesting widespread shallow fracturing and/or groundwater undersaturation.

 

Through the Alaska Transportable Array deployment of over 200 stations, we create a 3‐D tomographic model of Alaska with sensitivity ranging from the near surface (<1 km) into the upper mantle (~140 km). We perform a Markov chain Monte Carlo joint inversion of Rayleigh wave ellipticity and phase velocities, from both ambient noise and earthquake measurements, along with receiver functions to create a shear wave velocity model. We also use a follow‐up phase velocity inversion to resolve interstation structure. By comparing our results to previous tomography, geology, and geophysical studies we are able to validate our findings and connect localized near‐surface studies with deeper, regional models. Specifically, we are able to resolve shallow basins, including the Copper River, Cook Inlet, Yukon Flats, Nenana, and a variety of other shallower basins. Additionally, we gain insight on the interaction between the upper mantle wedge, asthenosphere, and active and nonactive volcanism along the Aleutians and Denali volcanic gap, respectively. We observe thicker crust beneath the Brooks Range and south of the Denali fault within the Wrangellia Composite Terrane and thinner crust in the Yukon Composite Terrane in interior Alaska. We also gain new perspective on the Wrangell Volcanic Field and its interaction between surrounding asthenosphere and the Yakutat Terrane.