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The emergence of mobile platforms equipped with Global Positioning System technology enables real-time data collection affording opportunities for mining data applicable to rapid flood inundation assessment. The collected data can be employed to complement existing methods for rapid flood inundation assessment, such as remote sensing, to enhance situational awareness. In particular, telemetry-based digital trace data related to human activity have intrinsic advantages to be used for inundation assessment. In this study, we investigate the use of Mapbox telemetry data, which provides human activity indices with high spatial and temporal resolutions, for application in rapid flood inundation assessment. Using data from Hurricane Harvey in 2017 in Harris County, Texas, we (1) study anomalous fluctuations in human activities and analyze the differences in activity level between inundated and non-inundated areas and (2) investigate changes in the concentration of human activity, to explore the disruption of human activity as an indicator of flood inundation. Results show that both analyses can provide valuable rapid insights regarding flood inundation status. Anomalous activities can be significantly higher/lower in flooded areas compared with non-flooded areas. Also, the concentration of human activity during the flood propagation period across affected watersheds can be observed. This study contributes to the state of knowledge in smart flood resilience by investigating the application of ubiquitous telemetry-based digital trace data to enhance rapid flood inundation assessment. Accordingly, the use of such digital trace data could provide emergency managers and public officials with valuable insights to inform impact evaluation and response actions. 

This study quantifies the state of the art in the rapidly growing field of seasonal Arctic sea ice prediction. A novel multimodel dataset of retrospective seasonal predictions of September Arctic sea ice is created and analyzed, consisting of community contributions from 17 statistical models and 17 dynamical models. Prediction skill is compared over the period 2001–20 for predictions of pan-Arctic sea ice extent (SIE), regional SIE, and local sea ice concentration (SIC) initialized on 1 June, 1 July, 1 August, and 1 September. This diverse set of statistical and dynamical models can individually predict linearly detrended pan-Arctic SIE anomalies with skill, and a multimodel median prediction has correlation coefficients of 0.79, 0.86, 0.92, and 0.99 at these respective initialization times. Regional SIE predictions have similar skill to pan-Arctic predictions in the Alaskan and Siberian regions, whereas regional skill is lower in the Canadian, Atlantic, and central Arctic sectors. The skill of dynamical and statistical models is generally comparable for pan-Arctic SIE, whereas dynamical models outperform their statistical counterparts for regional and local predictions. The prediction systems are found to provide the most value added relative to basic reference forecasts in the extreme SIE years of 1996, 2007, and 2012. SIE prediction errors do not show clear trends over time, suggesting that there has been minimal change in inherent sea ice predictability over the satellite era. Overall, this study demonstrates that there are bright prospects for skillful operational predictions of September sea ice at least 3 months in advance.

 

Abstract The objective of this study was to investigate the importance of multiple county-level features in the trajectory of COVID-19. We examined feature importance across 2787 counties in the United States using data-driven machine learning models. Existing mathematical models of disease spread usually focused on the case prediction with different infection rates without incorporating multiple heterogeneous features that could impact the spatial and temporal trajectory of COVID-19. Recognizing this, we trained a data-driven model using 23 features representing six key influencing factors affecting the pandemic spread: social demographics of counties, population activities, mobility within the counties, movement across counties, disease attributes, and social network structure. Also, we categorized counties into multiple groups according to their population densities, and we divided the trajectory of COVID-19 into three stages: the outbreak stage, the social distancing stage, and the reopening stage. The study aimed to answer two research questions: (1) The extent to which the importance of heterogeneous features evolved at different stages; (2) The extent to which the importance of heterogeneous features varied across counties with different characteristics. We fitted a set of random forest models to determine weekly feature importance. The results showed that: (1) Social demographic features, such as gross domestic product, population density, and minority status maintained high-importance features throughout stages of COVID-19 across 2787 studied counties; (2) Within-county mobility features had the highest importance in counties with higher population densities; (3) The feature reflecting the social network structure (Facebook, social connectedness index), had higher importance for counties with higher population densities. The results showed that the data-driven machine learning models could provide important insights to inform policymakers regarding feature importance for counties with various population densities and at different stages of a pandemic life cycle. 

Given the increasing attention in forecasting weather and climate on the subseasonal time scale in recent years, National Oceanic and Atmospheric Administration (NOAA) announced to support Climate Process Teams (CPTs) which aim to improve the Madden‐Julian Oscillation (MJO) prediction by NOAA’s global forecasting models. Our team supported by this CPT program focuses primarily on the improvement of upper ocean mixing parameterization and air‐sea fluxes in the NOAA Climate Forecast System (CFS). Major improvement includes the increase of the vertical resolution in the upper ocean and the implementation of General Ocean Turbulence Model (GOTM) in CFS. In addition to existing mixing schemes in GOTM, a newly developed scheme based on observations in the tropical ocean, with further modifications, has been included. A better performance of ocean component is demonstrated through one‐dimensional ocean model and ocean general circulation model simulations validated by the comparison with in‐situ observations. These include a large sea surface temperature (SST) diurnal cycle during the MJO suppressed phase, intraseasonal SST variations associated with the MJO, ocean response to atmospheric cold pools, and deep cycle turbulence. Impact of the high‐vertical resolution of ocean component on CFS simulation of MJO‐associated ocean temperature variations is evident. Also, the magnitude of SST changes caused by high‐resolution ocean component is sufficient to influence the skill of MJO prediction by CFS.

 

Deep cycle turbulence (DCT) is a diurnally oscillating turbulence that penetrates into a stratified shear layer below the surface mixed layer, which is often observed in the eastern Pacific and Atlantic above the Equatorial Undercurrent (EUC). Here we present the simulation of DCT by a global ocean general circulation model (OGCM) for the first time. As thek‐εvertical mixing scheme is used in the OGCM, the simulation of observed DCT structure based on in situ microstructure measurements can be explicitly demonstrated. The simulated DCT is found in all equatorial ocean basins, and its characteristics agree very well with observations. Zonal and meridional variations of DCT in the entire equatorial Pacific and Atlantic are described through constructing the composite diurnal cycle. In the central Pacific where the maximum shear associated with EUC is deep, the separation of DCT from the surface mixed layer is much more prominent than other areas.