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The National Weather Service (NWS) is an agency of the United States federal government that is tasked with providing weather forecasts, warnings of hazardous weather, and other weather-related products to organizations and the public for the purposes of protection, safety, and general information. It is a part of the National Oceanic and Atmospheric Administration (NOAA) branch of the Department of Commerce, and is headquartered in Silver Spring, Maryland, within the Washington metropolitan area. The agency was known as the United States Weather Bureau from 1890 until it adopted its current name in 1970. The NWS performs its primary task through a collection of national and regional centers, and 122 local Weather Forecast Offices (WFOs). As the NWS is an agency of the U.S. federal government, most of its products are in the public domain and available free of charge. History Calls for the creation of a government weather bureau began as early as 1844, when the electrical

Adaptive Mitigation: Identification of the Dynamic Drivers of Effective Policy during the COVID-19 Pandemic

Thierry Bossy

Introduction: The unprecedented speed and scale of the COVID-19 pandemic necessitated the rapid implementation of untested public health measures to mitigate the consequences of viral spread. In the 8 months that have passed since the first recognized case, an enormous amount of research has been published evaluating the efficacy of the various policies implemented by different countries, however the majority of these studies focus on a specific region or time, over-representing high-income countries during periods of extreme transmission or “peak events”. The aim of this study is to provide a more general analysis that considers a global scope of the pandemic and the dynamic drivers that build effective policies to mitigate human interactions and slow the spread of disease. Methods: We collected a range of information regarding epidemic trends, weather, demographics, government response and mobility reports across the globe. We then built an hybrid Neural Network that combined an LSTM layer with a multilayer perceptron to infer the reproduction number from various non-epidemiological factors. The model was designed to predict the reproduction number (R-value) on 93 countries with available data and compare it to our ground truth estimate obtained from officially reported epidemiological data. Finally, we used an alternative model to assess the impact of public health measures on the epidemic. Findings: From the available features, we obtained the best performances using demographics combined with mobility features. The sanitary indices (beds/thousand, diabetes prevalence, ...) did not help the prediction and, more interestingly, the pressure indicator of historical weather forecast improved the prediction of the reproduction number by about 4.5%. This optimized model predicted the reproduction number with a mean absolute error of 0.254 across the 93 countries over the time of the epidemic. For many countries (Switzerland, United Kingdom, South Africa, ...) this error passed below 0.17. An alternative version of the model allowed us to estimate the impact of policies in terms of average reduction in reproduction number, and more importantly, allowed us to compare these trends between countries. For instance, we observe that the model showed that no policy had a positive impact in India as opposed to Switzerland, where most of them are associated to improved epidemic control. Conclusion: Understanding these complex interactions may allow individuals and policy makers to better adapt mitigation strategies to optimize the efficacy of the implemented policies.

2020

Modeling and Optimization of Terminal Airspace and Aircraft Arrival Subject To Weather Uncertainties

Wei Zhang

We develop an accurate model for the arrival traffic dynamics in terminal airspace that takes into account the weather forecast and runway configuration changes. The planning of runway configuration switching subject to weather constraints is formulated as a hybrid optimal control problem and a hierarchical approach is proposed to solve the problem. At the upper level of the hierarchy, a runway configuration sequence is determined based on weather forecast data and air traffic demand. At the lower level, the configuration switching times and the corresponding aircraft speeds are determined by solving a Mixed Integer Linear Program. We illustrate the utility of the approach by a case study inspired by operations in John F. Kennedy airport

American Institute of Aeronautics and Astronautics2011

Learning Decoupled Representations for Human Pose Forecasting

Alexandre Massoud Alahi, Yuejiang Liu, Taylor Ferdinand Mordan, Behnam Parsaeifard, Saeed Saadatnejad

Human pose forecasting involves complex spatiotemporal interactions between body parts (e.g., arms, legs, spine). State-of-the-art approaches use Long Short-Term Memories (LSTMs) or Variational AutoEncoders (VAEs) to solve the problem. Yet, they do not effectively predict human motions when both global trajectory and local pose movements exist. We propose to learn decoupled representations for the global and local pose forecasting tasks. We also show that it is better to stop the prediction when the uncertainty in human motion increases. Our forecasting model outperforms all existing methods on the pose forecasting benchmark to date by over 20%. The code is available online: https://github.com/vita-epfl/decoupled-pose-prediction

IEEE2021

Adaptive Mitigation: Identification of the Dynamic Drivers of Effective Policy during the COVID-19 Pandemic

Thierry Bossy

2020