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Tropical weather phenomena—including tropical cyclones (TCs) and equatorial waves—are influenced by planetary‐to‐convective‐scale processes; yet, existing data sets and tools can only capture a subset of those processes. This study introduces a convection‐permitting aquaplanet simulation that can be used as a laboratory to study TCs, equatorial waves, and their interactions. The simulation was produced with the Model for Prediction Across Scales‐Atmosphere (MPAS‐A) using a variable resolution mesh with convection‐permitting resolution (i.e., 3‐km cell spacing) between 10°S and 30°N. The underlying sea‐surface temperature is given by a zonally symmetric profile with a peak at 10°N, which allows for the formation of TCs. A comparison between the simulation and satellite, reanalysis, and airborne dropsonde data is presented to determine the realism of the simulated phenomena. The simulation captures a realistic TC intensity distribution, including major hurricanes, but their lifetime maximum intensities may be limited by the stronger vertical wind shear in the simulation compared to the observed tropical Pacific region. The simulation also captures convectively coupled equatorial waves, including Kelvin waves and easterly waves. Despite the idealization of the aquaplanet setup, the simulated three‐dimensional structure of both groups of waves is consistent with their observed structure as deduced from satellite and reanalysis data. Easterly waves, however, have peak rotation and meridional winds at a slightly higher altitude than in the reanalysis. Future studies may use this simulation to understand how convectively coupled equatorial waves influence the multi‐scale processes leading to tropical cyclogenesis.

 

Abstract The Sierras de Córdoba (SDC) range in Argentina is a hotspot of deep moist convection initiation (CI). Radar climatology indicates that 44% of daytime CI events that occur near the SDC in spring and summer seasons and that are not associated with the passage of a cold front or an outflow boundary involve a northerly low-level jet (LLJ), and these events tend to preferentially occur over the southeast quadrant of the main ridge of the SDC. To investigate the physical mechanisms acting to cause CI, idealized convection-permitting numerical simulations with a horizontal grid spacing of 1 km were conducted using Cloud Model 1 (CM1). The sounding used for initializing the model featured a strong northerly LLJ, with synoptic conditions resembling those in a previously postulated conceptual model of CI over the region, making it a canonical case study. Differential heating of the mountain caused by solar insolation in conjunction with the low-level northerly flow sets up a convergence line on the eastern slopes of the SDC. The southern portion of this line experiences significant reduction in convective inhibition, and CI occurs over the SDC southeast quadrant. The simulated storm soon acquires supercellular characteristics, as observed. Additional simulations with varying LLJ strength also show CI over the southeast quadrant. A simulation without background flow generated convergence over the ridgeline, with widespread CI across the entire ridgeline. A simulation with mid- and upper-tropospheric westerlies removed indicates that CI is minimally influenced by gravity waves. We conclude that the low-level jet is sufficient to focus convection initiation over the southeast quadrant of the ridge. 

Increased use of three‐dimensional (3D) imaging data has led to a need for methods capable of capturing rich shape descriptions. Semi‐landmarks have been demonstrated to increase shape information but placement in 3D can be time consuming, computationally expensive, or may introduce artifacts. This study implements and compares three strategies to more densely sample a 3D image surface.

Three dense sampling strategies: patch, patch‐thin‐plate spline (TPS), and pseudo‐landmark sampling, are implemented to analyze skulls from three species of great apes. To evaluate the shape information added by each strategy, the semi or pseudo‐landmarks are used to estimate a transform between an individual and the population average template. The average mean root squared error between the transformed mesh and the template is used to quantify the success of the transform.

The landmark sets generated by each method result in estimates of the template that on average were comparable or exceeded the accuracy of using manual landmarks alone. The patch method demonstrates the most sensitivity to noise and missing data, resulting in outliers with large deviations in the mean shape estimates. Patch‐TPS and pseudo‐landmarking provide more robust performance in the presence of noise and variability in the dataset.

Each landmarking strategy was capable of producing shape estimations of the population average templates that were generally comparable to manual landmarks alone while greatly increasing the density of the shape information. This study highlights the potential trade‐offs between correspondence of the semi‐landmark points, consistent point spacing, sample coverage, repeatability, and computational time.

 

Abstract During the last few decades, scientific capabilities for understanding and predicting weather and climate risks have advanced rapidly. At the same time, technological advances, such as the Internet, mobile devices, and social media, are transforming how people exchange and interact with information. In this modern information environment, risk communication, interpretation, and decision-making are rapidly evolving processes that intersect across space, time, and society. Instead of a linear or iterative process in which individual members of the public assess and respond to distinct pieces of weather forecast or warning information, this article conceives of weather prediction, communication, and decision-making as an interconnected dynamic system. In this expanded framework, information and uncertainty evolve in conjunction with people’s risk perceptions, vulnerabilities, and decisions as a hazardous weather threat approaches; these processes are intertwined with evolving social interactions in the physical and digital worlds. Along with the framework, the article presents two interdisciplinary research approaches for advancing the understanding of this complex system and the processes within it: analysis of social media streams and computational natural–human system modeling. Examples from ongoing research are used to demonstrate these approaches and illustrate the types of new insights they can reveal. This expanded perspective together with research approaches, such as those introduced, can help researchers and practitioners understand and improve the creation and communication of information in atmospheric science and other fields. 

Abstract Binary supermassive black holes (BSBHs) are expected to be a generic byproduct from hierarchical galaxy formation. The final coalescence of BSBHs is thought to be the loudest gravitational wave (GW) siren, yet no confirmed BSBH is known in the GW-dominated regime. While periodic quasars have been proposed as BSBH candidates, the physical origin of the periodicity has been largely uncertain. Here we report discovery of a periodicity (P=1607±7 days) at 99.95% significance (with a global p-value of ∼10−3 accounting for the look elsewhere effect) in the optical light curves of a redshift 1.53 quasar, SDSS J025214.67−002813.7. Combining archival Sloan Digital Sky Survey data with new, sensitive imaging from the Dark Energy Survey, the total ∼20-yr time baseline spans ∼4.6 cycles of the observed 4.4-yr (restframe 1.7-yr) periodicity. The light curves are best fit by a bursty model predicted by hydrodynamic simulations of circumbinary accretion disks. The periodicity is likely caused by accretion rate modulation by a milli-parsec BSBH emitting GWs, dynamically coupled to the circumbinary accretion disk. A bursty hydrodynamic variability model is statistically preferred over a smooth, sinusoidal model expected from relativistic Doppler boost, a kinematic effect proposed for PG1302−102. Furthermore, the frequency dependence of the variability amplitudes disfavors Doppler boost, lending independent support to the circumbinary accretion variability hypothesis. Given our detection rate of one BSBH candidate from circumbinary accretion variability out of 625 quasars, it suggests that future large, sensitive synoptic surveys such as the Vera C. Rubin Observatory Legacy Survey of Space and Time may be able to detect hundreds to thousands of candidate BSBHs from circumbinary accretion with direct implications for Laser Interferometer Space Antenna.